Science, Irving K. Barber Faculty of (Okanagan)
Biology, Department of (Okanagan)
DSpace
UBCO
Turner, Ian
Doctor of Philosophy - PhD
University of British Columbia
Livestock production systems are associated with large environmental impacts, substantially contributing to the ongoing transgressions of planetary boundaries. Egg production is generally associated with lower environmental impacts than other livestock systems. However, impact reductions must still be realized if eggs are to be part of a sustainable food system. Concurrently, the egg industry must also navigate animal welfare challenges, which are a driving force in ongoing transitions away from conventional cage production systems to alternatives in Canada, and elsewhere globally. This dissertation reports research undertaken to support evidence-based decision making in the Canadian egg industry as it navigates this transition and seeks to contribute to a more sustainable food system. The research is grounded in life cycle thinking and its associated analytical framework, life cycle assessment (LCA), presenting a transdisciplinary integration of LCA with the disciplines of animal welfare science and operations research. Updated baseline LCA models of Canadian, non-organic egg production were developed based on primary data collected from farmers, and best publicly available data for other supply chain activities. These models subsequently supported application of data envelopment analysis, and life cycle optimization to investigate environmental impact mitigation potentials of efficiency improvements on egg farms, and optimization of feed formulations to minimize environmental impacts. Further, a novel animal welfare life cycle impact assessment method was proposed for assessment of laying hens. This method was used with the baseline LCA models to optimize the distribution of Canadian non-organic egg production in alternative housing systems for animal welfare and environmental impacts. Throughout, this research suggests that substantial environmental impact reductions may be realized on Canadian egg farms that outweigh potential increases in production costs. Further, as the housing system transition continues, the environmental- and animal welfare-optimal distribution of Canadian egg production is 100% in enriched housing, regardless of stakeholder priorities for improved environmental or animal welfare outcomes. Overall, this research provides principled decision support to the Canadian egg industry as they continue to navigate this transitionary period, while also making large methodological contributions that may catalyze further transdisciplinary development of LCA methods as a key tool in the sustainability science discipline.
Investigating sustainability improvement potential in the Canadian egg industry through integration of life cycle assessment, animal welfare, and operations research by Ian Turner B.Sc., The University of Toronto, 2017 A DISSERTATION SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF Doctor of Philosophy in THE COLLEGE OF GRADUATE STUDIES (Interdisciplinary Studies) (Sustainability) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) February 2025 © Ian Turner, 2025 ii The following individuals certify that they have read, and recommend to the College of Graduate Studies for acceptance, the dissertation entitled: Investigating sustainability improvement potential in the Canadian egg industry through integration of life cycle assessment, animal welfare, and operations research submitted by Ian Turner in partial fulfilment of the requirements for the degree of Doctor of Philosophy. Dr. Nathan Pelletier, Irving K. Barber Faculty of Science Supervisor Dr. Tina Widowski, University of Guelph Supervisory Committee Member Dr. Jeff Andrews, Irving K. Barber Faculty of Science Supervisory Committee Member Dr. Amir Ardestani-Jaafari, Faculty of Management University Examiner Dr. Greg Thoma, Colorado State University External Examiner iii Abstract Livestock production systems are associated with large environmental impacts, substantially contributing to the ongoing transgressions of planetary boundaries. Egg production is generally associated with lower environmental impacts than other livestock systems. However, impact reductions must still be realized if eggs are to be part of a sustainable food system. Concurrently, the egg industry must also navigate animal welfare challenges, which are a driving force in ongoing transitions away from conventional cage production systems to alternatives in Canada, and elsewhere globally. This dissertation reports research undertaken to support evidence-based decision making in the Canadian egg industry as it navigates this transition and seeks to contribute to a more sustainable food system. The research is grounded in life cycle thinking and its associated analytical framework, life cycle assessment (LCA), presenting a transdisciplinary integration of LCA with the disciplines of animal welfare science and operations research. Updated baseline LCA models of Canadian, non-organic egg production were developed based on primary data collected from farmers, and best publicly available data for other supply chain activities. These models subsequently supported application of data envelopment analysis, and life cycle optimization to investigate environmental impact mitigation potentials of efficiency improvements on egg farms, and optimization of feed formulations to minimize environmental impacts. Further, a novel animal welfare life cycle impact assessment method was proposed for assessment of laying hens. This method was used with the baseline LCA models to optimize the distribution of Canadian non-organic egg production in alternative housing systems for animal welfare and environmental impacts. Throughout, this research suggests that substantial environmental impact reductions may be realized on Canadian egg farms that outweigh potential increases in production costs. Further, as the housing system iv transition continues, the environmental- and animal welfare-optimal distribution of Canadian egg production is 100% in enriched housing, regardless of stakeholder priorities for improved environmental or animal welfare outcomes. Overall, this research provides principled decision support to the Canadian egg industry as they continue to navigate this transitionary period, while also making large methodological contributions that may catalyze further transdisciplinary development of LCA methods as a key tool in the sustainability science discipline. v Lay Summary Livestock systems are key drivers of environmental change, leading to calls to reduce consumption of livestock products in order to improve the overall sustainability of the global food system. Despite this, demand for livestock products is anticipated to increase in the coming years, hence necessitating large impact reductions. Additionally, livestock systems must also manage animal welfare impacts, which may act synergistically or antagonistically with the environmental impacts characteristic of different livestock production strategies. This dissertation presents research seeking to integrate assessments of environmental and animal welfare impacts in the Canadian egg industry with mathematical techniques to investigate on-farm efficiency improvements, and mathematical optimization. It is intended to provide Canadian egg farmers with valuable decision support information as they navigate the ongoing housing system transition, while also developing novel methods and frameworks that may be improved upon in the future to support further sustainable development of food systems. vi Preface I, Ian Turner, gathered and developed all components contained within this dissertation under the supervision of Dr. Nathan Pelletier, and in consultation with my supervisory committee members Dr. Tina Widowski and Dr. Jeff Andrews. No generative AI was used in this research. This included investigation of all research objectives, and analysis of all results. Four of the eight chapters in this dissertation are written in the form of manuscripts for publication in scientific journals. Three of those manuscripts have already been published. The details of these publications, including any differences between the published results and those presented in the corresponding research chapters of this dissertation, are provided below: • Chapter 2 was published as a primary research article in Resources, Conservation and Recycling. Since publication, methodological updates have been made regarding upstream field crop production, and emissions modeling. All updated methods are detailed in chapter 2 and the associated appendix. I was responsible for the design, data collection, analysis, and writing of this manuscript. All co-authors contributed to reviewing and improving the manuscript. The citation for this publication is provided below. o Turner, I., Heidari, D., Pelletier, N. 2022. Life cycle assessment of contemporary Canadian egg production systems during the transition from conventional cage to alternative housing systems: Update and analysis of trends and conditions. Resour. Conserv. Recycl. 176, 105907. • Chapter 3 was published as a primary research article in Journal of Cleaner Production. Since publication, updates have been made to this chapter in line with those made to the previous chapter. I was responsible for the design, data collection, analysis, and writing vii of this manuscript. All co-authors contributed to reviewing and improving the manuscript. The citation for this publication is provided below. o Turner, I., Heidari, D., Pelletier, N. 2022. Environmental impact mitigation potential of increased resource use efficiency in industrial egg production systems. J. Clean. Prod. 354, 131743. • Chapter 4 was published as a primary research article in Sustainable Production and Consumption. No changes have been made to this article since publication. I was responsible for the design, data collection, analysis, and writing of this manuscript. All co-authors contributed to reviewing and improving the manuscript. The citation for this publication is provided below. o Turner, I., Heidari, D., Widowski T., Pelletier, N. 2023. Development of a life cycle impact assessment methodology for animal welfare with an application in the poultry industry. Sustain. Prod. Consum. 40, 30 – 47. • Chapter 5 was published as a review article in Renewable and Sustainable Energy Reviews. I was responsible for the design, data collection, analysis, and writing of this manuscript. All co-authors contributed to reviewing and improving the manuscript. The citation for this publication is provided below. o Turner, I., Bamber, N., Andrews, J., Pelletier, N. 2024. Systematic review of the life cycle optimization literature, and recommendations for performance of life cycle optimization studies. Renew. Sust. Energy Rev. 208, 115058. Chapters 6 and 7 are being prepared for publication. Details about these manuscripts, potential venues, and anticipated dates of submission are provided below. viii • Chapter 6 is intended to be submitted for publication as a research article in the journal Science of the Total Environment. The intended date of submission is January 31st, 2025. I am responsible for the design, data collection, analysis, and writing of this manuscript. All co-authors will contribute to reviewing and improving the manuscript. • Chapter 7 is intended to be submitted for publication as a research article in the journal Sustainable Production and Consumption. The intended date of submission is January 31st, 2025. I am responsible for the design, data collection, analysis, and writing of this manuscript. All co-authors will contribute to reviewing and improving the manuscript. ix Table of Contents Abstract ......................................................................................................................................... iii Lay Summary .................................................................................................................................v Preface ........................................................................................................................................... vi Table of Contents ......................................................................................................................... ix List of Tables .............................................................................................................................. xvi List of Figures ......................................................................................................................... xxxiii List of Abbreviations ................................................................................................................ xliii Acknowledgements .................................................................................................................... xlv Dedication ................................................................................................................................. xlvii 1. Introduction ................................................................................................................................1 1.1 Context and rationale ...................................................................................................... 1 1.2 Research questions .......................................................................................................... 8 1.3 Dissertation reading guide ............................................................................................ 10 1.4 Acknowledgement ........................................................................................................ 16 2. Life cycle assessment of Canadian, non-organic egg production systems, with differentiation by housing system type ......................................................................................17 2.1 Chapter synopsis ........................................................................................................... 17 2.2 Context and rationale .................................................................................................... 17 2.3 Methods......................................................................................................................... 19 2.3.1 Goal and scope definition ......................................................................................... 20 2.3.1.1 Objectives ......................................................................................................... 20 2.3.1.2 System boundaries ............................................................................................ 21 2.3.1.3 Functional unit and reference flow ................................................................... 23 2.3.1.4 Cut-off criteria and exclusions .......................................................................... 23 2.3.1.5 Co-product allocation........................................................................................ 24 2.3.2 Life cycle inventory .................................................................................................. 30 2.3.2.1 Foreground data sources and assumptions ........................................................ 31 2.3.2.1.1 Primary collection of laying hen data ......................................................... 31 x 2.3.2.1.2 Production weighting of breeder, hatchery, pullet, and layer operations .... 35 2.3.2.1.3 Data characterizing feed formulations, breeder, hatchery, and pullet operations 38 2.3.2.1.4 Modeling N, P, and methane emissions from poultry manure .................... 38 2.3.2.1.5 Modeling emissions from waste treatment.................................................. 46 2.3.2.2 Background data sources and assumptions ....................................................... 51 2.3.2.2.1 Modeling feed input supply chains ............................................................. 51 2.3.2.2.2 Modeling energy inputs to egg supply chain stages .................................... 52 2.3.2.3 Data quality and uncertainty assessment .......................................................... 52 2.3.3 Life cycle impact assessment .................................................................................... 56 2.3.3.1 Sensitivity analyses ........................................................................................... 60 2.3.3.1.1 Life cycle impact assessment method ......................................................... 60 2.3.3.1.2 Use of a vegetarian feed formulation .......................................................... 62 2.4 Results and discussion .................................................................................................. 64 2.4.1 Representativeness of collected egg production data ............................................... 64 2.4.2 Life cycle inventory results ....................................................................................... 66 2.4.3 Life cycle impact assessment results ........................................................................ 73 2.4.4 Sensitivity analyses ................................................................................................... 82 2.4.4.1 Life cycle impact assessment method ............................................................... 82 2.4.4.2 Use of a vegetarian feed formulation ................................................................ 84 2.5 Limitations and conclusions ......................................................................................... 95 3. Joint data envelopment analysis and life cycle assessment of Canadian, non-organic egg production systems.....................................................................................................................101 3.1 Chapter synopsis ......................................................................................................... 101 3.2 Context and rationale .................................................................................................. 102 3.3 Methods....................................................................................................................... 105 3.3.1 Life cycle assessment .............................................................................................. 105 3.3.2 Joint DEA+LCA ..................................................................................................... 106 3.3.3 Investigation of key drivers of farm level efficiency .............................................. 111 xi 3.3.4 Investigation of cumulative environmental impacts of egg production under different housing system transition scenarios .................................................................................... 113 3.4 Results and discussion ................................................................................................ 114 3.4.1 Determination of the most appropriate production possibility set to describe Canadian egg production .................................................................................................... 114 3.4.2 Determination of efficiency targets for Canadian egg farms, and calculation of environmental impact mitigation potentials associated with industry-wide increases in efficiency............................................................................................................................. 115 3.4.3 Investigating environmental impacts under different housing system transition scenarios .............................................................................................................................. 126 3.5 Limitations and conclusions ....................................................................................... 132 4. Development of a type 1 animal welfare life cycle impact assessment methodology for assessment of Canadian laying hens .........................................................................................135 4.1 Chapter synopsis ......................................................................................................... 135 4.2 Context and rationale .................................................................................................. 136 4.3 Methods....................................................................................................................... 137 4.3.1 Identification of requisite LCIA methods components ........................................... 137 4.3.2 Testing of proposed method with a case study of the Canadian egg industry ........ 147 4.4 Results and discussion ................................................................................................ 149 4.4.1 Identification of type 1 animal welfare LCIA method components ....................... 149 4.4.1.1 Animal welfare definitions, areas of protection, and stakeholder categories . 151 4.4.1.2 Impact categories and subcategories ............................................................... 154 4.4.1.3 Inventory indicators and data requirements .................................................... 156 4.4.1.3.1 Biological function .................................................................................... 160 4.4.1.3.2 Natural behaviour ...................................................................................... 163 4.4.1.3.3 Affective state ........................................................................................... 166 4.4.1.3.4 Summary of the proposed animal welfare LCIA method ......................... 169 4.4.1.4 Characterization factors .................................................................................. 173 4.4.2 Testing the proposed method with a case study of the Canadian egg industry ...... 175 4.5 Limitations and conclusions ....................................................................................... 192 xii 5. Systematic review of the life cycle optimization (LCO) literature, and development of generalized guidelines for performance of LCO studies ........................................................195 5.1 Chapter synopsis ......................................................................................................... 195 5.2 Context and rationale .................................................................................................. 196 5.3 Methods....................................................................................................................... 197 5.3.1 Study selection ........................................................................................................ 197 5.3.2 Data collection and synthesis .................................................................................. 201 5.4 Results and discussion ................................................................................................ 204 5.4.1 Characteristics of reviewed studies ......................................................................... 204 5.4.2 Choice of optimization framework ......................................................................... 209 5.4.3 Aggregated and disaggregated objective functions ................................................ 215 5.4.4 Decision tree ........................................................................................................... 219 5.5 Limitations and conclusions ....................................................................................... 225 6. Life cycle optimization of laying hen feed formulations considering environmental impacts and economic costs.......................................................................................................228 6.1 Chapter synopsis ......................................................................................................... 228 6.2 Context and rationale .................................................................................................. 228 6.3 Methods....................................................................................................................... 230 6.3.1 Identification of reference feed mill locations ........................................................ 230 6.3.2 Regionalized modeling of feed ingredients ............................................................ 232 6.3.2.1 Field crops and processed crop products ........................................................ 232 6.3.2.2 Corn dried distillers grains with solubles (DDGS) ......................................... 236 6.3.2.3 Limestone ........................................................................................................ 236 6.3.2.4 Bakery by-product .......................................................................................... 237 6.3.2.5 Rendered animal products............................................................................... 238 6.3.2.6 Vitamin supplements and synthetic amino acids ............................................ 240 6.3.3 Estimation of ingredient transportation distances ................................................... 242 6.3.4 Estimation of ingredient procurement and transportation costs ............................. 246 6.3.4.1 Ingredient procurement costs .......................................................................... 246 6.3.4.2 Ingredient transportation costs ........................................................................ 248 xiii 6.3.4.2.1 Truck transport .......................................................................................... 248 6.3.4.2.2 Rail transport ............................................................................................. 249 6.3.5 Identification of laying hen nutrient requirements.................................................. 250 6.3.6 Identification of ingredient minimum and maximum inclusion rates ..................... 254 6.3.7 Optimization problem definition ............................................................................. 259 6.3.8 Estimation of the environmental impact mitigation potentials of using optimal feed formulations on Canadian egg farms .................................................................................. 266 6.3.9 Sensitivity analysis – Extension of optimization framework to include other ingredients ........................................................................................................................... 268 6.4 Results and discussion ................................................................................................ 269 6.4.1 Single-objective, economically optimal feeds ........................................................ 269 6.4.2 Single-objective, GHG optimal feeds ..................................................................... 273 6.4.3 Bi- and multi-objective optimal feeds..................................................................... 282 6.4.4 Environmental impact mitigation potential of using optimized feeds in Canadian egg production systems.............................................................................................................. 296 6.4.5 Sensitivity analysis - Extension of optimization framework to include other ingredients ........................................................................................................................... 298 6.5 Limitations and conclusions ....................................................................................... 304 7. Optimization of Canadian egg production systems for environmental and animal welfare objectives .....................................................................................................................................308 7.1 Chapter synopsis ......................................................................................................... 308 7.2 Context and rationale .................................................................................................. 308 7.3 Methods....................................................................................................................... 310 7.3.1 Environmental and animal welfare impacts of different egg housing systems ...... 310 7.3.2 Optimization problem definition ............................................................................. 311 7.3.2.1 Objective functions ......................................................................................... 311 7.3.2.2 Constraints ...................................................................................................... 315 7.3.2.3 Weighted sum solver....................................................................................... 316 7.3.3 Sensitivity analysis.................................................................................................. 321 7.4 Results and discussion ................................................................................................ 324 xiv 7.5 Limitations and conclusions ....................................................................................... 332 8. Conclusions .............................................................................................................................334 8.1 Research contributions and reflections ....................................................................... 334 8.2 Next steps and future directions .................................................................................. 337 References ...................................................................................................................................341 Appendices ..................................................................................................................................416 Appendix A ............................................................................................................................. 416 A.1 Agricultural production of field crops .................................................................... 416 A.2 Exclusions from field crop systems ........................................................................ 416 A.3 Allocation in field crop systems ............................................................................. 418 A.4 Modeling fertilizer inputs to field crop systems ..................................................... 422 A.5 Modeling transportation in field crop systems........................................................ 424 A.6 Irrigation water volumes for field crops ................................................................. 425 A.7 Biological nitrogen fixation .................................................................................... 426 A.8 Field level direct N2O emissions from field crops .................................................. 426 A.9 Field level indirect N2O emissions from field crops ............................................... 430 A.10 Field level non-GHG nitrogenous emissions .......................................................... 432 A.11 Modeling of pesticides and associated emissions ................................................... 435 A.12 Background datasets used for modeling field crop systems ................................... 439 A.13 Modeling field crop processing .............................................................................. 441 A.14 Modeling livestock derived feed ingredients .......................................................... 443 A.15 Modeling other feed ingredients ............................................................................. 451 A.16 Supplementary tables .............................................................................................. 453 A.17 Supplementary figures ............................................................................................ 570 Appendix B ............................................................................................................................. 580 B.1 Supplementary tables .............................................................................................. 580 B.2 Supplementary figures ............................................................................................ 584 Appendix C ............................................................................................................................. 593 C.1 Components of a type 1 LCIA method ................................................................... 593 C.2 Literature reviewed ................................................................................................. 594 xv C.3 Supplementary tables .............................................................................................. 625 C.4 Supplementary equations ........................................................................................ 626 C.5 Potential areas for future improvement of proposed method .................................. 627 Appendix D ............................................................................................................................. 631 D.1 PRISMA Checklist.................................................................................................. 631 D.2 Literature reviewed ................................................................................................. 654 Appendix E ............................................................................................................................. 680 E.1 Supplementary tables .............................................................................................. 681 E.2 Supplementary figures ............................................................................................ 733 xvi List of Tables Table 2-1. Categorization and proportions of co-products and waste products at each step in the egg supply chain. If no percentages are given for specific flows, 100% of that flow is categorized as indicated in the column heading. .............................................................................................. 27 Table 2-2. Calculated allocation factors for all co-products identified for all foreground system processes in the Canadian egg supply chain. ................................................................................ 29 Table 2-3. Assumed proportions of production for each step in the layer supply chain used for calculation of production weighted average factors. Slashes represent no data reported for that specific province-activity combination. ........................................................................................ 37 Table 2-4. Production-weighted average values for EF1 and FracLeach used in calculation of nitrogenous emissions from deposition of poultry manure on agricultural fields. Note that values for EF1 have been scaled by 0.84, as suggested by Liang et al., (2020) for N from manure sources........................................................................................................................................... 45 Table 2-5. Impact categories assessed by the CML-IA baseline LCIA method and included in this analysis, and associated category indicator units ................................................................... 60 Table 2-6. Impact categories assessed in a sensitivity analysis using the TRACI 2.1 LCIA method, and associated category indicator units........................................................................... 62 Table 2-7. Percent representativeness of collected egg production data based on number of farms across egg producing provinces and Canada-wide. Cage-free represents total coverage across both single- and multi-tier cage non-cage housing and free range, as the number of farms in each province for these systems was provided as just a single number. The “overall” row shows the percentage of egg farms included in the sample for each region regardless of housing system type. Zeros indicate no surveys were collected representing that housing system for that xvii province. Slashes indicate no housing systems of that type exist in that province. Supplementary data collected during 2017 was not included in calculations of representativeness. .................... 65 Table 2-8. Life cycle inventory data by housing system for the production of one tonne of eggs in layer facilities in Canada, based on 2012 data reported by Pelletier, (2017) and data collected during 2017 and Summer and Fall 2019. Blank spaces indicate inventory data that was not included in the model for that particular housing system. ............................................................ 67 Table 2-9. Life cycle environmental impacts per tonne of eggs produced in each non-organic housing system in the Canadian egg industry, and a Canadian production weighted average, estimated using the CML IA Baseline LCIA method. Superscript letters denote significant differences across housing systems within impact categories. Estimated impacts are presented ± standard error of the mean ............................................................................................................ 76 Table 3-1. Assumed percentage of total non-organic egg production used in each housing system transition scenario used in scenario analysis .............................................................................. 114 Table 3-2. Average percent efficiency scores, as well as the variance and range of scores across each housing system as calculated using DEA ........................................................................... 116 Table 3-3. Production-weighted average efficiency targets for feed and pullet inputs per tonne of eggs produced as calculated using DEA per housing system, compared to current baseline averages....................................................................................................................................... 119 Table 3-4. Estimated percent reductions in impacts per tonne of eggs produced in each housing system under DEA-efficient conditions relative to baseline impacts as estimated in chapter 2. 121 Table 4-1. The four most commonly used definitions of animal welfare in the literature. ........ 151 xviii Table 4-2. Candidate inventory indicators for each impact category that were utilized in a minimum 5% of all sources reviewed, presented as a percentage of total papers reviewed in which the measure was collected. ............................................................................................... 158 Table 4-3. Breakdown of the proposed requisite components of an animal welfare LCIA method for laying hens. Columns have been labelled as referenced in text. In inventory data requirements, Y/N refers to data collection by means of asking farmers a question to which they may reply yes or no. .................................................................................................................... 169 Table 4-4. LCI data used to calculate animal welfare impacts per tonne of eggs produced in each housing system in Canada. With the exception of the number of pullets required to produce one tonne of eggs, inventory data are presented as the probability associated with each measure as calculated from primary survey data collected from Canadian egg farmers representing management practices and animal welfare outcomes on farms in 2019. .................................... 175 Table 5-1. Distribution of the systems studied by the LCO literature reviewed across ISIC classification sections.................................................................................................................. 205 Table 6-1. Feed mill reference locations for each of the five provinces included in regionalized optimization of laying hen feed formulations. ........................................................................... 231 Table 6-2. Assumed market prices for ingredients included in regionalized optimization of laying hen feed formulations. ................................................................................................................ 248 Table 6-3. Nutritional constraints used in regionalized optimization of laying hen feed formulations as derived from Lohmann LSL Classic breed management guides ...................... 253 Table 6-4. Summary of minimum and maximum ingredient inclusion rates used as constraints in regionalized optimization of laying hen feed formulations ........................................................ 257 xix Table 6-5. Weights used for multi-objective optimization with five objective functions across four different scenarios for regionalized optimization of laying hen feed formulations. ........... 266 Table 7-1. Three different constraint scenarios for multi objective optimization of Canadian egg production for environmental and animal welfare outcomes, and the market year those constraints are intended to represents. ........................................................................................ 316 Table 7-2. Weights used for multi-objective optimization of the distribution of non-organic Canadian egg production in different housing systems with 8 objective functions. .................. 320 Table 7-3. Weights used for multi-objective optimization of the distribution of non-organic Canadian egg production in different housing systems with 8 objective functions for sensitivity analysis ........................................................................................................................................ 323 Table A 1. Percentage of total production of each crop attributable to each RU. Zero values indicate no production of that crop in that particular RU ........................................................... 453 Table A 2. Assumed percentages of aboveground crop residues removed from fields for each crop modeled. .............................................................................................................................. 454 Table A 3. Allocation factors calculated for allocation of impacts between corn, barley, and wheat grain and harvested residues based on gross chemical energy content of co-products .... 454 Table A 4. Recalculated fertilizer inputs per kilogram of barley produced in each RU (kg/kg) 455 Table A 5. Recalculated fertilizer inputs per kilogram of corn produced in each RU (kg/kg) ... 459 Table A 6. Recalculated fertilizer inputs per kilogram of wheat produced in each RU (kg/kg) 461 Table A 7. Recalculated fertilizer inputs per kilogram of soy produced in each RU (kg/kg) .... 465 Table A 8. Recalculated fertilizer inputs per kilogram of canola produced in each RU (kg/kg) 468 Table A 9. Assumed distances for transportation of inputs to farm, used for all crops and all RUs...................................................................................................................................................... 470 xx Table A 10. Conversion factors used for calculation of energy consumption data, when necessary. .................................................................................................................................... 471 Table A 11. Variables and associated units used in calculation of field-level direct N2O emissions from field crop systems .............................................................................................. 471 Table A 12. Scaled values of EF1 used in calculation of field-level direct N2O emissions for each RU for N inputs derived from synthetic, crop residues, and mineralized sources...................... 472 Table A 13. Assumed above- and below-ground crop residue yields and N contents used for estimation of emissions associated with crop residue management for each crop. .................... 473 Table A 14. Variables and associated units used in calculation of field-level indirect N2O emissions from volatilization of N. ............................................................................................. 474 Table A 15. Variables and associated units used in calculation of field-level indirect N2O emissions from leaching of N. .................................................................................................... 475 Table A 16. Variables and associated units used in calculation of field-level P losses .............. 476 Table A 17. Percent of total pesticides applied as herbicides, fungicides, insecticides, and other, as adapted from table 9-6 of the CRSC methodology report ((S&T)2 Consultants Inc., 2022a)...................................................................................................................................................... 477 Table A 18. Assumed herbicide mix used for all RUs, and the corresponding products used to model them for each crop and RU. ............................................................................................. 477 Table A 19. Processes used for modeling energy inputs to field crop production systems, and any modification made to them. ........................................................................................................ 478 Table A 20. Processes used for modeling production of synthetic fertilizers, and modifications made to make them more representative of Canadian production conditions. ........................... 480 xxi Table A 21. Percent inclusion rate, and assumed nutrient contents and associated sources for calculation of nutrient mass balances for estimation of manure management-related emissions from breeder flocks. .................................................................................................................... 484 Table A 22. Percent inclusion rate, and assumed nutrient contents and associated sources for calculation of nutrient mass balances for estimation of manure management-related emissions from non-organic pullets. ............................................................................................................ 485 Table A 23. Percent inclusion rate, and assumed nutrient contents and associated sources for calculation of nutrient mass balances for estimation of manure management-related emissions from non-organic layers. ............................................................................................................. 488 Table A 24. Estimated amounts of N applied to fields as poultry manure per tonne of eggs produced in each housing system ............................................................................................... 491 Table A 25. Closest canola processing facilities and assumed transportation distances for processing of canola for inputs to egg supply chain feeds. ......................................................... 492 Table A 26. Closest soybean processing facilities and assumed transportation distances for processing of soy for inputs to egg supply chain feeds. ............................................................. 493 Table A 27. Complete vegetarian feed formulation adapted from Turner et al. (2023b) for use in sensitivity analysis in chapter 2 as described by section 2.3.3.1.2 ............................................. 495 Table A 28. Comparison of protein, N, and P content per tonne of conventional feed, and the vegetarian feed used in sensitivity analysis as described in section 2.3.3.1.2. ........................... 501 Table A 29. Recalculated pesticide inputs per kilogram of barley produced in each RU (g/kg) 502 Table A 30. Recalculated pesticide inputs per kilogram of canola produced in each RU (g/kg) 504 Table A 31. Recalculated pesticide inputs per kilogram of corn produced in each RU (g/kg) .. 505 Table A 32. Recalculated pesticide inputs per kilogram of soy produced in each RU (g/kg) .... 506 xxii Table A 33. Recalculated pesticide inputs per kilogram of wheat produced in each RU (g/kg) 507 Table A 34. Recalculated field level emissions per kilogram of barley produced in each RU (kg/kg) ......................................................................................................................................... 509 Table A 35. Recalculated field level emissions per kilogram of canola produced in each RU (kg/kg) ......................................................................................................................................... 511 Table A 36. Recalculated field level emissions per kilogram of corn produced in each RU (kg/kg) ......................................................................................................................................... 513 Table A 37. Recalculated field level emissions per kilogram of soy produced in each RU (kg/kg)..................................................................................................................................................... 514 Table A 38. Recalculated field level emissions per kilogram of wheat produced in each RU (kg/kg) ......................................................................................................................................... 515 Table A 39. Abiotic depletion impacts per kilogram of crop produced in each RU, excluding impacts related to SOC changes and perennial to annual land transitions. A blank space indicates that crop is not produced in that RU. .......................................................................................... 517 Table A 40. Abiotic depletion (fossil fuels) impacts per kilogram of crop produced in each RU, excluding impacts related to SOC changes and perennial to annual land transitions. A blank space indicates that crop is not produced in that RU. ................................................................. 519 Table A 41. Acidification impacts per kilogram of crop produced in each RU, excluding impacts related to SOC changes and perennial to annual land transitions. A blank space indicates that crop is not produced in that RU. ................................................................................................. 521 Table A 42. Eutrophication impacts per kilogram of crop produced in each RU, excluding impacts related to SOC changes and perennial to annual land transitions. A blank space indicates that crop is not produced in that RU. .......................................................................................... 523 xxiii Table A 43. Freshwater aquatic ecotoxicity impacts per kilogram of crop produced in each RU, excluding impacts related to SOC changes and perennial to annual land transitions. A blank space indicates that crop is not produced in that RU. ................................................................. 524 Table A 44. Global warming impacts per kilogram of crop produced in each RU, excluding impacts related to SOC changes and perennial to annual land transitions. A blank space indicates that crop is not produced in that RU. .......................................................................................... 526 Table A 45. Human toxicity impacts per kilogram of crop produced in each RU, excluding impacts related to SOC changes and perennial to annual land transitions. A blank space indicates that crop is not produced in that RU. .......................................................................................... 528 Table A 46. Marine aquatic ecotoxicity impacts per kilogram of crop produced in each RU, excluding impacts related to SOC changes and perennial to annual land transitions. A blank space indicates that crop is not produced in that RU. ................................................................. 530 Table A 47. Ozone layer depletion impacts per kilogram of crop produced in each RU, excluding impacts related to SOC changes and perennial to annual land transitions. A blank space indicates that crop is not produced in that RU. .......................................................................................... 532 Table A 48. Photochemical oxidation impacts per kilogram of crop produced in each RU, excluding impacts related to SOC changes and perennial to annual land transitions. A blank space indicates that crop is not produced in that RU. ................................................................. 534 Table A 49. Terrestrial ecotoxicity impacts per kilogram of crop produced in each RU, excluding impacts related to SOC changes and perennial to annual land transitions. A blank space indicates that crop is not produced in that RU. .......................................................................................... 535 Table A 50. Estimated impacts per tonne of breeder, pullet, and layer feed milled, excluding SOC changes and impacts of perennial to annual land use transitions. ...................................... 538 xxiv Table A 51. Estimated impacts per functional unit for supply chain steps in the Canadian egg industry (kg/functional unit) ....................................................................................................... 539 Table A 52. Comparison of estimated impacts per kilogram of barley produced from previous field crop production models used by Pelletier (2017), and production-weighted national average models derived from the CRSC reports. A negative value for percent decrease indicates that estimated impacts increased relative to the models used by Pelletier (2017). ............................ 540 Table A 53. Comparison of estimated impacts per kilogram of canola produced from previous field crop production models used by Pelletier (2017), and production-weighted national average models derived from the CRSC reports. A negative value for percent decrease indicates that estimated impacts increased relative to the models used by Pelletier (2017). ............................ 542 Table A 54. Comparison of estimated impacts per kilogram of corn produced from previous field crop production models used by Pelletier (2017), and production-weighted national average models derived from the CRSC reports. A negative value for percent decrease indicates that estimated impacts increased relative to the models used by Pelletier (2017). ............................ 544 Table A 55. Comparison of estimated impacts per kilogram of soy produced from previous field crop production models used by Pelletier (2017), and production-weighted national average models derived from the CRSC reports. A negative value for percent decrease indicates that estimated impacts increased relative to the models used by Pelletier (2017). ............................ 546 Table A 56. Comparison of estimated impacts per kilogram of wheat produced from previous field crop production models used by Pelletier (2017), and production-weighted national average models derived from the CRSC reports. A negative value for percent decrease indicates that estimated impacts increased relative to the models used by Pelletier (2017). ............................ 548 xxv Table A 57. Percent contributions to abiotic depletion impacts per tonne of eggs produced in different housing systems, estimated using the CML-IA LCIA method .................................... 551 Table A 58. Percent contributions to abiotic depletion (fossil fuels) impacts per tonne of eggs produced in different housing systems, estimated using the CML-IA LCIA method ................ 552 Table A 59. Percent contributions to acidification impacts per tonne of eggs produced in different housing systems, estimated using the CML-IA LCIA method .................................... 553 Table A 60. Percent contributions to eutrophication impacts per tonne of eggs produced in different housing systems, estimated using the CML-IA LCIA method .................................... 554 Table A 61. Percent contributions to freshwater aquatic ecotoxicity impacts per tonne of eggs produced in different housing systems, estimated using the CML-IA LCIA method ................ 555 Table A 62. Percent contributions to GHG emissions per tonne of eggs produced in different housing systems, estimated using the CML-IA LCIA method ................................................... 556 Table A 63. Percent contributions to human toxicity impacts per tonne of eggs produced in different housing systems, estimated using the CML-IA LCIA method .................................... 557 Table A 64. Percent contributions to marine aquatic ecotoxicity impacts per tonne of eggs produced in different housing systems, estimated using the CML-IA LCIA method ................ 558 Table A 65. Percent contributions to ozone layer depletion impacts per tonne of eggs produced in different housing systems, estimated using the CML-IA LCIA method .................................... 559 Table A 66. Percent contributions to photochemical oxidation impacts per tonne of eggs produced in different housing systems, estimated using the CML-IA LCIA method ................ 560 Table A 67. Percent contributions to terrestrial ecotoxicity impacts per tonne of eggs produced in different housing systems, estimated using the CML-IA LCIA method .................................... 561 xxvi Table A 68. Life cycle environmental impacts per tonne of eggs produced in each non-organic housing system in the Canadian egg industry, and a Canadian production weighted average, estimated using the TRACI 2.1 LCIA method. Superscript letters denote significant differences across housing systems within impact categories. Estimated impacts are presented ± standard error of the mean. ........................................................................................................................ 562 Table A 69. Life cycle environmental impacts per tonne of eggs produced in each non-organic housing system in the Canadian egg industry using a vegetarian feed formulation, estimated using the CML IA baseline LCIA method. Estimated impacts are presented ± standard error of the mean. All estimated impacts are significantly lower than baseline estimated impacts presented in Table 2-9. ................................................................................................................ 564 Table A 70. Synthetic amino acid mix used for modeling conventional feed formulations in Canada for pullets and layers. ..................................................................................................... 567 Table B 1. Coefficients and r2 values for the regression analyses performed between total dozens produced and number of hens housed, per housing system in order to inform the choice of PPS for DEA. ...................................................................................................................................... 580 Table B 2. Coefficients and R2 values for the regression analyses performed between dozens produced per hen and number of hens housed, per housing system in order to inform the choice of PPS for DEA. .......................................................................................................................... 580 Table B 3. Estimated impacts per tonne of eggs produced in each non-organic housing system, assuming all farms are operating at maximum observed levels of pullet- and feed-use efficiency within housing systems, estimated using the CML IA Baseline LCIA method ......................... 581 Table B 4. Estimated impacts per tonne of eggs produced in Canada under different housing system transition scenarios, assuming all farms are operating at the maximum levels of feed- and xxvii pullet-use efficiency currently realized within housing systems, compared to current Canadian average impacts estimated using the CML IA Baseline LCIA method. ..................................... 582 Table C 1. Summary of characterization factors associated with each inventory indicator in the proposed animal welfare LCIA method developed in Chapter 4 ............................................... 625 Table D 1. Completed PRISMA checklist describing the systematic review performed in chapter 5, as adapted from Page et al. (2021) .......................................................................................... 631 Table E 1. Mode of transportation, and cumulative transportation distance by truck and rail for barley produced in each production location to be transported to each reference feed mill location for the regionalized feed formulation optimization performed in chapter 6 ............................... 681 Table E 2. Mode of transportation, and cumulative transportation distance by truck and rail for corn produced in each production location to be transported to each reference feed mill location for the regionalized feed formulation optimization performed in chapter 6 ............................... 683 Table E 3. Mode of transportation, and cumulative transportation distance by truck and rail for peas produced in each production location to be transported to each reference feed mill location for the regionalized feed formulation optimization performed in chapter 6 ............................... 684 Table E 4. Mode of transportation, and cumulative transportation distance by truck and rail for wheat produced in each production location to be transported to each reference feed mill location for the regionalized feed formulation optimization performed in chapter 6 ............................... 686 Table E 5. Mode of transportation, and cumulative transportation distance by truck and rail for canola oil and meal produced in each production location to be transported to each reference feed mill location for the regionalized feed formulation optimization performed in chapter 6 ......... 688 xxviii Table E 6. Mode of transportation, and cumulative transportation distance by truck and rail for soybean meal produced in each production location to be transported to each reference feed mill location for the regionalized feed formulation optimization performed in chapter 6 ................. 691 Table E 7. Mode of transportation, and cumulative transportation distance by truck and rail for corn DDGS produced in each production location to be transported to each reference feed mill location for the regionalized feed formulation optimization performed in chapter 6 ................. 694 Table E 8. Mode of transportation, and cumulative transportation distance by truck and rail for bakery meal produced in each production location to be transported to each reference feed mill location for the regionalized feed formulation optimization performed in chapter 6 ................. 696 Table E 9. Mode of transportation, and cumulative transportation distance by truck and rail for limestone produced in each production location to be transported to each reference feed mill location for the regionalized feed formulation optimization performed in chapter 6 ................. 698 Table E 10. Mode of transportation, and cumulative transportation distance by truck and rail for synthetic amino acids and vitamin supplements produced in each production location to be transported to each reference feed mill location for the regionalized feed formulation optimization performed in chapter 6 ........................................................................................... 699 Table E 11. Mode of transportation, and cumulative transportation distance by truck and rail for rendered animal meal produced in each production location to be transported to each reference feed mill location for the regionalized feed formulation optimization performed in chapter 6 . 700 Table E 12. Economically optimal feed formulation for feed milled in Abbotsford, British Columbia as identified by single-objective optimization for lowest cost ................................... 703 Table E 13. Economically optimal feed formulation for feed milled in Lethbridge, Alberta as identified by single-objective optimization for lowest cost ........................................................ 703 xxix Table E 14. Economically optimal feed formulation for feed milled in Wallenstein, Ontario as identified by single-objective optimization for lowest cost ........................................................ 704 Table E 15. Economically optimal feed formulation for feed milled in St. Hyacinthe, Quebec as identified by single-objective optimization for lowest cost ........................................................ 705 Table E 16. Economically optimal feed formulation for feed milled in Truro, Nova Scotia as identified by single-objective optimization for lowest cost ........................................................ 706 Table E 17. Estimated impacts per tonne of economically optimal feed milled at each reference feed mill location for impact categories not impacted by different treatments of upstream SOC and perennial to annual land use transitions ............................................................................... 707 Table E 18. Estimated impacts per tonne of economically optimal feed milled at each reference feed mill location for impact categories that are influenced by different treatments of upstream SOC and perennial to annual land use transitions ...................................................................... 708 Table E 19. GHG optimal feed formulation excluding SOC and LUC for feed milled in Abbotsford, British Columbia, as identified by single-objective optimization for lowest GHG emissions ..................................................................................................................................... 709 Table E 20. GHG optimal feed formulation excluding SOC and LUC for feed milled in Lethbridge, Alberta, as identified by single-objective optimization for lowest GHG emissions 710 Table E 21. GHG optimal feed formulation excluding SOC and LUC for feed milled in Wallenstein, Ontario, as identified by single-objective optimization for lowest GHG emissions..................................................................................................................................................... 711 Table E 22. GHG optimal feed formulation excluding SOC and LUC for feed milled in St. Hyacinthe, Quebec, as identified by single-objective optimization for lowest GHG emissions 711 xxx Table E 23. GHG optimal feed formulation excluding SOC and LUC for feed milled in Truro, Nova Scotia, as identified by single-objective optimization for lowest GHG emissions ........... 712 Table E 24. GHG optimal feed formulation including SOC and LUC for feed milled in Abbotsford, British Columbia, as identified by single-objective optimization for lowest GHG emissions ..................................................................................................................................... 713 Table E 25. GHG optimal feed formulation including SOC and LUC for feed milled in Lethbridge, Alberta, as identified by single-objective optimization for lowest GHG emissions 714 Table E 26. GHG optimal feed formulation including SOC and LUC for feed milled in Wallenstein, Ontario, as identified by single-objective optimization for lowest GHG emissions..................................................................................................................................................... 715 Table E 27. GHG optimal feed formulation including SOC and LUC for feed milled in St. Hyacinthe, as identified by single-objective optimization for lowest GHG emissions .............. 716 Table E 28. GHG optimal feed formulation including SOC and LUC for feed milled in Truro, Nova Scotia, as identified by single-objective optimization for lowest GHG emissions ........... 716 Table E 29. Estimated life cycle environmental impacts per tonne of economically optimal feed milled at each reference mill location, excluding SOC and LUC............................................... 718 Table E 30. Estimated life cycle environmental impacts per tonne of economically optimal feed milled at each reference mill location, including SOC and LUC ............................................... 719 Table E 31. Estimated life cycle environmental impacts per tonne of GHG optimal feed milled at each reference mill location, excluding SOC and LUC .............................................................. 720 Table E 32. Estimated life cycle environmental impacts per tonne of GHG optimal feed milled at each reference mill location, including SOC and LUC .............................................................. 721 xxxi Table E 33. Bi-objective optimal feed formulation excluding SOC and LUC for feed milled in Abbotsford, British Columbia, optimized for cost and GHG emissions .................................... 722 Table E 34. Bi-objective optimal feed formulation excluding SOC and LUC for feed milled in Lethbridge, Alberta, optimized for cost and GHG emissions ..................................................... 722 Table E 35. Bi-objective optimal feed formulation excluding SOC and LUC for feed milled in Wallenstein, Ontario, optimized for cost and GHG emissions ................................................... 723 Table E 36. Bi-objective optimal feed formulation excluding SOC and LUC for feed milled in St. Hyacinthe, Quebec, optimized for cost and GHG emissions...................................................... 724 Table E 37. Bi-objective optimal feed formulation excluding SOC and LUC for feed milled in Truro, Nova Scotia, optimized for cost and GHG emissions ..................................................... 725 Table E 38. Bi-objective optimal feed formulation including SOC and LUC for feed milled in Abbotsford, British Columbia, optimized for cost and GHG emissions .................................... 726 Table E 39. Bi-objective optimal feed formulation including SOC and LUC for feed milled in Lethbridge, Alberta, optimized for cost and GHG emissions ..................................................... 726 Table E 40. Bi-objective optimal feed formulation including SOC and LUC for feed milled in Wallenstein, Ontario, optimized for cost and GHG emissions ................................................... 727 Table E 41. Bi-objective optimal feed formulation including SOC and LUC for feed milled in St. Hyacinthe, Quebec, optimized for cost and GHG emissions...................................................... 728 Table E 42. Bi-objective optimal feed formulation including SOC and LUC for feed milled in Truro, Nova Scotia, optimized for cost and GHG emissions ..................................................... 729 Table E 43. Estimated environmental impacts per tonne of feed milled at each reference mill location for bi-objective optimal feeds, excluding SOC and LUC , optimized for cost and GHG emissions ..................................................................................................................................... 730 xxxii Table E 44. Estimated environmental impacts per tonne of feed milled at each reference mill location for bi-objective optimal feeds, inccluding SOC and LUC, optimized for cost and GHG emissions ..................................................................................................................................... 731 Table E 45. Costs per tonne of feed milled at each reference location for each different MOO scenario and for each different treatment of SOC changes and perennial to annual land transitions..................................................................................................................................................... 732 xxxiii List of Figures Figure 1-1. Dissertation overview describing major research outputs from each chapter, and how chapters contribute to one another ................................................................................................ 11 Figure 2-1. System boundaries for the cradle-to-farm-gate attributional environmental LCA of the Canadian, non-organic egg supply chain ................................................................................ 22 Figure 2-2. Relative percentage of impacts per tonne of eggs produced in difference housing systems. Conventional cage production is the reference system. Estimated using the CML IA Baseline LCIA method ................................................................................................................. 79 Figure 2-3. Percent contributions to total acidification impacts per tonne of eggs produced in each housing system as estimated using the CML IA Baseline LCIA method. ........................... 81 Figure 2-4. Percent contributions to total eutrophication impacts per tonne of eggs produced in each housing system as estimated using the CML IA Baseline LCIA method. ........................... 81 Figure 2-5. Percent contributions to total GHG emissions per tonne of eggs produced in each housing system as estimated using the CML IA Baseline LCIA method..................................... 82 Figure 2-6. Relative impacts per tonne of eggs produced across housing systems estimated using the TRACI 2.1. LCIA method ...................................................................................................... 83 Figure 2-7. Relative percentage of impacts per tonne of eggs produced in conventional cage production systems using a conventional and vegetarian diet, estimated using the CML IA baseline LCIA method. ................................................................................................................. 85 Figure 2-8. Percent contributions to total acidification impacts per tonne of eggs produced in each housing system when using a vegetarian feed formulation as estimated using the CML IA Baseline LCIA method ................................................................................................................. 87 xxxiv Figure 2-9. Percent contributions to total eutrophication impacts per tonne of eggs produced in each housing system when using a vegetarian feed formulation as estimated using the CML IA Baseline LCIA method. ................................................................................................................ 87 Figure 2-10. Percent contributions to total GHG emissions per tonne of eggs produced in each housing system when using a vegetarian feed formulation as estimated using the CML IA Baseline LCIA method. ................................................................................................................ 88 Figure 3-1. Flow chart describing the modified five-step method for DEA+LCA used in this analysis ........................................................................................................................................ 110 Figure 3-2. Box and whisker plot depicting variability in estimated efficiency scores for farms operating different non-organic housing systems in the Canadian egg industry. Boxes indicate the first and third quartiles on the edges, and include a median line. Whiskers represent 1.5 X the interquartile range, while points outside the whiskers are considered outliers........................... 116 Figure 3-3. Trace plot for the lasso model generated for enriched cage production systems. Number along the top axis represents the number of variables in the generated model. The leftmost dotted vertical line indicates the value of the tuning parameter λ at which the MSE of the generated model was minimized. The rightmost dotted vertical line represents the value of the tuning parameter λ at which the MSE of the generated model was within one standard error of the minimum MSE.................................................................................................................. 118 Figure 3-4. Estimated differences in relative contributions to acidification impacts per tonne of eggs produced in each housing system under baseline and DEA-efficient conditions ............... 123 Figure 3-5. Estimated differences in relative contributions to eutrophication impacts per tonne of eggs produced in each housing system under baseline and DEA-efficient conditions ............... 124 xxxv Figure 3-6. Estimated differences in relative contributions to greenhouse gas emissions per tonne of eggs produced in each housing system under baseline and DEA-efficient conditions .......... 125 Figure 3-7. Relative percentage of acidifying, eutrophying, and GHG emissions compared to the current baseline under each housing system transition scenario. ............................................... 128 Figure 4-1. Relative mortality risk across different housing systems associated with the production of 1 tonne of eggs. Values associated with each bar are unitless, with higher values representing worse welfare outcomes. ........................................................................................ 179 Figure 4-2. Relative risk of morbidity/injury associated with the production of 1 tonne of eggs across different housing systems. Values associated with each bar are unitless, with higher values representing worse welfare outcomes. ........................................................................................ 180 Figure 4-3. Relative scores for fulfillment of basal behavioural needs across housing systems associated with production of 1 tonne of eggs. Values associated with each bar are unitless, with higher values representing better welfare outcomes. .................................................................. 182 Figure 4-4. Contribution of different behaviours supported by each housing system to the overall fulfilment of additional behavioural needs. Conventional housing received a score of 0 as no furnishings are provided for hens to fulfill additional behavioural needs in these systems. Values associated with each bar are unitless, with higher values representing better welfare outcomes...................................................................................................................................................... 184 Figure 4-5. Risk of injurious behaviours associated with production of 1 tonne of eggs in different housing systems. Values associated with each bar are unitless, with higher values representing worse welfare outcomes. ........................................................................................ 185 xxxvi Figure 4-6. Risk of positive and negative affective associated with production of 1 tonne of eggs in different housing systems. Values associated with each bar are unitless, with higher values representing better welfare outcomes for both positive and negative affective state. ................ 187 Figure 5-1. Flow chart describing the results of the literature search and selection process used to arrive at the final body of literature reviewed during PRISMA systematic review .................... 200 Figure 5-2. Total number of sources reviewed from each year within the temporal boundaries of this review, and the number of sources within each year that studied systems attributable to different ISIC sections. Section letters refer to the corresponding sections listed in Table 5-1. 206 Figure 5-3. Percentage of different optimization frameworks applied in the literature reviewed...................................................................................................................................................... 208 Figure 5-4. Changes in the number of LCO studies published per year, and the proportion of studies employing mathematical programming, evolutionary metaheuristics, or other approaches as optimization frameworks between 2012 and 2023. ................................................................ 209 Figure 5-5 Decision tree to aid LCO practitioners in making methodological choices for performance of LCO studies. Ovals represent different optimization frameworks practitioners may employ to solve their problem. ........................................................................................... 224 Figure 6-1. Composition per tonne of economically optimal feed at each reference feed mill location ........................................................................................................................................ 272 Figure 6-2. Contributions to total costs per tonne of economically optimal feed milled at each reference feed mill location ........................................................................................................ 273 Figure 6-3. Relative impacts per tonne of feed milled at Abbotsford for both economically and GHG optimal feeds, across both treatments of SOC and LUC. The economically optimal feed excluding SOC and LUC is the reference category. ................................................................... 281 xxxvii Figure 6-4. Costs per tonne of feed milled at each reference location for each different multi-objective optimization scenario and each treatment of SOC and LUC ...................................... 290 Figure 6-5. Estimated impacts per tonne of multi-objective optimal feed milled at Abbotsford for each optimization scenario for those impact categories included in the MOO, excluding upstream SOC changes and perennial to annual land transitions ............................................................... 294 Figure 6-6. Estimated impacts per tonne of multi-objective optimal feed milled at Wallenstein for each optimization scenario for those impact categories included in the MOO, excluding upstream SOC changes and perennial to annual land transitions ............................................................... 295 Figure 6-7. Estimated impacts per tonne of multi-objective optimal feed milled at Truro for each optimization scenario for those impact categories included in the MOO, excluding upstream SOC changes and perennial to annual land transitions ............................................................... 295 Figure 6-8. Relative percentage of impacts per tonne of eggs produced in conventional cage systems using a single objective optimal feed for lowest cost, and a multi-objective optimal feed with strong preference for minimization of environmental impacts milled at Abbotsford. ....... 297 Figure 6-9. Relative percentage of impacts per tonne of eggs produced in free range systems using a single objective optimal feed for lowest cost, and a multi-objective optimal feed with strong preference for minimization of environmental impacts milled at Abbotsford. ............... 298 Figure 6-10. Relative percentage of impacts per tonne multi objective optimal feed milled with a strong preference for environmental impact minimization (i.e., Scenario 1) at Wallenstein excluding SOC and LUC, when feeds when phosphate supplements are excluded and included as potential feed ingredients in optimization. .................................................................................. 302 Figure 6-11. Relative percentage of impacts per tonne multi objective optimal feed milled with a strong preference for economic cost minimization (i.e., Scenario 4) at Wallenstein excluding xxxviii SOC and LUC, when feeds when phosphate supplements are excluded and included as potential feed ingredients in optimization.................................................................................................. 303 Figure 7-1. Relative environmental impacts per tonne of eggs produced in Canada under current Canadian average conditions, and in an animal welfare and environmentally optimal scenario in which 100% of non-organic eggs are produced in enriched cage systems ................................. 325 Figure A 1. Percent contributions to total abiotic depletion impacts per tonne of eggs produced in each housing system, estimated using the CML IA Baseline LCIA method.............................. 570 Figure A 2. Percent contributions to total abiotic depletion (fossil fuels) impacts per tonne of eggs produced in each housing system, estimated using the CML IA Baseline LCIA method. 570 Figure A 3. Percent contributions to total freshwater aquatic ecotoxicity impacts per tonne of eggs produced in each housing system, estimated using the CML IA Baseline LCIA method. 571 Figure A 4. Percent contributions to total human toxicity impacts per tonne of eggs produced in each housing system, estimated using the CML IA Baseline LCIA method.............................. 571 Figure A 5. Percent contributions to total marine aquatic ecotoxicity impacts per tonne of eggs produced in each housing system, estimated using the CML IA Baseline LCIA method.......... 572 Figure A 6. Percent contributions to total ozone layer depletion impacts per tonne of eggs produced in each housing system, estimated using the CML IA Baseline LCIA method.......... 572 Figure A 7. Percent contributions to total photochemical oxidation impacts per tonne of eggs produced in each housing system, estimated using the CML IA Baseline LCIA method.......... 573 Figure A 8. Percent contributions to total terrestrial ecotoxicity impacts per tonne of eggs produced in each housing system, estimated using the CML IA Baseline LCIA method.......... 573 xxxix Figure A 9. Relative percentage of impacts per tonne of eggs produced in enriched cage production systems using a conventional and vegetarian diet, estimated using the CML IA baseline LCIA method. ............................................................................................................... 574 Figure A 10. Relative percentage of impacts per tonne of eggs produced in single tier non-cage production systems using a conventional and vegetarian diet, estimated using the CML IA baseline LCIA method. ............................................................................................................... 574 Figure A 11. Relative percentage of impacts per tonne of eggs produced in multi tier non-cage production systems using a conventional and vegetarian diet, estimated using the CML IA baseline LCIA method. ............................................................................................................... 575 Figure A 12. Relative percentage of impacts per tonne of eggs produced in free range production systems using a conventional and vegetarian diet, estimated using the CML IA baseline LCIA method......................................................................................................................................... 575 Figure A 13. Percent contributions to total abiotic depletion impacts per tonne of eggs produced in each housing system when using a vegetarian feed formulation, estimated using the CML IA Baseline LCIA method. .............................................................................................................. 576 Figure A 14. Percent contributions to total abiotic depletion (fossil fuels) impacts per tonne of eggs produced in each housing system when using a vegetarian feed formulation, estimated using the CML IA Baseline LCIA method. .......................................................................................... 576 Figure A 15. Percent contributions to total freshwater aquatic ecotoxicity impacts per tonne of eggs produced in each housing system when using a vegetarian feed formulation, estimated using the CML IA Baseline LCIA method. .......................................................................................... 577 xl Figure A 16. Percent contributions to total human toxicity impacts per tonne of eggs produced in each housing system when using a vegetarian feed formulation, estimated using the CML IA Baseline LCIA method. .............................................................................................................. 577 Figure A 17. Percent contributions to total marine aquatic ecotoxicity impacts per tonne of eggs produced in each housing system when using a vegetarian feed formulation, estimated using the CML IA Baseline LCIA method................................................................................................. 578 Figure A 18. Percent contributions to total ozone depletion impacts per tonne of eggs produced in each housing system when using a vegetarian feed formulation, estimated using the CML IA Baseline LCIA method. .............................................................................................................. 578 Figure A 19. Percent contributions to total photochemical oxidation impacts per tonne of eggs produced in each housing system when using a vegetarian feed formulation, estimated using the CML IA Baseline LCIA method................................................................................................. 579 Figure A 20. Percent contributions to total terrestrial ecotoxicity impacts per tonne of eggs produced in each housing system when using a vegetarian feed formulation, estimated using the CML IA Baseline LCIA method................................................................................................. 579 Figure B 1. Estimated differences in relative contributions to abiotic depletion impacts per tonne of eggs produced in each housing system under baseline and DEA-efficient conditions estimated using the CML IA Baseline LCIA method ................................................................................. 585 Figure B 2. Estimated differences in relative contributions to abiotic depletion (fossil fuels) impacts per tonne of eggs produced in each housing system under baseline and DEA-efficient conditions estimated using the CML IA Baseline LCIA method ............................................... 586 xli Figure B 3. Estimated differences in relative contributions to freshwater aquatic ecotoxicity impacts per tonne of eggs produced in each housing system under baseline and DEA-efficient conditions estimated using the CML IA Baseline LCIA method ............................................... 587 Figure B 4. Estimated differences in relative contributions to human toxicity impacts per tonne of eggs produced in each housing system under baseline and DEA-efficient conditions estimated using the CML IA Baseline LCIA method ................................................................................. 588 Figure B 5. Estimated differences in relative contributions to marine aquatic ecotoxicity impacts per tonne of eggs produced in each housing system under baseline and DEA-efficient conditions estimated using the CML IA Baseline LCIA method ................................................................. 589 Figure B 6. Estimated differences in relative contributions to ozone depletion impacts per tonne of eggs produced in each housing system under baseline and DEA-efficient conditions estimated using the CML IA Baseline LCIA method ................................................................................. 590 Figure B 7. Estimated differences in relative contributions to photochemical oxidation impacts per tonne of eggs produced in each housing system under baseline and DEA-efficient conditions estimated using the CML IA Baseline LCIA method ................................................................. 591 Figure B 8. Estimated differences in relative contributions to terrestrial ecotoxicity impacts per tonne of eggs produced in each housing system under baseline and DEA-efficient conditions estimated using the CML IA Baseline LCIA method ................................................................. 592 Figure E 1. Relative impacts per tonne of feed milled at Abbotsford for both economically and GHG optimal feeds, across both treatments of SOC and LUC. The economically optimal feed excluding SOC and LUC is the reference category. ................................................................... 733 xlii Figure E 2. Relative impacts per tonne of feed milled at Lethbridge for both economically and GHG optimal feeds, across both treatments of SOC and LUC. The economically optimal feed excluding SOC and LUC is the reference category. ................................................................... 734 Figure E 3. Relative impacts per tonne of feed milled at St. Hyacinthe for both economically and GHG optimal feeds, across both treatments of SOC and LUC. The economically optimal feed excluding SOC and LUC is the reference category. ................................................................... 735 Figure E 4. Relative impacts per tonne of feed milled at Truro for both economically and GHG optimal feeds, across both treatments of SOC and LUC. The economically optimal feed excluding SOC and LUC is the reference category. ................................................................... 736 Figure E 5. Estimated impacts per tonne of multi-objective optimal feed milled at Lethbridge for each optimization scenario for those impact categories included in the MOO, excluding upstream SOC changes and perennial to annual land transitions ............................................................... 737 Figure E 6. Estimated impacts per tonne of multi-objective optimal feed milled at St Hyacinthe for each optimization scenario for those impact categories included in the MOO, excluding upstream SOC changes and perennial to annual land transitions ............................................... 737 Figure E 7. Relative percentage of impacts per tonne of eggs produced in conventional cage systems using a single objective optimal feed for lowest cost, and a multi-objective optimal feed with strong preference for minimization of environmental impacts milled at Wallenstein. ...... 738 Figure E 8. Relative percentage of impacts per tonne of eggs produced in free range systems using a single objective optimal feed for lowest cost, and a multi-objective optimal feed with strong preference for minimization of environmental impacts milled at Wallenstein ................ 739 xliii List of Abbreviations AoP – Area of protection C – Carbon CH4 – Methane CO2 – Carbon dioxide CN – Canadian National CP – Canadian Pacific CRS – Constant returns to scale CRSC – Canadian Roundtable for Sustainable Crops CSR – Corporate social responsibility DAP – Diammonium phosphate DDGS – Dried distillers grains with solubles DEA – Data Envelopment Analysis DMU – Decision making unit EF – Emissions factor eLCA – Environmental life cycle assessment GHG – Greenhouse gas GIS – Geographic Information Systems GTE – Global technical efficiency HSD – Honestly Significant Difference IPCC – Intergovernmental Panel on Climate Change ISIC – International Standard Industrial Classification of all Economic Activities K – Potassium LASSO – Least Absolute Shrinkage and Selection Operator LCA – Life cycle assessment LCC – Life cycle costing LCI – Life cycle inventory LCIA – Life cycle impact assessment LCO – Life cycle optimization LEAP – Livestock Environmental Assessment and Performance xliv LUC – Land use change MAP – Monoammonium phosphate MSE – Mean squared error N – Nitrogen N2O – Nitrous oxide NESTT – National Environmental Sustainability and Technology Tool NH3 – Ammonia NIR – National Inventory Report NOx – Nitrogen oxides NSGA – Non-dominated sorting genetic algorithm P – Phosphorus PCA – Principal component analysis PPS – Production possibility set PRISMA – Preferred Reporting Items for Systematic reviews and Meta Analyses RU – Reconciliation Unit S - Sulphur sLCA – Social life cycle assessment SOC – Soil organic carbon TRACI – Tool for the Reduction and Assessment of Chemical and other environmental Impacts VRS – Variable returns to scale xlv Acknowledgements To begin, I would like to acknowledge and thank my supervisor, Dr. Nathan Pelletier, for his guidance and unwavering patience throughout my time in the Food Systems PRISM Lab. His mentorship, encouragement, and support were instrumental in my journey towards completing this dissertation, and I will be forever grateful for being made to feel like a respected, valued member of the LCA community. I would similarly like to extend my gratitude to my research committee members, Dr. Tina Widowski and Dr. Jeff Andrews, who provided support and guidance as I dove headlong into disciplines in which I had little to no previous experience. Finally, I’d like to acknowledge Dr. James Bamber of Logisymetrix for the work he has done developing automation code for our lab. Use of this code saved countless hours of time, and without it the scope of this dissertation would have been greatly diminished. I would like to thank all members of the LCA community who I’ve had the pleasure to collaborate, and build friendships with since I first starting working in this field in 2018. This includes all past and present members of the Food Systems PRISM Lab, as well as all those who have made my experiences at conferences like LCA of Food so pleasurable. Whether discussing the merits and drawbacks of LCA methods, or discussing future personal or professional plans over dinner and drinks, I have had the opportunity to build enduring friendships that I’m sure will never be forgotten, and are what form the basis for making graduate school so special. Lastly, I’d like to thank all my past teachers, professors, family, friends, and my partner, Nicole Bamber, for the support and encouragement they have provided through my entire educational journey. To my parents, who were unfortunately gone too early to see this accomplishment, I hope you would be proud of this work. To my stepfather, sister, aunts, uncles, xlvi and grandparents, I will forever be grateful for everything you have done, no matter how small or large, in supporting my goals and pushing me to be better. To Nicole, thank you for being both my harshest critic, and most vocal supporter. To our cat Matilda, I’m sorry I had to remove your many contributions you wrote so thoughtfully while stepping on my keyboard. Completing this work has been a challenge, and it is just as much a testament to myself as it is to everyone who has taken this journey with me. xlvii Dedication “I have no idea where this will lead us, but I have a definite feeling it will be a place both wonderful and strange” – Special Agent Dale Cooper. 1 1. Introduction 1.1 Context and rationale Environmental change as a direct result of human actions has become a defining characteristic of the 20th and 21st centuries (Remondo et al., 2024). In response, global governance systems have come together in attempts to develop resources, frameworks, and pathways forward for humanity that both maintain economic prosperity and improve social justice while remaining within the biophysical limits of earth systems, first as the Brundtland Report (Brundtland, 1987), and more recently as the UN Sustainable Development Goals (United Nations, 2022). In spite of these efforts, early defintions of safe operating spaces for humanity with respect to global biophysical systems suggested that, at the time, three of nine planetary boundaries had already been transgressed, with others nearing transgression if business continued as usual (Rockström et al., 2009). Recent estimates now suggest that six of nine planetary boundaries have been transgressed, while a seventh is close to being breached and only marginal improvements have been made in an eighth (Richardson et al., 2023). Work towards these goals resulted in the conception of sustainability science as a scientific discipline, focused on questions related to meeting the needs of present generations without compromising the ability of future generations to meet their own needs (Kates et al., 2001). As a discipline, sustainability science may be described according to a set of key characteristics: first, that research undertaken in this discipline may generally be characterized by a common purpose, rather than a common set of methods (Spangenberg, 2011); second, that it spans all levels of spatial resolution (i.e., from local to global); and third, that it requires participation of stakeholders throughout the value chain (Kates et al., 2001). As a result, research questions investigated in the sustainability science discipline are often complex, and must 2 overcome the key challenge of resolving potentially competing interests, as rarely will a single solution maximize positive outcomes for all stakeholders in a system (Kates et al., 2001). In response to this, integration of knowledge from multiple distinct scientific disciplines in an inter- or trans-disciplinary manner has become the dominant research paradigm in sustainability science (Schaltegger et al., 2013), as analysis of issues from just a single persepctive may lead to erroneous conclusions (Sanders and Wagner, 2011). Differences between inter- and trans-disciplinary research are reviewed well by Choi and Pak (2006) who summarize that interdisciplinarity involves synthesis of multiple disciplines to generate novel discourse and knowledge integration, while transdisciplinarity seeks to subvert disciplinary boundaries and generate novel frameworks and perspectives that draw from multiple disciplines. In both cases, these approaches are distinct from multi-disciplinary approaches, which Spangenberg (2011) argues are insufficient for sustainability science, and that instead research within this discipline must be at least interdisciplinary-ready. Food systems operate at the intersection of many of the Sustainable Development Goals. Production of food makes large contributions to the ongoing breaching of planetary boundaries, including being responsible for up to one third of total anthropogenic greenhouse gas (GHG) emissions (Crippa et al., 2021) along with large nutrient surpluses leading to deterioration of the Nitrogen (N) and Phosphorus (P) cycles (Conijn et al., 2018; Kahiluoto et al., 2014), freshwater use, degradation of biosphere integrity, and other environmental impacts (Gerten et al., 2020). Production and distribution of food is also associated with many social benefits and impacts that may differ between products (Varela-Ortega et al., 2021) and geographic locations (Andrade et al., 2022). This is similarly true for local and global economic benefits and impacts (Altobelli 3 and Henke, 2024), all of which make important contributions to food system sustainability outcomes (Daly, 1992; Purvis et al., 2019). Within the context of global food systems, a disproportionate share of environmental impacts are attributable to livestock production (Pelletier and Tyedmers, 2010). This has led to suggestions that shifting consumption patterns away from livestock-derived foods may be an efficacious way to decrease the environmental impacts of food production and consumption (Tobi et al., 2019). Contrary to this, however, shifting income demographics and increasing levels of individual purchasing power and affluence are expected to result in large increases in demand for livestock-based foods over time (Govoni et al., 2022, 2021). Taken together, these trends suggest that significant improvements must be made to the environmental performance of livestock productions if they are to remain a viable part of human diets moving forward. In addition to environmental impacts, animal welfare impacts must also be considered. This presents a unique social challenge not faced by non-animal based agri-food systems. Animal welfare in industrial livestock production is growing in status as an issue of importance in many areas around the world (Clark et al., 2016), and is increasingly playing a role in individual purchasing decisions for livestock products (Heerwagen et al., 2014; Humble et al., 2021; Teixeira et al., 2018). Consumers regularly report, for example, an increased willingness to pay for livestock products associated with perceived higher levels of animal welfare (Ochs et al., 2018). Given this trend, the inclusion of animal welfare outcomes amongst other attributes is imperative when assessing and seeking to improve the sustainability of industrial livestock systems. As the environmental performance of specific livestock systems may be highly dependent on housing and management characteristics (Pelletier, 2017; Ross et al., 2017), so, too, may associated animal welfare outcomes (Delsart et al., 2020; Lay Jr et al., 2011; Tiezzi et 4 al., 2019). This may, therefore, create potential synergies or trade-offs – not only with respect to environmental and animal welfare impacts generally, but also between different kinds of environmental and animal welfare impacts. Weeks et al., (2016), for example, identified decreasing mortality rates as simultaneously improving both environmental and welfare outcomes on industrial egg farms, while Broom (2019) discusses a number of interventions that may result in synergistic or antagonistic interactions between environmental and animal welfare impacts. The Canadian egg industry is one livestock sector that is currently navigating potential synergies and antagonisms in pursuit of improved environmental and animal welfare outcomes. In 2023, the industry produced approximately 800 million dozen eggs on ~ 1200 industrial egg farms spread across all ten of Canadas provinces and one territory (Egg Farmers of Canada, 2023). In recent years, this industry has made significant strides towards improvement of the environmental outcomes associated with Canadian egg production. This includes the publication of Egg Farmers of Canada’s inaugural sustainability report (Egg Farmers of Canada, 2020), the launch of the National Environmental Sustainability and Technology Tool (NESTT) (Egg Farmers of Canada, 2023), and assessment of a variety of potential strategies for the reduction of environmental impacts related to farm-level energy use (Li, 2020), waste valorization (Kanani et al., 2020), improvement of nitrogen-use efficiency (Ershadi et al., 2021), and other ongoing projects. This work has culminated in a large-scale review of possible net-zero GHG emissions technologies to determine their potential efficacy and further research needs (Grassauer et al., 2023) in order to support the industry commitment to reach net-zero GHG emissions by 2050 (Zaks, 2023). Throughout the literature, improving the impacts associated with production of feed has emerged as a key lever for improvement of the environmental impacts of egg 5 production systems, as this input is responsible for up to 68% of the GHG emissions of egg production, and similar or higher shares of other environmental impacts as well (Pelletier, 2017). Additionally, the Canadian egg industry is currently in a transitionary period as conventional cage systems are being phased out of use following introduction of the new Code of Practice for the Care and Handling of Pullets and Laying Hens in 2017 (National Farm Animal Care Council, 2017). In line with this Code, egg production in Canada is expected to completely transition away from conventional cage production systems to alternatives by 2036, including enriched cage/colony, single- and multi-tier aviary, free range, and certified organic housing. Similar transitions are occurring in many jurisdictions around the world (Shields and Mirle, 2013; The Council of the European Union, 1999; Vogeler, 2021), driven primarily by animal welfare concerns associated with conventional cage systems (Caputo et al., 2023). In contrast to conventional systems, alternative systems may generally be characterized by greater space allowances per bird, and the opportunity to perform additional behaviors not afforded by conventional systems through the inclusion of furnishings such as perches, nest boxes, scratch mats, and in some cases, outdoor access (van Staaveren et al., 2018a). Given the potential synergies and trade-offs in sustainability attributes in the Canadian egg industry, depending on the housing system that is chosen, it is necessary that potential management practices and interventions be assessed in a principled manner to support informed decision making. The most commonly applied framework for sustainability assessment is life cycle thinking, a systems-based approach which seeks to holistically consider all relevant flows of materials, energy, and wastes, and all relevant interactions between the supply chain steps of any given good or service (Pelletier, 2015). Life cycle thinking is an important paradigm in contemporary sustainability measurement and management as it facilitates the identification of 6 potential trade-offs between different sustainability impacts, and aids in the identification of the potential for burden shifting from one impact or supply chain activity to another. Knowledge gained through the use of a life-cycle thinking-based approach may then be used to support informed decision making in choosing between sustainability management alternatives, and help ensure net-positive outcomes from the decisions that are made. The ability to identify potential trade-offs, in particular, is a key benefit of life-cycle thinking-based approaches, as sustainability challenges may often be described as “wicked problems” – that is, they are often complex, difficult to define, and any proposed solutions may lead to further challenges that must be solved as well (Brown et al., 2010). Life cycle thinking is operationalized using life cycle assessment (LCA), the application of which is standardized by the ISO14040-14044 document series (ISO, 2006a, 2006b). The dominant application of the LCA framework is for the assessment of environmental impacts using environmental LCA (eLCA), though it may also be applied to assess social (i.e., social LCA, or sLCA), and economic impacts (i.e., life cycle costing, or LCC), with the combination of all three methods forming the basis of life cycle sustainability assessment (Klopffer, 2003). While all of these methods have been applied to analyze food systems (see, for example, Pelletier (2018, 2017) and Gunluk et al. (2014)), it has been argued that improvements must be made in the inter- and trans-disciplinary applications of the LCA framework to better represent the discipline of sustainability science, and to better support achievement of the Sustainable Development Goals (Troullaki et al., 2021). More broadly, inter- and trans-disciplinary approaches are vital to the management of wicked problems such as sustainability, because these approaches facilitate an integration of knowledge from a diverse array of contributors and 7 stakeholders, helping to drive new understandings of complex problems, and development of innovative solutions for them (Lawrence, 2010). Within the context of livestock systems, it is imperative that assessments integrate knowledge and best practices from the disciplines of environmental assessment and animal welfare science, bridging existing philosophical and practical gaps to best support sustainability policy decision making. Further, integration of these methods with techniques from the discipline of operations research may allow for greater leveraging of potential relationships between environmental and animal welfare characteristics to provide enhanced decision support. Many definitions have been proposed for the field of operations research since its inception during World War Two, as reviewed by Bhunia et al. (2019). Broadly, operations research refers to the use of methods to solve complex problems related to systems management with the goal of providing decision support to managers, policy makers, and other relevant stakeholders. Operations research may integrate statistical methods for efficiency assessment, machine learning, mathematical optimization, and others, and can provide substantial additional value when combined with sustainability assessment frameworks, such as LCA to improve the sustainability of industrial processes (Heidari et al., 2021b). On this basis, this dissertation reports research undertaken to investigate potential sustainability improvement strategies for the Canadian egg industry in a trans-disciplinary manner, leveraging methods from the disciplines of LCA, animal welfare science, and operations research. Two overarching research objectives guided development of the research chapters. The first was to investigate the sustainability improvement potential in the Canadian egg industry as it navigates the ongoing housing system transition. This research objective supports development of an in-depth understanding of potential avenues for sustainability improvement related to 8 increasing farm-level efficiencies, optimization of feed formulations, and optimization of the distribution of non-organic Canadian egg production across housing systems considering differences in environmental and animal welfare impacts between these systems. The second research objective was to make meaningful contributions to LCA as a tool in the field of sustainability science by developing and applying frameworks drawing from the disciplines of animal welfare science and operations research in a transdisciplinary manner. This research objective supports and seeks to catalyze continued development and innovation in the discipline of sustainability science to improve the utility of the field to support achievement of the Sustainable Development Goals. Accomplishment of these research objectives are supported by development of a novel method for assessment of animal welfare in laying hens that is built upon the LCA framework, and the development of generalized guidelines for performance of a process optimization study incorporating life-cycle based metrics based on current best practices in the literature. The specific research questions associated with each chapter of this dissertation, as well as a thesis reading guide indicating how the results of each chapter contribute to the methods and results employed in subsequent chapters follows. 