Science, Irving K. Barber Faculty of (Okanagan)
Biology, Department of (Okanagan)
DSpace
UBCO
Zargar Ershadi, Shiva
Master of Science - MSc
University of British Columbia
Rapid population growth and the associated rising demand for food are driving agricultural expansion and intensification. One important consequence is increased use and loss of reactive nitrogen (Nr) in agriculture – in particular in livestock production - precipitating Nr cascades with multiple impacts on ecosystems. Among livestock production systems, egg production is the fastest growing segment worldwide. Improving nitrogen use efficiency (NUE) is a key priority for enabling more environmentally sustainable egg production. The objective of this research is to provide information to support NUE improvements along egg production supply chains while simultaneously minimizing “systems level” resource consumption and negative environmental impacts. NUE is influenced by feed production practices, feed composition, feed conversion efficiency, and manure management practices. Technological and management changes targeting these variables along egg supply chains to improve NUE can influence system-level environmental performance and resource efficiency. To consider the relative efficacy of Nr emissions mitigation options with respect to improving NUE and reducing resource use and emissions, life cycle assessment (LCA) methodology was employed. Potentially efficacious NUE improvement options (taking into account environmental, technical and economic criteria) were identified based on a literature review. Priority strategies were: “4Rs” practices and biochar addition to cropland for the feed input production stage; reduced crude protein diet via synthetic amino acid supplementation for the feed formulation stage; use of ammonia scrubbers in barns along with use of manure belts; biochar addition to the stored manure; and manure incorporation at time of field application. LCA, NUE, and N footprint results iv indicated that each mitigation option might be more or less suitable depending on kinds of environmental impacts considered. The application of scrubbers and biochar addition to the stored manure resulted in 10% and 3% higher NUE compared to the baseline, with considerable reduction of acidification and eutrophication impacts. However, they resulted in higher energy consumption, abiotic depletion and ozone layer depletion. Results clearly indicate that there is trade-offs among different impact categories and NUE for most NUE strategies. Hence, efforts to improve NUE must take into account a variety of potential resource and environmental impacts to ensure informed decision making.
LIFE CYCLE ASSESSMENT OF NITROGEN EFFICIENCY STRATEGIES FOR CANADIAN EGG SUPPLY CHAINS by Shiva Zargar Ershadi M.Sc., University of Mohaghegh Ardebili, 2017 B.Sc., University of Guilan, 2014 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Biology) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) July 2020 © Shiva Zargar Ershadi, 2020 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, a thesis/dissertation entitled: LIFE CYCLE ASSESSMENT OF NITROGEN EFFICIENCY STRATEGIES FOR CANADIAN EGG SUPPLY CHAINS submitted by Shiva Zargar Ershadi in partial fulfillment of the requirements for the degree of Master of Science in Biology Examining Committee: Dr. Nathan Pelletier, Irving K. Barber Faculty of Science/ Faculty of Management Supervisor Dr. Goretty Dias, School of Environment, Enterprise and Development, University of Waterloo Supervisory Committee Member Dr. Melanie Jones, Irving K. Barber Faculty of Science Supervisory Committee Member Dr. Kasun Hewage, School of Engineering University Examiner iii Abstract Rapid population growth and the associated rising demand for food are driving agricultural expansion and intensification. One important consequence is increased use and loss of reactive nitrogen (Nr) in agriculture – in particular in livestock production - precipitating Nr cascades with multiple impacts on ecosystems. Among livestock production systems, egg production is the fastest growing segment worldwide. Improving nitrogen use efficiency (NUE) is a key priority for enabling more environmentally sustainable egg production. The objective of this research is to provide information to support NUE improvements along egg production supply chains while simultaneously minimizing “systems level” resource consumption and negative environmental impacts. NUE is influenced by feed production practices, feed composition, feed conversion efficiency, and manure management practices. Technological and management changes targeting these variables along egg supply chains to improve NUE can influence system-level environmental performance and resource efficiency. To consider the relative efficacy of Nr emissions mitigation options with respect to improving NUE and reducing resource use and emissions, life cycle assessment (LCA) methodology was employed. Potentially efficacious NUE improvement options (taking into account environmental, technical and economic criteria) were identified based on a literature review. Priority strategies were: “4Rs” practices and biochar addition to cropland for the feed input production stage; reduced crude protein diet via synthetic amino acid supplementation for the feed formulation stage; use of ammonia scrubbers in barns along with use of manure belts; biochar addition to the stored manure; and manure incorporation at time of field application. LCA, NUE, and N footprint results iv indicated that each mitigation option might be more or less suitable depending on kinds of environmental impacts considered. The application of scrubbers and biochar addition to the stored manure resulted in 10% and 3% higher NUE compared to the baseline, with considerable reduction of acidification and eutrophication impacts. However, they resulted in higher energy consumption, abiotic depletion and ozone layer depletion. Results clearly indicate that there is trade-offs among different impact categories and NUE for most NUE strategies. Hence, efforts to improve NUE must take into account a variety of potential resource and environmental impacts to ensure informed decision making. v Lay Summary The agricultural sector is one of the main sources of reactive nitrogen (Nr) emissions, which have diverse impacts on ecosystems. There are numerous technologies and management strategies for reducing Nr emissions and improving nitrogen use efficiency in agriculture. To evaluate the relative efficacy of a subset of mitigation options that are potentially most