1.2 Research questions This thesis investigates six general research questions, each of which may be associated with more specific sub-questions. These questions are: 1) What are the current (i.e. baseline) life cycle environmental impacts of egg production in Canada? a. What are the environmental impact hotspots and key foci for environmental impact mitigation in non-organic Canadian egg production systems? 9 2) What levels of resource use efficiency are currently realized within Canadian egg production systems, and how could increases in resource-use efficiency lead to environmental impact mitigation? a. How do levels of resource use efficiency compare across individual farms operating the same housing systems, and how do levels of resource use efficiency compare across different housing systems? b. How do environmental impacts of alternative housing systems compare to conventional systems if operating at the maximum levels of efficiency currently realized among farms from which data were sourced for this analysis? 3) How can animal welfare assessment be integrated into the LCA framework in a manner that is internally consistent with methodological norms in both the animal welfare science and LCA, while adequately accounting for a number of different contributions to animal welfare and being able to discern trade-offs in welfare impacts in a meaningful manner? a. How do animal welfare impacts compare across different egg production systems? 4) What opportunities and challenges exist with respect to the integration of mathematical optimization within the LCA framework, and how could these methods be best implemented to identify environmental impact mitigation opportunities in the Canadian egg industry? 5) How can mathematical optimization techniques be applied to Canadian laying hen feed formulations to optimize feed for least economic and environmental cost? 10 a. What are the optimal non-organic laying hen feed formulations from an economic and environmental perspective when considering regionalized production of ingredients? b. What is the environmental impact mitigation potential of using feeds optimized taking into account environmental impacts compared to feeds optimized solely for least economic cost? 6) How can mathematical optimization techniques be applied to Canadian egg production systems in order to optimize egg production for environmental and animal welfare outcomes? a. What is the optimal distribution of egg production across different housing systems when considering environmental and animal welfare objectives? Does this distribution change if all housing systems are assumed to be operating at peak levels of pullet- and feed-use efficiency? b. How do the environmental and animal welfare impacts of this optimal distribution compare to current impacts? 1.3 Dissertation reading guide This dissertation includes six core research chapters, alongside this introductory chapter and a concluding chapter that together contribute to answer the six research questions (and associated sub-questions) defined in section 1.2. A brief description of each chapter, and how they contribute answering the research questions is provided below. A graphical summary of the relationship between different research chapters and research questions, including how chapters contribute to subsequent chapters is provided in Figure 1-1. 11 Chapter two presents the methods and results for the baseline, cradle-to-farm-gate, production-weighted Canadian average LCA models of egg production in five different non-organic housing systems: conventional and enriched cages, single- and multi-tier non-cage, and free range systems. These models were developed based on primary data collected directly from Canadian egg farmers describing cumulative resource use and productivity on their farms in the years 2019 (with some supplemental data from 2017), as well as the current best available secondary data describing upstream processes in the Canadian egg supply chain. Organic egg production systems are excluded from these analyses due to a lack of similarly updated data describing processes upstream in the organic supply chain, such as organic field crop production. These upstream processes included pullet, hatchery, and breeder flock operations and associated Figure 1-1. Dissertation overview describing major research outputs from each chapter, and how chapters contribute to one another 12 manure management, feed milling processes, and regional production of Canadian field crops. Emissions associated with manure management, and field level emissions from crop production systems were estimated in line with current best practices outlined by the IPCC (2019), and life cycle impact assessment (LCIA) results are presented with respect to one tonne of eggs produced at farm gate for each housing system. The development of these models contributed to answering research question one. Chapter three presents the methods and results for joint LCA and data envelopment analysis (DEA) of non-organic Canadian egg production. This chapter builds upon the baseline models developed in chapter two, using the primary data that was collected to determine current levels of feed- and pullet-use efficiency per tonne of eggs produced on Canadian egg farms. This efficiency analysis was subsequently used to determine target efficiency values for feed- and pullet-use that would be required for Canadian egg farms to be operating at the maximum levels of efficiency currently observed among the sampled population, which were then combined to generate efficient, production-weighted average pullet and feed inputs to Canadian egg production systems. The environmental impacts of these efficient systems were subsequently quantified in a methodologically consistent manner with chapter two, and comparisons in estimated impacts per tonne of eggs produced were made under efficient and baseline conditions to determine the environmental impact mitigation potentials of improved feed- and pullet-use efficiency. This allowed research question two to be answered. Chapter four presents the methods and results associated with development of a novel LCIA method for animal welfare assessment of laying hens and testing of the proposed method using a case study of the Canadian egg industry for answering research question three. The proposed method was developed in line with current best practices for development of a type-1 13 social LCIA method, including definition an area of protection and stakeholders, impact categories and subcategories, and associated inventory indicators, inventory data requirements, and characterization factors. Requisite components were identified based on reviews of the animal welfare literature to ensure that the proposed method was in line with current paradigms within animal welfare science. This method was developed with the goals of minimizing the need for normative decisions during assessment and ensuring that the data requirements were sufficiently general that assessments may be performed at the industry-wide scale that is generally characteristic of most LCA research. The proposed method was subsequently tested with a case study of the Canadian egg industry based on primary data collected from Canadian egg farms detailing animal welfare outcomes, management practices, and housing system characteristics for different egg production systems. This testing was done to ensure that the results of the proposed method were in line with expectations based on the animal welfare literature. Chapter five presents a systematic literature review of the life cycle optimization (LCO) literature, performed in line with the Preferred Reporting Items for Systematic reviews and Meta Analyses (PRISMA) method. This literature review collected data regarding current practices within the LCO literature, including the types of systems studied, LCA and optimization frameworks used, and characteristics of optimization problem definitions in order to answer research question four. The collected data was subsequently used to inform further discussions around key methodological choices to be made during performance of an LCO study, including problem definition and type of optimization framework employed. The results of this review and the information gained during the further investigation and discussion were then used to develop 14 generalized guidelines for performance of LCO studies according to specific practitioner and study-specific goals. Chapter six presents the methods and results of an LCO study seeking to optimize Canadian laying hen feed formulations (research question five). This optimization was performed taking into account regional differences in raw material production and processing impacts of potential feed ingredients, as well as associated impacts and costs of transportation and ingredient procurement. Optimizations were performed with respect to five different reference feed mill locations situated across Canada (representing distinct climate regions), two different treatments of modeling field-level soil carbon fluxes and impacts associated with perennial to annual land use transitions, and four different sets of objective functions. The optimizations performed included: single-objective optimizations for least economic cost and least GHG emissions associated with one tonne of feed milled; bi-objective optimization to simultaneously minimize economic costs and GHG emissions; and finally, multi-objective optimization to simultaneously minimize economic costs, GHG emissions, and acidification, eutrophication, and human toxicity impacts. For the multi-objective optimizations, four different sets of weighting factors representing different preferences for minimization of different objectives were assessed. Knowledge regarding performance of an LCO study gained during development of chapter five was directly used in chapter six to aid in problem definition and the choice of an optimization framework. A subset of the optimal feed formulations identified by the solved optimizations problems were subsequently input into the baseline conventional cage and free range Canadian egg production LCA models developed in chapter two to determine the impact mitigation potential of using environmentally optimal laying hen feeds, and to determine if and how use of such feeds may change impact hotspots within the Canadian egg supply chain. 15 Finally, chapter seven presents the methods and results for optimization of the distribution of production between different non-organic housing systems in the Canadian egg industry for environmental and animal welfare objectives. This chapter integrates the key research outcomes from all previous chapters to answer research question six. The optimization performed in this chapter takes into account constraints on the distribution of production in conventional cage housing systems imposed by the ongoing transition away from conventional cages (National Farm Animal Care Council, 2017). A total of thirty different optimization scenarios were explored, representing different combinations of constraints and preferences for minimizing environmental impacts, and minimizing negative while maximizing positive animal welfare impacts. These optimization scenarios were analyzed using both baseline estimates of environmental impacts across housing systems from chapter two, as well as the DEA-efficient estimates of impacts across housing systems from chapter three. Finally, the optimal feed formulations identified in chapter six are integrated into the optimal housing system distributions to investigate total environmental impact reduction potential. Following the six research chapters, chapter eight provides a synthesizing discussion of the key findings from this dissertation, limitations of the various analyses performed, and significance and utility of the methods, models, and frameworks developed throughout. This chapter concludes with a discussion of future research opportunities that arise from the research, towards the ultimate goals of not only improving sustainability outcomes in the Canadian egg industry, but also in catalyzing continued innovation in the LCA discipline to improve and expand upon existing methodologies in support of the goal of improved, life cycle-based sustainability decision making. 16 1.4 Acknowledgement The work described in this dissertation was made possible by the support of a number of funding organizations. Funding sources included the National Sciences and Engineering Research Council of Canada/Egg Farmers of Canada Industrial Research Chair in Sustainability, Egg Farmers of Canada, and the Egg Industry Center at Iowa State University. The funding sources had no involvement in study design, analysis, or interpretation of data, writing, or in the process of submitting this dissertation to the University of British Columbia. I would like to acknowledge Egg Farmers of Canada and the various provincial egg boards for the aid they provided in farmer recruitment for collection of the primary data used in chapters two, three, four, six, and seven. This data was foundational in the completion of this thesis, and I hope that this strong industry partnership may continue to help support future endeavors in the PRISM Lab towards the goal of improving sustainability in the Canadian egg industry. 17 2. Life cycle assessment of Canadian, non-organic egg production systems, with differentiation by housing system type 2.1 Chapter synopsis The supply-managed Canadian egg industry produces over 789 million dozen eggs per year, approximately 48% of which are produced in conventional cages (Egg Farmers of Canada, 2023). In 2017, the industry committed to a complete transition to alternative (i.e. enriched cage, single- and multi-tier non-cage, free range, and organic) production systems by 2036. This transition may have significant sustainability implications. In this chapter baseline LCA models are developed representing the cradle to farm gate Canadian, non-organic egg supply chain, updated from previous models developed by Pelletier (2017) using 2019 data. These models are based on a large (relative to previously collected data sets) primary data set collected directly from Canadian egg farmers, and integrate current best available data and practices regarding upstream field crop modeling, and estimation of emissions related to manure management systems. Compared to the previous baseline assessments, input levels generally decreased for all housing systems. Major reductions in environmental impacts per tonne of eggs produced in each housing system were observed, attributable primarily to updated models of field crop production systems. The baseline models developed in this chapter are applied in subsequent chapters for efficiency assessment, and mathematical optimization. 2.2 Context and rationale Recently, Pelletier (2017) performed the first LCA of the Canadian egg industry, differentiated by housing system type, using data representative of farm operations in 2012. The results of this study made significant contributions to the development of the sustainability goals outlined in Egg Farmers of Canada’s inaugural sustainability report (Egg Farmers of Canada, 18 2020), and were consistent with previous LCA studies of egg production in identifying feed and pullet inputs, as well as manure management, as key drivers of environmental impacts. While this study was of substantial value to the Canadian egg industry, a number of factors necessitate updating the baseline Canadian egg production LCA models so as to better support further sustainability research for the industry. First, the data used by Pelletier (2017) were representative of a time period prior to the initiation of transition away from conventional cage-based production in Canada. In a similar study of Dutch egg production, Dekker et al., (2011) hypothesize that a ban on conventional cages would increase cumulative environmental impacts of egg production, highlighting the importance of updating assessments as the industry navigates this transition. Additionally, given the importance of feed production in determining the environmental impacts of egg production systems, it is imperative that feed ingredient production models be updated regularly using the best available data in order to increase the accuracy of these assessments. On this basis, the research presented in this chapter seeks to update the baseline environmental LCA models of Canadian, non-organic egg production previously developed by Pelletier (2017). Specifically, this research makes seven key updates to these models: - Use of updated models of field crop production for feed inputs based on the most recent (i..e, 2022) data describing Canadian field crop systems derived from the carbon footprint reports from the Canadian Roundtable for Sustainable Crops (CRSC) ((S&T)2 Consultants Inc., 2022a); - Use of a greatly expanded data set representative of a larger proportion of Canadian egg production capacity (i.e., data from 206 laying facilities compared to 104); - Use of data more recent in temporal scope (i.e. from 2019 compared to 2012); 19 - Use of methods for estimation of emissions from manure management systems and field deposition of manure, in line with IPCC (2019) as opposed to IPCC, (2006); - Use of updated methods for allocation of impacts between co-products throughout the egg supply chain, including the use of metabolic-partitioning based allocation factors for co-products at egg farms, in line with Arulnathan et al. (2022) as opposed to allocation at the layer stage based on gross chemical energy content of co-products; - Differentiation of life cycle inventories and impact assessment results for both single- and multi-tier non-cage production systems; - Expansion of the number of environmental impacts considered calculated using a different life cycle impact assessment methodsuite The results associated with some of these updates have been published previously in Turner et al. (2022) (i.e., use of an more recent, expanded data set, and differentiation of life cycle inventories for single- and multi-tier non-cage systems). The results presented in this chapter represent further updates related to the use of updated field crop models and allocation methods, new methods for estimation of manure management and waste emissions, and use of a new life cycle impact assessment method. The results presented in this chapter should therefore be considered the most current estimates of the life cycle environmental impacts of Canadian egg production, barring additional updates published in the future. 2.3 Methods The ISO 14044 standard for LCA (ISO, 2006b) defines four distinct phases for performance of an LCA, each with their own requirements and guidelines. These stages include: 1) Goal and scope definition 2) Life cycle inventory (LCI) 20 3) Life cycle impact assessment (LCIA) 4) Life cycle interpretation The first three of these phases are covered in the methods section of this chapter, while the fourth phase relates to the results and discussion section. Short introductions to each of the first three phases are given the beginning of each associated methods subsection. No introduction to the life cycle interpretation phase is given at the beginning of the results and discussion section, as this phase is focused on interpretation of the results in line with the goal and scope phase. Rather, in the results and discussion phase, the cumulative results of the first three phases are analyzed using contribution analyses and statistical tests, including discussion of uncertainty in the estimated results, and identification of key impact hotspots. 2.3.1 Goal and scope definition The first phase of an LCA is the goal and scope definition phase. In this phase, the contextual and methodological aspects of the study are described. The ISO 14044 standard (ISO, 2006b) defines mandatory components that must be defined during this phase, with close adherence to these guidelines intended to maximize transparency, and improve consistency and reproducibility. 2.3.1.1 Objectives The goal of this analysis was to perform a cradle-to-farm gate attributional environmental LCA of non-organic egg production in the Canadian, supply-managed egg industry, differentiated by housing system. The analysis performed in this chapter updates the previous assessment performed by Pelletier (2017) and develops the baseline models employed in subsequent chapters of this dissertation for investigation of environmental impact mitigation potentials, and mathematical optimization. 21 2.3.1.2 System boundaries The system boundaries for this analysis include all relevant material, energy, and emission flows linked to all processes in the cradle-to-farm gate egg supply chain in Canada (Figure 2-1). These include breeder flock, hatchery, pullet, and layer facilities, as well as feed input supply chains and feed milling. Modeling of layer systems was supported by collection of primary data from Canadian egg farmers characterizing hen performance and resource use on farms, as described in section Primary collection of laying hen data2.3.2.1.1. Modeling of upstream feed milling, breeder, hatchery, and pullet operations was based on data previously collected by Pelletier (2017). Production of field crops upstream of feed mills was modeled based on current best publicly available data. Transportation between processes, and treatment of wastes generated by processes (i.e., manure, and portions of spent hens and mortalities that are not valorized) are also considered. Processes for valorization of spent hens and/or mortalities (i.e., rendering, and further processing of hens for human consumption) are outside of the system boundaries of this analysis. However, the portions of spent hens and mortalities that are valorized are considered co-products, and have therefore been allocated to as described in section 2.3.2.1.4. 22 Figure 2-1. System boundaries for the cradle-to-farm-gate attributional environmental LCA of the Canadian, non-organic egg supply chain 23 2.3.1.3 Functional unit and reference flow The functional unit and reference flow for this analysis is one tonne of eggs at the farm gate. It has been assumed that there are no differences in function (i.e., nutritional differences, etc.) between eggs produced in different housing systems. Estimated impacts for upstream processes (i.e., field crop production, feed milled, and operation of breeder, hatchery, and pullet facilities) have also been calculated and appended, with results presented according to a relevant metric for each life cycle stage. 2.3.1.4 Cut-off criteria and exclusions For foreground processes and those background processes sourced from Pelletier (2017) and the CRSC reports inputs and emissions associated with production, maintenance, and decommissioning of infrastructure and capital equipment were excluded as these impacts generally make small contributions to total environmental impacts when amortized against total production over their life-span ((S&T)2 Consultants Inc., 2022a; Li, 2020). This includes equipment and infrastructure used for field crop production (i.e., tractors, harvesters, grain drying bins, etc.), and infrastructure for housing and waste management at breeder, hatchery, pullet, and layer facilities (i.e., barn infrastructure, incinerators for mortalities/spent hens, etc.). Additional exclusions related to field crop production systems are given in the complete description of methods for modeling field crop systems in appendix A, sections A.1 to A.12. Disposal of wastes such as packaging have also been excluded, as have antibiotics, cleaners, and poultry enteric fermentation due to their negligible relevance. In some cases, during primary data collection, farmers indicated “other” methods for dealing with spent hens and mortality than those provided (i.e., human consumption, rendering, composting, burial, and incineration). The percentage of spent hens and/or mortalities dealt with using these “other” methods varies across 24 housing systems, ranging from 0% to 18%. Given the wide array of activities that could be used as alternatives for dealing with spent hens and mortalities, and a lack of data regarding what exactly these alternatives are, they were excluded. Production of eggs in organic systems have also been excluded from this analysis due to a lack of updated data describing organic field crop production in Canada as well as the minor share contributed to overall egg production volumes. In the previous baseline LCA of Canadian egg production systems performed by Pelletier (2017), impacts related to land use changes, changes in soil organic carbon (SOC), and carbon dioxide (CO2) sequestration in agricultural land were excluded due to high levels of uncertainty in the magnitude and longevity of these effects. The CRSC reports provide estimates of SOC changes and CO2 sequestration related to management practices and perennial to annual land transitions which were calculated in line with methods employed in the 2022 Canadian National Inventory Report (NIR) (Environment and Climate Change Canada, 2022). Given the potential uncertainty around these estimates impacts associated with SOC changes and perennial to annual land transitions have been excluded from the results presented in this chapter. However, the potential impacts of SOC changes and perennial to annual land transition on the estimated impacts of egg production are explored in detail in chapter 6. Given the regional specificity of these estimates, discussion of these impacts is more appropriate in that chapter, as it investigates feed formulation optimization with respect to a much greater degree of regional granularity than is explored in this chapter. 2.3.1.5 Co-product allocation Many economic activities are multi-functional, producing multiple co-products that may be used in other economic activities. The ISO 14044 standard for LCA (ISO, 2006b) provides a three-tier hierarchy for addressing multi-functionality in LCA in order to partition estimated 25 impacts between co-products. According to this hierarchy, the preferred solution for addressing multi-functionality is subdivision of the modeled system to represent each co-product individually, or expansion of the system to include the functions of the co-products. If such approaches are infeasible, the ISO 14044 standard requires allocation of impacts between co-products first on the basis of some underlying physical relationship. If no such physical relationship can be defined, it is then suggested that allocation be carried out on the basis of some other relationship between co-products, such as relative economic value (ISO, 2006b). Whenever allocation was necessary, it was done on the basis of a physical relationship as there is a strong rationale in the literature for avoiding allocation based on relative economic value of co-products (Pelletier et al., 2015). Importantly, the ISO Standards do not dictate that a single basis for allocation must be applied consistently throughout the entire system modeled, but rather that similar inputs and outputs be allocated to on a consistent basis (ISO, 2006b). This allows for use of different bases for allocation throughout the system, which is useful in instances where a consistent approach cannot be used due to data quality constraints. During upstream production of field crops, lignocellulosic biomass that is distinct from the primary crop product may be co-produced as a crop residue, such as straw that is produced during wheat cultivation (X. Li et al., 2012b). Crop residues may be a valuable co-product of field crop systems if they are removed from fields (Jaleta et al., 2013; Karan and Hamelin, 2021). In contrast, retention of residues on fields may also provide many agro-ecological benefits, (Ranaivoson et al., 2017), but may lead to increased field-level nitrous oxide (N2O) emissions through microbial N mineralization and nitrification of residues (Abalos et al., 2022; Chen et al., 2013). In some cases, field-level emissions related to crop residue management practices may be key determinants of the life cycle environmental impacts of field crop systems (Bamber et al., 26 2023, 2022b). In the CRSC reports, it is assumed that a negligible amount of crop residues are removed from fields for all crops, and crop residues are instead treated as waste products with all impacts related to decomposition of residues allocated to production of the crop product. This choice was driven by a lack of publicly available information regarding removal rates for residues from Canadian field crop systems ((S&T)2 Consultants Inc., 2022a). While this assumption is likely valid for some crops, it may not be valid for others – particularly when crop residues may be an important biofuel feedstock (Karan and Hamelin, 2021), or have use as livestock bedding. Potential crop residue removal rates were therefore investigated for corn, wheat, barley, canola, soy, and peas to determine what portion of crop residues may be removed from fields and therefore considered a co-product. When residues were removed from fields, impacts were allocated to this co-product on the basis of gross chemical energy content between residues and the crop product. Crop residues not removed from fields were considered a waste product, and field level emissions were calculated accordingly, as described in appendix A, section A.8and A.9. A complete description of assumed residue removal rates, gross chemical energy contents, and calculation of allocation factors for upstream field crop systems is available in appendix A, section A.3. Similarly, many stages of the egg supply chain are also multifunctional. Co-products and wastes for breeder, hatchery, and pullet facilities were identified based on data reported by Pelletier (2017) regarding the final uses of outputs from upstream life cycle stages including breeder, hatchery, and pullet operations. The identified co-products for breeder flocks include eggs produced for hatcheries, and portions of both mortalities and spent hens. The co-products for hatcheries included female chicks for pullet facilities, discarded eggs, and egg-shells. Co-products for pullet facilities included laying hens to be placed in layer barns, and a portion of 27 mortalities. Finally, co-products for layer facilities included eggs for further grading and processing, and portions of both spent hens and mortalities. A complete breakdown of the co-products identified for each life cycle stage, and the relative proportions considered co-products if not 100% are presented in Table 2-1. Table 2-1. Categorization and proportions of co-products and waste products at each step in the egg supply chain. If no percentages are given for specific flows, 100% of that flow is categorized as indicated in the column heading. Life cycle stage Co-products Wastes Breeder flocks Eggs Spent hens – 70% Spent hens – 30% Mortalities – 44% Mortalities – 56% Hatcheries Female chicks Discarded chicks Discarded eggs Egg shells – 76% Egg shells – 24% Pullet facilities Pullets Mortalities – 21% Mortalities – 79% Laying facilities - Conventional Eggs Spent hens – 72% Spent hens – 28% Mortalities – 8% Mortalities – 92% Laying facilities - Enriched Eggs Spent hens – 67% Spent hens – 33% Mortalities – 11% Mortalities – 89% 28 Life cycle stage Co-products Wastes Laying facilities – Single tier non-cage Eggs Spent hens – 77% Spent hens – 23% Mortalities – 23% Mortalities – 77% Laying facilities – Aviary Eggs Spent hens – 76% Spent hens – 24% Mortalities – 13% Mortalities – 87% Laying facilities – Free range Eggs Spent hens – 79% Spent hens – 21% Mortalities Allocation between co-products at breeder flocks, hatcheries, and pullet facilities was performed based on gross chemical energy content of the co-products in the absence of robust metabolic energy partitioning models for these life cycle stages that align with the identified co-products (Arulnathan et al., 2022). For all of these steps, eggs were assumed to have a total gross chemical energy content of 1520 kcal/kg (Egg Farmers of Canada, 2018), while chicken meat (whether as mortalities, spent hens, or female chicks) was assumed to have a gross chemical energy content of 1906.43 kcal/kg Pelletier (2017). For allocation at hatcheries, egg shells were assumed to have a gross chemical energy content of 239 kcal/kg (Heuzé et al., 2011). Unlike the upstream supply chain steps, allocation between eggs, and valorized spent hens and mortalities at the layer phase were calculated in line with Arulnathan et al. (2022) using a method based on 29 models of metabolic partitioning of feed energy in hens. A complete description of this allocation method, it’s rationale, and the underlying metabolic models used in calculation of allocation factors is available in Arulnathan et al. (2022). Table 2-2 lists all allocation factors for all co-products at breeder, hatchery, pullet, and layer facilities. Default background physical allocation factors were used for background processes upstream of breeder facilities and field crop production processes. Table 2-2. Calculated allocation factors for all co-products identified for all foreground system processes in the Canadian egg supply chain. Life cycle stage Co-product Allocation factor Breeder Eggs 0.88 Spent hens 0.11 Mortalities 0.01 Hatcheries Female chicks 0.65 Discarded eggs 0.33 Egg shells 0.02 Pullet rearing Pullets 0.99 Mortalities 0.01 Laying facilities - Conventional Eggs 0.96 Spent hens 0.04 Mortalities 0.00 Laying facilities - Enriched Eggs 0.96 Spent hens 0.04 30 Life cycle stage Co-product Allocation factor Mortalities 0.00 Laying facilities – Single-tier non-cage Eggs 0.96 Spent hens 0.04 Mortalities 0.00 Laying facilities – Multi-tier non-cage/Aviary Eggs 0.96 Spent hens 0.04 Mortalities 0.00 Laying facilities – Free range Eggs 0.96 Spent hens 0.04 2.3.2 Life cycle inventory The second phase of an LCA is the LCI phase (ISO, 2006b). This phase involves the collection of data characterizing the material and energy inputs and emissions associated with the supply chain activities comprising the system of interest. Collection of high quality LCI data is central to robust life cycle assessment research and associated sustainability management decisions. These data enable assessment of the environmental impacts of a product system across many different impact categories in an additive manner using formalized impact assessment methodologies in the LCIA phase (Ford et al., 2012). The methodological choices made during the goal and scope phase provide the initial plan for conducting the LCI (ISO, 2006b), which may be iterated upon if challenges are encountered (Rebitzer et al., 2004). This phase includes 31 definition of “foreground” processes – those for which case-specific primary data are to be collected and used – and “background” processes – which are those for which more general, secondary data are typically used (Guinée et al., 2002). Data for background processes may be sourced from literature, or may be taken from public or industry-developed LCI databases, which provide high-quality data characterizing frequently used commodities that are both transparent in their sourcing, and consistent in their documentation (Rebitzer et al., 2004). 2.3.2.1 Foreground data sources and assumptions 2.3.2.1.1 Primary collection of laying hen data Primary data characterizing egg production was collected via surveys administered to Canadian egg farmers to collect data on housing system type and characteristics, management practices, on farm resource use, and hen performance. A copy of the survey may be found on the Open Science Framework at https://osf.io/xrckz/?show=view. Housing systems were defined in accordance with Canadian standards (National Farm Animal Care Council, 2017). Only commercial egg farms operating under the Canadian supply management system were included in the study. A description of the Canadian egg industry, including the supply management system, is available in Pelletier (2017). Survey responses were collected in two ways. First, responses were collected via in-person interviews with farm owners/managers. Commercial laying hen farmers were recruited for voluntary participation through their respective provincial egg boards. Once recruited, producers were instructed to answer survey questions with respect to their most recently completed flock. In instances where farmers had multiple flocks operating simultaneously, they were instructed to complete survey questions with respect to their largest flock. In instances in which a producer operated a split flock (i.e. a single barn divided into two 32 sides each housing an independent flock), each side was treated as a separate, distinct barn. In such cases, farmers were asked to answer survey questions only with respect to the most recently completed side of the barn. Once a single survey was completed, farmers operating multiple flocks were invited to fill out additional surveys for other flocks at their discretion. Second, the survey was made available online using the University of British Columbia survey tool provided by Qualtrics (2005). Access links to the survey (both English and French versions) were provided to the provincial egg boards for distribution to commercial egg farmers electronically during Fall 2019. All instructions for answering survey questions were the same for online survey responses as they were for in-person responses. In addition, to increase sample sizes, data collected during Summer and Fall 2019 was supplemented with data previously collected in 2017 as part of a collaborative effort between Egg Farmers of Canada and the World Wildlife Fund. The surveys used for each of these data collection activities shared largely the same questions, with some minor differences. Relevant information from this supplementary data source was incorporated alongside the data collected in 2019 for further analysis. All data, which were collected under guarantee of non-disclosure with respect to individual facilities, were coded to ensure confidentiality for individual producers. All useable information was extracted from both complete and incomplete surveys. In cases where further clarification was necessary or to fill in missing values or correct obvious errors, follow ups were conducted if farmers had indicated their consent and provided contact information when filling out the survey. When follow ups were unsuccessful, obvious errors were excluded, and invalid responses were considered to be missing values. Importantly, errors or missing responses in one survey question did not necessitate complete exclusion of the entire survey. 33 Following collection of primary survey data for layer operations, separate production weighted averages were calculated for each non-energy input based on a four-step process. All eggs across each housing system were assumed to have an average mass of 60g. This mass represents a midpoint between “large” and “extra large” sized eggs, rounded up, as defined by the Canadian Food Inspection Agency (Canadian Food Inspection Agency, 2021). Since all calculations were production weighted, all those survey responses that did not provide information on production were removed. Next, all responses were scaled to a one-year long (i.e. 365 day) laying cycle in order to account for variability in cycle length between flocks (average length: 365.4 ± 14.11 days; maximum: 434 days; minimum: 308 days). After scaling, production outliers were informally identified in accordance with Laurikkala et al. (2000). Using this method, a data point was identified as an outlier if the value was greater than quartile 3 + 1.5 x interquartile range, or less than quartile 1 – 1.5 x interquartile range. Dozens of eggs produced per hen per year was chosen as the metric for outlier detection to account for variability in production due to differences in flock sizes between surveys. Production outliers were detected individually for different housing systems to account for variability in production across systems. If a survey response was found to be a production outlier it was removed completely. Following removal of production outliers, a second stage of outlier identification was performed using the same method for each of the individual inputs. If a survey was found to be an outlier for a specific input, or it did not provide a response for that input, it was removed from calculation of the production weighted average. It is important to note that this did not preclude other values from that data set being used in calculation of other production weighted averages. 34 Outlier detection and calculation of production weighted averages for energy inputs was difficult due to low response rates and large variability in reported energy consumption across farms, which was interpreted to represent challenges in reporting rather than operational realities alongside actual differences in sources and amounts of energy used. Energy consumption data was collected for electricity, diesel, gasoline, liquified petroleum gas/propane, fuel oil, and natural gas. Across every barn surveyed, electricity was the only consistently used type of energy for lighting, ventilation, and other automated systems. Since electricity is consistently used to some degree in all barns, data sets for which electricity consumption data were not provided were removed in calculation of production weighted electricity consumption. In some cases, farmers reported electricity consumption in terms of the amount of money spent during that production period. When this was the case, consumption was calculated based on average electricity prices in each province as reported by Urban (2020). Outliers for electricity consumption were identified through comparison of calculated values with the ranges reported by Pelletier (2017) in supplementary tables S16-20. This approach was taken rather than the interquartile range approach due to the large degree of variability exhibited in the collected data, and the small sample size with respect to electricity consumption If a value fell outside of the previously reported range, it was identified as an outlier and removed. This method of outlier detection was not applied to electricity consumption for either single- or multi-tier non-cage barns because the range previously reported by Pelletier (2017) in supplementary table S18 did not differentiate between non-cage systems. As such, only obvious outliers identified via visual inspection were removed to preserve the variability of the data sets. 35 In contrast to electricity, other forms of energy are variously used across farms for processes such as heating, running backup generators, barn cleaning, etc. Since the other sources of energy covered by the survey are not consistently applied across all farms, if a farm did not report consumption levels for a particular fuel it was assumed to be zero. These zeroes were subsequently included in calculation of production weighted average consumption for each energy source. Outlier detection could not be performed on these data due to the inconsistent use of these energy sources, the inability to differentiate between zero and missing values, and the large variation seen in reported energy consumption levels across those farms that did report consumption. As such, only obvious outliers on the very high end of the spectrum that could be identified visually were removed. In some cases, insufficient data was collected for calculation of production weighted averages for non-electricity energy sources. When this was the case, the average values presented in Pelletier (2017) supplementary tables S15-20 were used. Specifically, the following values reported by Pelletier (2017) were used: all non-electricity energy inputs for single-tier non-cage production; and all non-electricity energy inputs for multi-tier non-cage, and free range production with the exception of diesel. No updated data were calculated for transportation values. As such, these values are the same as those reported by Pelletier (2017). The values reported for non-cage production were applied to both single- and multi-tier non-cage models. Conversion factors used for calculating energy consumption where necessary are presented in Table A 10. 2.3.2.1.2 Production weighting of breeder, hatchery, pullet, and layer operations Throughout this analysis, production-weighted averages were used, when possible, to make the resulting LCA more representative of Canadian average conditions. Proportions of total 36 production of breeder eggs, chicks, pullets, and layers attributable to each province were used to derive appropriate energy grid mixes, and emissions factors for estimation of nitrogenous emissions following deposition of poultry manure applied on agricultural fields. Provincial production data for breeder operations were taken from StatsCan (2022), describing hatching egg production in each province, while provincial production data for hatcheries were taken from StatsCan (2022b), describing the number of chicks hatched in each province. In both cases, these data were collected during the 2021 Canadian Census of Agriculture. Production of hatching eggs in Newfoundland and Labrador, Prince Edward Island, and the Northwest Territories were excluded from the breeder production weighted average because StatsCan considers the collected data for these provinces too unreliable to be published. Chicks hatched in Newfoundland and Labrador were excluded from the hatchery production weighted average because the factors these production weighted averages are used for (i.e., energy grid mixes and emission factors) do not have data representative of Newfoundland and Labrador. Provincial production data for pullets were taken from StatsCan (2024), representing the number of layer type pullets placed during 2023. In this data, aggregated data is provided for Atlantic Canada, which is assumed to be representative of Newfoundland and Labrador, Prince Edward Island, Nova Scotia, and New Brunswick. In the absence of more granular information, it was assumed that the proportion of pullets placed in each province in Atlantic Canada was the same as that of eggs produced. Provincial production data for laying hens was based on limits to federal egg production quota allocated to each province in 2023, as described in the Egg Farmers of Canada 2022 Annual Report (Egg Farmers of Canada, 2023). Information is not available regarding the distribution of hens in each housing system within provinces, so the same production weighting for laying hens was assumed to apply to all housing systems. Similar to 37 calculation of production weighting factors for chicks, production of pullets and eggs in Newfoundland and Labrador was excluded because data representative of Newfoundland and Labrador is not available to support their calculation. Differences in resource use and emissions across cage- and non-cage pullet systems were not considered as Pelletier (2017) did not previously produce models differentiating pullet production by housing system type. The assumed distribution of production for breeder, hatchery, pullet, and layer operations used in production weighting are given in Table 2-3. Table 2-3. Assumed proportions of production for each step in the layer supply chain used for calculation of production weighted average factors. Slashes represent no data reported for that specific province-activity combination. Province Breeder Hatchery Pullet Layer Nova Scotia 4.23 2.50 1.37 3.20 New Brunswick 4.54 2.70 2.24 1.95 Prince Edward Island / 0.00 0.37 0.5 Quebec 28.97 29.70 24.45 20.68 Ontario 33.38 28.12 45.43 36.26 Manitoba 6.25 4.10 8.91 9.34 Saskatchewan 2.54 3.89 3.19 4.62 Alberta 6.32 11.61 10.60 10.52 British Columbia 13.77 17.37 3.44 12.42 Northwest Territories / 0.00 / 0.47 38 2.3.2.1.3 Data characterizing feed formulations, breeder, hatchery, and pullet operations LCI data characterizing upstream processes in the egg supply chain, including breeder, hatchery, and pullet operations, as well as feed formulations representative of those used in breeder, pullet, and layer operations were taken from data previously collected by Pelletier (2017). No modifications have been made to this data. However, previously Pelletier (2017) calculated manure management related emissions for breeder and pullet operations using IPCC (2006) methods. Estimated emissions from manure management systems for breeder and pullet flocks have been re-calculated herein according to IPCC (2019) methods, as described in section 2.3.2.1.4. 2.3.2.1.4 Modeling N, P, and methane emissions from poultry manure Actual manure removal amount data were not collected, as previous efforts to collect estimates of manure volumes from farmers resulted in a data set of low resolution and quality (Pelletier, 2017). A standard manure excretion rate was assumed, scaled relative to the feed conversion efficiencies calculated for each housing system (Pelletier, 2017; Pelletier et al., 2014). This excretion rate did not take into account the impact of hen genetics, which may be an important factor in determining feed conversion efficiency (Yuan et al., 2017, 2015). Nitrogen and phosphorous excretion rates were calculated based on a nutrient mass balance model using feed composition and assuming that 2.2% of hen body mass is N and 0.6% is P, and that eggs contain 1.7% N and 0.21% P in accordance with Koelsch (2007). Assumed nutrient contents for all ingredients included in breeder, pullet, and layer feeds, as well as the literature sources from 39 which these assumed nutrient contents were derived are available in appendix A, Table A 21 to Table A 23. Poultry manure is a source of nitrogenous and P losses to the environment, and may also be a source of methane emissions, though to a much lesser extent. Losses to different environmental compartments may occur from manure storage systems on farms, as well as following its deposition on agricultural fields, which is the predominant use of poultry manure (Bolan et al., 2010). Previous analyses indicate that emissions from manure management are key drivers of the life cycle environmental impacts of egg production systems (Pelletier, 2017), making them priority targets for improvement of sustainability outcomes in the industry (Ershadi et al., 2020; Kanani et al., 2020). Improvement requires identification of types of emissions and losses, as well as their magnitudes (Giltrap et al., 2010). While measurement of the various nutrient losses and emissions from manure management systems is possible (as reviewed by Hristov et al. (2019)), doing so is impractical at scale due to the number of factors that are spatially and temporally dependent and the time/resource intensiveness of such measurements (Congreves et al., 2016; Radcliffe et al., 2009). The use of models for estimation of nutrient emissions and losses is hence necessary. Both process-based and empirical models are available for estimation of nitrogenous emissions related to manure management. In a review of egg production LCA studies, Turner et al. (2023a) found that the majority of studies estimate nitrogenous emissions using the empirical IPCC Tier 1 and 2 methods, including the previous analysis of Canadian egg production (Pelletier, 2017). Overall, very few studies employed process-based models for estimation of nitrogenous emissions. 40 Direct emissions of nitrous oxide (N2O) may be released from manure management systems through nitrification and incomplete denitrification of the N in manure (IPCC, 2019). N2O is a particularly potent GHG, and may act as a precursor from which other nitrogenous emissions may be formed as part of a nitrogen “cascade” (Galloway et al., 2006; Vogeler et al., 2013). Subsequently, indirect emissions may then contribute to other environmental impacts, such as acidification, eutrophication, human health and biodiversity impacts. Accurate accounting of nitrogenous emissions from manure management systems is therefore key to assessment of the life cycle environmental impacts of poultry, and other livestock production systems. Here, nitrogenous emissions were estimated using a combination of IPCC Tier 1 and 2 methods (IPCC, 2019). Use of these methods is consistent with recommendations from the Livestock Environmental Assessment and Performance (LEAP) guidelines for calculating GHG emissions and fossil energy demand from poultry supply chains (FAO, 2016a), and update the methods used by Pelletier (2017), who previously used methods based on IPCC (2006). Direct N2O emissions from manure management were calculated according to equation 10.25 from IPCC (2019), which, for the purposes of this study, may be simplified to the equation 𝑘𝑔 𝑑𝑖𝑟𝑒𝑐𝑡 𝑁2𝑂 𝑒𝑚𝑖𝑠𝑠𝑖𝑜𝑛𝑠𝑡𝑜𝑛𝑛𝑒 𝑜𝑓 𝑒𝑔𝑔𝑠 =𝑘𝑔 𝑁 𝑒𝑥𝑐𝑟𝑒𝑡𝑒𝑑𝑡𝑜𝑛𝑛𝑒 𝑜𝑓 𝑒𝑔𝑔𝑠 ∗ 𝐸𝐹3(𝑆) ∗ 4428 since only a single livestock species is considered in this study, and because this study does not account for the potential flow of poultry manure to anaerobic digestor systems (currently minimal in Canada). A weighted emission factor EF3(S) was calculated based on the proportion of different manure management systems in use in Canada, as collected as part of the primary data collection described in 2.3.2.1.1. Default emission factors for different manure management systems were used in this calculation: 0.001, 0.005, and 0 kg N2O-N x kg N excreted-1 for dry 41 manure storage systems (both covered and uncovered), covered liquid storage systems, and uncovered liquid storage systems, respectively (IPCC 2019, table 10.21). Liquid manure storage systems are those defined by the IPCC as systems in which manure is stored or treated in a liquid/slurry form, such as in lagoons, ponds, tanks, or pits (IPCC, 2019). Indirect N2O emissions may result from manure management systems through two main pathways: volatilization of ammonia (NH3) or nitrogen oxides (NOx), and nitrate leaching, each of which leads to subsequent N2O emissions. Volatilization of NH3 and NOx from manure management systems are calculated according to equation 10.26 in IPCC (2019), which may be simplified here as 𝑘𝑔 𝑁𝑣𝑜𝑙𝑎𝑡𝑖𝑧𝑒𝑑 𝑎𝑠 𝑁𝐻3 𝑎𝑛𝑑 𝑁𝑂𝑥𝑡𝑜𝑛𝑛𝑒 𝑜𝑓 𝑒𝑔𝑔𝑠=𝑘𝑔 𝑁 𝑒𝑥𝑐𝑟𝑒𝑡𝑒𝑑𝑡𝑜𝑛𝑛𝑒 𝑜𝑓 𝑒𝑔𝑔𝑠∗ 𝐹𝑟𝑎𝑐𝐺𝑎𝑠𝑀𝑆 (𝑇,𝑆) where FracGAsMS (T,S) is the average default value of 0.44 for poultry manure with and without litter, in accordance with table 10.22 in IPCC (2019). Indirect N2O emissions from volatilized NH3 and NOx are subsequently calculated according to equation 10.28 in IPCC (2019), which is simplified here as 𝑘𝑔 𝑖𝑛𝑑𝑖𝑟𝑒𝑐𝑡 𝑁2𝑂 𝑒𝑚𝑖𝑠𝑠𝑖𝑜𝑛𝑠𝑡𝑜𝑛𝑛𝑒 𝑜𝑓 𝑒𝑔𝑔𝑠= 𝑘𝑔 𝑁𝑣𝑜𝑙𝑎𝑡𝑖𝑧𝑒𝑑 𝑎𝑠 𝑁𝐻3 𝑎𝑛𝑑 𝑁𝑂𝑥𝑡𝑜𝑛𝑛𝑒 𝑜𝑓 𝑒𝑔𝑔𝑠∗ 𝐸𝐹4 ∗ 4428 where EF4 is the default value of 0.01 kg N2O-N/(kg NH3-N + NOx-N) volatilised (IPCC 2019, table 11.3). 42 Indirect N2O emissions from nitrate leaching are calculated according to equations 10.27 and 10.29 in IPCC (2019). First, equation 10.27 is used to calculate the amount of N lost to leaching, which is simplified here as 𝑘𝑔 𝑁𝑙𝑒𝑎𝑐ℎ𝑒𝑑𝑡𝑜𝑛𝑛𝑒 𝑜𝑓 𝑒𝑔𝑔𝑠=𝑘𝑔 𝑁 𝑒𝑥𝑐𝑟𝑒𝑡𝑒𝑑𝑡𝑜𝑛𝑛𝑒 𝑜𝑓 𝑒𝑔𝑔𝑠∗ 𝐹𝑟𝑎𝑐𝐿𝑒𝑎𝑐ℎ𝑀𝑆 (𝑇,𝑆) where FracLeachMS (T,S) represents the fraction of managed manure N that is leached from the manure management system. A default value of 0 kg N is used for FracLeach, in line with table 10.22 in IPCC (2019). Indirect N2O emissions from the leached fraction are subsequently calculated according to equation 10.29 in IPCC (2019), simplified here as 𝑘𝑔 𝑖𝑛𝑑𝑖𝑟𝑒𝑐𝑡 𝑁2𝑂 𝑒𝑚𝑖𝑠𝑠𝑖𝑜𝑛𝑠𝑙𝑒𝑎𝑐ℎ𝑎𝑡𝑒𝑡𝑜𝑛𝑛𝑒 𝑜𝑓 𝑒𝑔𝑔𝑠=𝑘𝑔 𝑁𝑙𝑒𝑎𝑐ℎ𝑒𝑑𝑡𝑜𝑛𝑛𝑒 𝑜𝑓 𝑒𝑔𝑔𝑠∗ 𝐸𝐹5 ∗ 4428 where EF5 is equal to a weighted emission factor representing the proportion of liquid and dry manure management systems indicated in the collected data. This weighted factor is derived from the assumption that default leaching rates are 0 kg N2O-N/ kg N leaching/runoff for dry manure management systems, and 0.02 kg N2O-N/ kg N leaching/runoff for liquid manure management systems (IPCC 2019, table 11.3). This assumption does not distinguish between covered and uncovered manure management systems as literature generally indicates that leaching is greater from liquid manure management systems than from solid systems (Burger and Venterea, 2008; Karimi et al., 2017). Nitrogenous emissions are also released following application of manure to agricultural land. As with emissions from manure management systems, field level emissions include both 43 direct N2O emissions, and indirect emissions via volatilization and leaching (IPCC, 2019). Calculation of these emissions is based on the amount of N applied to fields after manure management, excluding losses of N as emissions from manure management systems, and excluding the amount of N taken away from the system due to management of mortalities using either rendering, incineration, or burial. For those mortalities and spent hens that are composted, it is assumed that the hens are composted in the manure pile. The N and P in their body mass is assumed to subsequently contribute to losses of N and P from manure management systems, rather than calculating separate emissions associated specifically with composting of spent hens and mortalities. To calculate the portion of N applied to fields, the total N losses from manure management systems are subtracted from the total N excreted. This amount is also supplemented by the N content of any manure that was indicated in survey responses to not be stored but rather directly applied to fields. Amounts of N applied to fields per tonne of eggs produced in different housing systems are presented in Table A 24. All field level nitrogenous emissions from application of N to agricultural fields are calculated following a combination of IPCC (2019) Tier 1 protocols and Tier 2 protocols. Field level direct N2O emissions from manure application are calculated according to equation 11.1 in IPCC (2019), such that 𝐷𝑖𝑟𝑒𝑐𝑡 𝑁2𝑂 𝑒𝑚𝑖𝑠𝑠𝑖𝑜𝑛𝑠𝑡𝑜𝑛𝑛𝑒 𝑜𝑓 𝑒𝑔𝑔𝑠 𝑝𝑟𝑜𝑑𝑢𝑐𝑒𝑑 =𝑘𝑔 𝑁 𝑎𝑝𝑝𝑙𝑖𝑒𝑑 𝑡𝑜 𝑓𝑖𝑒𝑙𝑑𝑡𝑜𝑛𝑛𝑒 𝑜𝑓 𝑒𝑔𝑔𝑠∗ 𝐸𝐹1 In the CRSC field crop carbon footprints, regionalized values of EF1 are provided for calculation of direct N2O emissions from Canadian agricultural soils ((S&T)2 Consultants Inc., 2022a). A production weighted average value of EF1 was therefore calculated based on these 44 reported emission factors. In some cases, multiple values of EF1 were given for different regions within a province. When this was the case, an average provincial value for EF1 was first calculated. These provincial values were subsequently production weighted and aggregated based on the proportions of layer production in each province (Table 2-3). However, during calculation of this production weighted average emission factor, production in Newfoundland and Labrador, and the Northwest Territories were excluded because they do not have regional values for EF1 in the CRSC reports. Additionally, Liang et al., (2020) developed scaling factors for EF1 for calculation of direct N2O emissions on agricultural fields, assigning a scaling factor of 0.84 for nitrogen from manure sources. A detailed discussion of these scaling factors is given in appendix A, section A.8. The value of EF1 used has been scaled according to the factor for manure N suggested by Liang et al., (2020). Since no data is available regarding the distribution of layers within specific housing systems within each province, the same production weighted values of EF1 are used for all housing systems. The production weighted average value of EF1 for calculation of direct N2O emissions from manure applied to agricultural soils is given in Table 2-4. Indirect emissions of N2O from leaching and runoff after manure application are calculated in accordance with equation 11.10 in IPCC (2019) such that 𝐼𝑛𝑑𝑖𝑟𝑒𝑐𝑡 𝑁2𝑂𝐿𝑒𝑎𝑐ℎ𝑡𝑜𝑛𝑛𝑒 𝑜𝑓 𝑒𝑔𝑔𝑠=𝑘𝑔 𝑁 𝑎𝑝𝑝𝑙𝑖𝑒𝑑 𝑡𝑜 𝑓𝑖𝑒𝑙𝑑𝑡𝑜𝑛𝑛𝑒 𝑜𝑓 𝑒𝑔𝑔𝑠∗ 𝐹𝑟𝑎𝑐𝐿𝑒𝑎𝑐ℎ ∗ 𝐸𝐹5 Similar to the value of EF1 used for calculation of direct N2O emissions from deposition of layer manure, the 2022 Canadian NIR (Environment and Climate Change Canada, 2022) provides provincially regionalized values for FracLeach for calculation of N leached from 45 agricultural soils. Production weighted average values were therefore calculated for FracLeach using the same methods as for calculation of a production weighted average EF1 (Table 2-4). Table 2-4. Production-weighted average values for EF1 and FracLeach used in calculation of nitrogenous emissions from deposition of poultry manure on agricultural fields. Note that values for EF1 have been scaled by 0.84, as suggested by Liang et al., (2020) for N from manure sources. EF1 FracLeach Breeder 0.011 0.25 Pullets 0.011 0.24 Layers 0.010 0.23 Additional nitrogenous emissions may also be released through volatilization of manure applied to fields in the forms of NH3 and NOx, with additional indirect N2O emissions released from the volatized fraction. NH3 and NOx emissions volatized from field-applied manure are calculated according to equation 11.9 in IPCC (2019) such that 𝑘𝑔 𝑖𝑛𝑑𝑖𝑟𝑒𝑐𝑡 𝑁2𝑂 𝑒𝑚𝑖𝑠𝑠𝑖𝑜𝑛𝑠𝑣𝑜𝑙𝑎𝑡𝑖𝑙𝑖𝑧𝑒𝑑𝑡𝑜𝑛𝑛𝑒 𝑜𝑓 𝑒𝑔𝑔𝑠=𝑘𝑔 𝑁 𝑎𝑝𝑝𝑙𝑖𝑒𝑑 𝑡𝑜 𝑓𝑖𝑒𝑙𝑑𝑠𝑡𝑜𝑛𝑛𝑒 𝑜𝑓 𝑒𝑔𝑔𝑠∗ 𝐹𝑟𝑎𝑐𝑔𝑎𝑠𝑚 ∗ 𝐸𝐹4 where FracGASM is equal to the default value of 0.21 kg NH3-N + NOx-N/ kg N applied or deposited, and EF4 is equal to the default value of 0.01 kg N2O-N/kg NH3-N + NOx-N volatilised (IPCC 2019, table 11.3). In contrast to other livestock species, methane (CH4) emissions from poultry manure are relatively low (Loyon et al., 2007). Methane emissions may still occur when manure decomposes anaerobically as in liquid manure management systems, and only occur to a negligible degree in 46 dry management systems and following application of manure on agricultural fields (IPCC, 2019). IPCC protocols dictate that Tier 2 methods should be used when modeling a livestock species responsible for a significant share of a country’s CH4 emissions (IPCC, 2019). While the agricultural sector is responsible for approximately 30% of Canadian CH4 emissions per year, the majority of these emissions are attributable to large ruminants (Environment and Climate Change Canada, 2022). Use of Tier 1 methods is therefore considered sufficient for estimation of CH4 emissions associated with poultry manure management, and these methods have been used in this analysis. Default emissions factors for solid and liquid manure management systems were used, and manure was assumed to be 80% volatile solids, representing a middle value of the range of volatile solid contents (i.e., 70% - 90%) found by Kanani (2021) in a review of the literature investigating the efficacy of the use of poultry manure in anaerobic digestion systems. As with calculation of nitrogenous emissions, weighted emission factors were derived prior to calculation of emissions taking into account proportions of different manure management systems reported in the collected survey data, and different emissions factors for solid and liquid manure management systems. CH4 emissions were then calculated according to equation 10.22 in IPCC (2019), which may be simplified to 𝑘𝑔 𝐶𝐻4𝑡𝑜𝑛𝑛𝑒𝑠 𝑜𝑓 𝑒𝑔𝑔𝑠 × 𝑦𝑒𝑎𝑟−1=[𝐸𝐹7 ∗𝑣𝑜𝑙𝑎𝑡𝑖𝑙𝑒 𝑠𝑜𝑙𝑖𝑑𝑠 𝑒𝑥𝑐𝑟𝑒𝑡𝑒𝑑 𝑡𝑜𝑛𝑛𝑒𝑠 𝑜𝑓 𝑒𝑔𝑔𝑠 × 𝑦𝑒𝑎𝑟−1]1000 In accordance with IPCC protocols, potential CH4 emissions following deposition of poultry manure on agricultural fields have been excluded, as these emissions are assumed to be negligible (IPCC, 2019). 2.3.2.1.5 Modeling emissions from waste treatment 47 In some cases, waste flows were also identified from the different processes within the egg supply chain. These include portions of spent hens and mortalities from breeder flocks, pullets, and layer barns, discarded chicks, and a portion of discarded egg-shells from hatcheries. In accordance with ISO 14044 (ISO, 2006b), impacts from waste treatment are to be allocated to the processes producing that waste. It is therefore necessary to estimate impacts of the various methods of waste treatment used during each process. In general, when spent hens or mortalities were indicated to be composted, it was assumed that these were composted in the manure produced at each respective stage in the egg supply chain. Emissions associated with composting of spent hens and mortalities were therefore not calculated directly; rather, the portions of N and P in the hen body mass composted was assumed to contribute to the amounts of N and P available for losses from manure management systems. The exception to this was in modeling of composting of discarded chicks from hatcheries, including destroyed male chicks. Pelletier (2017) previously reported that 100% of discarded chicks from hatcheries are composted. Since there is no manure management processes associated with hatcheries, emissions associated with composting of discarded chicks were calculated. Composting may lead to the release of CO2 emissions under aerobic conditions and CH4 emissions under anaerobic conditions, as well as the release of N2O emissions, which may vary significantly in magnitude depending on the composting conditions (IPCC, 2019) . Emissions of CH4 and N2O associated with composting are calculated in line with methods employed in the Canadian 2022 NIR (Environment and Climate Change Canada, 2022), which excludes emissions of CO2 because these emissions are biogenic in nature. Accordingly, CH4 emissions are calculated according to IPCC Tier 1 methods from IPCC (2006) using Tier 3 emissions factors sourced from the 2022 Canadian NIR (Environment and Climate Change Canada, 2022). 48 The 2006 IPCC guidelines are specified here because the 2019 IPCC guidelines made no refinement to methods used for estimating emissions from composting. CH4 emissions from composting of discarded chicks are therefore calculated according to equation 4.1 from IPCC (2006), 𝐶𝐻4 = ∑(𝑀𝑖 × 𝐸𝐹𝑖)𝑛𝑖 where Mi is the mass of organic wet waste treated of different types, and EFi is the emission factor associated with that type of waste. The 2022 Canadian NIR (Environment and Climate Change Canada, 2022) provides different Tier 3 values for EFi for composting of yard waste, municipal solid waste, biosolids/manure, and a mixture of these different types of waste. It is assumed that discarded chicks are considered biosolids. The IPCC (2006) guidelines provide default emission factors for calculating emissions from composting on both a dry and wet weight basis, under the assumption that the moisture content of the waste treated is 60%. Since the Canadian NIR specifies that the given emission factors are on a wet weight basis, it is assumed that they follow this assumption that treated waste is 60% moisture content. This assumed moisture content is significantly different than the moisture contents of chicken viscera, which is assumed to be 73.7%, as taken from Feedipedia (Heuzé et al., 2012a). To determine an appropriate value of EFi for composting of discarded chicks, the emission factor for biosolids given by the Canadian NIR were scaled to the assumed dry matter content of discarded checks. This gave a value of EFi of 5.31 g CH4 kg discarded chicks -1 on a dry weight basis. Composting may also lead to emissions of N2O. Emissions of N2O associated with composting of discarded chicks were calculated in the same manner as CH4 emissions, using a scaled emission factor for EFi based on that presented for biosolids in the 2022 Canadian NIR 49 (Environment and Climate Change Canada, 2022). N2O emissions from composting of discarded chicks were therefore calculated according to equation 4.2 from IPCC (2006), 𝑁2𝑂 = ∑(𝑀𝑖 × 𝐸𝐹𝑖)𝑛𝑖 where Mi is the mass of organic wet waste treated of different types, and EFi is the emission factor associated with that type of waste. A value of 0.27 g N2O kg discarded chicks -1 on a dry weight basis was used for EFi. Other methods of disposal of spent hens and mortalities that were accounted for include burial/landfilling, and incineration. Incineration of wastes may be associated with emissions of CO2, CH4, and N2O. Emissions associated with incineration of spent hens and mortalities were calculated according to IPCC Tier 1 methods (IPCC, 2019). CO2 emissions from incineration of mortalities and spent hens may be estimated using equation 5.1 in IPCC (2019). However, one of the factors used in this equation, FCF, represents the fossil carbon content of the waste being incinerated. Default values of FCF for waste incineration are given in table 2.5 of IPCC (2019). Of the types of waste for which default FCF values are given, it is assumed that spent hens and mortalities are most similar to food wastes, which have a default FCF value of 0, as the carbon content of the waste is assumed to be 100% biogenic. It is therefore assumed that there are no CO2 emissions associated with incineration of mortalities and spent hens. CH4 emissions may also be released during incineration of wastes as a result of incomplete combustion. CH4 emissions associated with incineration of spent hens and mortalities are estimated using equation 5.4 from IPCC (2019), 𝐶𝐻4 = 𝐼𝑊 𝑋 𝐸𝐹 𝑋 10−6 50 where IW is the amount of waste incinerated, EF is the emission factor representing kg CH4 released per tonne of waste incinerated, and 10-6 is a conversion factor for tonnes to kilograms. IPCC default factors were used for 𝐸𝐹, under the assumption that mortalities and spent hens are incinerated in a batch-type (i.e., start-stop) incinerator. IPCC (2019) further provides default emission factors for batch-type stoker and fluidised bed incinerators. Since it is unknown what type of incinerator is being used for incineration of spent hens and mortalities, an average emission factor was calculated, giving a value of 148.5 kg CH4 released per tonne of waste incinerated. N2O emissions associated with incineration of spent hens and mortalities are estimated using equation 5.5 from IPCC (2019), 𝑁2𝑂 = 𝐼𝑊 𝑋 𝐸𝐹 𝑋 10−6 where IW is the amount of waste incinerated, EF is the emission factor representing kg N2O released per tonne of waste incinerated, and 10-6 is a conversion factor for kilograms to tonnes. A default emission factor of 60 g N2O released per tonne of waste incinerated was used, in line with the default value given in the 2022 Canadian NIR (Environment and Climate Change Canada, 2022) for incineration of municipal solid wastes, which was the most similar waste category to spent hens and mortalities. All emission factors for calculation of emissions associated with incineration of spent hens and mortalities are given on a wet weight basis. Emissions associated with burial/landfilling of wastes were also estimated based on IPCC (2019) Tier 1 methods. CH4 emissions associated with burial/landfilling of spent hens, mortalities, and egg shells were estimated according to equation 3.6 from IPCC (2019), 𝐶𝐻4 = 𝐷𝐷𝑂𝐶𝑚 𝑑𝑒𝑐𝑜𝑚𝑝 × 𝐹 ×1612 51 where DDOCm decomp is the decomposable degradable organic carbon contained in the landfilled waste (i.e., the portion of organic carbon in decomposing material that will degrade), F is the fraction of CH4 by volume in the generated landfill gas and 16/12 is the molecular weight ratio of CH4/C. F is assumed to have a default value of 0.5. DDOCm decomp is estimated according to equation 3.5 in IPCC (2019), 𝐷𝐷𝑂𝐶𝑚 𝑑𝑒𝑐𝑜𝑚𝑝 = 𝐷𝐷𝑂𝐶𝑚 × (1 − 𝑒−𝑘) where DDOCm is the amount of decomposable degradable organic carbon accumulated, and k is a reaction constant. K is assumed to take a default value of 12, as per table 3.4 in IPCC (2019) for rapidly degrading wastes, which includes food wastes (assumed to be most similar to egg shells, spent hens, and mortalities). DDOCm is estimated according to equation 3.2 from IPCC (2019), 𝐷𝐷𝑂𝐶𝑚 = 𝑊 × 𝐷𝑂𝐶 × 𝐷𝑂𝐶𝑓 × 𝑀𝐶𝐹 where W is the amount of waste landfilled, DOC is the degradable organic carbon content of the waste, DOCf is the fraction of degradable organic carbon which decomposes, and MCF is a methane correction factor. DOC is assumed to have a default value of 36% based on the value given for food wastes, while DOCf is assumed to have a default value of 0.7 for highly degradable wastes according to table 3 in IPCC (2019). MCF is assumed to have a value of 1, in line with assumptions used for calculating emissions related to the landfilling of wastes in the 2022 Canadian NIR (Environment and Climate Change Canada, 2022). 2.3.2.2 Background data sources and assumptions 2.3.2.2.1 Modeling feed input supply chains Baseline feed formulations for breeders, pullets, and laying hens were taken from Pelletier (2017). These feed formulations represent production-weighted average compositions 52 based on primary survey data representative of 2012, excluding those contributing less than 0.1% of the total on a mass basis. A similar cut-off threshold was applied during calculation of national, production-weighted average feed formulations for breeders and pullets unless the feed inputs were already modelled for inclusion in layer feeds, in which case these ingredients were included despite their relatively small contributions. This led to the inclusion of a small amount of ruminant fat (0.005% by mass) in the conventional pullet feed formulation. Broadly, ingredients in the modeled feed formulations may be classified into three categories: field crops and their derivatives (i.e., corn, wheat, canola oil and meal, etc.), livestock-derived feed ingredients (i.e., animal fat, meat and bone meal, etc.), and other ingredients (i.e., vitamin and mineral supplements, synthetic amino acids, etc.). An in-depth description of how all ingredients included in these feed formulations were modeled is available in appendix A, sections A.1 to A.15. 2.3.2.2.2 Modeling energy inputs to egg supply chain stages Provincial electricity models as well as models for all other energy inputs were taken from the ecoinvent database version 3.9, and were production weighted according to the factors described in Table 2-3. Since no data is available regarding the distribution of laying hens in different housing systems across provinces, the same production weighted average energy mixes have been used for all housing systems. Energy inputs to each housing system were defined as described previously in section 2.3.2.1.1. 2.3.2.3 Data quality and uncertainty assessment In accordance with the ISO 1044 Standard (ISO, 2006b), data quality was assessed for foreground (i.e. egg production and layer manure management) processes, as well as background processes from Pelletier (2017). These include pullet, hatchery, and breeder flocks and 53 associated manure management activities, feed milling processes for layer, pullet, and breeder flocks, and feed crop production models. Data quality was assessed for each flow into and out of these processes using the default ecoinvent pedigree matrix, as described by Table 1 in Ciroth et al. (2016). Data quality entries for breeder, hatchery, pullet, and feed mill activities were taken from those given in Pelletier (2017), with modifications made to the temporal representativeness entries to reflect the context of the current study. Specifically, data quality was assessed with regards to reliability, completeness, and temporal, geographical, and technological correlation. Importantly, data quality assessment of this nature includes a degree of subjectivity. For each flow assessed, data quality scores were defined in relation to how well the data represented by the flow actually represented the modeling goal for each flow. Data quality was not assessed for background processes from LCI databases. Based on common practice for uncertainty assessment in LCA (Bamber et al., 2019), parameter uncertainty (i.e. uncertainty in inventory data) was propagated using Monte Carlo simulations. Using this method, a set number of simulations are run on a mathematical model in which each individual simulation samples possible input values from probability distributions defined for each parameter. The results of each simulation are subsequently used to determine the distribution around the estimated results of the model (Stevens, 2022). When used in an LCA model, the distribution defined for each model input represents a combination of parameter uncertainty (i.e., uncertainty associated with the estimated LCI values), and contributions to uncertainty related to data quality (i.e., uncertainty associated with different pedigree matrix entries, as defined by Ciroth et al. (2016)). These two uncertainty sources are subsequently combined to determine the probability distribution around each LCI value. 54 Where feasible, uncertainty factors for egg production were based on a combination of calculated geometric standard deviations, and uncertainty contributions from the pedigree matrix entries, as defined in Ciroth et al. (2016). Null values were not taken into account during calculation of geometric standard deviations for energy flows. In some cases, small sample sizes resulted in calculation of uncertainty factors several times higher than what would be expected for the agricultural sector, as noted by Frischknecht et al. (2005). Because of this, calculated uncertainty values greater than 5 were removed and replaced with the corresponding generic, sector-specific uncertainty value from Frischknecht et al. (2005). Five was chosen as an upper bound because it is the highest possible base uncertainty value defined by Frischknecht et al. (2005). For background processes taken from Pelletier (2017), default sectoral uncertainty factors from Frischknecht et al. (2005) were assigned to each flow as data are unavailable from which geometric standard deviations could be calculated. When performing Monte Carlo simulations, a key parameter to set is the number of simulations. Little information is available in the literature providing practitioners with a robust means with which to set the number of simulations. A wide range is represented in the literature, with both 1000 and 10,000 iterations being used commonly, while some studies perform up to 50,000 iterations (Igos et al., 2019). Generally, it is thought that the accuracy of the final estimates increases as the number of iterations increases, and that the choice of number of iterations used is primarily a trade-off between acceptable accuracy, and computational intensity (Michiels and Geeraerd, 2020). Rosenbaum et al. (2018) suggest that the number of iterations should be set based on comparisons in uncertainty measures (i.e., mean, standard deviation, etc.) from multiple simulations run using increasing numbers of iterations until the uncertainty 55 measures do not differ substantially from one another across simulations. However, as indicated by Michiels and Geeraerd (2020), how practitioners define an acceptable threshold for change across simulations is itself a subjective decision that may be study specific. Others have suggested definition of metrics such as the “uncertainty ratio”, representing the division of the 97.5th percentile by the 2.5th percentile, as a means to assess the impact of changing numbers of simulations on uncertainty measures. This metric was used by Steinmann et al. (2014), who indicate that the difference in uncertainty ratio when using 1000 and 10,000 Monte Carlo simulations was <1%, suggesting that increasing the number of iterations had little impact on the estimated uncertainty measures. Chen and Corson (2014) came to a similar conclusion, finding that increasing the number of iterations from 1000 to 5000 resulted in minor improvements to the stability of the estimated mean values, but more than tripled the computational run time, a trade-off that was deemed unacceptable. With regards to this trade-off, however, Michiels and Geeraerd (2020) argue that, in the context of computational time, such an increase is relatively insignificant and that lack of computational power should not be used as an excuse to justify performing a smaller number of simulations. In contrast, there is little in the literature to support reducing the number of simulations used. Rather, Lesage et al. (2019) indicate that, when using a smaller number of simulations, the dispersion of the resulting estimates do not reach convergence, representing a convergence error in Monte Carlo simulation. Since, from the perspective of error, the point of sampling in Monte Carlo simulation is to reduce convergence error (Galitzine and Boyd, 2015), utilization of a number of a small number of iterations that inadequately reduces this error should be avoided. It must be noted, however, that reduction of convergence error in Monte Carlo simulation assumes 56 that input parameters to the simulation are independent (Galitzine and Boyd, 2015), an assumption that often does not hold in LCA modeling (Rosenbaum et al., 2018). Whether potential correlations between input parameters actually have an impact on uncertainty measures in LCA is unclear, as indicated by Groen and Heijungs, (2017), who show that correlations may only be impactful some times. Overall, not only is it unclear if current common practices for uncertainty assessment in LCA are confounded by correlations within models, it is also unclear if different numbers of iterations in Monte Carlo simulations meaningfully impact estimated uncertainty around LCA results. While such questions may be mathematically interesting, Lesage et al. (2019) offer a more pragmatic approach, suggesting that there are more pressing challenges in LCA methodologies to focus on than determining the appropriate number of iterations for use in Monte Carlo sampling. With this in mind, uncertainty was propagated throughout the models developed in this analysis using Monte Carlo simulation with 1000 iterations, in line with common practice in the LCA field (Bamber et al., 2019). Uncertainty due to characterization models was not assessed, while uncertainty due to methodological choices was explored using sensitivity analyses, as described in section 2.3.3.1.1. 2.3.3 Life cycle impact assessment The third phase of an LCA is the LCIA phase (ISO, 2006b). In this phase, the results of the previous inventory analysis phase are converted into common-unit measurements and aggregated into impact assessment categories which describe the potential burdens of the system (Guinée et al., 2002). Additional steps may be taken to further process the results, including normalization, grouping, and weighting, though use of these steps are governed by additional 57 rules (ISO, 2006b). Completing this phase provides practitioners with a set of values expressing the potential impacts of the study system in terms of impact categories previously identified in the goal and scope definition phase. These values, in turn, help form the basis for the fourth and final phase of an LCA, the interpretation phase (ISO, 2006b). In this phase, impact assessment methods and impact categories are defined, and the compiled inventory data for the system under study undergo the processes of classification and characterization. During classification, inventory data are assigned on a qualitative basis to the pre-selected impact categories. Once flows have been classified according to which impact category they will contribute, they are aggregated to a common unit for each impact category using characterization models and characterization factors. This enables determining their individual and overall contributions to the quantified environmental burdens (Guinée et al., 2002). For example, characterization factors would be used to convert emissions of greenhouse gasses to air collected in the life cycle inventory phase to the common unit of kg CO2 equivalents, a category indicator for the global warming potential impact category. It must be noted, however, that characterization factors are the products of characterization models, which may vary in the accuracy with which they represent the relationship between flows and reference species, thus potentially biasing the results (Pennington et al., 2004). As such, it is imperative that impact assessment methodologies are chosen carefully based on the robustness of their underlying characterization models. A number of pre-selected suites of impact assessment methodologies are often included in LCA software. Their use should be informed by the goal and scope, best practices according to the literature, relevance, and other factors (Reap et al., 2008). It is also during this phase that the impact assessment results are to be subjected to sensitivity analyses, which are intended to evaluate uncertainty based on methodological choices 58 made during model construction (Reap et al., 2008). Two types of sensitivity analysis may be performed: a local sensitivity analysis, which examines the effect of a change in a single model parameter at a time, and a global sensitivity analysis, which determines how much each parameter contributes to the overall variance of the results (Groen et al., 2017). The ISO14044 standard (ISO, 2006b) requires that a comprehensive set of impact categories be assessed during the LCIA phase. Previous assessments of the Canadian egg industry have included different impact categories. Pelletier (2017) assessed a total of six different impacts: acidifying, eutrophying, and GHG emissions, and land, water, and energy use, using a combination of the CML 2 baseline 2000 and the Cumulative Energy Demand LCIA methods (Guinée et al., 2002). Herein, impacts are assessed using the CML-IA baseline LCIA method. This method was originally created by the University of Leiden in the Netherlands in 2001, with the most recent update occurring in 2016. This method is considered to be state-of-the-art for a European context, though the underlying characterization models used are considered to be location independent and globally relevant (Mikosch et al., 2022). In the context of this analysis, since the goal is to estimate impacts at the national average scale, a location-independent, generic LCIA method is sufficient. Further, since another goal of this analysis is to update the previous baseline LCA of the Canadian egg industry performed by Pelletier (2017), use of this LCIA method is in line with this goal as the CML-IA Baseline method used in this analysis is an updated version of the CML 2 Baseline 2000 method used previously. Finally, use of the CML LCIA method is also generally in line with recommendations from the LEAP guidelines for accounting of nutrient flows and associated impacts in livestock supply chains, though it is suggested for North American practitioners to test the validity of the results using this LCIA method against those estimated using the “tool for the reduction and assessment of 59 chemical and other environmental impacts” (TRACI) LCIA method (FAO, 2016b). The TRACI method was originally developed by the US EPA in 2003 (Bare, 2002) with the goal of providing an assessment tool consistent with existing standards and guidelines established by the US EPA, and included site-specific characterization factors for acidification, eutrophication, and smog formation. The methodology was subsequently updated, expanding upon the characterization factors originally included, but removing the focus on site-dependent assessment (Bare, 2011). Therefore, in line with recommendations from the LEAP guidelines, a sensitivity analysis is performed using the TRACI 2.1 LCIA method, as detailed in 2.3.3.1.1. The complete CML LCIA method is divided into baseline and non-baseline portions, with the baseline portion including the 11 impact categories most commonly applied in LCA (Universiteit Leiden, 2016). Importantly, while the CML-IA baseline and CML 2 baseline 2000 LCIA methods are related, they are fundamentally different methods. Direct comparisons between impacts estimated herein, and those estimated by Pelletier (2017) are therefore not made, because such comparisons would not be made on a methodologically consistent basis. Other points of methodological inconsistency throughout this analysis and that performed by Pelletier (2017), including with respect to sourcing of background data sets, allocation methods, etc. also preclude direct comparisons. A complete list of the impact categories assessed using this method and their category indicator units is given in Table 2-5. Impact categories assessed by the CML-IA baseline LCIA method and included in this analysis, and associated category indicator units. All LCIA calculations were performed in openLCA version 2.1. Significant differences in estimated impacts across housing systems were identified using one-factor ANOVA with post-hoc Tukey’s Honestly Significant Difference (HSD) tests (α=0.05), based on descriptive statistics derived from the Monte Carlo simulations performed. Statistical tests were performed 60 using the SciPy Python package (Virtanen et al., 2020). Production-weighted Canadian average impacts were excluded from tests for significant differences since these impacts represent an aggregation of the impacts for all housing systems. Table 2-5. Impact categories assessed by the CML-IA baseline LCIA method and included in this analysis, and associated category indicator units Impact category Category indicator unit Abiotic depletion Kg Sb eq Abiotic depletion (fossil fuels) MJ Acidification Kg SO2 eq Eutrophication Kg PO43- eq Freshwater Aquatic ecotoxicity Kg 1,4-DB eq Global warming Kg CO2 eq Human toxicity Kg 1,4-DB eq Marine aquatic ecotoxicity Kg 1,4-DB eq Ozone layer depletion Kg CFC-11 eq Photochemical oxidation Kg C2H4 eq Terrestrial ecotoxicity Kg 1,4-DB eq 2.3.3.1 Sensitivity analyses 2.3.3.1.1 Life cycle impact assessment method To determine the potential impact of the chosen LCIA method on the estimated impacts per tonne of eggs produced, a sensitivity analysis was performed using different LCIA methods. LCIA methods may differ from one another in their underlying characterization models, which 61 may lead to differences in the estimated impacts of the system under study. The impacts of LCIA method choice on the estimated results may be impact category-dependent, with some impact categories, such as climate change, differing very little across LCIA methods due to relative harmonization of underlying characterization models compared to others, such as ecotoxicity (Chen et al., 2021). In spite of this, while the magnitude of estimated impacts may change across different LCIA methods, it is possible that choice of LCIA methods may not be impactful for the study conclusions, particularly when making comparisons between similar systems (Cherubini et al., 2018). In line with recommendations from the LEAP Guidelines for accounting of nutrient flows and associated impacts in livestock supply chains for North American practitioners (FAO, 2016b), a sensitivity analysis using the TRACI 2.1 LCIA method was performed. The impact categories considered in this sensitivity analysis were acidification, eutrophication, fossil fuel depletion, global warming, and ozone depletion (Table 2-6). These impact categories were chosen because they are included in both the CML-IA baseline method and the TRACI 2.1 method. While both of these LCIA methods provide metrics for estimating ecotoxicity impacts, the TRACI 2.1 method only uses a single ecotoxicity impact category while CML-IA baseline uses three, each representing a different environmental compartment. Due to this incongruence in compartments, ecotoxicity was excluded from this sensitivity analysis. Similarly, the impact categories carcinogenics, non-carcinogenics, respiratory effects, and smog were excluded as they are included in the TRACI 2.1 method and do not have suitable analogs in the CML-IA baseline method. Tests for significant differences were performed using one-factor ANOVA with post-hoc Tukey’s Honestly Significant Difference (HSD) (α=0.05), performed using the SciPy Python package (Virtanen et al., 2020). Production-weighted Canadian average impacts were excluded 62 from tests for significant differences since these impacts represent an aggregation of the impacts for all housing systems. Table 2-6. Impact categories assessed in a sensitivity analysis using the TRACI 2.1 LCIA method, and associated category indicator units Impact category Category indicator unit Acidification Kg SO2 eq Eutrophication Kg N eq Fossil fuel depletion MJ surplus Global warming Kg CO2 eq Ozone depletion Kg CFC-11 eq 2.3.3.1.2 Use of a vegetarian feed formulation Across all housing systems, it was found that production of feed made substantial contributions to total life cycle environmental impacts. In the previous baseline LCA models of the Canadian egg industry developed by Pelletier (2017), it was observed that estimated impacts per tonne of eggs produced were substantially lower in organic production systems, despite poorer observed levels of resource efficiency, particularly with respect to feed conversion. This trend was driven by the absence of rendered animal products in organic poultry feeds, an additional regulation that applies to certified organic egg production in Canada (CGSB, 2015). A similar trend was also observed in a more recent analysis of the U.S. egg industry, in which environmental impacts in cage-free production systems were estimated to be lower than those of caged systems despite worse levels of resource use efficiency, with differences driven by a reported lack of animal products in feeds milled for cage-free production (Turner et al., 2023b). 63 Given the large contributions to impacts attributable to the inclusion of rendered animal products in feed, a sensitivity analysis was performed in which Canadian egg production systems were assumed to employ a feed free of rendered animal products. The vegetarian feed formulation used for this purpose was the cage-free layer formulation from the U.S. reported by Turner et al. (2023b), production models for which were adapted to represent Canadian conditions. These adaptations include the use of Canadian production processes for field crop ingredients, and the use of methodologically equivalent processes for production of other ingredients as described in appendix A, sections A.1 to A.15. This sensitivity analysis was performed under the assumption of nutritional adequacy of the vegetarian diet. The complete vegetarian feed formulation is available in Table A 27. Energy inputs required to mill the modeled vegetarian feed are assumed to be the same as those for the conventional feed formulation. Emissions from manure management systems were also recalculated as part of this analysis. Comparisons of the nutrient contents, amounts of N and P excreted, and manure management emissions associated with the conventional and vegetarian feed formulations are available in Table A 28. Protein and N contents between the conventional and vegetarian formulations are comparable, differing <1% between the two. In contrast, the P content of the vegetarian diet is approximately 37% higher than that of the conventional feed, driven primarily by the inclusion of specific phosphate supplements in the vegetarian feed. This higher P content may impact comparisons of estimated results between the vegetarian and baseline feeds, particularly with respect to eutrophication impacts. Comparisons were therefore made in light of this potentially limiting factor. Further investigations into the potential use of vegetarian feed formulations in the Canadian egg industry as an environmental impact reduction strategy should further focus on nutritional equivalency across potential alternatives. It is assumed, however, that for this comparison each of the feed 64 formulations investigated are nutritionally adequate for laying hens. Estimated impacts for each housing system using the conventional and vegetarian feed formulations were compared for significant differences using Student’s t tests (α=0.05) based on descriptive statistics taken from the Monte Carlo simulations performed. 2.4 Results and discussion 2.4.1 Representativeness of collected egg production data In total, 206 surveys were received during the Summer and Fall 2019 data collection periods from which data could be extracted. Of these surveys, 73% (151/206) were fully completed, while the remainder were partially completed. The largest number of responses was received for conventional systems (46%). Representativeness of the collected sample ranged from 0% to 50% for individual housing systems within provinces. Nationally, this sample represented 13% of conventional production, 25% of enriched production, and 26% of cage-free (i.e. single- and multi-tier non-cage and free range). Representativeness of the total number of farms throughout provinces and nationally range from 0% to 44% (Table 2-7). These data were further supplemented with additional survey responses (56 for conventional cage, 13 for enriched cage, 3 for single-tier non cage, 2 for multi-tier non cage, and 5 for free range) from WWF data collected in 2017. 65 Table 2-7. Percent representativeness of collected egg production data based on number of farms across egg producing provinces and Canada-wide. Cage-free represents total coverage across both single- and multi-tier cage non-cage housing and free range, as the number of farms in each province for these systems was provided as just a single number. The “overall” row shows the percentage of egg farms included in the sample for each region regardless of housing system type. Zeros indicate no surveys were collected representing that housing system for that province. Slashes indicate no housing systems of that type exist in that province. Supplementary data collected during 2017 was not included in calculations of representativeness. Region1 BC AB SK MB ON QC NB NS PEI NFLD NWT CAN Conventional 11.43 13.6 33.33 16.81 6.42 10.23 41.67 25 0 0 0 12.5 Enriched 28.57 22.58 44.44 46.15 12.5 16.21 50 20 0 0 / 24.87 Cage-free 33.33 50 50 20 19.57 7.89 50 16.67 0 0 / 25.77 Overall 18.79 20.32 35.62 23.67 9.37 12.5 43.75 15.63 0 0 0 16.76 1BC – British Columbia; AB – Alberta; SK – Saskatchewan; MB – Manitoba; ON – Ontario; QC – Quebec; NB – New Brunswick; NS – Nova Scotia; PEI – Prince Edward Island; NFLD – Newfoundland and Labrador; NWT – Northwest Territories; CAN - Canada 66 2.4.2 Life cycle inventory results Complete tables describing those inventory values for field crop-derived feed inputs that were re-calculated from those presented in the CRSC reports (i.e., fertilizers, pesticides, and field-level emissions) are available in appendix A, Table A 4 toTable A 8, and Table A 29 to Table A 38. Tables describing the LCI data used for modeling of feed milling, breeder, hatchery, and pullet operations are available in Pelletier, (2017). Since the data used for modeling these supply chain steps was unchanged from those previously presented, they are not further discussed. With the exception of those inputs that remained constant between the models reported herein and those reported in Pelletier, (2017), material/resource input and output levels per tonne of eggs produced generally decreased across all housing systems with few exceptions (Table 2-8). Notable gains were made in feed conversion efficiency, reflected by lower feed input usage, in all housing systems except for free range, which exhibited much higher levels of feed use than previously reported. Pullet inputs (i.e. number of ~20 week old laying hens placed in the barn at the beginning of the lay cycle) per tonne of eggs produced decreased across all housing systems. Mortality rates were lower in conventional, single-, and multi-tier non-cage systems, and higher than previously reported in enriched and free range systems. Observed decreases in output levels, including manure and mass of mortalities and spent hens, result from the observed decreases in associated input levels (i.e. increased efficiency with respect to feed and pullet inputs). 67 Table 2-8. Life cycle inventory data by housing system for the production of one tonne of eggs in layer facilities in Canada, based on 2012 data reported by Pelletier, (2017) and data collected during 2017 and Summer and Fall 2019. Blank spaces indicate inventory data that was not included in the model for that particular housing system. Conventional Enriched Single-tier non-cage Multi-tier non-cage Free range Pelletier 2017 Updated 2019 Pelletier 2017 Updated 2019 Pelletier 2017 Updated 2019 Pelletier 2017 Updated 2019 Pelletier 2017 Updated 2019 Inputs Pullets 55.95 50.74 54.69 51.37 56.43 52.15 56.43 51.97 55.66 54.72 Mass per Pullet (kg) 1.38 1.38 1.41 1.41 1.45 1.45 1.45 1.45 1.71 1.71 Transport (tonne-km) 11.17 11.17 7.67 7.67 12.98 12.98 12.98 12.98 4.96 4.96 Feed (tonnes) 2.00 1.92 2.15 1.95 2.13 2.06 2.13 2.06 2.17 2.48 68 Conventional Enriched Single-tier non-cage Multi-tier non-cage Free range Pelletier 2017 Updated 2019 Pelletier 2017 Updated 2019 Pelletier 2017 Updated 2019 Pelletier 2017 Updated 2019 Pelletier 2017 Updated 2019 Transport (t-km) 110.36 110.36 100.19 100.19 138.65 138.65 138.65 138.65 118.12 118.12 Water (m3) 3.12 3.41 2.63 3.45 4.05 3.37 4.05 3.79 2.82 3.53 Electricity (kwh) 216.62 201.08 216.62 236.16 360.22 393.6 360.22 398.69 525.73 306.55 Diesel (l) 0.58 0.19 0.58 1.03 0.84 0.84 0.84 1.07 0.43 0.13 Gasoline (l) 0.60 0.11 0.60 0.20 0.48 0.48 0.48 0.48 0.31 0.31 Fuel oil (l) 0.04 0.027 0.04 0.008 0 0 0 0 0 0 LPG (l) 1.19 0.079 1.19 1.81 5.89 5.89 5.89 5.89 1.86 1.86 Natural Gas (m3) 5.67 0.68 5.67 2.93 2.79 2.79 2.79 2.79 0.07 5.38 Outputs Eggs (t) 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 69 Conventional Enriched Single-tier non-cage Multi-tier non-cage Free range Pelletier 2017 Updated 2019 Pelletier 2017 Updated 2019 Pelletier 2017 Updated 2019 Pelletier 2017 Updated 2019 Pelletier 2017 Updated 2019 Mass per egg (g) 59.29 60 59.85 60 59.82 60 59.82 60 60.33 60 Spent hens (kg)1 93.69 83.80 89.99 80.26 100.55 94.33 100.55 95.31 99.80 94.94 Transport (t-km) 31.19 31.19 31.19 31.19 7.00 7.00 Manure2 (t) 1.14 1.09 1.14 1.03 1.14 1.10 1.14 1.10 1.14 1.30 Truck transport (t-km) 17.98 17.98 3.62 3.62 3.62 3.62 9.13 9.13 Tractor transport (t-km) 3.26 3.26 1.15 1.15 0.38 0.38 0.38 0.38 70 Conventional Enriched Single-tier non-cage Multi-tier non-cage Free range Pelletier 2017 Updated 2019 Pelletier 2017 Updated 2019 Pelletier 2017 Updated 2019 Pelletier 2017 Updated 2019 Pelletier 2017 Updated 2019 Excreted N (kg) 30.72 32.33 34.5 33.23 33.60 35.81 33.60 35.79 34.76 46.97 Excreted P (kg) 6.02 6.06 6.68 6.33 6.49 6.57 6.49 6.55 6.70 8.81 Flock Mortality rate (%) 3.18 2.85 1.73 2.35 5.08 4.80 5.08 3.48 7.14 8.68 Mass of mortalities (kg)4 2.58 2.10 1.50 1.91 4.64 4.05 4.64 2.93 7.56 9.04 Truck transport (t-km) 8.00E-02 8.00E-02 4.00E-03 4.00E-03 9.00E-02 9.00E-02 9.00E-02 9.00E-02 71 Conventional Enriched Single-tier non-cage Multi-tier non-cage Free range Pelletier 2017 Updated 2019 Pelletier 2017 Updated 2019 Pelletier 2017 Updated 2019 Pelletier 2017 Updated 2019 Pelletier 2017 Updated 2019 Tractor transport (t-km) 1.00E-03 1.00E-03 4.00E-03 4.00E-03 4.00E-04 4.00E-04 4.00E-04 4.00E-04 4.00E-05 4.00E-05 (1) Conventional: 52% to human consumption, 20% to rendering, 24% to composting, 1% to incineration, 4% to other; Enriched: 47% to human consumption, 20% to rendering, 18% to composting, 0% to incineration, 15% to other; Single-tier non-cage: 38% to human consumption, 33% to rendering, 28% to composting, 0% to incineration, 0% to other; Multi-tier non-cage: 64% to human consumption, 13% to rendering, 24% to composting, 0% to incineration, 0% to other; Free range: 40% to human consumption, 38% to rendering, 3% to composting, 0% to incineration, 18% to other. (2) Based on US layer production scaled to relative feed use efficiency. (3) Conventional: 4% to uncovered liquid storage, 5% to covered liquid storage, 22% to uncovered dry storage, 66% to covered dry storage, 2% to direct land application; Enriched: 0% to uncovered liquid storage, 2% to covered liquid storage, 18% to uncovered dry storage, 73% to covered dry storage, 7% to direct land application; Single-tier non-cage: : 0% to uncovered liquid storage, 0% to covered liquid storage, 38% to uncovered dry storage, 62% to covered dry storage, 0% to direct land application; Multi-tier non- 72 cage: 0% to uncovered liquid storage, 0% to covered liquid storage, 9% to uncovered dry storage, 91% to covered dry storage, 0% to direct land application; Free range: 0% to uncovered liquid storage, 0% to covered liquid storage, 12% to uncovered dry storage, 88% to covered dry storage, 0% to direct land application. (4) Conventional: 8% to rendering, 75% to composting, 4% to incineration, 12% to burial, 1% to other; Enriched: 11 % to rendering, 73% to composting, 4% to incineration, 1% to burial, 12% to other; Single-tier non-cage: 23% to rendering, 54% to composting, 23% to incineration, 0% to burial, 0% to other; Multi-tier non-cage: 13% to rendering, 84% to composting, 3% to incineration, 0% to burial, 0% to other; Free range: 0% to rendering, 54% to composting, 22% to incineration, 12% to burial, 12% to other. 73 2.4.3 Life cycle impact assessment results LCIA results for field crop production and feed milling, breeder, hatchery, and pullet operations are presented in appendix A, Table A 39 to Table A 51. Notably, substantial decreases in environmental impacts per kilogram of field crops produced were observed when comparing production weighted average models derived from the CRSC reports with those used previously to model field crop production in Pelletier, (2017). Impacts per kilogram of barley and corn were estimated to decrease across all impacts assessed, while those of canola, soy, and wheat were generally estimated to decrease with few exceptions. Generally, when impacts were estimated to have increased, these increases were mostly seen in toxicity-related impact categories (i.e., freshwater and marine, and terrestrial ecotoxicity, and human toxicity), likely as a result of new methods for modeling field level pesticide emissions. In contrast, global warming potential, and acidification and eutrophication were estimated to decrease substantially across all crops. Global warming potential was estimated to decrease by ~78% in barley, ~44% in canola, ~65% in corn, ~20% in soy, and 36% in wheat. Complete comparisons of estimated impacts per kilogram of crop produced using the previous field crop models used by Pelletier, (2017), and those used in this analysis are available in appendix A, Table A 52 to Table A 56. Comparatively, impact intensity per kilogram of N fertilizers produced has decreased substantially under the new processes for modeling N fertilizer production compared to those previously used by Pelletier, (2017), across all crops. The smallest percent decrease in impacts per kilogram of nitrogen fertilizer produced was seen in human toxicity impacts for corn production, which saw a decrease of approxiamtely 75% in the production-weighted national average N fertilizer mix. In other cases, these differences were very large, including a decrease of an order of magnitude in GHG emissions per kilogram of N fertilizer produced. 74 Table 2-9 describes estimated impacts per tonne of non-organic eggs produced per housing system, as well as production-weighted Canadian average impacts per tonne of eggs produced across all housing systems. Results associated with sensitivity analyses are presented in section 2.4.4. Significant differences in estimated impacts between housing systems are also indicated. Across all impact categories assessed, estimated impacts per tonne of eggs produced were lower in caged (i.e., conventional and enriched) than in cage free (i.e., single- and multi-tier non-cage and free range) production systems. Of the caged systems, impacts were lower in conventional systems across all impact categories assessed except for global warming potential, which was estimated to be lower in enriched systems, and photochemical oxidation, which was estimated to be the same in both systems. Generally, however, estimated impacts per tonne of eggs produced in conventional and enriched systems were quite similar across all impact categories (i.e. they only differed by a maximum of approximately 3%). Usually, estimated impacts within caged (i.e., conventional and enriched), and non-cage (i.e., single-tier and multi-tier) systems were not significantly different from one another. Acidification was the only impact category in which estimated impacts differed significantly between conventional and enriched systems. Similarly, significant differences between single- and multi-tier non-cage systems were only observed in the impact categories eutrophication, global warming, and marine aquatic ecotoxicity. In contrast, estimated impacts in single- and multi-tier non-cage systems differed significantly from those of both conventional and enriched systems for all impact categories except for terrestrial ecotoxicity, while for human toxicity single-tier systems differed significantly from both caged systems while multi-tier systems did not differ significantly from either. Finally, estimated impacts for free range systems differed significantly from those of all other housing systems for the majority of the impact categories 75 assessed. The exceptions to this were human toxicity, where free range impacts did not differ significantly from those of single- or multi-tier non-cage systems, and marine aquatic ecotoxicity, where free range impacts differed significantly from those of multi-tier non-cage but not single-tier non-cage systems. Across all impact categories assessed, terrestrial ecotoxicity showed the lowest number of significant differences between housing systems, with only the differences between free range and conventional cage, and free range and multi-tier non-cage systems estimated to be significant. Generally impacts per tonne of eggs produced were estimated to be higher in cage free systems. Of the cage free systems, free range production was estimated to have the highest impacts per tonne of eggs produced across all impacts assessed. This trend reflects the observed trend in LCI data, which indicated that resource use efficiency was consistently the poorest in free range systems. Impacts in single- and multi-tier non-cage systems were estimated to differ from one another only marginally (i.e., <1% differences across all impacts assessed). Figure 2-2 shows the relative percentage of impacts across different housing systems per tonne of eggs produced, using conventional cage production as a reference. With the exception of free range systems, impacts per tonne of eggs produced in all other systems were within 16% of each other. Comparatively, impacts in free range systems were ~ 20%- 29% higher than those of conventional cage systems per tonne of eggs produced. 76 Table 2-9. Life cycle environmental impacts per tonne of eggs produced in each non-organic housing system in the Canadian egg industry, and a Canadian production weighted average, estimated using the CML IA Baseline LCIA method. Superscript letters denote significant differences across housing systems within impact categories. Estimated impacts are presented ± standard error of the mean Impact Unit Conventional Enriched Single tier non-cage Multi tier non-cage/Aviary Free range Canadian average Abiotic depletion Kg Sb eq 7.59 x 10-3 ± 1.92 x 10-5 a 7.73 x 10-3 ± 2.03 x 10-5 a 8.22 x 10-3 ± 2.06 x 10-5 b 8.21 x 10-3 ± 1.90 x 10-5 b 9.34 x 10-3 ± 5.98 x 10-5 c 7.74 x 10-3 ± 1.34 x 10-5 Abiotic depletion (fossil fuels) MJ 7932.50 ± 42.72 a 8179.92 ± 27.95 a 9162.57 ± 28.82 b 9165.28 ± 23.17 b 9842.23 ± 55.38 c 8183.68 ± 24.17 Acidification Kg SO2 eq 66.53 ± 0.36 a 67.79 ± 0.39 b 72.21 ± 0.39 c 71.93 ± 0.40 c 85.71 ± 0.47 d 67.85 ± 0.26 Eutrophication Kg PO43- eq 20.54 ± 0.09 a 20.61 ± 0.10 a 22.33 ± 0.10 b 22.25 ± 0.11 c 26.40 ± 0.12 d 20.85 ± 0.07 77 Impact Unit Conventional Enriched Single tier non-cage Multi tier non-cage/Aviary Free range Canadian average Freshwater Aquatic ecotoxicity Kg 1,4-DB eq 671.86 ± 5.06 a 687.42 ± 3.93 a 741.82 ± 5.41 b 741.18 ± 7.01 b 823.42 ± 6.04 c 687.19 ± 7.73 Global warming Kg CO2 eq 1469.49 ± 4.67 a 1455.08 ± 4.03 a 1579.29 ± 11.53 b 1577.32 ± 3.85 c 1772.38 ± 13.74 d 1481.51 ± 3.15 Human toxicity Kg 1,4-DB eq 518.47 ± 13.76 a 530.73 ± 11.29 a 583.54 ± 15.46 b 583.20 ± 15.28 ab 641.47 ± 11.82 bc 531.76 ± 11.94 Marine aquatic ecotoxicity Kg 1,4-DB eq 549922.10 ± 11023.26 a 567936.46 ± 8504.90 a 642699.93 ± 7937.88 b 643240.87 ± 7315.31 c 683869.19 ±6625.14 bd 568538.89 ± 9324.89 78 Impact Unit Conventional Enriched Single tier non-cage Multi tier non-cage/Aviary Free range Canadian average Ozone layer depletion Kg CFC-11 eq 2.49 x 10-5 ± 5.69 x 10-8 a 2.54 x 10-5 ± 6.58 x 10-8 a 2.70 x 10-5 ± 6.68 x 10-8 b 2.70 x 10-5 ± 6.35 x 10-8 ab 3.05 x 10-5 ± 1.93 x 10-7 c 2.54E-05 ± 4.29 x 10-8 Photochemical oxidation Kg C2H4 eq 0.33 ± 2.93 x 10-3 a 0.33 ± 4.08 x 10-3 a 0.36 ± 2.84 x 10 -3 b 0.36 ± 2.99 x 10-3 ab 0.40 ± 3.01 x 10-3 c 0.34 ± 3.46 x 10-3 Terrestrial ecotoxicity Kg 1,4-DB eq 27.16 ± 1.07 abcd 27.61 ± 0.89 abcd 28.86 ± 1.32 abcd 28.80 ± 1.30 abcd 32.92 ± 1.00 bc 27.58 ± 0.99 79 Figure 2-2. Relative percentage of impacts per tonne of eggs produced in different housing systems. Conventional cage production is the reference system. Estimated using the CML IA Baseline LCIA method. Across the majority of the impacts assessed, production of feed was the largest contributor for all housing systems. The only impact categories to which feed was not the largest contributor were acidification and eutrophication, which were primarily driven by impacts related to manure management. However, feed production was the second largest contributor to these impacts. When feed was the largest contributor to impacts, contributions from feed ranged from approximately 61% to 84%, depending on the impact category and housing system. For acidification and eutrophication, manure management emissions contributed approximately 62%- 70% of impacts. Production of pullets also made relatively large contributions to impacts, and was generally the second or third largest contributor, depending on the impact category. With the exception of marine aquatic ecotoxicity, feed, pullets, and manure management cumulatively accounted for >90% of total impacts per tonne of eggs produced in all impact 020406080100120140Relative % of impacts per tonne of eggsConventional eggsEnrichedSingle tierMulti-tierFree rangeCanadian average 80 categories and all housing systems. In marine aquatic ecotoxocity, these three were still responsible for approximately 79% - 87% of impacts, depending on the housing system. Generally, transportation, water inputs, and management of waste mortalities and/or spent hens made negligible contributions to total impacts per tonne of eggs produced in all housing systems for all impact categories. Contributions to impacts from farm-level energy use were also small across the majority of the impacts assessed (i.e., <7%), with few exceptions. For abiotic depletion (fossil fuels) farm-level energy use contributed approximately 6% - 13%. In single- and multi-tier non-cage systems, these inputs contributed approximately 10% of human toxicity impacts, and <6% of human toxicity impacts in other housing systems. Finally, these inputs contributed approximately 10% - 18% of impacts for the impact category marine aquatic ecotoxicity. For all housing systems and all impacts assessed, water inputs and waste management never contributed >0.5% of total impacts per tonne of eggs produced. Contributions related to transportation were slightly larger, though they were never greater than approximately 5% of total impacts for any given impact category-housing system combination, with the greatest contributions observed in abiotic depletion (fossil fuels) in single- and multi-tier non-cage systems. Figure 2-3 to Figure 2-5 show contributions to total acidifying, eutrophying, and GHG emissions per tonne of eggs produced in each housing system, illustrating the variable contributions from feed, pullets, and manure management to different impact categories. Tables describing the complete numerical contribution analyses to all impact categories (Table A 57 to Table A 67), as well as additional figures illustrating contribution analyses to all other impacts assessed (Figure A 1 to Figure A 8) are available in appendix A. 81 Figure 2-3. Percent contributions to total acidification impacts per tonne of eggs produced in each housing system as estimated using the CML IA Baseline LCIA method. Figure 2-4. Percent contributions to total eutrophication impacts per tonne of eggs produced in each housing system as estimated using the CML IA Baseline LCIA method. 0.0020.0040.0060.0080.00100.00120.00Percent contribution to impactWaste treatmentWaterTransportationEnergyPulletsManure managementFeed0.0020.0040.0060.0080.00100.00120.00Percent contribution to impactWaste treatmentWaterTransportationEnergyPulletsManure managementFeed 82 Figure 2-5. Percent contributions to total GHG emissions per tonne of eggs produced in each housing system as estimated using the CML IA Baseline LCIA method. 2.4.4 Sensitivity analyses 2.4.4.1 Life cycle impact assessment method Impacts per tonne of eggs produced estimated using the TRACI 2.1 LCIA method showed many of the same general trends in impacts as observed when using the CML IA Baseline LCIA method. For both LCIA methods, estimated impacts were lower for caged (i.e., conventional and enriched) than for cage-free (i.e., single- and multi-tier non-cage, and free range) housing systems. Estimated impacts were the lowest in conventional cage systems for three of the five impact categories assessed (fossil fuel depletion, acidification, and ozone depletion), while enriched cage systems had the lowest impacts for the remaining two impact categories (global warming and eutrophication). This trend was almost identical to that observed for similar impact categories assessed using the CML IA Baseline LCIA method, with the exception of eutrophication, which was estimated to be marginally lower in conventional than 020406080100120Conventional Enriched Single tierfree runMulti-tierfree runFree rangePercent contribution to impactWaste treatmentWaterTransportationEnergyPulletsManure managementFeed 83 enriched systems when using the CML IA baseline LCIA method. Similarly, when using the TRACI 2.1. LCIA method, impacts were estimated to be very similar between single- and multi-tier housing systems (i.e., <1% difference across all impact categories assessed), while free range systems were consistently estimated to have the highest impacts (Figure 2-6). Figure 2-6. Relative impacts per tonne of eggs produced across housing systems estimated using the TRACI 2.1. LCIA method Also similarly to when using the CML IA baseline LCIA method, estimated impacts between conventional and enriched, and between single-tier and multi-tier non-cage systems were generally found to not differ significantly from one another. The exception to this was for eutrophication impacts, where enriched systems were estimated to have significantly lower impacts than conventional systems, and global warming, where multi-tier free systems were estimated to have significantly lower impacts than single-tier non-cage systems. Across all impact categories, impacts in single- and multi-tier non-cage systems were estimated to be significantly higher than those of conventional and enriched systems, and impacts in free range systems were estimated to be significantly higher than those of all other housing systems. While 020406080100120140Fossil fueldepletionGlobal warming Acidification Ozone depletion EutrophicationRelative % of impacts per tonne of eggsConventionalEnrichedSingle tierMulti tierFree rangeCanadian average 84 use of the TRACI 2.1. LCIA method resulted in some differences in the absolute magnitude of impacts compared to those estimated using the CML IA baseline LCIA method, direct comparisons in the estimated values for impact categories between the two LCIA methods should not be drawn due to the potential for methodological inconsistencies in the underlying classification and characterization models used between the two impact categories, as well as, in some cases, differences in category indicator units (for example, the CML IA Baseline method uses the unit kg PO43- eq for eutrophication, while the TRACI 2.1 method uses the unit kg N eq). Nonetheless, that both LCIA methods result in the same general trends in estimated impacts across housing systems indicates that choice of LCIA method makes relatively minor contributions to uncertainty around the conclusions drawn in this chapter. A complete table of the impacts per tonne of eggs produced estimated using the TRACI 2.1. LCIA method presented numerically is available in appendix A, Table A 68. 2.4.4.2 Use of a vegetarian feed formulation Use of feeds free of rendered animal products has been associated with lower environmental impacts per tonne of eggs produced (Pelletier, 2017). Use of a vegetarian feed formulation from the U.S. egg industry adapted for Canadian production conditions in non-organic laying systems resulted in statistically significant decreases to all assessed environmental impacts across all housing systems (p value < 0.001). These decreases exhibited considerable variability in magnitude related to the relative importance of feed as a determinant for each impact. The smallest decrease was observed for acidification, with impacts decreasing by approximately 11% per tonne of eggs produced across all housing systems when using a vegetarian feed. In contrast, the largest decrease was observed in ozone depletion, which was estimated to decrease by approximately 54% - 56%, depending on the housing system. Across all 85 other impacts, use of a vegetarian feed formulation was estimated to decrease impacts per tonne of eggs by approximately 13% - 47%. Relative estimated impacts per tonne of eggs produced in conventional cage systems using conventional and vegetarian feed formulations are presented in Figure 2-7. Similar figures for all other housing systems are available in appendix A, Figure A 9 to Figure A 12. Figure 2-7. Relative percentage of impacts per tonne of eggs produced in conventional cage production systems using a conventional and vegetarian diet, estimated using the CML IA baseline LCIA method. Use of a vegetarian diet also resulted in changes to relative contributions of different inputs to each impact assessed. Generally, the relative importance of feed in determining impacts decreased across all impact categories. For example, when using a conventional diet, feed contributed approximately 17% of total acidification impact across all housing systems, compared to contributing approximately 4% of impacts when using a vegetarian feed. These reductions in relative contributions from feed generally resulted in greater relative contributions 0102030405060708090100Relative percentage of impactsConventional dietVegetarian feed 86 coming from manure management and pullet production, which contributed approximately 10% and 3% more to total impacts when using a vegetarian feed. While the reductions in relative contributions from feed were generally substantial when using a vegetarian feed formulation, these reductions were insufficient to displace feed as the largest contributor to all impacts assessed except for acidification and eutrophication, each of which remain dominated by manure management. Figure 2-8 to Figure 2-10 show contributions to total acidifying, eutrophying, and GHG emissions per tonne of eggs produced in each housing system using a vegetarian feed formulation, and may be compared to Figure 2-3 to Figure 2-5, which show the same information using the conventional formulation. A complete table giving numerical estimates of all impacts per tonne of eggs produced in each housing system using the vegetarian feed formulation is available in appendix A, Table A 69. Additional figures illustrating contribution analyses to all other impacts assessed using the vegetarian feed formulation are available in appendix A, Figure A 13 to Figure A 20. 87 Figure 2-8. Percent contributions to total acidification impacts per tonne of eggs produced in each housing system when using a vegetarian feed formulation as estimated using the CML IA Baseline LCIA method Figure 2-9. Percent contributions to total eutrophication impacts per tonne of eggs produced in each housing system when using a vegetarian feed formulation as estimated using the CML IA Baseline LCIA method. 0102030405060708090100Conventional Enriched Single tier freerunMulti-tier freerunFree rangePercent contribution to impactWaste treatmentWaterTransportationEnergyPulletsManure managementFeed0102030405060708090100Conventional Enriched Single tier freerunMulti-tier freerunFree rangePercent contribution to impactWaste treatmentWaterTransportationEnergyPulletsManure managementFeed 88 Figure 2-10. Percent contributions to total GHG emissions per tonne of eggs produced in each housing system when using a vegetarian feed formulation as estimated using the CML IA Baseline LCIA method. While direct comparisons should not be made between the impacts per tonne of eggs produced in the Canadian egg industry presented previously by (Pelletier, 2017) and those estimated in this analysis due to many methodological inconsistencies, the general trend of substantially lower estimated impacts is nonetheless notable. In particular, the carbon footprints of Canadian eggs produced in different housing systems estimated in this analysis are substantially lower than previous estimates for Canadian production systems (Pelletier, 2017), and systems elsewhere in the world (Dekker et al., 2011; Leinonen et al., 2012; Taylor et al., 2014; Turner et al., 2023a). The main driver for these lower impacts is substantially lower impacts of field crop production of feed inputs compared to the models of field crop production previously used by (Pelletier, 2017). Three key characteristics were identified that led to the estimated reductions in field crop production impacts: 0102030405060708090100Conventional Enriched Single tier freerunMulti-tier freerunFree rangePercent contribution to impactWaste treatmentWaterTransportationEnergyPulletsManure managementFeed 89 1. Substantial improvements to system efficiency related to large increases in field crop yields. Compared to the field crop models employed by (Pelletier, 2017), negligible yield increases were observed for soybean production models; however, across corn, peas, wheat, barley, and canola, yield increases ranged from approximately 8% to 43% when using data derived from the CRSC reports. Specifically, corn yields increased by approximately 8%, pea yields by 12%, wheat yields by 21%, barley yields by 28%, and canola yields by 43%. Yield data used in the CRSC reports were obtained from Statistics Canada on a small area basis, and represented three-year average values from 2018 to 2020. That a three-year average value was used rather than yields from a single year lends credibility to the estimated increases in yield, as use of an average value over time better controls for particularly low or high yields in any given year. Bamber et al. (2023) suggest that a five-year average value for yield may be preferred to further smooth any anomalous years, but that a three-year average is still of higher quality than use of a single year. 2. Reductions in estimated field level N2O emissions. Compared to the field crop models previously used by (Pelletier, 2017), field level N2O emissions were estimated to have dropped per kilogram of crop produced. The lowest estimated reduction was for soy (11%), while the highest was for barley (47%). These reductions are driven by the use of updated data regarding crop residue yields and N contents from Thiagarajan et al. (2018), residue removal rates (when relevant), and updated emissions factors and corresponding scaling factors for N inputs from crop residues taken from the CRSC reports ((S&T)2 Consultants Inc., 2022a) and Liang et al., (2020), respectively. 90 3. Changes to upstream modeling of nitrogenous fertilizer production processes. (Pelletier, 2017) does not report which version of the ecoinvent database was used during their model development, nor which process was used for modeling upstream nitrogenous fertilizer production. However, given the publication date of the work, it is likely that work was performed using version 3.4, or an earlier version, compared to version 3.9 used in this analysis. When ecoinvent version 3.7 was released, it included significant changes to how the database modeled production of synthetic fertilizers, which have continued to be used in subsequent versions (Moreno Ruiz et al., 2020). Since it is unknown exactly which process from which version of ecoinvent was used by (Pelletier, 2017), it is not possible to make comparisons in impact intensity between that and the processes used to model N fertilizer production in this analysis. However, it is likely that the differences in impact intensity between these processes is substantial, given that impacts per kilogram of field crop produced exhibited large decreases, even when both estimated N fertilizer application rates and field level N2O emissions were estimated to have increased compared to previous levels, as was the case in corn production. Given the large contributions to many impacts made by feed production, these three factors may be particularly impactful in reducing estimated impacts per tonne of eggs produced in all housing systems. It is possible that the reductions may be overestimated, as the temporal cutoff for the yield data used in the CRSC reports is 2020. In contrast, 2021 represented an anomalous year for global field crop production, as the global field crop sector experienced lower than expected yields, including in Canada (Agriculture and Agri-Food Canada, 2021). Barley yields, for example, decreased by approximately 39% in 2021 compared to 2020, though recovered to pre-2021 levels in the following years, while canola yields decreased by 91 approximately 32%. Unlike barley, however, canola yields have yet to recover, with 2023 data indicating that average yields are still 12% lower than they were in 2020. Wheat and pea yields were also observed to decrease in 2021 and have not yet recovered to 2020 levels as of 2023, while yields of soybeans and corn remained relatively unchanged over this time period. This is likely due to asymmetry in the climate events leading to the observed yield reductions disproportionately impacting the prairie provinces, while major corn and soy producing regions of the country (i.e., Ontario, Quebec, and Eastern Canada) were relatively unaffected. As wheat, peas, and processed canola cumulatively represent ~25% of the conventional laying hen feed formulation by mass, it is likely that incorporation of more recent yield data would mitigate some of the observed reductions in environmental impacts per tonne of eggs produced. Similarly, it is possible that reductions due to the use of new, upstream fertilizer production models may also be overestimated. While the updates to fertilizer production modeling made by the ecoinvent database represent large improvements compared to previous versions used by (Pelletier, 2017), accurate data representative of Canadian fertilizer production processes remains a key data gap in Canadian agri-food LCA research (Turner et al., 2020). Regardless, all calculated results strongly reaffirm the need for continual reduction of the environmental impacts of feed. The results of the sensitivity analysis clearly indicate the potential efficacy of the avoidance of rendered animal products in poultry feed as an impact mitigation strategy, and replacement with alternative, low impact feed ingredients. Use of a vegetarian feed formulation led to substantial reductions in estimated impacts per tonne of eggs produced in all housing systems. This is due to the disproportionately high impacts of rendered animal products in poultry feeds. Although these ingredients only account for 4.4% of the conventional feed formulation on a mass basis, they are responsible for substantial shares of 92 many environmental impacts of feed production, including approximately 60% of GHG emissions, 70% of acidification impacts, and 94% of terrestrial ecotoxicity impacts. Alternatives could include single cell proteins (Cheng and Kim, 2022; Nkhalambayausi-Chirwa and Lebisto, 2012), valorized food waste products (Boumans et al., 2022), insect meals (El-Hack et al., 2020) and others. However, while this strategy may seem efficacious in reducing the impacts associated with feed production, it may be beneficial to perform further research using a consequential LCA-based approach in order to investigate the potential market-mediated impacts in such a shift in ingredient sourcing. Another potential feed-based strategy for improving environmental outcomes is regionalized feed input sourcing based on raw material sustainability performance. Given the relatively low importance of transportation in determining environmental impacts, it may be beneficial to source feed ingredients from areas associated with lower impacts of production even if this implies transportation distances. Regional differences in production-related impacts may become even more pronounced for field crop-derived ingredients if regional changes in soil carbon are considered, as it may be possible for ingredients sourced from certain regions to exhibit a negative carbon footprint if changes to soil carbon are included (Bamber, 2023; Bamber et al., 2023). However, all possible alternatives should be considered based on careful analysis of their comparative life cycle environmental impacts using a consistent and principled allocation approach that maintains the natural science basis of the models. It may also be beneficial to incorporate other sustainability metrics, including those related to economic and social performance, in order to ensure that interventions result in net-positive sustainability outcomes. While regionalized ingredient sourcing may be beneficial for environmental objectives, for example, increased transportation distances may have a large impact on ingredient procurement 93 costs that must be taken into account during the decision-making process. Such an analyses is performed later in chapter 6 of this dissertation, though further analyses could also be performed – particularly incorporating a consequential perspective to determine the market-mediated impacts of an altered allocation of feed ingredients to laying hen feed production. Further discussion of this is given in chapter 6. While some heterogeneity was observed in estimated impacts per tonne of eggs produced, these results make a compelling contribution to the current ongoing discourse regarding the transition away from conventional cage egg production systems currently underway in Canada. While this transition is largely being driven by the increasing status of animal welfare as an issue of concern worldwide (Hötzel, 2018; Vanhonacker et al., 2010; You et al., 2014) rather than issues related to differences in environmental impacts between egg production systems, it has been suggested by some that this transition will result in net negative environmental sustainability outcomes for the global egg industry (Stadig et al., 2016). The results from this analysis, however, indicate that differences in impacts per tonne of eggs produced across different housing systems are relatively small, with the exception of free range systems. That these differences are relatively small indicates that any potential negatives to environmental sustainability resulting from this transition could be overcome through improved efficiencies, supply chain optimization, improved management practices, or other strategies. It should be noted that, since the ban on new conventional cage housing (effective 2035 for existing systems) was established in Canada in 2017, free range production has experienced the least amount of growth of any non-organic system as production has transitioned away from conventional cages (Egg Farmers of Canada, 2023). 94 The differentiation between single- and multi- tier non-cage systems in this analysis is a key development compared to (Pelletier, 2017). Non-cage housing is currently the third most widespread housing system in Canada, behind conventional and enriched cage housing systems (Egg Farmers of Canada, 2023). The proportion of farms employing non-cage systems is expected to grow as the industry transitions away from conventional cages, though at a slower rate than that of enriched production. Importantly, the development of single- and multi-tier non-cage production system models as presented here will play an important role in informing egg farmers and policy makers of the environmental sustainability benefits and trade-offs associated with these systems. The results, for example, indicate that environmental performance in multi-tier non-cage systems does not generally differ significantly from that of single-tier systems, both of which were significantly outperformed, in most cases, by enriched production systems. This knowledge will aid farmers in making informed decisions as they continue to navigate the ongoing housing system transition. Of note, however, this study did not take into account potential differences in land use between different systems. Lower stocking densities in cage-free systems necessitate greater farm-level land use by these systems, which may also be exacerbated in organic systems due to higher levels of land use in organic field crop production (Pelletier et al., 2008). Differences in cumulative land use between housing systems should also be taken into account in sustainability decision making, as increases in organic egg production may have large-scale ripple effects in field crop production with respect to how land is allocated between conventional and organic field crops, and how crop products are allocated between human and animal consumption (Pelletier et al., 2008). There may also be trade-offs to consider across other environmental impacts not assessed herein. 95 While the current study makes considerable methodological advancements compared to previous LCA models of Canadian egg production systems, many limitations remain. Specifically, the model did not make any updates to pullet production processes, nor to estimated feed formulations. Rather, for both of these, LCI data was derived from prior analyses of Canadian egg production systems, based on data collected in 2012 (Pelletier, 2017). Given the differences in resource use efficiencies observed when comparing LCI data characterizing layer systems between this analysis and the previous analysis, it is likely that there have similarly been changes in efficiencies in pullet production systems, which could further improve the estimated environmental performance of egg production systems. Similarly, given the large contributions to total impacts made by feed production, updated feed formulation models would also be very useful in ensuring estimated impacts are accurate. Technological advancements in feed formulation, increased use of low-opportunity cost biomass, and other changes may have been made to the feed milling industry since this data was collected, which may improve the environmental outcomes associated with feed production processes. Updated feed formulations should serve as a key focus for future data collection efforts during assessment of the Canadian egg industry. 2.5 Limitations and conclusions As the Canadian egg industry continues to transition to alternative housing systems, farmers, policy makers, and other key stakeholders will require robust information to make informed decisions about the potential sustainability implications. These updated baseline LCA models of non-organic Canadian egg production, differentiated by housing system type, may also be used to support efforts to assess other potential environmental impact mitigation strategies, including efficiency assessment, feed formulation optimization, and optimization for 96 environmental impact reduction. These assessments may provide valuable insights for the Canadian egg industry to progress towards the sustainability goals outlined by Egg Farmers of Canada in their inaugural sustainability report (Egg Farmers of Canada, 2020). The models reported herein have been developed in accordance with current best practices for modeling emissions from manure management systems, while also incorporating updated methods for calculating allocation factors between co-products, and updated life cycle inventory data for both egg production, and field crop production systems. While these represent valuable updates compared to the previous baseline LCA models of Canadian egg production systems developed by Pelletier (2017), the models still have a number of limitations that may be improved upon in the future. Limitations include: 1. Use of data taken from Pelletier (2017) for modeling of breeder flock, hatchery, and pullet production operations. It is possible that the impacts of these processes may be overestimated, as the data used to model them is representative of operations in the year 2012. It is likely that these processes have also increased in resource use efficiency, similar to increases observed in egg production systems, meaning the impacts of these processes may be lower than currently estimated. Further, the pullet models used do not differentiate between pullet production in caged and non-cage systems, which may further impact estimated contributions to environmental impacts associated with pullet production. Updating the models of these processes could lead to further estimated reductions in impacts per tonne of eggs produced. 2. Use of data taken from Pelletier (2017) for modeling the conventional feed formulation. The formulation used in this analysis represent a production-weighted average formula based on data collected in 2012. This formulation is not representative of any specific 97 formulation, and also may not adequately represent how formulations have changed since the data was collected. It is possible, for example, that there are now higher inclusion rates for synthetic amino acids and other supplements that may replace ingredients in the current modeled formulation. 3. Exclusion of impacts related to production and use of antibiotics and other medical products. Antibiotics and other medical products may make important contributions to environmental impacts if impacts of their production is high and resulting yield increases are insufficient to offset them (Nyberg et al., 2021; Stone et al., 2011; Webb et al., 2020). Unfortunately, data is unavailable for characterizing the use of these products in the Canadian egg industry, and the proprietary nature of information regarding how they are produced makes their inclusion in models difficult. 4. The potential for bias in the collected sample resulting from the method used to collect updated data (i.e., voluntary self-reporting). Using this method, it is possible that the collected sample may be skewed towards those farmers that believe they are already operating sustainably, or are interested in making continual improvements to sustainability outcomes, which may lead to underestimation of environmental impacts. Various methods are available for analysis of the degree of bias introduced through the use of self-reported data (see for example (Da Silva and Skinner, 2021; Hanley and Sikka, 2012). Unfortunately, due to the data collection procedure used, analysis of this bias could not be performed. This increased potential for bias should be taken into account during interpretation of the reported impacts, and future studies should carefully consider data collection strategies such that potential biases may be minimized. 98 5. Primary data describing productivity, resource use, and management practices in alternative systems are representative of a time period only a few years into the ongoing housing system transition. In the time between data collection and production of this dissertation, it is possible that there have been additional changes to resource use and management practices in alternative systems as farmers have gained experience operating them. The Code of Practice, for example, provides both final and transitional requirements for a number of housing system characteristics that must be met depending on when flocks were placed (NFACC, 2017). Further, as farmers continue to gain experience with these alternative systems, it is possible that resource use efficiencies on farm will improve (Khan et al., 2022). Given these changes, it is likely that additional data should be collected in the near future in order to provide a more accurate description of conditions and ongoing changes to them in Canadian egg production systems. The recently released NESTT (Arulnathan, 2023) may prove vital in efforts to continually update baseline LCA models of Canadian egg production, as it may be used as a data gathering tool and repository for data that may be used to support development of updated LCA models. 6. Potential changes in impacts related to hen feather colour were not investigated. Feather colour may be an important determinant of feed conversion efficiency (Yuan et al., 2017, 2015), which is a key driver of many environmental impacts. Within Canada, hens housed in non-cage systems are predominantly brown feathered, while caged systems are dominated by white feathered birds. Given this, and the potential role of genetics in determining levels of resource use, it is possible that the comparisons in impacts between housing systems may be confounded by the relative distribution of brown- and white- 99 feathered birds across different housing systems. Additional research in the future should investigate the potential impact of hen feather colour on farm-level resource use efficiency, and associated environmental impacts. Additionally, in the future it would be beneficial to continue to update models of field crop production in light of the large role that feed production plays in determining the environmental impacts of egg production in Canada. This could include collection of primary data from Canadian field crop producers to ensure that models are as representative of Canadian production conditions as possible, as well as improvements to emissions modeling methods and methods for assessment of soil carbon dynamics. Another key point of potential criticism (albeit not stemming from a sound interpretation of ISO 14044) is the choice of allocation methods used (i.e., allocation based on metabolic partitioning, and on gross chemical energy content of co-products) to calculate process contributions to total environmental impact. A potential, commonly chosen alternative to this method is allocation between co-products on the basis of relative economic value (Ardente and Cellura, 2012; Bohnes and Laurent, 2019; Kyttä et al., 2022). The ISO 14044 Standard for LCA (ISO, 2006b) dictates that economic allocation should only be employed when allocation cannot be avoided, and when allocation based on some underlying physical relationship between co-products is not possible. If used in this analysis, economic allocation could result in reductions to estimated impacts per tonne of eggs produced by considering the animal products included in feed formulations to be burdenless due to their relatively low value (Adedokun et al., 2019; Reddy and Santosh, 2016; Shen et al., 2019; Zou et al., 2021). This choice would also result in lower allocation factors to co-products throughout the egg supply chain, such as spent hens, which would offset this effect. While inclusion of animal by-products in poultry feed is one 100 method through which valorization of products that might otherwise become wastes is possible, and these products may be good sources of minerals, protein, fats, and micronutrients in feed (Adedokun et al., 2019; Alfaia et al., 2019; Mora et al., 2019), impacts must be allocated to these products on the basis of a physical relationship (such as energy content, as done here) in order to maintain the natural science basis underpinning LCA (ISO, 2006a, 2006b). Published justifications and rationales for use of economic allocation have been strongly criticized (Kyttä et al., 2022; Bohnes and Laurent, 2019; Pelletier and Tyedmers, 2011). Given the large role played by feed in determining the environmental impacts of egg production, differentiation and selection of alternative, low-impact feed ingredients is a priority. However, such exploration cannot be performed on the basis of models that explicitly assume an equivalency between “cheap” and “low impact”. Overall, in spite of these limitations and potential criticisms, the models reported herein provide substantial value to sustainability measurement and management in the Canadian egg industry, and will usefully support future research. Utilization of these models in future work may help support long-term net improvements in sustainability outcomes in the Canadian egg industry as it continues to navigate the housing system transition. 101 3. Joint data envelopment analysis and life cycle assessment of Canadian, non-organic egg production systems 3.1 Chapter synopsis Much of the global egg industry is currently transitioning from conventional cage to alternative (i.e. enriched cage, single- and multi-tier non-cage, and free ran) housing systems. While this transition is primarily motivated by animal welfare concerns, it also has significant potential to alter the environmental footprint of egg production, which is the fastest growing livestock sector worldwide. Understanding the benefits, impacts and improvement opportunities characteristic of alternative systems is hence imperative to ensuring net-positive sustainability outcomes. This requires attention to current resource efficiency levels, key variables that influence efficiency, as well as the environmental impact mitigation potential of efficiency gains for specific interventions and housing systems. In this chapter, joint data envelopment analysis (DEA) and LCA are applied to Canadian egg production systems. It was found that egg farms are generally operating at high levels of efficiency relative to one another with respect to feed and pullet inputs per tonne of eggs produced both within and between housing system types. DEA results suggest that feed and pullet inputs could decrease across all housing systems between approximately 3.55% and 13.22%, translating to environmental impact reductions of up to approximately 15%. Least shrinkage and selection operator models were unable to identify key drivers of efficiency for any system except enriched colony housing where an increase in lay cycle length of 1 day was associated with minor increases in efficiency, and the use of brown birds over white birds was associated with a 0.95% decrease in efficiency. Further research is necessary to determine key drivers of efficiency that may represent priority strategies for farmers to increase efficiency and 102 decrease environmental impacts. Scenario analyses were used to calculate the cumulative environmental impacts of egg production assuming different distributions of production across housing systems and that DEA-efficient conditions are realized for all farms in each scenario. In all scenarios, 0% of production was attributed to conventional cages, reflecting a complete transition away from conventional production systems over time. Results of these scenarios indicate that, contrary to suggestions in the literature, the transition away from conventional cage production systems to alternatives may not be detrimental for the environmental impacts of the Canadian egg industry, provided that the industry continues to evolve towards currently best-reported efficiency levels. 3.2 Context and rationale Currently, industrial egg production predominantly takes place in conventional cage housing systems, which provide farmers with a greater degree of environmental control, and facilitate increased automation (Pelletier et al., 2018). However, due to increasing animal welfare concerns on a global scale (Ochs et al., 2018; Pelletier et al., 2018; Vanhonacker et al., 2012, 2010; You et al., 2014), egg production in many countries is transitioning away from conventional cages in favour of alternative systems (Balsiger, 2016; Egg Farmers of Canada, 2023; Shields et al., 2017; Stadig et al., 2016). Though driven primarily by animal welfare concerns, this transition may also influence the environmental footprint of egg production systems, due to differences in the environmental impacts between different housing systems (Dekker et al., 2011; Leinonen et al., 2012; Pelletier, 2017). An in-depth understanding of the differences in environmental impacts, current levels of resource-use efficiency and variability between farms, potential for increases in resource-use efficiency, and key variables that drive improved efficiency is therefore necessary to support informed decision making during this 103 transition such that net-positive sustainability outcomes can be realized. This includes better understanding of how the environmental impacts of egg production systems may change with increases in resource-use efficiency, and under different housing system transition scenarios. These goals may be met through the joint application of the efficiency benchmarking tool data envelopment analysis (DEA), and environmental LCA. DEA is a non-parametric frontier analysis method commonly used to explore technical efficiency (i.e. the relationship between operational input and output levels) (Bauer et al., 1998), and allocative efficiency (i.e. the relationship between economic inputs and outputs) (Chen et al., 2019). The analysis is performed on groups of units where individual units are called decision making units (DMUs). DMUs may be individual farms, factories, business, etc. that are homogenous in input and output types, but with differences in specific amounts of each. Efficiency levels realized by individual DMUs are compared against one another, and the maximum level of efficiency realized within the system is identified. This level of efficiency represents an efficiency frontier on which at least one DMU is situated, while all others operating at lower efficiency levels are enveloped by it. Those DMUs that are situated on the frontier are therefore classified as “efficient”, while those enveloped by the frontier are classified as “inefficient”. The efficiency frontier then represents a benchmark for other DMUs, with deviations from it representing potential efficiency gains (Cooper et al., 2007). Efficiency gains may be realized through different means, depending on the orientation of the DEA, which is determined by the goal of the analysis. If the goal is to identify DMUs that are overutilizing resources, an input orientation is appropriate; if the goal is to identify DMUs that are underproducing outputs, an output orientation is appropriate (Cook et al., 2014). Input oriented DEA seeks to reduce excesses of inputs while holding output levels constant; 104 conversely, output oriented DEA aims to maximize outputs given constant input levels. Additionally, DEA may also be mixed, or non-oriented. These analyses seek to simultaneously reduce input excesses and maximize outputs. Depending on the orientation chosen, target values are calculated for each inefficient DMU representing the amounts of inputs/outputs needed for DMUs to reach the identified efficiency frontier (Cooper et al., 2007). The calculation of these target values represents one possible point of integration between DEA and LCA, as the efficiency targets may be used in LCA for calculation of environmental impacts of the system operating at an efficient level. These calculations may then be compared with current baselines to indicate the environmental impact mitigation potential of industry-wide efficiency gains. Applications of DEA to egg production systems have been limited. Ilham et al. (2019) applied DEA to the egg supply chain in Indonesia, including farmers, breeders, retailers, etc., while Yusuf and Malomo (2007) applied DEA to egg production in Nigeria, and Dogan et al. (2018), and Küçükönder et al. (2019) to production in Turkey. None of these applications, however, combined DEA and LCA to assess the environmental impact mitigation potential of efficiency gains. The current study therefore aims to remedy this critical knowledge gap with the first reported application of joint LCA+DEA to egg production systems based on a large, representative data set collected directly from farmers across the Canadian egg industry. While taking a case study approach focused on Canadian egg farms, the research has direct implications and transferability to egg industries worldwide. In particular, this research aims to identify key variables that drive resource use efficiency on egg farms, which will provide farmers and policy makers with priority targets for improving sustainability outcomes in egg production systems. Further, this research provides the first published estimate of industry-wide changes in environmental impact that result from a transition away from conventional cage systems. 105 3.3 Methods 3.3.1 Life cycle assessment Chapter 2 and appendix A describe the process used for collection of primary data characterizing resource use on Canadian egg farms, and all methodological choices and assumptions made during development of the baseline cradle-to-farm gate attributional LCA models of Canadian egg production systems. This includes a complete description of data sources and assumptions for upstream modeling of field crop production, feed milling, breeder, hatchery, and pullet operations, and procedures for allocation between co-products and emissions modeling. These baseline models are used to quantify the environmental impact mitigation potentials of increased feed- and pullet-use efficiency on Canadian egg farms suggested by joint data envelopment analysis and LCA. The LCA models developed in this chapter using the efficiency targets calculated during the DEA employ the same LCA modeling principles as those used in development of the baseline models, with one key difference. In chapter 2, uncertainty was assessed in line with common practices within the LCA field (Bamber et al., 2019). In this chapter, uncertainty is not assessed in the LCA models developed using DEA target values as the inputs to these models represent projections onto a mathematical plane, rather than collected primary data. Uncertainty information may be incorporated into DEA when determining target efficiency values through the use of extended techniques such as fuzzy DEA (Hatami-Marbini et al., 2011), but such techniques were not explored during development of this dissertation. Otherwise, all LCA modeling principles are held constant as described in chapter 2. These methods are not repeated in this chapter to avoid redundancy. A detailed description of the methodology employed for the joint DEA-LCA follows. 106 3.3.2 Joint DEA+LCA Two main frameworks have been proposed for joint LCA+DEA. Vázquez-Rowe et al. (2010) describe a five-step methodology, while Iribarren et al. (2010) describe a three-step method. The two methods are quite similar, with the three-step method better suited for when environmental impacts are to be included in the DEA models (Iribarren et al., 2010). Here, a modified five-step approach is used, as the inclusion of impacts in the DEA models is not a goal of the assessment. Vázquez-Rowe et al. (2010) describe the following five steps for joint LCA+DEA: 1) Creation of separate LCIs for each individual DMU 2) Performance of LCIA calculations for each DMU 3) Performance of DEA using the LCIs generated in step 1 to generate efficiency target values for each DMU 4) LCIA calculations for each DMU using the efficiency targets generated in step 3 5) Comparison of impacts calculated in step 2 and 4 Since farm-level analyses were not the goal of this research, rather than performing LCIA calculations for each individual farm as in steps 2 and 4, a single LCIA calculation was performed using production weighted averages of operational inputs and outputs prior to application of DEA, followed by a second LCIA calculation using production weighted averages based on the target values for each farm (step 3). Slacks-based non-radial measures of efficiency were used here to better account for the possibility of inputs and outputs varying non-proportionally to one another. Input orientation was 107 chosen for this analysis as farmers have more control over farm level inputs than they do outputs. Since farmers do not have direct control over egg production levels maximization of production while maintaining input levels is infeasible, as would be suggested by an output orientation. This decision is also supported by previous work done by Ramos et al. (2014), in which joint DEA and LCA was applied to the Atlantic mackerel fishery in the Basque Country of Spain. In their work, Ramos et al. (2014) note that the rigid quota system applied to the fishery makes it more reasonable to use an input-oriented approach, as maximal outputs are constrained by the system. The constraints applied to fishing outputs in this system are similar to those imposed on Canadian egg farmers under the supply management system (Heminthavong, 2015), which limits the number of hens housed on any single farm (and therefore production of that farm, indirectly) based on the quota allocation that the producer receives. As such, given the management context of the Canadian egg industry, the DEA analysis must be input oriented. To inform the choice of production possibility set (PPS) to be used, regression analyses were performed for each housing system to investigate the property of scalability. Specifically, for each housing system, dozens of eggs produced per hen and total dozens produced were regressed on the number of hens housed. If the property of scalability holds for the Canadian egg industry, it is expected that there would be little to no relationship between dozens of eggs per hen and total number of hens housed, and hence a strongly linear relationship between total dozens produced and number of hens housed. The results of these regression analyses subsequently informed the choice of PPS, because if scalability holds then a constant returns to scale (CRS) model is more appropriate than a variable returns to scale (VRS) model. 108 To maximize the discriminatory power of the DEA analysis, it is important to take into account both the number of DMUs included in the analysis, as well as the nature and number of inputs and outputs included in the analysis. Rules guiding the number of DMUs included in the analysis are thus far underdeveloped and consensus is lacking (Sarkis, 2002; Wilson, 2018). A number of rules of thumb have been developed relating the number of DMUs to the number of inputs and outputs included in the analysis, as reviewed by Wilson (2018). It has also been argued that the number of DMUs included is inconsequential to the analysis, and that these rules of thumb have no statistical basis and should only be used as a matter of convenience (Cook et al., 2014). Due to this lack of consensus, all farms for which data is available for all inputs and outputs chosen in the analysis are included in the DEA. In total, 166 farms (86 conventional, 41 enriched, 6 single-tier non-cage, 15 multi-tier non-cage, 8 free range, and 10 certified organic DMUS) were included in the DEA models. Similarly, little guidance is available on choosing specific inputs and outputs for inclusion in DEA. Vázquez-Rowe et al. (2010) suggest inclusion of the most relevant inputs and outputs without guidance on what defines relevance. The inputs included were chosen based on the results of the baseline LCA model whose development was described in section 2.4.3. Based on the results of this model, it is clear that across all impact categories the most relevant processes for inclusion in DEA models are related to feed and pullet inputs, as well as manure management. While significant variability was observed in reported farm-level energy inputs, indicating the potential for gains in on-farm energy use efficiency, on-farm energy use makes relatively small contributions to the majority of environmental impacts studied. Based on this, as well as the concerns with data availability and data quality previously described, farm-level 109 energy inputs were not considered in this DEA. Similarly, farm-level water and transportation inputs also make small contributions to the majority of impacts investigated, and were therefore excluded from the DEA models. Finally, while manure management processes make large contributions to many impact categories, it is more efficacious to include feed inputs in DEA while excluding manure outputs for two reasons: first, reductions in feed inputs will reduce both impacts associated with feed, and impacts associated with manure management due to lower amounts of manure being excreted; and second, changes to manure management systems would be much more difficult for farmers to implement than changes to feed inputs. DEA models were therefore generated using feed and pullets as inputs, and eggs as the only output. These inputs and outputs were then used to find target values for each DMU included in the analysis. No input or output weighting was considered. The targets were subsequently used to create production-weighted averages based on the DMUs included to create DEA efficient LCIs for each housing system. All DEA modeling utilized PIM-DEA software (Emrouznejad and Thanassoulis, 2011). A flowchart describing the complete LCA+DEA method is available in Figure 3-1. Since uncertainty was not assessed in the LCA models developed in this chapter, statistical tests were not performed to determine if increasing efficiency resulting in significant changes to estimated environmental impacts per tonne of eggs produced. 110 Figure 3-1. Flow chart describing the modified five-step method for DEA+LCA used in this analysis Generate baseline, production weighted average LCA models of Canadian egg production systemsCompile farm-level feed and pullet inputs, and egg outputs for each DMUGenerate input-oriented SBM DEA models for each of the five non-organic housing systemsCompiled farm-level feed and pullet input target values for each DMUUse target values to calculate new production-weighted average feed and pullet inputs under efficient conditions for each housing systemCalculate environmental impacts under efficient conditions using new production weighted averagesCompare efficient environmental impacts to baseline environmental impacts 111 3.3.3 Investigation of key drivers of farm level efficiency Following calculation of efficiency levels for each DMU, the relationship between efficiency scores and different housing system characteristics and management practices were investigated. Potential predictor variables were chosen on the basis of data availability for the DMUs for which efficiency scores were calculated. The potential predictor variables included were length of lay cycle, mortality rate, space allowance, whether or not the pullet rearing system matched the laying system, the feeding system used (i.e. chain and trough, feed cart, or feed pan), number of feedings per day, type of feed (i.e. crumble, mash, pellets, other) hours of light received per day, average barn temperature, and whether the farm used white or brown birds. In spite of the potential “skipper effects” (Vázquez-Rowe and Tyedmers, 2013) on eco efficiency levels realized on farms, farmer experience was not considered as an explanatory variable as this information was not collected. The least shrinkage and selection operator (LASSO) was chosen as the most appropriate framework for investigating relationships between efficiency levels and the predictor variables. LASSO is a regularization method that shrinks coefficient estimates relative to the number of coefficients in the fitted model through the imposition of a tuning parameter, λ, and an ℓ1 penalty term which takes the form ∑|𝛽𝑗| , thereby performing variable selection concurrently with model fitting. The coefficients predicted by the lasso, ?̂?𝜆𝐿, therefore minimize the quantity Residual sum of squares + λ ∑|βj|pj=1 for the predicted model. Use of this penalty term forces the coefficients associated with some predictor variables to zero, removing them from the model and increasing model interpretability. By constraining and shrinking the estimated coefficients, model variance can be substantially 112 reduced with minimal other drawbacks. This is especially useful in cases where the number of predictor variables is greater than the sample size, as is the case here for some housing systems. LASSO was determined to be a more appropriate technique for this investigation than the more commonly used multiple linear regression (Doumpos and Cohen, 2014; Hauner, 2005; Picazo-Tadeo et al., 2011; Shen et al., 2011) for several reasons. First, it has been argued that regression tools are insufficient for this application because DEA results are serially correlated with one another (Simar and Wilson, 2007), a characteristic that does not confound LASSO in the same way it would multiple linear regression. Second, as there are more predictor variables than DMUs in some housing systems, dimensionality reduction or variable selection techniques are required. While non-linear principal component analysis could be used for dimensionality reduction of qualitative and quantitative data (Linting et al., 2007; Linting and Van Der Kooij, 2012), model fitting with principal components would greatly reduce interpretability, and significantly hamper application of any generated knowledge. LASSO, in contrast, preserves the original variables during model fitting, making interpretation and application of generated knowledge much easier. Tan et al. (2021) have previously used LASSO in a DEA study of the hotel industry in a similar manner as described here. Since the goal of this analysis was not to generate models for further prediction of efficiency scores, the data for each housing system was not divided into training and testing subsets for validation of the generated model, nor would this be possible given the small sample sizes for cage-free systems. As such, the predictive power of the generated models cannot be assessed. Cross-validation was performed during the model generation process in order to determine the optimal value of λ that provided the best fit model, based on mean squared error (MSE). One drawback of the LASSO is the requirement for complete sets of observations for 113 each potential predictor variable. As such, if a data set did not provide data for a single predictor variable, it had to be omitted from the modelling process. Given this constraint, LASSO models could not be generated for single-tier non-cage or organic production systems. If a predictor exhibited no variability within a housing system, it was omitted during model generation (for example, feather colour was not used during conventional model generation since only white birds were used). No weights were imposed on predictor variables during model fit. All LASSO models were generated using the glmnet package (Friedman et al., 2010) in R studio (2019). 3.3.4 Investigation of cumulative environmental impacts of egg production under different housing system transition scenarios Following calculation of environmental impacts per tonne of eggs produced in each housing system under DEA efficient conditions, the cumulative, production-weighted average environmental impacts of egg production in Canada were calculated using the proportions of production in each housing system in 2020, as reported by Egg Farmers of Canada (2023). Importantly, only a single proportion is presented for free-run production (i.e. not broken down into single- and multi-tier production). As such, equal proportions were assumed, which may over- or under-estimate proportions in either single- or multi-tier non-cage systems. The baseline production-weighted average environmental impacts were then compared against production-weighted average impacts of different scenarios, representing different distributions of production between housing systems following the transition away from conventional cages. Five different transition scenarios were considered, with proportions of production attributable to each housing system chosen based on observed trends in the housing system transition as reported by Egg Farmers of Canada (2022) The scenarios considered are presented in Table 3-1, 114 indicating the percentage of total non-organic production attributable to each housing system. The production-weighted average environmental footprints for each scenario assume the efficiency gains and impact reductions identified by joint LCA+DEA have been realized for each housing system. Table 3-1. Assumed percentage of total non-organic egg production used in each housing system transition scenario used in scenario analysis Percentage of total non-organic production Scenario 1 2 3 4 5 Enriched 50 33 25 40 40 Single-tier non-cage 0 33 25 0 20 Multi-tier non-cage 50 33 25 40 20 Free range 0 0 25 10 10 3.4 Results and discussion 3.4.1 Determination of the most appropriate production possibility set to describe Canadian egg production Across all housing systems, total eggs produced exhibited a strong linear relationship with total number of hens housed, indicated by high r2 values. Further, little to no relationship was seen between dozens of eggs produced per hen and total number of hens housed, as indicated by very low r2 values. Based on these results, it is clear that the property of scalability holds for the Canadian egg industry, indicating that a CRS model is the most appropriate PPS for 115 use in DEA. Subsequently, the calculated efficiency scores are representative of global technical efficiency (GTE). r2 values describing the relationships between production metrics and number of hens house are presented in appendix B, Table B 1 and Table B 2. 3.4.2 Determination of efficiency targets for Canadian egg farms, and calculation of environmental impact mitigation potentials associated with industry-wide increases in efficiency Generally, high levels of efficiency were seen within all housing systems (Figure 3-2, Table 3-2). Free range systems exhibited the lowest average percent efficiency, but still exhibited a high degree of efficiency. However, free range systems also exhibited a substantially larger amount of variability in efficiency scores (i.e. almost three times higher than the next highest system) indicating significant heterogeneity in reported feed and pullet input levels across farms. Otherwise, variability was fairly comparable across all other housing systems (i.e. variance between 3.52 and 5.55). Across all housing systems, the lowest efficiency achieved by any single farm was 88.59% relative to the efficiency frontier (i.e. the top performer). Three farms were identified as operating efficiently (i.e., on the efficiency frontier) in each of conventional, enriched, and multi-tier non-cage production systems, two farms were identified as operating efficiently in free range systems, and only a single efficient farm was identified in single-tier non-cage systems. 116 Figure 3-2. Box and whisker plot depicting variability in estimated efficiency scores for farms operating different non-organic housing systems in the Canadian egg industry. Boxes indicate the first and third quartiles on the edges, and include a median line. Whiskers represent 1.5 X the interquartile range, while points outside the whiskers are considered outliers. Table 3-2. Average percent efficiency scores, as well as the variance and range of scores across each housing system as calculated using DEA Average efficiency score Variance Range Conventional 96.43 3.78 9.21 Enriched 96.87 3.52 6.92 Single-tier non-cage 96.66 4.87 6.64 Multi-tier non-cage 97.63 5.55 8.25 Free range 96.07 15.30 11.14 Percent efficiency (%)86889092949698100ConventionalEnrichedSingle-tierMulti-tierPercent efficiency (%) 117 Across the LASSO models generated for conventional, aviary, and free range production systems, coefficients for all included predictor variables were shrunk to 0. This indicates that an intercept model (i.e. a straight line) was the fitted model that resulted in the lowest MSE for each of these housing systems. As such, from this analysis we cannot conclude that any of the predictor variables used in these models are useful predictors of feed and pullet use efficiency. In contrast, the LASSO model generated for enriched housing shrank all coefficients to 0 except for those associated with length of lay cycle, and use of brown birds. The coefficients for these variables, however, remained small, indicating minor relationships with feed- and pullet-use efficiency. Specifically, an increase in length of lay cycle by 1 day was associated with a 0.001% increase in efficiency, while the use of brown birds was associated with a decrease in efficiency of 0.95%. It must be noted, however, that the trace plot associated with this model fit indicates that an intercept model with no variables included has an MSE within one standard error of the minimum MSE found with the two variables model (Figure 3-3). While not a robust statistical test, this relationship indicates that the degree of confidence in the generated model should be low, and that a relationship between the included predictor variables, and the observed efficiency scores cannot be assumed. 118 Calculated efficiency targets for feed and pullet inputs indicated that input levels may decrease for many DMUs. Table 3-3 reports the current baseline input levels of feed and pullets per tonne of eggs produced per housing system, and calculated values based on production-weighted averages if the industry was operating under efficient conditions. Notably, under efficient conditions, all housing systems except for free range use less than 2 tonnes of feed per tonne of eggs produced, a level of feed use efficiency currently only realized by conventional and enriched systems in the baseline LCA models. Further, under efficient conditions the number of pullets required to produce 1 tonne of eggs is fewer than 50 in conventional, enriched, and both free-run systems. All decreases in inputs under the DEA efficient models range from 3.55% to 13.22%. Free range production exhibited the greatest potential efficiency gains for feed inputs, Figure 3-3. Trace plot for the lasso model generated for enriched cage production systems. Number along the top axis represents the number of variables in the generated model. The leftmost dotted vertical line indicates the value of the tuning parameter λ at which the MSE of the generated model was minimized. The rightmost dotted vertical line represents the value of the tuning parameter λ at which the MSE of the generated model was within one standard error of the minimum MSE. 119 and single-tier non-cage production exhibited the greatest potential efficiency gains for pullet inputs. Table 3-3. Production-weighted average efficiency targets for feed and pullet inputs per tonne of eggs produced as calculated using DEA per housing system, compared to current baseline averages. Feed (tonnes) Pullets (items) Baseline DEA efficient % reduction Baseline DEA efficient % reduction Conventional 1.92 1.79 6.77 50.74 47.32 6.74 Enriched 1.95 1.79 8.21 51.73 48.23 6.77 Single-tier non-cage 2.06 1.88 8.74 52.15 46.86 10.14 Multi-tier non-cage 2.06 1.96 4.85 51.97 49.4 4.95 Free range 2.42 2.10 13.22 54.72 51.06 6.69 These decreases in feed and pullet inputs when operating efficiently translated to industry wide reductions in environmental impacts across all impact categories and housing systems compared to the baseline levels. Table 3-4 reports estimated percent impact reduction potentials for each impact and housing system. Numerical estimates of the impacts per tonne of eggs produced in each housing system are presented in appendix B, Table B 3, which may be compared to Table 2-9, which presents the numerical estimates of the baseline results. Figure 3-4 to Figure 3-6 show differences in contributions to acidification and eutrophication, and GHG 120 emissions per tonne of eggs produced in each housing system. Additional figures showing similar information for the other impacts are available in appendix B, Figure B 1to Figure B 8. 121 Table 3-4. Estimated percent reductions in impacts per tonne of eggs produced in each housing system under DEA-efficient conditions relative to baseline impacts as estimated in chapter 2. Impact category Conventional Enriched Single-tier non-cage Multi-tier non-cage Free range Abiotic depletion 6.44 7.65 8.39 4.85 11.93 Abiotic depletion (fossil fuels) 5.87 6.78 7.17 4.09 10.41 Acidification 8.17 9.55 10.70 6.09 14.73 Eutrophication 8.04 9.44 10.52 5.99 14.51 Freshwater aquatic ecotoxicity 6.29 7.30 8.12 4.63 11.19 Global warming potential 6.21 7.42 8.20 4.69 11.44 Human toxicity 6.18 7.25 7.78 4.47 11.20 Marine aquatic ecotoxicity 5.78 6.66 7.03 4.01 10.19 122 Impact category Conventional Enriched Single-tier non-cage Multi-tier non-cage Free range Ozone layer depletion 6.47 7.65 8.46 4.87 11.88 Photochemical oxidation 6.05 7.25 7.95 4.57 11.23 Terrestrial ecotoxicity 6.61 7.68 8.88 5.05 11.73 123 Figure 3-4. Estimated differences in relative contributions to acidification impacts per tonne of eggs produced in each housing system under baseline and DEA-efficient conditions 0102030405060708090100Baseline Efficient Baseline Efficient Baseline Efficient Baseline Efficient Baseline EfficientConventional Enriched Single tier free run Multi-tier free run Free rangePercent contribution to impactWaste treatmentWaterTransportationEnergyPulletsManure managementFeed 124 Figure 3-5. Estimated differences in relative contributions to eutrophication impacts per tonne of eggs produced in each housing system under baseline and DEA-efficient conditions 0102030405060708090100Baseline Efficient Baseline Efficient Baseline Efficient Baseline Efficient Baseline EfficientConventional Enriched Single tier free run Multi-tier free run Free rangePercent contribution to impactWaste treatmentWaterTransportationEnergyPulletsManure managementFeed 125 Figure 3-6. Estimated differences in relative contributions to greenhouse gas emissions per tonne of eggs produced in each housing system under baseline and DEA-efficient conditions0102030405060708090100Baseline Efficient Baseline Efficient Baseline Efficient Baseline Efficient Baseline EfficientConventional Enriched Single tier free run Multi-tier free run Free rangePercent contribution to impactWaste treatmentWaterTransportationEnergyPulletsManure managementFeed 126 3.4.3 Investigating environmental impacts under different housing system transition scenarios Previously, Pelletier (2018) assessed changes in the life cycle environmental impacts of the Canadian egg industry over a 50 year time period from 1962 to 2012, indicating that impacts per tonne of eggs produced in 2012 were approximately one third of what they were in 1962. This decrease was attributed to increased efficiency in supporting activities along the egg supply chain, as well as changes in feed composition, animal health, and productivity on egg farms. Particularly notable was a 42.62% decrease in pullet inputs, and a 35.28% decrease in feed inputs between 1962 and 2012 in Canada (Pelletier, 2018b). While the gains in pullet and feed use efficiency reported here following DEA are only 6.25% and 8.86% respectively, it is important to note that these efficiency gains were calculated using data sets covering a 7 year time difference only, rather than the 50 year difference used in Pelletier (2018). Linear extrapolation of the efficiency gains calculated here to a similar time span would amount to a 50-year decrease of 44.64% for pullet inputs, and a 63.29% decrease for feed inputs. While the 50 year pullet reductions would be similar to those reported by Pelletier (2018), the projected efficiency gains for feed inputs are almost double those previously reported. This represents potentially significant long term mitigation potential for all environmental impacts assessed if this trend continues, as feed inputs are the largest contributor to environmental impacts across all housing systems. It is likely, however, that at some point a biological limit to feed conversion efficiency would be reached, limiting further impact reductions through improved farm-level efficiencies alone. It may therefore be more efficacious to realize farm-level efficiency gains in concert with other improvements, such as optimization of feed formulations for least environmental impacts in order to further reduce the environmental impacts of Canadian egg production systems. 127 Assuming the potential efficiency gains and environmental impact reductions identified by DEA are realized, the majority of the environmental impacts investigated were lower than the current Canadian production weighted average presented in chapter 2, regardless of the housing system distribution. There were some exceptions in which impacts per tonne of eggs were greater under different transition scenarios, though when these increases occurred they were generally small (i.e., <4%). Marine aquatic ecotoxicity impacts were estimated to increase by 1% in scenarios 1 and 2, and 4% in scenario 3 compared to the current Canadian production weighted average, while this impact was estimated to decrease by approximately 9% in both scenarios 4 and 5. The only other estimated increase was for freshwater aquatic ecotoxicity in scenario 3, though this increase was marginal (i.e., <1%). These increases may be attributed to a greater proportion of eggs produced in housing systems with higher impacts than those of conventional cage systems, which currently make up the largest share of the baseline production-weighted average egg model. In all other cases, estimated impacts per tonne of eggs produced were lower for all scenarios. Scenarios 4 and 5 generally had the lowest impacts per tonne of eggs produced. Figure 3-7 presents the relative percentage impacts for acidification and eutrophication impacts, and GHG emissions per tonne of eggs produced across each transition scenario compared to the production weighted average Canadian baseline. All estimated impacts for each transition scenario are presented in appendix B, Table B 4. 128 Figure 3-7. Relative percentage of acidifying, eutrophying, and GHG emissions compared to the current baseline under each housing system transition scenario. Globally, egg farmers are transitioning away from conventional cage to alternative production systems, which may be characterized by environmental trade-offs (Dekker et al., 2011; Leinonen et al., 2012; Pelletier, 2017). To help facilitate this transition, it is imperative that the trade-offs in environmental impacts between different systems are understood (and mitigated) alongside other potential trade-offs, such as those associated with animal welfare, so as to ensure net positive outcomes from sustainability decision making. Further, it is also important to understand levels of, and variability within on-farm resource use efficiency across different housing systems such that priority strategies can be derived to improve environmental sustainability outcomes in egg production. The research presented here contributes towards this goal by reporting the first application of joint LCA + DEA to egg production systems. Using a case study of the Canadian egg industry, differentiated by housing system, pullet and feed inputs were used as inputs to DEA. As indicated in chapter 2, these inputs make large contributions (i.e. ∼10–27% and ∼40–84%, respectively) to the total environmental impacts of 0102030405060708090100Baseline Scenario 1 Scenario 2 Scenario 3 Scenario 4 Scenario 5Acidification Eutrophication GHG emissions 129 egg production in Canada. Assuming industry-wide increases in use-efficiency of these resources to the peak levels currently realized by individual farms, feed inputs were reduced by between 4.85% and 13.22%, depending on the housing system. These increases in feed-use efficiency translated to potential decreases in total environmental impacts, indicating that improvement of feed- and pullet-use efficiency could be an efficacious way to reduce the environmental impacts associated with Canadian egg production. In addition to the environmental impact mitigation potential, increases in feed-use efficiency may also represent significant cost savings to farmers, as feed costs represent the largest share of cost of production (Emam and Hassan, 2010). While increased feed use efficiency may lead to reductions in environmental impacts, however, potential further reductions associated with use of least environmental cost feed formulations were not considered in this analysis. Potential impact reductions associated with decreasing the impacts of feed production likely far outstrip those achievable via mainstreaming current best reported feed conversion efficiencies (Pelletier, 2018b). In addition to feed, pullet inputs also make significant contributions to the total life cycle environmental impacts of egg production in Canada. Under the industry-wide efficiency scenario, pullet inputs were reduced by 3.55%–10.14%, depending on the housing system. These decreases also translated to decreases in the majority of environmental impacts. While reductions in environmental impacts related to increased pullet-use efficiency are likely smaller than those possible through feed-related interventions due to the relative differences in contributions to impacts made by these two inputs (Pelletier, 2017), it is possible this analysis may underestimate these reductions. In development of the baseline egg production models, both primary egg production, and upstream feed production processes were updated based on primary data, and current best publicly available data sets for Canadian field crop production. No such updates 130 were performed for the pullet production models, which were used as described in Pelletier (2017). Comparisons of the LCI data for the baseline egg production processes developed in chapter 2 indicated that both feed- and pullet-use efficiency generally improved between 2019 and previously reported levels representative of 2012 (Pelletier, 2017). It is likely that similar improvements have been made in resource-use efficiency in pullet production processes, however no updated data has been collected to improve these models. Improvement of models representing upstream pullet production should therefore be a priority for future environmental sustainability research in the Canadian egg industry. Unfortunately, few concrete results were obtained regarding key farm variables and management practices associated with higher levels of resource-use efficiency due to issues related to data availability and data quality in cage-free systems, and a high degree of homogeneity in efficiency scores in caged systems. Specifically, the only variables found to be associated with changes in efficiency were the use of brown feathered birds, and an increase in lay cycle length by a one day, associated with an efficiency decrease of 0.95% and increase of 0.001% respectively, in enriched systems only. Identification of increased cycle length as a variable associated with improved resource-use efficiency in enriched systems is interesting as it is contrary to results obtained from a recent analysis of extended lay cycles in the Canadian egg industry which indicated that as cycle length extends, efficiency decreases (Pelletier and Doyon, 2023). Given the contrary nature of these findings, it is clear that additional analyses of lay cycle extension as a means of environmental sustainability improvement in the Canadian egg industry is warranted. Such analyses must, however, also take into account the potential animal welfare and economic implications of this strategy to holistically assess the potential sustainability impacts (Arulnathan et al., 2024). 131 Generally, however, it is promising that farms using alternative systems are already consistently operating at quite high levels of efficiency and homogeneity with respect to feed and pullet inputs relative to one another. Further statistical analysis is necessary, though, to determine key variables that impact efficiency, as well as even further environmental impact mitigation potential. Interestingly, under efficient conditions conventional, enriched, and single- and multi-tier non-cage production systems all had feed inputs under 2 tonnes and pullet inputs under 50 birds per tonne of eggs produced, while free range and organic systems were not able to reduce inputs below these thresholds. Current trends in the proportion of hens housed in each type of system within the Canadian egg industry indicate that farmers are transitioning to enriched and non-cage systems at a higher rate than free range systems (Egg Farmers of Canada, 2023). This is a promising sign for the Canadian egg industry as it aims to meet the goals outlined in its inaugural sustainability report (Egg Farmers of Canada, 2020), as these housing systems appear to be able to operate at higher levels of resource use efficiency than are achievable in free range systems. To further investigate how the environmental footprint of Canadian egg production may change following industry-wide efficiency gains and a complete transition away from conventional cages, scenarios were designed to calculate cumulative environmental impacts per tonne of eggs produced. Generally, these scenarios indicated that production-weighted environmental impacts per tonne of eggs produced in Canada may decrease as farmers continue to transition away from conventional cages, provided that the identified efficiency-gains for each housing system type may be realized on an industry-wide basis. Additional research is clearly warranted to further identify key drivers of efficiency on Canadian egg farms, and to determine strategies to effectively communicate these practices to farmers and implement them on Canadian egg farms. The recently release National Environmental Sustainability and Technology 132 Tool (NESTT) may be crucial in this goal by providing farmers a means to track levels of resource use efficiency and environmental impacts on their farm, and additional information for farmers to consult to determine which potential sustainability improvement strategies are best suited for their operation (Arulnathan, 2023). 3.5 Limitations and conclusions The work presented in this chapter represents the first reported application of joint LCA + DEA to egg production systems. Results indicate that Canadian egg farms are operating at high levels of feed- and pullet-use efficiency, with relatively little variability, except for in free range systems. Nonetheless, industry-wide increases in pullet- and feed-use efficiency up to current efficiency frontiers may result in decreases in environmental impacts of up to 12%. One key downfall of DEA is that it is a relative assessment – that is, the efficiency frontier identified in DEA is representative of the highest relative level of efficiency realized by one or multiple DMUs at a given point in time (Cooper et al., 2007). It is possible (indeed, likely), therefore, that the current highest realized level of efficiency is not representative of the maximal efficiency achievable within the system. It is hence important that future research make use of more advanced, predictive techniques capable of predicting levels of efficiency beyond those currently realized by any single farm. Process optimization using machine learning techniques is an emerging field within agri-food research that may be leveraged to predict levels of efficiency beyond those realized by any individual farm, and optimize input and output levels across multiple distinct objectives (Villalba et al., 2019). An additional limitation of the approach used here is the exclusion of uncertainty assessment of the calculated efficiency target values. A number of techniques have been 133 developed for extension of DEA to include uncertainty assessment drawing from traditional statistics, fuzzy set theory, and robust optimization (Dia, 2004; Dyson and Shale, 2010; Sadjadi and Omrani, 2010; Shiraz et al., 2014; Skevas et al., 2012; Wu et al., 2022). A detailed description of strategies for uncertainty assessment in DEA is available in Wu et al. (2022). To best benefit from these methods, however, strategies must be identified to ensure the robust collection of accurate data characterizing farm-level energy inputs on Canadian egg farms. As indicated in chapter 2, identified collection of high-quality energy use data as a key source of uncertainty in the baseline LCA models used here, and, as a result of this poor data quality, energy inputs could not be included in the developed DEA models. It is likely that inclusion of these inputs would represent a potentially large source of uncertainty in DEA results, and would likely greatly increase heterogeneity in observed efficiency scores beyond that seen here. Additional research is also necessary to identify key drivers of efficiency to serve as foci for efficiency improvement strategies. These variables may be identified in the future using more sophisticated modelling methods than those used here, though they would require larger amounts of higher quality data than are currently available. Cluster analysis may also be beneficial in order to determine key variables that are common among farms with similar environmental impacts as a means of identifying management practices that would be beneficial to the industry. Scenario analyses assuming different distributions of production across housing systems following a complete transition away from conventional cages indicated that this transition will likely be beneficial for the environmental sustainability of industrial egg production, assuming that the indicated potential efficiency gains are realized. Identification of key drivers of resource-use efficiency will help ensure farmers are able to realize these efficiency gains. Further, as farmers gain experience with alternative systems, efficiency levels will likely increase, and 134 environmental impacts decrease, in line with expectations based on the “skipper effect.” When considering these results alongside the animal welfare benefits of transitioning away from conventional cages, it seems likely that the ongoing housing system transition could potentially be beneficial for the overall sustainability of the egg industry if accompanied by concerted efforts to mainstream the currently best-reported efficiency levels. 135 4. Development of a type 1 animal welfare life cycle impact assessment methodology for assessment of Canadian laying hens 4.1 Chapter synopsis To date, assessment of animal welfare impacts remains largely unconsidered in LCA. Though previous attempts have been made to integrate animal welfare assessment into the LCA framework, these attempts have been insufficient in their coverage of the numerous factors contributing to animal welfare impacts. Here, a novel type 1 (i.e, reference scale) life cycle impact assessment method is proposed for animal welfare assessment of laying hens. This includes identification of all requisite components of a life cycle impact assessment method (i.e., area of protection, stakeholder and impact categories, impact subcategories, inventory indicators and data requirements, and characterization factors) based on a review of the animal welfare literature, in line with best practices in both the animal welfare science, and life cycle assessment fields. The proposed method is subsequently tested using a case study of the Canadian egg industry, and levels of relative risk for different impact subcategories related to animal biological health, behaviour, and affective state are calculated. This method provides results in line with expectations based on the animal welfare literature. Further, the process used for development of this method is generalizable, and may be applied to development of similar methods for assessment of other livestock species, as the area of protection, stakeholder and impact categories, and impact subcategories are not species specific. This method improves upon previous efforts to incorporate animal welfare assessment into the LCA framework. Continued improvement is necessary however, particularly with respect to incorporation of additional hen life cycle stages, modeling of affective state and positive welfare contributions, and uncertainty assessment. Continued development of animal welfare LCIA methods is necessary given the 136 growing status of animal welfare as an issue of concern worldwide, and to ensure net-positive sustainability outcomes in food systems. 4.2 Context and rationale As the environmental impacts of food production systems are increasingly scrutinized (Rogelj et al., 2018), social and economic impacts must also be considered to ensure sustainability interventions result in net positive outcomes (Gunasekera, 2010). The adoption of systems-level, life-cycle thinking-based approaches such as LCA allows for simultaneous investigation of many sustainability impacts related to potential environmental, social, and economic outcomes, helping to avoid burden shifting from one impact or life cycle stage to another (Guinée et al., 2002). Holistic assessment of sustainability outcomes is of particular importance for livestock systems, which are responsible for a disproportionate share of food system environmental impacts (Marques et al., 2020), and consumption of which is expected to increase with increases in global population and shifts in income demographics (Govoni et al., 2021). Largely absent from LCA studies of livestock systems are parallel assessment of animal welfare outcomes on a methodologically consistent basis. Animal welfare is growing in status as an important issue worldwide (Clark et al., 2016). While there are examples of joint LCA and animal welfare assessment (Weeks et al. 2016), these often make use of independent systems for assessing animal welfare, such as the Welfare Quality system (Welfare Quality, 2009a). Joint assessments must, however, be made on a methodologically consistent basis to reduce potential uncertainty and biases that may be introduced into the results through the choice of specific welfare assessment frameworks. Incorporation of animal welfare assessment into the LCA 137 framework should therefore be a high priority to help reinforce the validity of the conclusions being made from these assessments. In this chapter, a novel animal welfare LCIA method is developed and tested using a case study of the Canadian egg industry. This chapter explores the various challenges associated with incorporating animal welfare assessment principles into the LCA framework and seeks to overcome those challenges in an interdisciplinary manner. Through incorporation of knowledge gained from an in-depth review of the animal welfare literature, this chapter examines how to reconcile conceptions of animal welfare with the LCA framework and quantifies animal welfare impacts associated with Canadian egg production systems. 4.3 Methods 4.3.1 Identification of requisite LCIA methods components To develop a type 1 animal welfare LCIA method, all requisite components of this type of method had to be identified. This included a relevant Area of Protection (AoP), stakeholder categories, impact categories and subcategories, and associated inventory indicators and characterization factors. Additional discussion describing each of these components is available in appendix C, section C.1. To begin, literature was consulted to identify common definitions of animal welfare. In particular, Fraser (2008), Mellor (2016), literature from the Welfare Quality project (2009a), and the Farm Animal Welfare Council (2009) were considered based on expert advice (Widowski, pers. comm). The identified definitions were subsequently assessed for their dependence on normative decisions, and their consideration of positive welfare impacts. It was preferred that normative value choices be minimized in line with ISO guidelines for LCA (ISO, 2006b), and 138 the chosen definition allow for assessment of both negative and positive welfare impacts so as to be consistent with current animal welfare science. Following identification of the best suited animal welfare definition, AoPs used in LCA were identified from the eLCA and sLCA literature. Consulted works included Schaubroeck and Rugani (2017), who provide an overview of common frameworks and associated AoPs used in the eLCA field, as well as the ILCD Handbook for LCIA Methodologies (2010), the UNEP sLCA Guidelines (UNEP, 2020), and the work of Dreyer (2006) and Ridoutt et al. (2016). Existing AoPs were assessed for their ability to incorporate the key principles of animal welfare as identified during assessment of the different animal welfare definitions. AoPs were not assessed with regards to their explicit inclusion of animal welfare, as no such AoP has been previously defined. Rather, existing AoPs were assessed for the applicability of their principles to the key principles of animal welfare following the chosen definition. Next, UNEP sLCA guidelines (2020) were consulted to identify the different stakeholder categories that are commonly used. In addition, guidance on stakeholder categories was taken from Tallentire et al. (2019), which includes discussion on integration of animal welfare into these categories. Impact categories were then defined in accordance with the previously chosen definition of animal welfare with three key goals: 1) Coverage of all requisite tenets of the chosen animal welfare characterization 2) Ensuring the ability to differentiate between different welfare tenets 3) Ensuring the ability to differentiate between different hen housing systems. Impact categories were further broken down through identification of subcategory indicators. Little guidance is available regarding the choice of impact subcategories. The UNEP sLCA guidelines (2020) note that their impact subcategories were defined with the goal of 139 comprehensiveness and taking into account consensus best practices where possible. The use of consensus best practices to define animal welfare impact subcategories is not possible as no such consensus has been reached, as evidenced by the different welfare characterizations used throughout the literature. Nonetheless, useful information may still be derived from these guidelines. In particular, the goal of comprehensiveness may be applied to the definition of animal welfare impact subcategories. Applying this principle, impact subcategories were defined to cover the entire spectrum of welfare states encompassed by the impact categories. Guidance in defining the different welfare states possible under each impact category were taken from Mellor (2016), the Farm Animal Welfare Council (2009), Scherer et al. (2018), Tallentire et al. (2019), Welfare Quality (2009a) and LayWel (2006) welfare assessment methods. Additional guidance was also taken from ISO14044 and the ILCD Handbook (ISO 2006a; ILCD 2010), which note that the defined subcategories must avoid double counting of impacts except in instances where inventory indicators may contribute to multiple impact categories. Following definition of impact categories and impact subcategories, inventory indicators were identified for each subcategory through review of the animal welfare literature, in line with recommendations made by Huijbregts et al. (2017). Review of the literature to identify important animal welfare measures to include as inventory indicators is also in line with the work done by Zira et al. (2020) in their adaptation of the animal welfare LCIA method developed by Tallentire et al. (2019). Literature were selected using a Web of Science topic search for the keywords “laying hen” AND “welfare” OR “health” OR “behaviour” OR “affective state”. This combination of keywords ensured the identification of resources dealing with either welfare as a whole, or one of the three tenets of animal welfare individually. The common terms “poultry”, and “chicken” were not used in this search to avoid results relevant to other poultry species, 140 including broilers, turkeys, ducks, etc. This was particularly important in avoidance of results relevant to broiler production, as the welfare challenges facing broilers and laying hens are significantly different (Welfare Quality, 2009a). In addition, only sources related to the laying phase of life were included. Sources referring to welfare in parent stock, hatcheries, pullet rearing, and depopulation were excluded, except where studies were investigating the impacts of treatments during the earlier life stages on the laying phase because assessment of welfare impacts across other life cycle stages is currently out of scope. This division of life stages is in line with that employed by the Welfare Quality assessment method (Welfare Quality, 2009b). Incorporation of additional life stages of laying hens should be a priority area of improvement to be explored in the future. Results were filtered to include sources published between 2009 and 2021 to ensure that potential inventory indicators were current. Additional relevant books and grey literature were identified via consultation with experts in the field of laying hen welfare (Widowski, pers. comm). These additional sources were not subjected to the same temporal bounds as journal sources to accommodate consideration of foundational contributions to animal welfare literature, particularly in the case of on-farm welfare assessment frameworks. Sources were reviewed to determine what measures of animal welfare were applied in each assessment. All collected welfare measures were tallied, and the percent of resources in which each measure was utilized was calculated. Each unique welfare measure was also sorted according to which of the three tenets of animal welfare (i.e., biological function, affective state, and naturalness) they refer. In some cases, measures were assigned to multiple tenets. For example, the measure “feed consumption” may contribute to both biological function (eating is necessary to maintain health) and affective state (eating decreases feelings of hunger). 141 Inventory indicators for each impact subcategory were chosen from the compiled list of welfare measures based on several criteria. First, any obvious relationships between subcategories and potential inventory indicators were considered. In absence of such relationships, welfare measures first had to be sufficiently represented across the reviewed literature to be considered. Due to the broad nature of the topics covered by the reviewed materials, the percent occurrence for the vast majority of welfare measures across all impact categories was very low (i.e. <1%). Sufficient representation within the reviewed literature was therefore defined liberally here as occurring in at least 5% of all resources reviewed. Second, potential contributing factors to each impact subcategory were considered, and those welfare measures appearing in at least 5% of all sources were investigated to determine if any singular, or combination of measures could sufficiently cover expected contributors to each subcategory. Inventory data requirements and availability for each inventory indicator were also considered at this point, with priority given to inventory indicators only requiring one inventory datum. Doing so minimizes the number of aggregations needed to calculate subcategory results, hence reducing potential information loss (UNEP, 2020) . Within the welfare literature, measures may also be classified as animal based, resource based, or management based (Munoz et al., 2018; Polgár et al., 2019). While animal based measures of welfare are often preferred as they are more direct measurements, resource and management based indicators may be used when animal-based measures are unreliable, not sensitive enough, or not feasible (Munoz et al., 2018; Polgár et al., 2019; Welfare Quality, 2009a). Potential inventory indicators were therefore also classified as either animal, resource, or management based. Preference was given to animal based measures for use as inventory indicators. However, difficulties in data collection at the farm- and industry-scale needed for 142 LCA work often precluded the feasibility of such indicators. On this basis, resource- or management-based counterparts for animal-based inventory indicators were used when they were more feasible for industry-scale data collection. This emphasis on data collection feasibility is in line with the selection of inventory indicators as performed by Tallentire et al. (2019). Similar recommendations regarding data availability and collection feasibility are also made by Mattila et al. (2012) for category indicator selection in environmental LCIA methods, highlighting the importance of this consideration in LCIA methods development. Measures of productivity such as rate of lay, measures of egg quality such as egg weight, and physiological measures such as blood plasma corticosterone concentrations were excluded as potential inventory indicators. Production and egg quality measures were disregarded because these parameters remain relatively unaffected by different treatments across many welfare studies (Bello and Korver 2019; Ramos-Vidales et al. 2019). Moreover, at an industrial scale, producers may face an economic incentive to sacrifice some individual welfare in exchange for increased performance across the entire group (Norwood and Lusk, 2011). Physiological measures were disregarded due to the infeasibility of large-scale data collection and difficulty in interpretability without in-depth knowledge of biochemical pathways. This is in line with recommendations by Mattila et al. (2012), who suggest indicators should be easily understandable by key stakeholders, such as policy makers. In all cases, inventory indicators were assessed for their fitness for use according to three key factors: if there were clear connections to any impact subcategories, and potential ease of data collection and applicability at industrial scales. In limited cases, additional inventory indicators were also proposed if they were identified as an important issue in the welfare literature, even if that indicator did not occur in >5% of studies reviewed. 143 Following identification of the inventory indicators, it was necessary to determine the bases for characterization and aggregation of inventory data to estimate impacts. During this process, characterization factors derived from characterization models are used to relate individual pieces of inventory data to the impacts to which they contribute. Characterization factors, therefore, represent the relative magnitude of the contributions of a specific inventory data to specific impacts. Further, these factors serve to convert all contributing inventory data to a single unit such that they may subsequently be aggregated to calculate the final impact result (Hauschild et al., 2013). Three different types of inventory indicators were identified for use: binary, ordinal, and numerical. Further, different impact subcategories have different numbers of inventory indicators associated with them. Different methods of characterization are therefore needed depending on the type of inventory indicator. When multiple inventory indicators contribute to the same impact subcategories, consistency in the characterization methods used was the primary goal to help facilitate aggregation of multiple inventory indicators. It was also a goal to minimize the use of normative value assumptions regarding the relative contribution of different inventory indicators to their associated impact subcategories. When only a single inventory indicator contributed to an impact subcategory, it was given a characterization factor of 1 such that that the calculated impact would be equal to the value of the inventory indicator. The exception to this was in the behavioural needs fulfilled impact category in which a reference point had to be defined against which space allowance could be compared. The chosen reference point should represent the minimum amount of floor space required by hens to perform basic behaviours, such as eating, drinking, movement, etc. Here, the reference point was defined as 294.5 square inches per bird. This value was chosen under the assumption that it represented the absolute minimum necessary 144 space for a hen to comfortably fulfill their basic behavioural needs, regardless of housing system type, according to the Canadian Code of Practice for laying hens (NFACC, 2017). This reference point was chosen because the case study used for testing the developed method was representative of the Canadian egg industry. Standards for other jurisdictions may require different minimum amounts of space, and may be used in place of this reference point to provide a degree of regionalization to the estimated impacts. To avoid making normative assumptions, binary inventory indicators, such as whether certain furnishings were provided to hens to facilitate performance of specific behaviours were assigned characterization factors of 0 to denote “absence” and 1 to denote “presence”. Similarly, three-level ordinal inventory indicators were assigned characterization factors of 0, 0.5, and 1 to represent “poor”, “average”, and “good” respectively. Use of these characterization factors for ordinal variables adequately maintains the natural order of these variables without making assumptions regarding how good or bad a particular piece of inventory data is. One exception to this is in the fulfillment of additional behavioural needs impact subcategory, where the absence of specific furnishings providing hens the ability to perform highly motivated behaviours is still given a characterization factor of 0. However, the presence of different furnishings are assigned characterization factors based on the differences in motivational strength to perform each of the associated behaviours. Motivational strength may be measured in many different ways in the animal welfare literature. These include preference tests (for example Engel et al. (2019), as well as tests in which the willingness-to-work of the animal to gain access to a resource or means to perform a behaviour are assessed. The latter of these tests are based on consumer-demand theories of economics (Kirkden et al., 2003), and aim to assess the maximum cost an animal is willing to pay to perform a behaviour or access a resource. 145 Various costs may be imposed, including the narrowing of a space that must be crossed (J. J. Cooper & Appleby, 1996), forcing animals to undergo social interactions (Freire, Appleby, & Hughes, 1997), or by loading push doors with varying amounts of weight that animals must open (Widowski & Duncan, 2000) in order to access an opportunity to fulfill a behavioural need. The motivational strength to perform a specific behaviour is ideal for use as a characterization factor, as different behaviours are associated with different levels of motivation (Weeks & Nicol, 2006). The motivational strength, therefore, may be used to represent the behavioural benefit a hen receives from performing a behaviour. To determine appropriate characterization factors for additional behavioural needs based on motivational strength, literature searches were necessary. First, all resources in which the behaviours in question were measured that were previously reviewed during the identification of inventory indicators were revisited to assess how they measured motivational strength. These resources were assessed to see if they performed tests in which hens were made to push an amount of weight to gain access to the means to perform a behaviour. This method of determining motivational strength was chosen as the most easily quantifiable, interpretable, and generalizable for use in determining characterization factors. Since none of the resources previously reviewed performed these tests, additional literature searches were necessary with an expanded temporal scope to capture resources not previously included in identification of inventory indicators. Subsequently, additional Web of Science searches were performed using the key words “laying hen”, and each behaviour identified as an inventory indicator. These searches did not have a temporal boundary. The results from these searches were subsequently searched again using the term “motivation” to find only those resources that dealt with measuring the motivational strength for the specific behaviour. The results of this second search 146 were then assessed individually to find sources that measured motivational behaviour by means of a weighted door test. In some cases, only a single resource resulted from these searches, while in other cases, multiple resources were identified. In the case of multiple resources, the sources were reviewed to determine which was the most highly generalizable. Specifically, sources only investigating motivational strength, and not the impacts of a specific treatment on it, were preferred. (i.e. just a measurement of motivation, as opposed to motivation as a function of or in response to a stimulus). Once an appropriate source was chosen, the maximum amount of weight hens were willing to push to access the means to perform the behaviour of interest was used as a characterization factor. Importantly, these characterization factors representing motivational strength were not used in calculation of results for the positive affective state impact subcategory. Rather, for this impact subcategory the standard characterization factors of 0 or 1 were applied to the binary inventory indicators indicating presence or absence of specific furnishings. This was done to avoid the need for normative assumptions regarding the affective contribution of different behaviours as, although they are linked, motivational strength and contributions to affective state differ (Barbano and Cador, 2007; Carreras et al., 2017; Fountain et al., 2020). Generally, contributions to affective state may be characterized according to three main factors: valence, arousal, and duration. Valence refers to whether the contribution is positive or negative; arousal refers to the magnitude of the contribution; and duration refers to the duration of the contribution (Mendl and Paul, 2020). Many methods have been developed for measurement of affective arousal and duration in animals. These include the taking of gross physiological measurements such as heart rate, molecular physiological measurements such as blood stress hormone concentrations, and behavioural analysis over a period of time in response to a stimulus (Graf 147 and Senn, 1999; Heinrich et al., 2009; Sutherland et al., 2002). While the arousal states associated with different affects could be useful as characterization factors, the connections between these measurements and the associated affects are not currently sufficiently robust for use as such (Mendl and Paul, 2020). Similarly, incorporating duration of affective contribution into characterization factors is difficult due to difficulties in understanding duration in a non-lab setting. Therefore, given the current laying hen research into affective state, valence is the only parameter that may be used as a characterization factor. Those inventory indicators that are thought to contribute to a positive affective state are given a characterization factor of 1, while those contributing to a negative affective state are given a characterization factor of -1. In calculation of results for each impact subcategory, simple addition was used as the basis for aggregation. Since impact subcategory results are meant to be relative indicators of risk rather than actual quantitative indications of the magnitude of different impacts, as would be the case with a type 2 LCIA method, potential issues related to units are avoided. As such, final impact subcategory results are unitless. 