relevant for egg supply chains, life cycle assessment modelling was employed. The results showed trade-offs between strategies with respect to nitrogen use efficiency, resource use, and emissions reduction. For example, ammonia scrubber application in the barn was found to be a suitable strategy to improve NUE and reduce greenhouse gas emissions, but increased energy consumption and ozone depleting emissions. vi Preface I, Shiva Zargar Ershadi, conceived and developed the material presented in this thesis under the supervision of Dr. Nathan Pelletier. The research supervisor has reviewed the manuscripts and the thesis and has provided critical and valuable feedback. In addition, Dr. Goretty Dias and Dr. Melanie Jones, the supervisory committee members, have reviewed the thesis and provided constructive feedback. Chapters 3-5 of this thesis have been prepared as individual manuscripts that are currently published, under review, or will be submitted to peer-reviewed journals for publication. The details of each chapter are described below. Chapter 3 A version of this chapter was published in the Journal of Cleaner Production:  Zargar S, Dias G, Heidari MD, and Pelletier N. 2020. Improving nitrogen use efficiency in crop-livestock systems: A review of mitigation technologies and management strategies, and their potential applicability for egg supply chains. The purpose of this manuscript was to review and synthesize literature related to technologies and management strategies influencing reactive nitrogen emissions in crop and livestock production systems. Mitigation options are analyzed according to technical, environmental, and economic criteria. A subset that might be considered priority strategies but that should first be subject to detailed, life cycle assessment research in order to ascertain potential benefits and trade-offs are identified. Shiva Zargar Ershadi conducted the literature review, analyzed the results, and wrote the manuscript. Co-authors provided feedback and input into the direction of the study and edited the manuscript prior to publication. vii Chapter 4 A version of this chapter has been written and will be submitted to the International Journal of Life Cycle Assessment:  Zargar S, Heidari MD, Dutta B, Dias G, and Pelletier N. A Review of Simulation Models to Estimate Reactive Nitrogen Emissions in Crop-Livestock Supply Chains for Use in Process-based Life Cycle Assessment. The objective of this manuscript was to identify, characterize, and compare available models to simulate reactive nitrogen (Nr) emissions in crop and/or livestock production systems in order to identify those best suited for use in process-based LCAs. Shiva Zargar Ershadi carried out the literature review, analyzed the results, and wrote the manuscript. Co-authors provided feedback and input into the direction of the study and edited the manuscript. Chapter 5 A version of this chapter has been written with the purpose of submission to the Journal of Resources, Conservation & Recycling:  Zargar S, Heidari MD, Dutta B, Dias G, and Pelletier N. Environmental Life Cycle Assessment of Mitigation Technologies and Management Strategies to Improve Nitrogen Use Efficiency Along Industrial Egg Supply Chains. The objective of this manuscript was to use process-based environmental life cycle assessment to evaluate and compare a priority subset of technologies and management strategies for improving the nitrogen use efficiency of egg production supply chains while minimizing resource consumption and environmental impacts. A series of life cycle viii inventory models were developed and assessed to represent the technologies and management strategies identified in chapter 2. In addition, the nitrogen emission models identified in chapter 3 were utilized to estimate nitrogen emissions for each supply chain stage and technology/management strategy combination. Shiva Zargar Ershadi carried out the research, was responsible for the data collection, created the models, analyzed and interpreted the results, and wrote the manuscript. Co-authors provided feedback and input into the direction of the study and edited the manuscript. ix Table of Contents Abstract ................................................................................................................................... iii Lay Summary ...........................................................................................................................v Preface ..................................................................................................................................... vi Table of Contents ................................................................................................................... ix List of Tables ........................................................................................................................ xix List of Figures ..................................................................................................................... xxvi List of Abbreviations ....................................................................................................... xxviii Acknowledgements ........................................................................................................... xxxii Dedication ......................................................................................................................... xxxiv Chapter 1: Introduction ..........................................................................................................1 1.1 Synopsis .................................................................................................................1 1.2 The relevance of nitrogen use efficiency ...............................................................1 1.2.1 The microbial nitrogen cycling groups and major pathways ................................3 1.2.2 Factors affecting the nitrogen cycle ......................................................................6 1.2.3 Major sources of reactive nitrogen emissions into the environment ....................9 1.2.4 Nitrogen footprint tools.......................................................................................11 1.3 Background information on N emissions from crop-livestock production .........14 1.3.1 Feed production ..................................................................................................16 1.3.2 Feed formulation .................................................................................................16 1.3.3 Manure management ...........................................................................................17 x 1.4 Egg production in Canada ....................................................................................19 1.5 Research objectives ..............................................................................................20 1.6 Thesis structure ....................................................................................................22 Chapter 2: The Methodology of Life Cycle Assessment .....................................................23 2.1 Synopsis ...............................................................................................................23 2.2 Life Cycle Assessment .........................................................................................23 2.2.1 Goal and scope ....................................................................................................26 2.2.2 Life cycle inventory ............................................................................................30 2.2.3 Life cycle impact assessment ..............................................................................36 2.2.4 Life cycle interpretation ......................................................................................41 2.3 Source of difficulties in life cycle assessment .....................................................45 Chapter 3: Improving Nitrogen Use Efficiency in Crop-Livestock Systems: A Review of Mitigation Technologies and Management Strategies, and Their Potential Applicability for Egg Supply Chains ...............................................................................................................48 3.1 Synopsis ...............................................................................................................48 3.2 Nitrogen use efficiency in crop-livestock systems ..............................................49 3.3 Methods................................................................................................................53 3.3.1 Search strategy and literature selection ...............................................................53 3.3.2 Detailed review criteria .......................................................................................55 3.4 Results and Discussion ........................................................................................57 3.4.1 Feed input production .........................................................................................58 3.4.1.1 Fertilization ..............................................................................................62 xi 3.4.1.2 Fertilizer type ...........................................................................................63 3.4.1.3 Inhibitors ..................................................................................................64 3.4.1.4 Irrigation ..................................................................................................65 3.4.1.5 Cover crops ..............................................................................................66 3.4.1.6 Intercrops .................................................................................................67 3.4.1.7 Biochar addition to soil ............................................................................68 3.4.1.8 Comparative efficacy and feasibility of NUE strategies/technologies for feed crop production ................................................................................................69 3.4.2 Feed formulation .................................................................................................71 3.4.2.1 Reduced crude protein .............................................................................73 3.4.2.2 Synthetic amino acids ..............................................................................73 3.4.2.3 Enzyme supplementation .........................................................................74 3.4.2.4 Comparative efficacy and feasibility of NUE strategies/technologies for feed formulation .......................................................................................................75 3.4.3 Manure management ...........................................................................................75 3.4.3.1 Manure management in housing ..............................................................76 3.4.3.1.1 Ventilation.............................................................................................79 3.4.3.1.2 Floor systems and manure collection ....................................................81 3.4.3.2 Manure storage.........................................................................................85 3.4.3.2.1 Type of storage .....................................................................................88 3.4.3.2.2 Manure treatment in storage .................................................................91 3.4.3.3 Manure land application practices ...........................................................95 xii 3.4.3.3.1 Injection ................................................................................................97 3.4.3.3.2 Incorporation .........................................................................................97 3.4.3.3.3 Spreading ..............................................................................................98 3.4.3.3.4 Comparative efficacy and feasibility of NUE strategies/technologies for manure land application .................................................................................98 3.5 Conclusions and recommendations......................................................................99 Chapter 4: A Review of Simulation Models to Estimate Reactive Nitrogen Emissions in Crop-Livestock Supply Chains for Process-based Life Cycle Assessment .......................102 4.1 Synopsis .............................................................................................................102 4.2 The importance of nitrogen emission models in crop-livestock systems ..........103 4.3 Methods..............................................................................................................107 4.3.1 Search strategy ..................................................................................................107 4.3.2 Criteria for model selection ..............................................................................107 4.3.2.1 Informative Criteria ...............................................................................109 4.3.2.2 Comparative Criteria ..............................................................................109 4.4 Results ................................................................................................................110 4.4.1 Model characteristics ........................................................................................118 4.4.1.1 Model type .............................................................................................118 4.4.1.2 Considered nitrogen species ..................................................................119 4.4.1.3 Spatial scale ...........................................................................................119 4.4.1.4 Country ..................................................................................................120 4.4.1.5 Cost ........................................................................................................121 xiii 4.4.1.6 Accessibility and availability of supporting information .......................121 4.4.1.7 Robustness .............................................................................................122 4.4.1.8 Required input data ................................................................................123 4.4.1.9 Ease of application .................................................................................124 4.4.1.10 Capacity to quantify uncertainty ..............................................................124 4.5 Discussion ..........................................................................................................124 4.6 Conclusions ........................................................................................................128 Chapter 5: Environmental Life Cycle Assessment of Mitigation Technologies and Management Strategies to Improve Nitrogen Use Efficiency along Industrial Egg Supply Chains ................................................................................................................................130 5.1 Synopsis .............................................................................................................130 5.2 Efficiency and environmental impacts in the egg sector ...................................132 5.3 Methods..............................................................................................................135 5.3.1 Nitrogen use efficiency .....................................................................................135 5.3.2 Life cycle assessment ........................................................................................137 5.3.2.1 Goal and scope definition ......................................................................137 5.3.2.1.1 Description of the conventional layer supply chain in Canada...........138 5.3.2.1.2 System boundary .................................................................................138 5.3.2.1.3 Functional unit ....................................................................................141 5.3.2.2 Life cycle inventory analysis .................................................................141 5.3.2.2.1 Data acquisition, assumptions, and limitations ...................................141 Feed input production ........................................................................................143 xiv Feed formulation ................................................................................................156 Manure management ..........................................................................................160 Egg production ...................................................................................................170 5.3.2.2.2 Modeling Nr, P, and methane emissions from poultry manure in the barn, storage, and land application stages ..........................................................172 5.3.2.2.3 Uncertainty assessment .......................................................................173 5.3.2.3 Life cycle impact assessment .................................................................173 5.4 Results and discussion .......................................................................................174 5.4.1 Life cycle NUE and N footprint for feed input production ..............................175 5.4.2 Life cycle NUE and N footprint for feed formulation ......................................188 5.4.3 Life cycle NUE and N footprint for manure management: Barn ......................195 5.4.4 Life cycle NUE and N footprint for manure management: Storage .................198 5.4.5 Life cycle NUE and N footprint for manure management: Field application ..202 5.4.6 Life cycle NUE and N footprint for egg production under baseline and alternative scenarios ...................................................................................................205 5.4.7 Sensitivity analyses ...........................................................................................219 5.4.7.1 Rate of biochar addition to soil ..............................................................219 5.4.7.2 Emission factors applied in the barn and manure storage stages ...........222 5.4.7.3 Effects of choice of emissions model ....................................................223 5.4.7.4 Acid scrubber: Acid, water, and electricity usage .................................224 5.4.8 Uncertainty analyses .........................................................................................226 5.4.9 Interpretation .....................................................................................................228 xv 5.5 Conclusions and recommendations....................................................................230 Chapter 6: Conclusions and Future Research Direction ....................................................233 6.1 Main findings .....................................................................................................233 6.2 Research Contributions ......................................................................................235 6.3 Limitations and future directions .......................................................................236 Bibliography .........................................................................................................................239 Appendices ............................................................................................................................319 Appendix A - Emission models descriptions .................................................319 A.1 Emission models for crop production systems ...................................319 