4.3.2 Testing of proposed method with a case study of the Canadian egg industry Following identification of characterization factors and determination of the aggregation process, the complete proposed method was tested using a case study of the Canadian egg industry to ensure it was able to identify trade-offs in impacts across housing systems, and that the results were consistent with those expected based on the animal welfare literature. This case study is based on the same dataset used previously in development of baseline eLCA models of Canadian egg production systems as presented in chapter 2. A detailed description of data collection practices, including definition of different housing systems, farmer recruitment strategies, representativeness of collected data, and steps taken in data processing is available in 148 section 2.3.2.1.1. Importantly, organic production systems were included in this analysis despite their exclusion from the analyses performed in chapters 2 and 3. Organic systems were excluded from the previous analyses due to insufficient data to update upstream LCA models of organic field crop production systems. However, since these models are not relevant for this analysis, organic systems have been included in this case study. The functional unit of this assessment was one tonne of eggs produced at farm gate, in line with the functional unit chosen in chapter 2. Use of a common functional unit better facilitates comparisons between environmental and animal welfare impacts of different housing systems across studies. The system boundary of this assessment includes only the laying phase of the hen life cycle. This differs significantly from the assessment performed in chapter 2, in which pullet, hatching, and breeder facilities were all included. Production weighted average mortality rates and space allowances per housing system were used in calculation of impacts for the mortality and behavioural needs impact category. All other impacts were calculated using a probabilistic approach under which the inventory data used represented the probability of a specific welfare contribution occurring. Impacts were subsequently multiplied by the production weighted average number of hens need to produce 1 tonne of eggs in each housing system to relate the impacts to a functional unit of 1 tonne of eggs produced. This multiplicative step also allowed for the number of hens affected in each housing system to be taken into account during impact calculation, a key consideration pointed out in previous animal welfare LCIA methods development attempts by Scherer et al. (2018). As with Tallentire et al. (2019), this study is aimed at a scientific audience of animal welfare and LCA practitioners, in hopes of catalyzing further development of animal welfare LCIA methods covering different species and life cycle stages. This study also has an additional intended audience of policy makers and farmers. Recently, Egg Farmers of Canada released an 149 updated code of practice for the care and handling of laying hens and pullets (National Farm Animal Care Council, 2017), mandating the complete transition of the Canadian egg industry away from conventional cage production in favour of alternative systems. This Code of Practice, however, provides no guidance for farmers to help them choose to which alternative system to transition. As such, in addition to the results of other LCA studies such as that described in chapter 2, the assessment performed here is also intended to aid farmers in making informed decisions regarding which housing system best corresponds to their priorities, taking into account both animal welfare outcomes and previously reported environmental outcomes. 4.4 Results and discussion 4.4.1 Identification of type 1 animal welfare LCIA method components The four characterizations of animal welfare proposed by Fraser (1997), the Welfare Quality project (2009a), the Farm Animal Welfare Council (2009), and Mellor (2016) are most commonly invoked in the welfare literature (Table 4-1). Others have been reviewed by Fisher (2009). Many different definitions of animal welfare exit in the literature which are differently suited for their use in an animal welfare LCIA method. Both the Five Freedoms and the Four Welfare Principles definitions embody explicit normative judgements – that which is “good” in the case of the Four Welfare Principles, and desirable “freedoms” in the Five Freedoms definition. The Five Domains definition does not explicitly label domains as good or bad but does specify what constitutes positive or negative aspects for each. In contrast, the Fraser et al. (1997) framework, which simply states that welfare is comprised of three conceptions (biological function, naturalness/the ability to perform natural behaviours, and affective state), is the least dependent on explicit normative judgements. Some of the other definitions reviewed by Fisher (2009) match this framework in terms of dependence on normative judgements, such as that 150 based on allostasis as proposed by Korte et al. (2007), but do not include a behavioural component, which is an important component of welfare (Yeates, 2018). While the Three Circles framework proposed by Fraser et al. (1997) is the least dependent on explicit normative choices, it must be noted that this definition references a behavioural component of welfare in terms of “natural” behaviours, as opposed to referencing behaviour agnostically, or in terms of appropriateness or welfare promotion. The classification of behaviours as “natural” is a controversial topic in the animal welfare literature, due to difficulties in defining what is meant by “natural” (Špinka, 2006; Yeates, 2018). Rather, it is now more common to address behaviour in terms of behavioural needs and priorities of the animal (see for example Weeks and Nicol 2006). In spite of the use of “naturalness” to describe the behavioural component of welfare, the Three Circles Framework as proposed by Fraser et al. (1997) is the best suited animal welfare characterization for LCIA methods development as it is the lest dependent on normative value judgements, and was chosen as the definition to use during methods development. It is important to note that normative value judgments cannot be completely avoided, since a goal of this method is to incorporate positive welfare impacts. Subjective judgments regarding that which is positive or negative are therefore unavoidable; however, the simplicity of the chosen definition allows those choices to be made in line with the welfare literature, rather than having those decisions already pre-made through the choice of welfare definition, helping to minimize total necessary value choices in line with ISO guidelines (ISO, 2006a). This also allows for changes in classification over time regarding that which is positive and negative, allowing the proposed method to be further updated as additional knowledge is generated within the animal welfare science field. 151 Table 4-1. The four most commonly used definitions of animal welfare in the literature. Source Definition Fraser et al. (1997) Three-circles: biological function, naturalness, and affective state Welfare Quality (2009) Four welfare principles: good feeding, good housing, good health, appropriate behaviour Farm Animal Welfare Council (2009) Five freedoms: freedom from hunger and thirst, freedom from pain, injury, and disease, freedom from distress, freedom from discomfort, and freedom to express behaviours that promote well-being Mellor (2016) Five domains: nutrition, environment, health, behaviour, and affective state (each of which have positive and negative aspects) 4.4.1.1 Animal welfare definitions, areas of protection, and stakeholder categories Many different AoPs have been defined for use in both eLCA and sLCA, as reviewed by Schaubroeck and Rugani (2017), the ILCD Handbook for LCIA Methodologies (2010), and the Guidelines for sLCA of Products (UNEP, 2020). Animal welfare impacts are not explicitly covered by any current AoP; however, the human health and well-being AoP proposed by Dreyer et al. (2006) applies many of the same principles of Fraser’s (1997) definition of animal welfare to human welfare. This AoP is now recognized as the primary AoP for sLCA (UNEP, 2020). Although specific to human welfare, the human health and well-being AoP hence 152 provides a suitable model for defining a new animal welfare AoP. It is not uncommon for a new AoP to be proposed where necessary (see for example, Dreyer et al. 2006 and Ridoutt et al. 2016). There is also significant value in doing so given the increasing societal concern with animal welfare issues worldwide (Ochs et al., 2018; Vanhonacker et al., 2012). According to Schaubroeck and Rugani (2017), three challenges must be overcome when defining a new AoP: 1) The identification and framing of which areas should be the primary target of sustainability efforts upon which impacts should be assessed 2) The ability to account for interconnectedness among areas of protection 3) The ability to assess both damages and benefits to the area of protection These problems appear to be surmountable. First, it has already been demonstrated that animal welfare is an area that should be targeted in sustainability efforts, using different philosophical and ethical approaches (Bourdeau, 2004; Buller et al., 2018; Johnson, 2019). Second, defining animal welfare as a novel area of protection lays the foundation for future research into its relationships with other AoPs. Links between animal welfare and the natural environment, for example, may be further explored through elucidation of trade-offs existing between environmental and animal welfare outcomes (see for example Weeks et al. 2016; Pelletier 2017). Finally, the need to assess both damages and benefits is already recognized in the welfare literature, and a variety of strategies are available (Mellor, 2016). In short, definition of a new animal welfare AoP is achievable in line with current practices within the LCA field, and is necessary for effective integration of animal welfare assessment into the LCA framework. The UNEP sLCA guidelines (2020) identify five primary stakeholder categories: workers, local community, society, consumers, and other value chain actors. Stakeholder 153 categories are expected to encompass groupings of all those impacted by the system being studied. Use of these categories to organize impacts is rooted in the concept of corporate social responsibility (CSR) and stakeholder theory, in which there are two basic tenets: managers must pay attention to the opinions of stakeholders, and that there are stakeholders that exist beyond just shareholders (Parmar et al., 2010; UNEP, 2020). It has recently been argued that the agricultural sector must do a better job employing the concept of CSR and accounting for their social impacts (Luhmann and Theuvsen, 2017). Following this, and in accordance with stakeholder theory, the question then becomes whether or not animals are stakeholders in agricultural systems. According to Freeman’s classic definition, a stakeholder is “any group or individual who can affect or is affected by the achievements of the organization’s objective” (Freeman 2010, pg. 25). By this definition, animals are stakeholders in their production systems as they are primarily responsible for realizing production objectives of the system. More formalized stakeholder identification may take place using tools for stakeholder analysis (Reed et al., 2009). In the absence of such analyses it is possible that animals may be considered their own stakeholder category in animal production systems, in line with the accepted definition of a stakeholder. Such a definition of a new stakeholder category is in line with the UNEP sLCA guidelines, which note that additional stakeholders may be identified for use in studies (UNEP, 2020). If, however, the goal of the existing stakeholder categories is to encompass all groups impacted upon by the system of interest without requiring expansion from their current form, Tallentire et al. (2019) make a compelling argument as to how animal welfare concerns may be adopted into any of the existing stakeholder categories. In their study, for example, Tallentire et al. (2019) use worker hours as an activity variable, defined as the collective hours of life 154 associated with production of one kilogram of broiler chicken meat. This use of worker hours suggests animals may be considered a subgrouping of the workers stakeholder group. Likewise, they also include explanations as to how animals may be considered subgroups of any of the other stakeholder categories (See Tallentire et al. 2019, Box 1). The UNEP sLCA guidelines (2020) further consider ethical treatment of animals as an impact subcategory in the society stakeholder group, suggesting inclusion of animals as members of society. On this basis, it would seem reasonable to include animals as a new stakeholder group while remaining consistent with the existing guidelines for sLCA. For simplicity, the animals are here considered as their own stakeholder group. 4.4.1.2 Impact categories and subcategories Using the chosen definition of welfare (Fraser et al., 1997), one impact category is defined representing each of the three tenets of animal welfare: biological function, natural behaviour, and affective state. These impact categories adequately represent the broad array of contributing factors to animal welfare (Mellor, 2016) without potentially restricting factors from contributing to more than one impact category - a hallmark of the LCA framework. Definition of these impact categories also allows for clear delineation between different tenets of animal welfare, thereby facilitating assessment of trade-offs between each and between different hen housing systems. Under these impact categories, for example, the health benefits of conventional cage systems (Lay Jr et al., 2011; Rodenburg et al., 2008; Sherwin et al., 2010) will be presented alongside the behavioural drawbacks and impacts on affective state (Lay Jr et al., 2011), hence allowing for a more complete picture of welfare status and easier comparisons with the impacts of other systems. 155 Each impact category is further divided into impact subcategories, in line with the structure of a type 1 LCIA method (UNEP, 2020). These subcategories must address both animal sentience and needs (Broom, 2011), and must be representative of the complete spectrum of possible welfare states under each impact category so as to be in line with current paradigms in animal welfare science. A number of different resources were consulted in definition of possible welfare states under each impact category, including work on animal welfare characterizations (Farm Animal Welfare Council, 2009; Mellor, 2016), the integration of animal welfare into the LCA framework (Scherer et al., 2018; Tallentire et al., 2019), and on-farm welfare assessment tools (LayWel, 2006; Welfare Quality, 2009a). Using guidance from these documents, the biological function and affective state impact categories were broken down into two impact subcategories, while natural behaviours was broken down into three. Biological function may be subdivided into three states intended to encompass all possibilities: alive and in good biological health, alive with compromised biological health due to injury or disease, and death. Since alive and in good health represents the inverse of the other two possible states under this impact category, it is excluded as an impact subcategory to avoid double counting, in line with ISO14044 (ISO, 2006b) and the ILCD Handbook (ILCD, 2010), and to simplify the developed method to the greatest degree. The two impact subcategories are therefore morbidity/injury, and mortality. Affective state is similarly divided into two impact subcategories: positive and negative affective state. An animal is considered to have good welfare when they generally feel positive affects, such as happiness and comfort, and poor welfare when they generally feel negative affects, such as fear, pain, sadness, frustration, etc. (Ahloy-Dallaire et al., 2018). 156 Natural behaviours is divided into three impact subcategories. Generally, the consulted works investigated behaviours that may be categorized as injurious, such as aggressive pecking directed towards different parts of the body, and behaviours that may be categorized as normal or non-injurious. Significant work has gone into identifying the behavioural needs, priorities, and preferences of laying hens (see, for example, Weeks and Nicol 2006), which may serve as a basis for an impact subcategory assessing normal or rewarding behaviours. To further increase the ability of the natural behaviour impact category to differentiate between housing systems, it is beneficial to define a third impact subcategory focused on meeting the basal behavioural needs of laying hens. Including this third subcategory is supported by justifications made for legislation banning conventional cages, which regularly cite insufficient space allowance for birds to access the opportunities to perform basic behaviours such as moving, turning around, etc. (see for example European Union 1999). This increased differentiation may be useful, for example, in comparisons between enriched cage and cage-free systems. Though these systems may provide opportunities for hens to perch at a similar rate, large differences in space allowance between the two (van Staaveren et al., 2018b) may impact the ability of the systems to support more basic behaviours, which would be highlighted by the fulfillment of a basal behavioural needs impact subcategory. 4.4.1.3 Inventory indicators and data requirements A total of 367 resources were reviewed to support identification of inventory indicators for each impact subcategory. A complete list of the reviewed sources is available in appendix C, section C2. 1107 unique welfare measures were documented across these sources. Many more measures were attributed to biological function (929) than either natural behaviours (182) or affective state (447). The average incidence plus/minus standard deviation (i.e. number of 157 resources in which the measurement was taken) of any single indicator was 0.93 ± 2.62%, 1.17 ± 1.94%, and 1.15 ± 2.57% for biological function, natural behaviour, and affective state, respectively. Significant overlap was observed between affective state indicators and indicators attributed to the other two impact categories. Specifically, of the 447 indicators attributable to affective state, only 118 (~26%) are unique to affective state. Overlap between indicators of biological function and affective state was expected as measures representing damage to bird health may also be associated with increases in potential stress, pain, and fear when receiving the damage, or relief from anxious or depressive affective states when damage is being treated (Daigle et al., 2015; El-Lethey et al., 2000; Nasr et al., 2013). Similarly, significant overlap exists between behavioural and affective state indicators due to the interconnectedness between behaviour and affective state. Play behaviour, for example, has been proposed as an indicator of positive affective state (Ahloy-Dallaire et al., 2018; Held and Špinka, 2011), and the ability to perform behavioural needs is associated with positive affective state and/or a reduction in negative affective state (Ross et al., 2019; Wichman et al., 2012). After removal of all indicators represented in <5% of all sources, as well as production, egg quality, and physiological indicators, a total of 12 candidate inventory indicators remained for biological function, 11 for natural behaviours, and 21 for affective state (Table 4-2). All candidate indicators across the impact categories were animal based. 158 Table 4-2. Candidate inventory indicators for each impact category that were utilized in a minimum 5% of all sources reviewed, presented as a percentage of total papers reviewed in which the measure was collected. Biological function Natural behaviour Affective state Candidate inventory indicator % Incidence Candidate inventory indicator % Incidence Candidate inventory indicator % Incidence Feed intake 27.25 Behaviour - Eating 10.08 Feed intake 27.25 Bird weight 25.07 Behaviour - Walking 10.08 Keel damage/deformation 16.89 Mortality rate 21.25 Behaviour - Aggressive (Unspecified) 9.26 Feather Damage - Back 15.80 Keel damage/deformation 16.89 Behaviour - Preening 8.99 Feather damage - Breast 11.72 Feather Damage - Back 15.80 Behaviour - Standing 8.45 Feather Damage - Tail 11.44 159 Biological function Natural behaviour Affective state Feed conversion ratio 13.08 Behaviour - Drinking 7.90 Behaviour - Eating 10.08 Feather damage - Breast 11.72 Behaviour - dustbathing 7.63 Behaviour - Walking 10.08 Feather Damage - Tail 11.44 Behaviour - Sitting 6.81 Feather Damage - General 9.54 Feather Damage - General 9.54 Behaviour – Exploratory pecking 5.99 Feather Damage - Wings 9.26 Feather Damage - Wings 9.26 Behaviour - Perching 5.72 Behaviour - Aggressive (Unspecified) 9.26 Feather Damage - Vent/Cloaca 8.99 Behaviour - Nesting 5.18 Feather Damage - Vent/Cloaca 8.99 Feather Damage – Neck (general) 7.63 Behaviour - Preening 8.99 Behaviour - Standing 8.45 Behaviour - Drinking 7.90 160 Biological function Natural behaviour Affective state Feather Damage – Neck (general) 7.63 Behaviour - dustbathing 7.63 Behaviour - Sitting 6.81 Behaviour – Exploratory pecking 5.99 Behaviour - Perching 5.72 Behaviour - Nesting 5.18 4.4.1.3.1 Biological function Comparisons between the defined impact subcategories for biological function and candidate inventory indicators yields some clear choices in defining inventory indicators. Notably, mortality rate is one of the most collected welfare measures. This serves as an ideal inventory indicator for the mortality subcategory as it is a direct measurement of the impact subcategory, and may be easily collected at industry-scale. Indices for keel bone damage, bird weight, and feather damage (both generally and to specific areas of the body) dominate the remainder of the candidate inventory indicators for biological function. Taking into account data availability, keel bone damage is less suitable for 161 use an inventory indicator for the morbidity/injury subcategory due to measurement difficulties, as accurate assessment requires specialized training (Petrik et al., 2013) and would require a sufficiently large sample to ensure representativeness. General feather condition, in contrast, represents a good inventory indicator for the morbidity/injury subcategory. This may be easily approximated by farm managers throughout entire flocks (as in Decina et al. 2019a, b, for example), enabling collection of necessary data at an industry-wide scale. Feather condition plays an important role in thermoregulation for laying hens, with replacement of damaged feathers representing a significant metabolic stressor for the animal (Lindström et al., 1993; Rubinstein and Lightfoot, 2012; van Zeeland and Schoemaker, 2014). Feather condition may deteriorate as a result of feather pecking from other birds, a common problem in intensive production systems (Hartcher et al., 2016). While not reported in >5% of studies reviewed, foot condition is also an important welfare consideration that could be collected at an industry-wide scale (Rørvang et al. 2019; Ali et al. 2020). Foot injuries and conditions such as foot pad dermatitis are likely to be painful to the birds and detrimental to their ability to move. These conditions may be caused by aggressive behaviours from other birds and/or other environmental characteristics, including flooring materials in caged systems, and litter quality in non-cage systems (Keutgen et al., 1999; Krause et al., 2011; Rørvang et al., 2019). For these reasons, foot condition is also included as an inventory indicator for the morbidity/injury impact subcategory. While feather and foot condition represent two significant contributors to injury in laying hen systems, they do not adequately cover all possible welfare contributors to the morbidity/injury subcategory. Though not one of the most commonly used welfare measures of biological function, disease incidence is also an important contributor to laying hen morbidity. 162 Laying hens may contract a number of different diseases, including avian influenza, bronchitis, pneumonia, as well as infection by Salmonella, E. coli, and other internal and external parasites (Hong et al., 2017; Welfare Quality, 2009a). Many different diseases and parasites were represented among the unique measures of animal welfare throughout the literature including, but not limited, to Salmonella (Kouam et al., 2019), E. coli (Zhao et al., 2013), mites (Louton et al., 2017), and helminths (Maurer et al., 2013). While no measurement of a single disease or parasite was collected commonly enough to exceed the 5% cut off applied, given the important contribution that lack of diseases and parasites make to overall animal health, it is clear that measurement of this factor must be included. As such, general disease incidence may also serve as a reasonable inventory indicator for the morbidity/injury subcategory. Taken together, therefore, the morbidity/injury impact subcategory may be represented by the three inventory indicators feather condition, foot condition, and general disease incidence. In each case, the associated inventory data may be collected easily through approximation by farmers, allowing for data collection at an industry-wide scale. Of those indicators of biological function that occurred in 5% of studies or more, there are also some that are unfit to serve as inventory indicators for either impact subcategory. None of feed intake, body weight, or feed conversion ratio are fit for use as inventory indicators. Links between feed intake, feed conversion ratio, and biological health would not be clear enough to key stakeholders (i.e. policy makers, farmers, etc.) to warrant their inclusion as inventory indicators. While the link between body weight and biological health would be more clear to stakeholders, the use of body weight as an inventory indicator is limited by data availability due to difficulties in achieving a representative sample throughout flocks. 163 Together, all inventory indicators proposed for the impact subcategories mortality, and morbidity/injury are animal based, keeping in line with the preference for animal-based measurements within the welfare literature (Munoz et al., 2018; Polgár et al., 2019; Welfare Quality, 2009a). Their inventory data requirements are sufficiently simple to satisfy at an industry-scale, and are clear, and easily understandable to policy makers in line with the overall goals of inventory indicator definition. 4.4.1.3.2 Natural behaviour All candidate inventory indicators identified for the natural behaviour impact category were animal-based measures of specific behaviours. Laying hens possess a considerable behavioural repertoire, as evidenced by the large number of behaviours reported in the literature (see for example Nicol et al. 2009, 2011; Rentsch et al. 2019). It has been noted, however, that it is neither practical nor necessary for hens to be able to perform all behaviours in their repertoire (Weeks and Nicol, 2006). Rather, hens have different motivational strengths for different behaviours, which may be quantified using different methods (Duncan and Kite, 1987; Kirkden et al., 2003; Manning and Dawkins, 2012). For practicality, therefore, inventory indicators for the additional behavioural needs fulfillment subcategory should only represent those behaviours which hens are most strongly motivated to perform, and, inversely, will experience the greatest degree of frustration if unable to perform (Yeates, 2018). It is important, however, to recognize the differences in how behavioural data may be collected in a small-scale lab setting compared to how it may be collected in a farm- and industry-scale setting, such as is needed for LCA. Many behavioural studies performed in a lab setting make use of dedicated observers and observational periods, or video recording during which all behaviours a hen performs are identified and codified. While such observations may 164 become feasible in the future, particularly with the advent of precision animal agriculture and AI technologies such as those described by Nakarmi et al. (2014), significant obstacles remain with regards to their industry-scale application (Fernandes et al., 2020). As such, taking into account this data availability concern, inventory indicators for the natural behaviour subcategory must instead focus on whether or not hens have the opportunity to perform highly motivated behaviours, rather than on performance of the behaviours themselves. Doing so, however, requires the unavoidable use of resource- or management-based proxy measurements for the candidate inventory indicators, rather than animal-based measures. Of the behaviours most commonly assessed in the welfare literature, eating, drinking, walking, standing, sitting, and preening are all basic behaviours which require a minimum amount of space to perform (Engel et al., 2018; Mench and Blatchford, 2014). Facilitation of these behaviours has been regularly cited as a motivation for legislation banning conventional cage systems (Matheny and Leahy, 2007). As such, although not represented in more than 5% of reviewed sources, space allowance is the most suitable inventory indicator for the impact subcategory investigating fulfillment of behavioural needs. Further, the accompanying inventory data, space allowance per bird, is easily collected on a large scale (as in van Staaveren et al. 2018, for example), and is easily understandable by policy makers. Dustbathing, perching, and nesting are also three of the most commonly collected direct behavioural measures in the welfare literature for which data regarding whether the hens can perform the behaviour or not is easily collected. Hens have been found to be strongly motivated to perform these behaviours (Campbell et al., 2016; Hester, 2014; Weeks and Nicol, 2006). In line with welfare literature on motivational strength of hens to perform different behaviours, perching, nesting, and dustbathing behaviours are hence proposed as inventory indicators for the 165 additional behavioural needs fulfilled subcategory, with associated inventory data for whether or not hens are provided with the opportunities to perform these behaviours. In addition, exploratory pecking is also a commonly collected behavioural measure in the welfare literature. This behaviour is similar to foraging, another behaviour hens are strongly motivated to perform (de Jong et al., 2007). As such, the ability of hens to perform foraging behaviour is also included here. Performance of injurious behaviours (i.e. those behaviours that may be indicative of compromised welfare of the individual performing the behaviour, or detrimental to other members of the flock who are the recipients) is the third impact subcategory under the natural behaviours impact category. General aggressive behaviour is one of the most commonly collected measures of animal welfare and, along with feather pecking, among the most common detrimental behaviours performed by laying hens (Daigle et al., 2015; Giersberg et al., 2019). Additional injurious behaviours are also possible, including pecking of other body parts such as the vent and toes, which may be measured directly or via a proxy. Definition of inventory indicators for injurious behaviours is more challenging than for the other two behaviour impact subcategories for two reasons. First, there are not specific furnishings, enrichments, etc. that either enable, or completely halt performance of these behaviours. Second, behavioural assessment for specific vice behaviours is impractical to carry out on a large scale. Decina et al. (2019b) previously investigated associations between husbandry and management practices and feather damage, providing a potentially useful guide for definition of injurious behaviour inventory indicators. However, injurious behaviours are not limited to just feather pecking, so use of these management factors as inventory indicators and associated inventory data would not adequately cover all expected contributors to the vice behaviours 166 impact subcategory. As such, it is proposed that general incidence of vice behaviours, including feather, toe, and vent pecking, etc. be used as a inventory indicator for the vice behaviours subcategory. For this inventory indicator, the associated inventory data would be the percentage of flock surveyed in which injurious behaviours of any kind are experienced. 4.4.1.3.3 Affective state Notably, each indicator of affective state occurring in >5% of sources reviewed was also identified as a candidate indicator for the biological function impact category. This is because each of these indicators is also associated with specific affects, including pain, anxiety, satiety, and thermal comfort. Mellor et al. (2020) provide a good overview of the many affects different animal welfare states may be associated with. Though these measures were the most common, tests have also been developed to specifically assess affective state. These include human approach tests, novel object tests (used in the Welfare Quality Assessment Protocol for Poultry (2009a)), and tests of cognitive bias (applied exclusively in lab settings) among others (Krugmann et al., 2019; Larsen et al., 2018; Ross et al., 2019). While some of these tests are included in the Welfare Quality assessment protocol, specific tests for affective state are largely confined to lab settings only, and have faced various criticisms for potential observer bias, providing inconsistent results depending on experimental context, and for impracticality (Bethell, 2016; Campbell et al., 2019; Tuyttens et al., 2014). In the absence of dependable and practical tests for affective state able to be performed at a large enough scale and provide reliable enough results to fulfill the data needs of LCA, it becomes necessary to use measures associated with specific positive or negative states as subcategory indicators. In this case, therefore, data availability becomes a key consideration. 167 Further, given the close associations between fulfillment of behavioural needs with positive affective state, it is intuitive to use the same inventory indicators across these subcategories alongside other indicators indicative of other positive affective states. Use of inventory indicators as proxy measures for affective state, however, does come with the trade-off of lower interpretability in terms of welfare. Feed intake is the most commonly collected measure associated with a positive affective state, satiety (Mellor and Beausoleil, 2015). While this measure is easy to collect given the increased mechanization of the egg industry, which allows farmers to easily monitor and collect farm-level information in real time (Berckmans and Guarino, 2017; Morota et al., 2018), interpretation in the context of animal welfare is difficult as previously described, making it not suitable for inclusion as an inventory indicator for the affective state impact category. The remainder of the candidate indicators identified for affective state are associated with the affective states of pain and fear, and pleasure (Keutgen et al. 1999; Krause et al. 2011)). Previous work has investigated the associations between bone damage (Nasr et al., 2013; Rokavec and Semrov, 2020), and feather pecking behaviours along with corresponding damage (Daigle et al., 2015; Pichová et al., 2021) and affective state. The unsuitability of keel bone damage for use as an inventory indicator has been previously discussed in the biological function section. Inclusion of feather damage as a contributor to negative affective state is possible, however, using many of the same invnetory indicators previously included in the injurious behaviours impact subcategory. These inventory indicators will be used for negative affective state as well, therefore, to decrease total inventory data requirements. While the injurious behaviours subcategory uses performance of injurious behaviours in general as an inventory indicator, the proposed inventory indicators for negative affective state make reference to 168 specific behaviours more closely related to the specific negative affects of pain and fear. These include the presence of excessive nervousness, and death or injury due to feather pecking, vent pecking, other bird to bird aggression, or the presence of health issues requiring veterinary care within the flock. By using many of the same inventory indicators already proposed for other impact subcategories, the necessary data requirements are significantly reduced, while also more accurately reflecting the capacity of inventory indicators to make contributions to multiple impacts, as in traditional LCA. Similarly, a number of behavioural indicators were found which are likely associated with pleasurable affective states as defined by Mellor (2016). While no direct research exists investigating the impact of performing specific behaviours on the affective state of laying hens, there is evidence indicating that the motivation to perform behaviours is linked to the affective outcome of performance (Barbano and Cador, 2007; Carreras et al., 2017; Fountain et al., 2020). As such, it is reasonable to assume that behaviours hens are highly motivated to perform also result in a positive affective experience for the hen when performing that behaviour. However, it is also possible that some behaviours may be associated with increased pleasure, while others are associated with decreased frustration (Fraser and Duncan, 1998), which could change the characterization of the proposed inventory data across the proposed affective state inventory indicators. For this analysis, the former is assumed. Based on this assumption, the positive affective state impact subcategory will use the same inventory indicators as the additional behavioural priorities fulfilled impact subcategory, since this subcategory represents behaviours for which hens have a high degree of motivation. As with the negative affective state impact subcategory, this will help reduce total inventory data requirements, and allow inventory data to make contributions to multiple impacts. 169 4.4.1.3.4 Summary of the proposed animal welfare LCIA method A complete breakdown of all components for the proposed type 1 animal welfare LCIA method is presented in Table 4-3. Table 4-3. Breakdown of the proposed requisite components of an animal welfare LCIA method for laying hens. Columns have been labelled as referenced in text. In inventory data requirements, Y/N refers to data collection by means of asking farmers a question to which they may reply yes or no. A B C D E F Area of Protection Stakeholder Category Impact Categories Impact Subcategories Inventory Indicators Inventory Data Requirements Animal Welfare Animals Biological Function Mortality Mortality rate Mortality rate (% of hens placed) Morbidity/injury Health issues requiring veterinary care Health issues requiring veterinary care? Y/N Feather condition at end of lay cycle Score: Good, Average, Poor 170 A B C D E F Area of Protection Stakeholder Category Impact Categories Impact Subcategories Inventory Indicators Inventory Data Requirements Foot condition at end of lay cycle Score: Good, Average, Poor Natural Behaviours Behavioural needs fulfilled Space allowance Space allowance per bird Additional behavioural priorities fulfilled Perches provided Perches provided? Y/N Nests provided Nests provided? Y/N Foraging supported Behaviours supported? Y/N Dustbathing supported Behaviours supported? Y/N 171 A B C D E F Area of Protection Stakeholder Category Impact Categories Impact Subcategories Inventory Indicators Inventory Data Requirements Injurious behaviours Injurious behaviour presence Injurious behavioural problems within flock? Y/N Affective State Positive affective state Perches provided Perches provided? Y/N Nests provided Nests provided? Y/N Foraging supported Behaviours supported? Y/N Dustbathing supported Behaviours supported? Y/N 172 A B C D E F Area of Protection Stakeholder Category Impact Categories Impact Subcategories Inventory Indicators Inventory Data Requirements Negative affective state Presence of excessive nervousness Is there excessive nervousness within the flock? Y/N Presence of death or injury due to feather pecking Has there been death or injury due to feather pecking within the flock? Y/N Presence of death or injury due to vent pecking Has there been death or injury due to vent pecking 173 A B C D E F Area of Protection Stakeholder Category Impact Categories Impact Subcategories Inventory Indicators Inventory Data Requirements within the flock? Y/N Presence of death or injury due to other bird to bird aggression Has there been death or injury due to other bird to bird aggression within the flock? Y/N Presence of health issues requiring veterinary care Health issues requiring veterinary care? Y/N 4.4.1.4 Characterization factors For the mortality impact subcategory, only one inventory indicator was considered. As such, this was given a characterization factor of 1. The morbidity/injury impact subcategory is composed of 3 inventory indicators, one of which is a binary variable, while the other two are 3 174 level ordinal variables. The binary variable indicating if there have been any health problems requiring veterinary care is given a characterization factor of 1. Since feather and foot condition each have three possible levels, it is assumed that “good” is the baseline response and any responses other than good represent a negative welfare impact. In order to reflect the ordinal nature of the remaining two options, “average” is given a characterization factor of 0.5, while “poor” is given a characterization factor of 1 and “good” a characterization factor of 0 in both cases. For both the basal behavioural needs and injurious behaviours subcategories, only a single inventory indicator is used so each is given a characterization factor of 1. More specific characterization factors were identified for the additional behavioural needs subcategories, representing the motivational strength of a hen to perform each behaviour. Specifically, the characterization factor associated with perching behaviour is 8.625, taken from Olsson and Keeling (2002), and Olsson et al. (2002). Nesting behaviours were associated with a characterization factor of 18, taken from Kruschwitz et al. (2008). Dustbathing was given a characterization factor of 13.9, taken from Wichman et al. (2008). Only a single source was found that investigated motivational strength of foraging behaviours in laying hens (de Jong et al., 2007). This source, however, presented motivational strength in terms of newtons of force exerted by the bird, as opposed to the amount of weight that the bird was willing to push. To maintain consistency with the other characterization factors, it was therefore necessary to convert newtons of force into amounts of weight. This factor was calculated by comparing results presented by de Jong et al. (2007) and Wichman et al. (2008), both of which presented measures of motivational strength for some of the same behaviours with overlapping units. On this basis, a characterization factor of 14.66 was derived for foraging behaviour. 175 Both the positive and negative affective state subcategories are composed of distinct, binary inventory indicators. In the absence of information around the arousal level for each inventory indicator, all are given a characterization factor of 1 for positive affective state, and -1 for negative affective state, indicative of their respective valences. A complete table of characterization factors for all inventory indicators is presented in appendix C, Table C 1. 4.4.2 Testing the proposed method with a case study of the Canadian egg industry Table 4-4 presents the inventory data used for animal welfare impact calculations, differentiated by housing type. It is important to note that the probability that foraging behaviours are supported is calculated independently of the probability that dustbathing behaviours are supported. Support of foraging behaviours in non-cage systems was demonstrated through providing hens with outdoor access in the form of a range, a covered veranda or winter garden, or provisioning of specific additional enrichments such as peck stones or hanging straw bales for support of foraging behaviours. Support of foraging behaviours in enriched systems was demonstrated through the regular dropping of feed or other substrate on scratch mats. Supplementary equations 1-7, available in appendix C, present sample calculations of each subcategory impact for enriched housing systems. Importantly, the exact numbers calculated for each impact are themselves meaningless; rather, the calculated impacts for each housing system should be interpreted relative to those of all other housing systems. Table 4-4. LCI data used to calculate animal welfare impacts per tonne of eggs produced in each housing system in Canada. With the exception of the number of pullets required to produce one tonne of eggs, inventory data are presented as the probability associated with each measure as calculated from primary 176 survey data collected from Canadian egg farmers representing management practices and animal welfare outcomes on farms in 2019. Conventional Enriched Single-tier non-cage Multi-tier non-cage Free range Organic Sample size 79 43 13 22 7 9 Number of pullets to produce one tonne of eggs 50.74 51.37 52.15 51.97 54.72 54.34 P(mortality) 0.0285 0.0235 0.048 0.0348 0.0868 0.0342 P(disease) 0.015385 0.025 0.0625 0.142857 0.1667 0.333333 P(poor feather condition) 0.037736 0.085714 0.1875 0.105263 0.1667 0.2222 P(poor foot condition) 0 0 0 0 0 0 P(average feather condition) 0.3 0.31 0.38 0.37 0.33 0.33 P(average foot condition) 0.09 0.07 0.27 0 0.25 0 177 Conventional Enriched Single-tier non-cage Multi-tier non-cage Free range Organic Space allowance (in2/bird) 68.98 116.9 158.64 160.05 528.39 724.9 P(perching supported) 0 0.98 0.79 0.96 0.75 1 P(nesting supported) 0 1 1 1 1 1 P(foraging supported) 0 0.88 0.79 0.54 1 1 P(dustbathing supported) 0 0.19 1 1 1 1 P(injurious behaviours) 0.09 0.15 0.5 0.33 0.5 0.22 P(excessive nervousness) 0 0.08 0 0 0 0 P(feather pecking issues) 0.03 0.15 0.2 0.1 0.5 0.22 P(vent pecking issues) 0.05 0.03 0.3 0.1 0.33 0.11 178 Conventional Enriched Single-tier non-cage Multi-tier non-cage Free range Organic P(other aggressive behaviours) 0.03 0.03 0.2 0 0.33 0 Under the mortality impact subcategory, a larger number represents a worse animal welfare outcome as it indicates a greater risk of mortality within the system. Mortality risk is lower in the two caged systems compared to non-cage systems (Figure 4-1). The lowest overall risk of mortality was seen in enriched cage systems, while the highest was seen in free range systems. Surprisingly, organic production systems had the second lowest mortality risk of the non-cage systems. This is in direct contrast to LCA results presented by Pelletier (2017), in which organic production systems had the highest mortality rate, indicating it may be a result of sampling bias. It is possible, however, that this is not the case, as many factors may impact mortality rates, leading to large variability in reported mortality rates in organic systems throughout the literature (Leenstra et al., 2012; Steenfeldt and Nielsen, 2015; von Borell and Sørensen, 2004). 179 Figure 4-1. Relative mortality risk across different housing systems associated with the production of 1 tonne of eggs. Values associated with each bar are dimensionless, with higher values representing worse welfare outcomes. In contrast to mortality, organic production systems exhibited the highest overall risk associated with morbidity/injury (Figure 4-2). As with mortality, a larger number represents a higher risk and a worse impact. As in the mortality impact subcategory, caged systems had the lowest overall risk of morbidity/injury compared to non-cage systems. Unlike the mortality impact subcategory, however, the morbidity/injury risk in enriched cage systems was higher than in conventional cage systems. While differences exist among the non-cage systems, they are noticeably smaller than are the differences in mortality risk among non-cage systems. Considerably larger contributions to morbidity/injury risk are made by the incidence of health issues requiring veterinary care in non-cage systems. Relatively larger contributions are also made by poor feather condition in non-cage systems compared to caged systems. Notably, no 00.511.522.533.544.55Conventional Enriched Single tier Multi tier Free range OrganicRelative risk of mortality per tonne of eggs 180 contribution is made to the overall risk of morbidity/injury in organic systems by either average or poor foot condition. Overall, caged systems performed better than non-cage systems in the biological function impact category. These results are congruent with what would be expected based on the animal welfare literature (Lay Jr et al., 2011; Rodenburg et al., 2008). Figure 4-2. Relative risk of morbidity/injury associated with the production of 1 tonne of eggs across different housing systems. Values associated with each bar are dimensionless, with higher values representing worse welfare outcomes. No system except organic performed well in the basal behavioural needs impact category (Figure 4-3). In this impact subcategory a higher number is indicative of better performance. Of the production systems, only organic and free range systems received positive values, meaning they are the only systems under which current space allowances exceed the final maximum space allowance listed in the updated Code (National Farm Animal Care Council, 2017). This is unsurprising for organic systems, which received significantly better scores than free range systems, as they are required to provide hens with a greater amount of space, in line with 051015202530354045Relative risk of morbidity/injury per tonne of eggsAverage foot conditionAverage feather conditionPoor foot conditionPoor feather conditionHealth issues 181 Canadian organic certification standards (CGSB, 2015). It must be noted, however, that for single-tier non-cage systems, the final space allowance required for hens is greater than the indoor space allowance defined by organic standards (i.e., 294.5 square inches per bird compared to 258.2 square inches per bird), indicating that the results associated with this impact category may change as more producers come in line with the final non-organic housing system requirements. Notably, the negative impact associated with single-tier non-cage housing was larger than that of either conventional or enriched housing, despite a much larger space allowance. This can be attributed to the larger number of hens required to produce 1 tonne of eggs in single-tier systems compared to conventional or enriched. It must, however, be noted that the results calculated for this impact subcategory are based on the assumption of basal needs fulfillment scaling linearly with total space allowance. In reality, this may not be the case. The use of advanced machine learning techniques such as computer vision (Nakarmi et al., 2014), and radio-frequency identification technology (Ruhnke et al., 2019) continues to provide a more in depth look into how hens move in three-dimensional space. Such research could be vital in further defining the three dimensional spatial needs of laying hens, allowing for further refinement of this impact subcategory. 182 Figure 4-3. Relative scores for fulfillment of basal behavioural needs across housing systems associated with production of 1 tonne of eggs. Values associated with each bar are dimensionless, with higher values representing better welfare outcomes. All production systems exhibited positive impacts in the additional behavioural needs impact subcategory, except for conventional housing which received a score of 0 as hens in these systems are not provided with any furnishings to fulfill additional behavioural needs (Figure 4-4). Since the probability of perching, nesting, dustbathing, and foraging behaviours being supported in conventional systems are all zero, the calculated impact for these systems is also zero. In this impact subcategory, a higher score is indicative of better performance. Organic and free range systems performed best in this assessment due to their provisioning of litter and outdoor access, which permits both foraging and dust bathing behaviours. Outdoor access in these systems resulted in a probability of 1 for each of foraging and dustbathing behaviours being supported. While both single- and multi-tier systems also received a probability of 1 for -15000-10000-5000050001000015000200002500030000Conventional Enriched Single tier Multi tier Free range OrganicRelative fulfillment of basal behavioural needs per tonne of eggs 183 the support of dustbathing behaviours, they received a lower probability for the support of foraging behaviours. This led to relatively smaller contributions from foraging behaviours in impact calculations for these systems. Also, in assessment of additional behavioural needs fulfillment, organic and free range systems benefit from lower productive efficiency per hen compared to other systems. Because these systems require a greater number of hens to produce the functional unit, more hens receive the behavioural advantages of these systems compared to others. This is a point of similarity to the animal welfare LCIA method proposed by Scherer et al. (2018), as the results of this impact subcategory are highly sensitive to the number of animals required per functional unit. This also represents a clear trade-off with respect to environmental impacts, in which systems demonstrating the highest levels of productive efficiency have lower impacts, as indicated in chapters 2 and 3. Differences between single- and multi-tier non-cage systems were driven by differences in the contribution from support of perching and foraging behaviours. While the contribution from support of perching behaviours was higher in multi-tier systems, the difference in the foraging behaviour contribution outweighed the differences in perching contribution in favour of single-tier systems. This difference in support of foraging behaviours was sufficient that, when coupled with the slightly higher number of hens required to produce one tonne of eggs, the positive behavioural impacts observed in single-tier non-cage systems is slightly higher than that observed in multi-tier non-cage systems. Enriched housing performed quite well across all behaviours contributing to this impact except support of dustbathing behaviours, suggesting a focus on better provisioning of this behaviour in enriched systems may bring the behavioural benefits of enriched systems more in line with those observed in non-cage systems. All non-conventional housing systems similarly supported nesting 184 behaviours, making differences in nesting contributions based solely on the number of birds affected per housing system. Figure 4-4. Contribution of different behaviours supported by each housing system to the overall fulfilment of additional behavioural needs. Conventional housing received a score of 0 as no furnishings are provided for hens to fulfill additional behavioural needs in these systems. Values associated with each bar are dimensionless, with higher values representing better welfare outcomes. Impacts in the injurious behaviours impact subcategory follow much the same pattern as seen in the morbidity/injury subcategory, with some differences between cage free housing systems magnified, and others decreased (Figure 4-5). In this impact category, a higher number is indicative of worse performance. Notably, while free range production performs the worst for injurious behaviours (similar to the morbidity/injury subcategory), the differences between free range and the second worst performer, single-tier non-cage systems is much smaller. In contrast, the difference between multi-tier and organic systems became larger when compared to the differences observed in Figure 4-2. Enriched housing also performs worse than conventional 0500100015002000250030003500Conventional Enriched Single tier Multi tier Free range OrganicRelative fulfillment of additional behavioural needs per tonne of eggs DustbathingForagingNestingPerching 185 housing for injurious behaviours. It is unsurprising that this impact subcategory result would closely follow the trend shown in the morbidity/injury subcategory, as the two categories are closely linked. Poor feather condition (taken into account in the morbidity/injury impact subcategory), for example, would likely correlate highly with farmers indicating issues with feather pecking, as reflected by this impact subcategory. Where the results of this subcategory differ from those of the morbidity/injury subcategory is in the potential number of contributing factors. Since the inventory data used in calculation of these impact subcategory results deals with the probability of injurious behaviour occurring in general, it allows for accounting of those behaviours that do not result in feather or foot damage, such as general aggressive pecking, etc. Figure 4-5. Risk of injurious behaviours associated with production of 1 tonne of eggs in different housing systems. Values associated with each bar are dimensionless, with higher values representing worse welfare outcomes. Across all housing systems except conventional, positive affective contributions greatly outweighed negative contributions (Figure 4-6). This is due to the relatively high probabilities 051015202530Conventional Enriched Single tier Multi tier Free range OrganicRelative risk of injurious behaviour per tonne of eggs 186 associated with each inventory indicator contributing to positive affective state, and the low probabilities associated with each indicator contributing to negative affective state. In conventional housing systems, however, negative affective impacts still remained low due to the low probability of negative affective contributors occurring for each inventory indicator except death or injury due to aggressive behaviour. As in the additional behavioural needs impact subcategory, free range and organic production systems performed the best for positive affective state because of the combination of high probability of contributions occurring and the relatively higher number of hens required to produce one tonne of eggs in these systems. It must be noted at this point that, while inclusion of affective state in this impact assessment method represents a significant step forward in life cycle impact assessment of animal welfare compared to the previous work of Scherer et al. (2018) and Tallentire et al. (2019), continued refinement of affective state modeling is necessary. In particular, the method proposed here only scratches the surface with regards to contributions to affective state (of which there are many) (Mellor et al., 2020), and only accounts for the valence of the contribution in impact calculation. As animal welfare research into affective state contributors continues, additional contributions to affective state, as well as the level of arousal and duration associated with those contributions must be taken into account in order to ensure accuracy in calculation of affective state impacts. 187 Figure 4-6. Risk of positive and negative affective associated with production of 1 tonne of eggs in different housing systems. Values associated with each bar are dimensionless, with higher values representing better welfare outcomes for both positive and negative affective state. Based on a comprehensive review of literature, we have herein identified and defined the requisite components of a novel type 1 LCIA method for assessment of animal welfare of laying hens, including a new area of protection, stakeholder category, impact categories based on the definition of animal welfare proposed by Fraser et al. (1997), impact subcategories, inventory indicators, and inventory data requirements. This method goes beyond previous proposals by Scherer et al. (2018) and Tallentire et al. (2019) by accounting for a greater range of welfare tenets and contributors, as well as positive welfare contributions, in line with contemporary animal welfare literature. By taking into account the multi-criteria nature of animal welfare, the proposed method will allow for identification and joint consideration of such trade-offs between different housing systems on a methodologically consistent basis. These trade-offs may, in turn, be assessed in tandem with the environmental and social trade-offs exhibited by the different -100-50050100150200250Conventional Enriched Single tier Multi tier Free range OrganicRealtive contribution to positive and negative affective state per tonne of eggsPositive affective stateNegative affective state 188 housing systems (Pelletier, 2018a, 2017), further facilitating holistic assessment of the sustainability impacts of egg production. Importantly, the results calculated from this method are largely congruent with expectations based on the animal welfare literature. As expected, results calculated here indicate that caged systems have lower risks of mortality, and morbidity and injury compared to non-cage systems (Rodenburg et al. 2008; Sherwin et al. 2010; Lay Jr et al. 2011), while they perform relatively worse with regards to behavioural fulfillment. Calculation of positive and negative affective state impacts further integrate the results of the other impact subcategories, illustrating some notable trade-offs within housing systems. Free range systems, for example, are shown to have the second highest positive affective state impacts, while simultaneously having the greatest risk of negative affective state. While the results are largely congruent with expectations based on the literature, it is clear that further refinement of calculation of affective state impacts is necessary in the future. The approach taken here for modelling affective state impacts is relatively rudimentary given the many factors that contribute to affective state, as well as the many possible affective states (Mellor, 2016; Mellor et al., 2020). Incorporation of levels of arousal and duration of affective contributions would also serve to greatly increase the validity of the calculated affective state impacts. The proposed method has two additional merits of note. First, it allows for the contribution of the same inventory data to multiple inventory indicators and impact subcategories. While this a hallmark of environmental LCIA methods, such capabilities are not supported in many current welfare assessment methods. The Welfare Quality assessment method, for example, only accounts for the affective impact of feather condition (i.e. reduced thermal comfort), disregarding the role that feather condition plays in thermoregulation and 189 metabolic activity (Lindström et al., 1993; Rubinstein and Lightfoot, 2012; van Zeeland and Schoemaker, 2014). By allowing inventory data to contribute to multiple different impact subcategories, the new method generates a clearer picture of animal welfare as a whole. Second, the proposed method is fairly generalizable. Application of this method to systems other than laying hens would require identification of new inventory indicators and inventory data requirements specific to the species of interest. However, the underlying principles regarding the AoP, stakeholder category, and impact categories and subcategories are generally applicable to other common animal production systems, including broiler, beef, dairy, and pork production. Adaptation of this method for use in other production systems would be similar to the work done by Zira et al. (2020) in their adaptation of the method developed by Tallentire et al. (2019) for application to pork production systems. This method does, however, have some limitations. First, it is currently refers only to the productive stage of the hen life cycle. The life cycle of a laying hen has five distinct stages, including the maternal breeding flock, the hatchery and pullet facilities, the laying phase, and the end-of-life stage. Welfare considerations at all stages may have an impact on welfare during subsequent stages(Janczak et al. 2007; Haas et al. 2014; Janczak and Riber 2015). The welfare implications of different depopulation methods used during the end of life phase are also contested (Berg et al., 2014; Martin et al., 2018; Turner et al., 2012). This indicates the need for development of holistic welfare LCIA methods for all stages of the hen life cycle during, taking into account potential relationships in welfare status between different life stages. Such relationships continue to be explored in the welfare literature today (Peixoto et al. 2020a, b). Expansion of the proposed method to include welfare impacts from other hen life cycle stages should be a priority for future research. An in-depth discussion of other potential areas for 190 improvement of the proposed method is included in appendix C, section C5. The methods proposed here can nonetheless provide important inputs towards the integration of comprehensive animal welfare assessment methods into the LCA framework. Ultimately, this will allow stakeholders to make informed decisions based on which aspects of animal welfare and other sustainability indicators that they value most, and provide valuable decision support to egg farmers as they transition away from conventional cages in favour of alternative housing systems. An additional, debatable aspect of this animal welfare LCIA method is the multiplication of risks by the number of hens required to produce one tonne of eggs in each housing system. This step is important in linking animal welfare impacts with the functional unit used in calculation of environmental impacts via the number of animals affected so as to enable comparability. However, it produces the counterintuitive result that, in the case of positive contributions to welfare, overall welfare increases with the number of hens required to produce the functional unit. This is inconsistent with efforts to decrease the environmental impacts of egg production systems, which indicate that reducing the number of pullets per tonne of eggs produced would be beneficial for reducing overall environmental impacts. While reconciliation of these two results in the context of sustainability decision making is difficult, in the context of LCIA methods development, this ability to elucidate trade-offs between environmental and animal welfare impacts should be interpreted as a success in methodological development. By demonstrating the ability to identify trade-offs between animal welfare and environmental outcomes on a methodologically consistent basis, the methodology has also demonstrated its capacity to inform farmers, policy makers, and other stakeholders of these trade-offs. 191 An additional potential area for future development is in definition of additional reference points/acceptability thresholds against which calculated results may be compared. Currently, no specific references points are defined – rather, the calculated impacts of each housing systems act as reference points for all other housing systems, providing a suite of relative results. Identification of specific reference points representing welfare goals, such as an acceptable level of mortality, may be useful for application of this method for decision support. Such reference points should, however, take into account the specific industrial context in which the assessment is being performed. Acceptable levels of mortality, for example, may differ across different jurisdictions that employ different management practices such as longer lay cycle lengths, which would result in higher cumulative mortality. Definition of these reference points should also take into account the priorities of stakeholders in the value chain, similar to the process used for development of weighting factors for different environmental impacts for use in multi-criteria decision analysis by the European Commission (Sala et al., 2018a). By not setting specific reference points here, the proposed method is more generally applicable, and may be adapted for use by different industries and stakeholders groups with different priorities. This approach for defining reference points is similar to that used by Tallentire et al. (2019), who do not compare results against constant reference points and instead calculate reference points based on the collected inventory data. Overall, additional refinements and adaptations may be made to this method to further improve the integration of animal welfare outcomes into the LCA framework. As these improvements are made, stakeholders will have myriad sources of information available to them to support informed decision making with regards to sustainability initiatives. 192 4.5 Limitations and conclusions Elucidation of environmental and welfare trade-offs associated with egg production in alternative housing systems is of particular importance to egg industries. As producers continue to transition away from conventional cage production in favour of alternative production systems, farmers, policy makers, and other key stakeholders will require robust information on potential environmental, animal welfare, and other synergies or trade-offs associated with different housing systems to ensure net positive changes for long-term sustainability objectives. The results of this research will help support the decision-making process by providing an assessment of the animal welfare outcomes of different egg production systems on a methodologically consistent basis with environmental outcomes. Additionally, the method used here for development of a novel animal welfare LCIA method may provide significant value to LCA practitioners in two key ways. First, the work done here contributes to the currently small body of literature seeking to assess environmental and animal welfare impacts of livestock systems on a methodologically consistent basis. Holistic assessment in this manner must become the norm in the future to ensure that any potential sustainability interventions will result in net-positive outcomes. Second, the approach used here to develop this LCIA method may serve as a roadmap for development of similar methods for application to other livestock species. Specifically, the AoP, stakeholder categories, impact categories, and impact subcategories proposed here are not species-specific and may be applied to other livestock species. Development of novel LCIA methods for other species may therefore only require identification of appropriate inventory indicators and data requirements, and characterization factors. Identification of these components for other species may also follow the approach used here (i.e., identification based on rigorous review of the welfare literature), as this is in line with best 193 practices in the LCA field (Huijbregts et al., 2017). Further, Richter et al. (2024) draw heavily upon many of the concepts presented in the published version of this research chapter in their recent conceptual framework for consideration of animal welfare in sustainability assessments of foods. There are, however, limitations associated with the method developed in this chapter – in particular with respect to the choice of characterization factors used for assessment of fulfillment of additional behavioural needs. While all characterization factors chosen for this impact category were representative of motivational strengths for hens to perform the included behaviours, these factors were chosen from a number of different studies. As a result, the individual characterization factors may not be methodologically consistent with one another, and may be confounded by factors such as management practices during rearing, genetics, etc. In practice, it would be best to derive factors from a single study to ensure methodological consistency, using a closed economy-type setup. Use of such a setup may provide a more accurate representation of the motivational strength to perform different behaviours relative to one another, as demonstrated by Cooper and Mason (2000), who performed such an experiment with mink. Unfortunately, no similar experiment has been performed for laying hens. Continued development of this method should therefore focus on harmonizing chosen characterization factors to ensure methodological consistency, and may be the subject of sensitivity analyses, similar to how model uncertainty is commonly assessed in eLCA. In the future, additional improvements must be made to this methodology, including extension to additional factors influencing animal welfare outcomes, and to additional life stages. An additional future step would be the use of inventory data representing performance of specific behaviours, rather than data focused on provisioning of opportunities to perform behaviours. 194 Specifically, big data computer vision systems may be utilized to accurately collect and process large amounts of behavioural data, giving a more accurate picture of behavioural welfare. Similarly, moving to a model in which the actual behaviours performed are assessed would help alleviate challenges related to ongoing changes to the Canadian Code of Practice for the Care and Handling of Laying Hens (NFACC, 2017), which now dictate that furnishings to support behaviours must be provided in all housing systems, hindering the ability of the proposed method to differentiate between systems on the basis on behavioural needs fulfillment. As current understandings of animal welfare science continue to evolve, so too, must attempts to integrate animal welfare assessment into the LCA framework such that assessments are performed in accordance with contemporary animal welfare science, with a particular focus on animal-based measures of welfare. Such an interdisciplinary approach is vital to ensuring the sustainability of future food systems. 195 5. Systematic review of the life cycle optimization (LCO) literature, and development of generalized guidelines for performance of LCO studies 5.1 Chapter synopsis Life cycle optimization (LCO) refers to the integration of objectives calculated using a life-cycle based framework into mathematical optimization problems. Application of LCO may allow for substantial sustainability improvements in many industrial sectors, and provide valuable decision support towards achieving the UN Sustainable Development Goals. This study performed a PRISMA systematic review of LCO literature published between 2012 and 2023 with the goal of developing general guidelines for performance of LCO studies. Three hundred and one sources were reviewed to determine the industrial sector of the modeled system, the life cycle assessment framework used, how objective functions were defined, if uncertainty was included, and the optimization framework used. Results indicate a shift towards evolutionary-based optimization methods relative to previous reviews of the literature. Economic and environmental objective functions were most commonly assessed, while some studies have begun incorporating social objectives into their optimization. Based on the collected data, additional discussion was included related to choice of optimization framework, and definition of objective functions. The collected data and these additional discussions were used to develop a decision tree to aid practitioners in making methodological choices when performing LCO studies. This decision tree will help practitioners manage the trade-offs between the accuracy and efficiency of optimization methods based on the goals of their particular LCO study,and support increased uptake of LCO methodologies across industrial sectors. Increased uptake may provide significant value to researchers and policy makers by enabling investigation of potential sustainability improvement measures where all metrics are simultaneously optimized. 196 5.2 Context and rationale To meet the goals outlined by the 2030 Agenda for Sustainable Development (United Nations, 2015), improvements must be made in the sustainability outcomes of industrial systems (United Nations, 2022). The use of systems-level, life-cycle thinking based approaches such as LCA to analyze the impacts of systems and potential sustainability interventions is necessary to ensure net positive outcomes across many sustainability metrics (UNEP, 2004). The utility of LCA may further be increased through integration with operations research methods, such as mathematical optimization, to perform life cycle optimization (LCO) (Azapagic and Clift, 1998). “Optimization” refers to finding the best solution or set of solutions to a specific problem out of all possible solutions (Arora, 2015). LCO may be used for the optimization of resource use/environmental (Liang et al., 2021), social (Pahlevan et al., 2021), and economic impacts (Shani et al., 2021), thereby providing value in decision support towards achieving sustainability goals (United Nations, 2015). Increased uptake of LCO should therefore be promoted in life cycle-based sustainability research. Performance of an LCO study, however, requires many methodological choices to be made regarding study goals, problem definition, and the strategy used to find the optimal solution(s). Previously, Pieragostini et al. (2012) published a review of the LCO field, covering topics including system boundaries, objective functions, life cycle impact assessment methods, mathematical formulation of the optimization problems, and the computational tools used. Updating and significantly expanding upon this work is desirable given the age of this resource, and the advances that may have been made in the LCO field since. This work therefore provides an updated review of the LCO literature, covering studies published following the publication of Pieragostini et al. (2012). This research culminates in development of a generalized decision tree 197 intended to help practitioners make methodological choices in line with their study goals when performing an LCO study. 5.3 Methods A systematic review of the LCO literature was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method. The PRISMA method is a reporting guideline for all methodological aspects of systematic reviews and meta-analyses that is intended to address poor reporting and facilitate transparency and replicability (Page et al., 2021). This method includes a 27-item checklist and guidelines for writing abstracts to help ensure robust, consistent and reproducible reporting of systematic review methods and results. Analyses of its uses indicate that application of the method leads to more complete reporting of systematic reviews (Panic et al., 2013), and that the guidelines developed are broadly applicable across disciplines (Heidari et al., 2021b). This review was not registered prior to initiation, and no review protocol was prepared for review. A completed PRISMA checklist describing the review performed is available in appendix D, Table D 1. 5.3.1 Study selection To identify literature, the Web of Science Core Collection was searched. No other databases were consulted. The last date the database was consulted was 31 January 2024. Topic searches were performed using the keywords (“life cycle assessment” OR “life cycle analysis” OR "LCA" OR "LCO" OR "life cycle optimization") AND (optimi*) AND (algorithm), including all listed brackets to override default operator precedence in the Web of Science search function. A wildcard operator (“*”) was included in the search to help increase the body of literature captured. This wildcard operator represents any group of characters following those defined in the search. For example, the search term “optimi*” was therefore able to capture terms such as optimize, 198 optimized, optimization, etc. The term “algorithm” was included in the topic search to ensure LCO was actually performed in the captured literature, as opposed to capturing literature that only mentioned optimization in other contexts. To be considered for inclusion, studies had to meet a number of eligibility criteria. First, only literature published within the last twelve years (i.e., 2012-2023, inclusive) was considered. Sources published as early access in 2023 for publication in 2024 were excluded. Only primary research articles, written in English, were considered. Conference proceedings and review articles were excluded. No eligibility criteria were defined regarding the industrial sector of the systems being assessed in candidate literature for two reasons. First, it was expected that LCO studies could be more common for some industrial sectors compared to others, so restricting sources based on industrial sector could have large impacts on the number of sources available for review. Second, the goal of this review was to develop a generalizable framework for LCA practitioners wishing to perform LCO regardless of the system assessed. Hence, all industrial sectors should be considered to ensure that the guidelines are broadly applicable. Studies that met these eligibility requirements comprised the primary body of literature to be carried forward to the literature selection process. To be selected for review, sources were required to apply the LCA framework in conjunction with a mathematical optimization technique – that is, a life cycle framework had to be used in determining at least one objective function for optimization. This could include objective functions related to environmental impacts as measured using environmental LCA, as well as social and economic impacts as measured via social LCA or life cycle costing. This selection was carried out primarily based on article titles and abstracts, with further consultation of the main text whenever necessary. In some cases, sources were captured by the review terms 199 that were not in line with the selection criteria. Raeesi et al. (2021), for example, was not included because no optimization was performed in the study. Similarly, Das et al. (2021) was not included because LCA was not used in determining any of the objective functions for optimization. A single reviewer was responsible for performing the selection process to ensure consistency of application, and no automation tools were used during this process. A depiction of the literature search steps performed to arrive at the total body of literature to be reviewed is shown in Figure 5-1. 200 Figure 5-1. Flow chart describing the results of the literature search and selection process used to arrive at the final body of literature reviewed during PRISMA systematic review 201 5.3.2 Data collection and synthesis Data were collected from the selected literature by in-depth readings of articles, as well as any associated supplementary materials. Two reviewers were responsible for the data collection process. The reviewers were in close communication with one another during the data collection process to ensure consistency in data collection principles. No automation tools were used during the data collection process. The selected literature were reviewed for several characteristics. First, the product system investigated by each study was classified according to the International Standard Industrial Classification of All Economic Activities (ISIC) (United Nations, 2008) to determine in which industrial sectors LCO is more or less commonly applied. Next, the LCA framework used (i.e., attributional or consequential) was recorded, as were the specific objective function(s) and whether the optimization was constrained or unconstrained. The objective functions considered in each study were classified as one of four types: environmental, economic, social, and technical. Environmental objective functions included those impacts assessed by an environmental LCA, such as greenhouse gas emissions, acidification, eutrophication, and others. If human health impacts were included as an objective function (as in Ng et al. (2021), for example), they were classified as an environmental objective function. Traditionally, environmental LCA reports impacts using either mid-point indicators (i.e., risk-level) or end-point indicators (i.e., damage-level) (Guinée et al., 2002). Human health impacts are one example of an end-point environmental indicator, which represent an aggregation of multiple mid-point indicators to a single impact (Goedkoop et al., 2013). Social objective functions included those related to social impacts such as social acceptance, contributions to job creation, aggregated social impact scores, etc. Economic objective functions included those related to total 202 life cycle costs, operational costs, net present value, etc. Finally, technical objective functions included those related to physical characteristics of the particular system being modelled. These objective functions were generally study-specific, and included objectives such as strength of manufactured concrete aggregate (Dabbaghi et al., 2021), sensitivity of the alkylation reaction in industrial ethylbenzene production (Han et al., 2021), logistical objectives for transportation activities (Ng et al., 2021), and others. It was also recorded whether analyses used aggregated (i.e., total environmental/social impacts, etc.) or disaggregated (i.e., greenhouse gas emissions, profits, etc.) measures for any objective functions. If any aggregated objective functions were used, the study was noted as using aggregated objective functions even if other objective functions included disaggregated measures. Following this, information was collected from each source regarding the method used to perform the optimization. Specifically, it was noted what optimization algorithm/solver was used in each study, as well as the overarching optimization framework to which the method belonged. Specific algorithms/solvers were categorized into eight categories: evolution-based metaheuristics, swarm-based metaheuristics, physics-based metaheuristics, mathematical programming, study-specific algorithms, multiple, other, and unknown. The first five of these categories represent overarching optimization frameworks that were used in at least five of the sources reviewed. The exception to this was in the classification “study-specific algorithms,” which was used to account for those works that developed their own optimization algorithms as part of their analysis. Sources were categorized as “multiple” if they used multiple types of optimization for the same question to draw comparisons among outcomes. A source was classified as “unknown” when it did not state what algorithm/solver was used. The “other” category was used as a general category for all those optimization frameworks that were used in 203 fewer than five of the sources reviewed. Finally, it was noted whether or not the sources took uncertainty into account during optimization. No methods were used to assess the risk of bias in any of the studies included for review. Once the data were collected from each source, tallies were created to determine common practices in LCO studies. These tallies were subsequently used to inform topics for further discussion regarding specific characteristics, and strengths and weaknesses of different approaches to LCO. All tallies were produced using Microsoft Excel. No methods were employed to assess risk of bias due to missing results, nor to assess the certainty in the body of evidence collected. Based on the collected characteristics, the choice of optimization framework and the use of aggregated or disaggregated measures as objective functions were chosen as topics warranting further discussion. Discussion on the choice of optimization frameworks focused primarily on evolutionary optimization and mathematical programming as these frameworks were employed in approximately 79% of all studies reviewed, though limited discussion was also included on the use of metaheuristic versus complete solvers in general. The use of aggregated or disaggregated measures was chosen as a topic for two key reasons. First, this choice has potentially large implications on the accuracy, applicability, and interpretability of LCA results. Second, this choice may have large implications on computational complexity (i.e., computational run time and memory storage resources required to solve a problem) as a determining factor of problem complexity (Belinato et al., 2019) and dimensionality (Chen et al., 2015). The information gathered from these additional readings were then used to generate a generalized decision tree to aid the methodological decision-making process when initiating an LCO study. To be sufficiently generalizable, it was determined that the majority of nodes in the decision tree must refer to the goals of the LCO study to be carried out, rather than specific 204 aspects of the LCO problem formulation, in line with the no-free lunch theorem, which dictates that there is no single optimization framework that is best suited for all possible situations (Ho and Pepyne, 2002). 5.4 Results and discussion 5.4.1 Characteristics of reviewed studies Three hundred and one studies were selected for review (Figure 5-1). A complete list of all literature reviewed is available in appendix D, section D2. Many different industrial sectors were covered by the systems assessed in these studies. The most commonly studied systems were in ISIC sections C (Manufacturing), D (Electricity, gas, steam and air conditioning supply) and F (construction), which together accounted for ~71% of all systems studied by the reviewed literature. When classified into specific section classes, the majority of classes (i.e., ~69%) were represented by less than three studied systems, with many section classes (~43%) only including a single studied system. In comparison, section classes 3510 (electric power generation, transmission, and distribution) and 4100 (construction of buildings) were relatively over-represented, including forty-eight and forty-six studied systems, respectively. In total, only seven different section classes included ten or more studied systems, all of which were part of sections C, D, F, and H. While a relatively small percentage of the studied systems were attributable to ISIC section H (transportation and storage), when systems were sorted into this section they were commonly associated with section 5221 (service activities incidental to land transportation), as these sources often dealt with maintenance planning of roads, bridges, and other infrastructure relate to land transportation. The remainder of the systems studied were distributed across 6 other industrial sectors (Error! Reference source not found.Table 5-1). 205 Table 5-1. Distribution of the systems studied by the LCO literature reviewed across ISIC classification sections ISIC Section Letter ISIC Section Percentage of studied systems A Agriculture, forestry and fishing 9.0 B Mining and quarrying 1.3 C Manufacturing 29.2 D Electricity, gas, steam and air conditioning supply 20.6 E Water supply; sewerage, waste management and remediation activities 9.0 F Construction 21.3 G Wholesale and retail trade; repair of motor vehicles and motorcycles 1.0 H Transportation and storage 7.6 M Professional, scientific and technical activities 1.0 Figure 5-2 depicts the total number of reviewed studies published in each year within the temporal boundaries, as well as the number of studies published in each year sorted into specific ISIC sections. It is clear that both the number of LCO studies performed has increased steadily since 2012, and across a wider array of industrial sectors. In 2012, for example, only four ISIC sections were represented in the reviewed literature, whereas studies published in 2022 and 2023 were sorted into seven and six different ISIC sections, respectively. In both cases, the trend 206 towards increasing application of LCO across a larger variety of industrial sectors speaks to its growing relevance in sustainability management. Figure 5-2. Total number of sources reviewed from each year within the temporal boundaries of this review, and the number of sources within each year that studied systems attributable to different ISIC sections. Section letters refer to the corresponding sections listed in Table 5-1. The majority of the reviewed studies (~82%) performed multi-objective optimization, with an additional ~15% performing single-objective optimization. The remaining ~3% of studies reviewed chose to perform both single- and multi-objective optimization to compare results across different optimization scenarios. This observed focus on multi-objective optimization is in line with a previous review of the LCO literature, in which most studies were multi-objective (Pieragostini et al., 2012). The majority of the reviewed studies (~82%) performed multi-objective optimization, with an additional ~15% performing single-objective optimization. The remaining ~3% of studies reviewed chose to perform both single- and multi-objective optimization to compare results across 051015202530354045502012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023Number of studiesMHGFEDCBA 207 different optimization scenarios. This observed focus on multi-objective optimization is in line with a previous review of the LCO literature, in which most studies were multi-objective (Pieragostini et al., 2012). The majority of studies reviewed (~93%) performed constrained optimization, while the remainder performed unconstrained optimization. Objective functions most often dealt with environmental and economic impacts, with 94% and 75% of studies including at least one of these types of objective functions, respectively. In contrast, technical and social objective functions were considerably less represented in the reviewed literature, with approximately 28% and 6% of studies including at least one of these types of objective functions. The inclusion of social objectives in some of the sources represents an advancement in LCO compared to the literature reviewed by Pieragostini et al. (2012), who did not find any literature that included social objectives. This may be because the first version of the UNEP/SETAC Guidelines for Social LCA of Products (UNEP/SETAC, 2009) was published in 2009, close to the temporal cut-off of the previous review. The growth exhibited in accounting for social impacts in LCO studies is a promising sign of growing importance being placed on the social aspect of sustainability and the increased availability of methods to support meaningfully doing so. Approximately 74% of the studies reviewed utilized only disaggregated measures as objective functions, while the remaining 26% used at least one aggregated measure as an objective function. Many different optimization frameworks were represented in the reviewed literature (Figure 5-3). The most commonly used frameworks were evolution-based metaheuristics, followed by mathematical programming. Together, these accounted for roughly 79% of all studies reviewed (approximately 52% and 26%, respectively). Swarm-based metaheuristics and “Other” 208 were the only other categories of optimization frameworks that occurred in greater than 3% of the literature reviewed, accounting for approximately 6% and 8%, respectively. In total, metaheuristic methods cumulatively accounted for approximately 60% of the optimization frameworks employed in the reviewed literature. The high prevalence of metaheuristic methods is in contrast to previously reported trends in the LCO literature, in which all reviewed sources used some form of mathematical programming to solve their optimization problem (Pieragostini et al., 2012). This trend towards the use of metaheuristics and evolutionary optimization methods, in particular, is shown in Figure 5-4, which displays an increasing trend in the number of LCO studies published per year, and a large increase in the proportion of studies employing evolutionary metaheuristics since 2012. This increase in the use of evolution-based metaheuristic methods is accompanied by similarly large decreases in the prevalence of mathematical programming approaches. A small, though non-trivial, increase was also observed in the use of other optimization frameworks over time. Figure 5-3. Percentage of different optimization frameworks applied in the literature reviewed. Evolution-based metaheuristicMathematical programmingSwarm-based metaheuristicMultipleUnknownPhysics-based metaheuristicStudy specific algorithmOther 209 Figure 5-4. Changes in the number of LCO studies published per year, and the proportion of studies employing mathematical programming, evolutionary metaheuristics, or other approaches as optimization frameworks between 2012 and 2023. 5.4.2 Choice of optimization framework Which optimization framework to use is an important choice that practitioners must make during performance of an LCO study. Across the studies reviewed, mathematical programming and evolutionary optimization approaches were by far the most common, with the use of evolution-based metaheuristics increasing in prevalence over time. Mathematical programming approaches may be generally classified as complete (Neumaier, 2004), or deterministic solvers (Kvasov and Mukhametzhanov, 2018), while evolutionary (and other metaheuristic approaches, such as swarm-based metaheuristics) may be classified as approximate (Chen et al., 2017), incomplete (Neumaier, 2004), or stochastic solvers (Kvasov and Mukhametzhanov, 2018). Complete solvers guarantee identification of global optima by searching all points in a region first for feasibility according to the constraints, then by checking all feasible points for optimality 010203040506070051015202530354045502012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023Proportion of studies applying different optimization frameworks per yearTotal number of studies published per yearNumber of publications reviewedMathematical programmingEvolution-based metaheuristicOther 210 (Neumaier, 2004). These solvers require that additional rules be defined when searching for optimal solutions because the feasible space contains an infinite number of possible points (Neumaier et al., 2005). Without definition of these rules, complete solvers would take indefinitely long to arrive at an optimal solution (Neumaier, 2004). In contrast, approximate solvers use defined sets of rules to search the feasible space, with the expectation that, given an appropriate amount of time, an optimal solution will be found. Unlike complete solvers, approximate solvers cannot guarantee identification of global optima since they do not search every point within the feasible solution space. However, approximate methods may be significantly less computationally intensive, and can accommodate a greater degree of flexibility in the problem definition, particularly when optimization problems are complex (Silver, 2004). Flexibility in problem definition is an important point of differentiation between mathematical programing and evolution-based metaheuristic optimization frameworks. Generally, optimization problems may be defined as either linear or non-linear in nature. When defining the optimization problem, the presence of any non-linear function is sufficient to define the problem as a non-linear problem (Venkataraman, 2009). Non-linearities may be introduced into an optimization problem in any function that is used to define that problem, including both constraints and objective functions. Optimization problems may similarly be characterized based on the nature of the variables used to define the functions describing the problem. The use of discrete variables in the problem definition, for example, classifies the problem as an all-integer or mixed-integer problem (Antunes et al., 2016). Within the category of mathematical programming, specific optimization approaches are suitable for different types of problem definition. Linear programming, for example, is a type of mathematical programming only suitable for solving fully linear, continuous optimization 211 problems, while the related non-linear programming method is suitable for solving non-linear, continuous problems (Venkataraman, 2009). If variables taking the form of integers are included in the optimization problem, it then becomes necessary to use a different optimization approach within mathematical programming, such as mixed integer linear or non-linear programming. Examples of these different mathematical programming approaches for different types of problem definition may be seen in Li et al. (2020) and Vázquez and Guillén-Gosálbez, (2021). In contrast, metaheuristic methods, such as evolutionary optimization, are not problem specific, providing practitioners with substantially more flexibility in problem definition (Blum and Roli, 2003). As a result of this property, metaheuristic optimization methods can be useful when developing optimization frameworks, as in Miranda-Ackerman et al. (2017), for example. Optimization frameworks developed using metaheuristic methods may be easily adapted to alternative problems without having to use a new optimization approach, as would be necessary if using a problem-specific, mathematical programming approach. In addition, since these algorithms make use of a population of individuals, and allow for interactions between them, they are efficiently able to search the feasible space while mitigating the risk of getting stuck in a local optimum (Fausto et al., 2020). This stochastic search method, however, does not guarantee that a global optimum will be identified. Rather, the algorithms are only capable of approximating an optimal solution with a specific probability that approaches one as time approaches infinity. While many different types of metaheuristic approaches are available, evolution-based metaheuristics were by far the most commonly applied optimization frameworks in the literature reviewed. These metaheuristics are used to identify optimal solutions to problems by applying principles from natural evolution (Fausto et al., 2020). These methods are based on four key 212 premises: that there exists a population made up of individuals, many of which are able to reproduce; that the individuals of the population have a finite lifespan; that there is variation across the population; and that the ability to survive is positively associated with reproductive success (Simon, 2013). The non-dominated sorting genetic algorithm (NSGA) is the most well known of these methods. It was initially developed by Srinivas and Deb (1994), with subsequent iterations resulting in the NSGA II (Deb et al., 2002), and NSGA III (Qu et al., 2019). While evolutionary optimization today is synonymous with the use of genetic algorithms (Simon, 2013), other evolutionary optimization algorithms also exist including the Hypervolume Estimation algorithm (Bader and Zitzler, 2011), and the Strength Pareto Evolutionary Algorithm and its iterations (Zitzler et al., 2001). Additionally, some authors view swarm-based optimization methods as a subset of evolutionary optimization (see, for example, Wu et al. (2019)). To avoid confusion, however, evolutionary optimization and swarm optimization methods are differentiated herein based on the classification of metaheuristics proposed by Fausto et al. (2020). In a small portion of the reviewed literature, the authors used multiple optimization frameworks and compared performance between them. Capitanescu et al. (2016), for example, compared the performance of the Strength Pareto Evolutionary Algorithm II and NSGA II algorithms, finding the NSGA II performed the best for their specific problem. The generalizability of this finding to other problems, however, may be questionable in light of the no free lunch theorem (Ho and Pepyne, 2002). NSGA-II has two key features that allow it to perform well as an optimization algorithm. First, it applies the concept of elitism across all generations (Deb et al., 2002). which ensures that the best performing solutions are maintained throughout subsequent generations (Simon, 2013). Without elitism, the solutions that are used to 213 generate each subsequent generation would be chosen probabilistically, based on the relative fitness of each solution and the defined cross-over coefficients. This would allow for the possibility of losing the best performing solutions from a previous generation in subsequent generations. Second, NSGA-II applies the concept of non-dominated sorting (Deb et al., 2002). In multi-objective optimization, a solution is said to dominate another solution if it performs better with respect to at least one objective function (Bao et al., 2017). All optimal solutions to a multi-objective optimization problem will, therefore, be non-dominant relative to one another, and will not be dominated by any other sub-optimal solution. Regardless, all evolutionary optimization methods share the same general framework. First, an initial population representing potential solutions to the objective function(s) is generated randomly. The “fitness” of these solutions (i.e., how well different solutions perform according to the parameters of the optimization problem) is assessed, and reproduction occurs, including cross-over between individuals to produce a second generation of offspring. Cross-over occurs with a probability, such that it is more likely to occur between individuals with a greater degree of fitness, The population size is maintained across subsequent generations. Finally, as in nature, a mutation rate is defined. This rate is usually low, and represents a way through which additional variability is introduced into potential solutions. Beneficial mutations are maintained through the fitness assessment and cross-over mechanism, while detrimental mutations quickly drop out of the population. Mutation is an important mechanism to ensure that evolution will not be stalled prior to the defined termination conditions. Termination conditions may be based on a number of characteristics, including a set number of generations having been met, the fitness of the best solution being greater than some pre-defined threshold, the fitness of the best solution no longer improving across generations, or some other condition (Simon, 2013). 214 Following evolution-based metaheuristics, mathematical programming approaches were the most commonly applied optimization frameworks in the literature reviewed. The largest difference between mathematical programming techniques and metaheuristic optimization techniques, such as evolutionary optimization, is that mathematical programming techniques are problem-specific – that is, which algorithm is needed to solve a problem is dependent on the specific characteristics of that problem (Fausto et al., 2020). Many mathematical programming techniques have therefore been developed, each of which is specifically designed to solve a specific type of problem, such as integer programming, which is used when the problem contains decision variables that take the form of integers. As a result, there is no single consensus technique in the LCO literature reviewed that utilized mathematical programming. Differences in performance can be expected between metaheuristic methods and mathematical programming techniques. Biskas et al. (2005), for example, indicate that in some cases mathematical programming may outperform genetic algorithms and another common metaheuristic technique, particle swarm optimization, in terms of solution quality and computational time. The authors also indicate that mathematical programming techniques performed better in medium-size systems, while metaheuristic methods performed better in smaller systems. This is in direct contrast to the findings of Bezão Silveira et al. (2021), in which mathematical programming techniques were reportedly superior for low to medium sized systems, while metaheuristic techniques were superior in larger systems. This discrepancy further underlines the problem-specificity of mathematical programming techniques, as their performance may vary considerably across different types of problems. This is reflected in the work of Wolpert and Macready (1997) who indicate that, while there may be a certain 215 optimization technique that is best suited for a specific type of problem, there is no individual technique that is best suited for all problems. 5.4.3 Aggregated and disaggregated objective functions Another important methodological choice to be made when performing an LCO study is how many, and what, to include as objective functions. Broadly, there are three options: using only a single measure for a particular type of impact (e.g., using only greenhouse gas emissions as an environmental objective function); using multiple measures representing different objectives (e.g., using both greenhouse gas and eutrophying emissions as environmental objective functions); or using measures that aggregate multiple impacts into a single score (e.g., using disability-adjusted life years as an aggregated end-point for all environmental impacts). All three approaches are represented in the reviewed literature (see for example (Dabbaghi et al., 2021; Galimshina et al., 2021; Kiss and Szalay, 2020)). Approximately 74% of studies reviewed used only disaggregated measures, and 26% used at least one aggregate measure as objective functions. This decision is important for two reasons: first, for the implications it may have on the computational complexity of the optimization problem and, second, for the implications it may have for the interpretability of the final results. Computational complexity refers to the amount of computer resources (i.e., computational run time and memory storage resources) it takes to run an algorithm, which is represented by a function that depends on the inputs to the algorithm (Pudlák, 2013). The first iteration of NSGA, for example, was generally more complex than its successor, NSGA-II, which was developed with the goal of decreasing computational run time (Deb et al., 2002). The type and number of objective functions included in the problem directly contributes to the computational complexity by increasing the number of functions that must be evaluated, and by 216 increasing the amount of space that must be searched to find the optimal solution. Search space increases in an exponential manner as problems include more dimensions. Generally, therefore, problems that have higher dimensionality are also more computationally complex (Chen et al., 2015). Solving highly dimensional problems therefore represents a significant challenge. When the search space is too large, use of complete solvers such as mathematical programming techniques becomes infeasible, and approximate solvers, such as evolutionary- and other metaheuristics may be preferable. At high dimensions, however, performance of metaheuristics will also degrade (Chen et al., 2015). In general, as optimization problem formulations become more complex, the computational complexity of different algorithms for solving the problem will also increase, likely at different rates depending on the approach chosen (Voulgaropoulou et al., 2019). Reductions in computational complexity may be achieved by improving the ability of the algorithm to search the solution space (Cheng and Jin, 2015), by using surrogate or metamodels that adequately approximate functions in the problem definition but are easier to evaluate, or by simplifying the problem (Sayed et al., 2015). Use of single measures as objective functions (whether representing single life cycle impacts or aggregates of multiple impacts) is one method that practitioners may use to simplify LCO problems, and hence reduce the computational burden of solving them. Reducing the number of life cycle impacts used as objective functions may simplify LCO problems, but also comes with associated risks (Capitanescu et al., 2015). Using single life cycle impacts as objective functions (such as just using greenhouse gas emissions as an environmental objective) requires practitioners to make normative choices regarding which impacts they value the most. According to ISO guidelines for LCA (ISO, 2006a, 2006b), studies are to assess a set 217 of impacts that are both comprehensive with respect to the system being studies, and consistent with the goal and scope of the analysis. Use of a single midpoint impact as an objective function in an LCO study is likely not in line with ISO guidelines, nor consistent with the multi-criteria nature of LCA (Guinée et al., 2002). Further, inclusion of just a single midpoint impact as an objective function in an LCO study may lead to increased risks of burden shifting between impacts, as demonstrated by Algunaibet and Guillén-Gosálbez (2019). Hence, it is generally recommended to include all relevant impacts in LCA studies (Hellweg and Milà i Canals, 2014). Another strategy that may be used to simplify LCO problems is to instead use aggregated measures as objective functions that represent aggregations of multiple midpoint life cycle impacts, as done in approximately 26% of the literature reviewed. If practitioners wish to include multiple impacts of the same type (for example, multiple environmental impacts), use of aggregated indicators, such as those that aggregate environmental impacts to the end-point of human health damages, may increase the comprehensiveness of the study without commensurate increases in problem complexity. However, the potential trade-offs associated with this choice must be considered. Generally, endpoint impact measures are associated with a greater degree of uncertainty as these impacts occur further along the cause-effect chain, where exact mechanisms are more uncertain (Huijbregts et al., 2017), and there is less consensus in characterization (Bare and Gloria, 2006). Bare and Gloria (2006) and Lenzen (2006) provide a comprehensive analysis of additional factors to consider regarding the applicability of midpoint and endpoint indicators in LCA. Further, aggregation of multiple impacts into a single measure results in losses to interpretability of results with respect to any individual impact. While total life cycle impacts may be lower after optimization, it is possible that this includes increases in some impacts that are outweighed by decreases in others, resulting in net losses. As a whole, however, aggregated 218 measures are often more easily interpretable than are collections of midpoint measures, as their relevance is usually more clear (Guinée et al., 2002). The increases in computational complexity stemming from the inclusion of many midpoint level indicators as objective functions does, however, come with the benefit of decreased uncertainty in LCA results as midpoint level indicators have lower levels of uncertainty associated with them than their endpoint counterparts (Huijbregts et al., 2017), and a decreased risk of burden shifting across different impacts (Guinée et al., 2002). In contrast, LCO practitioners may also choose to include all relevant life cycle impacts in the optimization problem at the midpoint level as objective functions. As noted previously, doing so will increase computational complexity on an exponential scale. However, increases in computational complexity do not equate to the problem being unsolvable – rather, it will simply take longer to find the optimal solution. Perez-Gallardo et al. (2018) and Mele and Campana (2020), for example, both use the NSGA-II framework to solve an optimization problem that includes the eighteen midpoint environmental impacts included in the ReCiPe impact assessment method (Huijbregts et al., 2017) as environmental objective functions, while Qu et al. (2019) use the NSGA-III framework to optimize across up to fifteen objective functions at once. Other methods are also available for reducing computational complexity in ways unrelated to the objective functions. Bao et al. (2017), for example, discuss a number of different methods focused on the non-dominated sorting process for reducing computational complexity of NSGA-II. Discussions of methods for reducing computational complexity in some mathematical programming problems are available in Kannengießer et al. (2019), Örkcü et al. (2015), and den Hertog, (1994). 219 5.4.4 Decision tree Based on the results of the literature review, it is clear that there are strengths and weakness to all approaches to LCO. To help LCO practitioners make methodological choices when performing a study, a decision tree has been developed (Figure 5-5). This decision tree is intended to provide LCO practitioners with an opportunity to reflect upon the specific goal and scope of their study, and the context of their optimization problem to help them make informed decisions in selecting their optimization strategy. The information included in this decision tree is generally applicable to both single and multi-objective optimization problems. In this decision tree, nodes represent questions or actions practitioners must consider during definition of their strategy. When these decisions lead to specific optimization frameworks that may be used, these frameworks are represented by ovals. The branch of the decision tree dealing with robust optimization techniques does not extend further beyond the suggestion of robust optimization methods. This choice was made for two reasons: first, because robust optimization studies are currently rare in the LCO literature and, second, because robust optimization carries with it other mathematical theories and methodological choices that are beyond the scope of the current review. For an in-depth discussion of robust optimization, readers are pointed towards Gabrel et al. (2014), Beyer and Sendhoff (2007), Ben-Tal et al. (2009), Li et al. (2012, 2011), and Li and Floudas (2014). Overall, the decision tree seeks to aid LCO practitioners in managing the trade-off between accuracy and efficiency that may result from specific methodological choices. Following initial definition of the objective functions and constraints, and the choice as to whether or not uncertainty is to be included, practitioners are to reflect upon the dimensionality and complexity of their problem. If the defined problem is highly dimensional or includes 220 functions that are highly complex, practitioners are then asked if the problem can be simplified within the scope of their study. Potential simplifications may include the use of aggregated measures or surrogate models as objective functions, in which case they are instructed to return to the beginning of the flow chart and redefine their objectives and constraints. Importantly, there is a degree of ambiguity included in this node regarding what exactly constitutes a highly dimensional/complex problem. As discussed in section 5.4.3, dimensionality and function complexity can have important implications for choice of optimization approach and the efficiency with which the problem may be solved. However, setting a hard rule for what constitutes high dimensionality/complexity is infeasible, as these definitions are likely problem specific. For example, if practitioners choose to use many midpoint environmental impacts as objective functions, the optimization problem may be highly dimensional; however, if an attributional LCA framework is used (common in the literature reviewed), each environmental impact included may be represented by a simple linear function (Guinée et al., 2002). So, while the problem may be highly dimensional, the overall complexity of the problem may remain relatively low. This may be compared to a problem that includes fewer environmental impacts and fewer objective functions, but where impacts are assessed using a non-linear consequential LCA approach. In this situation, it may be possible for overall problem complexity to be higher in spite of lower dimensionality in the problem definition. This problem specificity is highlighted by the divergent results seen in Biskas et al., (2005) and Bezão Silveira et al. (2021) regarding relative performance of mathematical programming and metaheuristic optimization methods in systems of different complexity. If the problem cannot be simplified within the boundaries of the goal and scope of the analysis, metaheuristic optimization methods are suggested in light of their superior ability to solve complex problems compared to mathematical programming approaches, 221 as discussed in section 5.4.2. In line with the no free lunch theorem (Ho and Pepyne, 2002), practitioners are then instructed to examine potential metaheuristics, such as evolutionary- and swarm-based methods to determine which of the possible methods is best suited for the specific problem being solved. In contrast, if the defined problem is not highly dimensional/complex, practitioners are then asked to determine whether they prioritize solving speed, or a guarantee of finding global optima. If speed is prioritized, they are similarly pointed towards metaheuristic, approximate search methods that do not guarantee finding a global optima, but may more efficiently search feasible space than their counterparts, complete search methods. If practitioners instead prioritize finding guaranteed optima, they are finally asked to reflect upon specific characteristics of their study (i.e., linearity or nonlinearity, inclusion of binary variables, etc.) in order to determine the best fit mathematical programming technique for their specific problem structure. While these decisions are important, they have been left off of the decision tree. Rather, the decision tree makes the broad distinction between linear and non-linear programming techniques only as a means of reducing complexity of the tree. Additional information about various case-specific mathematical programming techniques can be found in Eiselt and Sandbolm (2000), Jeter (1986), and Cao et al. (2020). Similarly, while evolutionary metaheuristics are the only methods covered in-depth herein due to their wide-spread use in the LCO literature, many other methods are available for use by practitioners. For an overview of available metaheuristic methods, we suggest the work of Fausto et al. (2020), Dragoi and Dafinescu (2021), and Rajendran et al. (2022). Importantly, the decision tree does not provide LCO practitioners with recommendations for specific choices of optimization solvers that may be broadly applicable. Different 222 optimization algorithms may perform differently from one another under similar circumstances due to two key aspects of algorithm behaviour: exploration and exploitation. Exploration behaviour describes how well the algorithm is able to find novel areas within feasible solution space, while exploitation behaviour described how well the algorithm is able to harness the potential knowledge in feasible space to find an optimal solution. To be efficient, therefore, algorithms that find optimal solutions by searching within feasible space must establish an appropriate ratio between these two behaviours (Crepinsek et al., 2013). An algorithm with weak exploration, for example, is less likely to find novel regions within feasible space to exploit when searching for global optima, meaning it is more likely that only a local optima will be identified (Korošec and Eftimov, 2020). These behaviours are less important for complete optimization methods because they search all possible points within feasible space for a global optima. Tuning parameters specific to different optimization approaches – that is, optimizing the optimizers – is the best overall approach to improving the efficiency with which an optimization solver is able to find an optimal solution (Korošec and Eftimov, 2020). However, is it not possible to give general recommendations regarding specific optimization solvers to employ because of the no-free-lunch theorem in optimization (Ho and Pepyne, 2002). Finally, in cases where multi-objective optimization is performed, it is possible that the objectives may conflict with one another. When multi-objective problems are solved, therefore, a set of optimal solutions is found rather than a single optimal solution, with that set representing varying degrees of compromise between the objective functions. This set of optimal values is known as the Pareto optimal set (Arora, 2015). Application of the results of a solved multi-objective optimization problem therefore has an additional step compared to application of the results of single-objective problems: decision making. During the decision making process, those 223 applying the results of a multi-objective optimization problem must choose which solution from the Pareto optimal set is the most preferred (Kiani-Moghaddam et al., 2019). The decision making process therefore represents a point of integration with other methods to support decision making, such as multi-criteria decision analysis (Perez-Gallardo et al., 2018). Multi-criteria decision analysis may be used to choose the preferred optimal solution, according to practitioner preferences, societal values, or other criteria. Since these methods are applied following performance of an LCO study, they are omitted from the decision tree. Additional information on multi-criteria decision analysis and its applications is available in Bystrzanowska and Tobiszewski, (2018), and Ishizaka and Nemery (2013). 224 Figure 5-5 Decision tree to aid LCO practitioners in making methodological choices for performance of LCO studies. Ovals represent different optimization frameworks practitioners may employ to solve their problem. 225 5.5 Limitations and conclusions The synthesis of the data collected during this review may provide valuable decision support to LCO practitioners in defining their LCO problem and devising the strategy they will use to solve it. However, some limitations to this analysis remain. LCO is a rapidly evolving field, with contributions being made from both the fields of LCA and mathematical optimization. As shown in Figure 5-3, over the past twelve years there has been a marked shift in the optimization frameworks commonly employed in LCO studies away from mathematical programming-based solvers to evolutionary metaheuristics and other methods. This is in contrast to the earlier LCO literature reviewed by Pieragostini et al., (2012), in which mathematical programming approaches dominated. Further improvements to metaheuristic optimization methods continue to be made, including specialization of algorithms to more efficiently solve problems in specific industrial sectors such as power systems (Nassef et al., 2023) and mechanical design (Elaziz et al., 2022), and integration with machine learning-based methods to further expand the utility of algorithmic solvers (Song et al., 2022). Similarly, improvements continue to be made in LCA, particularly with respect to impact assessment. Novel methods for assessment of biodiversity impacts (Borgelt et al., 2024) and impacts on human health and nutrition (Scherer et al., 2024) are continuously being developed and improved upon in the environmental space, while novel methods for assessment of social impacts such as human well-being (Lindkvist and Ekener, 2023) animal welfare (Turner et al., 2023d), and others (Huarachi et al., 2020) are also becoming more common in the social space. While social impacts were relatively under-represented among the types of objective functions used in the reviewed literature compared to environmental, economic, and technical objectives, their usage 226 nonetheless reveals additional evolution that has occurred in the LCO field since the review by Pieragostini et al. (2012). An additional point of potential improvement in the future is with respect to the integration of uncertainty into optimization problems, and the use of robust optimization methods. Quantitative uncertainty assessment is relatively underrepresented within the LCA literature (Bamber et al., 2019). However, machine learning methods are now being integrated with LCA for better uncertainty assessment (Romeiko et al., 2024), and new methodologies are being developed to better deal with specific aspects of uncertainty. This includes dynamic LCA, which seeks to integrate information regarding impact time horizons, and how inventory data may change over time (Beloin-Saint-Pierre et al., 2020). In their previous review of the LCO literature, Pieragostini et al. (2012) did not cover any applications of robust optimization. The literature reviewed in this study indicated that application of robust optimization techniques continues to be relatively uncommon in the LCO field. Future research should focus on the potential use of robust optimization methods to provide effective decision support under uncertainty. In this review of the LCO literature, a large body of literature published between 2012 and 2023 has been reviewed to determine current methodological norms and practices in LCO. The data collected supported in-depth discussions of the implications of, and potential trade-offs associated with decisions that may be made during performance of an LCO study related to the optimization framework used, and objective function definition. This subsequently enabled creation of a decision tree that aims to provide LCO practitioners with general decision support for performance of an LCO study, based on the specific goals of the practitioner and the context of their study. This review also effectively highlights how the LCO field has evolved over time, 227 including the wide-spread use of metaheuristics as optimization solvers, increased treatment of many sustainability attributes at both the midpoint and endpoint level, and increased discussion around the preferred outcomes of optimization problems. The information presented herein, and the decision tree generated based on this systematic review will help support increased uptake of LCO by providing practitioners with guidance regarding methodological choices. Increased uptake of these methods may lead to large reductions in environmental impacts across numerous industrial sectors, including energy generation, manufacturing, construction, and agriculture, among others. 228 6. Life cycle optimization of laying hen feed formulations considering environmental impacts and economic costs 6.1 Chapter synopsis In this chapter, an optimization framework is developed and applied to investigate regional optimization of Canadian laying hen feed formulations, accounting for environmental and economic objectives. Regionalized models of feed ingredient production are developed, and the environmental impacts and economic costs of producing, procuring, and transporting those ingredients to five different reference feed mill locations across Canada are quantified, both including and excluding changes to soil organic carbon (SOC) and perennial to annual land transitions in field crop production. These impacts and costs are subsequently integrated into single, bi-, and multi-objective optimization models representing different combinations of objective functions and preferences between those objective functions to determine how formulations change according to different objectives. Following this, a subset of optimal formulations are integrated into baseline LCA models of Canadian egg production in conventional and free range production systems to investigate the environmental impact mitigation potential of using the optimized feeds on Canadian egg farms. A sensitivity analysis is also performed in which the developed framework is extended to include a larger set of potential ingredients in order to explore the impacts of methodological choice regarding the definition of decision variables on the final optimal formulations. 6.2 Context and rationale Feed production is a key contributor to many of the environmental impacts of poultry production (chapter 2). This makes feed a priority target for improving environmental sustainability outcomes. While improved feed use efficiency may meaningfully contribute to 229 environmental impact reductions (chapter 3), it is likely that these contributions may be relatively insignificant compared to impact mitigations that may be derived from the use of alternative feed formulations optimized for least environmental cost as biological limits to feed conversion efficiency are approached. As a result, alternative strategies, such as optimization of feed formulations for least environmental impacts must be explored. In egg production systems, feed formulations aim to use different quantities of ingredients in a mixture to meet the nutritional requirements of hens, and to best support production (Sahman et al., 2009). Traditionally, feed formulations are based on least-economic cost optimization models that satisfy nutritional requirements, with environmental impacts rarely taken into account (Garcia-Launay et al., 2018; Sahman et al., 2009; Uyeh et al., 2019). A recent review by Heidari et al. (2021a), however, highlighted the importance of accounting for environmental impacts in feed formulations, and proposed a framework for identifying regionally resolved, least economic- and environmental- cost feed formulation for laying hens based on the integration of LCA with a goal programming based approach. Optimization of livestock feed formulations for least environmental impacts has shown considerable impact mitigation potential in both pork (de Quelen et al., 2021) and dairy (Marques et al., 2022) production systems. Such an analysis, however, has never been carried out in the context of egg production systems. This work therefore seeks to develop and apply a framework for regionalized optimization of Canadian laying hen feed formulations for environmental and economic objectives to answer the fifth research question of this dissertation. The framework developed in this chapter may subsequently be adapted to take into account different nutritional constraints, and the potential for inclusion of alternative, potentially low impact feed ingredients such as cell proteins, (Cheng and Kim, 2022; Nkhalambayausi-Chirwa 230 and Lebisto, 2012), valorized food waste products (Boumans et al., 2022), and insect meals (El-Hack et al., 2020). 6.3 Methods The baseline Canadian production weighted average laying hen feed formulation for non-organic laying hens as developed by Pelletier (2017) served as the starting point for this analysis. This baseline feed formulation was developed according to producer reported inputs to layer feeds, with those ingredients contributing less than 0.1% of the national, production-weighted average feed composition excluded (Pelletier, 2017). The percent composition of this baseline feed formulation is presented in appendix A, Table A 23. Regional models of production were developed for all ingredients included in this formulation based on best publicly available data. This included models of field crop production based on carbon footprint reports from the CRSC, which provide estimated carbon footprints of major Canadian field crops on the reconciliation unit (RU) level, representing combinations of ecozones and provincial boundaries ((S&T)2 Consultants Inc., 2022a). 6.3.1 Identification of reference feed mill locations It is possible that the feed formulation that is economically- and environmentally optimal may differ depending on feed mill location due to differences in ingredients costs and environmental impacts associated with transportation. To explore how optimal feed formulations may differ between different feed mills, a set of reference feed mill locations was chosen for use in different optimization scenarios. The Animal Nutrition Association of Canada provides a list of commercial feed mills operating in Canada, last updated in March, 2024 (Animal Nutrition Association of Canada, 2024). This list provides locations for commercial feed mills operating in nine of ten Canadian provinces (excluding Prince Edward Island), and zero territories. From this 231 list, it is evident that industrial feed mills are not evenly distributed across Canada. Alberta, Ontario, and Quebec have the highest concentration of industrial feed mills, while a smaller number are found in British Columbia, Manitoba, New Brunswick, Nova Scotia, and Saskatchewan. Five provinces were chosen for inclusion: Ontario, Quebec, Alberta, British Columbia, and Nova Scotia. Ontario, Quebec, and Alberta were chosen because these three provinces have the highest concentration of poultry feed mills in the country. British Columbia and Nova Scotia were chosen as they represent two additional distinct sets of climate conditions (i.e. the Pacific and Atlantic coasts) and regional energy grid mixes compared to the other provinces. Specific reference mill locations were chosen within these provinces as the individual cities with the highest number of industrial feed mills in each province according to the Animal Nutrition Association of Canada. Ontario was the only province that did not have a single city with the largest number of industrial mills – rather, both Wallenstein and St. Mary’s were listed as having three mills operating within the town. For this analysis, Wallenstein was chosen as a reference location, since it is slightly more central to southern Ontario. However, the distance between St. Mary’s and Wallenstein is relatively small (i.e., <100 km), so it was anticipated that this choice would not have a major impact on estimated results. The reference feed mill locations, and the number of industrial feed mills operating in the chosen city for each of these five provinces are given in Table 6-1. Table 6-1. Feed mill reference locations for each of the five provinces included in regionalized optimization of laying hen feed formulations. Province Reference mill location Number of industrial feed mills in this city British Columbia Abbotsford 5 232 Province Reference mill location Number of industrial feed mills in this city Alberta Lethbridge 5 Ontario Wallenstein 3 Quebec St. Hyacinthe 5 Nova Scotia Truro 3 6.3.2 Regionalized modeling of feed ingredients 6.3.2.1 Field crops and processed crop products LCA models characterizing regionalized production of Canadian field crops (i.e., barley, canola, corn, peas, soybeans, and wheat) were developed based on current best publicly available data taken from the recent carbon footprint reports of Canadian commodity field crops prepared for the CRSC ((S&T)2 Consultants Inc., 2022a). All methodological choices and assumptions made during development of these models, as well as models representing processing of canola and soybeans are available in chapter 2, section 2.3.2.2., and appendix A, sections A 1 to A 13, and are not repeated here. When used in chapter 2, these models do not include impacts related to changes in SOC, nor those related to perennial to annual land use transitions. Data to support the inclusion of these impacts are available in the CRSC reports ((S&T)2 Consultants Inc., 2022a). Since SOC dynamics and impacts related to land use changes (LUC) are regionally dependent, these may be a significant factor in decision making regarding regionalized sourcing of different feed ingredients. To investigate how optimal feed formulations may change depending based on regional differences in LUC patterns and SOC dynamics, a second set of field crop models were 233 developed for this analysis that included both SOC changes, and impacts related to perennial to annual LUC. These models are largely the same as those employed previously in chapter 2, with some changes. First, direct fluxes of CO2 representing field-level CO2 emissions and sequestration as a result of on-farm management practices were included in the field crop models. SOC fluxes in the CRSC reports are estimated based on the enhanced IPCC Tier 2 method that has been developed for estimation of soil carbon fluxes from cropland remaining cropland ((S&T)2 Consultants Inc., 2022a) as described in the Canadian NIR (Environment and Climate Change Canada, 2022). These methods are based on the IPCC Good Practice Guidance for Land Use, Land Use Change, and Forestry (LULUCF) (IPCC, 2006), and assume that changes in SOC in agricultural soils are driven by specific changes in management practices. Specifically, it is assumed that if there are no changes in soil management, then there are no associated changes to SOC. In contrast, increases in use of conservation tillage practices, use of perennial cropping systems, and increases in crop yield or crop residue C inputs are assumed to increase SOC stocks and thus lead to removals of CO2 from the atmosphere. The relevant changes in land management practices used for estimation of SOC changes in the CRSC reports are taken from the most recent Canadian Census of Agriculture. Additional soil carbon fluxes may also be promoted through shifts in land use from annual to perennial cropping systems, which may lead to an increase in SOC sequestration, and vice versa. All field crops included in this analysis are annual crops. In the CRSC reports, SOC changes resulting from an annual to perennial shift are not attributed to the annual crop, while those from a perennial to annual shift (i.e., changes promoting release of SOC) are attributed to the annual crop. 234 For both SOC changes related to management practices and LUC, the methods for estimation applied in the CRSC reports are regionally specific, but not crop-specific. Rather, net SOC changes were estimated for each RU as a whole, and these emission were then allocated to the crops produced in each RU proportionally based on total productivity of field crops within that RU. Importantly, it must be noted that the time frame over which agricultural SOC sequestration is expected to occur is relatively short. Following changes in tillage regimes, SOC levels eventually reach a new, stable equilibirum, usually assumed to occur ~20 years following the management change (IPCC, 2003). Upon reaching this equilibrium, continued sequestration of SOC cannot be assumed, and the potential benefits to sustainability outcomes that may incur on the basis of SOC sequestration should no longer be accounted for. The CRSC reports assume that this equilibrium has not yet been reached, though it has been suggested that Canadian soils, particularly in the Prairie provinces, are beginning to reach equilibration (Ashton et al., 2023). This will therefore be an important factor to consider in future work, as upon reaching equilibrium continued sequestration of SOC cannot be assumed, and the potential benefits to sustainability outcomes that may incur on the basis of SOC sequestration should no longer be accounted for. Taken together, however, the data available in the CRSC reports indicate that there may be a large degree of variability in SOC fluxes in different regions in Canada, with Eastern Canadian soils generally losing SOC, and Western Canadian soils generally sequestering SOC. In addition to direct fluxes of CO2, SOC losses may also be accompanied by N mineralization that may contribute additional N that may be lost as field-level N2O emissions. Development of new field crop models including SOC changes therefore also required re-calculation of field level N2O emissions to account for N mineralization, represented by the FSOM 235 term in Table A 11. In the CRSC reports, FSOM is estimated using a combination of estimated changes in SOC for each RU, and default IPCC Tier 1 methods assuming a 10:1 C:N ratio ((S&T)2 Consultants Inc., 2022a). This 10:1 C:N ratio differs from that used in the 2022 Canadian NIR (Environment and Climate Change Canada, 2022), which instead applies an 11:1 C:N ratio based on data collected from soil samples taken in Saskatchewan. Rates of N mineralization associated with decreases in SOC may be impacted by many regionally-specific soil and climate factors (Elrys et al., 2021; Karki et al., 2021), which may contribute to the differences observed between the default IPCC and the Saskatchewan-specific C:N ratios. Since this analysis includes field crops produced across all of Canada, rather than just Saskatchewan or the Prairies, the default 10:1 C:N ratio has been used. Importantly, contributions to direct N2O emissions from N mineralization only occur when there is an associated loss of SOC. As a result field level N2O emissions are estimated to be the same for those RUs which are not estimated to be experiencing SOC losses. Recalculation of N2O emissions for RUs estimated to have net SOC losses followed a three-step approach: 1) The SOC losses associated with each RU had to be back calculated based on factors reported in the CRSC reports. The reports do not provide estimates of SOC changes directly – rather, they report these changes in terms of their contribution to the carbon footprint of each crop (i.e., kg CO2eq/tonne of crop at an appropriate moisture content). The associated changes in SOC were therefore back-calculated by multiplying the reported kg CO2eq/tonne of crop at an appropriate moisture content by 12/44 (i.e., a conversion factor for CO2 to C). 2) The default 10:1 C:N ratio was applied to the estimated C loss to estimate amounts of N mineralized in association with SOC losses. This factor was used as the term FSOM. 236 3) Field level emissions were recalculated using the new factor following methods outline in appendix A, section A 8. 6.3.2.2 Corn dried distillers grains with solubles (DDGS) Distillers grains are commonly produced as a co-product of bioethanol production (Beef Cattle Research Council, 2024). Possible production locations for corn dried distillers grains with solubles (DDGS) production were identified based on a map of Canadian ethanol production facilities from Renewable Industries Canada, a non-profit organization formerly known as the Canadian Renewable Fuels Association (Renewable Industries Canada, 2024). Each biofuel production location listed on the map using corn as a feedstock was considered a possible production facility for corn DDGS. It is possible that this list is not exhaustive, as some facilities were listed without specifying a feedstock. In the absence of specific information regarding the feedstocks used at these facilities, they have been excluded from this analysis. For corn DDGS produced in each facility it was assumed that the corn inputs were sourced from the closest RU to the processing facility, as estimated using the same procedures for determining source RUs for soybean and canola processing as detailed in appendix A, section A 13. Production of corn DDGS was modeled based on a maize distillers grains production process for the US taken from the Agrifootprint 6.3 database (Blonk et al., 2022a), modified to reflect appropriate regional production conditions for each possible production location (i.e., energy grid mixes and corn grain sourcing). 6.3.2.3 Limestone Little information is available regarding production of limestone in Canada. Statistics Canada provides some information on total production of limestone in each province and territory between 2019 and 2023. However, the only provinces with reported values for 237 production are Ontario and British Columbia, with production in Ontario being approximately 10 times higher than in British Columbia. The available data indicates that Ontario is responsible for approximately 28 – 44% of Canadian limestone production in any given year (Statistics Canada, 2024). Information from the Government of New Brunswick estimates that 84% of limestone production occurs in Ontario and Quebec (Keith and Webb, 2015). In this analysis, it is assumed that limestone is produced in either Quebec or Ontario. Specific information describing limestone production locations could not be found to use as a reference location for estimating transportation distances. However, Tomlinson et al. (2008) and Catling et al. (2014) both indicate that alvars and limestone barrens in Canada are highly concentrated in the area of the Quebec City – Windsor Corridor. On this basis, Toronto and Montreal were chosen as reference locations to represent limestone production in Ontario and Quebec, respectively, with transportation distances to each reference feed mill estimated accordingly, as described in section 6.3.3. Production of limestone was modeled based on a limestone production process for Quebec taken from the ecoinvent 3.9 database, with modifications made to reflect possible production conditions in Ontario where necessary. 6.3.2.4 Bakery by-product Bakery by-products may be used as an energy source in livestock diets, with their inclusion representing a form of waste valorization (Kaltenegger et al., 2020). In 2021, the National Zero Waste Council produced a report providing an overview of circular food solutions in Canada. In it, they provide a map of circular food initiatives across the country, indicating that such initiatives exist in all ten provinces and two of three territories (National Zero Waste Council, 2021). While the report does not provide specific locations of these initiatives, they do indicate that over half of the initiatives are found in major urban centres and surrounding areas. 238 Taken together, this information is sufficient to approximate potential source locations for bakery by-product. It has been assumed that there are possible sources in Vancouver, Calgary, Saskatoon, Winnipeg, Toronto, Montreal, Fredericton, and Halifax. Transportation distances to reference feed mills were estimated accordingly, as described in section 6.3.3. Production of bakery meal was modeled based on a wheat flour production process for the US taken from the Agrifootprint 6.3 database (Blonk et al., 2022a), in line with modeling choices made when modeling a similar ingredient in U.S. laying hen feed formulations (Turner et al., 2023a). Modifications were made to this process to reflect appropriate regional energy grid mixes, and regional sourcing of wheat grain, which was assumed to be sourced from the closest RU to the production facility. 6.3.2.5 Rendered animal products Rendering of livestock carcasses is a common practice in Canada for producing livestock co-products, such as meat and bone meal, feather meal, and rendered animal fat. Little information is publicly available regarding the locations of livestock rendering facilities in Canada. The Government of Ontario indicates that there are six rendering facilities operating in the province (Government of Ontario, 2022) and provide locations of each, while the Government of Nova Scotia indicates that there is a single rendering facility operating in the province, but does not provide its location (Perennia, 2021). This facility is assumed to be in Halifax, as it is a relatively central point in the province. The only other information available regarding rendering facility locations came from the websites for two companies, West Coast Reduction (West Coast Reduction Ltd., 2024), and Sanimax (Sanimax, 2024). In total, 18 livestock rendering facilities were identified across Canada from these four sources. Of these, 16 were considered as possible production locations for this analysis. One facility in Calgary was 239 excluded, as both Sanimax and West Coast Reductions indicated they had a facility in Calgary, and potential differences in transportation distances and ingredient costs and impacts that may be present between the two companies were assumed to be negligible. The other excluded facility was a facility operated by West Coast Reduction in Nanaimo, which was excluded due to the difficulties in accurately estimating transportation distances and costs associated with boat transportation from Vancouver Island to Mainland British Columbia. Transportation distances between all production locations and reference feed mill locations were estimated as described in section 6.3.3. Production of rendered animal products was modeled based on a US animal meal production process taken from the Agrifootprint 6.3 database (Blonk et al., 2022a), with modifications made to be reflective of production conditions at each different location. Inputs of field crops to the livestock systems upstream of the rendering facility were modeled using production-weighted Canadian average field crop models, rather than regional models of field crop production. Average systems were used because the identified locations of processing facilities do not necessarily correlate with either the locations of the animal production systems, or the feed mills that supply these systems, and because trying to speculate on where the field crops that enter the animal production systems are sourced would not meaningfully improve the quality of the model without increasing uncertainty. These production-weighted average field crop models were developed in accordance with the proportions of production of each relevant field crop in each RU as described in appendix A, Table A 1. Additionally, it has been assumed that the proportions of animal product inputs to the rendering facility are constant at all locations, and are the same as those used in prior modeling of these ingredients (chapter 2) due to a lack of data reflecting potential availability of different animal products at each rendering site. 240 For use in the optimization problems, all rendered animal products were aggregated into a single ingredient due to an inability to differentiate between production of specific products with the data sets available for modeling. Additionally, while the rendering process is regionalized according to the potential source locations, further regionalization of upstream processes (i.e, ruminant, swine, and poultry supply chain processes contributing to animal meal production) was not carried out due to a lack of information on regionalized livestock production. 6.3.2.6 Vitamin supplements and synthetic amino acids Little information is available regarding sourcing of supplements and other feed additives in the Canadian poultry industry. The Canadian Animal Health Institute provides a list of full members, representing companies that are licensed to market medications and/or registered veterinary health products in Canada (Canadian Animal Health Institute, 2024). There are 18 full members. To determine potential sources of poultry feed additives, the websites for all full members of the Canadian Animal Health Institute were searched to determine if each company sold either poultry-specific or general livestock feed additives. Of the full members, five companies were identified as possible sources of poultry feed additives: Solvet, Elanco, Huvepharma, Phibro, and Zoetis. Addresses for each of these companies were taken from the Canadian Animal Health Institute list, and transportation distances to reference mill locations were estimated as described in section 6.3.3. It is possible that these transportation distances may not be truly representative of truck transportation distances for these ingredients if companies have manufacturing facilities in locations other than those listed as their address. The website of each identified company was searched for any additional information regarding specific locations of production facilities, but no further information was found. It was assumed that all possible additives were produced at each of these possible locations. In the future, efforts should 241 be made to collect more representative data regarding sourcing of feed additives by poultry feed mills. One of these companies (Phibro) is located in the U.S. For production of synthetic methionine, threonine, and valine, and mineral supplements, data was unavailable to meaningfully differentiate between impacts of production at different locations as these production processes can only be modeled generically. For these ingredients, therefore, it has been assumed that the impacts of production are constant at all five possible locations, with transportation distances calculated accordingly. In contrast, for production of synthetic lysine it is possible to meaningfully differentiate between production locations, as this process includes an input of wheat. Canadian production facilities were therefore assumed to source wheat from the RU closest to each company. Production of synthetic lysine in Teaneck, NJ, was excluded due to a lack of data representing U.S. wheat production on a methodologically consistent basis with all other wheat models. Carbon footprint models of U.S. non-durum wheat production have been created by Bamber et al. (2023) using the best publicly available data derived from government statistics and third party LCI databases. However, these models did not include any impacts associated with land use changes, which are included in all of the Canadian field crop models. Since the impacts associated with land use change are regionally dependent, it is possible that their exclusion from the available model of U. S. wheat production could lead to over- or under-estimated impacts of synthetic lysine production relative to production modeled in Canada. Production of synthetic lysine in the U.S. was therefore excluded to avoid potential biases that could result from methodological differences. Canadian models of synthetic lysine were also modified to take into account other regional differences in production, such as provincial energy grid mixes. 242 6.3.3 Estimation of ingredient transportation distances Distances to transport ingredients from their production locations to the reference mill locations were estimated using different approaches for different ingredient types. Two modes of transportation were considered: truck and rail transport. Truck was assumed to be the “default” mode of transportation”, with ingredients instead transported by rail if the distance from production location to reference feed mill location was deemed to be too large. Detailed descriptions of specific protocols used to estimate transportation distances, and to determine the most appropriate mode of transportation for each possible ingredient to each reference feed mill location are provided below. For all field crop ingredients that do not require additional processing, RU centroids were identified using geographic information systems (GIS), and transportation distances by truck from centroids to reference feed mill destinations were estiamted using Google Maps. When directions could not be found from RU centroids to destinations, a representative point was chosen on the closest road that would allow for distances to be calculated (for example, the centroid for RU 28 was found to be in Prince Albert National Park in Saskatchewan, so a representative point was chosen on the highway outside the park for use instead). Proxy locations to represent RU centroids were chosen for RUs 22, 23, 28, and 41. For those field crop ingredients that required further processing (i.e., canola and soy), methods used for identifying processing locations, and the RUs from which these processing locations sourced raw ingredients are described in appendix A, section A 13. Subsequently, truck transportation distances between the identified processing facilities and the reference feed mill locations were estimated using Google Maps. For all other ingredients, potential production locations were identified as 243 described previously in section 6.3.2, and truck transportation distances from these locations to the reference feed mills were estimated using Google Maps. In addition to truck transport, transportation by rail was also included as a potential means of transporting ingredients from production locations to reference feed mill locations. To determine whether an ingredient would be transported by rail or by truck, a multi-step process was followed which is briefly outlined below. A more in-depth discussion of data sources and assumptions used during this process subsequently follows: 1) Identify potential rail shipping and receiving locations for the different kinds of goods that include potential ingredients 2) Determine truck transportation distances from ingredient production locations to the closest appropriate rail shipping location, and from the closest appropriate rail receiving locations to each of the reference feed mill locations 3) Determine a general cut-off value representing the maximum distance an ingredient would be shipped by truck from its production location directly to feed mill locations 4) Examine the potential need for case-specific exceptions to this cut-off distance and determine if any exceptions are required To determine potential rail shipping and receiving locations, the websites for the Canadian National Railway (CN), and Canadian Pacific Railway (CP) were consulted. These companies are the two major rail freight operators in Canada (Transport Canada, 2023). In terms of market shares, CN transports approximately 54% of total Canadian class 1 freight in the 2021-2022 crop year compared to approximately 46% by CP (Parming, 2023). Stations operated by CP are limited to Alberta, Manitoba, and Saskatchewan, while CN services these provinces as well as British Columbia, Ontario, Quebec, and New Brunswick. For this analysis, it was assumed 244 that rail tracks owned by each company may be utilized by the other company (i.e., full track sharing), a practice that has been put in place in some areas in Canada to improve operational efficiency (Cairns, 2014). It is possible that this assumption may not be truly reflective of Canadian rail transportation logistics; however, more accurate modeling of rail transportation lines would require in-depth knowledge of Canadian rail transport logistics and routes. Further, data required to estimate rail-company specific distances are not publicly available. CN does provide a calculator that may be used to estimate rail transit distances via CN owned and operated tracks, however its use is limited to customers of the company. CP does not provide a similar service. The potential ingredients for use in feed formulations may be generally classified into two shipping categories: grains, and bulk goods. CN provides a list of all liquid and dry bulk distribution centres they operate (CN, 2019a), which are sites that handle processed field crops including meals and oils, as well as other food grade liquids and solids. CP provides information on shipping services for forest and industrial products, which is said to include food products such as feed meals (CPKC, 2023a). However, unlike CN, CP does not provide a list of their distribution centres that handle these goods. Rail transportation of all bulk ingredients (i.e., processed field crop ingredients, supplements, etc.) was therefore assumed to only occur between CN distribution centres. Truck transportation distances from bulk ingredient production locations to the closest CN bulk good shipping centre were subsequently estimated using Google Maps. In contrast, both CN and CP provide lists of all distribution centres that handle grains (CN, 2019b; CPKC, 2023b). Similar to bulk goods, truck transportation distances between RU centroids and the closest grain shipping center, and the closest grain receiving centre to each reference feed mill location were estimated using Google Maps. 245 Following identification of these transportation distances, a cut-off distance of 244 kilometers was defined to represent the maximum distance an ingredient would be transported by truck from its production location directly to the reference feed mill before it would transported by rail instead. This value was chosen as it represents the sum of the median transportation distance from RU centroids to the closest grain shipping stations, and the median transportation distance from grain receiving stations to the reference feed mill locations. Median distances were chosen to set this cut-off rather than mean distances to better control for differences in the density and availability of rail shipping and receiving locations across the country. This cut-off value was applied to transportation of both grain and bulk goods. Upon applying this cut-off, some case-specific exceptions had to be made. Specifically, an exception had to be made when the distance from ingredient production facilities to the closest rail shipping station was greater than 244km, but the rail shipping station closest to the ingredient production location and the rail receiving station closest to the reference feed mill location were the same rail stations. This was the case, for example, for corn DDGS produced in Red Deer, Alberta. While the truck shipping distance between Red Deer and the reference feed mill in Lethbridge was 353km, meaning it may instead be transported by rail, the closest bulk good shipping facility to Red Deer and the closest bulk good receiving facility to Lethbridge are both in Calgary. In this instance, therefore, and any similar instances, ingredients were instead assumed to be shipped solely by truck. Tables describing production locations, the primary mode of transportation (i.e., rail or truck) between production locations and reference feed mill locations, and cumulative truck and rail transport distances for each possible ingredient are given in appendix E, Table E 1 to Table E 11. 246 6.3.4 Estimation of ingredient procurement and transportation costs 6.3.4.1 Ingredient procurement costs To include economic objectives in the optimizations performed it was necessary to determine the costs associated with production and transportation of each potential ingredient. The total cost associated with each ingredient is comprised of two components: the market cost of the ingredient, and the transportation cost. Market costs for each ingredient were drawn from a number of different sources. Prices for peas, barley, corn, and wheat were taken from the Outlook for Principle Field Crops report published by AAFC and Statistics Canada, which provide average prices for commodity field crops produced in Canada on a per tonne basis (Agriculture and Agri-Food Canada and Statistics Canada, 2023). Prices for rendered meat and bone meal, animal fat, feather meal, soybean meal, and canola meal were similarly taken from information from Agriculture and Agri-Food Canada (2022) providing the selling price of bulk feed ingredients at different points throughout the country, based on survey data. Information was only available for the prices of these ingredients in single locations, so it was assumed the prices were constant across Canada. Prices for meat and bone meal, animal fat, and feather meal were available at the feed mill level, while soybean and canola meal prices were listed as free-on-board prices, which were assumed to be representative of feed mill prices. Since rendered animal products are modeled as a single, aggregated ingredient as described previously, it has been assumed that the price for this ingredient is the average of each of the individual prices for meat and bone meal, feather meal, and animal fat (i.e., $1.08). The assumed priced for canola oil was taken from Miller et al. (2012), who performed a techo-economic assessment to find the optimal scale of a canola oil processing plant in Alberta in order to minimize the cost of production. Miller et al. (2012) provide canola oil prices in terms of 2008 CAD$ per litre, which 247 was adjusted for inflation to 2023 CAD$, and converted to price on a mass basis assuming an average density of 0.917 g/cm3 in line with Dunford (2018). The price for synthetic DL-methionine supplements was taken from All About Feed, and is representative of synthetic DL-methionine prices in the U.S. for October 2023 (All About Feed, 2023). Synthetic lysine, threonine, and valine were also included as potential ingredients in this optimization. Data could not be found estimating the costs of these synthetic amino acids, so they were assumed to be the same as that of synthetic DL-methionine. The price for corn DDGS was taken from the National Weekly Grain Co-Products Report produced by the USDA, which provides prices for corn DDGS in 11 states in US$ per short ton. An average value was taken from these 11 states and converted to prices per metric tonne (USDA, 2023). The price for bakery by-product was taken from Kwak and Kang (2006), and is representative of prices in Korea as a more temporally or geographically representative estimate could not be found. The price for limestone supplements was taken from the ecoinvent database version 3.9. Finally, for mineral additives, defining a price was difficult due to a lack of available data regarding use of specific additives from the potentially large number that are available. Costs for vitamin and mineral additives were therefore assumed to be the same as those for synthetic DL-methionine. Where necessary, ingredient prices were converted to Canadian dollars using exchange rates assuming that $1 US is equal to $1.38 CAD, and that €1 is equal to $1.46 CAD. All assumed ingredient prices are presented in Table 6-2. When multiple production locations were identified for potential ingredients, it was assumed that market prices were constant across different production locations, and that costs only differed with respect to transportation costs. 248 Table 6-2. Assumed market prices for ingredients included in regionalized optimization of laying hen feed formulations. Ingredient Price (CAD per kilogram) Peas 0.44 Barley 0.42 Corn 0.30 Wheat 0.40 Soybean meal 0.69 Canola meal 0.55 Canola oil 1.19 Meat and bone meal 0.50 Feather meal 0.88 Animal fat 1.87 DL methionine 3.93 Limestone 0.02 Total mineral additives 3.93 Corn DDGS 0.24 Bakery by product 1.73 6.3.4.2 Ingredient transportation costs 6.3.4.2.1 Truck transport Costs associated with ingredient transport by truck are comprised of a fixed and a variable component. The fixed component represents the cost associated with loading and 249 unloading of trucks at ingredient pick-up and drop-off, while the variable component represents the costs associated with transportation of the ingredient, including those associated with driver wages, fuel, and maintenance, and is a function of the distance transported. Assumed transportation costs were taken from Miller et al. (2012), who estimate that typical fixed cost for truck transportation in North America to be $3.26 CAD per tonne, and the typical variable cost to be $0.10 CAD per tonne-kilometer transported. While Miller et al. (2012) do not explicitly state that the estimated costs are given in CAD, it is assumed this is the case as the analysis the authors performed was focused on provisioning of agricultural feed stocks for production of biofuels in Alberta. Estimated costs associated with freight trucking were not adjusted for inflation. It is possible that the real truck freight costs may be between 9% lower, and 52% higher than those used in this analysis, representing the percentage change in Canadian price index for freight trucking between 2013 and 2022 (Statistics Canada, 2022), depending on how the goods transported are classified. In the absence of specific information regarding transportation of agricultural products, the unadjusted costs from Miller et al. (2012) have been used. It is not anticipated that this choice would make a substantial difference in optimal ingredient mix as differences between ingredients in truck freight costs are driven by transportation distances, while other components of the total cost function remain fixed. Total ingredient cost at each reference feed mill location for those ingredients transported by truck may therefore be described by the function 𝑇𝑜𝑡𝑎𝑙 𝑐𝑜𝑠𝑡 = 𝑃𝑟𝑜𝑐𝑢𝑟𝑒𝑚𝑒𝑛𝑡 𝑐𝑜𝑠𝑡 + (2 × 3.26) + (0.1 × 𝑇𝑟𝑢𝑐𝑘 𝑡𝑟𝑎𝑛𝑠𝑝𝑜𝑟𝑡 𝑑𝑖𝑠𝑡𝑎𝑛𝑐𝑒) where the fixed transportation cost is doubled to reflect ingredient loading at the production facility and unloading at the reference feed mill location. 6.3.4.2.2 Rail transport 250 Costs associated with rail shipping were taken from Parming (2023), who estimates costs of rail freight in US cents per tonne mile. This estimate was converted to cents CAD per tonne kilometer of transport, assuming 1.38$ CAD per US dollar, and 1.61 km per mile. This gave rail freight an estimated cost of approximately 0.036$ CAD per tonne kilometer transported. In addition to the cost of transporting ingredients by rail, the total cost of rail freight also includes costs associated with truck transportation of ingredients from production facilities to shipping stations, and from receiving stations to the feed mill. Total ingredient cost at each reference feed mill location for those ingredients transported by rail may therefore be described by the function 𝑇𝑜𝑡𝑎𝑙 𝑐𝑜𝑠𝑡 = 𝑃𝑟𝑜𝑐𝑢𝑟𝑒𝑚𝑒𝑛𝑡 𝑐𝑜𝑠𝑡 + (3.26 + 0.1𝑎) + (0.036𝑏) + (3.26 + 0.1𝑐) where a and c represent transportation distances by truck to the rail shipping station, and from the receiving station to feed mills, respectively, and b represents the transportation distance by rail. Parming (2023) does not report any fixed costs associated with rail shipping, so none have been assumed here. Rather, it is assumed that any costs associated with transferring ingredients to/from trucks to rail shipping containers is included in the costs estimated by Parming (2023). Transportation distances between rail shipping and receiving stations were estimated using SeaRates.com. 6.3.5 Identification of laying hen nutrient requirements In order for a feed formulation to be viable, the formulation must meet all of the nutritional requirements of laying hens. A number of factors may influence the dietary needs of laying hens. For example, the type of housing systems in which hens are housed (i.e., caged or cage-free) may alter hen nutritional requirements. However, literature comparing nutrient requirements across housing systems is relatively limited and often provides contradictory results, making development of feeding guidelines in different housing systems difficult. Some 251 suggest that differences in management practices are more determining of nutrient intakes than are differences in housing systems (Karcher et al., 2015). Nutrient requirements may also be impacted by specific productivity goals of the system. For example, feeding diets higher in methionine may result in larger egg sizes, which may be more profitable for producers. Regardless, while subtle changes may be made, it is necessary that upper and lower boundaries for feed nutrient contents are strictly adhered to in order to ensure hen welfare and productivity do not deteriorate (Moss et al., 2021). Other additional factors that influence hen nutritional requirements include both hen genetics and timing within the lay cycle – birds may have different nutritional requirements at different points throughout the lay cycle. The management guide for Babcock White layers, for example, indicates that hens should be fed a diet that is between 18 and 18.5% crude protein during the productive phase from 16 weeks of age to 38 weeks of age, and that this content should decrease to between 15 and 15.5% from 78 weeks of age to the end of the cycle (Babcock, 2022). This differs slightly from recommendations from the management guide for Lohmann LSL Classic hens, which suggests that dietary crude protein content should begin at 18% during phase 1 of the lay cycle (i.e., 19 to approximately 50 weeks of age), eventually decreasing to 16.8% during phase 3 of the lay cycle (i.e., from 70 weeks of age to the end of the cycle) (Lohmann, 2022). Given the potential variability across different sources, the choice of sources from which to take nutritional requirements is important and may result in changes to the optimal feed formulation that is identified. When optimization of feed formulations has been explored in other livestock species, data on nutrient requirements has been taken from national databases (Castrodeza et al., 2005; Dooyum et al., 2018), and the literature (Babić and Perić, 2011; Şahman et al., 2018). In Canada, the current Code of Practice for the Care and Handling of 252 Pullets and Laying Hens does not provide specific guidance on the nutritional requirements of laying hens. Rather, this code dictates that all birds must receive feed that meets daily nutrient requirements to ensure good health and to avoid metabolic and nutritional disorders, and recommends that practitioners consult breed-specific management guides and/or qualified advisors when formulating feed (National Farm Animal Care Council, 2017). In line with recommendations from the Code of Practice, the nutrient requirements used herein were therefore derived from breed management guides. According to primary data collected as described in chapter 2, section 2.3.2.1.1, three specific breeds of hens account for the majority (~77%) of non-organic Canadian egg production: Lohmann LSL Classic (41.98%), Shaver White (18.62%), and DeKalb White (16.42%). Since Lohmann LSL Classic hens represent the largest share of Canadian egg production, including more than twice as much production as the next most common breed of hens, nutrient requirements were defined with respect to the Lohmann LSL Classic breed management guides. Lohmann provides two different management guides for their Lohmann LSL Classic hen: one for caged production systems (Lohmann, 2022), and one for alternative (i.e., non-cage and free range) systems (Lohmann, 2021). Within each of these management guides, Lohmann suggests a phase-based feeding structure to reflect potential changes in hen nutritional requirements throughout the life of the bird. In the caged production guide, the three phases are delineated as approximately 19 to 50 weeks of age, 50 to 70 weeks of age, and 70 weeks of age onwards. Determination of a single set of nutrient constraints for all Lohmann LSL Classic production therefore followed a four-step process. First, the nutrient requirements as percentage of dietary composition was taken from the management guides for all listed nutrients and for all three phases. Second, it was determined what proportion of the lay cycle was attributable to each 253 phase, assuming hens were placed at 18 weeks of age and an average Canadian lay cycle length of 52 weeks (i.e., 70 weeks of age in total), as reported in chapter 2. Third, these proportions were used as weighting factors to determine a phase weighted average nutrient composition for both caged and alternative systems. Finally, of the share of Canadian egg production from Lohmann LSL Classic hens, it was determined what proportion was attributable to caged and alternative systems. These proportions were subsequently used to create a single set of production-weighted average nutrient requirements from the phase-weighted requirements. This set of nutrient constraints was subsequently used in feed formulation optimization with respect to Lohmann LSL Classic hens. The production-weighted average minimum nutrient requirements used for optimization are presented in Table 6-3. In both the caged and alternative housing management guides, it is noted that regular phase feeding energy content is 11.4 MJ of metabolizable energy per kilogram of feed. This number has therefore been used as the required energy content. Table 6-3. Nutritional constraints used in regionalized optimization of laying hen feed formulations as derived from Lohmann LSL Classic breed management guides Nutrient Minimum percentage in diet, unless otherwise indicated Metabolizable energy 11.4 MJ/kg Protein 17.85 Calcium 4.22 Phosphorus 0.59 Lysine 0.92 Methionine/Cysteine 0.83 254 Nutrient Minimum percentage in diet, unless otherwise indicated Tryptophane 0.20 Threonine 0.64 6.3.6 Identification of ingredient minimum and maximum inclusion rates For many of the ingredients included in the baseline feed formulation, upper limits exist over which additional consumption may be harmful to laying hens. Jacob and Pescatore (2012), for example, provide an in-depth review of the use of barley as an ingredient in poultry diets, noting that high levels of barley consumption by poultry may lead to gastrointestinal issues due to the high concentration of nonstarch poly-saccharides in barley grain. For this reason, it was necessary to define constraints representing maximum allowable intakes of each possible feed ingredient to ensure no upper limits were bypassed that may compromise hen health. Data on maximum inclusion rates were taken from a number of different sources. Canola meal was assumed to have a maximum inclusion rate of 20%, based on Oryschak et al. (2020). Feather meal was assumed to have a maximum inclusion rate of 5%, based on Senkoylu et al. (2005). Feedipedia suggests that the optimal inclusion rate in layer feeds for corn DDGS is 15% (Heuzé et al., 2015b), though others have found conflicting results at both higher and lower inclusion rates. Loar II et al. (2010) found that inclusion of corn DDGS in laying hen diets at up to 32% did not negatively impact egg productivity or quality measures, while Shalash et al. (2010) suggest that inclusion at rates greater than 10% may have detrimental effects. Based on these, it was assumed that 15% was the maximum inclusion rate. Estimates of the maximum inclusion rate for canola oil are highly variable in the literature. Ceylan et al. (2011) found that 255 inclusion of canola oil in laying hen diets at a rate of 3% does not negatively impact egg productivity or quality metrics, while Yuan et al. (2019) found that inclusion at a rate of 4% significantly decreases layer performance. Finally, Gao et al. (2021) reviewed studies investigating inclusion of canola oil in laying hen diets, concluding that inclusion at a rate of up to 5% does not significantly decrease performance and may even improve layer performance, while a rate of 6% does significantly reduce productivity. Based on these studies, a maximum inclusion rate of 3% was assumed for canola oil. While this may be a conservative assumption based on the literature, inclusion of canola oil at a rate of 3% would be an almost ten-fold increase in the amount of canola oil in the diet compared to the current baseline of 0.39% as reported in chapter 2. Similar variability was seen in the literature regarding maximum inclusion rates for soybean meal. A 19.8% soybean meal diet was used as a control by Harms and Russell (2004), which is similar to the 19.29% soybean meal diet used without negative effects on hen productivity by Shi et al. (2012). A 26.31% soybean meal diet was used without negative effects by Burley et al. (2013). Based on these, 26% was chosen as a maximum upper limit for soybean meal. Finally, a maximum inclusion rate of 30% was chosen for bakery by-product, in line with Olafadehan et al. (2010). For wheat, corn, and peas, it was assumed that there was no maximum inclusion rate in the feed formulation. This choice for wheat was based on Cufadar et al. (2010), who showed that neither egg production nor quality were impacted by inclusion of up to 60% wheat in laying hen feed. The authors found the same results for inclusion of corn at up to 60% of the diet, while Harms and Russell (2004) used a 70.35% corn diet as their control diet. On an industrial scale, U.S. laying hen feed formulations reported by Turner et al. (2023b) indicate a similarly high tolerance for corn in both caged and cage-free laying hen feeds (i.e., approximately 52% and 256 54% of feed on a mass basis). Finally, peas were assumed to have no upper limit based on information available on Feedipedia (Heuzé et al., 2017a). For some ingredients, specific maximum upper limits could not be identified due to data availability constraints. For both meat and bone meal and rendered animal fats a lack of information regarding the specific animal types from which these ingredients were derived meant constraints could not be defined because they may be different for different animal sources. For example, Orda et al. (2012) found that inclusion of porcine-derived blood meals in laying hen diets at a rate of 4% did not negatively impact productivity parameters, while Çatlı et al. (2012) found that inclusion of cattle-derived meal at the same rate did negatively impact productivity parameters, although this analysis was performed using post-molt birds. More generally, van Krimpen et al. (2010) found that inclusion of processed animal protein in laying hen diets at rates of 12% significantly reduced productivity, while inclusion of these products at rates of up to 8.96% may actually improve productivity. A maximum upper limit of 8.96% was therefore chosen in line with van Krimpen et al. (2010). Similar constraints could not be placed on rendered animal fats. Ravindran et al. (2016) provide an in-depth review of the use of rendered animal fats in poultry diets, including the role of fats as energy sources, and the physiological basis for fat utilization by poultry. Since animal products are modeled here as a single aggregated ingredient due to data availability constraints, it has been assumed that animal products may make up a maximum of 8.96% of the diet on a mass basis. This maximum inclusion rate is substantially higher than the amount currently included in the baseline feed formulation described in chapter 2 (i.e, 4.44% of the diet by mass). Finally, some ingredients are included in the diets as supplements with the goal of ensuring adequate levels of single nutrients are met. These ingredients include limestone, which 257 is a source of calcium in poultry diets, trace vitamins and minerals, and DL-methionine, lysine, threonine, and valine, which are included in order to meet poultry amino acid requirements. Since these ingredients only provide single nutrients, specific constraints were not placed around the amount of each that may be present in the diet; rather, constraints defined around nutritional requirements are sufficient as these ingredients will only be included in order to supplement other ingredients when they cannot meet the nutritional constraints on their own. Further, since these ingredients represent potentially important supplements, restricting the amounts of each that may be present in the diet increases the risk of the optimization not being able to find a feasible solution if nutritional constraints cannot be met within the maximum inclusion rates of all ingredients and supplements. Since the exact compositions of vitamin and mineral supplements used as a feed ingredients are unknown, a minimum inclusion rate is defined for this ingredient that equals the amount included in the baseline diet described in chapter 2. This minimum inclusion rate assumes that this amount of supplementation is required to ensure nutritional requirements are adequately met for those nutrients not accounted for during definition of the nutritional constraints. A complete description of all assumed minimum and maximum inclusion rates are given in Table 6-4. Table 6-4. Summary of minimum and maximum ingredient inclusion rates used as constraints in regionalized optimization of laying hen feed formulations Ingredient Minimum inclusion rate (%) Maximum inclusion rate (%) Source Corn NA Harms and Russell (2004), Cufadar et al. 258 Ingredient Minimum inclusion rate (%) Maximum inclusion rate (%) Source (2010) Corn dried distillers grains 15% Heuzé et al. (2015) Soybean meal 26 Burley et al. (2013) Bakery by-product 30 Olafadehan et al. (2010) Wheat NA Cufadar et al. (2010) Meat and bone meal 8.96 van Krimpen et al. (2010) Feather meal 5 Senkoylu et al. (2005) Animal fat NA Peas NA Heuzé et al. (2017) Canola meal 20 Oryschak et al. (2020) 259 Ingredient Minimum inclusion rate (%) Maximum inclusion rate (%) Source Canola oil 3 Ceylan et al. (2011) Limestone NA Barley 30 Jacob and Pescatore (2012) Trace vitamins 0.99 NA DL-methionine NA 6.3.7 Optimization problem definition Without a loss of generality, an optimization problem may be described by min 𝑓𝑚(𝑥) where m are the optimization objectives, known as objective functions, subject to 𝑔𝑗(𝑥) ≤ 0 ℎ𝑘(𝑥) = 0 where g represents the inequality constraints and h represents the equality constraints acting upon the optimization problem, and the values the decisions variables may take are bounded by upper and lower limits described by 260 𝑥𝑖𝐿 ≤ 𝑥𝑖 ≤ 𝑥𝑖𝑈 For definition of objective functions, life cycle environmental impacts associated with production and transportation of each potential ingredient to each reference mill location were estimated using the CML IA baseline LCIA method. When defining objective functions for an optimization problem, inclusion of many objectives increases problem complexity and may be detrimental to performance of the optimization algorithm. Chapter 5 describes in detail various strategies that may be used to simplify optimization problems, including reduction of the number of objective functions to be considered. To reduce the number of objective functions considered, pairwise correlations between environmental impacts per kilogram of ingredient produced in each possible location were computed. When correlations between environmental impacts were greater than 0.9, only one of the impact categories was maintained for use in the optimization problem, with the impact category that is most easily communicable retained. While choice of which specific impact categories were retained for optimization was a subjective value choice, the high degree of correlation between potential environmental objective functions means that this choice should have little impact upon the final optimal results, and that results would not change substantially if a different suite of impact categories was retained instead. The following impact categories were therefore excluded as possible objective functions: - Abiotic depletion, due to a high correlation with human toxicity - Abiotic depletion (fossil fuels), due to a high correlation with GHG emissions - Freshwater aquatic ecotoxicity, due to a high correlation with acidification - Ozone layer depletion, due to a high correlation with acidification - Terrestrial ecotoxicity, due to a high correlation with acidification - Photochemical oxidation, due to a high correlation with GHG emissions 261 - Marine aquatic ecotoxicity, due to a high correlation with human toxicity Correlations between impacts were checked for estimated impacts across both treatments of SOC and perennial to annual land use transitions for field crops. When SOC changes and perennial to annual land transitions were included, the correlation between photochemical oxidation and global warming potential fell below the 0.9 threshold to 0.89. In spite of this, photochemical oxidation was still excluded in the optimization problems including SOC changes and perennial to annual land transitions for consistency in problem definition across the different treatments. Relaxation of the correlation threshold for exclusion in this case does not impact any other impact categories, as the next highest correlation between any two impacts is 0.82, between marine aquatic ecotoxicity, which is already excluded, and eutrophication. The environmental impacts identified for potential inclusion in the optimization problems were therefore acidification, eutrophication, GHG emissions, and human toxicity. In total, four different sets of objective functions were defined with a total of 162 decision variables. Two of these optimization problems were single-objective problems, while the third and fourth were bi- and multi-objective problems, respectively. Bi- and multi-objective optimization problems were formulated and solved because it was likely that economic and environmental objectives would be conflicting – particularly when SOC changes were considered – given the heterogeneity in environmental impacts for the same ingredient produced at different places across Canada. The first set of objective functions was a single objective that sought to minimize the economic cost of production of 1 tonne of feed. This objective may be described by the equation min ∑(𝑥𝑖)(𝑖𝑛𝑔𝑟𝑒𝑑𝑖𝑒𝑛𝑡 𝑐𝑜𝑠𝑡𝑖)𝐽𝑖 262 The second set of objective functions was also a single objective that sought to minimize the GHG emissions associated with production of one tonne of feed. The single objective function for this problem may be described by the equation min ∑(𝑥𝑖)(𝑖𝑛𝑔𝑟𝑒𝑑𝑖𝑒𝑛𝑡 𝐺𝐻𝐺 𝑒𝑚𝑖𝑠𝑠𝑖𝑜𝑛𝑠𝑖)𝐽𝑖 The third set of objective functions was a bi-objective problem that sought to simultaneously minimize both cost and GHG emissions, as described by the equations above. Finally, the fourth set of objective functions was a multi-objective problem that included five different objective functions. This set of objective functions included both of the previous objectives to minimize economic costs and GHG emissions, while also adding in objectives to minimize acidification, eutrophication, and human toxicity impacts. These additional objective functions may be described by the equations min ∑(𝑥𝑖)(𝑖𝑛𝑔𝑟𝑒𝑑𝑖𝑒𝑛𝑡 𝑎𝑐𝑖𝑑𝑖𝑓𝑖𝑐𝑎𝑡𝑖𝑜𝑛 𝑖𝑚𝑝𝑎𝑐𝑡𝑠𝑖)𝐽𝑖 min ∑(𝑥𝑖)(𝑖𝑛𝑔𝑟𝑒𝑑𝑖𝑒𝑛𝑡 𝑒𝑢𝑡𝑟𝑜𝑝ℎ𝑖𝑐𝑎𝑡𝑖𝑜𝑛 𝑖𝑚𝑝𝑎𝑐𝑡𝑠𝑖)𝐽𝑖 min ∑(𝑥𝑖)(𝑖𝑛𝑔𝑟𝑒𝑑𝑖𝑒𝑛𝑡 ℎ𝑢𝑚𝑎𝑛 𝑡𝑜𝑥𝑖𝑐𝑖𝑡𝑦 𝑖𝑚𝑝𝑎𝑐𝑡𝑠𝑖)𝐽𝑖 The single-objective optimization problems were solved using a linear programming approach utilizing the simplex algorithm. In line with recommendations given in chapter 5, this approach was deemed most appropriate as the problems are characterized by having low dimensionality and simple functions to evaluate, solving speed is of low priority, and the problem is fully linear with respect to the objective and constraint functions. 263 In contrast, the bi- and multi-objective optimization problems were solved using a weighted sum approach. This approach involves conversion of a multi-objective optimization problem into a single objective problem seeking to minimize linear combinations of objective functions multiplied by scalar weights. This approach may be defined by the equation 𝑈 = ∑ 𝑤𝑖𝐹𝑖(𝑥)𝑘𝑖=1 where U is the linear combination of objective functions i=1 to k multiplied by the associated weights, wi. If all weights are positive, and if the feasible solution space is convex, then any solution that minimizes the linear combination of objective functions may be considered pareto optimal across all possible weights (Marler and Arora, 2010). Since all constraint functions in this problem formulation are linear, which, by definition, are convex functions, the feasible space is also convex, meaning all solutions to this problem using this approach are pareto optimal (Marler and Arora, 2010). Importantly, use of the weighted sum approach to optimization is, in this case, functionally equivalent to use of a metaheuristic method such as NSGA 2 combined with a post hoc multi-criteria decision analysis technique. In both cases, pareto-optimal solutions may be identified, with a decision-making process performed to choose a single solution among the suite of pareto-optimal solutions identified. When using a weighted sum approach, however, the decision making step is included in the solving process, negating the need for use of multi-criteria decision analysis techniques following identification of pareto optimal solutions. Assuming similar weights were used, use of either the weighted sum approach or a metaheuristic method identifying a complete set of pareto optimal solutions then subject to a decision making process should result in the same single optimal solution being identified. 264 When using a weighted sum approach, weights must be defined for each objective function that reflect their relative importance, with weights generally constrained such that they are greater than zero and the sum of all weights is equal to one. An iterated weighted sum approach may subsequently be used such that many pareto optimal points are identified (Williams and Kendall, 2017). One key consideration when setting objective function weights is the magnitude of the objective functions relative to one another, as it may be possible for one objective function to disproportionately contribute to the weighted sum being minimized if the magnitude of the objective is substantially higher than the others, even if it is assigned a relatively low weight. All objective functions used in the bi- and multi-objective optimizations were therefore min-max normalized such that the maximum value was 1 and the minimum value was 0 in order to avoid such issues, as suggested by Marler and Arora (2010). For the bi-objective optimization problem, only a single set of weights was defined, with each objective function weighted equally (i.e., wi = 0.5). Definition of this problem with these weights allows the bi-objective optimization solution to represent a mid-point between the solutions to each single-objective optimization problem. For the multi-objective problem, a two-step approach was used to define weights. First, a set of weights was defined representing relative weights of economic cost and environmental impacts. Four sets of weights were defined, with cost being assigned weights of 0, 0.25, 0.5, and 0.75, and the total weights for environmental impacts assigned such that the sums equal to one (i.e., 1, 0.75, 0.5, and 0.25). Second, the total environmental impacts weight was broken down into individual weights representing each environmental objective function. Weights for individual environmental objectives were derived from suggested weights for environmental impacts reported by the European Commission (Sala et al., 2018b), who derived weights for different impacts during 265 stakeholder workshops held by UNEP/SETAC. Weights were defined for 16 environmental impacts, many of which were not assessed during this analysis. Additionally, in the case of human toxicity and eutrophication impacts, weights were defined for multiple impact subtypes. For human toxicity, weights were defined for cancer and non-cancer effects, while for eutrophication weights were defined for terrestrial, freshwater, and marine eutrophication. This is in contrast to the environmental impacts assessed in this analysis, which assessed only a single impact category each for human toxicity and eutrophication. To define weights for use in the weighted sum optimization, the final weighting factors for impacts recommended by the European Commission were therefore proportionally re-distributed among the impact categories included in this optimization. Since the documentation for the CML-IA baseline LCIA method (Guinée et al., 2002) is not clear if the eutrophication and human toxicity impacts assessed represent one, or a combination of the impact categories included in the European Commission weighting factors, average weighting factors for these impact categories were used. These factors subsequently underwent a second round of proportional re-weighting such that they summed to the chosen weighting factor for cumulative environmental impacts associated with each chosen weight for economic costs. A complete breakdown of all weights used is shown in Table 6-5. In total, therefore, 65 different optimization problems were defined and solved. This included: - 5 single objective problems to find economically optimal formulations (i.e., one problem per reference mill location) - 10 single objective problems to find GHG optimal formulations (i.e., one problem per reference feed mill location across two soil carbon treatments) - 10 bi-objective optimization problems (i.e., one problem per reference feed mill location across two soil carbon treatments) 266 - 40 multi-objective optimization problems (i.e., one problem per reference feed mill location across four different objective weighting scenarios and two soil carbon treatments) All optimization problems were implemented and solved using the CPLEX Optimization Studio. Table 6-5. Weights used for multi-objective optimization with five objective functions across four different scenarios for regionalized optimization of laying hen feed formulations. Scenario Economic objective weighting factor GHG emissions weighting factor Acidification weighting factor Eutrophication weighting factor Human toxicity weighting factor 1 0 0.65 0.06 0.19 0.10 2 0.25 0.49 0.05 0.14 0.07 3 0.5 0.32 0.03 0.10 0.05 4 0.75 0.16 0.02 0.05 0.02 6.3.8 Estimation of the environmental impact mitigation potentials of using optimal feed formulations on Canadian egg farms Following identification of optimal feed formulations for each set of objective functions and mill locations, a subset of formulations were chosen to incorporate into a subset of the baseline housing-system specific LCA models developed in chapter 2 to determine the potential differences in estimated impacts per tonne of eggs produced using different feeds. Incorporation of these optimal formulations into the chosen baseline egg production LCA models is not 267 intended to be exhaustive; rather, this is intended as an illustrative example of how the optimal formulation framework may be incorporated into egg production system management to improve environmental sustainability outcomes, and how impacts may differ depending on the relative weight placed on different objectives during formula optimization. The baseline housing egg production models for conventional cage and free range production were chosen for this investigation since they have the best and worst observed feed conversion efficiencies, respectively. Abbotsford and Wallenstein were chosen as the reference mill locations for this analysis, as feeds produced at these mills were generally observed to have the highest and lowest economic cost and environmental impacts per tonne of feed milled, respectively. The single-objective, economically optimal, and scenario 1 multi-objective optimal (i.e., the multi-objective optimal feed with no weighting for the economic objective) were chosen as the formulations for incorporation into the baseline egg production models. These two formulations were chosen as they exhibit the greatest variability in objective function weighting across the different optimizations performed. Despite being structurally the same LCA models as those presented in chapter 2, for this analysis direct comparisons are not drawn between the baseline results given in chapter 2 and those estimated here as the baseline feed formulation used in chapter 2 is a production-weighted average formulation that is not representative of any single specific feed formulation. As a result, this formulation does not adhere to the same nutritional constraints imposed upon the formulas identified through life cycle optimization. Specifically, compared to the nutritional constraints used during optimization described in Table 6-3, the baseline feed formulation described in chapter 2 is deficient in all nutrients except metabolizable energy, and protein content. This does not mean that Canadian poultry feed mills are providing feeds that do not adequately meet the 268 nutritional demands of laying hens; rather, this is an artifact of the sampling and aggregation process used by Pelletier (2017) to arrive at the baseline feed formulation used in chapter 2. Instead, for this analysis, the single-objective optimal feed that minimizes economic costs will be considered the reference feed against which the multi-objective optimal feed is compared, as the majority of poultry feeds are currently formulated on a least cost basis (Heidari et al., 2021a). All of the formulations chosen were those from optimizations performed including SOC changes and perennial to annual land transitions. These decisions were made in order to maximize potential variability in final results. All protocols used for estimation of manure management emissions and LCA modeling protocols were as described previously so they are not described again in this chapter. Energy requirements per tonne of feed milled are assumed to be constant at all reference feed mill locations, in line with those used in chapter 2. 6.3.9 Sensitivity analysis – Extension of optimization framework to include other ingredients Upon examination of the optimal feed formulations, a sensitivity analysis was designed in which a phosphate supplement was added as a potential ingredient. This sensitivity analysis was performed for two reasons. The first was to illustrate the potential for extension of the optimization framework developed in this chapter to include other ingredients not previously included in the laying hen feed formulation presented by Pelletier (2017). The ability to continually iterate upon this analysis through incorporation of alternative ingredients may provide a promising pathway for future research into incorporation of alternative feed ingredients, such as valorized food wastes, insect-derived ingredients, and other sources of low opportunity cost biomass into laying hen feeds. The second was because of the identification of phosphate content as an active constraint across the optimizations performed. This observation 269 led to the choice of phosphate supplements as the ingredient to illustrate this extension, as it also allows for investigation of if the phosphate nutrient requirement identified in section 6.3.5 was driving the inclusion of high impact rendered animal products in environmentally optimal feed formulations. A synthetic phosphate supplement was therefore modeled as in a recent LCA of the U.S. egg industry (Turner et al., 2023a), as a supplement of diammonium phosphate with an assumed P content of 25.88% and N content of 18%. This supplement was assumed to be produced at the same production locations as all other supplements included, and the cost was assumed to be the same as that of DL-methionine. In performing this sensitivity analysis, the objective values for all decision variables were re-normalized using the same procedure previously described to accurately account for the new decision variables, and two new multi-objective optimization models were developed using the objective weights for scenarios 1 and 4, as described previously in Table 6-5. These optimizations were performed excluding upstream SOC changes and perennial to annual land transitions, and were performed for the Wallenstein feed mill. 6.4 Results and discussion 6.4.1 Single-objective, economically optimal feeds Across the five reference feed mill locations, the economically optimal feed formulations used the same mix of ingredients, with small differences in exact ingredient mixes and sourcing locations. For all economically optimal feeds, corn and canola meal were the predominant ingredients, cumulatively accounting for approximately 59 – 60% of the economically optimal feeds at each reference mill locations on a mass basis. All economically optimal formulations also included soybean meal, corn DDGS, limestone, rendered animal meal, and synthetic methionine and vitamin supplements. The majority of these ingredients were sourced from the 270 production facility closest to the reference feed mill location, reflecting the lower cost of transportation associated with this choice. The exception to this was soybean meal, which was instead sourced from production facilities that minimized the distances between the facility and the closest bulk goods rail shipping station. With regards to ingredient selection, the specific ingredient mix chosen was generally driven by constraints related to dietary micronutrient contents. For all reference mill locations, phosphorus and methionine + cysteine contents were active constraints, while calcium content was an active constraint for feeds milled in Abbotsford, Lethbridge, and Truro. In contrast, lysine, tryptophan, threonine, and crude protein content were inactive constraints for all feed mills, reflecting that these nutrients are easily supplied by the mix of ingredients represented by the decision variables. In all cases, the economically optimal formulations included the maximum allowable amounts of canola meal, corn DDGS, and rendered animal products in the diets. This included the economically optimal feed milled at Truro, Nova Scotia, which included the maximum amounts of these ingredients despite their closest production facilities being located in Manitoba and Ontario, respectively. That these ingredients are included at the maximum allowable rates in all formulations reflects the relative efficiency with which these ingredients are able to deliver specific nutrients relative to their procurement costs. Rendered animal products, for example, are valuable as the potential ingredient with the highest amount of available phosphorus, while canola meal and corn DDGS have the second and third highest amounts. Both rendered animal products and corn DDGS are also good sources of methionine, having the second and fourth highest concentrations of this amino acid of the potential ingredients. 271 Across all reference feed mill locations, total cost per tonne of feed milled for the economically optimal formulations ranged from $463.02 to $505.04, with the cheapest feed milled in Wallenstein, and the most expensive feed milled in Abbotsford. Figure 6-1 shows the composition of one tonne of economically optimal feed at each reference mill location, while Figure 6-2 shows contributions to total costs attributable to each ingredient. Across all economically optimal feed formulations, corn, canola meal, and rendered animal meal were the greatest contributors to cost per tonne of feed milled, cumulatively accounting for approximately 70-75% of total costs. Also of note is the disproportionately high contribution to total costs associated with vitamin supplementation, which accounted for approximately 8% of total costs despite only accounting for 1% of the optimal feeds on a mass basis, reflecting the relatively high procurement cost associated with these ingredients. The complete feed formulations, including breakdowns of where ingredients are sourced from, and contributions from each ingredient to estimated costs per tonne of feed milled are available in appendix E, Table E 12to Table E 16. Estimated impacts per tonne of economically optimal feed milled at each reference location are presented in appendix E, Table E 17 and Table E 18. Two tables are used to report estimated impacts, as only a subset of impact categories (i.e., acidification, eutrophication, and GHG emissions) are influenced by the treatment of SOC and perennial to annual LUC. One table therefore reports estimated impacts for those impact categories not influenced by the treatment of SOC and LUC, while the other reports those impact categories that are influenced. Production of rendered animal products was the largest contributor to all impact categories assessed across all reference feed mill locations and both treatments of SOC and perennial to annual land transitions. Despite only accounting for approximately 9% of the economically optimal feed formulations on a mass basis, this ingredient was responsible for 272 anywhere from 35% – 97% of impacts per tonne of eggs produced. Canola meal was generally the second most impactful ingredient across the majority of impacts and reference locations assessed, although the maximum contribution to any impact made by canola meal was approximately 27%, which is relatively more proportionate to the amount of canola meal in each formulation (i.e., 20% by mass for all reference mill locations). Figure 6-1. Composition per tonne of economically optimal feed at each reference feed mill location 0.00100.00200.00300.00400.00500.00600.00700.00800.00900.001000.00Abbotsford Lethbridge Wallenstein St. Hyacinthe TruroComposition per tonne of feed milled (kgs)Animal mealVitamin supplementsMethionineLimestoneCorn DDGSSoybean mealCanola mealCorn 273 Figure 6-2. Contributions to total costs per tonne of economically optimal feed milled at each reference feed mill location 6.4.2 Single-objective, GHG optimal feeds Similar to the economically optimal feed formulations, the GHG optimal formulations were consistent in ingredient makeup regardless of reference mill location, or SOC and LUC treatment. In all cases, GHG optimal feeds were comprised of corn, canola oil and meal, soybean meal, corn DDGS, limestone, vitamin supplements, and rendered animal meal. Unlike the economically optimal formulations, none of the GHG optimal formulations included synthetic methionine, and all included canola oil. All of the GHG-optimal feeds may be characterized by high amounts of corn, canola meal, corn DDGS, and soybean meal, which cumulatively accounted for 80% of all of the GHG-optimal formulations on a mass basis. Similar to the economically optimal formulations, the GHG-optimal formulations were primarily constrained by phosphorus, calcium, and metabolizable energy contents, which were active constraints for all optimal formulations. Unlike the economically-optimal formulations, methionine + cysteine contents were never identified as an active constraint, indicating that the GHG optimal mix of 0.00100.00200.00300.00400.00500.00600.00Abbotsford Lethbridge Wallenstein St. Hyacinthe TruroCost per tonne of feed milled ($CAD/tonne)Animal mealVitamin supplementsMethionineLimestoneCorn DDGSSoybean mealCanola mealCorn 274 ingredients sufficiently meets this nutritional requirement, leading to the exclusion of synthetic methionine from formulations compared to those that are economically optimal. Unlike the economically optimal feeds, when optimizing for GHG emissions ingredients were not always procured from the production locations situated closest to the feed mill locations. This was particularly notable in sourcing of canola meal and oil. When SOC and LUC were excluded from the optimizations, the feed mills in Abbotsford and Lethbridge sourced canola meal from the production facility in Nipawin, Saskatchewan, which sourced canola from RU 22 in Manitoba, rather than from Alberta production facilities sourcing canola from British Columbia or Alberta. This procurement choice is reflective of differences in production impacts of canola meal in different locations, indicating that that these differences are sufficiently large to offset the increased impacts of transportation associated with transporting these products from Manitoba to Saskatchewan for processing, then to feed mills in Alberta and British Columbia. This choice is particularly notable for the Lethbridge feed mill, as canola meal was chosen to be sourced from outside of Alberta rather than from the production facility in Lethbridge that sources canola from RU 37 in Alberta. In contrast, when SOC and LUC were included in the optimizations, all feed mills sourced canola meal from the production facility in Yorkton, Saskatchewan, which sourced canola from RU 29 in Saskatchewan. Of the canola producing RUs, RU 29 is estimated to have the greatest amount of net SOC sequestration, with sequestration in this RU estimated to be approximately 17% greater than in the next highest sequestering RU. Similarly, it was found that it was more beneficial for mills in Wallenstein, St. Hyacinthe, and Truro to source canola oil from canola grown and processed in Saskatchewan rather than from closer production and processing facilities in Manitoba. These three mills also included the maximum allowable amount of soybean meal and relatively less canola meal in 275 their optimal formulation, the opposite trend from that observed at the Abbotsford and Lethbridge mills, indicating that the ideal mix of protein sources from a GHG emissions perspective is regionally dependent. A similar trend in not sourcing ingredients from the closest possible production facilities is also observed in the sourcing pattern for corn DDGS at the Lethbridge feed mill. Regardless of the treatment of SOC and LUC, the GHG optimal formulation at the Lethbridge mil sources corn DDGS from the production facility in Minnedose, Manitoba, which sources corn from RU 24 in Manitoba, rather than from a production facility in Red Deer, which sources corn from RU 37 in Alberta. Compared to other RUs, particularly in Manitoba and the other provinces further east, RU 37 produces a small amount of corn (Table A 1), with relatively high impacts. Across both treatments of SOC and LUC, GHG emissions of corn production in RU 37 are substantially higher than those of RU 24. Differences in energy grid mixes (i.e., a predominantly fossil fuel based grid in Alberta compared to a predominantly hydroelectric grid in Manitoba) utilized during corn processing (i.e., ethanol production and subsequent drying) to produce corn DDGS further contributes to differences in impacts of producing corn DDGS at these two locations. These differences are sufficiently large to offset the GHG emissions associated with an increase in cumulative truck transportation distance of approximately 21%, and an additional 1307 km of rail transportation required to transport corn DDGS from Minnedosa, MB, to Lethbridge, compared to transporting the same ingredient from Red Deer, AB. Compared to the economically optimal feeds, the GHG optimal feeds did not include as high amounts of rendered animal products, though these products were still included in GHG optimal feeds regardless of the treatment of SOC and LUC. While all of the economically 276 optimal feeds included the maximum allowable amounts of rendered animal products, all of the GHG optimal feeds reduced the amount of rendered animal products included by approximately 39%. This result was surprising, as rendered animal products are generally key drivers of many environmental impacts including GHG emissions, as demonstrated by the results presented in chapter 2. In spite of the high carbon footprint of these products, rendered animal products were still included in all GHG optimal feeds due to their high P content relative to other ingredients. Across all single-objective GHG optimal feeds, P was identified as an active constraint, indicating a relative amount of difficulty in minimizing GHG emissions while meeting this nutritional need. This result is likely driven by the methodological choice to constrain potential ingredients in these optimizations to just those that were included in the baseline feed formulation originally reported by Pelletier (2017), and is a noted limitation of this work that is discussed in greater detail in section 6.5. In practice, it is likely that feed mills are utilizing phosphorus-specific supplements to meet hen nutritional demands. This assertion is supported by recent survey data collected from feed mills in the U.S., where it was found that phosphate-specific supplements were included in higher quantities in feeds milled for cage-free pullets and laying hens, which did not included any rendered animal products (Turner et al., 2023b). In light of this methodological choice and the resulting optimal formulations, a sensitivity analysis was performed in which the pool of decision variables was expanded to include a phosphate supplement was included as a potential ingredient. The implications of including a phosphate specific supplement as a potential ingredient is discussed in greater detail in section 6.4.5. This sensitivity analysis also demonstrates the adaptability of the optimization framework developed in this chapter, illustrating the relative ease with which alternative ingredients may be included as decision variables for future optimizations. 277 Across all reference feed mill locations, total costs per tonne of feed milled ranged from $544.69 to $589.97 per tonne when SOC and LUC were excluded, and $529.60 to $599.57 when SOC and LUC were included. As with the economically optimal feeds, the cheapest feed was milled at the Wallenstein feed mill while the most expensive was milled at the Abbotsford mill, regardless of the treatment of SOC and LUC, reflecting the relative differences in ingredient availability and associated production and transportation costs and emissions for the two regions. Compared to the economically optimal feeds, GHG optimal feeds are at least ~5% and ~8% more expensive per tonne when SOC and LUC are included and excluded, respectively, when comparing the most expensive economically optimal feed to the least expensive GHG optimal feeds. This increase in price is expected as in the GHG optimal formulations, mills are not always sourcing ingredients from the closest production locations as there are often locations further away that cost more to transport ingredients to the mill from, but have lower GHG emissions than closer production locations. When SOC and LUC are included in the optimizations, the range in prices of GHG optimal feeds is wider than that of the optimal feeds when SOC and LCU are excluded, reflecting the relatively high levels of heterogeneity in estimated SOC dynamics across Canada. The complete GHG optimal feed formulations both including and excluding SOC and LUC, including breakdowns of where ingredients are sourced from, and contributions from each ingredient to estimated costs per tonne of feed milled are available in appendix E, Table E 19 to Table E 28. When optimizing for least GHG emissions, emissions were estimated to drop between ~22% to ~36% per tonne of feed milled, depending on the mill location, and SOC treatment compared to the single objective economically optimal feed. These emissions drops were largely attributable to the approximately 40% decrease in rendered animal products included in the GHG 278 optimal feeds compared to the economically optimal feeds for all reference mill locations and SOC and LUC treatments. When SOC and LUC were excluded, the Lethbridge feed mill showed the lowest GHG emissions mitigation potential, with the GHG optimal feed having approximately 22% lower emissions than the economically optimal feed at the same mill, while GHG optimal feeds milled at Abbotsford, Wallenstein, and Truro had approximately 25% lower GHG emissions than their economic counterparts. When SOC and LUC were included, GHG mitigation potentials increased for all reference mill locations, with the greatest emissions reductions potential observed for the Truro feed mill, where GHG emissions were approximately 36% lower than those of the economically optimal feed excluding SOC and LUC, and 32% lower than those of the economically optimal feed including SOC and LUC. The Truro feed mill has the largest potential improvement in GHG emissions because incorporation of regional sourcing allows this mill to source ingredients from production locations that are further away which often have lower production impacts and highest estimated SOC sequestration than similar ingredient production locations in Atlantic Canada. In addition to GHG emissions reductions, environmental impacts per tonne of GHG optimal feed were also lower for the vast majority of impact categories compared to economically optimal feeds for all reference mill locations regardless of the treatment of SOC and LUC. Generally, environmental impact reductions of between 2% to 36% were observed for other impact categories, depending on the mill location and treatment of SOC and LUC. Photochemical oxidation was the only impact that was estimated to increase when milling GHG optimal feeds compared to economically optimal feeds, with impacts increasing 10% to 20% per tonne of feed milled, depending on the mill location and SOC and LUC treatment. Generally, rendered animal products make the largest contributions to photochemical oxidation impacts. 279 Contributions to photochemical oxidation from this ingredient drop proportionally to the drop in the inclusion of this ingredient in GHG optimal feeds compared to economically optimal feeds; however, this drop is offset by large increases in the amount of soybean meal included in GHG optimal feeds, as well as large increases in cumulative transportation distance required when milling GHG optimal feeds. Processing of soybeans into soybean meal includes emissions of hexane to air, which is responsible for approximately 85% of the photochemical oxidation impacts associated with production of soybean meal. The much larger proportions of soybean meal included in the GHG optimal feeds therefore increase these hexane emissions, primarily driving the observed increases in photochemical oxidation of GHG optimal feeds. Contributions from transportation to photochemical oxidation also increase substantially – for example, at the St. Hyacinthe mill, the contribution from transportation increases approximately 131% for GHG optimal feeds compared to economically optimal feeds – reflecting the larger transportation distances that result from not sourcing ingredients from the closest production facility possible. Overall, however, use of single-objective optimization for least GHG emission feed formulations generally results in large decreases in both GHG emissions, and other environmental impacts compared to economically optimal feeds. While these feeds are more expensive per tonne of feed milled, it is worth noting that potential impact reductions are generally far greater in magnitude than expected increases in costs. Figure 6-3 shows the relative impacts per tonne of feed milled for the single-objective economic and GHG optimal feeds, both including and excluding SOC and LUC, milled at the Wallenstein feed mill, using the economically optimal formulation excluding SOC and LUC as the reference formulation. Similar figures for all other reference mill locations are available in appendix E, Error! Reference source not found. to Error! Reference source not found., while complete tables of estimated impacts per tonne of feed milled for each 280 single-objective optimal formulation at each reference mill location are given in appendix E, Table E 29 to Table E 32. 