A.1.1 AGRIFLUX .................................................................................319 A.1.2 ANIMO ........................................................................................319 A.1.3 CANDY .......................................................................................320 A.1.4 CENTURY ...................................................................................321 A.1.5 CERES .........................................................................................321 A.1.6 CREAMS .....................................................................................321 A.1.7 DAISY .........................................................................................322 A.1.8 DAYCENT...................................................................................322 A.1.9 ECOSYS ......................................................................................323 A.1.10 EPIC ...........................................................................................324 A.1.11 INCA ..........................................................................................325 A.1.12 LEACHN ...................................................................................325 A.1.13 LIXIM ........................................................................................326 xvi A.1.14 NCYCLE ....................................................................................326 A.1.15 NGAS .........................................................................................326 A.1.16 NLEAP .......................................................................................327 A.1.17 RZWQM ....................................................................................327 A.1.18 SOILN ........................................................................................328 A.1.19 SOILNDB ..................................................................................328 A.1.20 STICS .........................................................................................329 A.1.21 STONE .......................................................................................329 A.1.22 SWAT ........................................................................................330 A.1.23 Thinkstep ....................................................................................330 A.1.24 TNT2 ..........................................................................................331 A.1.25 TOUGHREACT-N ....................................................................332 A.1.26 VOLT’AIR .................................................................................332 A.1.27 WHNSIM ...................................................................................333 A.1.28 WNMM ......................................................................................334 A.2 Emission models for livestock production systems ............................335 A.2.1 AGRAMMON .............................................................................335 A.2.2 ALFAM ........................................................................................335 A.2.3 GNL .............................................................................................336 A.2.4 MANNER ....................................................................................336 A.2.5 MITERRA ....................................................................................337 A.2.6 TAN-mass balance .......................................................................337 xvii A.3 Emission models for combined crop-livestock production systems ...338 A.3.1 APEX ...........................................................................................338 A.3.2 CAPRI ..........................................................................................339 A.3.3 DNDC ..........................................................................................339 A.3.4 FarmN ..........................................................................................341 A.3.5 GLEAM .......................................................................................341 A.3.6 HOLOS ........................................................................................342 A.3.7 IFSM ............................................................................................342 A.3.8 IPCC .............................................................................................343 A.3.9 IULIA ...........................................................................................344 A.3.10 NARSES ....................................................................................345 A.3.11 NEMA ........................................................................................345 A.3.12 NitroScape ..................................................................................346 A.3.13 OVERSEER ...............................................................................346 A.3.14 SALCA.......................................................................................347 Appendix B Emission models required input data ........................................349 Appendix C - Monto Carlo results .................................................................356 C.1 Feed input production..........................................................................356 C.2 Feed formulation .................................................................................371 C.3 Manure management: Barn .................................................................374 C.4 Manure management: Storage .............................................................376 C.5 Manure management: Field .................................................................380 xviii C.6 Egg production supply chains .............................................................382 xix List of Tables Table 1-1 Definitions and calculations of NUE .......................................................................12 Table 2-1 Similarities and differences between ACLCA and CLCA. .....................................27 Table 3-1 Search terms ............................................................................................................54 Table 3-2 Criteria, sub-criteria and indicators selected to characterize and compare reported technologies and strategies to mitigate nitrogen emissions along crop-livestock supply chains. ......................................................................................................................................55 Table 