281 Figure 6-3. Relative impacts per tonne of feed milled at Abbotsford for both economically and GHG optimal feeds, across both treatments of SOC and LUC. The economically optimal feed excluding SOC and LUC is the reference category. 020406080100120140Economically optimal - SOC and LUCexcludedEconomically optimal - SOC and LUCincludedGHG optimal - SOC and LUC excludedGHG optimal - SOC and LUC included 282 6.4.3 Bi- and multi-objective optimal feeds Generally, the economic results of the bi-objective optimization models followed the same pattern as observed in the single-objective optimization models for feed cost. As in the single-objective economic optimal models, the most expensive feed from the bi-objective optimization models was milled in Abbotsford across both treatments of SOC changes and perennial to annual land transitions. Unlike the single-objective models, however, the least expensive feed in the bi-objective models was milled in Lethbridge, rather than Wallenstein. The differences in cost between the optimal feeds at Lethbridge and Wallenstein were, however, very small. Costs per tonne of economic and GHG optimal feeds at Wallenstein were $3.58 and $1.53 higher than those at Lethbridge when SOC changes and perennial to annual land transitions were excluded, and included, respectively. When both GHG emissions and costs were considered in the optimization models, costs per tonne of the optimal feeds milled were approximately 10% - 14% and 12% - 17% higher than those in the single-objective economically optimal feeds when SOC and perennial to annual land transitions were excluded and included, respectively. Across the bi-objective optimization models, costs per tonne of feed mill were higher at all reference mill locations when upstream changes in SOC and perennial to annual land transitions were included. These increases were generally small, ranging from approximately 2 – 4%, depending on mill location. The largest increase in cost was observed at Abbotsford, while the smallest was observed at Wallenstein. Across all of the reference mill locations and both treatments of SOC and perennial to annual land transitions, the ingredients chosen in the bi-objective optimal formulations were consistent, with all formulations made up of corn, canola oil and meal, soybean meal, corn 283 DDGS, limestone, vitamin supplements, and rendered animal products. Generally, as reference mill locations moved from west to east, optimal feed formulations included less corn and rendered animal products, and more canola oil and soybean meal, when SOC changes and perennial to annual land transitions were excluded. This trend for corn, in particular, appears counterintuitive given the distribution of corn production in Canada, the vast majority of which occurs in Ontario. However, for both the Abbotsford and Lethbridge feed mills, corn included in the optimal formulations was sourced from RU 37, in Alberta. Total corn production in this RU is comparatively small, accounting for <1% of total Canadian corn production (Table A 1). Given the small amount of corn production in this RU, it may not be practical in reality for feed mills in western Canada to be sourcing corn produced in this RU. Since there were no constraints included in these optimizations related to feasibility of ingredient sourcing, it may therefore be possible that these results are not truly representative of optimal feeds in reality, as ingredient availability may be a limiting factor. Inclusion of additional constraints related to ingredient availability and procurement feasibility should be explored in the future to make the optimization problem formulations more representative of actual ingredient sourcing capabilities. This is discussed in greater detail in section 6.5. While the ingredient makeup of the optimal formulations was generally consistent for each mill location across different soil carbon treatments, some differences were observed with respect to ingredient sourcing. This was particularly true for canola oil and meal, as inclusion of SOC changes and perennial to annual land transitions often led to sourcing these ingredients from RUs estimated to be sequestering carbon, even when this choice resulted in greater transportation distances for these ingredients. This was also a contributing factor to the observed increases in price per tonne of feed milled when SOC changes and perennial to annual land 284 transitions were included. For example, when SOC changes and perennial to annual land transitions were excluded, the Lethbridge feed mill sourced canola oil from a production facility in Camrose, Alberta, which sourced canola from RU 35, in south-central Alberta. In comparison, when SOC changes and perennial to annual land transitions were included, this ingredient was instead sourced from a facility in Yorkton, Saskatchewan, which sourced canola from RU 29, in south-eastern Saskatchewan. In both cases, the same amount of canola oil was included in the optimal formulation. However, using canola originating from RU 29 instead of RU 35 resulted in increasing required truck transportation distance by approximately 59%, and increasing required rail transportation by approximately 164%. Carbon sequestration per tonne of canola produced, however, was estimated to be approximately 65% higher in RU 29 than in RU 35. Because of this difference in carbon sequestration, canola oil produced in Yorkton was estimated to have a net negative carbon footprint at processor gate and have a carbon footprint approximately 112% lower than that of canola oil produced in Camrose when SOC and LUC were included. In contrast, when SOC and LUC are excluded, canola oil produced in Yorkton is estimated to have a carbon footprint that is approximately 2% higher than that of canola oil produced in Camrose, further highlighting the potential importance of choices related to SOC modeling when considering feeds optimized to minimize environmental impacts. In total, when the bi-objective optimization models including and excluding SOC and LUC gave the same ingredient mix and amounts with different sources for those ingredients at the Lethbridge feed mill, changes in chosen ingredient production locations were responsible for an approximately 10% decrease in carbon footprint. This translated to a net decrease in carbon footprint per tonne of feed of approximately 7%, as some of the reductions resulting from alternative ingredient sources were offset by the increases in transportation required to get ingredients to mills from production 285 locations further away. Further, given that GHG emissions and cost reductions were equally weighted in this bi-objective optimization, this difference in soil carbon sequestration and carbon footprint of canola oil production at these possible production locations was sufficient to offset the increased costs associated with the additional transportation costs and emissions associated with delivery of the ingredient to the feed mill. Similarly, inclusion of SOC changes and perennial to annual land transitions led to changing the source of corn at the Truro feed mill from RU 5 in Nova Scotia to RU 6 in Prince Edward Island. While neither of these RUs were estimated to be sequestering SOC, net SOC emissions were estimated to be approximately 92% lower per tonne of corn produced in RU 6 compared to RU 5 when SOC changes and perennial to annual land transitions were included. This difference similarly offset increase in costs and emissions associated with additional transportation of this ingredient to the mill. Also of note, none of the bi-objective optimal feed formulations included any peas, despite their low carbon footprint compared to many of the other possible ingredients. This may indicate that, although their carbon footprint was estimated to be lower than many other ingredients, peas are inefficient at delivering limiting nutrients (i.e., phosphorus and calcium) compared to other ingredients, leading to their exclusion from the optimal formulations. While peas are a potentially good source of crude protein – they have the fifth highest crude protein content of possible ingredients – ingredients such as canola meal and rendered animal products are higher in crude protein and calcium, and substantially higher in phosphorus. The complete bi-objective optimal feeds at each reference mill location and for each SOC treatment, including breakdowns of where ingredients are sourced from, and contributions from each ingredient to estimated costs per tonne of feed milled are available in appendix E, Table E 33 to Table E 42. 286 Per tonne of feed milled, life cycle GHG emissions were lower for all reference feed mill locations when SOC changes and perennial to annual land transitions were included except for St. Hyacinthe, where impacts are approximately 4% higher. Across both SOC treatments, the optimal feeds at St. Hyacinthe were consistent in both amounts included, and ingredient sourcing locations for all ingredients included in the optimization except for canola meal. When SOC changes were excluded, the St. Hyacinthe mill was sourcing canola meal from the facility in St. Agathe, Manitoba, which was sourcing canola from RU 23, in Manitoba. In contrast, when SOC changes were included, canola meal was instead sourced from the processing facility in Yorkton, Saskatchewan, which sourced canola from RU 29, in Alberta. In both RUs 23 and 29, it is estimated that canola production is associated with net soil carbon sequestration, with sequestration estimated to be approximately 15% greater in RU 29. When SOC changes and perennial to annual land transitions were included, the greater carbon sequestration occurring in RU 29 was sufficient to offset the impacts of the use of a predominantly fossil fuel based electricity grid to process canola in Saskatchewan compared to the predominantly hydroelectric grid in Manitoba. However, this is insufficient to fully offset the impacts associated with increased transportation, leading to the small increase in estimated impacts observed. In contrast, when SOC changes are included, canola meal produced from canola grown in RU 23 does not benefit from as great an estimated amount of soil carbon sequestration, meaning that sourcing canola meal from this location with this treatment of SOC would result in higher total life cycle impacts of milled feed if it was instead sourced from canola grown in RU 23. For all other reference mill locations, inclusion of SOC changes and perennial to annual land transitions resulted in reductions in GHG emissions per tonne of feed milled of between approximately 7% and 13%. Inclusion of SOC changes and perennial to annual land transitions 287 did not result in reduction in estimated impacts per tonne of feed milled for all impact categories. Rather, whether specific impacts were estimated to increase or decrease was dependent on the specific feed mill location. Substantial variability was observed in the magnitude of these changes in impacts, which ranged from reductions of approximately 15% for terrestrial ecotoxicity at Abbotsford, to increases of 27% for marine aquatic ecotoxicity at St. Hyacinthe. For all impacts assessed, estimated impacts per tonne of feed milled were higher at St. Hyacinthe when SOC changes and perennial to annual land transitions were included. For feeds milled at Wallenstein and Truro, inclusion of SOC changes and perennial to annual land transitions resulted in net reductions in 2 and 3 impact categories, respectively. In contrast, under this treatment of SOC, net reductions in impacts were observed at Abbotsford and Lethbridge for 6 impact categories, suggesting that feed mills in Western Canada may disproportionately benefit from inclusion of SOC changes and perennial to annual land transitions in optimal feed formulations for least environmental cost, compared to mills in Eastern Canada. This is because soil carbon sequestration is estimated to be occurring more readily in Western Canadian soils, so feed mills in Western Canada are still able to procure these ingredients when economic objectives are highly favoured since these production locations are closer to mills, resulting in lower transportation costs. It is possible that this pattern may change if minimization of GHG emissions was weighted sufficiently greater than minimization of economic costs. Estimated life cycle environmental impacts per tonne of feed milled at each mill location for both treatments of SOC and LUC for the bi-objective optimal formulations are available in appendix E, Table E 43 and Table E 44. The multi-objective optimizations also followed the same general pattern in price across different reference mill locations as observed in other optimizations, with the most expensive 288 feeds milled in Abbotsford, and the least expensive feeds milled in Wallenstein, regardless of the treatment of SOC changes and perennial to annual land transitions. Costs per tonne of feed milled were generally higher when SOC changes and perennial to annual land transitions were included, reflecting the sourcing of ingredients from more distant RUs that may have lower impacts due to sequestration of soil carbon. The exceptions to this were feeds milled at Wallenstein and St. Hyacinthe in scenario 1, where costs per tonne were higher when SOC changes and perennial to annual transitions were excluded. In both cases, this may be attributed to a greater amount of soybean meal and a lower amount of canola meal in the optimal diets, as soybean meal is a more expensive ingredient on a per kilogram basis. Generally, however, differences in optimal formulation cost across different SOC treatments at the same mill were small, with the greatest observed difference being an approximate 6% increase in price at Truro when SOC changes were included compared to excluded. As an increasing amount of weight was placed on economic objectives, the differences in costs per tonne of feed milled at the same reference locations across different SOC treatments decreased. This culminated in the optimal formulations identified in scenario 4 being the same for each location, regardless of the treatment of SOC changes and perennial to annual transitions. Comparisons in cost per tonne of feed milled at each location for each SOC treatment are presented in Figure 6-4, while numerical prices are presented in appendix E, Table E 45. Given this convergence, from a practical standpoint it may be argued that, when optimizing for both economic costs and multiple environmental impacts, inclusion of changes to SOC and perennial to annual land transitions are only relevant if minimization of environmental impacts is at least equally weighted with cost minimization. Reductions in total costs per tonne of feed milled from scenario 1 to scenario 4 289 (i.e., least to most weight on economic cost minimization) ranged from approximately 12% - 18%. 290 Figure 6-4. Costs per tonne of feed milled at each reference location for each different multi-objective optimization scenario and each treatment of SOC and LUC 0100200300400500600700Excluding SOCand LUCIncluding SOCand LUCExcluding SOCand LUCIncluding SOCand LUCExcluding SOCand LUCIncluding SOCand LUCExcluding SOCand LUCIncluding SOCand LUCExcluding SOCand LUCIncluding SOCand LUCAbbotsford Lethbridge Wallenstein St. Hyacinthe TruroCost ($CAD per tonne of feed milled)Scenario 1Scenario 2Scenario 3Scenario 4 291 As in all other optimizations, ingredient selection in the multi-objective optimal formulations was generally consistent across all mill locations, optimization scenarios, and SOC treatments. In all cases, optimal formulations were comprised on corn, canola meal, soybean meal, corn DDGS, limestone, vitamin supplements, and rendered animal products. Optimal formulations for scenarios 1 through 3 also included canola oil, which was not included in the optimal formulation for scenario 4. Unlike all other formulations, those identified for scenario 4 also included a small amount of synthetic methionine. As in the bi-objective optimization, ingredient amounts in optimal formulations were generally similar for the same mill location and optimization scenario across the two different treatments of SOC changes, with the majority of differences relating to ingredient sourcing locations. Many of the trends in ingredient sourcing observed in the bi-objective optimizations across different SOC treatments were also observed in the multi-objective optimizations. The complete multi-objective optimal feeds at each reference mill location and for each SOC treatment, including breakdowns of where ingredients are sourced from, and contributions from each ingredient to estimated costs per tonne of feed milled are available on the OpenScience Framework at https://osf.io/9wupf/?view_only=4ab8aec8a94c45c291d7eecb74ea5eea. As anticipated, life cycle environmental impacts per tonne of feed milled generally increased as less weight was placed on minimization of environmental impacts and more weight on minimization of costs for all reference feed mill locations, and for both treatments of SOC and LUC. This was not necessarily the case, however, for all impact categories or mill locations. For example, across both SOC treatments, photochemical oxidation impacts per tonne of feed milled at Abbotsford were counterintuitively observed to decrease as less weight was placed on minimization of environmental impacts. This is the same trend as was observed in the single 292 objective, GHG optimal feeds described previously in section 6.4.2, and is similarly driven by the decreasing proportion of soybean meal included in formulations as more weight is placed on minimizing costs. In some cases, it was also possible that environmental impacts may increase from one weighting scenario to another, then decrease in a subsequent scenario for the same mill location, and vice versa. For example, at the St. Hyacinthe feed mill with SOC changes and perennial to annual land transitions included, ozone layer depletion impacts were observed to increase very slightly (i.e., <0.5%) between optimization scenarios 1 and 2, then decrease by approximately 3% from scenario 2 to 3, before increasing again by approximately 21% from scenario 3 to 4, resulting in a net increase of approximately 18% from scenario 1 to scenario 4. The observed increase between scenarios 1 and 2 is driven primarily by a small change in sourcing of corn DDGS between these scenarios, with scenario 1 sourcing DDGS from Tiverton, Ontario, while scenario 2 sources DDGS from Aylmer, Ontario. Both of these DDGS production facilities source corn required for ethanol production from the same RU (RU 17), but the transportation requirement to source corn is approximately 42% higher for Aylmer than Tiverton, leading to net small increases in the contribution of DDGS to photochemical oxidation impacts at the feed mill. The observed decrease from scenario 2 to 3 is driven by decreasing transportation distances for many ingredients associated with reducing formulation costs as the preference for decreasing costs increases. Finally, the large increase from scenario 3 to scenario 4 is attributable to the approximately 56% increase in rendered animal products included in the optimal formulation for scenario 4, which only becomes relevant as a driver of differences in impacts across scenarios at this point because the optimal formulations for scenarios 1 to 3 all include the same amount of rendered animal products. 293 Overall, little consistency was observed with respect to how estimated impacts per tonne of feed milled changed across different optimization scenarios for different impact categories, reference mill locations, and SOC treatments. This is because trends in impacts are driven by a complex interplay between many different factors, including ingredient mixes, sourcing decisions at the feed mill, as well as sourcing decisions further upstream at ingredient production facilities, as illustrated by trend in ozone depletion described above. Though impacts were usually observed to increase as less weight was assigned to environmental impact reduction, at which weight distribution the greatest increase in estimated impacts occurred was also inconsistent. For feeds milled at Abbotsford and Lethbridge, the largest increase in impacts per tonne of feed milled was generally observed between scenarios 2 and 3, regardless of SOC treatment. In contrast, for feeds milled at Wallenstein, St. Hyacinthe, and Truro, the largest increase in impacts per tonne usually occurred between scenarios 3 and 4. These results are likely indicative of substantial heterogeneity in impacts associated with ingredient production, and relative ease of ingredient procurement for feed mills located in Western versus Eastern Canada. These results also suggest that, from a practical standpoint, regionally-specific input regarding stakeholder priorities on minimization of economic costs and environmental impacts at the feed milling step may be necessary to derive the greatest degree of benefit from incorporation of environmental impacts into feed formulation tools. While one specific set of weights may result in the greatest improvements in sustainability outcomes for feed mills in one specific location, that same set of weights may result in little benefit, or potentially even worse sustainability outcomes for a feed mill in another location. Figure 6-5 to Figure 6-7 show the trend in life cycle impacts per tonne of feed milled for those impact categories included in the multi-objective optimization for both SOC treatments for feeds milled at Abbotsford, 294 Wallenstein, and Truro, respectively. These three mills were chosen as examples as they illustrate three distinct trends in how impacts change over different optimization scenarios. In all cases, results from Scenario 1 are the reference relative to which all other impacts are presented. Similar figures for Lethbridge and St. Hyacinthe are available in appendix E, Error! Reference source not found. and Error! Reference source not found. Tables of complete LCIE results for all reference mill locations, optimization scenarios, and SOC and LUC treatments are available on the OpenScienceFramework at https://osf.io/9wupf/?view_only=4ab8aec8a94c45c291d7eecb74ea5eea. Figure 6-5. Estimated impacts per tonne of multi-objective optimal feed milled at Abbotsford for each optimization scenario for those impact categories included in the MOO, excluding upstream SOC changes and perennial to annual land transitions 020406080100120140Acidification GHG emissions Eutrophication Human toxicityPercent impactsScenario 1Scenario 2Scenario 3Scenario 4 295 Figure 6-6. Estimated impacts per tonne of multi-objective optimal feed milled at Wallenstein for each optimization scenario for those impact categories included in the MOO, excluding upstream SOC changes and perennial to annual land transitions Figure 6-7. Estimated impacts per tonne of multi-objective optimal feed milled at Truro for each optimization scenario for those impact categories included in the MOO, excluding upstream SOC changes and perennial to annual land transitions 020406080100120140160Acidification GHG emissions Eutrophication Human toxicityPercent impactsScenario 1Scenario 2Scenario 3Scenario 4020406080100120140160Acidification GHG emissions Eutrophication Human toxicityPercent impactsScenario 1Scenario 2Scenario 3Scenario 4 296 6.4.4 Environmental impact mitigation potential of using optimized feeds in Canadian egg production systems While incorporation of environmental impacts into feed formulation optimization problems generally resulted in decreased environmental impacts per tonne of feed milled, such reductions may not translate to commensurate decreases in impacts per tonne of eggs produced at farm gate if the nutrient profiles of the optimal feeds result in increased N excretion and increased emissions from manure management systems. To investigate this, the single objective, economically optimal, and multi-objective optimal for scenario 1 feeds milled at Abbotsford and Wallenstein were incorporated into the baseline conventional cage and free range egg production models described by chapter 2. For the purposes of these comparisons, the single-objective economically optimal feed is considered to be the reference case, rather than the model results presented in chapter 2. Use of the multi-objective optimal feed in the baseline egg production models resulted in decreases in estimated impacts per tonne of eggs produced in eight of the eleven impact categories assessed for both housing systems and both reference feed mill locations. Decreases in estimated impacts per tonne of eggs produced were observed for abiotic depletion, abiotic depletion (fossil fuels), freshwater aquatic ecotoxicity, global warming potential, human toxicity, marine aquatic ecotoxicity, ozone layer depletion, and terrestrial ecotoxicity, while increases in impacts were observed for acidification, eutrophication, and photochemical oxidation. The magnitude of the observed changes was dependent on the specific impact category, housing system, and reference mill location, and ranged from approximately 3% - 32%. The greatest reductions were observed in terrestrial ecotoxicity, with estimated reductions in impacts >30% across both housing system and reference mill locations. In contrast, when impacts were estimated to increase, these increases ranged from approximately 1% - 14%, 297 with the acidification impacts estimated to increase the most. The relative percentage of impacts per tonne of eggs produced in conventional cage and free range systems using the two different feeds milled at Abbotsford are shown in Figure 6-8 and Figure 6-9. Similar figures for feeds milled at Wallenstein are available in appendix E, Error! Reference source not found. Error! Reference source not found.. Figure 6-8. Relative percentage of impacts per tonne of eggs produced in conventional cage systems using a single objective optimal feed for lowest cost, and a multi-objective optimal feed with strong preference for minimization of environmental impacts milled at Abbotsford. 020406080100120Relative percentage of impacts (%)SOO Cost Feed MOO S1 Feed 298 Figure 6-9. Relative percentage of impacts per tonne of eggs produced in free range systems using a single objective optimal feed for lowest cost, and a multi-objective optimal feed with strong preference for minimization of environmental impacts milled at Abbotsford. 6.4.5 Sensitivity analysis - Extension of optimization framework to include other ingredients Inclusion of a phosphate supplement as a potential ingredient in the feed formulation optimization models led to large changes in the optimal formulations suggested. For both sets of objective function weights, the optimal formulations identified included supplemental phosphate, while rendered animal products were completely removed from the formulations. The results of this sensitivity analysis verify the hypothesis that rendered animal products were being included in optimal formulations primarily due to their high P content relative to other potential ingredients. Similarly, inclusion of a phosphate supplement as a potential ingredient resulted in large increases in the proportions of corn included in the optimal formulations for both sets of 020406080100120Relative percentage of impacts (%)SOO Cost Feed MOO S1 Feed 299 objective weights. When a phosphate supplement was included, the percentage of corn in the optimal diet at Wallenstein in the first weight scenario increased from approximately 22% to 56%, and from 39% to 52% in the fourth weight scenario. Canola oil and meal, and corn DDGS were also removed completely from the optimal formulation at Wallenstein for the first weighting scenario, while synthetic methionine was added. The optimal formulation at Wallenstein for the fourth weighting scenario also saw the removal of canola meal from the optimal formulation when supplemental phosphate was included as a potential ingredient, while the amount of soybean meal included increased and synthetic lysine was included in optimal formulations for the first time. Interestingly, unlike all other supplements included (methionine, vitamins, and phosphate) which were sourced from the production facility in Mississauga, Ontario, synthetic lysine was instead sourced from the production facility in Kirkland, Quebec. This sourcing choice resulted in slightly increased shipping distance by truck to arrive at the Wallenstein mill, while also requiring transit by rail. In spite of this, the relative cost effectiveness of rail transport resulted in only minor increases in the price of synthetic lysine at the Wallenstein feed mill when sourced from Kirkland as opposed to Mississauga. Additionally, the impacts of producing synthetic lysine in Kirkland and transporting it to Wallenstein were estimated to be lower than those associated with production and transportation of lysine produced in Mississauga for all of the impact categories considered as objective functions in multi-objective optimization. Inclusion of a phosphate supplement as a potential ingredient in the optimization problems led to reduction in cost per tonne of feed milled for both of the scenarios tested. Compared to the optimal feed milled at Wallenstein excluding SOC and LUC with a strong preference for environmental impact minimization (i.e., Scenario 1), inclusion of a phosphate 300 supplement led to reductions in costs of optimal feeds of approximately 4%. When optimization was instead performed with a strong preference for economic cost minimization (i.e., Scenario 4), inclusion of a phosphate supplement led to relatively larger reductions in cost – optimal costs per tonne including a phosphate supplement were approximately 14% lower. That potential cost reductions were greater when preferences were stronger for minimizing economic costs is unsurprising. While phosphate supplements are greater than three times more expensive compared to rendered animal products, which were used to fulfill phosphate requirements in the optimizations described in sections 6.4.1 to 6.4.3, they are much more efficient at providing phosphorus on a per dollar basis. As a result, when phosphate supplements are a possible ingredient, relatively small amounts of them can be used to meet nutritional requirements so rendered animal products can be excluded from formulations in favour of lower impact and lower cost ingredients, such as corn. Inclusion of a phosphate supplement as a possible ingredient also led to reductions in all environmental impacts assessed compared to optimal formulations at Wallenstein without phosphate supplements as a potential ingredient. This included reductions in carbon footprint per tonne of feed produced of approximately 72% when environmental impact mitigation was strongly preferred, and approximately 70% when economic minimization was preferred. Across all impact categories, the minimum impact mitigation per tonne of feed produced when phosphate supplements were included as potential ingredients was 4% when environmental impact mitigation was strongly preferred, and 11% when economic minimization was preferred. Particularly striking were the mitigation potentials for terrestrial ecotoxicity, which were estimated to decrease by approximately 96% per tonne of feed milled following inclusion of phosphate supplements as a potential ingredient in the optimization preferring cost minimization. 301 Figure 6-10 Figure 6-11show the environmental impacts of optimal feeds milled at Wallenstein, excluding SOC and LUC, when phosphate supplements are included in the optimization as a potential ingredient relative to optimal formulations at the same mill without phosphate supplements as a potential ingredient. The complete feed formulations and estimated life cycle environmental impacts of the optimal feeds when phosphate supplements are included as an ingredient are available on the OpenScienceFramework at https://osf.io/9wupf/?view_only=4ab8aec8a94c45c291d7eecb74ea5eea. 302 Figure 6-10. Relative percentage of impacts per tonne multi objective optimal feed milled with a strong preference for environmental impact minimization (i.e., Scenario 1) at Wallenstein excluding SOC and LUC, when feeds when phosphate supplements are excluded and included as potential feed ingredients in optimization. 020406080100120Relative percentage of impacts (%)Without phosphateWith phosphate 303 Figure 6-11. Relative percentage of impacts per tonne multi objective optimal feed milled with a strong preference for economic cost minimization (i.e., Scenario 4) at Wallenstein excluding SOC and LUC, when feeds when phosphate supplements are excluded and included as potential feed ingredients in optimization. 020406080100120Relative percentage of impacts (%)Without phosphateWith phosphate 304 6.5 Limitations and conclusions While the feed formulation optimization framework developed in this chapter presents the first optimal formulations developed for laying hens taking into account environmental impacts, a number of improvements may be made in the future to address some key limitations of this approach. Five key limitations have been identified, each of which relates to a different component of the optimization problem formulation. The first limitation was the method used for estimating soil carbon changes associated with field crop production processes. While the methods used for estimating SOC changes in the CRSC reports are consistent with those utilized in the Canadian NIR, the accuracy of these estimates may limited as they are region specific, but not crop specific. Additionally, these methods do not account for factors such as the creation of new soil over time, or the potential permanence of sequestration effects, both of which may impact estimated SOC changes, and associated environmental impacts. Improvement of methods for estimation of SOC changes may yield more accurate characterizations of SOC dynamics in Canadian agricultural soils, which could, in turn, impact decisions on ingredient sourcing if SOC changes are considered during formulation and ingredient procurement, The second limitation is related to the set of nutritional constraints considered in this analysis. Currently, only a single set of nutritional constraints has been considered, representing production weighted average nutritional constraints for Lohmann LSL Classic hens housed in caged and cage free systems. While defining nutritional constraints in this manner was beneficial for this analysis as it represents average requirements for the most commonly used hen breed in Canada, these constraints are not actually representative of the nutritional requirements for any 305 birds in production in Canada. In the future, specific nutritional constraints should be used representing specific breeds in specific housing systems, in line with the Canadian Code of Practice for rearing of laying hens (National Farm Animal Care Council, 2017). This may result in different optimal formulations depending on the differences in nutritional requirements between strains and housing systems. This limitation is surmountable, however, provided hen management guides clearly state nutritional requirements, and the requisite nutritional contents of potential ingredients can be identified, at which point their implementation would be relatively trivial. The third limitation of this analysis was the choice to limit potential ingredients to those previously included in production-weighted Canadian average laying hen feed formulation reported by Pelletier (2017). This choice was made because the reported formulation provided the most up to date data regarding ingredients included in Canadian feed formulations, removing the need for speculation regarding potential ingredients that may available at different feed mill locations. It is likely that this list of ingredients is not exhaustive, and not representative of the complete set of possible ingredients available to Canadian feed mills for laying hen feeds. To address this limitation, the collection of updated data from Canadian feed mills producing laying hen feeds should be a priority in order to develop a more representative list of potential ingredients to use in formulation optimization. If this data were to be collected, the sensitivity analysis performed in this chapter extending the optimization framework to include phosphate supplements as a potential ingredient clearly indicate that incorporation of other ingredients is possible. Use of a set of potential ingredients that are more representative of those actually available to Canadian feed mills would provide more robust results with respect to optimal formulations given different environmental and economic objectives. Such extensions of 306 available ingredients could also include other alternative ingredients such as such as insect meals (Dominguez Aldama, 2024), valorized food wastes (Siddique et al., 2024), and other low impact, low opportunity cost biomass. The fourth major limitation of this analysis was the lack of constraints representing ingredient availability from different production locations. In this analysis, it was assumed that feed mills were able to procure as much of an ingredient as needed from any possible production location. As a result of this assumption, it was often observed that optimal formulations milled at Western Canadian feed mills were including large amounts of corn produced in RU 37 in Alberta. Corn production in Western Canada is relatively rare – there are only two corn producing RUs west of Manitoba (RUs 29, in Saskatchewan, and 37, in Alberta), which cumulatively represent >1% of total corn production in Canada (Table A 1). In reality, it is unlikely that corn is produced in sufficiently large amounts in Western Canada to support its inclusion in poultry feeds. Rather, it is more likely that wheat would be used in greater quantities in Western Canadian poultry feeds, as it is produced in larger quantities in the Prairie provinces. This was not reflected in the optimizations performed, however, because corn was estimated to be relatively lower impact (see Table A 39 and Table A 49), and cheaper (Table 6-2) than wheat. Further, Bamber et al. (in prep), estimate that only 45% of Canadian corn production is used for livestock feed. Assuming this estimate is representative, the amount of corn produced in RU 37 available for use in any livestock feed is likely very small, an even smaller proportion of which would likely be used in poultry feeds. This clearly illustrates that this is a potentially large limitation of the work described in this chapter, and that future efforts In the future should be made to identify shares of production of ingredients in different regions that are available for use as poultry feeds. To implement such constraints would require collection of data characterizing 307 total amounts of ingredients required and total amounts of feed milled to ensure that potentially scarce ingredients could be used efficiently, or be replaced with other, more plentiful ingredients. The final limitation identified to this analysis was the definition of weights for different objectives when multiple objectives were considered in optimization. While this analysis attempted to define weights in a systematic manner representative of many possible different combinations of stakeholder priorities, the assigned weights may not be representative of true stakeholder priorities. In the future, a collaborative approach must be considered such that consensus may be reached in weights assigned to different objectives during feed formulation optimization based on the opinions and priorities of relevant members of the egg value chain. Other improvements may also be made to the framework proposed in this chapter. Incorporation of carbon pricing schemes, for example, could result in substantial changes to the optimal formulations identified because different ingredient production locations would be further differentiated from one another on the basis of environmental impacts when considering ingredient prices rather than on just proximity to reference feed mill locations. Overall, however, the optimization framework developed in this chapter has been demonstrated to be sufficiently adaptable to consider alternative ingredients, and could also easily accommodate different environmental impacts, constraints, and objective function weights. Use of this framework may provide substantial value to the Canadian egg industry in their goal to become net-zero with respect to GHG emissions, as use of environmentally optimal feeds may lead to large impact reductions at egg farms. While these feeds are often associated with higher costs than feeds milled with the goal of minimizing economic costs, it is promising that, for many environmental impacts, the magnitude of estimated price increases was smaller than that of estimated impact reductions. 308 7. Optimization of Canadian egg production systems for environmental and animal welfare objectives 7.1 Chapter synopsis The Canadian egg industry is currently navigating a transition away from conventional cage to alternative housing systems. Alternative systems may be characterized by potentially large differences in associated environmental and animal welfare impacts. In this chapter, the baseline egg LCA models of Canadian egg production developed in chapter 2 are integrated with the animal welfare impacts estimated using the method developed in chapter 4 into a multi-objective optimization problem. Fifteen multi-objective optimization problems are defined and solved using a weighted sum approach, representing different combinations of constraints, and priorities for objective functions. The implications of the optimal distributions suggested are subsequently discussed in detail, including potential barriers to their implementation and how these barriers may be overcome. 7.2 Context and rationale The Canadian egg industry, and many others around the world, are currently in a transitionary period in which production in conventional cage housing systems is being phased out in favour of production in alternative systems, including enriched cage, single and multi tier non-cage, and free range sysems (National Farm Animal Care Council, 2017; Shields and Mirle, 2013; The Council of the European Union, 1999; Vogeler, 2021). While this transition is largely driven by perceived wide-spread animal welfare benefits that will results from the discontinued use of conventional cages (Caputo et al., 2023), it may also have large impacts on other sustainability attributes of Canadian egg production. Generally, (though not always), environmental impacts are higher in alternative systems than in conventional cages due to lower 309 levels of resource-use efficiency, particularly with respect to feed use which is a large contributor to many environmental impacts (chapter 2). How this transition occurs, including the new proportions of egg production attributable to each non-organic housing system may therefore have significant implications for both environmental and animal welfare outcomes in the Canadian egg industry. Decisions regarding which housing system to transition into from conventional cage production may be influenced by many factors, including but not limited to expected levels of resource-use efficiency in the new system – particularly with respect to feed, as it is the largest contributor to costs of production (Heidari et al., 2021a) – as well as the importance placed on different attributes of the new system, including expected animal welfare and environmental outcomes. Domestic trends in Canadian egg production indicate that the industry is in a period of long-term, sustained growth (Egg Farmers of Canada, 2023), congruent with the greater global trend of increasing production and consumption of livestock products (Govoni et al., 2022, 2021). Though the environmental impacts of eggs are relatively low compared to other livestock products, they are still higher than many non-livestock derived protein sources. In light of these impacts and expected upward trend in production and consumption, it is imperative that the housing system transition be navigated in such a manner that simultaneously accounts for both the animal welfare and environmental impacts of alternative systems, in light of potentially competing objectives. Such an approach must be based on a deeper understanding of the potential synergies and tradeoffs in environmental and animal welfare objectives that characterize alternative systems. One method for understanding these potential trade-offs and synergies is the development of mathematical optimization models to optimize the distribution of egg production in different housing system types in accordance with relative preferences for different environmental and 310 animal welfare objectives. Mathematical optimization may be used to explore a number of potential transition scenarios that align with various sets of stakeholder priorities and preferences for different objectives, accounting for differences in expected environmental and animal welfare outcomes of different housing systems. Development of an optimization framework to investigate the optimal distribution of production in non-organic housing systems may provide useful decision support for Canadian egg farmers currently navigating this transition, while also providing value as a consumer-facing tool to help consumers in understanding potential trade-offs between systems. This research chapter describes development and application of this framework, integrating research outcomes from all previous chapters in this dissertation. 7.3 Methods 7.3.1 Environmental and animal welfare impacts of different egg housing systems Estimated environmental impacts per tonne of eggs produced in each non-organic housing system in Canada under current Canadian-average and DEA-efficient conditions were used as reported in chapters 2 and 3, respectively. No changes were made to the estimated impacts prior to their use in optimization so the methods used in their derivation are not described again in this chapter. Readers are directed towards sections 2.3 and 2.4 for methods and results associated with current Canadian-average conditions, and sections 3.3 and 3.4 for methods and results associated with DEA-efficient conditions. Estimated animal welfare impacts per tonne of eggs produced in each non-organic housing system were taken from chapter 4, with associated methods and results available in sections 4.3 and 4.4. 311 7.3.2 Optimization problem definition As described in chapters 5 and 6, optimization problem definitions are comprised of three key features: objective functions, associated constraints, and the solver used to solve the problem. Individual sections are provided below defining each of these components. 7.3.2.1 Objective functions The number of objective functions included in an optimization problem directly influences the overall complexity of the problem, which may influence performance of the optimization algorithm used to solve the problem. In many cases, therefore, it may be beneficial to reduce the number of objective functions considered, particularly when some objective functions may be redundant. In total, 18 possible objective functions could be used in the optimization problems defined in this chapter: eleven representing the midpoint environmental impacts of egg production in each housing system as reported in chapters 2 and 3, and seven representing animal welfare impacts associated with egg production in each housing system as estimated in chapter 4. As described in chapter 6, a high degree of correlation between some environmental impact categories indicates potential redundancy in the complete suite of environmental objectives, meaning it may be possible to reduce the number of objectives considered and, as a result, problem complexity. Three potential methods were explored to remove redundant objectives. The first method is the same as that used for removing redundant objectives in chapter 6, with pairwise correlations calculated between each possible objective function, and removal of objectives that are highly correlated with one another, as defined by some pre-determined limit. Setting this limit at 0.9 as in chapter 6 would result in removal of a substantial number of possible impacts. It was found, for example, that freshwater aquatic ecotoxicity was highly correlated with all other 312 environmental impact categories, and four of the seven animal welfare impact categories, meaning that it could be removed from the optimization problem as it is likely a redundant impact. In total, this strategy suggests retention of six of 18 possible impact categories: acidification, eutrophication, marine aquatic ecotoxicity, terrestrial ecotoxicity, morbidity/injury, and fulfillment of additional behavioural needs. However, it must be noted that these correlations were only calculated using five replicates for each impact category (i.e., one estimate per housing system type). Given this small number of replicates, the robustness of these correlations may be questionable, and exclusion of impact categories on this basis may be tenuous. The second potential method explored for removal of redundant impacts was the integration of principal component analysis (PCA) as a data pre-processing step. In brief, PCA is a dimensionality reduction technique in which a large set of correlated variables are converted into principal components representing linear combinations of those variables. Principal components are calculated such that each subsequent principal component represents a decreasing amount of variation found in the original dataset. While the number of principal components computed is equal to the number of variables in the data set, dimensionality may be reduced by only retaining a subset of components which cumulatively represent an adequate amount of the original variability in the data set (James et al., 2013). Use of PCA for reduction of objective space in optimization problems has been previously proposed by Deb and Saxena (2005), with applications by Sabio et al., (2012) and Perez-Gallardo et al. (2018). When using PCA for dimensionality reduction, it is necessary to set a criterion used to determine the number of components retained in the reduced objective space. Deb and Saxena (2005) defined a threshold cutoff representing the amount of variance explained by the retained components, choosing a threshold value of 0.95. This contrasts with the approach used by Perez-Gallardo et 313 al. (2018) who instead apply the Kaiser-Guttman rule, which dictates that all principal components with associated eigenvalues >1 should be retained. In both cases, these criteria would result in the retention of two principal components. While the use of PCA in this manner would represent significant dimensionality reduction for this optimization problem, use of principal components to represent objective functions adds substantial complexity to the process of defining weights when using a weighted-sum approach to optimization. Since principal components represent combinations of potential objectives, their use does not allow for definition of weighting factors that represent different preferences for improved environmental and animal welfare outcomes. The final possible strategy for dimensionality reduction is to instead remove objective functions on the basis of structural similarity in the characterization models used for estimating impacts. For the animal welfare impacts, identifying potentially redundant impact categories may be done through comparison of the inventory data used to calculate each impact, as defined in chapter 4. On this basis, it is possible to exclude the positive and negative affective state, and injurious behaviours impact categories due to similarities in inventory data with the fulfillment of additional behavioural needs, and morbidity/injury impact categories. Making similar exclusions for the potential environmental impact categories is more difficult due to a lack of a similar depth of knowledge regarding the characterization models underlying the CML-IA Baseline impact assessment methods suite. However, in chapter 6 a sufficiently large number of replicates was available to exclude environmental impact categories from optimization models on the basis of high degrees of correlation between them. In that chapter, only the acidification, eutrophication, global warming potential, and human toxicity impact categories were retained. Since that chapter focused on feed production, and since feed is the largest contributor to the 314 majority of the impact categories considered as shown in chapter 2, it may be assumed that the same impact category exclusions identified in chapter 6 are also valid for this optimization. Given the uncertainty associated with excluding impact categories on the basis of correlations calculated in this chapter, and the inability to assign weights representing different levels of compromise between objectives when using principal components in optimization, this approach has been used to choose impact categories to be included in the optimization. Therefore, a total of 8 impact categories are included: acidification, eutrophication, global warming potential, and human toxicity as environmental impact categories, and mortality, morbidity/injury, and fulfillment of basal and additional behavioural needs as animal welfare impact categories. Unlike chapter 6, in which all objective functions were to be minimized, the optimization problems defined in this chapter include two objective functions to be maximized related to fulfillment of basal and additional behavioural needs. These objective functions were therefore multiplied by negative one before the optimization problems were solved so that all objective functions were being minimized. A multi-objective optimization approach was used in this chapter as it was expected there would be diverging signals regarding animal welfare and environmental outcomes associated with egg production in each housing system. Specifically, the results presented in chapter 2 clearly indicate that, generally, environmental impacts are greater in non-cage systems than in their cage counterparts. In contrast, the results presented in chapter 3 indicate that non-cage systems performed better with respect to behavioural welfare measures than caged systems. This indicates that there may be a trade-off with respect to environmental impacts and some welfare impacts, necessitating the use of a multi-objective optimization approach to reconcile these potential trade-offs. 315 7.3.2.2 Constraints For this optimization problem, constraints were defined in accordance with anticipated changes in market share of non-organic Canadian egg production attributable to each housing system. In total, three different sets of constraints were defined to represent current market conditions, as well as projected future market conditions. In the first scenario, an inequality constraint was defined such that the proportion of hens housed in conventional cage systems could not exceed the proportion currently housed in this type of system. This constraint was defined in accordance with the Canadian Code for Care and Handling of Laying Hens, which dictates that new construction of conventional cage systems is currently banned in Canada (National Farm Animal Care Council, 2017). In this scenario, the maximum upper limit market share for conventional cage housing was set at 48%, in line with data for 2023 presented in the most recent annual report from Egg Farmers of Canada (2023). For the second scenario, an inequality constraint was defined such that the maximum proportion of hens housed in conventional cage systems was 24%. This scenario was intended to represent 2030 market conditions as the halfway point between the current conditions in 2024, and 2036 market conditions in which the Canadian Code for Care and Handling of Laying Hens dictates that no hens are to be housed in conventional cage housing systems (National Farm Animal Care Council, 2017). In line with this timeframe, this scenario assumes that the maximum proportion of hens housed in conventional cages is half of the current market share. In the final scenario, an equality constraint was defined such that the market share for conventional cage systems was zero, while market shares for all other housing systems were unconstrained. This scenario was intended to reflect market conditions in 2036, representing the expected complete phase out of conventional cage egg production in Canada. Across all scenarios, the potential market share for 316 all alternative housing systems were assumed to be unconstrained, and no minimum market share was assumed for any alternative systems. While this could lead to suggestions that would require substantial re-structuring of the Canadian egg industry to practically implement, such as a complete transition of all egg production to just a single type of housing system, Doyon et al. (2023) indicate that Canadian consumers may be willing to update their beliefs and revise their choices on egg sourcing when provided with new information. This suggests that no minimum market shares should be assumed in optimization, as it may be possible for consumer preferences to coalesce to a single housing system type. A complete summary of the three scenario constraints is available in Table 7-1. Table 7-1. Three different constraint scenarios for multi objective optimization of Canadian egg production for environmental and animal welfare outcomes, and the market year those constraints are intended to represents. Scenario Market year Maximum conventional cage market share (%) 1 2024 48 2 2030 24 3 2036 0 7.3.2.3 Weighted sum solver A weighted sum approach was used to solve the defined optimization problems. A complete description of the weighted sum approach, including conditions that must be met to ensure that solutions are pareto optimal, is given in chapter 6, section 6.3.7, and hence is not repeated here. All necessary conditions to ensure pareto optimality are met by the optimization 317 problems defined in this chapter. Use of a weighted sum approach requires definitions of weights associated with each objective function, representing the relative importance of each objective within the optimization. Definition of the weights associated with each objective may have substantial impacts on the optimal solution identified, particularly when there may be competing objectives as may be the case with the animal welfare and environmental impacts associated with egg production in different housing systems. Little information is available regarding preferences for different product characteristics among Canadian egg consumers. Bejaei et al. (2011) used an online survey to collect data on attitudes and preferences from egg consumers in British Columbia in 2009, finding a strong increase in preference for cage-free specialty eggs including both non-cage and free range-eggs compared to data from 2007. These preferences were driven in part by animal welfare and environmental concerns associated with different egg production systems, though combinations of price, and perceived differences in nutritional contents between eggs from different systems were commonly considered to be relatively more important. While this study seemed to indicate a relatively strong preference for cage-free eggs, this preference has not been reflected in the proportions of hens kept in different housing systems at a national level. Rather, in the period from 2019 to 2023, only small increases were observed in proportions of non-organic hens kept in non-cage and free range systems, compared to much larger increases in the proportion of hens kept in enriched cage systems (Egg Farmers of Canada, 2023). Using more recently collected data from a sample of consumers in Quebec, Doyon et al. (2023) similarly found a disproportionately high percentage of consumers reporting purchasing non-cage and free range eggs relative to the Canadian market share of these systems. The results of these two studies combined with information regarding Canadian housing system market share strongly indicate 318 the potential for regional differences in consumer preferences and priorities across the Canadian egg market. Regional differences in consumer preferences for sustainability attributes is not uncommon (Zepeda et al., 2013), though similar data regarding preferences in other major egg producing provinces in Canada, such as Alberta, Manitoba, and Ontario, are unavailable. Similarly, in a meta-analysis Clark et al. (2017) found substantial heterogeneity in reported consumer willingness-to-pay for improved farm animal welfare attributes, which was dependent on both region and population demographic factors such as consumer age. Rahmani et al. (2019) found that consumers may exhibit strong preferences for eggs with reduced GHG emissions, with this preference potentially being stronger than that for improved animal welfare outcomes when these reductions were substantial (i.e., >20%). These data, however, were collected from Spanish consumers and may not be transferrable to Canadian consumers given regulatory differences between Canadian and European egg producers, such as the ban on conventional cage egg production that was implemented in European Union member states many years before the ban imposed in Canada in 2017 (National Farm Animal Care Council, 2017; The Council of the European Union, 1999). Similarly, it is suggested by Doyon et al. (2023) that Canadian consumers may have a stronger preference for lower environmental impacts than improved animal welfare outcomes, although this suggestion comes with the caveat that the preference may also be driven by lower market prices for consumers purchasing eggs with lower environmental impacts (i.e., caged eggs) compared to those with potentially improved animal welfare outcomes (i.e., cage free eggs). Taken together, it is clear that there is little definitive information in the literature regarding the relative strength of preferences among Canadian egg consumers for improved environmental and animal welfare impacts. On this basis, multiple sets of weights were defined 319 for each objective function representing different levels of compromise between objectives. Sets of weights were defined to represent three general preferences: equal weighting placed on both environmental and animal welfare impacts, stronger preference for environmental impacts, and stronger preference for animal welfare impacts. Weighting factors for the environmental objectives under each weighting scenario were the same as those defined previously in chapter 6. Of the animal welfare impacts considered, two impact categories related to the biological functionality of the animal, while the other two related to animal behaviour. When considering farm animal welfare, it is possible that different stakeholder groups may have different priorities regarding which component(s) of welfare are the most important. For example, the priorities of consumers and farmers may differ from one another (Alonso et al., 2020; Balzani and Hanlon, 2020; Ufer and Ortega, 2023). Since no authoritative source of weights for different animal welfare impacts is available, multiple optimization scenarios have been defined representing greater preference for improvements in biological function measures and lower preference for behavioural measures, and vice versa. While each of these components of welfare is further broken down into two subcategories, equal weighting has been assumed for subcategories within each overall welfare impact category. In total, therefore, five different sets of weights for optimization were defined. A complete overview of the weights for each objective in each scenario is given in Table 7-2. On this basis, fifteen distinct optimization problems were defined representing different combinations of three constraint scenarios and five objective weight scenarios. 320 Table 7-2. Weights used for multi-objective optimization of the distribution of non-organic Canadian egg production in different housing systems with 8 objective functions. GHG emissions Acidification Eutrophication Human toxicity Mortality Morbidity/Injury Basic behavioural needs fulfilled Additional behavioural needs fulfilled Sum environmental weights Sum animal welfare weights S1 0.125 0.125 0.125 0.125 0.13 0.13 0.13 0.13 0.5 0.5 S2 0.49 0.05 0.14 0.07 0.08 0.08 0.04 0.04 0.75 0.25 S3 0.49 0.05 0.14 0.07 0.04 0.04 0.08 0.08 0.75 0.25 S4 0.16 0.02 0.05 0.02 0.25 0.25 0.13 0.13 0.25 0.75 S5 0.16 0.02 0.05 0.02 0.13 0.13 0.25 0.25 0.25 0.75 321 7.3.3 Sensitivity analysis Upon analysis of the optimal results given from the different objective function weight and constraint scenarios, two case-specific sensitivity analyses were performed. In the first, a subset of the optimization problems were re-defined such that the environmental objective functions were based on estimated imapcts per tonne of eggs produced under DEA-efficient conditions, as reported in chapter 3. Assuming the efficiency gains reported in chapter 3 are realized, the relative differences in environmental impacts per tonne of eggs across housing systems shrink compared to current conditions (particularly with respect to free-run systems). A sensitivity analysis was therefore performed to determine if shrinking these differences would be sufficient to suggest a different optimal distribution of egg production. Objective function weighting scenarios 1, 2, and 4 (Table 7-2) were chosen for use in this sensitivity analysis in combination with constraint scenario 3 (i.e., zero conventional cage production, Table 7-1). Prior to running this sensitivity analysis, all impacts were re-normalized using min-max normalization. A second sensitivity analysis was also performed in which greater weight was placed on the basal behavioural needs impact category relative to the other animal welfare impacts. This impact category was chosen for sensitivity analysis as the objective value for each housing system is calculated based on space allowance, which has more variability across enriched and cage-free housing systems than the inventory data for any other animal welfare impacts. Under the high animal welfare priority scenario (i.e., total weight of animal welfare impact categories = 0.75), the breakeven point was determined representing how highly fulfillment of basal behavioural needs had to be weighted to give an optimal distribution of housing systems different than that suggested by any other optimization scenario. This point was determined to be 322 a weight of 0.45, while all other animal welfare impacts were assigned weights of 0.1. Following identification of this break-even point in the high animal welfare priority scenario, these weights were scaled to find corresponding weights in the equal and low animal welfare priority scenarios, to see if this increased preference for space allowance was sufficient to alter the optimal distributions previously identified. A complete breakdown of the weights used for each impact category during optimization for each sensitivity analysis is given in Table 7-3. 323 Table 7-3. Weights used for multi-objective optimization of the distribution of non-organic Canadian egg production in different housing systems with 8 objective functions for sensitivity analysis GHG emissions Acidification Eutrophication Human toxicity Mortality Morbidity/Injury Basic behavioural needs fulfilled Additional behavioural needs fulfilled Sum environmental weights Sum animal welfare weights S1 0.16 0.02 0.05 0.02 0.1 0.1 0.45 0.1 0.25 0.75 S2 0.125 0.125 0.125 0.125 0.07 0.07 0.3 0.07 0.5 0.5 S3 0.49 0.05 0.14 0.07 0.06 0.06 0.08 0.06 0.75 0.25 324 7.4 Results and discussion Across all 15 optimization scenarios investigated, it was found that the optimal solution was to produce all non-organic Canadian eggs in enriched cage systems. This was also found to be the optimal distribution for all three sensitivity analyses performed when using the environmental impacts of egg production under DEA-efficient, rather than current Canadian average conditions, and two of the three sensitivity analyses performed when using alternative objective function weights. The only optimization model that resulted in a different optimal distribution of egg production in housing systems was the scenario 1 sensitivity analysis, which included a strong preference for maximizing fulfillment of basic hen behavioural needs relative to all other objective functions. Instead, this optimization model indicated that 100% of Canadian non-organic egg production should occur in free range systems. That 100% enriched cage production was identified as the optimal solution in almost all models tested indicates that enriched cage systems represent an optimal compromise between these objectives. In practice, this solution is not substantially different than what may be expected in the Canadian egg industry over time, as enriched cage production has seen substantially larger growth in market share since the implementation of the ban on conventional cages compared to other alternative, non-organic systems (Egg Farmers of Canada, 2023). From an environmental perspective, such a shift would result in generally negligible changes (i.e., <0.5%) to the current national average environmental impacts per tonne of eggs produced in Canada (Figure 7-1). The majority of impacts would decrease slightly, while freshwater aquatic ecotoxicity and terrestrial ecotoxicity impacts would be expected to increase by <0.2% relative to the current national average. 325 Figure 7-1. Relative environmental impacts per tonne of eggs produced in Canada under current Canadian average conditions, and in an animal welfare and environmentally optimal scenario in which 100% of non-organic eggs are produced in enriched cage systems 0102030405060708090100Relative percent of impacts (%)Canadian averageEnriched 326 From an animal welfare perspective, such a shift would generally be expected to have a positive animal welfare impact for all those hens transitioning from conventional cage housing to enriched housing for most animal welfare impacts. For those hens, this transition would be expected to result in a lower risk of mortality, better support for basal and additional behavioural needs, and generally better positive affective state. There may, however, be some trade-offs for hen transitions from conventional cage systems, as this transition may also result in a moderately high risk of morbidity and injury as well as risk of being the victim of injurious behaviours, and, as a result, a larger contribution from negative affective state. For those non-organic hens currently housed in other alternative housing systems, including non-cage and free range systems, a transition to enriched housing would engender more trade-offs. Such a transition would generally be expected to provide hens with a lower risk of mortality, morbidity/injury, and of experiencing injurious behaviours. In contrast, this shift would also generally be expected to result in slightly worse outcomes for supporting additional behavioural needs, and smaller contributions to positive affective state. Smaller contributions to negative affective state may be expected for those hens transitioning from single-tier non-cage and free range housing systems, but not necessarily those from multi-tier aviary systems. A more in-depth discussion of the differences in estimated animal welfare metrics between different housing systems in available in chapter 4. While this transition would not be expected to substantially change the national average environmental impacts associated with Canadian egg production, if the efficiency gains suggested in chapter 3 were realized in enriched cage system, further impact reductions of approximately 9% per tonne of eggs could be achieved. Further reduction could likely be possible through the use of environmentally optimal feeds under DEA efficient conditions. 327 However, these reductions cannot be quantified as estimated impacts between current Canadian average conditions (including the average feed formulation reported by Pelletier (2017)), and production assuming the environmentally optimal feeds identified in chapter 6 are not methodologically consistent with one another, as discussed in section 6.3.8. Of note, the optimal solutions suggested in this chapter closely mirror the results of the scenario analysis performed in chapter 3, where 100% enriched cage production was found to lead to reductions in average environmental impacts per tonne of eggs produced. While some of the scenarios investigated in that chapter exhibited greater environmental impact reduction potentials, those scenarios generally included large increases in the proportion of organic egg production, which generally has lower environmental impacts than non-organic production due to additional regulations regarding feed composition (Pelletier, 2017). While large increases in organic production systems are technically possible, the likelihood of such a scenario is small, as the market share of organic eggs in Canada has exhibited little change since 2019 (Egg Farmers of Canada, 2023). Such a large increase would also have many ripple effects through other Canadian supply chains such as the field crop sector, which would likely have to see commensurate increases in market share of organic field crops in order to meet increased demand for organic feed ingredients. Given the current market structure of the Canadian egg supply chain, therefore, a complete transition to only enriched cage production systems would be more feasible. Additional, off-target effects of this transition could include different changes to economic outcomes for farmers depending on the housing system from which they are transitioning to enriched cages. Doyon et al. (2023) indicate that there may be differences in costs of production between different systems, with costs increasing when transitioning from conventional to enriched systems. Generally, enriched systems are expected to have higher costs 328 of production than conventional systems due to higher operating and capital costs; however, the increase in cost of production associated with transitioning from conventional to enriched systems is expected to be less than that associated with a transition from conventional to other alternative, cage-free systems (Matthews and Sumner, 2015). Alternatively, for those farmers already operating cage-free systems, they could likely expect overall decreases to cost of production if they were to transition to cage-free systems, although exact estimates of costs savings are unknown. Egg Farmers of Canada does provide publicly-available information on costs of production, but the data are not differentiated by housing system type (Egg Farmers of Canada, 2024). However, given that costs associated with purchasing feed are generally the largest contributor to total cost of production (Heidari et al., 2021a), and that feed-use efficiency is generally better in enriched systems than in cage-free systems, as indicated in chapter 2, it is likely that farmers would experience decreases to costs of production as a result of higher feed-use efficiency. To determine if expected reductions in cost of production would actually result in net-benefits with respect to economic outcomes, however, it would also be required to account for capital costs that may be associated with transitioning from cage-free to enriched production systems, as well as potentially decreased revenues that could be associated with no longer producing eggs for which a specialty designation and market premium exists, such as for free range eggs. These differences in production costs and potential reductions in revenue would have to be considered against the capital costs associated with transitions to enriched systems, amortized over the lifespan of the new system in order to determine the anticipated economic payback time of a transition. If the payback time is found to be longer than the expected lifespan of the new system, it may be argued that transition to enriched systems may not be beneficial from an economic perspective. If this is the case, it is possible that the realistic optimal 329 distribution of egg production in Canada includes a small market share of cage-free production similar to the current market share, while all other farmers currently still operating conventional cage systems transition solely to enriched systems. While environmental impact reductions could result from this transition if feed- and pullet-use efficiency were concurrently improved, it is possible that such efficiency improvements would not be realized on a large scale due to farmer inexperience with these new systems. Previously, Vázquez-Rowe and Tyedmers (2013) analyzed the impacts of “skipper effects” (i.e., producer skill and experience levels) on levels of eco-efficiency realized in Peruvian fisheries, finding that producer skill was a key determinant of eco-efficiency. While no correlations between predictor variables and eco-efficiency could be found in chapter 3, and the impact of producer skill and experience was not investigated, there is some evidence in the literature that this correlation is also the case on egg farms. Khan et al. (2022), for example, performed a similar analysis for layers, finding that farmer experience had a positive impact on realized levels of technical efficiency. It has similarly been indicated that that producer experience is also associated with lower farm-level losses (Mensah et al., 2022), and higher profits for farmers (Yevu and Onumah, 2021). In order for a complete transition to enriched cages to result in reductions in estimated environmental impacts on Canadian egg farms, it would be critical that effective systems be implemented for generation and sharing of knowledge among farmers – particularly those that are already operating enriched systems and therefore have a greater degree of experience with them. It has been shown that implementing systems to facilitate knowledge transfer between farmers can have a positive influence on the rate of uptake of new, beneficial practices (Cadger et al., 2016). Mapangisana et al. (2022) suggest that the formation of “learning alliances” 330 involving producers and other stakeholders throughout the value chain is an important strategy to help facilitate knowledge transfer between farmers. For Canadian egg farmers, such alliances could draw substantial value from the new National Environmental Sustainability and Technology Tool (NESTT) (Arulnathan, 2023), which provides famers with an effective portal to engage with current research in the Canadian egg industry, and means to track individual performance and benchmark against the performance of their peers. In cases where farmers are resistant to taking part in voluntary learning programs, Wood et al. (2022) suggest that mandatory learning programs can be beneficial. Barriers to adoption may include a lack of opportunities for farmers to trial the new systems and technologies prior to implementation, the potential complexity of the systems, and how compatible new systems are with pre-existing systems (Sewell et al., 2017). Regardless of the approach taken, however, it is crucial that strategies for promoting farmer learning and knowledge sharing must take into account the different manners in which different farmers may learn most effectively (Sligo and Massey, 2007). If systems for knowledge generation and sharing are implemented between farmers, it may be possible to realize wide-spread reductions in environmental impacts in Canadian egg production systems, even following the potential complete transition for the majority of farmers to a new system with which they have limited to no previous experience. In addition to potential difficulties on the producer side associated with a complete transition to enriched cage housing, there would likely also be difficulties to be navigated on the consumer side regarding public acceptance of such a change. Generally, consumers view cage-free systems as more beneficial for animal welfare than caged production systems (Ochs et al., 2018). When considering consumer preferences for specific attributes of egg production systems, consumers often still exhibit a preference for cage-free systems, even though enriched systems 331 are generally capable of meeting these preferences as well (Widmar et al., 2020). Although consumers often state a preference for cage-free eggs related to perceived animal welfare benefits, it has been demonstrated that consumer preferences may change upon receiving new information. Doyon et al. (2023) have demonstrated that Canadian consumer preferences may be changed in response to new information received during a survey process, and Ochs et al. (2019) demonstrated similar capabilities through the use of new information presented in video form. Alternative strategies for changing consumer preferences and behaviours may include marketing strategies, which have been indicated to be effective in changing consumer willingness-to-pay for animal welfare attributes (van Riemsdijk et al., 2023), as well as animal welfare labelling systems. Animal welfare labelling systems have been shown to be an effective tool for enhancing consumer education and changing consumer behaviours (Schwickert, 2023), though barriers exist to the potential efficacy of these systems. Barriers may include issues related to establishment of labelling schemes that accurately reflect the many different factors that contribute to animal welfare and potential trade-offs between them (Sørensen and Schrader, 2019), as well as consumer related challenges around trust and consistency in the labelling system, and general consumer understanding of what the labels mean (Ingenbleek and Krampe, 2022). Similar issues are also prevalent in the development of labelling schemes for other sustainability attributes (Cook et al., 2023). Generally, however, it is clear that implementation of the optimal egg production distributions suggested in this chapter would require substantial engagement from Canadian egg farmers and other value chain actors to ensure societal acceptance of this transition. 332 7.5 Limitations and conclusions Although all of the optimization problems solved in this chapter gave a solution that coalesced to a single optima, it is possible that this result is impacted by various limitations of the study. Limitations related to estimation of the environmental impacts, and of the animal welfare life cycle impact assessment method used for estimating the animal welfare impacts of each egg production system are discussed in depth in chapters 2 and 4, respectively, and so are not discussed in detail here. Nonetheless, it does bear mentioning that the results of this optimization are limited by the method with which animal welfare impacts of each housing system were estimated. As indicated in chapter 4, methods for integration of animal welfare assessment into the life cycle assessment framework are still nascent. Continued development of these methods may lead to increased accuracy of estimated welfare impacts, particularly with respect to differences between systems. Possible improvements include accounting for a wider range of factors that contribute to animal welfare outcomes, as well as potential interactions between life cycle stages that influence welfare outcomes. However, if the general framework for estimation of animal welfare impacts developed in chapter 4 is maintained, it is unlikely that the optimized results suggested in this chapter would change substantially. As evidenced by the sensitivity analyses performed, in order to get an optimal solution that does not suggest 100% of production in enriched systems, weighting factors for fulfillment of basal behavioural needs must be more than four times greater than that of any other animal welfare impact, and more than double that of any environmental impact. Given the relative unsensitivity of the optimum results to different sets of weighting factors, it is likely that to obtain a different optimal solution more factors contributing to animal welfare would have to be considered that differentiate outcomes in enriched and cage-free housing systems to a greater degree. 333 It may be possible, however, that other optimal solutions could be identified if other objectives and constraints were included in the optimization problems. For example, different optima may be identified if economic impacts were taken into account given expected differences in costs of production, capital costs associated with transitioning to new housing systems, and potential differences in revenues between different systems. With more consumer data, it would also be possible to include constraints related to expected changes in consumer behaviour and consumption patterns. It may be necessary, for example, to determine a minimum market share required to satisfy demand for cage free eggs in cases where consumers may be unwilling to change their preferences, even in light of labelling schemes, consumer education campaigns, etc. Additional data regarding consumer and farmer priorities for different animal welfare and environmental objectives would also be useful in order to ensure the set of weighting factors employed accurately represents the priorities of stakeholders in the egg value chain. Nonetheless, the optimization scenarios and sensitivity analyses performed in this chapter clearly point to a single optimal solution, indicating that, from an animal welfare and environmental perspective, it is likely optimal for all Canadian eggs to be produced in enriched cage systems. 334 8. Conclusions 8.1 Research contributions and reflections As the world population continues to grow, development and maintenance of a sustainable food system is a key target for sustainable development. While many have suggested that reduction in consumption of livestock-derived food products may be a key lever for development of such a food system (Tobi et al., 2019), consumer trends indicate demand for these products is expected to increase (Govoni et al., 2022, 2021). If this anticipated demand is to be met while staying within safe operating spaces for planetary boundaries, it is clear that substantial improvements must be made to reduce the environmental impacts of these systems without jeopardizing their associated social and economic benefits. The research presented in this dissertation contributes both to industry and public knowledge, and to the sustainability assessment literature. In chapter 2, updated baseline LCA models of Canadian egg production systems have been developed based on primary data collected from Canadian egg farmers, and best publicly available data for upstream field crop production systems. These models have subsequently supported investigation into many other sustainability improvement strategies for the Canadian egg industry, including waste valorization (Ershadi et al., 2021; Kanani, 2021), hen housing (Li, 2020; Vanbaelinghem, 2024), and lay cycle length (Pelletier and Doyon, 2023). These models have also been implemented in NESTT (Arulnathan, 2023), putting the information contained within them directly into the hands of Canadian egg farmers to support informed decision making on their farms, and to allow farmers to compare the environmental performance of their specific farm against average benchmark metrics representative of their peers. 335 The research presented in chapter 3 then builds upon the models baseline models developed in chapter 2, extending them through joint application of DEA and LCA. This chapter indicates that pullet- and feed-use efficiency gains are possible for many Canadian egg farmers relative to their peers, and that realizing these gains may lead to large reductions in environmental impacts. These findings further reaffirm the conclusions of chapter 2, identifying feed inputs as a priority focus for sustainability improvement efforts in the Canadian egg industry. In chapter 4, a novel animal welfare LCIA method was developed and tested using a case study of the Canadian egg industry. This method is in line with current paradigms in the discipline of animal welfare science, and provides results that are congruent with estimated impacts based on the literature. This method successfully integrates knowledge from the field of animal welfare science in an interdisciplinary manner, a notable departure from other methods developed with similar goals. This method makes contributions not only to industry knowledge regarding the relative differences in animal welfare impacts between different housing systems, but also make substantial contributions to the scientific literature as other researchers further attempt to integrate animal welfare metrics into sustainability assessments of food systems. Chapter 5 makes similarly novel contributions to the literature through development of generalized guidelines for performance of LCO studies. These guidelines provide practitioners an opportunity to reflect upon the specific goals and characteristics of their optimization problem, and make methodological choices accordingly. The guidance provided in chapter 5 draws heavily upon current best practices within the LCO literature, providing an updated review of the field compared to now outdated prior reviews. The knowledge developed in this chapter is 336 then directly operationalized in chapters 6 and 7, directly demonstrating the utility of these guidelines. Finally, chapters 6 and 7 make valuable contributions to Canadian egg farmers and industry partners seeking to meet the net-zero GHG goals of the Canadian egg industry. Chapter 6 develops a framework for regional optimization of laying hen feed formulations, taking into account differences in environmental impacts associated with production and transportation of ingredients produced in different areas to feed mills across the country. This framework also integrates many different sets of weights for different environmental and economic objectives, reflecting relative differences in stakeholder priorities during optimization, This results of the application of this framework indicate that optimization of feeds for environemtnal outcomes may make substantial contributions to reducing the environmental impacts of Canadian egg productions without commensurate increases in feed costs. In the future, this framework may be extended to consider alternative ingredients, nutritional constraints, and stakeholder priorities such that impact mitigation can be achieved in line with the priorities of Canadian egg value chain stakeholders. Finally, chapter 7 contributes t o the ongoing sustainability discourse regarding the transition away from conventional cage housing to alternative housing systems in Canada. While some researchers have suggested that such transitions may result in net negative sustainability outcomes for global egg industries (Stadig et al., 2016), the results of this chapter strongly indicate that a transition to enriched cage housing systems may be beneficial from an environmental and an animal welfare perspective. A complete transition to enriched cage production may result in net negatives for some animal welfare impacts – particularly in cases where cage free systems outperform enriched cages. However, across a large number of 337 optimizations performed exploring many different preferences for improvement of environmental and animal welfare objectives, the only situations in which a complete transition to enriched cage production was not preferred was when fulfillment of hen basic behavioural needs was strongly preferred compared to all other objectives. Additional research should be conducted using this optimization framework in combination with metrics representing actual stakeholder priorities for improvement of different objectives to verify these results. However, the trend in optimization results indicated in this chapter cannot be ignored, suggesting that enriched cage production systems represent an optimal compromise between environmental and animal welfare objectives, and that transition into these systems specifically should be prioritized in the Canadian egg industry. 8.2 Next steps and future directions Together, the research presented in this dissertation may help catalyze sustainability improvements in the Canadian egg industry. Of note, however, is that this research was largely focused on “supply-side” action as a means of sustainability improvement. Broadly, public policies implemented with the goal of improving sustainability outcomes may be described as “supply-side” (i.e., aimed at producers), or “demand-side” (i.e., aimed at consumers) (Caviglia-Harris et al., 2003; Stankovic et al., 2024). In all chapters, the research was performed with the goal of identifying improvement opportunities for Canadian egg producers, with the role of Canadian egg consumers in transitioning to a sustainable food system only discussed briefly in chapter 7. Although the research focuses on supply-side improvements, the potential merits and efficacies of demand-side strategies should not be ignored. Indeed, demand-side strategies may be efficacious tools for realizing improvements in food systems sustainability (Mueller et al., 2023), and will likely be necessary in the form of marketing, product labelling, and/or consumer 338 education programs so as to further stimulate the Canadian egg industry to navigate the ongoing housing system transition in a manner that is ecologically, socially, and economically sound. Successful implementation of demand-side strategies, however, faces substantial challenges, and is dependent on consumer priorities and preferences being malleable rather than fixed and unchanging (Creutzig et al., 2021). As discussed in chapter 7, it is likely that this is the case for Canadian egg consumers. In reality, for the Canadian egg industry to meet the net-zero targets outlined by Egg Farmers of Canada, an integrated approach incorporating both supply- and demand-side strategies for sustainability improvement will be necessary. Towards this end, demand-side information could be incorporated in weighting of optimization objectives in chapters 6 and 7, such that these weights more accurately represent the preferences and priorities of Canadian consumers, as well as other stakeholders in the Canadian egg supply chain. In completion of the research described in this dissertation, a number of limitations were identified. First among these limitations is uncertainty. In LCA, sources of uncertainty include parameter uncertainty, model uncertainty, and uncertainty due to methodological choices. Literature is not clear on which among these is the most important, and it is unlikely that all uncertainty can be accounted for in a study as there will always be unknown unknowns. It is best practice in the LCA field to provide estimates of uncertainty around results (Bamber et al., 2019). Uncertainty assessment was only performed in chapter 2 of this dissertation. In the future, more focus should be placed on the use of methods that support the inclusion of uncertainty, such as fuzzy DEA and robust optimization, each of which is discussed briefly in chapters 3 and 5, respectively. Use of these frameworks may enhance the potential decision support afforded by the frameworks developed in chapters 6 and 7 by accounting for potential uncertainty in the decision making process. Similarly, continued development of the animal welfare impact 339 assessment method presented in chapter 4 to account for more contributors to animal welfare, more life cycle stages, and the potential for interactions in welfare outcomes between stages is important. However, these improvements must be made in an interdisciplinary manner, drawing on the most current paradigms and up to date knowledge from the field of animal welfare science to ensure the accuracy of the estimated impacts, and confidence in the method. Achieving a high degree of confidence in this method will be vital if it is to be used to support decisions that are contradictory to pre-conceived notions regarding welfare held by consumers, as indicated in chapter 7. Importantly, no single solution should be considered as a silver bullet capable of overcoming all current challenges in development of a sustainable food system. Food systems exist at the nexus of basic human needs, environmental degradation, economic prosperity, and social and cultural identity. Efforts to solve an issue related to one of these factors without consideration of the others risks simply shifting burdens to other issue areas. Moving forward, an integrated, interdisciplinary approach to assessing potential strategies and solutions must be prioritized for improvement in both the Canadian egg industry, and the global food system as a whole. Assessments of strategies should be grounded in life cycle thinking so as to ensure that strategies result in net positive improvements, as it is possible that improvements in one supply chain step may lead to worsening impacts in another, as illustrated in chapter 6. Further, LCA models of potential improvement strategies should be based on the best available data and methods that enable accurately representing the true attributes and implications of the system under study. As seen in Pelletier (2018) and in chapter 2 of this dissertation, changes in LCI data over time should be expected, which may lead to substantial differences in estimated impacts. 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These reports source data from a variety of publicly available sources, including Statistics Canada, provincial growers guidelines for each crop, literature, and others. When RU-specific data sources are unavailable, average provincial sources may be used instead. In some cases no Canadian data is available, leading to the use of proxy data representative of U.S. practices for production of the same crops, or informed assumptions, such as those regarding transportation distances from field to bins for storage and drying of harvested crop. In all cases, efforts are made to use the best publicly available data for representing production practices for each crop in each RU. Detailed descriptions of the sources for all data points, as well as in-depth discussions regarding data quality, are available in the CRSC methods report ((S&T)2 Consultants Inc., 2022a), and the relevant individual crop reports. In addition to the RU-specific models that have been re-created , this analysis also includes aggregation of these models to the national level in order to represent production-weighted average conditions. The proportions of total amounts of each crop produced attributable to each RU are given in Table A 1, and are based on production data reported in each associated CRSC report. A.2 Exclusions from field crop systems In modeling of soybean production systems, production of soybeans in RUs 24 and 28 were excluded due to inconsistencies in reported fertilizer application rates. During modeling of field crop systems, fertilizer inputs for each crop-RU combination were re-calculated based on 417 the reported nutrient application rates, and appropriate fertilizer mixes defined in each crop report. During this re-calculation process, it was found that reported rates of sulfur fertilizer application for soybeans produced in RUs 24 and 28 could not be met when applying the reported amount of ammonium sulfate without substantially over-shooting the reported rate of N fertilization. This inconsistency in reporting meant that fertilizer inputs could not be re-calculated for soybeans produced in RUs 24 and 28, so they were subsequently excluded from this analysis. As a leguminous crop, it is possible that soybeans may be inoculated at the beginning of the growing season with nitrogen-fixing bacteria, which reside in root nodules of the legume in a symbiotic relationship with the plant (Alberta Agriculture and Forestry, 2018; Bamber et al., 2022a; Bromfield et al., 2017). Inoculation may result in increases in yield, and better yield-resilience during high-stress situations, such as drought (Albareda et al., 2009; Igiehon et al., 2021). Impacts associated with inoculant production were excluded from the CRSC soybean report ((S&T)2 Consultants Inc., 2022a). However, these impacts have been included in other LCA studies of Canadian legume production systems. Contributions to total impacts from inoculants are generally small, and dependent on the carrier with which the inoculant is applied (Alberta Agriculture and Forestry, 2018; Bamber et al., 2022a). Consultation of provincial and growers association guidelines for all provinces containing a soybean-producing RU revealed significant variability in suggested inoculation practices, ranging from inoculation at each sowing (Government of Saskatchewan, n.d.), to only every 7-10 years or more (OMAFRA, 2022). In general, literature suggests that inoculation practices for soybean systems should depend on regional climate factors, the specific cultivar being grown, application methods, and other factors (Hungria et al., 2017; OMAFRA, 2022; Sanzovo et al., 2023). Given a lack of clear 418 data with regards to regional inoculation practices, impacts associated with potential inoculation of soybean fields were excluded in line with the CRSC soybean report ((S&T)2 Consultants Inc., 2022a). Similarly, modeling of inoculant inputs was a noted challenge during previous analyses of Canadian pea and lentil systems, leading to the use of expert opinion as a data source for rates of inoculation, rather than using primary data collected from famers (Bamber et al., 2022a). While the decision to exclude impacts related to inoculation of soybean systems may not be entirely representative of current soybean production practices in Canada, it is unlikely that inclusion of impacts associated with inoculants would result in substantial changes to estimated impacts per functional unit (Araujo et al., 2020; Bamber et al., 2022a). A.3 Allocation in field crop systems Indiscriminate treatment of all crop residues as waste products may result in overestimation of the impacts associated with crop production, as all impacts are allocated to the crop product, with no impacts allocated to co-products. It was therefore necessary to identify residue management practices associated with each of the crops included, both for the purpose of proper allocation between co-products, and to ensure accurate accounting of field-level emissions associated with crop residue decomposition. In development of models characterizing Canadian, western-Canadian, and Saskatchewan canola production, Bamber et al. (2022b) found that canola residues are commonly left on fields and/or incorporated into soils. This was also found to be true of pea production systems, and is likely also true of soybean systems as both are leguminous crops that are capable of fixing atmospheric N. Retention of leguminous crop residues on fields may lead to large releases of N during decomposition (Bahl and Pasricha, 2000; Walley et al., 2007), thus reducing requirements for synthetic N fertilizers in subsequent crops (MacWilliam et al., 2014; Xing et al., 2017). For 419 soybean, pea, and canola production, therefore, it was assumed that no residues are removed from fields and there are no co-products of these systems which require allocation. For corn, wheat, and barley, it is likely that some portion of residues are removed from fields. Residues from all three of these crops are used as biofuel feedstocks (Canada Energy Regulator, 2023) and in livestock production systems in Canada (McCartney et al., 2006). Bamber et al. (2022b) previously estimated the percentage of wheat straw removed from fields, based on available information regarding total area of non-durum wheat cultivation in Saskatchewan in 2021 (Statistics Canada, 2023a), total area from which crop residues were removed in Saskatchewan in 2021 (Statistics Canada, 2021), and an average residue removal rate of 34.5% in Saskatchewan (Lafond et al., 2009). Based on these sources, they estimated that 8.3% of all wheat straw was removed from fields, reflecting 34.5% of straw removed from 24.1% of total non-durum wheat cultivated area. This assumption has been used in the current analysis. Estimates of rates at which corn residues are removed from fields show high variability. When removing corn stover residues from fields in southwestern Ontario, Gan et al. (2016) found that the marginal costs associated with stover removal increase significantly as removal rates exceed 25% due to costs associated with collection, baling, and subsequent nutrient replacement. This is lower than the 30% removal rate assumed by Duffy and Marchand (2013). In a review of the literature, Battaglia et al. (2020) found that the impacts of residue removal rates ≥90% on long-term crop performance is dependent on local climate conditions, suggesting that sustainable corn stover removal rates are regionally dependent. Other estimated sustainable removal rates of stover include 3.2 tonnes/ha (Hamedani et al., 2016), 3.52 tonnes/ha (Kludze et al., 2013), 2.01 tonnes/ha (Western Sarnia-Lambton Research Park, 2012), and 4.1 tonnes/ha 420 (Kludze et al., 2010). When these estimates are combined with the production-weighted average yield given in the CRSC report ((S&T)2 Consultants Inc., 2022b), and the proportions of biomass partitioning for Canadian field crops given by Liang et al. (2020), they correspond to removal rates of 28.52%, 17.91%, 36.54%, and 31.42%, respectively. An average rate of corn residue removal rate of 28.23% was calculated and used in the current analysis, excluding the rates suggested by Battaglia et al., (2020) since they are considerably higher than any others found. Estimates of the percentage of barley residues removed from fields were more difficult to identify. Alberta Agriculture and Forestry (2020) indicate that estimates on the percentage of barley straw removed are not readily available, and this percentage is likely dependent on yearly straw yields and market conditions. Depending on the method used to harvest straw, McCartney et al. (2006) estimate that harvested yield of barley straw may be between 33-54% of grain yield, by mass. Assuming that, on average, barley residue yields are 57.5% that of grain yields by mass (McCartney et al., 2006) combined with the production-weighted average yield given in the CRSC report ((S&T)2 Consultants Inc., 2022c), and the proportions of biomass partitioning for Canadian field crops given by Liang et al. (2020), this corresponds to a residue removal rate of 36.73%. A complete summary of the assumed rates of aboveground residue removal for each crop modeled is given in Table A 2. Since a portion of corn, wheat, and barley crop residues were assumed to be removed from fields as co-products, it was necessary to allocate impacts between these residues and the grain that is co-produced. LCA literature investigating field crop systems commonly allocated between grain and straw on the basis of both mass (i.e., Doran-Browne et al., 2015; Li et al., 2012a; Naudin et al., 2014) and economic value (i.e., Eady et al., 2012; Nguyen et al., 2012; Van 421 Middelaar et al., 2013). Less commonly, impacts are allocated between co-products on the basis of relative chemical energy content (Pelletier et al., 2010). Strong arguments have been made in the literature against the use of economic value as a basis for defining allocation factors (Pelletier and Tyedmers, 2011). Arulnathan et al. (2022) provides an in-depth discussion of the use of external- or internal-causality when choosing biophysical relationships to use as a basis for calculation of allocation factors. In it, the authors make strong arguments for the use of chemical energy content as an appropriate underlying biophysical basis upon which to define allocation factors (Arulnathan et al., 2022). The applicability of this to wheat production systems was explored in-depth by Bamber et al. (2022b), who calculated both mass- and chemical-energy based allocation factors for wheat grain and straw, finding that the calculated allocation factors differed very little between the two (i.e., 0.22% - 0.25%). Following Arulnathan et al. (2022) and Bamber et al. (2022b), allocation factors for crops and crop residues were calculated based on gross chemical energy content of co-products. For calculation of energy-based allocation factors, the mass of harvested residues was calculated based on crop residue yields given by Liang et al. (2020) and the percentages of residues removed shown in Table A 2, and the relative portions of the total harvested energy content between grain and residues were calculated. Assumed energy contents for corn stover were taken from Lizotte et al. (2015), who found corn residues in Eastern Ontario to have an average energy content of 17.41 MJ/kg DM. Assumed energy contents for corn grain were taken from Feedipedia (Heuzé et al., 2012b), which is consistent with sourcing of energy contents used in calculation of emissions from manure management as described in section 2.3.2.1.4. Energy contents for wheat grain and straw were similarly taken from Feedipedia for calculation of energy-based allocation factors (Heuzé et al., 2021, 2015a). It was assumed that barley straw had 422 an average energy content of 16.51 MJ/kg based on Agu et al. (2021), who analyzed the energy content of barley straw collected in Saskatchewan. This energy content is slightly lower than the value of 17.7 MJ/kg estimated by Satpathy et al. (2014), who also collected straw in Saskatchewan. The value estimated by Agu et al. (2021) was chosen as it is a more recent estimate. As with corn and wheat grain, the assumed energy content for barley grain was taken from Feedipedia (Heuzé et al., 2016). A complete description of the allocation factors calculated for wheat, corn, and barley grain and harvested residues is given in Table A 3. A.4 Modeling fertilizer inputs to field crop systems When reporting fertilizer application data, the CRSC reports do not provide data for application of specific fertilizer products. Rather, each report provides a summary of the amount of N, P, K, and S applied to crops in each RU as synthetic fertilizers, as well as a summary of the different types of fertilizers commonly used in Atlantic Canada, Quebec, Ontario, Manitoba, Saskatchewan, Alberta, and British Columbia. Applications of specific fertilizers were therefore calculated based on the reported nutrient application rates, and the reported fertilizer mix for each region. In some cases, the fertilizers included in this mix may supply multiple different nutrients. For example, mono-ammonium and di-ammonium phosphate (MAP and DAP, respectively) each supply both N and P, and ammonium sulphate supplies both N and sulphur (S). MAP and DAP were listed as the only P fertilizers applied in the CRSC reports, while four sources of S fertilizer were given: ammonium sulphate, elemental sulphur, potassium magnesium sulphate, and 50/50 blend of MAP and ammonium sulphate. The potential for fertilizers to supply multiple nutrients was taken into account during recalculation of fertilizer inputs by determining the amount of one nutrient applied as this fertilizer when meeting the required applications of another nutrient, and subtracting that from the required application. For example, 423 required rates of MAP, DAP, and ammonium sulphate application need to meet the given P and S requirements were first calculated. Following this, the amount of N applied as these fertilizers when meeting the P and S requirements were calculated and subtracted from the reported rate of N fertilization. The remaining N to be applied was subsequently distributed among the remaining single nutrient N fertilizers (i.e., ammonia, urea, urea ammonium nitrate, and ammonium nitrate/calcium ammonium nitrate), with the proportions of each re-weighted following exclusion of MAP, DAP and ammonium sulphate (AS). When determining the amount of P applied as both MAP and DAP, the fraction of N fertilizer coming from each as reported in table 3-4 of the CRSC methodology report ((S&T)2 Consultants Inc., 2022a) was added, and the proportion of each of that sum was used. This assumption is based on the logic that the amount of MAP and DAP being applied must be the same for both N and P fertilizers, as the supplied nutrients cannot be uncoupled from one another. This was treated differently than P applied as 50/50 MAP/AS blend, as it was assumed these nutrients were blended in bag and are not part of the same molecule, as in the case of N and S in AS. Some additional assumptions were also necessary during recalculation of fertilizers. For recalculation of fertilizers applied to soybeans, it was found that it was not possible to fulfill the reported P requirements using just MAP and DAP without substantially overshooting the reported N requirements. No other fertilizers were indicated in the CRSC reports that supply only P. Therefore, amounts of MAP and DAP applied were calculated with respect to the reported N requirements. To make up the amount of P still required following application of MAP and DAP to meet N requirements, it was assumed that triple superphosphate was applied as a P-only fertilizer. Triple superphosphate was chosen in line with the list of synthetic fertilizers applied to Canadian soybean production as reported in the Agrifootprint 6.3 database (Blonk et 424 al., 2022a). For calculation of S fertilizers, the CRSC methodology report ((S&T)2 Consultants Inc., 2022a) indicates that 80% of S application id in the form of ammonium sulphate, while specific proportions of the remaining S fertilizers are not given. It was therefore assumed that the remaining 20% of S fertilizer applications were distributed evenly between elemental sulphur, potassium magnesium sulphate, and 50/50 MAP/AS. Further, potassium magnesium sulphate was assumed to have a K2O content of 21.5%, in line with the International Plant Nutrition Institute (2022), and K2O was assumed to have a K content of 60%. Potassium magnesium sulphate was assumed to have a K content of 12.9%, representing 60% of 21.5%. In calculation of N fertilizers, the CRSC reports only provide aggregated values for application of ammonium nitrate and calcium ammonium nitrate. It was therefore assumed that these two fertilizers were applied in equal proportions. Finally, for calculation of P fertilizers, it was assumed that reported rates of P fertilization were given in terms of P2O5. Recalculated fertilizer inputs per kilogram of all field crops modeled are given in Table A 4 to Table A 8. A.5 Modeling transportation in field crop systems In the CRSC methodology report ((S&T)2 Consultants Inc., 2022a), it is indicated that emissions associated with transportation of all on-farm inputs are included in the estimated carbon footprints of each crop. However, data is not reported on assumed transportation distances for any inputs, nor is any information given from which assumed transportation distances may be calculated. Rather, the only reported transportation data states an assumed transportation distance of 5 km from field to crop storage bin. Little information is available regarding transportation distances for on-farm inputs to Canadian field crop systems. Assumed transportation distances were therefore taken from work done by Alberta Agriculture and Forestry (2018), who performed an LCA of pea production in Alberta. These distances were 425 collected using surveys administered to Alberta pea producers in 2015 and 2016, and use of these distances is in line with other work done for Pulse Canada (Bamber et al., 2020a). The assumed distances were applied to all crops and all regions modeled. While it is possible that these distances may not be representative of the true transportation distances for other crops and geographic regions within Canada, transportation of on-farm inputs generally makes small contributions to the environmental impacts of Canadian field crop systems (Bamber et al., 2020a; MacWilliam et al., 2014), so potential over- or under-estimation of transportation distances is anticipated to have trivial effects on the estimated environmental impacts. The 5 km transportation distance from field to storage bin post-harvest as assumed in the CRSC reports ((S&T)2 Consultants Inc., 2022a) has also been included. All assumed transportation distances are given in Table A 9. For transportation of pesticides, Alberta Agriculture and Forestry (2018) only provides transportation distances for herbicides and fungicides. It has been assumed that the transportation distance for insecticides is the average of these two distances. All conversion factors necessary for calculating masses of diesel, gasoline, and LPG are listed in Table A 10. A.6 Irrigation water volumes for field crops Irrigation plays a relatively minor role in Canadian field crop production systems. Of the approximately 62.2 million hectares of agricultural land in Canada (Government of Canada, 2022), it is estimated that approximately 1.22% is irrigated, with the majority (~72.5%) of irrigated farm land being in Alberta (Statistics Canada, 2023b). As a result, the CRSC reports only include energy use associated with irrigation in their carbon footprint estimates for RUs 35 and 37, both in southern Alberta, when relevant. In line with this choice, irrigation water volumes were calculated for these RUs. Irrigation volumes were calculated according to information presented in the report for each crop. When a portion of the harvested crop area is 426 irrigated, it is assumed that the irrigation rate is 3500 m3 per hectare, in line with the assumed value used for calculation of energy inputs related to irrigation in the CRSC report ((S&T)2 Consultants Inc., 2022a). A.7 Biological nitrogen fixation Peas and soybeans are nitrogen-fixing legume crops, which may provide an input of N for the subsequent crops in rotation. Biological nitrogen fixation was modeled using a system expansion and substitution approach, in line with Bamber et al. (2023). Using this approach, the N credit provided by peas and soybeans was modeled as an avoided input of synthetic ammonia fertilizer. Ammonia was chosen as the avoided product because it is the simplest synthetic N fertilizer, and used as a building block for all other N fertilizer types. Values for the N credit associated with pea and soybean production were taken from Barker (2007). A.8 Field level direct N2O emissions from field crops The CRSC reports make use of IPCC Tier 2 methods for estimation of direct N2O emissions from agricultural fields ((S&T)2 Consultants Inc., 2022a). The methods utilized are largely consistent with those employed in the Canadian NIR) (Environment and Climate Change Canada, 2022), with few exceptions. Rather than using the estimates of direct N2O emissions given in the CRSC report for each crop included, direct emissions have been re-modeled using updated methods on a methodologically consistent basis. The IPCC Tier 2 equation for estimation of direct N2O emissions from managed soils includes three different factors: those related to direct N2O emissions from N inputs to managed soils, direct N2O emissions from managed organic soils, and direct N2O emissions from urine and dung deposits on grazed soils. These are both outside of the scope of the current analysis and 427 are therefore excluded from estimation of direct N2O emissions. N2O emissions from managed soils may therefore be calculated according to equation 11.2 from IPCC (2019), 𝑁2𝑂𝐷𝑖𝑟𝑒𝑐𝑡 = (∑(𝐹𝑆𝑁 + 𝐹𝑂𝑁) × 𝐸𝐹𝑖 + (𝐹𝐶𝑅 + 𝐹𝑆𝑂𝑀) × 𝐸𝐹𝑖𝑛𝑖) ×4428 A complete description of all variables included in this equation is given in Table A 11. To differentiate this calculation from Tier 1 methods, the IPCC notes that the equation may be modified to accommodate different emission factors representing different N sources and different climate conditions. The CRSC reports make use of values for EF1 that are regionalized to the RU level, in line with those employed in the Canadian NIR (Environment and Climate Change Canada, 2022). It has additionally been proposed that these values of EF1 should be subsequently modified using scaling factors representing the readiness by which direct N2O emissions may be released from different nitrogen sources. Specifically, Liang et al. (2020) propose scaling factors of 1, 0.84, and 0.28 for EF1 with respect to N from synthetic, manure, and crop-residue sources based on data collected by Rochette et al. (2018), and other literature sources. While Liang et al. (2020) do not propose a scaling factor for EF1 related to direct N2O emissions from mineralization of soil N resulting from soil C losses (i.e., FSOM), the 2022 Canadian NIR (Environment and Climate Change Canada, 2022) applies the scaling factor for organic nitrogen sources (i.e., 0.84) to this source as well. While the CRSC reports do apply these scaling factors to both synthetic N and mineralized N, they also apply the 0.84 scaling factor to N sources from crop residues, rather than the 0.28 scaling factor proposed by Liang et al. (2020) ((S&T)2 Consultants Inc., 2022a). The authors justify this choice by stating that there is insufficient evidence to justify use of the lower value for crop residue N; however, relevant literature in support of this assertion is not cited, so the strength/merit of this choice is unclear. 428 Application of the 0.28 scaling factor for N derived from crop residues could lead to substantial reductions in estimated direct N2O emissions. It has previously been estimated, for example, that use of this scaling factor could lead to an ~8% reduction in the estimated GHG emissions per tonne of soybeans produced (Turner et al., 2023c). Given the lack of strong evidence given against the use of the crop residue scaling factor in the CRSC report, the 0.28 scaling factor proposed by Liang et al. (2020) for EF1 with respect to N from crop residues has been used. It must be noted, however, that following preparation of this dissertation, Agriculture and Agrifood Canada made the decision to discontinue the use of the 0.28 scaling factor, instead reverting to the 0.84 factor used previously. This change is therefore a noted limitation of the work presented in this dissertation. One additional important consideration with respect to modeling of direct N2O emissions as performed in the CRSC reports is the treatment of N derived from application of manure to agricultural fields. In calculation of N2O emissions, the CRSC reports assume that manure inputs to field crop systems are burdenless inputs, with field-level emissions associated with manure application allocated to the upstream livestock system producing the manure ((S&T)2 Consultants Inc., 2022a). This is consistent with the view of manure as a waste product of livestock systems, and field application of manure as a waste-treatment process as defined by ISO 14044 (ISO, 2006b). It is also consistent with previous LCA models of the Canadian (Pelletier, 2017) and American egg industries (Turner et al., 2023b). This assumption has also been applied herein, effectively setting the FON term of the above equation to 0. The scaled emission factors for each RU and N source are given in Table A 12. Regionalized values for FSN are taken from reported N fertilizer application rates for production of each crop in each relevant RU. Three factors are required for calculation of FCR: 429 the mass of crop residues produced and the N content of those residues (both above- and below-ground), and the proportion of aboveground residues removed from fields. Percentages of residues removed from fields for each crop are used as reported in Table A 2. In calculation of FCR, the CRSC reports generally use crop residue yields and N contents as estimated by Janzen et al. (2003), with few exceptions. For many crops, estimates of residue yields based on biomass partitioning models and residue N contents have subsequently been updated by Thiagarajan et al. (2018), but these updated values are not used in the CRSC reports or the 2022 Canadian NIR ((S&T)2 Consultants Inc., 2022a; Environment and Climate Change Canada, 2022), though no specific reason is given for not using them. The estimates of crop residue yields in Thiagarajan et al. (2018) are given with respect to different grain yields, which are subsequently production-weighted to give single values in Liang et al. (2020). For estimation of the carbon footprint of Canadian soybeans, neither of these estimates of residue N-content are used. Rather, a below-ground residue N content of 0.02 kg N kg residue -1 is used based on Cai et al. (2015). This choice is justified by noting that there is much variability in the literature regarding soybean below-ground residue N contents due to uncertainty regarding whether or not the root nodules that fix N may senesce during the growing season and hence may no longer be in existence following harvest. The value for below-ground residue N content from Cai et al. (2015) is double that reported by Thiagarajan et al. (2018), who estimate a below-ground residue N content of 0.01 kg N/ kg residue for soybeans. To ensure consistency across all crops included in this analysis, crop residue yields are taken from Liang et al. (2020), while above- and below-ground residue N contents are taken from Thiagarajan et al. (2018). A complete description of estimated residue yields and N contents is given in Table A 13. 430 A.9 Field level indirect N2O emissions from field crops N2O emissions may also occur on agricultural fields through two indirect pathways: volatilization and atmospheric deposition of N as NH3 and NOx, which subsequently contribute to N2O emissions in a similar manner as direct application of N as fertilizers, and leaching and runoff of N, which may subsequently contribute to soil nitrification and de-nitrification processes to create additional N2O (IPCC, 2019). The CRSC reports estimate indirect N2O emissions from atmospheric deposition of volatilized N using IPCC Tier 1 methods ((S&T)2 Consultants Inc., 2022a), according to equation 11.9 in IPCC (2019). Since the CRSC reports allocate the impacts associated with field application of manure to the upstream livestock system that produces it, and excludes grazed lands, this equation may be simplified to 𝑁2𝑂(𝐴𝑇𝐷) = ((𝐹𝑆𝑁 × 𝐹𝑅𝐴𝐶𝐺𝐴𝑆𝐹) × 𝐸𝐹4) ×4428 A complete description of all variables included in this equation is given in Table A 14. Here, indirect N2O emissions from atmospheric deposition of volatilized N have been remodeled using a combination of IPCC Tier 1 and Tier 2 factors. The 2022 Canadian NIR (Environment and Climate Change Canada, 2022) provides estimated values for FRACGASF regionalized at the provincial level which were not employed in the CRSC reports ((S&T)2 Consultants Inc., 2022a), but have been used here. A Tier 1 default value of 0.01 has been used for EF4, in line with previous work by Liang et al. (2020). Of note, the IPCC methods for calculating indirect N2O emissions from atmospheric deposition of volatilized N does not include N inputs from crop residues. Little information is available regarding whether or not N contributions from crop residues left on field contribute to 431 N volatilization and subsequent indirect N2O emissions. De Ruijter et al. (2010) found that volatilization of NH3 from crop residues was negligible when N content of plant matter was <2%, and that volatilization of ~10% was observed when N contents were approximately 4%; however, none of the crops included in that study were the same as those studied herein. Later, de Ruijter and Huijsmans (2019) developed a methodology for estimation of NH3 volatilization from crop residues, finding that crop residues may be significant sources of NH3 volatilization if the N content is sufficiently high. However, they found that residues from wheat, barley, grain maize, and rapeseed do not have sufficient N content to contribute to NH3 volatilization. Soy was not included in the crops studied. In general, the potential contribution to NH3 volatilization from crop residues is unclear, though it has also been suggested that retention of crop residues on fields may lead to increased volatilization from synthetic N sources (Abalos et al., 2022). Potential interaction effects between crop residues and synthetic N sources are also not included in IPCC methods. Given the lack of clarity on volatilization of N from crop residues, N inputs from crop residues has been excluded in calculation of indirect N2O emissions from atmospheric deposition of volatilized N, in line with the IPCC methods (IPCC, 2019). The CRSC reports estimate indirect N2O emissions from leaching/run-off from managed soils using a combined Tier 1 and Tier 2 approaches ((S&T)2 Consultants Inc., 2022a), according to equation 11.10 in IPCC (2019). Since the CRSC reports allocate the impacts associated with field application of manure to the upstream livestock system that produces it, and excludes grazed lands, this equation may be simplified to 𝑁2𝑂(𝐿) = ((𝐹𝑆𝑁 + 𝐹𝐶𝑅 + 𝐹𝑆𝑂𝑀) × 𝐹𝑟𝑎𝑐𝐿𝐸𝐴𝐶𝐻−(𝐻) × 𝐸𝐹5) ×4428 432 A complete description of all variables included in this equation is given in Table A 15. Values for FSN, FCR, and FSOM were used as previously described. RU-specific factors for FracLEACH were used as in the CRSC reports ((S&T)2 Consultants Inc., 2022a) that were developed for use in the Canadian NIR (Environment and Climate Change Canada, 2022). A default value of 0.011 was used for EF5, in line with both the CRSC reports ((S&T)2 Consultants Inc., 2022a) and the 2022 Canadian NIR (Environment and Climate Change Canada, 2022). A.10 Field level non-GHG nitrogenous emissions Since the CRSC reports only sought to develop carbon footprint models of Canadian field crop production systems, rather than full LCA models, estimates of non-GHG nitrogenous emissions, such as NH3 and NOx, and of losses related to P application were not reported. These therefore had to be estimated for the current analysis. In calculation of indirect N2O emissions from volatilization of inorganic nitrogen, the IPCC methods apply the aggregated variable FracGASF to represent the portion of N volatilized as both NH3 and NOx. To estimate emissions of NH3 and NOx separately, it is therefore necessary to disaggregate this value to represent the relative proportions of NH3 and NOx that are volatilized. Little information is available regarding the proportions of NH3 and NOx in volatilized N. Pelletier (2017) has previously assumed that 90% of volatilized N is in the form of NH3 while 10% is in the form of NOx for calculating these emissions with respect to Canadian field application of poultry manure. These proportions differ slightly from those suggested by Denmead et al. (2010), who estimate that approximately 17% of volatilized N is in the form of NOx while ~83% is in the form of NH3. These estimates, however, are based on a field trial in northern China using urea as a fertilizer. Bouwman et al., (2002) and Pan et al. (2016) provide in-depth reviews of possible ammonia volatilization rates from both synthetic and organic N sources, indicating that the rates of NH3 volatilization can be 433 significantly impacted by the specific mix of N sources used, fertilizer application rates and methods, type of crop to which the source is applied, and many regionally-specific climate and soil characteristics. The 2022 German NIR (Federal Environment Agency, 2022) recognizes that the default aggregate factor suggested by the IPCC may not be appropriate if trying to estimate non-GHG emissions, and therefore applies disaggregated factors for each based on Vos et al. (2022); however, neither source explicitly states what those individual factors are, nor are they necessarily applicable to Canadian conditions given the large differences in synthetic and organic fertilizer application rates, mixes, and other management practices observed between German and Canadian field crop systems (Bamber et al., 2023, 2022b). In the absence of specific disaggregated factors applicable to Canadian conditions, it has been assumed herein that 90% of volatilized N is in the form of NH3 while 10% is in the form of NOx, in line with Pelletier, (2017). While these assumed proportions may have a large degree of uncertainty associated with them, they nonetheless reflect the general conclusion that the majority of volatilized N is in the form of NH3, as noted by Denmead et al., (2010), Vos et al., (2022), and the 2022 German NIR (Federal Environment Agency, 2022). P from fertilizers applied to crop systems may also be lost to the environment through run-off to surface waters, and subsurface draining (Ye et al., 2018). These losses may be risk factors for eutrophication, and accumulation of P in surface soils may decrease P absorption rates, potentially exacerbating issues related to P losses in surface run-off (Kibet et al., 2011). Many different methods may be used to estimate P losses related to fertilizer application, ranging from the application of assumed loss rates to estimation using complex, process-based models (Dalgaard et al., 2006; Radcliffe et al., 2009; Schoumans et al., 2013, 2009). The various methods that may be used present a distinct trade-off in terms of model complexity and general 434 applicability. Process-based models are the most complex, with significant data requirements but the ability to estimate losses at higher levels of spatial resolution than other methods. Use of process-based models generally requires significant expertise to properly parameterize models, particularly given the many site-dependent factors that may impact P loss rates (Schoumans et al., 2009). Run-off of P to surface waters was estimated here using the SALCA-P emission model as described by Emmenegger et al. (2018). Use of this model is consistent with how P run-off is estimated in the ecoinvent database (Weidema et al., 2013). Run-off of P to surface waters was calculated according to the equation 𝑃𝑟𝑜 = 𝑃𝑟𝑜𝑙 × 𝐹𝑟𝑜 A complete description of all variables included in this equation is given in Table A 16. The term Fro is subsequently calculated according to the equation 𝐹𝑟𝑜 = 1 + (0.280) × 𝑃2𝑂5𝑚𝑖𝑛 where the term 𝑃2𝑂5𝑚𝑖𝑛 represents the quantity of mineral fertilizers applied to the crop system as P2O5 with the unit kg P2O5 applied x tonne of crop at an appropriate moisture content for market -1. There is some ambiguity in the CRSC reports regarding whether reported rates of P fertilization are given in terms of P applied or P2O5. It is assumed here that rates are reported in terms of P2O5 applied. This conclusion was arrived at through comparing rates of fertilization reported in the Canola CRSC report ((S&T)2 Consultants Inc., 2022d) with rates of P2O5 fertilization for canola production in Western Canada, as reported by MacWilliam et al., (2016). It should also be noted that calculation of the Fro term excludes P2O5 applied as manure, consistent with the environmental impacts from manure being allocated to the upstream livestock system. For calculation of P2O5 run-off, a value of 0.175 kg P2O5 run-off x hectare-1 is assumed 435 for arable land, in line with Prasuhn (2006). Use of this value is in line with previous modeling of P2O5 run-off from organic Canadian field crop systems, as described by Laage (2022). A.11 Modeling of pesticides and associated emissions As noted in the CRSC methodology report ((S&T)2 Consultants Inc., 2022a), data characterizing pesticide application rates to Canadian field crops is not formally tracked, while only limited data is publicly available regarding pesticide sales in different provinces. When data on pesticide application rates are available, they are generally aggregated and not given in terms of herbicides, insecticides, and fungicides. Alberta Environment and Parks (2020), for example, indicates an average pesticide application rate of 1.37 kg active ingredient per hectare, with no information on proportions of herbicides, insecticides, and fungicides. Overall, the CRSC methodology report ((S&T)2 Consultants Inc., 2022a) provides estimated pesticide application rates for Canada, Alberta, Quebec, and Ontario of 2.377, 1.37, 2.06, and 1.75 kg active ingredient per hectare (total across all pesticides). The values for Canada, Alberta, and Quebec are based on data representative of 2018, while the value for Ontario is based on data representative of 2013. Disaggregated values are provided for Canada, Alberta, and Quebec describing application rates for herbicides, fungicides, insecticides, and other pesticides (i.e., adjuvants) but no disaggregated values are given for Ontario. Further, it is unclear on what basis the CRSC reports disaggregate values for Canada, Alberta, and Quebec. The reported proportion of each type of pesticide in each jurisdiction is presented in Table A 17. For RUs in Alberta and Quebec, the disaggregated values are used as reported for these provinces. For RUs in all other provinces the disaggregated values given for Canada are assumed to be representative. In addition to the breakdown of total pesticides applied into herbicides, fungicides, insecticides, and other, it is also necessary to model the mix of active ingredients applied for 436 each type of pesticide. The CRSC methodology report ((S&T)2 Consultants Inc., 2022a) provides information on the top ten most common pesticide active ingredients in Canada by sales for 2018 as previously reported by Health Canada, but this does not provide any numerical information regarding the specific proportions of each pesticide used. Additional information regarding pesticide sales in 2018 in Alberta is also given with accompanying data indicating the total mass of each active ingredient sold. This list includes 10 herbicides and 3 adjuvants, but no insecticides or fungicides. To model the herbicide mix, it was assumed herbicides were applied proportionally to their total sales volume in Alberta in 2018. Often, specific active ingredients being modelled did not have representative production processes in the ecoinvent database. Proxy datasets were therefore used to model production of these herbicides, with appropriate datasets chosen based on the chemical properties of the herbicide being modeled, in line with previous work by Bamber et al. (2023, 2022b). A complete description of the assumed herbicide mix, and the type of herbicide used to model production of each, is given in Table A 18. This herbicide mix was assumed to be the same for all RUs. Since no information is available regarding the mix of fungicides and insecticides applied, all fungicides and insecticides have been modeled as inputs of unspecified pesticides. This may result in conservative estimates of potential toxicity impacts, representing a limitation of the developed models. Application of pesticides to field crop systems may also be associated with field-level emissions of the active ingredients, which may impact non-target organisms and lead to contamination of soils and water (Margni et al., 2002). In order to quantify these potential impacts, it is necessary to determine the amount of active ingredient that is lost, and to which environmental compartments these losses occur. Currently, processes in the ecoinvent database assume that all pesticides applied for crop production end up lost as emissions to soil 437 (Emmenegger et al., 2018). This assumption is not supported by other methods for modeling pesticide emissions, which assume that portions of the active ingredients applied are emitted to soil, water, and air in different fractions, such as in the methods proposed by Margni et al. (2002), the PestLCI model (Birkved and Hauschild, 2006), the recent OLCA-Pest Project (Nemecek et al., 2022), and guidelines for National Inventories from the European Commission (Sala et al., 2014). The guidelines provided by Sala et al. (2014) recognize that different fractions of applied pesticides may be lost to different environmental compartments, but do not provide estimates of those fractions. Application of the PestLCI model requires data on a number of regional factors that may impact the magnitude of emissions to different environmental compartments. Such data are unavailable, hence this method cannot be used. Margni et al. (2002) estimate that 95% of active ingredients applied are eventually lost, with 76.5%, 8.5%, and 10% emitted to soil, water, and air respectively, while the final 5% is taken up by the plant. This method has been applied in LCA studies of other Canadian agricultural systems, such as Bamber et al. (2020b) and Sanderson et al. (2019). The OLCA-Pest Project seeks to extend the PestLCI model and further develop methodological consensus on pesticide emissions models. It provides default emission factors for fractions of herbicides applied to cereal crops that are emitted to air, soil, water, or taken up by crops. The environmental compartments represented by these emission factors include two different compartments for losses to soil: losses to agricultural soils, and losses to natural or forest soils, with the latter representative of pesticides that are not applied on agricultural fields due to factors such as wind during application. These default emission factors have been used herein for modelling pesticide emissions. Notably, they assume that a larger portion of pesticides applied is taken up by the plant than the fraction previously suggested by Margni et al. (2002) (i.e., 22.2% compared to 5%), while assuming the same fraction is lost to 438 air (10%), and smaller fractions are lost to soil and water (67.71% and 0.02%, respectively). The authors further suggest that, for treatment of the fraction of pesticides taken up by the plant, a new environmental compartment should be defined that is representative of the crop itself, which should be further subdivided into compartments representative of the portion of crop used for human food, and non-food (i.e., animal feed, fuel, etc.) uses. They argue then that flows to the non-food compartments should be associated with human toxicity characterization factors of 0, since they do not directly impact on human toxicity, while flows to the food compartments should have characterization factors for human toxicity, and that human toxicity impacts related to this pesticide fraction should be allocated to the consumption phase. In line with these suggestions, impacts related to the fraction of applied herbicides that are taken up by the plant are excluded herein since consumption is outside of the system boundaries. Finally, Nemecek et al. (2022) only provide default emission factors for herbicides applied to cereal crops. However, these emission factors are assumed to be the same for applications of fungicides and insecticides as well. Modeling emissions of glufosinate was problematic due to unavailability of flows to appropriate environmental compartments. For modeling glufosinate emissions to the environmental compartments air, low population density, and all water environmental compartments, flows of glufosinate were unavailable. Emissions of glufosinate to these compartments were therefore instead modeled as emissions of glufosinate-ammonium. Similarly, flows of glufosinate were unavailable for modeling emissions to the environmental compartments soil, natural, and soil, forestry. These emissions were instead modeled as flows of glufosinate to the environmental compartment soil, unspecified. 439 Emissions of fungicides, insecticides, and other pesticides were modeled as emissions of unspecified insecticides and unspecified fungicides. No flows are available to model emissions of other types of pesticides, so these were distributed proportionally as flows of unspecified insecticides and fungicides. In modeling emissions of unspecified insecticides and fungicides, no flows are available representing losses of these chemicals to soil, natural, or soil forest. These releases have therefore instead been modeled as emissions to soil, unspecified. Similarly, there are no flows available representing losses of unspecified insecticides and fungicides to any air environmental compartment. Losses of unspecified insecticides and fungicides have therefore instead been re-distributed proportionally to the other environmental compartments. A.12 Background datasets used for modeling field crop systems Background datasets for modeling field crop systems were taken from the ecoinvent 3.9 database, and were intended to be representative of regionally-specific conditions when possible. If a background dataset for a specific region was not available, then data sets representing Canadian average conditions were used instead. If no background data set was available representing Canadian average conditions, data sets from other regions (generally the United States or Europe) were used instead, with modifications made in order to make these data sets more representative of Canadian conditions. General modifications included changing providers for energy grid mixes and water to be representative of Canada. In few cases, no modifications were made and background data sets representative of other geographic areas were used instead. Inputs of electricity and natural gas were modeled using provincially relevant processes, while inputs of propane/LPG were representative of Canadian conditions. Inputs of gasoline and diesel fuel were modeled with processes representative of global conditions. All processes used for modeling energy inputs to field crop systems are available in Table A 19. Inputs of water, such 440 as those used for irrigation, were modeled using providers representative of Quebec for all RUs in which irrigation was included. Background processes for production of fertilizers were similarly modeled using processes from the ecoinvent 3.9 database, with modifications made to make them more representative of fertilizer production in Canada. Information regarding some fertilizer production locations was taken from data collected by Cheminfo Services Inc. (2016), which also provided estimates of the carbon footprints of Canadian fertilizer production for use in the CRSC reports ((S&T)2 Consultants Inc., 2022a). Of the synthetic fertilizers included, the Cheminfo report does not provide production location data for four of them: elemental sulfur, potassium sulfate, DAP, and calcium ammonium nitrate. Production of DAP is assumed to occur at the same location as production of MAP, for which it is reported that production occurs in Alberta. Production of calcium ammonium nitrate is assumed to occur at the same locations as production of ammonium nitrate and urea ammonium nitrate, for which it is reported that production occurs in Alberta, Saskatchewan, Manitoba, and Ontario. Production of potassium sulfate is assumed to occur at the same location as production of ammonium sulfate, for which it is reported that production occurs in Alberta. While there are other production facilities for ammonium sulfate listed in the Cheminfo report, it is indicated that ammonium sulfate is produced at these facilities as a by-product of other industrial processes, while the facility in Alberta is a main producer of ammonium sulfate. This Alberta facility has therefore been assumed as the producer of ammonium sulfate. Production of triple superphosphate is assumed to occur in the same locations as MAP and DAP (i.e., Alberta). In Canada, the majority of elemental sulfur is produced as a by-product of natural gas production (Stone, 2008). Data from the Canada Energy Regulator indicates that the majority of 441 Canadian natural gas production occurs in Alberta and British Columbia, with production in these two provinces cumulatively accounting for ~98% (approximately 61% and 37%, respectively) of average monthly marketable natural gas in 2023 (Canada Energy Regulator, 2024). For RUs in British Columbia, elemental sulfur is therefore assumed to be sourced from B.C. natural gas production, while it is assumed to be sourced from Alberta natural gas production for all other RUs. Inputs of potash as a potassium (K) fertilizer were modeled as potassium chloride in line with Bamber et al., (2022a), with all production assumed to occur in Saskatchewan following information given by the Cheminfo Services report (Cheminfo Services Inc., 2016). All processes used for modeling of fertilizer inputs, as well as modifications made to these processes are available in Table A 20. A.13 Modeling field crop processing For canola and soy inputs to feed formulations, further processing is necessary to turn these products into oil and meal to be included in feed. Processes representing processing of soy and canola into oil and meal were therefore taken from the Agrifootprint 6.3 database (Blonk et al., 2022a), and altered to reflect Canadian conditions. Alterations including changing energy grid mixes to be more regionally representative. The baseline process for production of canola oil was based on Canadian processes already available in Agrifootprint, while the process for production of soybean meal was based on processes representative of U.S. production. The Canadian Oilseed Processors Association provides a map of 14 oilseed processing facilities operating in Canada: 11 in Western Canada (i.e., Saskatchewan, Alberta, and Manitoba), and 3 in Eastern Canada (i.e., Ontario and Quebec). The 11 plants in Western Canada crush only canola and include 2 facilities in Yorkton, Saskatchewan, while the 3 in Eastern Canada crush both canola and soybeans (Canadian Oilseed Processors Association, 2023). To 442 determine appropriate transportation distances for canola and soybeans produced in each region to processing plants, and to determine the most appropriate regional energy grid mix for processing of canola and soy produced in each region, distances from the centroids of each canola and soy producing region modeled to each of the possible processing plants were calculated. In the case of RUs 23, 28, and 41, driving distances could not be calculated from the geographical centroid to potential processing facilities, so proxies were used instead to represent RU centroids. For RUs 23 and 28, proxy latitude and longitudes were used, while Bella Coola, British Columbia was chosen as a proxy for the centroid of RU 41, from which driving distances to processing facilities could be estimated. It was subsequently assumed that canola and soy from each region would be transported to the closest facility available for processing. This allowed for identification of transportation distances, as well as the most regionally appropriate energy grid mix for processing of canola and soy produced in each region modeled. Transportation from canola and soy fields to processing facilities was assumed to occur by truck. The closest processing facilities, and assumed transportation distances for each canola and soy producing region modeled are given in Table A 25 and Table A 26, respectively. These regionally resolved models of soy and canola processing were subsequently production-weighted and aggregated to produce models that were nationally representative of canola and soy production and processing. For production weighting of these models, it was assumed that the proportions of processed soy and canola from each region were the same as the proportion of total production from each region as presented in Table A 1. 443 A.14 Modeling livestock derived feed ingredients Livestock-derived feed ingredients were modeled using processes from the Agrifootprint 6.3 database (Blonk et al., 2022a) based on a process representative of the production of animal meal in the U.S., with some modifications made to make this process more representative of Canadian conditions. Proportions of specific animal inputs from ruminant, swine, and poultry systems were used as described in Pelletier (2017). One key change that was made during modification of this model was the re-attribution of emissions from field application of livestock manure to the livestock production process, in line with the description of modeling N emissions from poultry manure management given in section 2.3.2.1.4. Rather, the Agrifootprint 6.3 database allocates field-level emissions related to manure application to the crop production system, while the manure input is considered to be burdenless. Emissions associated with transportation of manure to fields are also allocated fully to the field crop system (Blonk et al., 2022b). In order to ensure internal consistency with modeling of emissions related to manure management throughout the complete model, manure management emissions from swine and broiler systems upstream of the animal meal production process were recalculated using methods consistent with those described in section 2.3.2.1.4. This also included re-attribution of the emissions associated with transportation of manure to the agricultural fields to which it is applied to the livestock production process. The Agrifootprint 6.3 database only assumes that poultry and swine manure are field applied (Blonk et al., 2022a), so no re-attribution was required for the modelled dairy system. A number of steps were necessary for re-attribution of manure management emissions for broiler production systems upstream of the animal meal production process. These steps are outlined below: 444 1) The upstream process “Broiler fattening housing system, poultry manure without litter, at farm” is the upstream process which includes emissions related to manure management of the broiler system. This process includes estimates of both direct and indirect N2O emissions that are associated with manure management. According to the Agrifootprint methods report (Blonk et al., 2022a) emissions from manure management are calculated in accordance with methods used in the APS Footprint tool (Blonk Consultants, 2020), which in turn indicate that these emissions are calculated using IPCC Tier 2 methods (IPCC, 2019). Blonk Consultants (2020) provides the values for all variables used in calculation of both direct and indirect N2O emissions, including EF3(s) used in equation 10.25 (0.001), FracGasMS (T, S) used in equation 10.26 (0.4), FracLeachMS (T, S) used in equation 10.27 (0.1), EF4 used in equation 10.28 (0.01), and EF5 used in equation 10.29 (0.0075). Importantly, FracGasMS (T, S) is the only of these variables that takes different values depending on if the modelled poultry housing is with or without litter. The Agrifootprint process name indicates that this process represents production without litter. However, Agrifootprint documentation (Blonk et al., 2022a) indicates that the source for the data included in this data set is Putman et al. (2017), which models U.S. broiler production with litter. During the re-attribution process, it was found that the emissions in the Agrifootprint process were modeled using the value of FracGasMS (T, S) representing broiler housing with litter rather than without, despite the name of the process suggesting otherwise. 2) With the values of all variables used in calculation of direct and indirect N2O emissions, it was possible to back-calculate the amount of N excreted that would 445 result in the given estimated values of direct and indirect N2O emissions. The calculated estimate for amount of N excreted (i.e., the product of N (T, P) and Nex(T, P) used in equation 10.26 in IPCC (2019)) was 0.452 kg N. This calculated value was double checked by using it to calculate indirect N2O emissions using the methods outlined in the Agrifootprint documentation, which resulted in an estimate of indirect N2O emissions that is within a rounding error of the value presented in the Agrifootprint database. With this estimated value of N excretion, it was possible to recalculate manure management emissions in a manner consistent with those presented in section 2.3.2.1.4. Though upstream broiler feed formulations are available in agrifootprint, a nutrient mass balance approach similar to that used to estimate N excretion in layer systems was not used because such an approach requires additional pieces of data (i.e., mortality rates, masses of birds at placement and of mortalities, etc.) which are not available. 3) In order to estimate the emissions related to manure management systems, information is required on the proportions of manure handled in different management systems (i.e., solid and liquid systems, directly field applied, covered and uncovered, etc.). Little information is publicly avaialble regarding the proportions of different manure management systems currently used in the Canadian broiler industry. While an updated LCA of the Canadian broiler industry was completed in 2018 (Groupe Ageco, 2018), the methodological details underpinning this model are not publicly available. Rather, the most recent publicly available assessment of broiler production systems in Canada was completed in 2014, which assessed the environmental impacts of broiler production in Alberta (Quantis, 2014). In this 446 assessment, all manure is assumed to be stored and handled in a solid state. This assumption is in line with primary data collected for the Canadian egg industry, which indicate that only a small portion of manure produced on Canadian egg farms is stored in liquid form. For the purposes of recalculating manure-management related emissions it has therefore been assumed that all broiler manure in Canada is stored in a solid form, and that all manure is stored prior to on-field application. While information is unavaialble regarding whether these manure storage systems are covered or uncovered, this distinction is not relevant for emissions calcualtions as both covered and uncovered manure management systems have the same default emission factor for calculation of direct N2O emissions according to IPCC (2019). 4) Calculation of methane emissions according to IPCC (2019) requires definition of the amount of volatile solids excreted. Amounts of total solids and total manure handled are back-calculated from the N excretion rate using information on N content and dry matter content of broiler manure from the Government of Ontario (Ministry of Agriculture Food and Rural Affairs, 2023), which indicates broiler manure has an average dry matter content of 62.8%, and an average N content of 1.605%. Of note, this estimated N content is less than half of that previously assumed by Bamber et al. (2023) for North American poultry manure. However, Bamber et al. (2023) sourced their assumed poultry manure N content from Azeez and Van Averbeke (2010), which estimated manure N contents based on samples collected in South Africa, as this was the best available data at the time of the analysis. The lower value for N content given by the Government of Ontario has been used here, as this source is Canadian-specific, and substantially newer than that used by Bamber et al. (2023). It 447 is also necessary to define the volatile solids content of manure for calculation of methane emissions. A volatile solids content of 80% has been assumed, in line with the average value found for layer manure reported by Kanani (2021). 5) Calcualtion of emissions from field deposition of broiler manure were calculated in accordance with the methods outlined for field deposition of breeder, pullet, and layer manure in section 2.. For calcualtion of these emissions, a default value of FracgasM was used, in line with the 2022 Canadian NIR (Environment and Climate Change Canada, 2022). Similar to calculation of emissions from deposition of laying hen manure, production-weighted factors for EF1 and FracLeach were also calculated for broilers. Broiler production in Newfoundland and Labrador was excluded from this calculation as neither of these factors have values given for this province. The calculated value for EF1 was also scaled in accordance with Liang et al. (2020). Provincial production data for broiler meat were taken from StatsCan (2023), and were representative of 2022. 6) The Agrifootprint 6.3 database also includes emissions of non-methane volatile organic compounds and particulate matter from broiler manure management systems. These emissions were excluded during re-calculation of manure management emissions in order to maintain consistency with the methods used for calculating manure management emissions from breeder, pullet, and layer flocks. Field-level emissions of methane are also excluded, as discussed in section 2.3.2.1.4. 7) Emissions associated with transportation of broiler manure to agricultural fields were also re-attributed to the upstream broiler production process. Transportation of manure to the storage system and from the storage system to agricultural fields were 448 modeled as tractor and truck transport, respectively, with transportation distances assumed to be the same as those used by Pelletier (2017). Assumed manure transportation distances were 2.96 km by tractor and 15.77 km by truck. 8) Phosphate losses from manure applied to fields were re-calculated using the approach described in section 2.3.2.1.4, assuming a 2.8% phosphate run-off rate and an assumed P2O5 content of broiler manure of 1.545% based on data from the Government of Ontario (Ministry of Agriculture Food and Rural Affairs, 2023). A similar process was also used for re-attribution of emissions from swine manure management upstream of the animal meal process. According to Agrifootprint methods (Blonk et al., 2022a) emissions from manure management are calculated in accordance with methods used in the APS Footprint tool (Blonk Consultants, 2022). While the Agrifootprint methods (Blonk et al., 2022a) document indicates that emissions are calculated in accordance with the 2020 version of these methods, version 2, published in 2022, is the only available methods document for the pig module of the APS footprint tool. Emissions have therefore been recalculated following the 2022 version, which indicates that direct N2O emissions from manure management systems are calculated using IPCC Tier 2 methods (IPCC, 2019). Estimated N excretion was generally back-calculated as described for broilers with few exceptions. Specifically, when calculating N excretion from pig systems operating liquid slurry manure management systems without a natural crust cover, the emission factor for direct N2O emissions is 0. Estimates of N excretion therefore had to be back-calculated instead based on the given value for indirect N2O emissions. A value of 0.084 kg NO3-N per kg of N excreted was used during this calculation under the assumption that a default value of 0.7 kg total ammoniacal nitrogen / kg N excreted was used 449 previously, as there is no information given indicating otherwise, in line with the APS footprint methods employed in the Agrifootprint database (Blonk Consultants, 2022). In contrast, back calculations of N excreted from pig systems operating pit storage systems that store manure for <1 and >1 month were performed using the given values for direct N2O emissions, as these manure management systems do have emission factors for direct N2O. Other assumptions made during re-attribution of manure management emissions to swine systems include: 1) Total amount of manure excreted was calculated assuming a manure N content of 0.389% in line with (Bamber et al., 2023), who derived this value from data reported by the Government of Saskatchewan (2022) and assuming that pig slurry has a density of 1000 kg/m3 (Moral and Paredes, 2005). Of note, this assumed manure N content is very similar to that given by the Government of Ontario (Ministry of Agriculture Food and Rural Affairs, 2023), which estimates an average manure N content of 0.385% for manure stored at 18-35% dry matter. The assumed total solids content of this manure was 23.9%. Estimates of the volatile solids content of pig manure are relatively variable in the literature. Gomez et al. (2019) estimate a volatile solids content of 7.89% based on manure samples taken from Spain, Wang et al. (2020) estimate a volatile solids content of 17.03% based on samples from Ireland, and Rajagopal et al. (2022) estimate a volatile solids content of 23% from Canada. Despite being the highest value, the estimated volatile solids content from Rajagopal et al. (2022) has been used since it is representative of manure collected in Canada. Use of this value also therefore represents a conservative estimate of the methane emissions associated with Canadian pig production. For calcualtion of a methane emission factor, an average value has been used 450 of the default values given for cool and dry, and cool and moist climates for the emission factors from IPCC (2019) 2) The same phosphate run-off rate has been assumed for pig manure, with an assumed P2O5 content of 0.645% representing the average value for pig manure at 18-30% dry matter as given by the Government of Ontario (Ministry of Agriculture Food and Rural Affairs, 2023). 3) Production-weighted average values for FracLeach and EF1 were calculated as previously described based on data on the total head of hogs present in each province in 2023 (StatsCan, 2024b). In their reporting, StatsCan was unable to report the number of hogs present in Nova Scotia in order to maintain confidentiality requirements. Values of FracLeach and EF1 for Nova Scotia were therefore excluded during calculation of these factors. 4) For methane emissions from pit manure storage, the volatile solids content is kept the same as the slurry storage, but the total solids content is instead changed to 42.6%, representing average total solids content for pig manure stored from 30-100% dry matter as per Government of Ontario (Ministry of Agriculture Food and Rural Affairs, 2023). Default emission factors from IPCC (2019) from high productivity swine are used for pit storage of manure >1 month and <1 month. In IPCC (2019), different default methane emission factors are given for liquid/slurry storage. Agrifootprint documentation does not indicate which default methane emission factor was used for calculation of methane emissions form liquid/slurry storage. A weighted average value was therefore used based on the proportions of non liquid/slurry manure management (i.e., proportions of total pit 451 managed manure for <1 and >1 month) reported for pig production in North America in Agrifootprint 6.3. 5) The assumed P2O5 content of pit stored pig manure was 0.705% as per data from the Government of Ontario (Ministry of Agriculture Food and Rural Affairs, 2023). In addition, emissions from swine production systems related to enteric fermentation were not re-calculated, and were used as reported in the Agrifootprint database. Following re-attribution of manure management emissions to swine and broiler systems upstream of the animal meal process, it was noted that the Agrifootprint 6.3 database has some issues with mapping of flows to life cycle impact assessment methods. Incorrect flow mapping may lead to under-estimation of impacts, as flows that are not properly mapped would not be accounted for during calculation of estimated impacts. These flow mapping issues arose due to the inclusion of flows representing regionalized emissions which were not accounted for in the available LCIA methods. To solve these flow mapping issues, system processes were therefore generated from the modified animal meal production processes, and regionalized flows were aggregated and replaced with flows representing the same emissions to the same environmental compartments that would normally be accounted for during LCIA. A.15 Modeling other feed ingredients A number of other feed ingredients were also included in the baseline feed formulation models. One of these ingredients, monocalcium phosphate, is a calcium supplement that is included only in the pullet feed formulation. Datasets representing production of monocalcium phosphate supplements were not available, so this was instead modeled as inputs of 452 monoammonium phosphate, in line with methodological choices made for LCA models of U.S. egg production systems (Turner et al. 2023). Synthetic amino acids are also included in the feed formulations modeled. Previously, inputs of synthetic amino acids were modeled as DL-methionine due to data availability constraints (Pelletier, 2017). A larger number of life cycle inventories for a variety of synthetic amino acids are now available. These include inventories for L-lysine, L-threonine, valine, and tryptophan. In reporting feed formulations for different stages in the egg supply chain, Pelletier (2017) does not report amounts of specific amino acids included as supplements. Rather, a single aggregated value for “amino acids” is given. In the U.S. egg LCA model developed by Turner et al. (2023), amino acid supplements to feed formulations were modeled as a mix of DL-methionine, L-lysine, threonine, and valine. The relative proportions of each of these amino acid supplements used in the U.S. egg LCA model has also been applied herein for modeling amino acid supplementation in Canadian layer and pullet feed formulas. The assumed mix of synthetic amino acids, as well as the processes used to model their production and any modifications made to these processes to make them more representative of Canadian production conditions are show in Table A 70. Inventories for production of DL-methionine, L-threonine, and valine were taken from the Agrifootprint 6.3 database (Blonk et al., 2022a), while the inventory for the production of lysine was taken from the USDA LCA Commons (USDA-National Agricultural Library, 2014). 453 A.16 Supplementary tables Table A 1. Percentage of total production of each crop attributable to each RU. Zero values indicate no production of that crop in that particular RU RU Province1 Wheat Corn Soy Canola Barley 5 NS 0.03 0.38 0.20 0.00 0.03 6 PE 0.22 0.26 0.60 0.00 0.82 7 NB 0.06 0.21 0.15 0.00 0.17 11 QC 0.16 0.44 0.79 0.00 0.52 12 QC 0.49 18.94 12.52 0.00 0.33 15 QC 0.33 3.63 4.17 0.00 0.64 17 ON 7.23 57.22 57.67 0.00 0.96 19 ON 0.23 1.92 1.79 0.00 0.18 22 MB 0.39 0.89 0.83 0.44 0.12 23 MB 2.88 0.62 2.49 2.91 0.71 24 MB 14.94 14.68 16.82 12.93 4.99 28 SK 7.67 0.00 0.10 13.04 10.74 29 SK 15.80 0.15 1.11 21.26 15.75 30 SK 15.23 0.00 0.76 21.18 15.14 34 AB 9.73 0.00 0.00 10.79 13.00 35 AB 13.63 0.00 0.00 10.68 20.53 37 AB 10.69 0.65 0.00 6.45 14.75 39 BC 0.25 0.00 0.00 0.16 0.41 41 BC 0.01 0.00 0.00 0.01 0.08 454 RU Province1 Wheat Corn Soy Canola Barley 42 BC 0.02 0.00 0.00 0.14 0.13 Table A 2. Assumed percentages of aboveground crop residues removed from fields for each crop modeled. Crop Assumed % of aboveground residues removed Corn 28.23 Soy 0.00 Wheat 8.3 Canola 0.00 Peas 0.00 Barley 36.73 Table A 3. Allocation factors calculated for allocation of impacts between corn, barley, and wheat grain and harvested residues based on gross chemical energy content of co-products Corn Barley Wheat Grain Residues Grain Residues Grain Residues Allocation factor 0.77 0.23 0.72 0.28 0.90 0.10 455 Table A 4. Recalculated fertilizer inputs per kilogram of barley produced in each RU (kg/kg) RU AS1 Elemental sulfur Potassium Sulfate AS/S 50/50 blend Potash MAP2 DAP3 Ammonia Urea UAN4 AN5 CAN6 5 5.75E-03 1.15E-04 5.22E-04 1.85E-04 5.63E-03 6.14E-04 1.80E-02 0 3.13E-02 0 1.20E-02 1.20E-02 6 4.76E-03 9.52E-05 4.33E-04 1.54E-04 4.59E-03 5.13E-04 1.49E-02 0 2.59E-02 0 9.97E-03 9.97E-03 7 5.75E-03 1.15E-04 5.22E-04 1.85E-04 5.45E-03 6.19E-04 1.80E-02 0 3.12E-02 0 1.20E-02 1.20E-02 11 5.75E-03 1.15E-04 5.22E-04 1.85E-04 1.91E-02 3.46E-03 3.13E-02 2.86E-03 2.46E-02 1.77E-02 2.22E-03 2.22E-03 12 5.75E-03 1.15E-04 5.22E-04 1.85E-04 1.88E-02 3.46E-03 3.13E-02 2.86E-03 2.46E-02 1.77E-02 2.22E-03 2.22E-03 15 5.75E-03 1.15E-04 5.22E-04 1.85E-04 1.85E-02 3.46E-03 3.13E-02 2.86E-03 2.46E-02 1.77E-02 2.22E-03 2.22E-03 456 RU AS1 Elemental sulfur Potassium Sulfate AS/S 50/50 blend Potash MAP2 DAP3 Ammonia Urea UAN4 AN5 CAN6 17 5.05E-03 1.01E-04 4.59E-04 1.63E-04 1.05E-02 1.67E-02 8.98E-04 1.98E-03 2.06E-02 3.03E-02 1.02E-03 1.02E-03 19 5.21E-03 1.04E-04 4.73E-04 1.68E-04 1.06E-02 1.72E-02 9.26E-04 2.04E-03 2.12E-02 3.12E-02 1.05E-03 1.05E-03 22 7.31E-03 1.46E-04 6.65E-04 2.36E-04 6.12E-03 2.01E-02 0 7.40E-03 2.15E-02 1.42E-02 0 0 23 4.63E-03 9.27E-05 4.21E-04 1.49E-04 4.80E-03 1.72E-02 0 7.08E-03 2.06E-02 1.36E-02 0 0 24 4.68E-03 9.37E-05 4.26E-04 1.51E-04 4.17E-03 1.59E-02 0 6.71E-03 1.95E-02 1.29E-02 0 0 28 5.17E-03 1.03E-04 4.70E-04 1.67E-04 4.77E-03 1.69E-02 0 5.68E-03 2.85E-02 1.14E-02 0 0 457 RU AS1 Elemental sulfur Potassium Sulfate AS/S 50/50 blend Potash MAP2 DAP3 Ammonia Urea UAN4 AN5 CAN6 29 6.40E-03 1.28E-04 5.82E-04 2.07E-04 4.35E-03 1.75E-02 0 5.71E-03 2.87E-02 1.14E-02 0 0 30 6.67E-03 1.33E-04 6.06E-04 2.15E-04 2.72E-03 1.83E-02 0 5.85E-03 2.94E-02 1.17E-02 0 0 34 7.39E-03 1.48E-04 6.72E-04 2.38E-04 2.37E-03 1.40E-02 0 3.95E-03 3.23E-02 2.72E-03 0 0 35 5.67E-03 1.13E-04 5.15E-04 1.83E-04 3.31E-03 1.52E-02 0 4.05E-03 3.31E-02 2.79E-03 0 0 37 5.86E-03 1.17E-04 5.32E-04 1.89E-04 2.15E-03 1.66E-02 0 4.21E-03 3.44E-02 2.90E-03 0 0 39 5.75E-03 1.15E-04 5.22E-04 1.85E-04 4.39E-03 1.66E-02 0 1.01E-03 5.01E-02 0 0 0 458 RU AS1 Elemental sulfur Potassium Sulfate AS/S 50/50 blend Potash MAP2 DAP3 Ammonia Urea UAN4 AN5 CAN6 41 4.63E-03 9.26E-05 4.21E-04 1.49E-04 3.49E-03 1.34E-02 0 8.17E-04 4.03E-02 0 0 0 42 4.63E-03 9.26E-05 4.21E-04 1.49E-04 3.45E-03 1.34E-02 0 8.17E-04 4.03E-02 0 0 0 1 Ammonium sulfate 2 Mono-ammonium phosphate 3 Di-ammonium phosphate 4 Urea ammonium nitrate 5 Ammonium nitrate 6 Calcium ammonium nitrate 459 Table A 5. Recalculated fertilizer inputs per kilogram of corn produced in each RU (kg/kg) RU AS1 Elemental sulfur Potassium Sulfate Potash MAP2 DAP3 Ammonia Urea UAN4 AN5 CAN6 5 3.39E-03 1.20E-04 3.03E-04 8.25E-03 3.58E-04 1.04E-02 0 1.82E-02 0 7.01E-03 7.01E-03 6 3.39E-03 1.20E-04 3.03E-04 8.15E-03 3.60E-04 1.05E-02 0 1.83E-02 0 7.04E-03 7.04E-03 7 3.39E-03 1.20E-04 3.03E-04 8.00E-03 3.59E-04 1.05E-02 0 1.82E-02 0 7.03E-03 7.03E-03 11 1.35E-03 4.78E-05 1.20E-04 7.87E-03 9.56E-03 0 0 1.54E-02 2.21E-02 1.63E-03 1.63E-03 12 1.35E-03 4.79E-05 1.21E-04 7.82E-03 9.66E-03 0 0 1.55E-02 2.23E-02 1.64E-03 1.64E-03 15 1.35E-03 4.82E-05 1.21E-04 7.65E-03 9.59E-03 0 0 1.54E-02 2.22E-02 1.63E-03 1.63E-03 460 RU AS1 Elemental sulfur Potassium Sulfate Potash MAP2 DAP3 Ammonia Urea UAN4 AN5 CAN6 17 3.19E-03 1.13E-04 2.85E-04 1.13E-02 1.02E-02 8.74E-04 2.04E-03 1.09E-02 2.33E-02 1.35E-04 1.35E-04 19 3.23E-03 1.15E-04 2.89E-04 1.13E-02 1.03E-02 8.86E-04 2.06E-03 1.11E-02 2.36E-02 1.37E-04 1.37E-04 22 2.37E-03 8.43E-05 2.12E-04 7.44E-03 9.21E-03 0 5.91E-03 1.72E-02 1.14E-02 0 0 23 3.70E-03 1.32E-04 3.31E-04 4.84E-03 1.02E-02 0 5.74E-03 1.67E-02 1.10E-02 0 0 24 5.10E-03 1.81E-04 4.56E-04 5.23E-03 1.04E-02 0 5.77E-03 1.68E-02 1.11E-02 0 0 29 5.07E-03 1.80E-04 4.54E-04 5.12E-03 1.04E-02 0 4.22E-03 2.12E-02 8.43E-03 0 0 461 RU AS1 Elemental sulfur Potassium Sulfate Potash MAP2 DAP3 Ammonia Urea UAN4 AN5 CAN6 37 2.72E-03 9.66E-05 2.43E-04 2.70E-03 5.55E-03 0 3.45E-03 2.82E-02 2.38E-03 0 0 1 Ammonium sulfate 2 Mono-ammonium phosphate 3 Di-ammonium phosphate 4 Urea ammonium nitrate 5 Ammonium nitrate 6 Calcium ammonium nitrate Table A 6. Recalculated fertilizer inputs per kilogram of wheat produced in each RU (kg/kg) RU AS1 Elemental sulfur Potassium Sulfate AS/S 50/50 blend Potash MAP2 DAP3 Ammonia Urea UAN4 AN5 CAN6 5 4.11E-03 8.21E-05 3.73E-04 1.32E-04 4.44E-03 5.48E-04 1.60E-02 0 3.00E-02 0 1.16E-02 1.16E-02 462 RU AS1 Elemental sulfur Potassium Sulfate AS/S 50/50 blend Potash MAP2 DAP3 Ammonia Urea UAN4 AN5 CAN6 6 4.52E-03 9.03E-05 4.11E-04 1.46E-04 4.80E-03 6.03E-04 1.76E-02 0 3.31E-02 0 1.27E-02 1.27E-02 7 5.04E-03 1.01E-04 4.58E-04 1.62E-04 5.26E-03 6.72E-04 1.96E-02 0 3.68E-02 0 1.42E-02 1.42E-02 11 1.09E-03 2.19E-05 9.95E-05 3.53E-05 1.30E-02 7.25E-04 6.56E-03 2.00E-03 1.72E-02 1.24E-02 1.56E-03 1.56E-03 12 9.65E-04 1.93E-05 8.77E-05 3.11E-05 1.13E-02 6.39E-04 5.78E-03 1.76E-03 1.52E-02 1.10E-02 1.37E-03 1.37E-03 15 1.10E-03 2.20E-05 9.99E-05 3.54E-05 1.27E-02 7.28E-04 6.58E-03 2.01E-03 1.73E-02 1.25E-02 1.56E-03 1.56E-03 17 7.08E-03 1.42E-04 6.43E-04 2.28E-04 6.82E-03 1.24E-02 6.68E-04 1.72E-03 1.79E-02 2.63E-02 8.88E-04 8.88E-04 463 RU AS1 Elemental sulfur Potassium Sulfate AS/S 50/50 blend Potash MAP2 DAP3 Ammonia Urea UAN4 AN5 CAN6 19 9.79E-03 1.96E-04 8.90E-04 3.16E-04 9.29E-03 1.72E-02 9.24E-04 2.38E-03 2.48E-02 3.65E-02 1.23E-03 1.23E-03 22 4.70E-03 9.40E-05 4.27E-04 1.52E-04 5.20E-03 2.11E-02 0 9.79E-03 2.85E-02 1.88E-02 0 0 23 5.25E-03 1.05E-04 4.77E-04 1.69E-04 5.46E-03 1.84E-02 0 8.69E-03 2.53E-02 1.67E-02 0 0 24 4.38E-03 8.76E-05 3.98E-04 1.41E-04 3.58E-03 1.75E-02 0 8.33E-03 2.42E-02 1.60E-02 0 0 28 6.20E-03 1.24E-04 5.63E-04 2.00E-04 4.62E-03 1.94E-02 0 7.42E-03 3.73E-02 1.48E-02 0 0 29 8.38E-03 1.68E-04 7.62E-04 2.70E-04 3.77E-03 2.02E-02 0 7.62E-03 3.83E-02 1.52E-02 0 0 464 RU AS1 Elemental sulfur Potassium Sulfate AS/S 50/50 blend Potash MAP2 DAP3 Ammonia Urea UAN4 AN5 CAN6 30 8.14E-03 1.63E-04 7.40E-04 2.62E-04 2.24E-03 2.11E-02 0 7.55E-03 3.80E-02 1.51E-02 0 0 34 4.32E-03 8.64E-05 3.93E-04 1.39E-04 5.04E-03 1.50E-02 0 5.35E-03 4.37E-02 3.69E-03 0 0 35 1.04E-03 2.08E-05 9.45E-05 3.35E-05 3.52E-03 1.31E-02 0 4.96E-03 4.06E-02 3.43E-03 0 0 37 2.16E-03 4.33E-05 1.97E-04 6.98E-05 2.57E-03 9.59E-03 0 5.30E-03 4.34E-02 3.66E-03 0 0 39 4.63E-03 9.26E-05 4.21E-04 1.49E-04 3.90E-03 1.66E-02 0 1.10E-03 5.41E-02 0 0 0 41 5.21E-03 1.04E-04 4.73E-04 1.68E-04 4.33E-03 1.86E-02 0 1.23E-03 6.09E-02 0 0 0 465 RU AS1 Elemental sulfur Potassium Sulfate AS/S 50/50 blend Potash MAP2 DAP3 Ammonia Urea UAN4 AN5 CAN6 42 5.21E-03 1.04E-04 4.73E-04 1.68E-04 4.27E-03 1.86E-02 0 1.23E-03 6.09E-02 0 0 0 1 Ammonium sulfate 2 Mono-ammonium phosphate 3 Di-ammonium phosphate 4 Urea ammonium nitrate 5 Ammonium nitrate 6 Calcium ammonium nitrate Table A 7. Recalculated fertilizer inputs per kilogram of soy produced in each RU (kg/kg) RU AS1 Elemental sulfur Potassium Sulfate AS/S 50/50 blend Potash MAP2 DAP3 Triple superphosphate Ammonia Urea UAN4 AN5 CAN6 5 1.00E-03 2.00E-05 9.09E-05 3.23E-05 1.94E-02 4.08E-05 1.19E-03 9.93E-03 0 1.69E-03 0 6.52E-04 6.52E-04 466 RU AS1 Elemental sulfur Potassium Sulfate AS/S 50/50 blend Potash MAP2 DAP3 Triple superphosphate Ammonia Urea UAN4 AN5 CAN6 6 1.20E-03 2.40E-05 1.09E-04 3.87E-05 2.33E-02 4.89E-05 1.43E-03 1.19E-02 0 2.03E-03 0 7.82E-04 7.82E-04 7 1.30E-03 2.61E-05 1.19E-04 4.21E-05 2.53E-02 5.32E-05 1.55E-03 1.29E-02 0 2.21E-03 0 8.50E-04 8.50E-04 11 1.07E-03 2.14E-05 9.74E-05 3.46E-05 7.90E-03 4.70E-04 1.75E-03 4.07E-03 0 8.61E-04 0 1.48E-03 1.48E-03 12 9.09E-04 1.82E-05 8.26E-05 2.93E-05 6.70E-03 3.99E-04 1.48E-03 3.45E-03 0 7.31E-04 0 1.26E-03 1.26E-03 15 1.03E-03 2.07E-05 9.40E-05 3.34E-05 7.62E-03 4.54E-04 1.69E-03 3.93E-03 0 8.32E-04 0 1.43E-03 1.43E-03 17 2.12E-03 4.24E-05 1.93E-04 6.84E-05 2.86E-02 2.89E-03 4.69E-04 1.43E-02 0 5.58E-04 7.35E-05 1.57E-05 1.57E-05 467 RU AS1 Elemental sulfur Potassium Sulfate AS/S 50/50 blend Potash MAP2 DAP3 Triple superphosphate Ammonia Urea UAN4 AN5 CAN6 19 2.41E-03 4.83E-05 2.19E-04 7.79E-05 3.25E-02 3.29E-03 5.34E-04 1.63E-02 0 6.34E-04 8.36E-05 1.79E-05 1.79E-05 22 4.64E-03 9.28E-05 4.22E-04 1.50E-04 1.32E-02 1.70E-05 0 3.73E-02 1.63E-04 4.74E-04 3.13E-04 0 0 23 5.80E-03 1.16E-04 5.27E-04 1.87E-04 1.67E-02 1.93E-05 0 3.39E-02 1.85E-04 5.37E-04 3.55E-04 0 0 29 9.79E-03 1.96E-04 8.90E-04 3.16E-04 1.65E-02 3.51E-05 0 3.20E-02 3.12E-04 1.57E-03 6.23E-04 0 0 30 2.33E-02 4.67E-04 2.12E-03 7.53E-04 1.11E-02 3.37E-04 0 4.38E-02 2.99E-03 1.50E-02 5.98E-03 0 0 1 Ammonium sulfate 2 Mono-ammonium phosphate 3 Di-ammonium phosphate 4 Urea ammonium nitrate 5 Ammonium nitrate 468 6 Calcium ammonium nitrate Table A 8. Recalculated fertilizer inputs per kilogram of canola produced in each RU (kg/kg) RU AS1 Elemental sulfur Potassium Sulfate Potash MAP2 Ammonia Urea UAN4 AN5 CAN6 22 2.60E-02 5.20E-04 2.36E-03 9.49E-03 3.79E-02 1.87E-02 5.44E-02 3.59E-02 0 0 23 3.37E-02 6.73E-04 3.06E-03 1.20E-02 3.17E-02 4.16E-02 3.71E-02 0 0 0 24 2.80E-02 5.60E-04 2.55E-03 1.05E-02 3.27E-02 2.82E-02 4.38E-02 2.22E-02 8.88E-04 8.88E-04 28 3.63E-02 7.27E-04 3.30E-03 8.12E-03 2.79E-02 2.42E-02 4.96E-02 1.55E-02 0 0 29 3.30E-02 6.60E-04 3.00E-03 8.52E-03 2.98E-02 1.13E-02 6.56E-02 1.94E-02 1.12E-03 1.12E-03 30 3.23E-02 6.47E-04 2.94E-03 8.03E-03 3.29E-02 6.49E-03 7.35E-02 2.04E-02 1.77E-03 1.77E-03 34 3.80E-02 7.60E-04 3.45E-03 9.26E-03 3.17E-02 2.80E-02 6.13E-02 5.54E-03 0 0 35 3.17E-02 6.33E-04 2.88E-03 9.38E-03 2.69E-02 1.13E-02 6.78E-02 5.75E-03 6.92E-04 6.92E-04 37 3.60E-02 7.20E-04 3.27E-03 9.30E-03 3.31E-02 4.41E-03 9.66E-02 3.71E-03 0 0 39 2.90E-02 5.80E-04 2.64E-03 9.43E-03 3.17E-02 2.17E-03 1.07E-01 0 0 0 41 2.90E-02 5.80E-04 2.64E-03 9.43E-03 3.17E-02 2.17E-03 1.07E-01 0 0 0 42 2.90E-02 5.80E-04 2.64E-03 9.43E-03 3.17E-02 2.17E-03 1.07E-01 0 0 0 469 1 Ammonium sulfate 2 Mono-ammonium phosphate 3 Di-ammonium phosphate 4 Urea ammonium nitrate 5 Ammonium nitrate 6 Calcium ammonium nitrate 470 Table A 9. Assumed distances for transportation of inputs to farm, used for all crops and all RUs. Input/Output Distance (km) Seed 76a Fertilizer 23a Herbicides 22a Fungicides 36a Insecticides 29b Fuel 27a Harvested grain 5c a From Alberta Agriculture and Forestry (2018) b Average of herbicide and fungicide transportation distance from Alberta Agriculture and Forestry (2018) c From CRSC methodology report ((S&T)2 Consultants Inc., 2022a) 471 Table A 10. Conversion factors used for calculation of energy consumption data, when necessary. Energy type Conversion factor Diesel 38.4 MJ/L Diesel 0.85 kg/L Gasoline 34.9 MJ/L Gasoline 0.725 kg/L LPG 0.51 kg/L LPG 26 MJ/L LPG 50.22 MJ/kg Fuel Oil 890.13 kg/m3 Natural Gas 38 MJ/m3 Table A 11. Variables and associated units used in calculation of field-level direct N2O emissions from field crop systems Variable Description Unit 𝑁2𝑂𝐷𝑖𝑟𝑒𝑐𝑡 Direct field-level emissions of N2O kg N2O x tonne of crop at an appropriate moisture content for market -1 𝐹𝑆𝑁 Amount of N in synthetic fertilizers applied to soils, as N Kg N x tonne of crop at an appropriate moisture content for market -1 𝐹𝑂𝑁 Amount of N in animal manure, compost, sewage sludge, and other organic sources of N, as N Kg N x tonne of crop at an appropriate moisture content for market -1 𝐹𝐶𝑅 Amount of N in crop residues left on field (above- and below-ground residues), as N Kg N x tonne of crop at an appropriate moisture content for market -1 472 Variable Description Unit 𝐹𝑆𝑂𝑀 Amount of N in mineral soils that is mineralized in associated with loss of soil C from soil organic matter from changes to land use or management, as N Kg N x tonne of crop at an appropriate moisture content for market -1 𝐸𝐹𝑖 Emission factor for N2O emissions from different N inputs under different conditions Kg N2O-N x kg N input-1 4428 Conversion factor for conversion of N2O-N to N2O Dimensionless Table A 12. Scaled values of EF1 used in calculation of field-level direct N2O emissions for each RU for N inputs derived from synthetic, crop residues, and mineralized sources. RU Province EF1 Synthetic N EF1 Crop residue N EF1 Mineralized N 5 NS 0.018 0.005 0.015 6 PE 0.008 0.002 0.007 7 NB 0.014 0.004 0.012 11 QC 0.016 0.004 0.013 12 QC 0.021 0.006 0.017 15 QC 0.010 0.003 0.009 17 ON 0.015 0.004 0.012 19 ON 0.013 0.004 0.011 22 MB 0.012 0.003 0.010 23 MB 0.007 0.002 0.006 24 MB 0.006 0.002 0.005 473 RU Province EF1 Synthetic N EF1 Crop residue N EF1 Mineralized N 28 SK 0.004 0.001 0.004 29 SK 0.004 0.001 0.003 30 SK 0.004 0.001 0.004 34 AB 0.006 0.002 0.005 35 AB 0.005 0.001 0.004 37 AB 0.009 0.003 0.008 39 BC 0.004 0.001 0.003 41 BC 0.008 0.002 0.006 42 BC 0.012 0.003 0.010 Table A 13. Assumed above- and below-ground crop residue yields and N contents used for estimation of emissions associated with crop residue management for each crop. Crop Above-ground residue yield (kg/kg of DM crop) Below-ground residue yield (kg/kg of DM crop) Above-ground residue N content (kg N/ kg residue) Below-ground residue N content (kg N/ kg residue) Corn 1.05 0.51 0.009 0.008 Soy 1.53 0.55 0.007 0.010 Wheat 1.49 0.58 0.007 0.011 Canola 2.65 1.35 0.013 0.009 Barley 1.24 0.43 0.009 0.012 474 Crop Above-ground residue yield (kg/kg of DM crop) Below-ground residue yield (kg/kg of DM crop) Above-ground residue N content (kg N/ kg residue) Below-ground residue N content (kg N/ kg residue) Peas 2.28 0.49 0.021 0.022 Table A 14. Variables and associated units used in calculation of field-level indirect N2O emissions from volatilization of N. Variable Description Unit 𝑁2𝑂(𝐴𝑇𝐷) Amount of N2O produced from atmospheric deposition of N volatilized from managed soils kg N2O x tonne of crop at an appropriate moisture content for market -1 𝐹𝑆𝑁 Amount of N in synthetic fertilizers applied to soils, as N Kg N x tonne of crop at an appropriate moisture content for market -1 𝐹𝑅𝐴𝐶𝐺𝐴𝑆𝐹 Fraction of synthetic fertilizer N that volatilizes as NH3 and NOx Kg volatilized N x kg N input-1 𝐸𝐹4 Emission factor for N2O emissions from atmospheric Kg N2O x (kg NH3 + kg NOx volatilized)-1 475 Variable Description Unit deposition of N on soils and water surfaces 4428 Conversion factor for conversion of N2O-N to N2O Dimensionless Table A 15. Variables and associated units used in calculation of field-level indirect N2O emissions from leaching of N. Variable Description Unit 𝑁2𝑂(𝐿) Amount of N2O produced from leaching and run-off from managed soils kg N2O x tonne of crop at an appropriate moisture content for market -1 𝐹𝑆𝑁 Amount of N in synthetic fertilizers applied to soils, as N Kg N x tonne of crop at an appropriate moisture content for market -1 𝐹𝐶𝑅 Amount of N in crop residues left on field (above- and below-ground residues), as N Kg N x tonne of crop at an appropriate moisture content for market -1 𝐹𝑆𝑂𝑀 Amount of N in mineral soils that is mineralized in associated with loss of soil C Kg N x tonne of crop at an appropriate moisture content for market -1 476 Variable Description Unit from soil organic matter from changes to land use or management, as N 𝐹𝑟𝑎𝑐𝐿𝐸𝐴𝐶𝐻−(𝐻) Fraction of all N added to/mineralized in managed soils in regions where leaching and run-off occurs Kg leaching and run-off N x kg N input-1 𝐸𝐹5 Emission factor for N2O emissions from N leaching and run-off Kg N2O x (kg N leached or run-off)-1 4428 Conversion factor for conversion of N2O-N to N2O Dimensionless Table A 16. Variables and associated units used in calculation of field-level P losses Variable Description Unit 𝑃𝑟𝑜 Quantity of P lost through run-off to surface waters, as P2O5 kg P2O5 x tonne of crop at an appropriate moisture content for market -1 𝑃𝑟𝑜𝑙 The average quantity of P leached to surface waters for kg P2O5 run-off x hectare-1 477 Variable Description Unit a specific land-use category, as P2O5 𝐹𝑟𝑜 A correction factor for fertilization with P, as P2O5 Dimensionless Table A 17. Percent of total pesticides applied as herbicides, fungicides, insecticides, and other, as adapted from table 9-6 of the CRSC methodology report ((S&T)2 Consultants Inc., 2022a). Canada Alberta Quebec Herbicide 68.35 88.32 73.30 Fungicide 16.88 6.57 15.53 Insecticide 10.13 2.19 3.88 Other 4.64 2.92 7.28 Table A 18. Assumed herbicide mix used for all RUs, and the corresponding products used to model them for each crop and RU. Herbicide Percent of total herbicides applied Modeled as Glyphosate 67.57 Glyphosate Glufosinate 7.75 Organophosphorus-compund, unspecified MCPA 7.54 phenoxy-compound 2,4-D 5.23 phenoxy-compound 478 Herbicide Percent of total herbicides applied Modeled as Bromoxynil 3.00 nitrile-compound Bentazon 2.16 benzo[thia]diazole-compound Fluroxypyr 2.13 pyridine-compound Triallate 1.81 [thio]carbamate-compound Diquat 1.42 bipyridylium-compound Ethalfluralin 1.39 dinitroaniline-compound Table A 19. Processes used for modeling energy inputs to field crop production systems, and any modification made to them. Input Provider modelled as Modifications made to provider Electricity Province-specific electricity grid mixes NA LPG liquefied petroleum gas production, petroleum refinery operation | liquefied petroleum gas | APOS, U | CA NA 479 Input Provider modelled as Modifications made to provider Natural gas heat production, natural gas, at boiler condensing modulating >100kW | heat, district or industrial, natural gas | APOS, U | for specific provinces NA Gasoline market for petrol, unleaded, burned in machinery | petrol, unleaded, burned in machinery | Cutoff, U - GLO NA Diesel diesel, burned in agricultural machinery | diesel, burned in agricultural machinery | APOS, U - GLO NA 480 Table A 20. Processes used for modeling production of synthetic fertilizers, and modifications made to make them more representative of Canadian production conditions. Nutrient supplied Fertilizer Process used Modifications/assumptions made N Ammonia ammonia production, steam reforming, liquid | ammonia, anhydrous, liquid | Cutoff, U – RNA - modeled production in BC, AB, SK, MB, ON, and Canada based on proportions of ammonia produced in each province according to the Cheminfo Services Inc. (2016) - Provincially relevant processes used for heat inputs - Canadian provider used for natural gas input Urea urea production | urea | Cutoff, U – RNA – modeled production in AB, SK, MB, and ON - Ammonia provider changed to geographically appropriate ammonia production process as described above - Electricity providers changed to be provincially relevant Ammonium nitrate ammonium nitrate production | ammonium - Ammonia provider changed to geographically appropriate ammonia production process as described above 481 Nutrient supplied Fertilizer Process used Modifications/assumptions made nitrate | Cutoff, U – RNA – modeled production in AB, SK, MB, and ON - Electricity providers changed to be provincially relevant - Heat providers changed to be provincially relevant Urea ammonium nitrate urea ammonium nitrate production | urea ammonium nitrate mix | Cutoff, U – RNA – modeled production in AB, SK, MB, and ON - Ammonium nitrate provider changed to geographically appropriate production as previously described - Urea provider changed to geographically appropriate production as previously described - Electricity providers changed to be provincially relevant - Tap water provider changed to be representative of Quebec Calcium ammonium nitrate calcium ammonium nitrate production | calcium ammonium nitrate | Cutoff, U – RNA – modeled production in AB, SK, MB, and ON - Ammonia provider changed to geographically appropriate ammonia production process as described above - Electricity providers changed to be provincially relevant - Heat providers changed to be provincially relevant S Ammonium sulfate ammonium sulfate production | ammonium - Ammonia provider changed to Alberta ammonia production as described previously 482 Nutrient supplied Fertilizer Process used Modifications/assumptions made sulfate | Cutoff, U – Europe, RER – modeled production in AB - Electricity provider changed to be regionally relevant to Alberta Elemental sulfur natural gas production | sulfur | APOS, U |, representative of AB and BC None made P Mono-ammonium phosphate monoammonium phosphate production | monoammonium phosphate | Cutoff, U – RNA – modeled production in AB - Ammonia provider changed to Alberta ammonia production as described previously - Electricity provider changed to be regionally relevant to Alberta - Provider for phosphate rock changed to be representative of U.S. conditions bases on Cheminfo services report (2016) which indicates that the producer of MAP imports their phosphate rock Di-ammonium phosphate diammonium phosphate production | diammonium phosphate | Cutoff, U – RNA – modeled production in AB - Ammonia provider changed to Alberta ammonia production as described previously - Heat provider changed to be regionally relevant to Alberta - Provider for phosphoric acid changed to be representative of U.S. conditions under the assumption that phosphoric 483 Nutrient supplied Fertilizer Process used Modifications/assumptions made acid is imported similar to phosphate rock for MAP production Triple superphosphate triple superphosphate production | triple superphosphate | Cutoff, U , ROW – modeled production in AB - Electricity provider changed to be regionally relevant to Alberta - Heat provider changed to be regionally relevant to Alberta - Providers for phosphate rock and phosphoric acid changed to be representative of U.S. conditions in line with assumptions made for production of MAP and DAP K Potash potassium mining and benefication | potassium chloride | Cutoff, U – Saskatchewan - Provider for heat changed to be regionally relevant to SK - Modeled as potassium chloride Potassium sulfate potassium sulfate production | potassium sulfate | Cutoff, U – Europe, RER – modeled production in AB - Electricity provider changed to be regionally relevant to Alberta - Heat provider changed to be regionally relevant to Alberta - Provider for potassium chloride changed to be representative of potassium chloride production in SK as described above 484 Table A 21. Percent inclusion rate, and assumed nutrient contents and associated sources for calculation of nutrient mass balances for estimation of manure management-related emissions from breeder flocks. Ingredient % inclusion Protein content (%) N Content (%) P content (%) Source Notes Corn 36 9.5 1.52 0.26 Feedipedia Maize grain, North America value Soybean meal 20 55.2 8.83 0.62 Feedipedia Soybean meal, high protein, type 50 and similar value Wheat 27 14.58 2.33 0.31 INRAE-CIRAD AFZ Tables Soft wheat value Animal Fat 1 0 0.00 0.00 INRAE-CIRAD AFZ Tables 3 possible sources - Lard (i.e., pig), tallow (ruminants) and poultry. According to pages for each, they all have the same nutrient composition Limestone 4 0 0.00 0.02 INRAE-CIRAD 485 Ingredient % inclusion Protein content (%) N Content (%) P content (%) Source Notes AFZ Tables Trace Vitamins 9 0 0.00 0.00 Assumed to be a vitamin/micronutrient supplement without any other nutrients relevant to the calculations being performed here Table A 22. Percent inclusion rate, and assumed nutrient contents and associated sources for calculation of nutrient mass balances for estimation of manure management-related emissions from non-organic pullets. Ingredient % inclusion Protein content (%) N Content (%) P content (%) Source Notes Corn 36.24 9.5 1.52 0.26 Feedipedia Maize grain, North America value Corn dried distillers grains 2.52 29.5 4.72 0.71 Feedipedia Maize distillers dried grains and solubles value 486 Ingredient % inclusion Protein content (%) N Content (%) P content (%) Source Notes Soybean meal 16.36 55.2 8.83 0.62 Feedipedia Soybean meal, high protein, type 50 and similar value Bakery By-product 2.35 12.4 1.98 0.25 Feedipedia Bakery and viennoiserie byproducts value Wheat 28.97 14.58 2.33 0.31 INRAE-CIRAD AFZ Tables Soft wheat value Meat and Bone Meal 2.35 54.9 8.78 4.00 Feedipedia Meat and bone meal, high-fat value Feather Meal 0.34 85.5 13.68 0.76 Feedipedia Feather meal value Animal Fat 0.005 0 0.00 0.00 INRAE-CIRAD AFZ Tables 3 possible sources - Lard (i.e., pig), tallow (ruminants) and poultry. According to pages for each, they all 487 Ingredient % inclusion Protein content (%) N Content (%) P content (%) Source Notes have the same nutrient composition Peas 0.57 23.9 3.82 0.39 Feedipedia Pea seeds value Canola Meal 1 38.1 6.10 1.05 Feedipedia Rapeseed meal, 00 type, oil < 5% as fed, solvent extraction value Canola Oil 0.45 0 0.00 0.00 INRAE-CIRAD AFZ Tables Limestone 1.07 0 0.00 0.02 INRAE-CIRAD AFZ Tables Barley 7.02 11.8 1.89 0.34 Feedipedia Trace Vitamins 0.39 0 0.00 0.00 Assumed to be a vitamin/micronutrient supplement without 488 Ingredient % inclusion Protein content (%) N Content (%) P content (%) Source Notes any other nutrients relevant to the calculations being performed here DL-Methionine 0.22 0 0.00 0.00 Assumed to be 100% methionine Monocalcium phosphate 0.28 0 0.00 21.00 Assumed to have a P content of 21%, in line with assumptions made during the US egg study Table A 23. Percent inclusion rate, and assumed nutrient contents and associated sources for calculation of nutrient mass balances for estimation of manure management-related emissions from non-organic layers. Ingredient % inclusion Protein content (%) N Content (%) P content (%) Source Notes Corn 37.81 9.5 1.52 0.26 Feedipedia Maize grain, North America value 489 Ingredient % inclusion Protein content (%) N Content (%) P content (%) Source Notes Corn dried distillers grains 2.1 29.5 4.72 0.71 Feedipedia Maize distillers dried grains and solubles value Soybean meal 17.22 55.2 8.83 0.62 Feedipedia Soybean meal, high protein, type 50 and similar value Bakery By-product 1.38 12.4 1.98 0.25 Feedipedia Bakery and viennoiserie byproducts value Wheat 22.27 14.58 2.33 0.31 INRAE-CIRAD AFZ Tables Soft wheat value Meat and Bone Meal 3.01 54.9 8.78 4.00 Feedipedia Meat and bone meal, high-fat value Feather Meal 0.34 85.5 13.68 0.76 Feedipedia Feather meal value Animal Fat 1.09 0 0.00 0.00 INRAE-CIRAD 3 possible sources - Lard (i.e., pig), 490 Ingredient % inclusion Protein content (%) N Content (%) P content (%) Source Notes AFZ Tables tallow (ruminants) and poultry. According to pages for each, they all have the same nutrient composition Peas 0.63 23.9 3.82 0.39 Feedipedia Pea seeds value Canola Meal 0.97 38.1 6.10 1.05 Feedipedia Rapeseed meal, 00 type, oil < 5% as fed, solvent extraction value Canola Oil 0.39 0 0.00 0.00 INRAE-CIRAD AFZ Tables Limestone 8.33 0 0.00 0.02 INRAE-CIRAD AFZ Tables 491 Ingredient % inclusion Protein content (%) N Content (%) P content (%) Source Notes Barley 3.12 11.8 1.89 0.34 Feedipedia Trace Vitamins 0.99 0 0.00 0.00 Assumed to be a vitamin/micronutrient supplement without any other nutrients relevant to the calculations being performed here DL-Methionine 0.34 0 0.00 0.00 Assumed to be 100% methionine Table A 24. Estimated amounts of N applied to fields as poultry manure per tonne of eggs produced in each housing system Housing system Amount of N applied to fields per tonne of eggs produced (kg) Conventional 24.87 Enriched 22.15 Single tier non-cage 27.35 Multi tier non-cage 27.32 492 Housing system Amount of N applied to fields per tonne of eggs produced (kg) Free range 33.64 Table A 25. Closest canola processing facilities and assumed transportation distances for processing of canola for inputs to egg supply chain feeds. Canola producing RU Province Closest canola processing facility Transportation distance from RU centroid to facility (km) 22 MB Nipawin, SK 645 23 MB Ste. Agathe, MB 374 24 MB Harrowby, MB 216 28 SK Nipawin, SK 215 29 SK Yorkton, SK 164 30 SK Clavet, SK 251 34 AB Fort Saskatchewan, AB 367 35 AB Camrose, AB 92 37 AB Lethbridge, AB 181 39 AB Fort Saskatchewan, AB 690 493 Canola producing RU Province Closest canola processing facility Transportation distance from RU centroid to facility (km) 41 BC Fort Saskatchewan, AB 1383 42 BC Fort Saskatchewan, AB 892 Table A 26. Closest soybean processing facilities and assumed transportation distances for processing of soy for inputs to egg supply chain feeds. Soybean producing RU Province Closest soy processing facility Transportation distance from RU centroid to facility (km) 5 NS Becancour, QC 1084 6 PE Becancour, QC 1031 7 NB Becancour, QC 640 11 QC Becancour, QC 222 12 QC Becancour, QC 59.9 15 QC Becancour, QC 589 17 ON Hamilton, ON 115 494 Soybean producing RU Province Closest soy processing facility Transportation distance from RU centroid to facility (km) 19 ON Hamilton, ON 638 22 MB Windsor, ON 2648 23 MB Windsor, ON 2171 24 MB Windsor, ON 2009 28 SK Windsor, ON 2779 29 SK Windsor, ON 2443 30 SK Windsor, ON 2465 495 Table A 27. Complete vegetarian feed formulation adapted from Turner et al. (2023b) for use in sensitivity analysis in chapter 2 as described by section 2.3.3.1.2 Ingredient % inclusion Protein content (%) N Content (%) P content (%) Source Notes Alfalfa 0.83 18.3 2.93 0.27 https://www.feedipedia.org/node/275 Alfalfa, Dehydrated Calcium 2.53 0 0 0 Based on a production weighted average P content of calcium ingredients listed in the feed mill surveys for cage free layer production Canola Oil 0.47 0 0 0 https://fdc.nal.usda.gov/fdc-app.html#/food-details/172336/nutrients Corn 52.38 9.5 1.52 0.29 https://www.feedipedia.org/node/556 Maize grain, North America 496 Ingredient % inclusion Protein content (%) N Content (%) P content (%) Source Notes Corn meal 1.37 67.2 10.75 0.4 https://www.feedipedia.org/node/715 Corn Oil 1.19 0 0 0 https://fdc.nal.usda.gov/fdc-app.html#/food-details/748323/nutrients Corn dried distillers grains 8.05 27.02 4.32 0.66 https://grains.org/wp-content/uploads/2019/07/USGC-Precision-DDGS-Handbook-2019_07_02-WEB.pdf Protein represents average value of table 1, while P Is average from table 2 Flaxseed 0.27 18.3 2.93 0.64 https://fdc.nal.usda.gov/fdc-app.html#/food-details/169414/nutrients Flaxseed oil 0.30 0.11 0.02 0.00 https://fdc.nal.usda.gov/fdc-app.html#/food-details/167702/nutrients Limestone 7.39 0 0 0 https://www.feedtables.com/content/limestone 497 Ingredient % inclusion Protein content (%) N Content (%) P content (%) Source Notes L-Methionine 0.14 0 0 0 Other 0.65 0 0 0 Assumed to be zero Phosphate 0.94 0 0 26.44 Based on a production weighted average P content of phosphate ingredients listed in the feed mill surveys for cage free layer production Salt 0.23 0 0 0 Assumed to be zero in the absence of specific 498 Ingredient % inclusion Protein content (%) N Content (%) P content (%) Source Notes information regarding types of salts used. Soy oil 0.35 0 0 0 https://fdc.nal.usda.gov/fdc-app.html#/food-details/171411/nutrients Soybean 0.62 38 6.08 0.59 https://feedstuffs.farmcentric.com/mdfm/Feeess50/author/427/2015/11/Feedstuffs_RIBG_Ingredient_Analysis_Table_2016.pdf Soybean, full fat, cooked Soybean hulls 0.19 13.1 2.10 0.16 https://www.feedipedia.org/node/719 Soybean meal 19.35 46.7 7.47 2.85 https://ussec.org/wp-content/uploads/2015/10/20120123-Nutritional-value-white-paper-1.pdf Table 1 499 Ingredient % inclusion Protein content (%) N Content (%) P content (%) Source Notes Vegetable Oil 0.19 0 0.00 0 https://fdc.nal.usda.gov/fdc-app.html#/food-details/1103855/nutrients Wheat 0.63 12.69 2.03 0.38 https://feedstuffs.farmcentric.com/mdfm/Feeess50/author/427/2015/11/Feedstuffs_RIBG_Ingredient_Analysis_Table_2016.pdf Based on a weighted average of hard and soft grain wheat, with hard wheat accounting for 70% of US wehat production and soft accounting for 30%, in line with https://www.ers.usda.gov/topics/crops/wheat/wheat-sector-at-a-glance/ 500 Ingredient % inclusion Protein content (%) N Content (%) P content (%) Source Notes Wheat middlings 1.91 17.67 2.83 1.17 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6140900/ Average values from table 1 501 Table A 28. Comparison of protein, N, and P content per tonne of conventional feed, and the vegetarian feed used in sensitivity analysis as described in section 2.3.3.1.2. Conventional Vegetarian feed Protein content (kg/tonne) 199.67 198.49 N content (kg/tonne) 31.95 31.76 P content (kg/tonne) 4.41 6.02 502 Table A 29. Recalculated pesticide inputs per kilogram of barley produced in each RU (g/kg) RU Triallate (g/kg) Bentazon (g/kg) Diquat (g/kg) Ethalfluralin (g/kg) Glyphosate (g/kg) Bromoxynil (g/kg) Glufosinate (g/kg) Pesticides (unspecified), (g/kg) MCPA (g/kg) 2,4-D (g/kg) Fluroxypyr (g/kg) 5 0.010 0.012 0.008 0.008 0.383 0.017 0.044 0.262 0.043 0.030 0.012 6 0.008 0.010 0.007 0.006 0.311 0.014 0.036 0.213 0.035 0.024 0.010 7 0.010 0.012 0.008 0.008 0.379 0.017 0.044 0.260 0.042 0.029 0.012 11 0.009 0.011 0.007 0.007 0.353 0.016 0.040 0.190 0.039 0.027 0.011 12 0.009 0.011 0.007 0.007 0.353 0.016 0.041 0.190 0.039 0.027 0.011 15 0.009 0.011 0.007 0.007 0.352 0.016 0.040 0.190 0.039 0.027 0.011 17 0.009 0.011 0.007 0.007 0.330 0.015 0.038 0.226 0.037 0.025 0.010 19 0.009 0.011 0.007 0.007 0.347 0.015 0.040 0.238 0.039 0.027 0.011 22 0.008 0.010 0.006 0.006 0.306 0.014 0.035 0.210 0.034 0.024 0.010 23 0.007 0.009 0.006 0.006 0.269 0.012 0.031 0.184 0.030 0.021 0.008 24 0.007 0.008 0.005 0.005 0.260 0.012 0.030 0.178 0.029 0.020 0.008 503 RU Triallate (g/kg) Bentazon (g/kg) Diquat (g/kg) Ethalfluralin (g/kg) Glyphosate (g/kg) Bromoxynil (g/kg) Glufosinate (g/kg) Pesticides (unspecified), (g/kg) MCPA (g/kg) 2,4-D (g/kg) Fluroxypyr (g/kg) 28 0.007 0.009 0.006 0.006 0.275 0.012 0.032 0.188 0.031 0.021 0.009 29 0.008 0.009 0.006 0.006 0.286 0.013 0.033 0.196 0.032 0.022 0.009 30 0.009 0.011 0.007 0.007 0.342 0.015 0.039 0.234 0.038 0.026 0.011 34 0.006 0.007 0.005 0.005 0.220 0.010 0.025 0.043 0.025 0.017 0.007 35 0.005 0.006 0.004 0.004 0.203 0.009 0.023 0.040 0.023 0.016 0.006 37 0.006 0.007 0.005 0.005 0.222 0.010 0.025 0.043 0.025 0.017 0.007 39 0.010 0.012 0.008 0.008 0.380 0.017 0.044 0.261 0.042 0.029 0.012 41 0.008 0.010 0.006 0.006 0.306 0.014 0.035 0.209 0.034 0.024 0.010 42 0.008 0.010 0.006 0.006 0.306 0.014 0.035 0.209 0.034 0.024 0.010 504 Table A 30. Recalculated pesticide inputs per kilogram of canola produced in each RU (g/kg) RU Triallate (g/kg) Bentazon (g/kg) Diquat (g/kg) Ethalfluralin (g/kg) Glyphosate (g/kg) Bromoxynil (g/kg) Glufosinate (g/kg) Pesticides (unspecified), (g/kg) MCPA (g/kg) 2,4-D (g/kg) Fluroxypyr (g/kg) 22 0.015 0.017 0.011 0.011 0.546 0.024 0.063 0.374 0.061 0.042 0.017 23 0.012 0.014 0.009 0.009 0.447 0.020 0.051 0.307 0.050 0.035 0.014 24 0.012 0.015 0.010 0.010 0.463 0.021 0.053 0.317 0.052 0.036 0.015 28 0.012 0.014 0.009 0.009 0.445 0.020 0.051 0.305 0.050 0.034 0.014 29 0.012 0.015 0.010 0.010 0.462 0.021 0.053 0.317 0.052 0.036 0.015 30 0.014 0.017 0.011 0.011 0.517 0.023 0.059 0.355 0.058 0.040 0.016 34 0.010 0.012 0.008 0.008 0.377 0.017 0.043 0.074 0.042 0.029 0.012 35 0.009 0.011 0.007 0.007 0.346 0.015 0.040 0.068 0.039 0.027 0.011 37 0.010 0.012 0.008 0.008 0.388 0.017 0.044 0.076 0.043 0.030 0.012 39 0.014 0.017 0.011 0.011 0.535 0.024 0.061 0.366 0.060 0.041 0.017 41 0.014 0.016 0.011 0.010 0.507 0.022 0.058 0.347 0.057 0.039 0.016 42 0.014 0.017 0.011 0.011 0.533 0.024 0.061 0.365 0.059 0.041 0.017 505 Table A 31. Recalculated pesticide inputs per kilogram of corn produced in each RU (g/kg) RU Triallate (g/kg) Bentazon (g/kg) Diquat (g/kg) Ethalfluralin (g/kg) Glyphosate (g/kg) Bromoxynil (g/kg) Glufosinate (g/kg) Pesticides (unspecified), (g/kg) MCPA (g/kg) 2,4-D (g/kg) Fluroxypyr (g/kg) 5 0.004 0.005 0.003 0.003 0.146 0.006 0.017 0.100 0.016 0.011 0.005 6 0.004 0.004 0.003 0.003 0.132 0.006 0.015 0.090 0.015 0.010 0.004 7 0.004 0.005 0.003 0.003 0.152 0.007 0.017 0.104 0.017 0.012 0.005 11 0.004 0.004 0.003 0.003 0.141 0.006 0.016 0.076 0.016 0.011 0.004 12 0.003 0.003 0.002 0.002 0.109 0.005 0.013 0.059 0.012 0.008 0.003 15 0.003 0.004 0.003 0.002 0.120 0.005 0.014 0.065 0.013 0.009 0.004 17 0.003 0.003 0.002 0.002 0.107 0.005 0.012 0.073 0.012 0.008 0.003 19 0.003 0.004 0.003 0.003 0.123 0.005 0.014 0.084 0.014 0.009 0.004 22 0.004 0.004 0.003 0.003 0.137 0.006 0.016 0.094 0.015 0.011 0.004 23 0.003 0.004 0.003 0.003 0.128 0.006 0.015 0.088 0.014 0.010 0.004 24 0.003 0.004 0.003 0.003 0.128 0.006 0.015 0.088 0.014 0.010 0.004 506 RU Triallate (g/kg) Bentazon (g/kg) Diquat (g/kg) Ethalfluralin (g/kg) Glyphosate (g/kg) Bromoxynil (g/kg) Glufosinate (g/kg) Pesticides (unspecified), (g/kg) MCPA (g/kg) 2,4-D (g/kg) Fluroxypyr (g/kg) 29 0.006 0.008 0.005 0.005 0.238 0.011 0.027 0.163 0.027 0.018 0.008 37 0.003 0.003 0.002 0.002 0.095 0.004 0.011 0.019 0.011 0.007 0.003 Table A 32. Recalculated pesticide inputs per kilogram of soy produced in each RU (g/kg) RU Triallate (g/kg) Bentazon (g/kg) Diquat (g/kg) Ethalfluralin (g/kg) Glyphosate (g/kg) Bromoxynil (g/kg) Glufosinate (g/kg) Pesticides (unspecified), (g/kg) MCPA (g/kg) 2,4-D (g/kg) Fluroxypyr (g/kg) 5 0.010 0.012 0.008 0.007 0.360 0.016 0.041 0.247 0.040 0.028 0.011 6 0.012 0.014 0.009 0.009 0.433 0.019 0.050 0.296 0.048 0.033 0.014 7 0.013 0.015 0.010 0.010 0.470 0.021 0.054 0.322 0.052 0.036 0.015 11 0.010 0.011 0.008 0.007 0.359 0.016 0.041 0.194 0.040 0.028 0.011 12 0.008 0.010 0.006 0.006 0.308 0.014 0.035 0.166 0.034 0.024 0.010 15 0.009 0.011 0.007 0.007 0.352 0.016 0.040 0.190 0.039 0.027 0.011 507 RU Triallate (g/kg) Bentazon (g/kg) Diquat (g/kg) Ethalfluralin (g/kg) Glyphosate (g/kg) Bromoxynil (g/kg) Glufosinate (g/kg) Pesticides (unspecified), (g/kg) MCPA (g/kg) 2,4-D (g/kg) Fluroxypyr (g/kg) 17 0.009 0.011 0.007 0.007 0.333 0.015 0.038 0.228 0.037 0.026 0.010 19 0.010 0.012 0.008 0.008 0.383 0.017 0.044 0.262 0.043 0.030 0.012 22 0.013 0.015 0.010 0.010 0.472 0.021 0.054 0.323 0.053 0.036 0.015 23 0.013 0.015 0.010 0.010 0.485 0.022 0.056 0.332 0.054 0.037 0.015 29 0.013 0.016 0.010 0.010 0.490 0.022 0.056 0.336 0.055 0.038 0.015 30 0.017 0.020 0.013 0.013 0.623 0.028 0.071 0.427 0.070 0.048 0.020 Table A 33. Recalculated pesticide inputs per kilogram of wheat produced in each RU (g/kg) RU Triallate (g/kg) Bentazon (g/kg) Diquat (g/kg) Ethalfluralin (g/kg) Glyphosate (g/kg) Bromoxynil (g/kg) Glufosinate (g/kg) Pesticides (unspecified), (g/kg) MCPA (g/kg) 2,4-D (g/kg) Fluroxypyr (g/kg) 5 0.007 0.009 0.006 0.006 0.270 0.012 0.031 0.185 0.030 0.021 0.009 6 0.008 0.009 0.006 0.006 0.296 0.013 0.034 0.203 0.033 0.023 0.009 508 RU Triallate (g/kg) Bentazon (g/kg) Diquat (g/kg) Ethalfluralin (g/kg) Glyphosate (g/kg) Bromoxynil (g/kg) Glufosinate (g/kg) Pesticides (unspecified), (g/kg) MCPA (g/kg) 2,4-D (g/kg) Fluroxypyr (g/kg) 7 0.009 0.011 0.007 0.007 0.331 0.015 0.038 0.227 0.037 0.026 0.010 11 0.010 0.012 0.008 0.008 0.372 0.017 0.043 0.201 0.042 0.029 0.012 12 0.009 0.010 0.007 0.007 0.329 0.015 0.038 0.177 0.037 0.025 0.010 15 0.010 0.012 0.008 0.008 0.374 0.017 0.043 0.201 0.042 0.029 0.012 17 0.005 0.007 0.004 0.004 0.204 0.009 0.023 0.140 0.023 0.016 0.006 19 0.008 0.009 0.006 0.006 0.282 0.013 0.032 0.193 0.031 0.022 0.009 22 0.008 0.009 0.006 0.006 0.281 0.012 0.032 0.192 0.031 0.022 0.009 23 0.007 0.008 0.006 0.005 0.265 0.012 0.030 0.182 0.030 0.021 0.008 24 0.007 0.009 0.006 0.006 0.267 0.012 0.031 0.183 0.030 0.021 0.008 28 0.008 0.010 0.006 0.006 0.309 0.014 0.035 0.211 0.034 0.024 0.010 29 0.009 0.010 0.007 0.007 0.327 0.015 0.038 0.224 0.037 0.025 0.010 30 0.010 0.012 0.008 0.008 0.371 0.016 0.043 0.254 0.041 0.029 0.012 34 0.006 0.007 0.005 0.004 0.216 0.010 0.025 0.042 0.024 0.017 0.007 509 RU Triallate (g/kg) Bentazon (g/kg) Diquat (g/kg) Ethalfluralin (g/kg) Glyphosate (g/kg) Bromoxynil (g/kg) Glufosinate (g/kg) Pesticides (unspecified), (g/kg) MCPA (g/kg) 2,4-D (g/kg) Fluroxypyr (g/kg) 35 0.006 0.007 0.004 0.004 0.212 0.009 0.024 0.042 0.024 0.016 0.007 37 0.006 0.008 0.005 0.005 0.241 0.011 0.028 0.047 0.027 0.019 0.008 39 0.008 0.010 0.006 0.006 0.304 0.013 0.035 0.208 0.034 0.023 0.010 41 0.009 0.011 0.007 0.007 0.342 0.015 0.039 0.234 0.038 0.026 0.011 42 0.009 0.011 0.007 0.007 0.342 0.015 0.039 0.234 0.038 0.026 0.011 Table A 34. Recalculated field level emissions per kilogram of barley produced in each RU (kg/kg) RU Direct N2O (kg/kg) Indirect N2O due to volatilization (kg/kg) Indirect N2O from leaching (kg/kg) NH3 volatilized (kg/kg) NOx volatilized (kg/kg) NO3 leached (kg/kg) P2O5 runoff (kg/kg) 5 8.15E-04 2.44E-05 1.97E-04 1.69E-03 3.33E-04 1.26E-04 1.79E-04 6 3.19E-04 1.68E-05 1.74E-04 1.17E-03 2.30E-04 1.11E-04 1.78E-04 7 6.53E-04 2.03E-05 1.94E-04 1.41E-03 2.77E-04 1.23E-04 1.79E-04 510 RU Direct N2O (kg/kg) Indirect N2O due to volatilization (kg/kg) Indirect N2O from leaching (kg/kg) NH3 volatilized (kg/kg) NOx volatilized (kg/kg) NO3 leached (kg/kg) P2O5 runoff (kg/kg) 11 7.62E-04 2.54E-05 2.03E-04 1.76E-03 3.46E-04 1.29E-04 1.82E-04 12 9.84E-04 2.54E-05 2.02E-04 1.76E-03 3.46E-04 1.28E-04 1.82E-04 15 4.90E-04 2.54E-05 1.84E-04 1.76E-03 3.46E-04 1.17E-04 1.82E-04 17 6.24E-04 2.23E-05 1.59E-04 1.54E-03 3.04E-04 1.01E-04 1.79E-04 19 5.65E-04 2.30E-05 1.57E-04 1.59E-03 3.13E-04 1.00E-04 1.79E-04 22 5.15E-04 1.80E-05 1.23E-04 1.25E-03 2.46E-04 7.80E-05 1.80E-04 23 2.79E-04 1.70E-05 1.09E-04 1.18E-03 2.32E-04 6.92E-05 1.79E-04 24 2.24E-04 1.61E-05 9.87E-05 1.12E-03 2.20E-04 6.28E-05 1.79E-04 28 1.83E-04 1.84E-05 1.03E-04 1.28E-03 2.51E-04 6.54E-05 1.79E-04 29 1.50E-04 1.86E-05 9.54E-05 1.29E-03 2.53E-04 6.07E-05 1.79E-04 30 1.92E-04 1.91E-05 7.05E-05 1.32E-03 2.60E-04 4.49E-05 1.79E-04 34 2.25E-04 1.62E-05 1.04E-04 1.12E-03 2.21E-04 6.61E-05 1.78E-04 35 1.94E-04 1.67E-05 9.77E-05 1.16E-03 2.28E-04 6.22E-05 1.78E-04 511 RU Direct N2O (kg/kg) Indirect N2O due to volatilization (kg/kg) Indirect N2O from leaching (kg/kg) NH3 volatilized (kg/kg) NOx volatilized (kg/kg) NO3 leached (kg/kg) P2O5 runoff (kg/kg) 37 3.80E-04 1.75E-05 6.42E-05 1.21E-03 2.38E-04 4.08E-05 1.79E-04 39 1.87E-04 2.84E-05 1.27E-04 1.97E-03 3.88E-04 8.10E-05 1.79E-04 41 2.87E-04 2.29E-05 1.58E-04 1.59E-03 3.13E-04 1.00E-04 1.78E-04 42 4.47E-04 2.29E-05 6.00E-05 1.59E-03 3.13E-04 3.82E-05 1.78E-04 Table A 35. Recalculated field level emissions per kilogram of canola produced in each RU (kg/kg) RU Direct N2O (kg/kg) Indirect N2O due to volatilization (kg/kg) Indirect N2O from leaching (kg/kg) NH3 volatilized (kg/kg) NOx volatilized (kg/kg) NO3 leached (kg/kg) P2O5 runoff (kg/kg) 22 1.35E-03 4.43E-05 3.54E-04 3.07E-03 6.04E-04 2.25E-04 1.84E-04 23 7.53E-04 4.32E-05 3.22E-04 2.99E-03 5.89E-04 2.05E-04 1.82E-04 24 6.31E-04 4.31E-05 3.02E-04 2.99E-03 5.88E-04 1.92E-04 1.82E-04 28 4.34E-04 4.01E-05 2.77E-04 2.78E-03 5.46E-04 1.76E-04 1.81E-04 512 RU Direct N2O (kg/kg) Indirect N2O due to volatilization (kg/kg) Indirect N2O from leaching (kg/kg) NH3 volatilized (kg/kg) NOx volatilized (kg/kg) NO3 leached (kg/kg) P2O5 runoff (kg/kg) 29 3.47E-04 3.93E-05 2.53E-04 2.72E-03 5.36E-04 1.61E-04 1.82E-04 30 4.40E-04 3.99E-05 1.86E-04 2.77E-03 5.44E-04 1.18E-04 1.82E-04 34 6.48E-04 4.46E-05 3.22E-04 3.09E-03 6.08E-04 2.05E-04 1.82E-04 35 4.62E-04 3.61E-05 2.66E-04 2.50E-03 4.93E-04 1.69E-04 1.81E-04 37 9.75E-04 4.18E-05 1.83E-04 2.90E-03 5.70E-04 1.17E-04 1.83E-04 39 4.32E-04 6.05E-05 3.35E-04 4.19E-03 8.25E-04 2.13E-04 1.82E-04 41 8.00E-04 6.05E-05 4.79E-04 4.19E-03 8.25E-04 3.05E-04 1.82E-04 42 1.25E-03 6.05E-05 1.82E-04 4.19E-03 8.25E-04 1.16E-04 1.82E-04 513 Table A 36. Recalculated field level emissions per kilogram of corn produced in each RU (kg/kg) RU Direct N2O (kg/kg) Indirect N2O due to volatilization (kg/kg) Indirect N2O from leaching (kg/kg) NH3 volatilized (kg/kg) NOx volatilized (kg/kg) NO3 leached (kg/kg) P2O5 runoff (kg/kg) 5 5.05E-04 1.42E-05 1.35E-04 9.86E-04 1.94E-04 8.60E-05 1.77E-04 6 2.34E-04 1.19E-05 1.35E-04 8.22E-04 1.62E-04 8.60E-05 1.77E-04 7 4.05E-04 1.19E-05 1.33E-04 8.22E-04 1.62E-04 8.44E-05 1.77E-04 11 4.92E-04 1.55E-05 1.42E-04 1.08E-03 2.12E-04 9.06E-05 1.77E-04 12 6.35E-04 1.56E-05 1.42E-04 1.08E-03 2.12E-04 9.01E-05 1.77E-04 15 3.17E-04 1.56E-05 1.29E-04 1.08E-03 2.12E-04 8.23E-05 1.77E-04 17 4.32E-04 1.48E-05 1.18E-04 1.02E-03 2.02E-04 7.53E-05 1.78E-04 19 3.81E-04 1.48E-05 1.15E-04 1.02E-03 2.02E-04 7.29E-05 1.78E-04 22 4.03E-04 1.38E-05 9.96E-05 9.56E-04 1.88E-04 6.34E-05 1.77E-04 23 2.26E-04 1.35E-05 9.05E-05 9.37E-04 1.84E-04 5.76E-05 1.77E-04 24 1.89E-04 1.35E-05 8.49E-05 9.36E-04 1.84E-04 5.40E-05 1.77E-04 29 1.38E-04 1.35E-05 7.49E-05 9.36E-04 1.84E-04 4.77E-05 1.75E-04 514 RU Direct N2O (kg/kg) Indirect N2O due to volatilization (kg/kg) Indirect N2O from leaching (kg/kg) NH3 volatilized (kg/kg) NOx volatilized (kg/kg) NO3 leached (kg/kg) P2O5 runoff (kg/kg) 37 3.00E-04 1.35E-05 5.25E-05 9.37E-04 1.84E-04 3.34E-05 1.76E-04 Table A 37. Recalculated field level emissions per kilogram of soy produced in each RU (kg/kg) RU Direct N2O (kg/kg) Indirect N2O due to volatilization (kg/kg) Indirect N2O from leaching (kg/kg) NH3 volatilized (kg/kg) NOx volatilized (kg/kg) NO3 leached (kg/kg) P2O5 runoff (kg/kg) 5 1.77E-04 1.89E-06 9.10E-05 1.31E-04 2.57E-05 5.79E-05 1.77E-04 6 8.68E-05 1.89E-06 9.31E-05 1.31E-04 2.57E-05 5.92E-05 1.78E-04 7 1.55E-04 2.05E-06 9.24E-05 1.42E-04 2.80E-05 5.88E-05 1.78E-04 11 1.77E-04 2.53E-06 9.45E-05 1.75E-04 3.44E-05 6.01E-05 1.76E-04 12 2.15E-04 2.14E-06 9.19E-05 1.49E-04 2.92E-05 5.85E-05 1.76E-04 15 1.12E-04 2.44E-06 8.54E-05 1.69E-04 3.33E-05 5.44E-05 1.76E-04 17 1.57E-04 2.31E-06 8.00E-05 1.60E-04 3.16E-05 5.09E-05 1.79E-04 515 RU Direct N2O (kg/kg) Indirect N2O due to volatilization (kg/kg) Indirect N2O from leaching (kg/kg) NH3 volatilized (kg/kg) NOx volatilized (kg/kg) NO3 leached (kg/kg) P2O5 runoff (kg/kg) 19 1.45E-04 2.63E-06 7.89E-05 1.83E-04 3.59E-05 5.02E-05 1.79E-04 22 1.13E-04 1.13E-06 5.93E-05 7.81E-05 1.54E-05 3.77E-05 1.83E-04 23 6.79E-05 1.37E-06 5.55E-05 9.47E-05 1.86E-05 3.53E-05 1.82E-04 24 6.07E-05 1.68E-06 5.33E-05 1.16E-04 2.29E-05 3.39E-05 1.83E-04 29 3.84E-05 1.00E-06 4.85E-05 6.96E-05 1.37E-05 3.09E-05 1.77E-04 30 4.21E-05 2.55E-06 5.00E-05 1.77E-04 3.48E-05 3.18E-05 1.82E-04 Table A 38. Recalculated field level emissions per kilogram of wheat produced in each RU (kg/kg) RU Direct N2O (kg/kg) Indirect N2O due to volatilization (kg/kg) Indirect N2O from leaching (kg/kg) NH3 volatilized (kg/kg) NOx volatilized (kg/kg) NO3 leached (kg/kg) P2O5 runoff (kg/kg) 5 7.96E-04 2.30E-05 2.04E-04 1.59E-03 3.14E-04 1.30E-04 1.78E-04 6 4.01E-04 2.12E-05 2.18E-04 1.47E-03 2.90E-04 1.39E-04 1.79E-04 516 RU Direct N2O (kg/kg) Indirect N2O due to volatilization (kg/kg) Indirect N2O from leaching (kg/kg) NH3 volatilized (kg/kg) NOx volatilized (kg/kg) NO3 leached (kg/kg) P2O5 runoff (kg/kg) 7 7.70E-04 2.38E-05 2.31E-04 1.65E-03 3.25E-04 1.47E-04 1.79E-04 11 5.11E-04 1.52E-05 1.62E-04 1.05E-03 2.07E-04 1.03E-04 1.77E-04 12 5.92E-04 1.32E-05 1.50E-04 9.17E-04 1.80E-04 9.54E-05 1.76E-04 15 3.29E-04 1.52E-05 1.47E-04 1.05E-03 2.07E-04 9.34E-05 1.77E-04 17 5.62E-04 1.90E-05 1.56E-04 1.32E-03 2.59E-04 9.96E-05 1.78E-04 19 6.54E-04 2.63E-05 1.85E-04 1.82E-03 3.59E-04 1.18E-04 1.79E-04 22 6.62E-04 2.33E-05 1.56E-04 1.62E-03 3.18E-04 9.93E-05 1.80E-04 23 3.42E-04 2.08E-05 1.33E-04 1.44E-03 2.84E-04 8.49E-05 1.79E-04 24 2.76E-04 1.99E-05 1.22E-04 1.38E-03 2.71E-04 7.74E-05 1.79E-04 28 2.38E-04 2.42E-05 1.32E-04 1.67E-03 3.29E-04 8.39E-05 1.79E-04 29 1.98E-04 2.48E-05 1.24E-04 1.72E-03 3.38E-04 7.89E-05 1.80E-04 30 2.41E-04 2.40E-05 8.81E-05 1.66E-03 3.27E-04 5.60E-05 1.80E-04 34 2.95E-04 2.15E-05 1.34E-04 1.49E-03 2.93E-04 8.53E-05 1.78E-04 517 RU Direct N2O (kg/kg) Indirect N2O due to volatilization (kg/kg) Indirect N2O from leaching (kg/kg) NH3 volatilized (kg/kg) NOx volatilized (kg/kg) NO3 leached (kg/kg) P2O5 runoff (kg/kg) 35 2.31E-04 1.97E-05 1.17E-04 1.37E-03 2.69E-04 7.46E-05 1.78E-04 37 4.55E-04 2.08E-05 7.75E-05 1.44E-03 2.84E-04 4.93E-05 1.77E-04 39 2.04E-04 3.06E-05 1.43E-04 2.12E-03 4.17E-04 9.11E-05 1.79E-04 41 4.19E-04 3.44E-05 2.21E-04 2.38E-03 4.69E-04 1.41E-04 1.79E-04 42 6.53E-04 3.44E-05 8.41E-05 2.38E-03 4.69E-04 5.35E-05 1.79E-04 Table A 39. Abiotic depletion impacts per kilogram of crop produced in each RU, excluding impacts related to SOC changes and perennial to annual land transitions. A blank space indicates that crop is not produced in that RU. Abiotic depletion (kg SB eq) RU Barley Canola Corn Soy Wheat 5 2.63E-06 1.49E-06 1.87E-06 3.07E-06 6 2.17E-06 1.47E-06 2.25E-06 3.35E-06 7 2.62E-06 1.50E-06 2.44E-06 3.98E-06 518 Abiotic depletion (kg SB eq) RU Barley Canola Corn Soy Wheat 11 3.52E-06 9.62E-07 1.43E-06 2.36E-06 12 3.51E-06 9.18E-07 1.23E-06 2.13E-06 15 3.51E-06 9.55E-07 1.39E-06 2.49E-06 17 1.49E-06 1.03E-06 1.96E-06 3.88E-06 19 1.54E-06 1.07E-06 2.24E-06 5.80E-06 22 1.49E-06 4.61E-06 8.60E-07 3.73E-06 4.62E-06 23 1.28E-06 3.93E-06 8.70E-07 2.04E-06 4.95E-06 24 1.22E-06 4.16E-06 9.26E-07 4.70E-06 28 1.37E-06 4.29E-06 6.21E-06 29 1.42E-06 4.41E-06 1.12E-06 4.25E-06 7.89E-06 30 1.53E-06 4.63E-06 6.08E-06 8.21E-06 34 1.05E-06 4.00E-06 5.35E-06 35 1.02E-06 3.50E-06 2.97E-06 519 Abiotic depletion (kg SB eq) RU Barley Canola Corn Soy Wheat 37 1.11E-06 4.20E-06 8.74E-07 4.08E-06 39 1.55E-06 4.46E-06 6.36E-06 41 1.25E-06 4.47E-06 7.41E-06 42 1.25E-06 4.45E-06 7.66E-06 Table A 40. Abiotic depletion (fossil fuels) impacts per kilogram of crop produced in each RU, excluding impacts related to SOC changes and perennial to annual land transitions. A blank space indicates that crop is not produced in that RU. Abiotic depletion (fossil fuels) (MJ) RU Barley Canola Corn Soy Wheat 5 1.62 1.06 0.97 1.89 6 1.35 0.99 1.13 1.84 7 1.62 1.04 1.23 2.13 11 1.77 1.10 0.97 1.46 12 1.79 1.03 0.83 1.29 520 Abiotic depletion (fossil fuels) (MJ) RU Barley Canola Corn Soy Wheat 15 1.79 1.06 0.95 2.37 17 1.41 0.94 0.90 3.10 19 1.46 0.98 1.03 2.94 22 1.42 4.18 1.02 1.32 2.80 23 1.29 3.77 1.00 1.36 2.78 24 1.22 3.86 1.02 2.65 28 1.38 3.72 3.76 29 1.40 3.83 1.30 1.84 3.92 30 1.44 3.94 2.74 4.02 34 1.27 4.06 3.03 35 1.28 3.55 2.17 37 1.41 4.34 1.54 2.78 39 1.55 4.28 3.16 521 Abiotic depletion (fossil fuels) (MJ) RU Barley Canola Corn Soy Wheat 41 1.27 3.69 42 1.29 4.36 Table A 41. Acidification impacts per kilogram of crop produced in each RU, excluding impacts related to SOC changes and perennial to annual land transitions. A blank space indicates that crop is not produced in that RU. Acidification (kg SO2 eq) RU Barley Canola Corn Soy Wheat 5 2.82E-03 1.74E-03 7.64E-04 3.44E-03 6 2.05E-03 1.49E-03 8.48E-04 3.36E-03 7 2.47E-03 1.51E-03 9.26E-04 3.84E-03 11 3.04E-03 1.78E-03 8.08E-04 2.73E-03 12 3.06E-03 1.75E-03 6.84E-04 2.42E-03 15 3.06E-03 1.76E-03 7.80E-04 3.15E-03 17 2.42E-03 1.65E-03 8.08E-04 3.91E-03 522 Acidification (kg SO2 eq) RU Barley Canola Corn Soy Wheat 19 2.50E-03 1.67E-03 9.27E-04 4.96E-03 22 2.05E-03 6.72E-03 1.52E-03 1.08E-03 4.22E-03 23 1.89E-03 6.42E-03 1.52E-03 2.13E-03 4.07E-03 24 1.80E-03 6.44E-03 1.54E-03 3.89E-03 28 2.03E-03 6.12E-03 4.77E-03 29 2.06E-03 6.07E-03 1.64E-03 1.51E-03 5.50E-03 30 2.12E-03 6.19E-03 3.25E-03 5.52E-03 34 1.82E-03 6.70E-03 4.33E-03 35 1.85E-03 5.58E-03 3.28E-03 37 1.94E-03 6.51E-03 1.67E-03 3.85E-03 39 2.93E-03 8.62E-03 5.52E-03 41 2.37E-03 8.66E-03 6.31E-03 42 2.38E-03 8.66E-03 6.40E-03 523 Table A 42. Eutrophication impacts per kilogram of crop produced in each RU, excluding impacts related to SOC changes and perennial to annual land transitions. A blank space indicates that crop is not produced in that RU. Eutrophication (kg PO43- eq) RU Barley Canola Corn Soy Wheat 5 1.05E-03 7.08E-04 6.05E-04 1.26E-03 6 7.60E-04 5.93E-04 6.38E-04 1.16E-03 7 9.39E-04 6.38E-04 6.94E-04 1.38E-03 11 1.10E-03 7.24E-04 6.20E-04 1.08E-03 12 1.15E-03 7.43E-04 5.76E-04 1.01E-03 15 1.05E-03 6.79E-04 5.89E-04 1.14E-03 17 9.22E-04 6.64E-04 6.01E-04 1.34E-03 19 9.31E-04 6.72E-04 6.50E-04 1.65E-03 22 7.99E-04 2.44E-03 5.91E-04 7.64E-04 1.44E-03 23 7.04E-04 2.14E-03 5.91E-04 7.83E-04 1.32E-03 24 6.66E-04 2.13E-03 5.88E-04 1.25E-03 28 7.38E-04 2.00E-03 1.49E-03 524 Eutrophication (kg PO43- eq) RU Barley Canola Corn Soy Wheat 29 7.33E-04 1.97E-03 6.36E-04 9.61E-04 1.65E-03 30 7.56E-04 2.03E-03 1.48E-03 1.66E-03 34 6.83E-04 2.23E-03 1.37E-03 35 6.80E-04 1.88E-03 1.08E-03 37 7.48E-04 2.26E-03 6.96E-04 1.28E-03 39 9.44E-04 2.61E-03 1.64E-03 41 8.27E-04 2.75E-03 1.91E-03 42 8.37E-04 2.78E-03 1.95E-03 Table A 43. Freshwater aquatic ecotoxicity impacts per kilogram of crop produced in each RU, excluding impacts related to SOC changes and perennial to annual land transitions. A blank space indicates that crop is not produced in that RU. Freshwater aquatic ecotoxicity (kg 1,4-DB eq) RU Barley Canola Corn Soy Wheat 5 8.34E-02 5.05E-02 1.58E-01 9.76E-02 525 Freshwater aquatic ecotoxicity (kg 1,4-DB eq) RU Barley Canola Corn Soy Wheat 6 6.81E-02 4.91E-02 1.90E-01 1.06E-01 7 8.24E-02 5.00E-02 2.07E-01 1.28E-01 11 1.14E-01 3.70E-02 1.64E-01 7.38E-02 12 1.14E-01 3.63E-02 1.40E-01 6.70E-02 15 1.14E-01 3.68E-02 1.60E-01 8.14E-02 17 5.91E-02 3.92E-02 1.56E-01 1.51E-01 19 6.11E-02 4.03E-02 1.80E-01 2.20E-01 22 5.23E-02 1.57E-01 3.19E-02 2.63E-01 1.66E-01 23 4.48E-02 1.38E-01 3.27E-02 2.84E-01 1.80E-01 24 4.28E-02 1.46E-01 3.50E-02 1.70E-01 28 5.10E-02 1.54E-01 2.36E-01 29 5.23E-02 1.59E-01 4.29E-02 3.57E-01 2.96E-01 30 5.45E-02 1.66E-01 4.60E-01 3.06E-01 526 Freshwater aquatic ecotoxicity (kg 1,4-DB eq) RU Barley Canola Corn Soy Wheat 34 4.54E-02 1.87E-01 2.03E-01 35 4.55E-02 1.43E-01 1.08E-01 37 5.38E-02 1.83E-01 6.51E-02 1.57E-01 39 5.36E-02 1.59E-01 2.37E-01 41 4.33E-02 1.58E-01 2.76E-01 42 4.33E-02 1.59E-01 2.86E-01 Table A 44. Global warming impacts per kilogram of crop produced in each RU, excluding impacts related to SOC changes and perennial to annual land transitions. A blank space indicates that crop is not produced in that RU. Global warming (kg