3-3 Potential mitigation options for Nr emissions in feed input production systems. ...59 Table 3-4 Potential mitigation options for Nr emissions through feed formulation. ...............72 Table 3-5 Potential mitigation options for Nr emissions in poultry housing. ..........................77 Table 3-6 Potential mitigation options for Nr emissions during manure storage stage. ..........86 Table 3-7 Potential mitigation options for Nr emissions in manure land application. .............96 Table 4-1 Criteria and sub-criteria for identifying the best-suited models for considering Nr emissions in LCA. ..................................................................................................................108 Table 4-2 Summary of Nr emission models for crop production systems (presented alphabetically). F: Farm, R: Regional, N: National, G: Global. Criteria for which no information was found are indicated by; NA: Not Available. ...............................................111 Table 4-3 Summary of Nr models for livestock production systems (presented alphabetically). F: Farm, R: Regional, P: Province, N: National, C: Continental, G: Global. The criteria for which no information was found through reviewing the open literature is indicated by NA: Not Available. ...........................................................................................115 xx Table 4-4 Summary of Nr models for combined crop-livestock production systems (presented alphabetically). F: Farm, R: Regional, P: Province, N: National, C: Continental, G: Global. The criteria for which no information was found through reviewing the open literature is indicated by NA: Not Available. ...........................................................................................116 Table 4-5 Numbers of models considering different spatial scales for each agricultural production system context .....................................................................................................120 Table 4-6 Robustness of Nr emission models ........................................................................122 Table 4-7 Comparative evaluation of Nr simulation models based on the intended application context (only those models satisfying the defined threshold for each criterion for use in process-based LCA are indicated). ........................................................................................126 Table 5-1 Mitigation technologies and management strategies for NUE at key supply chain stages ......................................................................................................................................139 Table 5-2 Processes and scenarios for the considered technologies and management strategies at each supply chain stage ......................................................................................142 Table 5-3 LCI processes and databases used to model 1 tonne of corn production (baseline vs. alternative strategies) ........................................................................................................147 Table 5-4 LCI processes and databases used to model 1 tonne of soybean production (baseline vs. alternative strategies) ........................................................................................150 Table 5-5 LCI processes and databases used to model 1 tonne of wheat production (baseline vs. alternative strategies) ........................................................................................................153 Table 5-6 Formulations of the baseline and reduced crude protein with synthetic amino acids supplementation layer feeds (per tonne of feed). ...................................................................156 xxi Table 5-7 LCI processes and databases used to model the production of 1 tonne of baseline layer feed (baseline vs. reduced crude protein diet supplemented with synthetic amino acids) ......................................................................................................................................157 Table 5-8 LCI models and source databases used to model manure management in barns (baseline vs. application of an acid scrubber) ........................................................................162 Table 5-9 LCI models and source databases used to model manure management in storage (baseline vs. biochar addition) ...............................................................................................164 Table 5-10 LCI models and source databases used to model manure land application (baseline vs. incorporation) ....................................................................................................165 Table 5-11 LCI models and source databases used to model the manure management stage assuming combined application of all strategies ...................................................................167 Table 5-12 Life cycle inventory data, models, and source databases for the production of one tonne of eggs assuming application of all mitigation strategies ............................................170 Table 5-13 Selected life cycle impact assessment categories and category indicators..........174 Table 5-14 NUE and N footprint associated with 1 tonne of crop production (baseline vs. alternative strategies) .............................................................................................................177 Table 5-15 Life cycle impact assessment results for feed input production (per tonne of crop) applying the 4Rs strategy and biochar addition to the soil compared to the baseline ...........179 Table 5-16 NUE and N footprint of feed production for the baseline and reduced crude protein feeds (based on the amount of feed required to produce 1 tonne of eggs, and the associated level of N excretion) .............................................................................................189 xxii Table 5-17 Life cycle impact assessment results at the feed mill gate per tonne of feed input sourced for layer feeds ...........................................................................................................191 Table 5-18 Life cycle impact assessment results (per tonne of feed produced) for the baseline and reduced crude protein supplemented with synthetic amino acids (RCP) feeds. .............193 Table 5-19 NUE and N footprint associated with manure management in the barn under the baseline and alternative NUE (scrubber) scenario .................................................................195 Table 5-20 Comparative LCIA results for the baseline and alternative (an acid scrubber) manure management strategy in the layer hen barn per tonne of eggs produced (1140 kg manure). .................................................................................................................................196 Table 5-21 NUE and N footprint during manure storage for the baseline and alternative NUE strategies ................................................................................................................................199 Table 5-22 Comparative LCIA results for the baseline and alternative (biochar addition to manure) manure management strategies at the storage per tonne of eggs produced (1140 kg manure). .................................................................................................................................200 Table 5-23 NUE and N footprint per tonne of eggs produced associated with manure management at time of land application under the baseline (surface spreading) and alternative (incorporation following spreading) scenarios. .....................................................................202 Table 5-24 Comparative LCIA results for the baseline and alternative manure land application techniques (incorporation) per tonne of eggs produced (1140 kg manure). .......203 Table 5-25 Life cycle NUE and N footprints for the baseline and alternative scenarios per tonne of eggs produced. Negg: N content in 1 tonne egg, NM: N content in manure on field after emissions, Ns: N in spent hens. .....................................................................................206 xxiii Table 5-26 Comparative LCIA results for the baseline and alternative NUE strategies for 1 tonne of egg production. ........................................................................................................208 Table 5-27 LCIA results for feed input production under different assumed rates of biochar application (5, 7.5 and 10 tonnes biochar per ha) ..................................................................220 Table 5-28 LCIA results per 1 tonne of eggs produced, assuming that the combined emissions from manure in the barn and during storage are distributed at rates of 50% barn/50% storage, 25% barn/75% storage, and 75% barn/25% storage. ...............................223 Table 5-29 LCIA results for the manure management at the barn using an acid scrubber for production of 1 tonne of eggs under different rates of acid consumption as well as water and energy use ..............................................................................................................................225 Table 5-30 Top strategies for egg supply chains for reducing environmental impacts and nitrogen footprints and improving NUE. ...............................................................................229 Table A-1 Required input data for N emissions models for crop production systems ..........349 Table A-2 Required input data for N emission models for livestock production systems ....353 Table A-3 Required input data for N emissions models for combined crop-livestock production systems.................................................................................................................354 Table A-4 Uncertainty analysis results from a Monte Carlo simulation (1000 runs) for 1 tonne of corn production, including reference and alternative scenarios.. ............................356 Table A-5 Uncertainty analysis results from a Monte Carlo simulation (1000 runs) for 1 tonne soybean of production, including reference and alternative scenarios. .......................361 Table A-6 Uncertainty analysis results from a Monte Carlo simulation (1000 runs) for 1 tonne of wheat production, including reference and alternative scenarios. ...........................366 xxiv Table A-7 Uncertainty analysis results from a Monte Carlo simulation (1000 runs) for 1 tonne of feed production, including reference and alternative scenarios. ..............................371 Table A-8 Uncertainty analysis results from a Monte Carlo simulation (1000 runs) for manure management at barn for 1 tonne of egg production, including reference and alternative scenario.. ..............................................................................................................374 Table A-9 Uncertainty analysis results from a Monte Carlo simulation (1000 runs) for manure management at storage for 1 tonne of egg production, including reference and alternative scenarios. .............................................................................................................376 Table A-10 Uncertainty analysis results from a Monte Carlo simulation (1000 runs) for manure management at field for 1 tonne of egg production, including reference and alternative scenarios. ..............................................................................................................380 Table A-11 Uncertainty analysis results from a Monte Carlo simulation (1000 runs) for the entire egg production supply chain (1 tonne egg produced), including reference scenario (S1) and application of 4Rs strategy scenario (S2).. ......................................................................382 Table A-12 Uncertainty analysis results from a Monte Carlo simulation (1000 runs) for the entire egg production supply chain (1 tonne egg produced), including reference scenario (S1) and biochar addition to soil scenario (S3)..............................................................................384 Table A-13 Uncertainty analysis results from a Monte Carlo simulation (1000 runs) for the entire egg production supply chains (1 tonne egg produced), including reference scenario (S1) and reduced crude protein diet scenario (S4). ................................................................388 xxv Table A-14 Uncertainty analysis results from a Monte Carlo simulation (1000 runs) for the entire egg production supply chain (1 tonne egg produced), including reference scenario (S1) and application of scrubber in barn scenario (S5).. ...............................................................390 Table A-15 Uncertainty analysis results from a Monte Carlo simulation (1000 runs) for the entire egg production supply chain (1 tonne egg produced), including reference scenario (S1) and biochar addition to the stored manure scenario (S6). ......................................................392 Table A-16 Uncertainty analysis results from a Monte Carlo simulation (1000 runs) for the entire egg production supply chain (1 tonne egg produced), including reference scenario (S1) and manure incorporation