CO2 eq) RU Barley Canola Corn Soy Wheat 5 3.28E-01 2.14E-01 1.50E-01 3.93E-01 6 2.05E-01 1.52E-01 1.43E-01 2.96E-01 7 2.94E-01 1.91E-01 1.71E-01 4.11E-01 527 Global warming (kg CO2 eq) RU Barley Canola Corn Soy Wheat 11 3.25E-01 2.15E-01 1.55E-01 2.78E-01 12 3.69E-01 2.38E-01 1.48E-01 2.82E-01 15 2.72E-01 1.72E-01 1.33E-01 3.00E-01 17 2.62E-01 1.74E-01 1.32E-01 4.11E-01 19 2.54E-01 1.77E-01 1.43E-01 4.31E-01 22 2.29E-01 7.50E-01 1.36E-01 1.62E-01 4.04E-01 23 1.71E-01 5.52E-01 1.35E-01 1.54E-01 3.22E-01 24 1.53E-01 5.25E-01 1.28E-01 2.95E-01 28 1.56E-01 4.49E-01 3.58E-01 29 1.49E-01 4.20E-01 1.33E-01 1.96E-01 3.67E-01 30 1.56E-01 4.32E-01 2.97E-01 3.77E-01 34 1.51E-01 5.33E-01 3.17E-01 35 1.44E-01 4.26E-01 2.32E-01 528 Global warming (kg CO2 eq) RU Barley Canola Corn Soy Wheat 37 1.83E-01 5.82E-01 1.80E-01 3.22E-01 39 1.62E-01 4.67E-01 3.03E-01 41 1.69E-01 5.81E-01 4.07E-01 42 1.82E-01 6.48E-01 4.37E-01 Table A 45. Human toxicity impacts per kilogram of crop produced in each RU, excluding impacts related to SOC changes and perennial to annual land transitions. A blank space indicates that crop is not produced in that RU. Human toxicity(kg 1,4-DB eq) RU Barley Canola Corn Soy Wheat 5 1.47E-01 1.00E-01 1.28E-01 1.74E-01 6 1.21E-01 9.71E-02 1.54E-01 1.89E-01 7 1.47E-01 1.00E-01 1.68E-01 2.27E-01 11 2.02E-01 7.68E-02 1.12E-01 1.30E-01 12 2.02E-01 7.26E-02 9.44E-02 1.18E-01 529 Human toxicity(kg 1,4-DB eq) RU Barley Canola Corn Soy Wheat 15 2.02E-01 7.45E-02 1.08E-01 1.44E-01 17 1.02E-01 7.63E-02 1.35E-01 2.64E-01 19 1.05E-01 7.96E-02 1.55E-01 3.85E-01 22 9.01E-02 2.70E-01 6.57E-02 2.31E-01 2.91E-01 23 7.71E-02 2.37E-01 6.63E-02 2.60E-01 3.15E-01 24 7.37E-02 2.51E-01 7.04E-02 2.97E-01 28 8.50E-02 2.63E-01 4.03E-01 29 8.75E-02 2.72E-01 6.81E-02 2.83E-01 5.16E-01 30 9.16E-02 2.83E-01 4.01E-01 5.34E-01 34 7.44E-02 3.09E-01 3.52E-01 35 7.33E-02 2.39E-01 1.86E-01 37 8.20E-02 2.94E-01 9.09E-02 2.66E-01 39 9.11E-02 2.71E-01 4.12E-01 530 Human toxicity(kg 1,4-DB eq) RU Barley Canola Corn Soy Wheat 41 7.36E-02 2.72E-01 4.81E-01 42 7.37E-02 2.71E-01 4.99E-01 Table A 46. Marine aquatic ecotoxicity impacts per kilogram of crop produced in each RU, excluding impacts related to SOC changes and perennial to annual land transitions. A blank space indicates that crop is not produced in that RU. Marine aquatic ecotoxicity (kg 1,4-DB eq) RU Barley Canola Corn Soy Wheat 5 1.42E+02 9.75E-02 8.07E+01 1.89E+02 6 1.15E+02 9.46E-02 9.10E+01 2.03E+02 7 1.40E+02 9.75E-02 1.00E+02 2.38E+02 11 1.99E+02 7.49E-02 5.25E+01 1.44E+02 12 1.99E+02 7.07E-02 4.38E+01 1.32E+02 15 1.99E+02 7.26E-02 5.03E+01 1.58E+02 17 8.72E+01 7.44E-02 8.96E+01 2.52E+02 531 Marine aquatic ecotoxicity (kg 1,4-DB eq) RU Barley Canola Corn Soy Wheat 19 9.01E+01 7.75E-02 1.03E+02 3.49E+02 22 7.83E+01 2.37E+02 6.41E-02 1.73E+02 2.85E+02 23 6.70E+01 2.05E+02 6.46E-02 4.23E+02 3.04E+02 24 6.39E+01 2.16E+02 6.86E-02 2.89E+02 28 7.82E+01 2.32E+02 3.96E+02 29 8.01E+01 2.41E+02 6.62E-02 1.93E+02 4.75E+02 30 8.34E+01 2.52E+02 2.94E+02 4.90E+02 34 7.03E+01 2.70E+02 3.39E+02 35 7.12E+01 2.18E+02 2.01E+02 37 8.56E+01 2.85E+02 8.85E-02 2.74E+02 39 8.24E+01 2.44E+02 3.85E+02 41 6.65E+01 2.41E+02 4.48E+02 532 Marine aquatic ecotoxicity (kg 1,4-DB eq) RU Barley Canola Corn Soy Wheat 42 6.66E+01 2.44E+02 4.63E+02 Table A 47. Ozone layer depletion impacts per kilogram of crop produced in each RU, excluding impacts related to SOC changes and perennial to annual land transitions. A blank space indicates that crop is not produced in that RU. Ozone layer depletion (kg CFC-11 eq) RU Barley Canola Corn Soy Wheat 5 7.64E-09 3.38E-09 9.97E-09 6.95E-09 6 6.23E-09 3.08E-09 1.20E-08 7.54E-09 7 7.58E-09 3.49E-09 1.30E-08 8.46E-09 11 6.81E-09 3.05E-09 9.13E-09 9.27E-09 12 6.71E-09 2.48E-09 7.82E-09 8.19E-09 15 6.69E-09 2.96E-09 8.92E-09 1.01E-08 17 3.33E-09 2.60E-09 7.47E-09 6.77E-09 19 3.48E-09 2.94E-09 8.59E-09 7.99E-09 533 Ozone layer depletion (kg CFC-11 eq) RU Barley Canola Corn Soy Wheat 22 6.16E-09 1.52E-08 3.15E-09 1.34E-08 7.86E-09 23 5.42E-09 1.25E-08 2.96E-09 1.40E-08 7.54E-09 24 5.24E-09 1.30E-08 2.99E-09 7.49E-09 28 5.60E-09 1.24E-08 9.42E-09 29 5.78E-09 1.30E-08 5.17E-09 1.91E-08 9.55E-09 30 6.75E-09 1.44E-08 2.28E-08 1.05E-08 34 3.18E-09 7.70E-09 6.93E-09 35 3.05E-09 7.08E-09 6.31E-09 37 3.54E-09 8.65E-09 3.27E-09 7.87E-09 39 7.47E-09 1.50E-08 8.52E-09 41 6.02E-09 1.41E-08 9.65E-09 42 6.04E-09 1.50E-08 9.72E-09 534 Table A 48. Photochemical oxidation impacts per kilogram of crop produced in each RU, excluding impacts related to SOC changes and perennial to annual land transitions. A blank space indicates that crop is not produced in that RU. Photochemical oxidation (kg C2H4 eq) RU Barley Canola Corn Soy Wheat 5 2.69E-05 2.96E-05 7.00E-05 3.25E-05 6 2.20E-05 2.70E-05 8.32E-05 3.58E-05 7 2.65E-05 2.95E-05 9.06E-05 4.27E-05 11 3.33E-05 2.67E-05 7.19E-05 2.86E-05 12 3.38E-05 2.28E-05 6.09E-05 2.63E-05 15 3.38E-05 2.42E-05 6.95E-05 3.87E-05 17 1.78E-05 2.10E-05 6.52E-05 6.05E-05 19 1.83E-05 2.31E-05 7.46E-05 7.39E-05 22 1.86E-05 5.42E-05 2.33E-05 1.02E-04 5.84E-05 23 1.60E-05 5.01E-05 2.26E-05 1.43E-04 6.31E-05 24 1.53E-05 5.00E-05 2.35E-05 5.99E-05 28 1.74E-05 5.26E-05 8.10E-05 535 Photochemical oxidation (kg C2H4 eq) RU Barley Canola Corn Soy Wheat 29 1.79E-05 5.35E-05 1.55E-05 1.40E-04 1.01E-04 30 1.88E-05 5.53E-05 1.74E-04 1.05E-04 34 1.62E-05 5.36E-05 7.13E-05 35 1.60E-05 4.87E-05 3.99E-05 37 1.80E-05 6.09E-05 3.21E-05 5.72E-05 39 1.96E-05 5.63E-05 8.08E-05 41 1.60E-05 5.66E-05 9.44E-05 42 1.62E-05 5.71E-05 9.78E-05 Table A 49. Terrestrial ecotoxicity impacts per kilogram of crop produced in each RU, excluding impacts related to SOC changes and perennial to annual land transitions. A blank space indicates that crop is not produced in that RU. Terrestrial ecotoxicity (kg 1,4-DB eq) RU Barley Canola Corn Soy Wheat 5 3.56E-04 1.91E-04 4.49E-03 3.20E-04 536 Terrestrial ecotoxicity (kg 1,4-DB eq) RU Barley Canola Corn Soy Wheat 6 2.95E-04 1.80E-04 5.41E-03 3.47E-04 7 3.56E-04 1.93E-04 5.88E-03 4.04E-04 11 4.44E-04 1.78E-04 5.09E-03 3.22E-04 12 4.45E-04 1.60E-04 4.38E-03 2.86E-04 15 4.45E-04 1.70E-04 4.99E-03 5.29E-04 17 3.02E-04 1.60E-04 4.18E-03 5.94E-04 19 3.12E-04 1.71E-04 4.81E-03 5.50E-04 22 3.02E-04 5.57E-04 1.60E-04 6.81E-03 5.61E-04 23 2.67E-04 5.61E-04 1.59E-04 6.93E-03 5.66E-04 24 2.52E-04 5.82E-04 1.64E-04 5.45E-04 28 2.73E-04 5.48E-04 5.97E-04 29 2.77E-04 5.73E-04 2.32E-04 9.80E-03 7.78E-04 30 2.93E-04 6.01E-04 1.16E-02 8.08E-04 537 Terrestrial ecotoxicity (kg 1,4-DB eq) RU Barley Canola Corn Soy Wheat 34 2.44E-04 5.75E-04 6.05E-04 35 2.69E-04 5.15E-04 4.39E-04 37 3.02E-04 6.99E-04 6.04E-04 39 3.00E-04 6.16E-04 3.99E-04 6.00E-04 41 2.47E-04 6.05E-04 6.94E-04 42 2.52E-04 6.36E-04 7.13E-04 538 Table A 50. Estimated impacts per tonne of breeder, pullet, and layer feed milled, excluding SOC changes and impacts of perennial to annual land use transitions. Impact category Breeder Pullet Layer Abiotic depletion (kg SB eq) 8.62E-03 3.25E-03 3.40E-03 Abiotic depletion (fossil fuels) (MJ) 2311.85 2773.30 2758.39 Acidification (Kg SO2 eq) 3.02 6.24 6.1 Eutrophication (Kg PO43- eq) 1.05 2.36 2.29 Freshwater aquatic ecotoxicity (Kg 1,4-DB eq) 250.12 416.21 267.81 Global warming potential (kg CO2 eq) 299.15 631.4 488.39 Human toxicity (Kg 1,4-DB eq) 379.03 212.39 207.95 Marine aquatic ecotoxicity (Kg 1,4-DB eq) 305450.6 214026.18 203779.1 Ozone layer depletion (kg CFC-11 eq) 7.15E-06 1.33E-05 1.09E-05 539 Impact category Breeder Pullet Layer Photochemical oxidation (kg C2H4 eq) 0.11 0.16 0.13 Terrestrial ecotoxicity (Kg 1,4-DB eq) 2.85 22.37 11.09 Table A 51. Estimated impacts per functional unit for supply chain steps in the Canadian egg industry (kg/functional unit) Breeders Hatcheries Pullets Functional unit One tonne of eggs 1000 chicks sold to pullet operations 1000 pullets sold to layer operations Abiotic depletion (kg SB eq) 8.62E-03 3.25E-03 3.40E-03 Abiotic depletion (fossil fuels) (MJ) 2311.85 2773.30 2758.39 Acidification (kg SO2 eq) 3.02 6.24 6.1 Eutrophication (kg PO43- eq) 1.05 2.36 2.29 Freshwater aquatic ecotoxicity (kg 1,4-DB eq) 250.12 416.21 267.81 540 Breeders Hatcheries Pullets Functional unit One tonne of eggs 1000 chicks sold to pullet operations 1000 pullets sold to layer operations Global warming potential (kg CO2 eq) 299.15 631.4 488.39 Human toxicity (kg 1,4-DB eq) 379.03 212.39 207.95 Marine aquatic ecotoxicity (kg 1,4-DB eq) 305450.6 214026.18 203779.1 Ozone layer depletion (kg CFC-11 eq) 7.15E-06 1.33E-05 1.09E-05 Photochemical oxidation (kg C2H4 eq) 0.11 0.16 0.13 Terrestrial ecotoxicity (kg 1,4-DB eq) 2.85 22.37 11.09 Table A 52. Comparison of estimated impacts per kilogram of barley produced from previous field crop production models used by Pelletier (2017), and production-weighted national average models derived from 541 the CRSC reports. A negative value for percent decrease indicates that estimated impacts increased relative to the models used by Pelletier (2017). Impact category Pelletier (2017) field crop model Production-weighted national average field crop model from CRSC reports Percent decrease in estimated impacts Abiotic depletion (kg SB eq) 4.23E-06 9.52E-07 77.50 Abiotic depletion (fossil fuels) (MJ) 2.28 1.01 55.89 Acidification (kg SO2 eq) 9.77E-03 1.43E-03 85.38 Eutrophication (kg PO43- eq) 3.15E-03 5.24E-04 83.37 Freshwater aquatic ecotoxicity (kg 1,4-DB eq) 9.41E-02 3.68E-02 60.94 Global warming potential (kg CO2 eq) 5.27E-01 1.15E-01 78.14 Human toxicity (kg 1,4-DB eq) 1.55E-01 6.04E-02 60.92 542 Impact category Pelletier (2017) field crop model Production-weighted national average field crop model from CRSC reports Percent decrease in estimated impacts Marine aquatic ecotoxicity (kg 1,4-DB eq) 2.32E+02 5.72E+01 75.37 Ozone layer depletion (kg CFC-11 eq) 3.05E-08 3.35E-09 89.01 Photochemical oxidation (kg C2H4 eq) 7.36E-05 1.26E-05 82.87 Terrestrial ecotoxicity (kg 1,4-DB eq) 5.08E-03 2.01E-04 96.05 Table A 53. Comparison of estimated impacts per kilogram of canola produced from previous field crop production models used by Pelletier (2017), and production-weighted national average models derived from 543 the CRSC reports. A negative value for percent decrease indicates that estimated impacts increased relative to the models used by Pelletier (2017). Impact category Pelletier (2017) field crop model Production-weighted national average field crop model from CRSC reports Percent decrease in estimated impacts Abiotic depletion (kg SB eq) 6.15E-06 4.24E-06 30.99 Abiotic depletion (fossil fuels) (MJ) 3.56 3.87 -8.79 Acidification (kg SO2 eq) 1.57E-02 6.22E-03 60.36 Eutrophication (kg PO43- eq) 5.61E-03 2.05E-03 63.42 Freshwater aquatic ecotoxicity (kg 1,4-DB eq) 1.53E-01 1.60E-01 -5.15 Global warming potential (kg CO2 eq) 8.34E-01 4.69E-01 43.81 Human toxicity (kg 1,4-DB eq) 2.44E-01 2.71E-01 -11.16 544 Impact category Pelletier (2017) field crop model Production-weighted national average field crop model from CRSC reports Percent decrease in estimated impacts Marine aquatic ecotoxicity (kg 1,4-DB eq) 3.46E+02 2.41E+02 30.25 Ozone layer depletion (kg CFC-11 eq) 4.42E-08 1.17E-08 73.50 Photochemical oxidation (kg C2H4 eq) 1.09E-04 5.32E-05 51.17 Terrestrial ecotoxicity (kg 1,4-DB eq) 8.77E-03 5.79E-04 93.40 Table A 54. Comparison of estimated impacts per kilogram of corn produced from previous field crop production models used by Pelletier (2017), and production-weighted national average models derived from 545 the CRSC reports. A negative value for percent decrease indicates that estimated impacts increased relative to the models used by Pelletier (2017). Impact category Pelletier (2017) field crop model Production-weighted national average field crop model from CRSC reports Percent decrease in estimated impacts Abiotic depletion (kg SB eq) 5.46E-06 7.84E-07 85.64 Abiotic depletion (fossil fuels) (MJ) 2.51 0.77 69.18 Acidification (kg SO2 eq) 5.79E-03 1.28E-03 77.95 Eutrophication (kg PO43- eq) 1.99E-03 5.14E-04 74.18 Freshwater aquatic ecotoxicity (kg 1,4-DB eq) 7.06E-02 2.94E-02 58.42 Global warming potential (kg CO2 eq) 3.99E-01 1.38E-01 65.44 Human toxicity (kg 1,4-DB eq) 1.36E-01 5.77E-02 57.71 546 Impact category Pelletier (2017) field crop model Production-weighted national average field crop model from CRSC reports Percent decrease in estimated impacts Marine aquatic ecotoxicity (kg 1,4-DB eq) 1.63E+02 4.40E+01 72.96 Ozone layer depletion (kg CFC-11 eq) 5.57E-08 2.06E-09 96.30 Photochemical oxidation (kg C2H4 eq) 6.07E-05 1.70E-05 72.05 Terrestrial ecotoxicity (kg 1,4-DB eq) 3.59E-03 1.26E-04 96.49 Table A 55. Comparison of estimated impacts per kilogram of soy produced from previous field crop production models used by Pelletier (2017), and production-weighted national average models derived from 547 the CRSC reports. A negative value for percent decrease indicates that estimated impacts increased relative to the models used by Pelletier (2017). Impact category Pelletier (2017) field crop model Production-weighted national average field crop model from CRSC reports Percent decrease in estimated impacts Abiotic depletion (kg SB eq) 2.13E-06 1.85E-06 12.81 Abiotic depletion (fossil fuels) (MJ) 0.72 0.74 -2.75 Acidification (kg SO2 eq) 4.63E-03 8.65E-04 81.29 Eutrophication (kg PO43- eq) 1.15E-03 6.18E-04 46.31 Freshwater aquatic ecotoxicity (kg 1,4-DB eq) 2.02E-02 1.65E-01 -718.65 Global warming potential (kg CO2 eq) 1.75E-01 1.39E-01 20.48 Human toxicity (kg 1,4-DB eq) 9.66E-02 1.37E-01 -41.50 548 Impact category Pelletier (2017) field crop model Production-weighted national average field crop model from CRSC reports Percent decrease in estimated impacts Marine aquatic ecotoxicity (kg 1,4-DB eq) 7.47E+01 9.47E+01 -26.87 Ozone layer depletion (kg CFC-11 eq) 2.16E-08 8.23E-09 61.87 Photochemical oxidation (kg C2H4 eq) 8.49E-05 6.99E-05 17.65 Terrestrial ecotoxicity (kg 1,4-DB eq) 6.32E-04 4.54E-03 -618.54 Table A 56. Comparison of estimated impacts per kilogram of wheat produced from previous field crop production models used by Pelletier (2017), and production-weighted national average models derived from 549 the CRSC reports. A negative value for percent decrease indicates that estimated impacts increased relative to the models used by Pelletier (2017). Impact category Pelletier (2017) field crop model Production-weighted national average field crop model from CRSC reports Percent decrease in estimated impacts Abiotic depletion (kg SB eq) 3.41E-06 5.00E-06 -46.48 Abiotic depletion (fossil fuels) (MJ) 1.91 2.84 -48.56 Acidification (kg SO2 eq) 9.04E-03 3.98E-03 55.92 Eutrophication (kg PO43- eq) 2.53E-03 1.26E-03 50.24 Freshwater aquatic ecotoxicity (kg 1,4-DB eq) 8.13E-02 1.86E-01 -129.22 Global warming potential (kg CO2 eq) 4.68E-01 2.98E-01 36.37 Human toxicity (kg 1,4-DB eq) 1.32E-01 3.24E-01 -146.14 550 Impact category Pelletier (2017) field crop model Production-weighted national average field crop model from CRSC reports Percent decrease in estimated impacts Marine aquatic ecotoxicity (kg 1,4-DB eq) 1.86E+02 3.11E+02 -67.15 Ozone layer depletion (kg CFC-11 eq) 2.55E-08 7.41E-09 70.87 Photochemical oxidation (kg C2H4 eq) 5.95E-05 6.57E-05 -10.41 Terrestrial ecotoxicity (kg 1,4-DB eq) 4.64E-03 5.64E-04 87.85 551 Table A 57. Percent contributions to abiotic depletion impacts per tonne of eggs produced in different housing systems, estimated using the CML-IA LCIA method Conventional Enriched Single tier non-cage Multi tier non-cage Free range Feed 82.62 82.53 81.83 81.84 84.36 Manure management 0.21 0.04 0.03 0.03 0.04 Pullets 14.98 14.93 14.22 14.15 13.09 Energy 1.41 1.81 2.87 2.92 1.83 Transportation 0.69 0.60 0.96 0.96 0.60 Water 0.09 0.09 0.09 0.10 0.08 Waste treatment 0.00 0.00 0.00 0.00 0.00 552 Table A 58. Percent contributions to abiotic depletion (fossil fuels) impacts per tonne of eggs produced in different housing systems, estimated using the CML-IA LCIA method Conventional Enriched Single tier non-cage Multi tier non-cage Free range Feed 71.32 70.33 66.16 66.06 72.18 Manure management 0.73 0.06 0.12 0.11 0.23 Pullets 18.19 17.90 16.17 16.07 15.75 Energy 5.81 8.30 12.45 12.66 8.45 Transportation 3.86 3.33 5.03 5.01 3.32 Water 0.09 0.08 0.07 0.08 0.07 Waste treatment 0.00 0.00 0.00 0.00 0.00 553 Table A 59. Percent contributions to acidification impacts per tonne of eggs produced in different housing systems, estimated using the CML-IA LCIA method Conventional Enriched Single tier non-cage Multi tier non-cage Free range Feed 16.96 16.93 16.74 16.79 16.53 Manure management 66.81 66.84 67.65 67.62 69.86 Pullets 15.96 15.90 15.10 15.07 13.31 Energy 0.20 0.28 0.41 0.43 0.24 Transportation 0.07 0.06 0.09 0.09 0.05 Water 0.00 0.00 0.00 0.00 0.00 Waste treatment 0.00 0.00 0.00 0.00 0.00 554 Table A 60. Percent contributions to eutrophication impacts per tonne of eggs produced in different housing systems, estimated using the CML-IA LCIA method Conventional Enriched Single tier non-cage Multi tier non-cage Free range Feed 20.58 20.86 20.29 20.34 20.12 Manure management 62.68 62.09 63.35 63.32 65.69 Pullets 16.13 16.31 15.24 15.20 13.49 Energy 0.55 0.69 1.04 1.06 0.66 Transportation 0.05 0.05 0.07 0.07 0.04 Water 0.01 0.01 0.00 0.01 0.00 Waste treatment 0.00 0.00 0.00 0.00 0.00 555 Table A 61. Percent contributions to freshwater aquatic ecotoxicity impacts per tonne of eggs produced in different housing systems, estimated using the CML-IA LCIA method Conventional Enriched Single tier non-cage Multi tier non-cage Free range Feed 73.72 73.27 71.54 71.52 75.54 Manure management 0.23 0.05 0.03 0.03 0.04 Pullets 21.18 21.00 19.70 19.60 18.57 Energy 4.10 5.00 7.72 7.83 5.19 Transportation 0.68 0.59 0.93 0.93 0.59 Water 0.08 0.08 0.08 0.08 0.07 Waste treatment 0.00 0.00 0.00 0.00 0.00 556 Table A 62. Percent contributions to GHG emissions per tonne of eggs produced in different housing systems, estimated using the CML-IA LCIA method Conventional Enriched Single tier non-cage Multi tier non-cage Free range Feed 61.47 63.13 61.28 61.29 64.00 Manure management 16.21 13.13 13.30 13.27 14.40 Pullets 17.96 18.41 17.17 17.08 16.00 Energy 2.75 3.94 6.11 6.22 3.96 Transportation 1.49 1.34 2.09 2.09 1.32 Water 0.04 0.05 0.04 0.05 0.04 Waste treatment 0.08 0.01 0.00 0.00 0.29 557 Table A 63. Percent contributions to human toxicity impacts per tonne of eggs produced in different housing systems, estimated using the CML-IA LCIA method Conventional Enriched Single tier non-cage Multi tier non-cage Free range Feed 74.18 73.69 70.62 70.58 75.29 Manure management 2.17 1.76 1.73 1.72 1.95 Pullets 16.80 16.65 15.33 15.25 14.59 Energy 4.90 6.18 9.78 9.90 6.49 Transportation 1.81 1.58 2.42 2.42 1.56 Water 0.14 0.13 0.12 0.13 0.11 Waste treatment 0.00 0.00 0.00 0.00 0.00 558 Table A 64. Percent contributions to marine aquatic ecotoxicity impacts per tonne of eggs produced in different housing systems, estimated using the CML-IA LCIA method Conventional Enriched Single tier non-cage Multi tier non-cage Free range Feed 68.54 67.48 62.83 62.71 69.21 Manure management 0.51 0.10 0.07 0.07 0.09 Pullets 18.61 18.28 16.35 16.24 16.08 Energy 10.54 12.56 18.51 18.74 13.09 Transportation 1.62 1.40 2.09 2.08 1.39 Water 0.18 0.18 0.15 0.17 0.15 Waste treatment 0.00 0.00 0.00 0.00 0.00 559 Table A 65. Percent contributions to ozone layer depletion impacts per tonne of eggs produced in different housing systems, estimated using the CML-IA LCIA method Conventional Enriched Single tier non-cage Multi tier non-cage Free range Feed 80.72 80.60 79.82 79.83 82.71 Manure management 0.29 0.02 0.05 0.05 0.09 Pullets 16.99 16.93 16.10 16.03 14.89 Energy 0.75 1.34 2.33 2.38 1.21 Transportation 1.16 1.02 1.61 1.61 1.01 Water 0.10 0.10 0.09 0.10 0.08 Waste treatment 0.00 0.00 0.00 0.00 0.00 560 Table A 66. Percent contributions to photochemical oxidation impacts per tonne of eggs produced in different housing systems, estimated using the CML-IA LCIA method Conventional Enriched Single tier non-cage Multi tier non-cage Free range Feed 74.53 75.64 74.01 73.99 77.68 Manure management 5.33 2.87 2.48 2.47 2.57 Pullets 16.81 17.02 16.00 15.92 14.99 Energy 2.21 3.52 6.08 6.19 3.56 Transportation 0.98 0.87 1.36 1.36 0.87 Water 0.07 0.07 0.07 0.07 0.06 Waste treatment 0.07 0.01 0.00 0.00 0.27 561 Table A 67. Percent contributions to terrestrial ecotoxicity impacts per tonne of eggs produced in different housing systems, estimated using the CML-IA LCIA method Conventional Enriched Single tier non-cage Multi tier non-cage Free range Feed 75.53 75.56 76.14 76.22 78.25 Manure management 0.03 0.00 0.00 0.00 0.01 Pullets 24.12 24.08 23.31 23.22 21.38 Energy 0.18 0.23 0.36 0.37 0.24 Transportation 0.10 0.09 0.14 0.14 0.09 Water 0.04 0.04 0.04 0.05 0.04 Waste treatment 0.00 0.00 0.00 0.00 0.00 562 Table A 68. Life cycle environmental impacts per tonne of eggs produced in each non-organic housing system in the Canadian egg industry, and a Canadian production weighted average, estimated using the TRACI 2.1 LCIA method. Superscript letters denote significant differences across housing systems within impact categories. Estimated impacts are presented ± standard error of the mean. Impact Unit Conventional Enriched Single tier non-cage Multi tier non-cage/Aviary Free range Canadian average Acidification Kg SO2 eq 78.65 ± 2.56a 80.14 ±2.62a 85.36 ± 2.80b 85.03 ± 2.77b 101.35 ± 3.32c 80.21± 2.63 Eutrophication Kg N eq 18.43 ± 0.62a 18.07 ± 0.61a 20.13 ± 0.68b 20.07 ± 0.67b 23.58 ± 0.79c 18.57 ±0.63 Fossil fuel depletion MJ Surplus 1117.71 ± 37.59a 1152.03 ± 37.81a 1281.07 ±42.29b 1281.12 ± 41.77b 1386.04 ± 45.40c 1151.74 ±38.23 563 Impact Unit Conventional Enriched Single tier non-cage Multi tier non-cage/Aviary Free range Canadian average Global warming Kg CO2 eq 1456.24 ± 48.30a 1444.16 ± 47.27a 1570.11 ± 81.58b 1568.13 ± 51.17c 1763.60 ± 86.51d 1469.56 ± 48.95 Ozone depletion Kg CFC-11 eq 3.03E-0505 ± 9.82 x 10-7 a 3.09E-0505 ± 9.95 x 10-7 a 3.29E-0505 ± 1.07 x 10-6 b 3.29E-0505 ± 1.21 x 10-6 b 3.72E-05 ± 1.21 x 10-6 c 3.09E-05 ± 9.95 x 10-7 564 Table A 69. Life cycle environmental impacts per tonne of eggs produced in each non-organic housing system in the Canadian egg industry using a vegetarian feed formulation, estimated using the CML IA baseline LCIA method. Estimated impacts are presented ± standard error of the mean. All estimated impacts are significantly lower than baseline estimated impacts presented in Table 2-9. Impact Unit Conventional Enriched Single tier non-cage Multi tier non-cage/Aviary Free range Abiotic depletion Kg Sb eq 5.88E-03 ± 2.05 x 10-5 6.00E-03 ± 1.99 x 10-5 6.41E-03 ± 2.09 x 10-5 6.40E-03 ± 1.98 x 10-5 7.19E-03 ± 4.93 x 10-5 Abiotic depletion (fossil fuels) MJ 5334.873 ± 45.69 5538.642 ± 27.03 6384.73 ± 26.24 6391.21 ± 19.91 6602.712 ± 36.15 Acidification Kg SO2 eq 57.39 ± 0.36 58.49 ± 0.36 62.42 ± 0.39 62.15± 0.38 74.23 ± 0.34 Eutrophication Kg PO43- eq 18.21 ± 0.11 18.25 ± 0.12 19.83 ± 0.12 19.76 ± 0.11 23.47 ±0.13 565 Impact Unit Conventional Enriched Single tier non-cage Multi tier non-cage/Aviary Free range Freshwater Aquatic ecotoxicity Kg 1,4-DB eq 368.11 ± 4.91 378.54 ± 5.51 416.36 ± 4.76 416.09 ± 5.62 442.00 ± 13.36 Global warming Kg CO2 eq 935.00 ± 5.88 911.65 ± 4.40 1006.61 ± 10.35 1005.29 ± 3.98 1101.23 ± 12.49 Human toxicity Kg 1,4-DB eq 342.17 ± 15.91 351.44 ± 11.92 394.63 ± 16.91 394.50 ± 16.28 420.08 ± 18.30 Marine aquatic ecotoxicity Kg 1,4-DB eq 373784.52 ± 10460.28 388821.83 ± 8178.50 453971.79 ± 9101.76 454726.21 ± 10299.58 462688.8 ± 23598.27 Ozone layer depletion Kg CFC-11 eq 1.13E-05 ± 3.85 E-08 1.15E-05 ± 3.85 E-08 1.24E-05 ± 4.21 E-08 1.23E-05 ± 3.96E-08 1.34E-05 ± 7.60E-08 566 Impact Unit Conventional Enriched Single tier non-cage Multi tier non-cage/Aviary Free range Photochemical oxidation Kg C2H4 eq 0.26 ± 5.44 E-03 0.26 ± 3.62 E-03 0.28 ± 2.28 E-03 0.28 ± 2.89 E-03 0.31 ± 2.88 E-03 Terrestrial ecotoxicity Kg 1,4-DB eq 14.51 ± 1.01 14.75 ± 0.98 15.32 ± 1.38 15.27 ± 1.30 17.04 ± 0.80 567 Table A 70. Synthetic amino acid mix used for modeling conventional feed formulations in Canada for pullets and layers. Synthetic amino acid Percentage of total synthetic amino acid inputs Modeled with provider From Modifications Methionine 59.07 MetAMINO®, 99% DL-Methionine, at Evonik plant {BE} Energy, U Agrifootprint 6.3 System process – none possible Lysine 20.73 USDA LCA commons - Canadian average electricity grid mix used - market group for electricity, medium voltage | electricity, medium voltage | Cutoff, U - Water provider changed to be representative of Quebec, market for tap water | tap water | Cutoff, U – Ca-Qc - Input provider for wheat grain changed to Canadian average with different SOC scenarios - Provider for ammonia changed to a market made based on ammonia 568 Synthetic amino acid Percentage of total synthetic amino acid inputs Modeled with provider From Modifications production for fertilizers as described previously based on ChemInfo Services report - Flow Natural gas produced and combusted, at industrial furnace changed to natural gas, burned in gas turbine with provider natural gas, burned in gas turbine | natural gas, burned in gas turbine | Cutoff, U – CA - Flow Treatment, sewage, to wastewater treatment, class 4 changed to wastewater, average with provider treatment of wastewater, average, wastewater treatment | wastewater, average | Cutoff, U – CA, QC - Flow Maize starch, at plant changed to maize starch, with provider maize starch production | maize starch | 569 Synthetic amino acid Percentage of total synthetic amino acid inputs Modeled with provider From Modifications Cutoff, U – ROW. This provider has been Canadianized by swapping out energy and water inputs for Canadian market mixes, and maize grain from Canadian CRSC processes Threonine 3.11 ThreAMINO®, 98.5% L-Threonine, at Evonik plant {HU} Energy, U Agrifootprint 6.3 System process – none possible Valine 17.10 ValAMINO®, 98.0% L-Valine, at Evonik plant {SK} Energy, U Agrifootprint 6.3 System process – none possible 570 A.17 Supplementary figures Figure A 1. Percent contributions to total abiotic depletion impacts per tonne of eggs produced in each housing system, estimated using the CML IA Baseline LCIA method. Figure A 2. Percent contributions to total abiotic depletion (fossil fuels) impacts per tonne of eggs produced in each housing system, estimated using the CML IA Baseline LCIA method. 0.0010.0020.0030.0040.0050.0060.0070.0080.0090.00100.00Percent contribution to impactWaste treatmentWaterTransportationEnergyPulletsManure managementFeed0102030405060708090100Percent contribution to impactWaste treatmentWaterTransportationEnergyPulletsManure managementFeed 571 Figure A 3. Percent contributions to total freshwater aquatic ecotoxicity impacts per tonne of eggs produced in each housing system, estimated using the CML IA Baseline LCIA method. Figure A 4. Percent contributions to total human toxicity impacts per tonne of eggs produced in each housing system, estimated using the CML IA Baseline LCIA method. 0.0020.0040.0060.0080.00100.00120.00Percent contribution to impactWaste treatmentWaterTransportationEnergyPulletsManure managementFeed020406080100120Percent contribution to impactWaste treatmentWaterTransportationEnergyPulletsManure managementFeed 572 Figure A 5. Percent contributions to total marine aquatic ecotoxicity impacts per tonne of eggs produced in each housing system, estimated using the CML IA Baseline LCIA method. Figure A 6. Percent contributions to total ozone layer depletion impacts per tonne of eggs produced in each housing system, estimated using the CML IA Baseline LCIA method. 020406080100120Percent contribution to impactWaste treatmentWaterTransportationEnergyPulletsManure managementFeed020406080100120Percent contribution to impactWaste treatmentWaterTransportationEnergyPulletsManure managementFeed 573 Figure A 7. Percent contributions to total photochemical oxidation impacts per tonne of eggs produced in each housing system, estimated using the CML IA Baseline LCIA method. Figure A 8. Percent contributions to total terrestrial ecotoxicity impacts per tonne of eggs produced in each housing system, estimated using the CML IA Baseline LCIA method. 020406080100120Percent contribution to impactWaste treatmentWaterTransportationEnergyPulletsManure managementFeed020406080100120Percent contribution to impactWaste treatmentWaterTransportationEnergyPulletsManure managementFeed 574 Figure A 9. Relative percentage of impacts per tonne of eggs produced in enriched cage production systems using a conventional and vegetarian diet, estimated using the CML IA baseline LCIA method. Figure A 10. Relative percentage of impacts per tonne of eggs produced in single tier non-cage production systems using a conventional and vegetarian diet, estimated using the CML IA baseline LCIA method. 0102030405060708090100Conventional dietVegetarian feed0102030405060708090100Conventional dietVegetarian feed 575 Figure A 11. Relative percentage of impacts per tonne of eggs produced in multi tier non-cage production systems using a conventional and vegetarian diet, estimated using the CML IA baseline LCIA method. Figure A 12. Relative percentage of impacts per tonne of eggs produced in free range production systems using a conventional and vegetarian diet, estimated using the CML IA baseline LCIA method. 0102030405060708090100Conventional dietVegetarian feed0102030405060708090100Conventional dietVegetarian feed 576 Figure A 13. Percent contributions to total abiotic depletion impacts per tonne of eggs produced in each housing system when using a vegetarian feed formulation, estimated using the CML IA Baseline LCIA method. Figure A 14. Percent contributions to total abiotic depletion (fossil fuels) impacts per tonne of eggs produced in each housing system when using a vegetarian feed formulation, estimated using the CML IA Baseline LCIA method. 0.0010.0020.0030.0040.0050.0060.0070.0080.0090.00100.00Percent contribution to impactWaste treatmentWaterTransportationEnergyPulletsManure managementFeed0102030405060708090100Percent contribution to impactWaste treatmentWaterTransportationEnergyPulletsManure managementFeed 577 Figure A 15. Percent contributions to total freshwater aquatic ecotoxicity impacts per tonne of eggs produced in each housing system when using a vegetarian feed formulation, estimated using the CML IA Baseline LCIA method. Figure A 16. Percent contributions to total human toxicity impacts per tonne of eggs produced in each housing system when using a vegetarian feed formulation, estimated using the CML IA Baseline LCIA method. 0.0010.0020.0030.0040.0050.0060.0070.0080.0090.00100.00Percent contribution to impactWaste treatmentWaterTransportationEnergyPulletsManure managementFeed0102030405060708090100Percent contribution to impactWaste treatmentWaterTransportationEnergyPulletsManure managementFeed 578 Figure A 17. Percent contributions to total marine aquatic ecotoxicity impacts per tonne of eggs produced in each housing system when using a vegetarian feed formulation, estimated using the CML IA Baseline LCIA method. Figure A 18. Percent contributions to total ozone depletion impacts per tonne of eggs produced in each housing system when using a vegetarian feed formulation, estimated using the CML IA Baseline LCIA method. 0102030405060708090100Percent contribution to impactWaste treatmentWaterTransportationEnergyPulletsManure managementFeed0102030405060708090100Percent contribution to impactWaste treatmentWaterTransportationEnergyPulletsManure managementFeed 579 Figure A 19. Percent contributions to total photochemical oxidation impacts per tonne of eggs produced in each housing system when using a vegetarian feed formulation, estimated using the CML IA Baseline LCIA method. Figure A 20. Percent contributions to total terrestrial ecotoxicity impacts per tonne of eggs produced in each housing system when using a vegetarian feed formulation, estimated using the CML IA Baseline LCIA method. 0102030405060708090100Percent contribution to impactWaste treatmentWaterTransportationEnergyPulletsManure managementFeed0102030405060708090100Percent contribution to impactWaste treatmentWaterTransportationEnergyPulletsManure managementFeed 580 Appendix B B.1 Supplementary tables Table B 1. Coefficients and r2 values for the regression analyses performed between total dozens produced and number of hens housed, per housing system in order to inform the choice of PPS for DEA. Coefficient r2 value Conventional 26.79 0.96 Enriched 26.85 0.99 Single-tier non-cage 26.47 0.99 Multi-tier non-cage 25.83 0.99 Free range 26.26 0.99 Table B 2. Coefficients and R2 values for the regression analyses performed between dozens produced per hen and number of hens housed, per housing system in order to inform the choice of PPS for DEA. Coefficient r2 value Conventional 2.00 x10-6 <0.01 Enriched -7.00 x10-6 <0.01 Single-tier non-cage 1.00 x10-5 <0.01 Multi-tier non-cage -5.00 x10-5 0.27 Free range -2.00 x10-4 0.23 581 Table B 3. Estimated impacts per tonne of eggs produced in each non-organic housing system, assuming all farms are operating at maximum observed levels of pullet- and feed-use efficiency within housing systems, estimated using the CML IA Baseline LCIA method Impact category Conventional Enriched Single-tier non-cage Multi-tier non-cage Free range Abiotic depletion 7.16 x 10-3 7.20 x 10-3 7.60 x 10-3 7.89 x 10-3 8.29 x 10-3 Abiotic depletion (fossil fuels) 6827.83 6981.76 7835.61 8098.82 8073.77 Acidification 61.09 61.31 64.49 67.55 73.09 Eutrophication 18.89 18.67 19.98 20.92 22.57 Freshwater aquatic ecotoxicity 629.58 637.22 681.62 706.89 731.31 Global warming potential 1378.22 1347.13 1449.77 1503.42 1569.63 Human toxicity 486.43 492.24 538.12 557.11 569.65 Marine aquatic ecotoxicity 518149.35 530111.79 597507.52 617447.87 614210.99 582 Impact category Conventional Enriched Single-tier non-cage Multi-tier non-cage Free range Ozone layer depletion 2.33 x 10-5 2.34 x 10-5 2.47 x 10-5 2.56 x 10-5 2.69 x 10-5 Photochemical oxidation 0.31 0.31 0.33 0.34 0.36 Terrestrial ecotoxicity 25.36 25.49 26.30 27.35 29.05 Table B 4. Estimated impacts per tonne of eggs produced in Canada under different housing system transition scenarios, assuming all farms are operating at the maximum levels of feed- and pullet-use efficiency currently realized within housing systems, compared to current Canadian average impacts estimated using the CML IA Baseline LCIA method. Impact category Canadian average a Scenario 1 b Scenario 2 c Scenario 3 d Scenario 4 e Scenario 5 f Abiotic depletion 7.74 x 10-3 ± 1.34 x 10-5 7.54E-03 7.49E-03 7.74E-03 7.62E-03 6.81E-03 Abiotic depletion (fossil fuels) 8183.68 ± 24.17 7540.29 7562.34 7747.49 7593.63 6786.96 Acidification 67.85 ± 0.26 64.43 63.81 66.61 65.30 58.24 583 Impact category Canadian average a Scenario 1 b Scenario 2 c Scenario 3 d Scenario 4 e Scenario 5 f Eutrophication 20.85 ± 0.07 19.79 19.66 20.53 20.07 17.90 Freshwater aquatic ecotoxicity 687.19 ± 7.73 672.06 668.49 689.26 677.98 605.72 Global warming potential 1481.51 ± 3.15 1425.28 1419.11 1467.49 1439.71 1286.45 Human toxicity 531.76 ± 11.94 524.68 523.87 539.28 529.18 472.91 Marine aquatic ecotoxicity 568538.89 ± 9324.89 573780 575872 589820 577823 516457 Ozone layer depletion 2.54E-05 ± 4.29 x 10-8 2.45E-05 2.44E-05 2.52E-05 2.48E-05 2.21E-05 Photochemical oxidation 0.34 ± 3.46 x 10-3 0.33 0.32 0.34 0.33 0.29 Terrestrial ecotoxicity 27.58 ± 0.99 26.42 26.11 27.05 26.68 23.83 a 53.90% conventional; 33.26% enriched; 7.23% single tier non-cage; 10.70% multi tier non-cage; 14.32% free range 584 b 0% conventional; 50% enriched; 0% single tier non-cage; 50% multi tier non-cage; 0% free range c 0% conventional; 33% enriched; 33% single tier non-cage; 33% multi tier non-cage; 0% free range d 0% conventional; 25% enriched; 25% single tier non-cage; 25% multi tier non-cage; 25% free range e 0% conventional; 45% enriched; 0% single tier non-cage; 45% multi tier non-cage; 10% free range f 0% conventional; 40% enriched; 20% single tier non-cage; 20% multi tier non-cage; 10% free range B.2 Supplementary figures 585 Figure B 1. Estimated differences in relative contributions to abiotic depletion impacts per tonne of eggs produced in each housing system under baseline and DEA-efficient conditions estimated using the CML IA Baseline LCIA method 0102030405060708090100Baseline Efficient Baseline Efficient Baseline Efficient Baseline Efficient Baseline EfficientConventional Enriched Single tier freerunMulti-tier freerunFree rangePercent contribution to impactWaste treatmentWaterTransportationEnergyPulletsManure managementFeed 586 Figure B 2. Estimated differences in relative contributions to abiotic depletion (fossil fuels) impacts per tonne of eggs produced in each housing system under baseline and DEA-efficient conditions estimated using the CML IA Baseline LCIA method 0102030405060708090100Baseline Efficient Baseline Efficient Baseline Efficient Baseline Efficient Baseline EfficientConventional Enriched Single tier free run Multi-tier free run Free rangePercent contribution to impactWaste treatmentWaterTransportationEnergyPulletsManure managementFeed 587 Figure B 3. Estimated differences in relative contributions to freshwater aquatic ecotoxicity impacts per tonne of eggs produced in each housing system under baseline and DEA-efficient conditions estimated using the CML IA Baseline LCIA method 0102030405060708090100Baseline Efficient Baseline Efficient Baseline Efficient Baseline Efficient Baseline EfficientConventional Enriched Single tier free run Multi-tier free run Free rangePercent contribution to impactWaste treatmentWaterTransportationEnergyPulletsManure managementFeed 588 Figure B 4. Estimated differences in relative contributions to human toxicity impacts per tonne of eggs produced in each housing system under baseline and DEA-efficient conditions estimated using the CML IA Baseline LCIA method 0102030405060708090100Baseline Efficient Baseline Efficient Baseline Efficient Baseline Efficient Baseline EfficientConventional Enriched Single tier freerunMulti-tier freerunFree rangePercent contribution to impactWaste treatmentWaterTransportationEnergyPulletsManure managementFeed 589 Figure B 5. Estimated differences in relative contributions to marine aquatic ecotoxicity impacts per tonne of eggs produced in each housing system under baseline and DEA-efficient conditions estimated using the CML IA Baseline LCIA method 0102030405060708090100Baseline Efficient Baseline Efficient Baseline Efficient Baseline Efficient Baseline EfficientConventional Enriched Single tier freerunMulti-tier freerunFree rangePercent contribution to impactWaste treatmentWaterTransportationEnergyPulletsManure managementFeed 590 Figure B 6. Estimated differences in relative contributions to ozone depletion impacts per tonne of eggs produced in each housing system under baseline and DEA-efficient conditions estimated using the CML IA Baseline LCIA method 0102030405060708090100Baseline Efficient Baseline Efficient Baseline Efficient Baseline Efficient Baseline EfficientConventional Enriched Single tier freerunMulti-tier freerunFree rangePercent contribution to impactWaste treatmentWaterTransportationEnergyPulletsManure managementFeed 591 Figure B 7. Estimated differences in relative contributions to photochemical oxidation impacts per tonne of eggs produced in each housing system under baseline and DEA-efficient conditions estimated using the CML IA Baseline LCIA method 0102030405060708090100Baseline Efficient Baseline Efficient Baseline Efficient Baseline Efficient Baseline EfficientConventional Enriched Single tier freerunMulti-tier freerunFree rangePercent contribution to impactWaste treatmentWaterTransportationEnergyPulletsManure managementFeed 592 Figure B 8. Estimated differences in relative contributions to terrestrial ecotoxicity impacts per tonne of eggs produced in each housing system under baseline and DEA-efficient conditions estimated using the CML IA Baseline LCIA method 0102030405060708090100Baseline Efficient Baseline Efficient Baseline Efficient Baseline Efficient Baseline EfficientConventional Enriched Single tier freerunMulti-tier freerunFree rangePercent contribution to impactWaste treatmentWaterTransportationEnergyPulletsManure managementFeed 593 Appendix C C.1 Components of a type 1 LCIA method Development of a type 1 LCIA method requires definition of a number of key components. These include a relevant AoP, stakeholder categories, impact categories and subcategories, inventory indicators, and characterization factors. AoPs are categorical umbrellas representing endpoints of recognisable value to society (Guinée et al., 2002) under which impacts are grouped. Common areas of protection include human health, natural resources, the natural environment, and the man-made environment (Guinée et al., 2002), although new areas of protection are sometimes defined for environmental impacts (see for example Cao et al., 2015) and, in particular, for assessment of social impacts (see for example Dreyer et al., 2006; Neugebauer et al., 2014; Reitinger et al., 2011). According to ISO 14040, if LCIA results are to be reported to a third party, the category endpoints (i.e. the areas of protection) must be reported (ISO, 2006b). Within the area of protection, type 1 LCIA methods use stakeholder categories and impact categories as organizational groupings of the impacts being studied. Stakeholder categories are groupings of different stakeholders expected to share similar relationships to the system being studied, have similar interests in it, and be similarly impacted. The UNEP Guidelines (2020) identify five stakeholder categories: workers, local community, society, consumers, and other value chain actors. Within stakeholder categories, impact categories represent groupings of different impacts according to a single overarching theme, such as human rights, working conditions, health and safety, etc. in social LCA, or global warming potential, 594 acidification potential, eutrophication potential, etc. in environmental LCA (Guinée et al., 2002; UNEP, 2020) After grouping at the stakeholder and impact category level, type 1 LCIA methods further group impacts according to subcategories, which represent impacts within an impact or stakeholder category. While subcategory impact results may be aggregated to a single impact or stakeholder category score, aggregation of subcategory results on a scientifically valid basis is often difficult, leading studies to present a suite of subcategory results which together represent the bulk of the situation assessed (Pelletier, 2018a; UNEP, 2020). Subcategories are subsequently divided into inventory indicators, each with their own contributing inventory data. Calculation of subcategory results therefore requires aggregation of contributing data to inventory indicators, and subsequent aggregation of inventory indicators to subcategories through the process of characterization (UNEP, 2020). The bases for characterization and aggregation are largely dependent on the type of inventory data used (i.e. quantitative, semi-quantitative, or qualitative) and the goal of the study, and may be representative of things such as relative levels of risk (Benoit-Norris and Norris, 2015), or other relationships (Ciroth and Franze, 2011). C.2 Literature reviewed 2021 Bai Y, Wang R, Yang Y, et al (2021) Folic Acid Absorption Characteristics and Effect on Cecal Microbiota of Laying Hens. Front Vet Sci 8:1–15. https://doi.org/10.3389/fvets.2021.720851 Bartels T, Berk J, Cramer K, et al (2021) Research Note: A sip of stress. Effects of corticosterone supplementation in drinking water on feather corticosterone concentrations in layer pullets. Poult Sci 100:101361. https://doi.org/10.1016/j.psj.2021.101361 Borda-Molina D, Iffland H, Schmid M, et al (2021) Gut microbial composition and predicted functions are not associated with feather pecking and antagonistic behavior in laying hens. 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Summary of characterization factors associated with each inventory indicator in the proposed animal welfare LCIA method developed in Chapter 4 Impact subcategory Inventory indicator Characterization factor Mortality Mortality rate 1 Morbidity/injury Health issues requiring veterinary care “No”: 0 “Yes”: 1 Feather condition at end of lay cycle “Poor”: 0 “Average”: 0.5 “Good”: 1 Foot condition at end of lay cycle “Poor”: 0 “Average”: 0.5 “Good”: 1 Behavioural needs fulfilled Space allowance 1 Additional behavioural priorities fulfilled Perches provided “No”: 0 “Yes”: 8.625 Nests provided “No”: 0 “Yes”: 18 Foraging supported “No”: 0 “Yes”: 14.66 Dustbathing supported “No”: 0 “Yes”: 13.9 Injurious behaviours Injurious behaviour presence 1 Positive affective state Perches provided “No”: 0 “Yes”: 1 Nests provided “No”: 0 626 Impact subcategory Inventory indicator Characterization factor “Yes”: 1 Foraging supported “No”: 0 “Yes”: 1 Dustbathing supported “No”: 0 “Yes”: 1 Negative affective state Presence of excessive nervousness “No”: 0 “Yes”: -1 Presence of death or injury due to feather pecking “No”: 0 “Yes”: -1 Presence of death or injury due to vent pecking “No”: 0 “Yes”: -1 Presence of death or injury due to other bird to bird aggression “No”: 0 “Yes”: -1 Presence of health issues requiring veterinary care “No”: 0 “Yes”: -1 C.4 Supplementary equations Equation S1. Calculation of Mortality impacts for enriched housing systems 𝐼𝑚𝑝𝑎𝑐𝑡 = 𝑃(𝑚𝑜𝑟𝑡𝑎𝑙𝑖𝑡𝑦) × ℎ𝑒𝑛𝑠 𝑡𝑜 𝑝𝑟𝑜𝑑𝑢𝑐𝑒 𝑜𝑛𝑒 𝑡𝑜𝑛𝑛𝑒 𝑜𝑓 𝑒𝑔𝑔𝑠 𝐼𝑚𝑝𝑎𝑐𝑡 = 0.0235 𝑥 51.37 Equation S2. Calculation of Morbidity/injury impacts for enriched housing systems 𝐼𝑚𝑝𝑎𝑐𝑡 = [[𝑃(𝑑𝑖𝑠𝑒𝑎𝑠𝑒) + 𝑃(𝑝𝑜𝑜𝑟 𝑓𝑒𝑎𝑡ℎ𝑒𝑟 𝑐𝑜𝑛𝑑𝑖𝑡𝑖𝑜𝑛) + 𝑃(𝑝𝑜𝑜𝑟 𝑓𝑜𝑜𝑡 𝑐𝑜𝑛𝑑𝑖𝑡𝑖𝑜𝑛)]+ 0.5[𝑃(𝑎𝑣𝑒𝑟𝑎𝑔𝑒 𝑓𝑒𝑎𝑡ℎ𝑒𝑟 𝑐𝑜𝑛𝑑𝑖𝑡𝑖𝑜𝑛)+ 𝑃(𝑎𝑣𝑒𝑟𝑎𝑔𝑒 𝑓𝑜𝑜𝑡 𝑐𝑜𝑛𝑑𝑖𝑡𝑖𝑜𝑛)]] × ℎ𝑒𝑛𝑠 𝑡𝑜 𝑝𝑟𝑜𝑑𝑢𝑐𝑒 𝑜𝑛𝑒 𝑡𝑜𝑛𝑛𝑒 𝑜𝑓 𝑒𝑔𝑔𝑠 𝐼𝑚𝑝𝑎𝑐𝑡 = [[0.0.25 + 0.085 + 0] + 0.5[0.31 + 0.07]] × 51.37 627 Equation S3. Calculation of basal behavioural needs impacts for enriched housing systems 𝐼𝑚𝑝𝑎𝑐𝑡 = (116.99 − 294.5) × ℎ𝑒𝑛𝑠 𝑡𝑜 𝑝𝑟𝑜𝑑𝑢𝑐𝑒 𝑜𝑛𝑒 𝑡𝑜𝑛𝑛𝑒 𝑜𝑓 𝑒𝑔𝑔𝑠 𝐼𝑚𝑝𝑎𝑐𝑡 = (116.99 − 294.5) × 51.37 Equation S4. Calculation of additional behavioural needs impacts for enriched housing systems 𝐼𝑚𝑎𝑝𝑐𝑡 = [(𝑃(𝑝𝑒𝑟𝑐ℎ𝑖𝑛𝑔) × 𝑝𝑒𝑟𝑐ℎ𝑖𝑛𝑔 𝑚𝑜𝑡𝑖𝑣𝑎𝑡𝑖𝑜𝑛) + (𝑃(𝑛𝑒𝑠𝑡𝑖𝑛𝑔) × 𝑛𝑒𝑠𝑡𝑖𝑛𝑔 𝑚𝑜𝑡𝑖𝑣𝑎𝑡𝑖𝑜𝑛)+ (𝑃(𝑑𝑢𝑠𝑡𝑏𝑎𝑡ℎ𝑖𝑛𝑔) × 𝑑𝑢𝑠𝑡𝑏𝑎𝑡ℎ𝑖𝑛𝑔 𝑚𝑜𝑡𝑖𝑣𝑎𝑡𝑖𝑜𝑛)+ (𝑃(𝑓𝑜𝑟𝑎𝑔𝑖𝑛𝑔) × 𝑓𝑜𝑟𝑎𝑔𝑖𝑛𝑔 𝑚𝑜𝑡𝑖𝑣𝑎𝑡𝑖𝑜𝑛)] × ℎ𝑒𝑛𝑠 𝑡𝑜 𝑝𝑟𝑜𝑑𝑢𝑐𝑒 𝑜𝑛𝑒 𝑡𝑜𝑛𝑛𝑒 𝑜𝑓 𝑒𝑔𝑔𝑠 𝐼𝑚𝑝𝑎𝑐𝑡 = [(0.98 × 8.625) + (1 × 18) + (0.88 × 14.66) + (0.19 × 13.9)] × 51.37 Equation S5. Calculation of injurious behaviour impacts for enriched housing systems 𝐼𝑚𝑝𝑎𝑐𝑡 = 𝑃(𝑖𝑛𝑗𝑢𝑟𝑖𝑜𝑢𝑠 𝑏𝑒ℎ𝑎𝑣𝑖𝑜𝑢𝑟𝑠) × ℎ𝑒𝑛𝑠 𝑡𝑜 𝑝𝑟𝑜𝑑𝑢𝑐𝑒 𝑜𝑛𝑒 𝑡𝑜𝑛𝑛𝑒 𝑜𝑓 𝑒𝑔𝑔𝑠 𝐼𝑚𝑝𝑎𝑐𝑡 = 0.15 ∗ 51.37 Equation S6. Calculation of positive affective state impacts for enriched housing systems 𝐼𝑚𝑝𝑎𝑐𝑡 = [(𝑃(𝑝𝑒𝑟𝑐ℎ𝑖𝑛𝑔)) + (𝑃(𝑛𝑒𝑠𝑡𝑖𝑛𝑔)) + (𝑃(𝑑𝑢𝑠𝑡𝑏𝑎𝑡ℎ𝑖𝑛𝑔)) + (𝑃(𝑓𝑜𝑟𝑎𝑔𝑖𝑛𝑔))]× ℎ𝑒𝑛𝑠 𝑡𝑜 𝑝𝑟𝑜𝑑𝑢𝑐𝑒 𝑜𝑛𝑒 𝑡𝑜𝑛𝑛𝑒 𝑜𝑓 𝑒𝑔𝑔𝑠 𝐼𝑚𝑝𝑎𝑐𝑡 = (0.98 + 1 + 0.88 + 0.19) × 51.37 Equation S7. Calculation of negative affective state impacts for enriched housing systems 𝐼𝑚𝑝𝑎𝑐𝑡 = [𝑃(𝑒𝑥𝑐𝑒𝑠𝑠𝑖𝑣𝑒 𝑛𝑒𝑟𝑣𝑜𝑢𝑠𝑛𝑒𝑠𝑠) + 𝑃(𝑑𝑒𝑎𝑡ℎ 𝑜𝑟 𝑖𝑛𝑗𝑢𝑟𝑦 𝑑𝑢𝑒 𝑡𝑜 𝑓𝑒𝑎𝑡ℎ𝑒𝑟 𝑝𝑒𝑐𝑘𝑖𝑛𝑔)+ 𝑃(𝑑𝑒𝑎𝑡ℎ 𝑜𝑟 𝑖𝑛𝑗𝑢𝑟𝑦 𝑑𝑢𝑒 𝑡𝑜 𝑣𝑒𝑛𝑡 𝑝𝑒𝑐𝑘𝑖𝑛𝑔)+ 𝑃(𝑑𝑒𝑎𝑡ℎ 𝑜𝑟 𝑖𝑛𝑗𝑢𝑟𝑦 𝑑𝑢𝑒 𝑡𝑜 𝑜𝑡ℎ𝑒𝑟 𝑎𝑔𝑔𝑟𝑒𝑠𝑠𝑖𝑜𝑛)] × ℎ𝑒𝑛𝑠 𝑡𝑜 𝑝𝑟𝑜𝑑𝑢𝑐𝑒 𝑜𝑛𝑒 𝑡𝑜𝑛𝑛𝑒 𝑜𝑓 𝑒𝑔𝑔𝑠 𝐼𝑚𝑝𝑎𝑐𝑡 = [0.075 + 0.15 + 0.025 + 0.025] × 51.37 C.5 Potential areas for future improvement of proposed method A number of other improvements to assessment of animal welfare impacts in LCA are also warranted in the future. For example, further investigation into the biological basis for some behavioural patterns may help in further defining and delineating between the biological function 628 and behavioural impact categories. It may be argued, for example, that some non-normal behaviours such as feather pecking are pathologic in nature and therefore may be more representative of impaired biological function (Hartcher et al., 2016). In the case of feather pecking, the developed method aims to delineate between the results of the behaviour, accounted for in the morbidity/injury impact subcategory, the behaviour itself, accounted for in the injurious behaviour impact subcategory, as well as potential stressors that precipitate feather pecking behaviour in the negative affective state impact subcategory. Further research into the mechanisms behind behaviours such as feather pecking may support the re-classification of animal welfare contributors to different impact subcategories than are proposed here. Overall, the potential for continued refinement of the method provides motivation not only for further work in LCIA methods development, but also for continued multi-disciplinary collaboration between LCA and animal welfare science practitioners. Such collaborations will be invaluable in ensuring net positive sustainability outcomes for the global egg industry going forward. Additionally, the biological function impact subcategory is currently divided into impact subcategories based on the assumption that mortality, and morbidity and injury together represent the inverse of good biological health, which has been excluded to avoid double counting. In the future, it may be possible to define a third impact subcategory representative of positive contributions to laying hen health, based on factors such as how well feed formulations fulfill hen nutritional requirements, management practices that promote musculoskeletal health, etc. Inclusion of nutritional factors as positive contributors to biological health may be particularly fruitful, given the large volume of research into supplementation of laying hen diets with different additives (see for example Borelli et al. 2017; Kalus et al. 2020), as well as the common management practice of changing dietary composition depending on hen age (van 629 Staaveren et al., 2018b). Additional research is required to determine which management practices and housing system characteristics could be considered biological health promoting, and the degree to which they improve biological health before it can be included in this impact assessment method. The proposed method could also be improved through future integration with methods for uncertainty assessment, in line with best practices in environmental LCA (Bamber et al., 2019). It is particularly important to develop the means for incorporation of model uncertainty resulting from the characterization factors used, given potentially large differences in motivational strength for specific behaviours, and perception of affective experience between birds of different feather colours and strains (Fraisse and Cockrem, 2006; Rozempolska-Rucińska et al., 2017). A potentially significant source of model uncertainty in the method are the assumptions used in the basal behavioural needs fulfillment impact subcategory. This impact subcategory assumes that the largest final space requirement defined by the updated Canadian Code of Practice for the Care and Handling of Pullets and Laying Hens (NFACC 2017) is the minimum space requirement for any laying hen to fulfill their basal behavioural needs, and that fulfillment of these needs scales linearly with space allowance. These assumptions resulted in negative scores for all housing systems except free range, which received a relatively small positive score, and organic, which received a very high positive score. While the Canadian guidelines used here are the most appropriate as the proposed method was tested using a Canadian case-study, these requirements could differ across different jurisdictions. U.S. guidelines for cage-free production systems, for example, dictate that a minimum 144 square inches per bird is sufficient to allow for performance of normal behaviours (United Egg Producers, 2017), a significant decrease from the 294.5 square inches per bird used as a reference here. Use of a different reference value 630 regarding minimal require spaced allowances would results in large differences in calculated impacts. Identification of an optimal stocking density for use as a reference in impact calculation would be beneficial; however, identification of such a reference may be difficult given the various effects of stocking density. It has been suggested that both too low and too high stocking densities result in decreases in welfare (Campbell et al., 2017; Green and Xin, 2009; Kang et al., 2016; Khumput et al., 2019; Weimer et al., 2019). Identification of the optimal stocking density may be further complicated by different definitions of what is considered “optimal”, whether from a welfare perspective, economic perspective, or some other (Norwood and Lusk, 2011). It is also important to recognize that, currently, space allowances are defined in two-dimensions rather than three. Further refinement of the three dimensional spatial needs of laying hens to fulfill their basal behavioural needs may help to reduce the large discrepancy in scores between housing systems for this impact subcategory, and reduce the uncertainty associated with that calculation. The use of advanced machine learning techniques such as computer vision (Nakarmi et al., 2014), and radio-frequency identification technology (Ruhnke et al., 2019) continues to provide a more in depth look into how hens move in three dimensional space, which may be vital in further defining the three dimensional spatial needs of laying hens, allowing for further refinement of this impact subcategory. While accounting for the discrepancies outlined here would help reduce uncertainty associated with impact calculations, it must be noted that assessment of model uncertainty remains difficult in LCA, with sensitivity analyses the most common method of assessment (Bamber et al., 2019). Sensitivity analyses of animal welfare life cycle impacts would require further development of additional, similar animal welfare LCIA methodologies for comparison of results. 631 Appendix D D.1 PRISMA Checklist Table D 1. Completed PRISMA checklist describing the systematic review performed in chapter 5, as adapted from Page et al. (2021) Section Topic Item # Checklist item Brief description of adherence Location where item is reported Title Title 1 Identify the report as a systematic review The manuscript is identified as a systematic review in the title Title Abstract Abstract 2 See the PRISMA 2020 for Abstracts checklist See table S3 Table D1 Introduction Rationale 3 Describe the rationale for the review in the context of existing knowledge This work provides an updated review of the LCO literature, covering studies published following the publication of Pieragostini et al. (2012). Section 5.1 Objectives 4 Provide an explicit statement of the The current review therefore has two goals. First is to provide an updated analysis of the current state-of-art in Section 5.1 632 Section Topic Item # Checklist item Brief description of adherence Location where item is reported objective(s) or question(s) the review addresses LCO. Second is to use this information to develop a generalized decision tree to help practitioners make methodological decisions in LCO studies in a consistent and transparent manner. Methods Eligibility criteria 5 Specify the inclusion and exclusion criteria for the review and how studies were grouped for the syntheses. To be considered for inclusion, studies had to meet a number of eligibility criteria. First, only literature published within the last 12 years (i.e., 2012-2023, inclusive) were considered, while all other literature was excluded. Sources excluded includes those that were published as early access in 2023 Section 5.3.1 633 Section Topic Item # Checklist item Brief description of adherence Location where item is reported for publication in 2024. Further, only primary research articles, written in English, were considered. Conference proceedings and review articles were excluded. No eligibility criteria were defined regarding the industrial sector of the systems being assessed in candidate literature. Information sources 6 Specify all databases, registers, websites, organisations, reference lists and other sources searched or consulted to identify studies. Specify The Web of Science Core Collection was searched. No other databases were consulted. The last date the database was consulted during the initial literature search was January 31st, 2024. Section 5.3.1 634 Section Topic Item # Checklist item Brief description of adherence Location where item is reported the date when each source was last searched or consulted. Search strategy 7 Present the full search strategies for all databases, registers and websites, including any filters and limits used. Topic searches were performed using the keywords (“life cycle assessment” OR “life cycle analysis” OR "LCA" OR "LCO" OR "life cycle optimization") AND (optimi*) AND (algorithm), including all listed brackets which are used to override default operator precedence in the Web of Science search function. The temporal boundaries were 2012 to 2023, inclusive. Early access papers Section 5.3.1 635 Section Topic Item # Checklist item Brief description of adherence Location where item is reported were excluded. Only primary research articles, written in English, were considered. Conference proceedings and review articles were excluded Selection process 8 Specify the methods used to decide whether a study met the inclusion criteria of the review, including how many reviewers screened each record and each report retrieved, whether they worked independently, and if applicable, details of To be selected for review, sources were required to apply the LCA framework in conjunction with a mathematical optimization technique. This selection was carried out primarily based on article titles and abstracts, with further consultation of the main text whenever necessary. A single reviewer was responsible for performing the selection process to ensure consistency Section 5.3.1 636 Section Topic Item # Checklist item Brief description of adherence Location where item is reported automation tools used in the process. of application, and no automation tools were used during this process. Data collection process 9 Specify the methods used to collect data from reports, including how many reviewers collected data from each report, whether they worked independently, any processes for obtaining or confirming data from study investigators, and if applicable, details of Data were collected from the selected literature by in-depth readings of articles, as well as any associated supplementary materials. Two reviewers were responsible for the data collection process. The reviewers were in close communication with one another during the data collection process to ensure consistency in data collection principles. No automation tools were used during the data collection process. Section 5.3.2 637 Section Topic Item # Checklist item Brief description of adherence Location where item is reported automation tools used in the process. Data items 10a List and define all outcomes for which data were sought. Specify whether all results that were compatible with each outcome domain in each study were sought (e.g. for all measures, time points, analyses), and if not, the methods used to decide which results to collect. Methods used for optimization in each reviewed study Section 5.3.2 638 Section Topic Item # Checklist item Brief description of adherence Location where item is reported 10b List and define all other variables for which data were sought (e.g. participant and intervention characteristics, funding sources). Describe any assumptions made about any missing or unclear information. - Industrial classification of product system under study according to ISIC guidelines - LCA framework - Specific objective functions - Type of objective function - Use of aggregated/disaggregated measures as objective functions - Optimization solver/algorithm employed - Type of algorithm/solver - Whether optimization was constrained or not Section 5.3.2 639 Section Topic Item # Checklist item Brief description of adherence Location where item is reported Whether optimization was robust or not Study risk of bias assessment 11 Specify the methods used to assess risk of bias in the included studies, including details of the tool(s) used, how many reviewers assessed each study and whether they worked independently, and if applicable, details of automation tools used in the process. No methods were used to assess the risk of bias in any of the studies included for review. Section 5.3.2 640 Section Topic Item # Checklist item Brief description of adherence Location where item is reported Effect measures 12 Specify for each outcome the effect measure(s) (e.g. risk ratio, mean difference) used in the synthesis or presentation of results. NA NA Synthesis methods 13a Describe the processes used to decide which studies were eligible for each synthesis (e.g. tabulating the study intervention characteristics and comparing against the All sources that met selection criteria were eligible for all data syntheses Section 5.3.2 641 Section Topic Item # Checklist item Brief description of adherence Location where item is reported planned groups for each synthesis (item #5)). 13b Describe any methods required to prepare the data for presentation or synthesis, such as handling of missing summary statistics, or data conversions. NA NA 13c Describe any methods used to tabulate or visually display results of individual studies and syntheses. Once the data were collected from each source, tallies were created to determine common practices in LCO studies Section 5.3.2 642 Section Topic Item # Checklist item Brief description of adherence Location where item is reported 13d Describe any methods used to synthesise results and provide a rationale for the choice(s). If meta-analysis was performed, describe the model(s), method(s) to identify the presence and extent of statistical heterogeneity, and software package(s) used. NA NA 13e Describe any methods used to explore possible causes of heterogeneity NA NA 643 Section Topic Item # Checklist item Brief description of adherence Location where item is reported among study results (e.g. subgroup analysis, meta-regression). 13f Describe any sensitivity analyses conducted to assess robustness of the synthesised results. NA NA Reporting bias assessment 14 Describe any methods used to assess risk of bias due to missing results in a synthesis (arising from reporting biases). No methods were employed to assess risk of bias due to missing results, nor to assess the certainty in the body of evidence collected. Section 5.3.2 Certainty assessment 15 Describe any methods used to assess certainty No methods were employed to assess risk of bias due to missing results, nor Section 5.3.2 644 Section Topic Item # Checklist item Brief description of adherence Location where item is reported (or confidence) in the body of evidence for an outcome. to assess the certainty in the body of evidence collected. Results Study selection 16a Describe the results of the search and selection process, from the number of records identified in the search to the number of studies included in the review, ideally using a flow diagram. Flow diagram of selection process included Figure 5-1, section 5.4.1 16b Cite studies that might appear to meet the inclusion criteria, but Examples of exclusions given in methods description, citing Raeesi et al. (2021) and Das et al. (2021) Section 5.3.2 645 Section Topic Item # Checklist item Brief description of adherence Location where item is reported which were excluded, and explain why they were excluded. Study characteristics 17 Cite each included study and present its characteristics. General descriptions of data collected are provided. Citing each study included is infeasible due to the large number of studies included in the review. Section 5.4.1 Risk of bias in studies 18 Present assessments of risk of bias for each included study. NA NA Results of individual studies 19 For all outcomes, present, for each study: (a) summary statistics for Summaries of all collected data given Section 5.4.1 646 Section Topic Item # Checklist item Brief description of adherence Location where item is reported each group (where appropriate) and (b) an effect estimate and its precision (e.g. confidence/credible interval), ideally using structured tables or plots. Results of syntheses 20a For each synthesis, briefly summarise the characteristics and risk of bias among contributing studies. NA NA 20b Present results of all statistical syntheses NA NA 647 Section Topic Item # Checklist item Brief description of adherence Location where item is reported conducted. If meta-analysis was done, present for each the summary estimate and its precision (e.g. confidence/credible interval) and measures of statistical heterogeneity. If comparing groups, describe the direction of the effect. 20c Present results of all investigations of possible causes of heterogeneity among study results. NA NA 648 Section Topic Item # Checklist item Brief description of adherence Location where item is reported 20d Present results of all sensitivity analyses conducted to assess the robustness of the synthesised results. NA NA Reporting biases 21 Present assessments of risk of bias due to missing results (arising from reporting biases) for each synthesis assessed. NA NA Certainty of evidence 22 Present assessments of certainty (or confidence) in the body of evidence NA NA 649 Section Topic Item # Checklist item Brief description of adherence Location where item is reported for each outcome assessed. Discussion Discussion 23a Provide a general interpretation of the results in the context of other evidence. Discussion and synthesis of results probidede Sections 5.4.2 – 5.4.4 23b Discuss any limitations of the evidence included in the review. 23c Discuss any limitations of the review processes used. 23d Discuss implications of the results for practice, 650 Section Topic Item # Checklist item Brief description of adherence Location where item is reported policy, and future research. Other information Registration and protocol 24a Provide registration information for the review, including register name and registration number, or state that the review was not registered. This review was not registered prior to initiation. Section 5.3 24b Indicate where the review protocol can be accessed, or state that a protocol was not prepared. No review protocol was prepared for review Section 5.3 24c Describe and explain any amendments to NA NA 651 Section Topic Item # Checklist item Brief description of adherence Location where item is reported information provided at registration or in the protocol. Support 25 Describe sources of financial or non-financial support for the review, and the role of the funders or sponsors in the review. The work performed for this review was supported by Egg Farmers of Canada, the Natural Science and Engineering Research Council of Canada/Egg Farmers of Canada Industrial Research Chair in Sustainability, and the Canada Foundation for Innovation John Evans Leadership Fund. The funding sources had no involvement in study design, collection, analysis, or interpretation of Acknowledgements 652 Section Topic Item # Checklist item Brief description of adherence Location where item is reported data, manuscript preparation, or the decision to submit the article for publication. Competing interests 26 Declare any competing interests of review authors. The authors declare they have no competing interests, financial or otherwise, that may be impacted by publication of this manuscript. Acknowledgements Availability of data, code, and other materials 27 Report which of the following are publicly available and where they can be found: template data collection forms; data extracted from included studies; data used for all The complete body of literature from which the results were derived may be found in the associated supplementary information file. 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Environ Sustain Indic 2023;19:100263. https://doi.org/10.1016/j.indic.2023.100263. Cantú VH, Ponsich A, Azzaro-Pantel C, Carrera E. Capturing spatial, time-wise and technological detail in hydrogen supply chains: A bi-level multi-objective optimization approach. Appl Energy 2023;344:121159. https://doi.org/10.1016/j.apenergy.2023.121159. Chen Z, Wang D. Numerical analysis of a multi-objective maintenance decision-making model for sustainable highway networks: Integrating the GDE3 method, LCA and LCCA. Energy Build 2023;290:113096. https://doi.org/10.1016/j.enbuild.2023.113096. Chen D, Chen T, Feng Y. Comprehensive CO2 emissions analysis and optimization of plug-in hybrid electric vehicles. Proc Inst Mech Eng Part D J Automob Eng 2023. https://doi.org/10.1177/09544070231152519. Chen W, Zheng M, Tian N, Ding X, Li N, Zhang W. Project-based sustainable timing series decision-making for pavement maintenance using multi-objective optimization: An innovation in traditional solutions. 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Mode of transportation, and cumulative transportation distance by truck and rail for barley produced in each production location to be transported to each reference feed mill location for the regionalized feed formulation optimization performed in chapter 6 Abbotsford Lethbridge Wallenstein St. Hyacinthe Truro RU Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) 5 Rail 270.3 4766 Rail 276.7 3768 Rail 313.4 1442 Rail 282.4 285 Truck 56.9 0 6 Rail 216.3 4766 Rail 222.7 3768 Rail 259.4 1442 Rail 228.4 285 Truck 242 0 7 Rail 218.3 4766 Rail 224.7 3768 Rail 261.4 1442 Rail 230.4 285 Truck 338 0 11 Rail 434.3 4106 Rail 440.7 3109 Rail 477.4 842 Rail 446.4 241 Rail 559 665 12 Rail 178.3 4079 Rail 184.7 3083 Rail 221.4 607 Truck 69.3 0 Rail 303 285 15 Rail 505.3 4106 Rail 511.7 3109 Rail 548.4 842 Rail 517.4 241 Rail 630 665 17 Rail 137.4 3959 Rail 143.8 2966 Truck 88.8 0 Rail 149.5 532 Rail 262.1 532 19 Rail 646.3 3959 Rail 652.7 2966 Rail 689.4 81 Rail 658.4 532 Rail 771 532 22 Rail 181.3 2134 Rail 187.7 1138 Rail 224.4 1788 Rail 193.4 1969 Rail 306 2652 682 Abbotsford Lethbridge Wallenstein St. Hyacinthe Truro RU Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) 23 Rail 346.3 2086 Rail 352.7 1090 Rail 389.4 1821 Rail 358.4 2002 Rail 471 2687 24 Rail 66.6 1939 Rail 73 943 Rail 109.7 1981 Rail 78.7 2162 Rail 191.3 2847 28 Rail 261.3 1729 Rail 267.7 1038 Rail 304.4 2506 Rail 273.4 2689 Rail 386 3372 29 Rail 90.5 1729 Rail 96.9 711 Rail 133.6 2592 Rail 102.6 2605 Rail 215.2 3289 30 Rail 83.6 1407 Rail 90 411 Rail 126.7 2606 Rail 95.7 2739 Rail 208.3 3226 34 Rail 416.3 1076 Rail 422.7 571 Rail 459.4 3155 Rail 428.4 3191 Rail 541 3875 35 Rail 245.3 1076 Rail 251.7 571 Rail 288.4 3155 Rail 257.4 3191 Rail 370 3875 37 Rail 168.3 1064 Truck 174 0 Rail 211.4 2953 Rail 180.4 3083 Rail 293 3768 39 Rail 512.3 729 Rail 518.7 1304 Rail 555.4 3959 Rail 524.4 3995 Rail 637 4678 41 Rail 737.3 729 Rail 743.7 1304 Rail 780.4 3959 Rail 749.4 3995 Rail 862 4678 42 Rail 186.3 729 Rail 192.7 1304 Rail 229.4 3959 Rail 198.4 3995 Rail 311 4678 683 Table E 2. Mode of transportation, and cumulative transportation distance by truck and rail for corn produced in each production location to be transported to each reference feed mill location for the regionalized feed formulation optimization performed in chapter 6 Abbotsford Lethbridge Wallenstein St. Hyacinthe Truro RU Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) 5 Rail 270.3 4766 Rail 276.7 3768 Rail 313.4 1442 Rail 282.4 285 Truck 56.9 0 6 Rail 216.3 4766 Rail 222.7 3768 Rail 259.4 1442 Rail 228.4 285 Truck 242 0 7 Rail 218.3 4766 Rail 224.7 3768 Rail 261.4 1442 Rail 230.4 285 Rail 343 0 11 Rail 434.3 4106 Rail 440.7 3109 Rail 477.4 842 Rail 446.4 241 Rail 559 665 12 Rail 178.3 4079 Rail 184.7 3083 Rail 221.4 607 Truck 69.3 0 Rail 303 285 15 Rail 505.3 4106 Rail 511.7 3109 Rail 548.4 842 Rail 517.4 241 Rail 630 665 17 Rail 137.4 3959 Rail 143.8 2966 Truck 88.8 0 Rail 149.5 532 Rail 262.1 532 19 Rail 646.3 3959 Rail 652.7 2966 Rail 689.4 81 Rail 658.4 532 Rail 771 532 22 Rail 181.3 2134 Rail 187.7 1138 Rail 224.4 1788 Rail 193.4 1969 Rail 306 2652 23 Rail 346.3 2086 Rail 352.7 1090 Rail 389.4 1821 Rail 358.4 2002 Rail 471 2687 684 Abbotsford Lethbridge Wallenstein St. Hyacinthe Truro RU Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) 24 Rail 66.6 1939 Rail 73 943 Rail 109.7 1981 Rail 78.7 2162 Rail 191.3 2847 29 Rail 90.5 1729 Rail 96.9 711 Rail 133.6 2592 Rail 102.6 2605 Rail 215.2 3289 37 Rail 168.3 1064 Truck 174 0 Rail 211.4 2953 Rail 180.4 3083 Rail 293 3768 Table E 3. Mode of transportation, and cumulative transportation distance by truck and rail for peas produced in each production location to be transported to each reference feed mill location for the regionalized feed formulation optimization performed in chapter 6 Abbotsford Lethbridge Wallenstein St. Hyacinthe Truro RU Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) 5 Rail 270.3 4766 Rail 276.7 3768 Rail 313.4 1442 Rail 282.4 285 Truck 56.9 0 7 Rail 216.3 4766 Rail 222.7 3768 Rail 259.4 1442 Rail 228.4 285 Truck 242 0 22 Rail 181.3 2134 Rail 187.7 1138 Rail 224.4 1788 Rail 193.4 1969 Rail 306 2652 685 Abbotsford Lethbridge Wallenstein St. Hyacinthe Truro RU Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) 23 Rail 346.3 2086 Rail 352.7 1090 Rail 389.4 1821 Rail 358.4 2002 Rail 471 2687 24 Rail 66.6 1939 Rail 73 943 Rail 109.7 1981 Rail 78.7 2162 Rail 191.3 2847 28 Rail 261.3 1729 Rail 267.7 1038 Rail 304.4 2506 Rail 273.4 2689 Rail 386 3372 29 Rail 90.5 1729 Rail 96.9 711 Rail 133.6 2592 Rail 102.6 2605 Rail 215.2 3289 30 Rail 83.6 1407 Rail 90 411 Rail 126.7 2606 Rail 95.7 2739 Rail 208.3 3226 34 Rail 416.3 1076 Rail 422.7 571 Rail 459.4 3155 Rail 428.4 3191 Rail 541 3875 35 Rail 245.3 1076 Rail 251.7 571 Rail 288.4 3155 Rail 257.4 3191 Rail 370 3875 37 Rail 168.3 1064 Truck 174 0 Rail 211.4 2953 Rail 180.4 3083 Rail 293 3768 39 Rail 512.3 729 Rail 518.7 1304 Rail 555.4 3959 Rail 524.4 3995 Rail 637 4678 41 Rail 737.3 729 Rail 743.7 1304 Rail 780.4 3959 Rail 749.4 3995 Rail 862 4678 42 Rail 186.3 729 Rail 192.7 1304 Rail 229.4 3959 Rail 198.4 3995 Rail 311 4678 686 Table E 4. Mode of transportation, and cumulative transportation distance by truck and rail for wheat produced in each production location to be transported to each reference feed mill location for the regionalized feed formulation optimization performed in chapter 6 Abbotsford Lethbridge Wallenstein St. Hyacinthe Truro RU Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) 5 Rail 270.3 4766 Rail 276.7 3768 Rail 313.4 1442 Rail 282.4 285 Truck 56.9 0 6 Rail 216.3 4766 Rail 222.7 3768 Rail 259.4 1442 Rail 228.4 285 Truck 242 0 7 Rail 218.3 4766 Rail 224.7 3768 Rail 261.4 1442 Rail 230.4 285 Truck 338 0 11 Rail 434.3 4106 Rail 440.7 3109 Rail 477.4 842 Rail 446.4 241 Rail 559 665 12 Rail 178.3 4079 Rail 184.7 3083 Rail 221.4 607 Truck 69.3 0 Rail 303 285 15 Rail 505.3 4106 Rail 511.7 3109 Rail 548.4 842 Rail 517.4 241 Rail 630 665 17 Rail 137.4 3959 Rail 143.8 2966 Truck 88.8 0 Rail 149.5 532 Rail 262.1 532 19 Rail 646.3 3959 Rail 652.7 2966 Rail 689.4 81 Rail 658.4 532 Rail 771 532 22 Rail 181.3 2134 Rail 187.7 1138 Rail 224.4 1788 Rail 193.4 1969 Rail 306 2652 23 Rail 346.3 2086 Rail 352.7 1090 Rail 389.4 1821 Rail 358.4 2002 Rail 471 2687 687 Abbotsford Lethbridge Wallenstein St. Hyacinthe Truro RU Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) 24 Rail 66.6 1939 Rail 73 943 Rail 109.7 1981 Rail 78.7 2162 Rail 191.3 2847 28 Rail 261.3 1729 Rail 267.7 1038 Rail 304.4 2506 Rail 273.4 2689 Rail 386 3372 29 Rail 90.5 1729 Rail 96.9 711 Rail 133.6 2592 Rail 102.6 2605 Rail 215.2 3289 30 Rail 83.6 1407 Rail 90 411 Rail 126.7 2606 Rail 95.7 2739 Rail 208.3 3226 34 Rail 416.3 1076 Rail 422.7 571 Rail 459.4 3155 Rail 428.4 3191 Rail 541 3875 35 Rail 245.3 1076 Rail 251.7 571 Rail 288.4 3155 Rail 257.4 3191 Rail 370 3875 37 Rail 168.3 1064 Truck 174 0 Rail 211.4 2953 Rail 180.4 3083 Rail 293 3768 39 Rail 512.3 729 Rail 518.7 1304 Rail 555.4 3959 Rail 524.4 3995 Rail 637 4678 41 Rail 737.3 729 Rail 743.7 1304 Rail 780.4 3959 Rail 749.4 3995 Rail 862 4678 42 Rail 186.3 729 Rail 192.7 1304 Rail 229.4 3959 Rail 198.4 3995 Rail 311 4678 688 Table E 5. Mode of transportation, and cumulative transportation distance by truck and rail for canola oil and meal produced in each production location to be transported to each reference feed mill location for the regionalized feed formulation optimization performed in chapter 6 Abbotsford Lethbridge Wallenstein St. Hyacinthe Truro Source RU Processing location Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) 22 Nipawin, SK Rail 607.3 1754 Rail 624.4 2226 Rail 593.4 2424 Rail 706 3109 Rail 747 905 23 St Agathe, MB Rail 113.2 2156 Rail 130.3 1788 Rail 99.3 1969 Rail 211.9 2652 Rail 252.9 1307 24 Harrowby, MB Rail 349.3 1754 Rail 366.4 2226 Rail 335.4 2424 Rail 448 3109 Rail 489 905 28 Nipawin, SK Rail 607.3 1754 Rail 624.4 2226 Rail 593.4 2424 Rail 706 3109 Rail 747 905 689 Abbotsford Lethbridge Wallenstein St. Hyacinthe Truro Source RU Processing location Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) 29 Yorkton, SK Rail 354.3 1754 Rail 371.4 2226 Rail 340.4 2424 Rail 453 3109 Rail 494 905 30 Clavet, SK Rail 536.3 1754 Rail 553.4 2226 Rail 522.4 2424 Rail 635 3109 Rail 676 905 34 Fort Saskatchewan, AB Rail 88.3 1098 Rail 105.4 3155 Rail 74.4 3191 Rail 187 3875 Rail 228 343 35 Camrose, AB Rail 170.8 1098 Rail 187.9 3155 Rail 156.9 3191 Rail 269.5 3875 Rail 310.5 343 37 Lethbridge, AB Rail 289.3 857 Rail 306.4 3122 Rail 275.4 3252 Rail 388 3939 Truck 0 0 690 Abbotsford Lethbridge Wallenstein St. Hyacinthe Truro Source RU Processing location Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) 39 Fort Saskatchewan, AB Rail 88.3 1098 Rail 105.4 3155 Rail 74.4 3191 Rail 187 3875 Rail 228 343 41 Fort Saskatchewan, AB Rail 88.3 1098 Rail 105.4 3155 Rail 74.4 3191 Rail 187 3875 Rail 228 343 42 Fort Saskatchewan, AB Rail 88.3 1098 Rail 105.4 3155 Rail 74.4 3191 Rail 187 3875 Rail 228 343 691 Table E 6. Mode of transportation, and cumulative transportation distance by truck and rail for soybean meal produced in each production location to be transported to each reference feed mill location for the regionalized feed formulation optimization performed in chapter 6 Abbotsford Lethbridge Wallenstein St. Hyacinthe Truro Source RU Processing location Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) 5 Becancourt, QC Rail 209.3 4128 Rail 349 3278 Rail 226.4 842 Truck 115 0 Rail 308 665 6 Becancourt, QC Rail 209.3 4128 Rail 349 3278 Rail 226.4 842 Truck 115 0 Rail 308 665 7 Becancourt, QC Rail 209.3 4128 Rail 349 3278 Rail 226.4 842 Truck 115 0 Rail 308 665 11 Becancourt, QC Rail 209.3 4128 Rail 349 3278 Rail 226.4 842 Truck 115 0 Rail 308 665 692 Abbotsford Lethbridge Wallenstein St. Hyacinthe Truro Source RU Processing location Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) 12 Becancourt, QC Rail 209.3 4128 Rail 349 3278 Rail 226.4 842 Truck 115 0 Rail 308 665 15 Becancourt, QC Rail 209.3 4128 Rail 349 3278 Rail 226.4 842 Truck 115 0 Rail 308 665 17 Hamilton, ON Rail 72.3 3968 Rail 212 3122 Truck 88.8 0 Rail 58.4 607 Rail 171 1442 19 Hamilton, ON Rail 72.3 3968 Rail 212 3122 Truck 88.8 0 Rail 58.4 607 Rail 171 1442 22 Windsor, ON Rail 377.3 3968 Rail 517 3122 Truck 311 0 Rail 363.4 607 Rail 476 1442 23 Windsor, ON Rail 377.3 3968 Rail 517 3122 Truck 311 0 Rail 363.4 607 Rail 476 1442 693 Abbotsford Lethbridge Wallenstein St. Hyacinthe Truro Source RU Processing location Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) 29 Windsor, ON Rail 377.3 3968 Rail 517 3122 Truck 311 0 Rail 363.4 607 Rail 476 1442 30 Windsor, ON Rail 377.3 3968 Rail 517 3122 Truck 311 0 Rail 363.4 607 Rail 476 1442 694 Table E 7. Mode of transportation, and cumulative transportation distance by truck and rail for corn DDGS produced in each production location to be transported to each reference feed mill location for the regionalized feed formulation optimization performed in chapter 6 Abbotsford Lethbridge Wallenstein St. Hyacinthe Truro Production location Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) Red Deer, AB Rail 222.3 857 Truck 353 0 Rail 239.4 3122 Rail 208.4 3252 Rail 321 3939 Weyburn, SK Rail 174.3 1754 Rail 314 905 Rail 191.4 2226 Rail 160.4 2424 Rail 273 3109 Minnedosa, MB Rail 288.3 2156 Rail 428 1307 Rail 305.4 1788 Rail 274.4 1969 Rail 387 2652 Tiverton, ON Rail 279.3 3968 Rail 419 3122 Truck 119 0 Rail 265.4 607 Rail 378 1442 Sarnia, ON Rail 298.3 3968 Rail 438 3122 Truck 189 0 Rail 284.4 607 Rail 397 1442 695 Abbotsford Lethbridge Wallenstein St. Hyacinthe Truro Production location Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) Chatham, ON Rail 301.3 3968 Rail 441 3122 Truck 235 0 Rail 287.4 607 Rail 400 1442 Aylmer, ON Rail 198.3 3968 Rail 338 3122 Truck 132 0 Rail 184.4 607 Rail 297 1442 Havelock, ON Rail 245.3 3981 Rail 385 3135 Rail 262.4 81 Rail 231.4 532 Rail 344 532 Johnstown, ON Rail 436.3 3981 Rail 576 3135 Rail 453.4 81 Rail 422.4 532 Rail 535 532 696 Table E 8. Mode of transportation, and cumulative transportation distance by truck and rail for bakery meal produced in each production location to be transported to each reference feed mill location for the regionalized feed formulation optimization performed in chapter 6 Abbotsford Lethbridge Wallenstein St. Hyacinthe Truro Production location Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) Vancouver, BC Truck 71.4 0 Rail 212 857 Rail 89.4 3968 Rail 58.4 4101 Rail 171 4788 Calgary, AB Rail 72.3 857 Truck 212 0 Rail 89.4 3122 Rail 58.4 3252 Rail 171 3939 Saskatoon, SK Rail 550.3 1754 Rail 690 905 Rail 567.4 2226 Rail 536.4 2424 Rail 649 3109 Winnipeg, MB Rail 72.3 2156 Rail 212 1307 Rail 89.4 1788 Rail 58.4 1969 Rail 171 2652 Toronto, ON Rail 72.3 3981 Rail 212 3135 Truck 131 0 Rail 58.4 532 Rail 171 532 697 Abbotsford Lethbridge Wallenstein St. Hyacinthe Truro Production location Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) Montreal, QC Rail 72.3 4101 Rail 212 3252 Rail 89.4 607 Truck 65.6 0 Rail 171 285 Fredericton. NB Rail 182.3 4693 Rail 322 3844 Rail 199.4 1195 Rail 168.4 606 Rail 281 285 Halifax, NS Rail 336.3 4788 Rail 476 3939 Rail 353.4 1442 Rail 322.4 285 Truck 94 0 698 Table E 9. Mode of transportation, and cumulative transportation distance by truck and rail for limestone produced in each production location to be transported to each reference feed mill location for the regionalized feed formulation optimization performed in chapter 6 Abbotsford Lethbridge Wallenstein St. Hyacinthe Truro Production location Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) Toronto, ON Rail 72.3 3981 Rail 212 3135 Truck 131 0 Rail 58.4 532 Rail 171 532 Montreal, QC Rail 72.3 4101 Rail 212 3252 Rail 89.4 607 Truck 65.6 0 Rail 171 285 699 Table E 10. Mode of transportation, and cumulative transportation distance by truck and rail for synthetic amino acids and vitamin supplements produced in each production location to be transported to each reference feed mill location for the regionalized feed formulation optimization performed in chapter 6 Abbotsford Lethbridge Wallenstein St. Hyacinthe Truro Production location Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) Calgary, AB Rail 72.3 857 Truck 212 0 Rail 89.4 3122 Rail 58.4 3252 Rail 171 3939 Mississauga, ON Rail 100.3 3981 Rail 240 3135 Truck 109 0 Rail 86.4 532 Rail 199 532 Ottawa, ON Rail 275.3 4101 Rail 415 3252 Rail 292.4 607 Truck 273 0 Rail 374 285 Teaneck, NJ, USA a Rail 641.3 3968 Rail 781 3122 Truck 785 0 Rail 627.4 607 Rail 740 1442 Kirkland, QC Rail 100.9 4101 Rail 240.6 3252 Rail 118 607 Truck 85.3 0 Rail 199.6 285 a Production of synthetic lysine in Teaneck, NJ was excluded 700 Table E 11. Mode of transportation, and cumulative transportation distance by truck and rail for rendered animal meal produced in each production location to be transported to each reference feed mill location for the regionalized feed formulation optimization performed in chapter 6 Abbotsford Lethbridge Wallenstein St. Hyacinthe Truro Production location Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) Vancouver, BC Truck 69.9 0 Rail 212 857 Rail 89.4 3968 Rail 58.4 4101 Rail 171 4788 Calgary, AB Rail 72.3 857 Truck 212 0 Rail 89.4 3122 Rail 58.4 3252 Rail 171 3939 Edmonton, AB Rail 127.1 1098 Rail 266.8 343 Rail 144.2 3155 Rail 113.2 3191 Rail 225.8 3875 Lethbridge, AB Rail 289.3 857 Truck 0 0 Rail 306.4 3122 Rail 275.4 3252 Rail 388 3939 Saskatoon, SK Rail 550.3 1754 Rail 690 905 Rail 567.4 2226 Rail 536.4 2424 Rail 649 3109 701 Abbotsford Lethbridge Wallenstein St. Hyacinthe Truro Production location Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) Atwood, ON Rail 191.3 3968 Rail 331 3122 Truck 39.3 Rail 177.4 607 Rail 290 1442 Dundas, ON Rail 81.3 3968 Rail 221 3122 Truck 78.2 Rail 67.4 607 Rail 180 1442 Hamilton, ON Rail 72.3 3968 Rail 212 3122 Truck 88.8 Rail 58.4 607 Rail 171 1442 St Albert, ON Rail 229.3 4101 Rail 369 3252 Rail 246.4 607 Truck 224 0 Rail 328 285 Renfrew, ON Rail 366.3 4101 Rail 506 3252 Rail 383.4 607 Truck 361 0 Rail 465 285 Smiths Falls, ON Rail 316.3 4101 Rail 456 3252 Rail 333.4 607 Truck 311 0 Rail 415 285 702 Abbotsford Lethbridge Wallenstein St. Hyacinthe Truro Production location Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) Mode Truck (km) Rail (km) Bolton, ON Rail 123.7 3981 Rail 263.4 3135 Truck 106 Rail 109.8 532 Rail 222.4 532 Montreal, QC Rail 72.3 4101 Rail 212 3252 Rail 89.4 607 Truck 65.6 0 Rail 171 285 Verchères, QC Rail 127.3 4101 Rail 267 3252 Rail 144.4 607 Truck 59.4 0 Rail 226 285 Lévis, QC Rail 95.8 4128 Rail 235.5 3278 Rail 112.9 842 Truck 191 0 Rail 194.5 665 Kentville, NS Rail 411.3 4788 Rail 551 3939 Rail 428.4 1442 Rail 397.4 285 Truck 137 0 703 Table E 12. Economically optimal feed formulation for feed milled in Abbotsford, British Columbia as identified by single-objective optimization for lowest cost Ingredient Production location a Amount in one tonne of feed Cost per kilogram Total cost Corn 37 403.07 0.36 145.63 Canola meal 42 200.00 0.60 120.90 Soybean meal 19 41.26 0.85 34.87 Corn DDGS Red Deer 150.00 0.30 44.90 Limestone Toronto 105.37 0.18 18.51 Methionine Calgary 0.80 3.97 3.20 Vitamin supplements Calgary 9.90 3.97 39.35 Animal meal Vancouver 89.60 1.09 97.69 Total Cost 505.04 a If a number is given, it refers to a specific RU where that crop is grown Table E 13. Economically optimal feed formulation for feed milled in Lethbridge, Alberta as identified by single-objective optimization for lowest cost Ingredient Production location a Amount in one tonne of feed Cost per kilogram Total cost Corn 37 403.07 0.32 129.25 Canola meal 37 200.00 0.55 110.65 Soybean meal 19 41.26 0.83 34.20 704 Ingredient Production location a Amount in one tonne of feed Cost per kilogram Total cost Corn DDGS Red Deer 150.00 0.28 41.78 Limestone Toronto 105.37 0.16 16.81 Methionine Calgary 0.80 3.95 3.18 Vitamin supplements Calgary 9.90 3.96 39.16 Animal meal Lethbridge 89.60 1.08 97.06 Total Cost 472.10 a If a number is given, it refers to a specific RU where that crop is grown Table E 14. Economically optimal feed formulation for feed milled in Wallenstein, Ontario as identified by single-objective optimization for lowest cost Ingredient Production location a Amount in one tonne of feed Cost per kilogram Total cost Corn 17 394.81 0.31 123.24 Canola meal 23 200.00 0.63 126.66 Soybean meal 19 44.56 0.70 31.29 Corn DDGS Tiverton 150.00 0.26 38.27 Limestone Toronto 110.34 0.04 4.01 Methionine Mississauga 0.78 3.94 3.09 Vitamin supplements Mississauga 9.90 3.94 39.05 705 Ingredient Production location a Amount in one tonne of feed Cost per kilogram Total cost Animal meal Atwood 89.60 1.09 97.41 Total Cost 463.02 a If a number is given, it refers to a specific RU where that crop is grown Table E 15. Economically optimal feed formulation for feed milled in St. Hyacinthe, Quebec as identified by single-objective optimization for lowest cost Ingredient Production location a Amount in one tonne of feed Cost per kilogram Total cost Corn 12 394.81 0.31 122.47 Canola meal 23 200.00 0.64 127.33 Soybean meal 11 44.56 0.70 31.40 Corn DDGS Aylmer 150.00 0.29 42.99 Limestone Montreal 110.34 0.03 3.19 Methionine Kirkland 0.78 3.94 3.09 Vitamin supplements Kirkland 9.90 3.94 39.02 Animal meal Verchères 89.60 1.09 97.59 Total Cost 467.09 a If a number is given, it refers to a specific RU where that crop is grown 706 Table E 16. Economically optimal feed formulation for feed milled in Truro, Nova Scotia as identified by single-objective optimization for lowest cost Ingredient Production location a Amount in one tonne of feed Cost per kilogram Total cost Corn 5 403.07 0.31 124.53 Canola meal 23 200.00 0.67 134.45 Soybean meal 11 41.26 0.75 30.98 Corn DDGS Havelock 150.00 0.30 44.98 Limestone Montreal 105.37 0.05 5.67 Methionine Kirkland 0.80 3.97 3.19 Vitamin supplements Kirkland 9.90 3.97 39.27 Animal meal Kentville 89.60 1.10 98.29 Total Cost 481.37 a If a number is given, it refers to a specific RU where that crop is grown 707 Table E 17. Estimated impacts per tonne of economically optimal feed milled at each reference feed mill location for impact categories not impacted by different treatments of upstream SOC and perennial to annual land use transitions Feed mill location Abiotic depletion Abiotic depletion (fossil fuels) Freshwater aquatic ecotoxicity Human toxicity Marine aquatic ecotoxicity Ozone layer depletion Photochemical oxidation Terrestrial ecotoxicity Abbotsford 4.22E-03 6824.27 494.14 482.83 433.33 433.02 449.91 494.14 Lethbridge 4.01E-03 6003.18 315.22 294.33 257.58 255.04 281.56 315.22 Wallenstein 4.02E-03 4898.88 384706 369451 259824 258446 306785 384706 St. Hyacinthe 3.94E-03 4978.33 2.06E-05 1.88E-05 1.86E-05 1.86E-05 1.93E-05 2.06E-05 Truro 4.26E-03 5486.65 0.26 0.25 0.23 0.24 0.24 0.26 708 Table E 18. Estimated impacts per tonne of economically optimal feed milled at each reference feed mill location for impact categories that are influenced by different treatments of upstream SOC and perennial to annual land use transitions Acidification Eutrophication GHG emissions Feed mill location Excluding SOC and LUC Including SOC and LUC Excluding SOC and LUC Including SOC and LUC Excluding SOC and LUC Including SOC and LUC Abbotsford 10.53 10.53 3.61 3.63 1044.42 1060.43 Lethbridge 9.83 9.83 3.47 3.48 971.35 970.40 Wallenstein 9.63 9.63 3.27 3.29 911.31 867.51 St. Hyacinthe 9.71 9.71 3.31 3.33 941.23 896.00 Truro 9.91 9.91 3.35 3.38 971.47 940.31 709 Table E 19. GHG optimal feed formulation excluding SOC and LUC for feed milled in Abbotsford, British Columbia, as identified by single-objective optimization for lowest GHG emissions Ingredient Production location a Amount in one tonne of feed Cost per kilogram Total cost Corn 29 254.99 0.38 96.19 Canola oil 35 30.00 1.25 37.58 Canola meal 35 200.00 0.61 122.55 Soybean meal 17 193.01 0.85 163.14 Corn DDGS Minnedosa 150.00 0.35 52.83 Limestone Toronto 107.60 0.18 18.90 Vitamin supplements Calgary 9.90 3.97 39.35 Animal meal Vancouver 54.50 1.09 59.42 Total Cost 589.96 a If a number is given, it refers to a specific RU where that crop is grown 710 Table E 20. GHG optimal feed formulation excluding SOC and LUC for feed milled in Lethbridge, Alberta, as identified by single-objective optimization for lowest GHG emissions Ingredient Production location a Amount in one tonne of feed Cost per kilogram Total cost Corn 29 254.99 0.34 87.10 Canola oil 35 30.00 1.24 37.19 Canola meal 35 200.00 0.60 119.96 Soybean meal 17 193.01 0.83 160.01 Corn DDGS Minnedosa 150.00 0.34 50.39 Limestone Toronto 107.60 0.16 17.16 Vitamin supplements Calgary 9.90 3.95 39.15 Animal meal Lethbridge 54.50 1.08 59.04 Total Cost 29 254.99 570.00 a If a number is given, it refers to a specific RU where that crop is grown 711 Table E 21. GHG optimal feed formulation excluding SOC and LUC for feed milled in Wallenstein, Ontario, as identified by single-objective optimization for lowest GHG emissions Ingredient Production location a Amount in one tonne of feed Cost per kilogram Total cost Corn 17 218.95 0.31 68.34 Canola oil 29 30.00 1.31 39.39 Canola meal 29 168.69 0.67 113.53 Soybean meal 17 260.00 0.70 182.56 Corn DDGS Tiverton 150.00 0.26 38.27 Limestone Toronto 107.61 0.04 3.91 Vitamin supplements Mississauga 9.90 3.94 39.05 Animal meal Atwood 54.85 1.09 59.64 Total Cost 544.69 a If a number is given, it refers to a specific RU where that crop is grown Table E 22. GHG optimal feed formulation excluding SOC and LUC for feed milled in St. Hyacinthe, Quebec, as identified by single-objective optimization for lowest GHG emissions Ingredient Production location a Amount in one tonne of feed Cost per kilogram Total cost Corn 6 218.95 0.34 74.34 712 Ingredient Production location a Amount in one tonne of feed Cost per kilogram Total cost Canola oil 35 30.00 1.33 39.78 Canola meal 29 168.69 0.68 114.20 Soybean meal 12 260.00 0.70 183.24 Corn DDGS Tiverton 150.00 0.29 44.21 Limestone Montreal 107.61 0.03 3.11 Vitamin supplements Kirkland 9.90 3.94 39.02 Animal meal Verchères 54.85 1.09 59.75 Total Cost 557.65 a If a number is given, it refers to a specific RU where that crop is grown Table E 23. GHG optimal feed formulation excluding SOC and LUC for feed milled in Truro, Nova Scotia, as identified by single-objective optimization for lowest GHG emissions Ingredient Production location a Amount in one tonne of feed Cost per kilogram Total cost Corn 6 218.95 0.33 71.70 Canola oil 35 30.00 1.36 40.85 Canola meal 29 168.69 0.71 120.22 Soybean meal 12 260.00 0.75 195.27 713 Ingredient Production location a Amount in one tonne of feed Cost per kilogram Total cost Corn DDGS Havelock 150.00 0.30 44.98 Limestone Montreal 107.61 0.05 5.79 Vitamin supplements Kirkland 9.90 3.97 39.27 Animal meal Montreal 54.85 1.10 60.17 Total Cost 578.25 a If a number is given, it refers to a specific RU where that crop is grown Table E 24. GHG optimal feed formulation including SOC and LUC for feed milled in Abbotsford, British Columbia, as identified by single-objective optimization for lowest GHG emissions Ingredient Production location a Amount in one tonne of feed Cost per kilogram Total cost Corn 29 254.99 0.38 96.19 Canola oil 29 30.00 1.29 38.83 Canola meal 29 200.00 0.65 130.90 Soybean meal 17 193.01 0.85 163.14 Corn DDGS Minnedosa 150.00 0.35 52.83 Limestone Toronto 107.60 0.18 18.90 714 Ingredient Production location a Amount in one tonne of feed Cost per kilogram Total cost Vitamin supplements Calgary 9.90 3.97 39.35 Animal meal Vancouver 54.50 1.09 59.42 Total Cost 599.57 a If a number is given, it refers to a specific RU where that crop is grown Table E 25. GHG optimal feed formulation including SOC and LUC for feed milled in Lethbridge, Alberta, as identified by single-objective optimization for lowest GHG emissions Ingredient Production location a Amount in one tonne of feed Cost per kilogram Total cost Corn 29 254.99 0.34 87.10 Canola oil 29 30.00 1.28 38.35 Canola meal 29 200.00 0.64 127.64 Soybean meal 17 193.01 0.83 160.01 Corn DDGS Minnedosa 150.00 0.34 50.39 Limestone Toronto 107.60 0.16 17.16 Vitamin supplements Calgary 9.90 3.95 39.15 Animal meal Lethbridge 54.50 1.08 59.04 715 Ingredient Production location a Amount in one tonne of feed Cost per kilogram Total cost Total Cost 578.83 a If a number is given, it refers to a specific RU where that crop is grown Table E 26. GHG optimal feed formulation including SOC and LUC for feed milled in Wallenstein, Ontario, as identified by single-objective optimization for lowest GHG emissions Ingredient Production location a Amount in one tonne of feed Cost per kilogram Total cost Corn 17 254.99 0.31 79.59 Canola oil 29 30.00 1.31 39.39 Canola meal 29 200.00 0.67 134.61 Soybean meal 17 193.01 0.70 135.52 Corn DDGS Tiverton 150.00 0.26 38.27 Limestone Toronto 107.60 0.04 3.91 Vitamin supplements Mississauga 9.90 3.94 39.05 Animal meal Atwood 54.50 1.09 59.25 Total Cost 529.60 a If a number is given, it refers to a specific RU where that crop is grown 716 Table E 27. GHG optimal feed formulation including SOC and LUC for feed milled in St. Hyacinthe, as identified by single-objective optimization for lowest GHG emissions Ingredient Production location a Amount in one tonne of feed Cost per kilogram Total cost Corn 6 254.99 0.34 86.58 Canola oil 29 30.00 1.32 39.51 Canola meal 29 200.00 0.68 135.40 Soybean meal 12 193.01 0.70 136.03 Corn DDGS Tiverton 150.00 0.29 44.21 Limestone Montreal 107.60 0.03 3.11 Vitamin supplements Kirkland 9.90 3.94 39.02 Animal meal Verchères 54.50 1.09 59.36 Total Cost 543.22 a If a number is given, it refers to a specific RU where that crop is grown Table E 28. GHG optimal feed formulation including SOC and LUC for feed milled in Truro, Nova Scotia, as identified by single-objective optimization for lowest GHG emissions Ingredient Production location a Amount in one tonne of feed Cost per kilogram Total cost Corn 6 254.99 0.33 83.50 717 Ingredient Production location a Amount in one tonne of feed Cost per kilogram Total cost Canola oil 29 30.00 1.35 40.58 Canola meal 29 200.00 0.71 142.54 Soybean meal 17 193.01 0.77 147.66 Corn DDGS Havelock 150.00 0.30 44.98 Limestone Montreal 107.60 0.05 5.79 Vitamin supplements Kirkland 9.90 3.97 39.27 Animal meal Montreal 54.50 1.11 60.70 Total Cost 565.02 a If a number is given, it refers to a specific RU where that crop is grown 718 Table E 29. Estimated life cycle environmental impacts per tonne of economically optimal feed milled at each reference mill location, excluding SOC and LUC Feed mill location Abiotic depletion Abiotic depletion (fossil fuels) Acidification Eutrophication Freshwater aquatic ecotoxicity GHG emissions Human toxicity Marine aquatic ecotoxicity Ozone layer depletion Photochemical oxidation Terrestrial ecotoxicity Abbotsford 4.22E-03 6824.27 10.53 3.61 494.14 1044.42 315.22 384705.73 2.06E-05 0.26 21.73 Lethbridge 4.01E-03 6003.18 9.83 3.47 482.83 971.35 294.33 369451.08 1.88E-05 0.25 21.68 Wallenstein 4.02E-03 4898.88 9.63 3.27 433.33 911.31 257.58 259823.84 1.86E-05 0.23 21.47 St. Hyacinthe 3.94E-03 4978.33 9.71 3.31 433.02 941.23 255.04 258445.80 1.86E-05 0.24 21.48 Truro 4.26E-03 5486.65 9.91 3.35 449.91 971.47 281.56 306785.02 1.93E-05 0.24 21.52 719 Table E 30. Estimated life cycle environmental impacts per tonne of economically optimal feed milled at each reference mill location, including SOC and LUC Feed mill location Abiotic depletion Abiotic depletion (fossil fuels) Acidification Eutrophication Freshwater aquatic ecotoxicity GHG emissions Human toxicity Marine aquatic ecotoxicity Ozone layer depletion Photochemical oxidation Terrestrial ecotoxicity Abbotsford 4.22E-03 6824.27 10.53 3.63 494.14 1060.43 315.22 384705.60 2.06E-05 0.26 21.73 Lethbridge 4.01E-03 6003.18 9.83 3.48 482.83 970.40 294.33 369450.95 1.88E-05 0.25 21.68 Wallenstein 4.02E-03 4898.88 9.63 3.29 433.33 867.51 257.58 259823.71 1.86E-05 0.23 21.47 St. Hyacinthe 3.94E-03 4978.33 9.71 3.33 433.02 896.00 255.04 258445.67 1.86E-05 0.24 21.48 Truro 4.26E-03 5486.65 9.91 3.38 449.91 940.31 281.56 306784.89 1.93E-05 0.24 21.52 720 Table E 31. Estimated life cycle environmental impacts per tonne of GHG optimal feed milled at each reference mill location, excluding SOC and LUC Feed mill location Abiotic depletion Abiotic depletion (fossil fuels) Acidification Eutrophication Freshwater aquatic ecotoxicity GHG emissions Human toxicity Marine aquatic ecotoxicity Ozone layer depletion Photochemical oxidation Terrestrial ecotoxicity Abbotsford 3.72E-03 5941.32 7.57 2.62 370.31 785.94 276.82 325987.07 1.54E-05 0.29 13.88 Lethbridge 3.68E-03 5624.11 7.27 2.56 360.36 757.81 266.78 311592.63 1.51E-05 0.28 13.86 Wallenstein 3.68E-03 4458.91 6.81 2.37 333.01 687.04 242.86 239271.52 1.48E-05 0.28 14.07 St. Hyacinthe 3.61E-03 4650.41 6.82 2.37 335.89 702.67 242.94 242836.11 1.49E-05 0.28 14.13 Truro 3.71E-03 5107.22 6.98 2.42 345.45 737.59 257.17 260149.56 1.54E-05 0.29 14.16 721 Table E 32. Estimated life cycle environmental impacts per tonne of GHG optimal feed milled at each reference mill location, including SOC and LUC Feed mill location Abiotic depletion Abiotic depletion (fossil fuels) Acidification Eutrophication Freshwater aquatic ecotoxicity GHG emissions Human toxicity Marine aquatic ecotoxicity Ozone layer depletion Photochemical oxidation Terrestrial ecotoxicity Abbotsford 3.81E-03 6186.62 7.44 2.61 375.76 742.02 285.47 335475.69 1.59E-05 0.29 13.90 Lethbridge 3.89E-03 5919.96 7.20 2.55 367.60 682.67 280.01 323281.36 1.64E-05 0.28 13.89 Wallenstein 3.71E-03 4573.06 6.95 2.43 329.49 615.67 245.36 244384.94 1.48E-05 0.26 13.77 St. Hyacinthe 3.75E-03 4820.43 6.99 2.43 334.77 631.83 251.12 253169.00 1.52E-05 0.27 13.83 Truro 3.88E-03 4833.18 7.00 2.43 336.83 624.30 257.80 259577.29 1.51E-05 0.27 13.80 722 Table E 33. Bi-objective optimal feed formulation excluding SOC and LUC for feed milled in Abbotsford, British Columbia, optimized for cost and GHG emissions Ingredient Production location a Amount in one tonne of feed Cost per kilogram Total cost Corn 37 308.74 111.54 Canola oil 35 18.06 22.63 Canola meal 35 200.00 122.55 Soybean meal 17 140.02 118.35 Corn DDGS Weyburn 150.00 48.97 Limestone Toronto 106.85 18.77 Vitamin supplements Calgary 9.90 39.35 Animal meal Vancouver 66.42 72.41 Total Cost 554.58 a If a number is given, it refers to a specific RU where that crop is grown Table E 34. Bi-objective optimal feed formulation excluding SOC and LUC for feed milled in Lethbridge, Alberta, optimized for cost and GHG emissions Ingredient Production location a Amount in one tonne of feed Cost per kilogram Total cost Corn 37 308.74 99.00 723 Ingredient Production location a Amount in one tonne of feed Cost per kilogram Total cost Canola oil 35 18.06 22.39 Canola meal 37 200.00 110.65 Soybean meal 17 140.02 116.09 Corn DDGS Red Deer 150.00 41.78 Limestone Toronto 106.85 17.04 Vitamin supplements Calgary 9.90 39.16 Animal meal Lethbridge 66.42 71.95 Total Cost 518.07 a If a number is given, it refers to a specific RU where that crop is grown Table E 35. Bi-objective optimal feed formulation excluding SOC and LUC for feed milled in Wallenstein, Ontario, optimized for cost and GHG emissions Ingredient Production location a Amount in one tonne of feed Cost per kilogram Total cost Corn 17 254.99 79.59 Canola oil 29 30.00 39.39 Canola meal 23 200.00 126.66 Soybean meal 17 193.01 135.52 Corn DDGS Tiverton 150.00 38.27 724 Ingredient Production location a Amount in one tonne of feed Cost per kilogram Total cost Limestone Toronto 107.60 3.91 Vitamin supplements Mississauga 9.90 39.05 Animal meal Atwood 54.50 59.25 Total Cost 521.65 a If a number is given, it refers to a specific RU where that crop is grown Table E 36. Bi-objective optimal feed formulation excluding SOC and LUC for feed milled in St. Hyacinthe, Quebec, optimized for cost and GHG emissions Ingredient Production location a Amount in one tonne of feed Cost per kilogram Total cost Corn 12 254.99 79.10 Canola oil 29 30.00 39.51 Canola meal 23 200.00 127.33 Soybean meal 12 193.01 136.03 Corn DDGS Aylmer 150.00 42.99 Limestone Montreal 107.60 3.11 Vitamin supplements Kirkland 9.90 39.02 Animal meal Verchères 54.50 59.36 725 Ingredient Production location a Amount in one tonne of feed Cost per kilogram Total cost Total Cost 526.45 a If a number is given, it refers to a specific RU where that crop is grown Table E 37. Bi-objective optimal feed formulation excluding SOC and LUC for feed milled in Truro, Nova Scotia, optimized for cost and GHG emissions Ingredient Production location a Amount in one tonne of feed Cost per kilogram Total cost Corn 5 254.99 78.78 Canola oil 29 30.00 40.58 Canola meal 23 200.00 134.45 Soybean meal 12 193.01 144.96 Corn DDGS Havelock 150.00 44.98 Limestone Montreal 107.60 5.79 Vitamin supplements Kirkland 9.90 39.27 Animal meal Montreal 54.50 60.70 Total Cost 549.51 a If a number is given, it refers to a specific RU where that crop is grown 726 Table E 38. Bi-objective optimal feed formulation including SOC and LUC for feed milled in Abbotsford, British Columbia, optimized for cost and GHG emissions Ingredient Production location a Amount in one tonne of feed Cost per kilogram Total cost Corn 37 254.99 92.13 Canola oil 29 30.00 38.83 Canola meal 35 200.00 122.55 Soybean meal 17 193.01 163.14 Corn DDGS Red Deer 150.00 44.90 Limestone Toronto 107.60 18.90 Vitamin supplements Calgary 9.90 39.35 Animal meal Vancouver 54.50 59.42 Total Cost 579.22 a If a number is given, it refers to a specific RU where that crop is grown Table E 39. Bi-objective optimal feed formulation including SOC and LUC for feed milled in Lethbridge, Alberta, optimized for cost and GHG emissions Ingredient Production location a Amount in one tonne of feed Cost per kilogram Total cost Corn 37 308.74 99.00 Canola oil 29 18.06 23.09 727 Ingredient Production location a Amount in one tonne of feed Cost per kilogram Total cost Canola meal 35 200.00 119.96 Soybean meal 17 140.02 116.09 Corn DDGS Red Deer 150.00 41.78 Limestone Toronto 106.85 17.04 Vitamin supplements Calgary 9.90 39.16 Animal meal Lethbridge 66.42 71.95 Total Cost 528.07 a If a number is given, it refers to a specific RU where that crop is grown Table E 40. Bi-objective optimal feed formulation including SOC and LUC for feed milled in Wallenstein, Ontario, optimized for cost and GHG emissions Ingredient Production location a Amount in one tonne of feed Cost per kilogram Total cost Corn 17 254.99 79.594 Canola oil 29 30.00 39.391 Canola meal 29 200.00 134.607 Soybean meal 17 193.01 135.520 Corn DDGS Tiverton 150.00 38.274 Limestone Toronto 107.60 3.912 728 Ingredient Production location a Amount in one tonne of feed Cost per kilogram Total cost Vitamin supplements Mississauga 9.90 39.047 Animal meal Atwood 54.50 59.253 Total Cost 529.60 a If a number is given, it refers to a specific RU where that crop is grown Table E 41. Bi-objective optimal feed formulation including SOC and LUC for feed milled in St. Hyacinthe, Quebec, optimized for cost and GHG emissions Ingredient Production location a Amount in one tonne of feed Cost per kilogram Total cost Corn 12 254.99 79.10 Canola oil 29 30.00 39.51 Canola meal 29 200.00 135.40 Soybean meal 12 193.01 136.03 Corn DDGS Aylmer 150.00 42.99 Limestone Montreal 107.60 3.11 Vitamin supplements Kirkland 9.90 39.02 Animal meal Verchères 54.50 59.36 Total Cost 534.52 a If a number is given, it refers to a specific RU where that crop is grown 729 Table E 42. Bi-objective optimal feed formulation including SOC and LUC for feed milled in Truro, Nova Scotia, optimized for cost and GHG emissions Ingredient Production location a Amount in one tonne of feed Cost per kilogram Total cost Corn 6 254.99 83.50 Canola oil 29 30.00 40.58 Canola meal 29 200.00 142.54 Soybean meal 12 193.01 144.96 Corn DDGS Havelock 150.00 44.98 Limestone Montreal 107.60 5.79 Vitamin supplements Kirkland 9.90 39.27 Animal meal Montreal 54.50 60.70 Total Cost 562.31 a If a number is given, it refers to a specific RU where that crop is grown 730 Table E 43. Estimated environmental impacts per tonne of feed milled at each reference mill location for bi-objective optimal feeds, excluding SOC and LUC , optimized for cost and GHG emissions Feed mill location Abiotic depletion Abiotic depletion (fossil fuels) Acidification Eutrophication Freshwater aquatic ecotoxicity GHG emissions Human toxicity Marine aquatic ecotoxicity Ozone layer depletion Photochemical oxidation Terrestrial ecotoxicity Abbotsford 3.81E-03 6082.94 8.38 2.91 410.41 854.76 284.57 343357.95 1.67E-05 0.27 16.54 Lethbridge 3.76E-03 5755.20 8.13 2.91 409.69 844.93 282.20 348599.24 1.61E-05 0.27 16.57 Wallenstein 3.63E-03 4418.82 6.95 2.40 321.68 703.09 235.89 228670.62 1.46E-05 0.26 13.76 St. Hyacinthe 3.54E-03 4752.52 7.04 2.44 323.87 746.02 237.27 230455.58 1.50E-05 0.27 13.82 Truro 3.75E-03 5098.28 7.21 2.48 336.25 767.18 254.88 259132.54 1.55E-05 0.27 13.85 731 Table E 44. Estimated environmental impacts per tonne of feed milled at each reference mill location for bi-objective optimal feeds, inccluding SOC and LUC, optimized for cost and GHG emissions Feed mill location Abiotic depletion Abiotic depletion (fossil fuels) Acidification Eutrophication Freshwater aquatic ecotoxicity GHG emissions Human toxicity Marine aquatic ecotoxicity Ozone layer depletion Photochemical oxidation Terrestrial ecotoxicity Abbotsford 3.70E-03 6136.80 7.54 2.66 381.34 745.66 288.48 349373.30 1.54E-05 0.29 13.98 Lethbridge 3.73E-03 5851.75 8.07 2.88 408.37 785.37 281.73 346934.65 1.62E-05 0.27 16.57 Wallenstein 3.71E-03 4572.81 6.95 2.43 329.50 615.66 245.56 244241.12 1.48E-05 0.26 13.77 St. Hyacinthe 4.00E-03 6506.94 7.27 2.53 355.67 777.26 296.06 292636.91 1.66E-05 0.29 13.97 Truro 3.86E-03 5359.24 7.17 2.48 345.64 672.89 267.67 276712.95 1.57E-05 0.28 13.87 732 Table E 45. Costs per tonne of feed milled at each reference location for each different MOO scenario and for each different treatment of SOC changes and perennial to annual land transitions Abbotsford Lethbridge Wallenstein St. Hyacinthe Truro Scenario Excluding SOC Including SOC Excluding SOC Including SOC Excluding SOC Including SOC Excluding SOC Including SOC Excluding SOC Including SOC 1 589.96 595.71 570.01 574.98 545.14 529.60 559.16 543.22 580.68 562.31 2 586.10 587.36 551.51 574.98 521.65 529.60 526.72 542.00 554.50 562.31 3 510.96 510.96 476.63 485.94 521.65 521.65 526.45 526.45 523.41 554.23 4 506.69 506.69 472.10 472.10 465.63 465.63 469.77 469.77 484.64 484.64 733 E.2 Supplementary figures Figure E 1. Relative impacts per tonne of feed milled at Abbotsford for both economically and GHG optimal feeds, across both treatments of SOC and LUC. The economically optimal feed excluding SOC and LUC is the reference category. 020406080100120Economically optimal - SOC and LUC excludedEconomically optimal - SOC and LUC includedGHG optimal - SOC and LUC excludedGHG optimal - SOC and LUC included 734 Figure E 2. Relative impacts per tonne of feed milled at Lethbridge for both economically and GHG optimal feeds, across both treatments of SOC and LUC. The economically optimal feed excluding SOC and LUC is the reference category. 020406080100120Economically optimal - SOC and LUC excludedEconomically optimal - SOC and LUC includedGHG optimal - SOC and LUC excludedGHG optimal - SOC and LUC included 735 Figure E 3. Relative impacts per tonne of feed milled at St. Hyacinthe for both economically and GHG optimal feeds, across both treatments of SOC and LUC. The economically optimal feed excluding SOC and LUC is the reference category. 020406080100120140Economically optimal - SOC and LUC excludedEconomically optimal - SOC and LUC includedGHG optimal - SOC and LUC excludedGHG optimal - SOC and LUC included 736 Figure E 4. Relative impacts per tonne of feed milled at Truro for both economically and GHG optimal feeds, across both treatments of SOC and LUC. The economically optimal feed excluding SOC and LUC is the reference category. 020406080100120140Economically optimal - SOC and LUC excludedEconomically optimal - SOC and LUC includedGHG optimal - SOC and LUC excludedGHG optimal - SOC and LUC included 737 Figure E 5. Estimated impacts per tonne of multi-objective optimal feed milled at Lethbridge for each optimization scenario for those impact categories included in the MOO, excluding upstream SOC changes and perennial to annual land transitions Figure E 6. Estimated impacts per tonne of multi-objective optimal feed milled at St Hyacinthe for each optimization scenario for those impact categories included in the MOO, excluding upstream SOC changes and perennial to annual land transitions 020406080100120140160Acidification GHG emissions Eutrophication Human toxicityPercent impactsScenario 1Scenario 2Scenario 3Scenario 4020406080100120140160Acidification GHG emissions Eutrophication Human toxicityPercent impactsScenario 1Scenario 2Scenario 3Scenario 4 738 Figure E 7. Relative percentage of impacts per tonne of eggs produced in conventional cage systems using a single objective optimal feed for lowest cost, and a multi-objective optimal feed with strong preference for minimization of environmental impacts milled at Wallenstein. 020406080100120Relative percentage of impacts (%)SOO Cost Feed MOO S1 Feed 739 Figure E 8. Relative percentage of impacts per tonne of eggs produced in free range systems using a single objective optimal feed for lowest cost, and a multi-objective optimal feed with strong preference for minimization of environmental impacts milled at Wallenstein 020406080100120Relative percentage of impacts (%)SOO Cost Feed MOO S1 Feed
Thesis/Dissertation
eng
Interdisciplinary Studies
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Pelletier, Nathan
Investigating sustainability improvement potential in the Canadian egg industry through integration of life cycle assessment, animal welfare, and operations research
Text