scenario (S7).. ..............................................................................397 Table A-17 Uncertainty analysis results from a Monte Carlo simulation (1000 runs) for the entire egg production supply chain (1 tonne egg produced), including reference scenario (S1) and application of all strategies scenario (S8).. .....................................................................399 xxvi List of Figures Figure 1-1 N input-output mass balance in the system of crop and livestock production. ......11 Figure 2-1 Research methodology: life cycle assessment .......................................................23 Figure 2-2 Streamlined procedures for LCI analysis (ISO 2006b). .........................................32 Figure 4-1 The number of models simulating emissions of various Nr species in agricultural systems. ..................................................................................................................................118 Figure 4-2 Numbers of models to simulate Nr emissions in different countries ...................121 Figure 5-1 Diagram and system boundaries for the cradle-to-farm gate environmental life cycle assessment of NUE technologies and management strategies for the Canadian egg industry ..................................................................................................................................140 Figure 5-2 Comparative LCIA results for corn production under baseline and alternative NUE strategies. ......................................................................................................................183 Figure 5-3 Comparative LCIA results for soybean production under baseline and alternative NUE strategies. ......................................................................................................................186 Figure 5-4 Comparative LCIA results for wheat production under baseline and alternative NUE strategies. ......................................................................................................................188 Figure 5-5 Comparative LCIA results for control diet and reduced crude protein diet supplemented with synthetic amino acids..............................................................................195 Figure 5-6 Comparative LCIA results for manure management in the barn for the baseline and alternative (acid scrubber) NUE strategy ........................................................................198 Figure 5-7 Comparative LCIA results for manure storage for the baseline and alternative (biochar addition to manure) NUE strategy ...........................................................................201 xxvii Figure 5-8 Comparative LCIA results for manure management at time of field application for the baseline and alternative (incorporation) strategies ...........................................................204 Figure 5-9 Comparative LCIA results for 1 tonne of egg production for the baseline and alternative NUE strategies.. ...................................................................................................216 Figure 5-10 Comparative LCIA results for 1 tonne of eggs produced relative to the baseline.. ................................................................................................................................218 Figure 5-11 Comparative LCIA results for corn production under different assumed rates of biochar application (5, 7.5 and 10 tonne biochar per ha) .......................................................221 xxviii List of Abbreviations Abiotic resource depletion AD Abiotic resource depletion–fossil AD(fossil) Acidification AC Adapting Mosaic AM Anaerobic digestion AD Aquatic eutrophication AEU Area of protection AoP Attributional life cycle assessment ALCA Best management practice BMP Biological nitrogen fixation BNF Black Soldier Fly Larvae BSFL Carcinogens Ca Climate change CcH Commission of the European Communities CEC Consequential life cycle assessment CLCA Crude protein CP Cumulative energy demand CED Ecotoxicity Ec Energy use EnU Environmental impact assessment EIA xxix Environmental life cycle assessment e-LCA Environmental protection agency EPA Equivalent eq Eutrophication EU Fossil Fuel Depletion FFD Freshwater aquatic ecotoxicity FE Freshwater Eutrophication FEU Functional unit FU Global Orchestration GO Global warming potential GWP Greenhouse gas GHG Human toxicity HT Input-Output IO International organization for standard ISO International Reference Life Cycle Data System ILCD Land use LU Life Cycle Assessment LCA Life cycle costing LCC Life cycle impact assessment LCIA Life cycle inventory LCI Life cycle sustainability assessment LCSA xxx Marine eutrophication ME Millennium Ecosystem Assessment MEA Mineral use MU Nitrogen N Nitrogen agronomic efficiency NAE Nitrogen oxide NOx Nitrogen use efficiency NUE Non-renewable energy use NRE Numeral unit spread assessment pedigree NUSAP Order from Strength OS Ozone layer depletion OLD Particulate matter formation PMF Photochemical oxidant formation potential POFP Photochemical ozone creation POC Radiation R Resource Depletion RD Respiratory inorganics RI Respiratory inorganics RiO Respiratory organics RO Sensitivity analysis SA Standard deviation SD xxxi Social life cycle assessment SLCA Society of Environmental Toxicology & Chemistry SETAC Subsurface slurry applicator SSA Technogarden TG Terrestrial Acidification TA Terrestrial eutrophication TEU Terrestrial ecotoxicity TE Total Kjeldahl N TKN Uncertainty analysis UA United Nations Environmental Program UNEP Water use WU xxxii Acknowledgements I would like to express my sincere gratitude to my supervisor, Dr. Nathan Pelletier, for his trust, constant support, guidance, inspiration, dedication to my success, critical insights into the project, and endless patience throughout this academic journey. I am also thankful for his funding of my master's project. Among the many great conversations with Dr. Pelletier, one of them has become a pillar of my academic life. His attitude towards the “five Cs”- Curiosity, Creativity, Communication, Critical thinking, and Commitment to being a scholar - was incredibly motivational. Thanks a million! I would like to acknowledge and thank Dr. Goretty Dias and Dr. Melanie Jones, members of my supervisory committee, for their guidance, expertise, valuable comments, and constructive suggestions. Despite their busy schedules, they always had time to provide me with invaluable, creative and constructive comments. I am also grateful for the positive and constructive environment in the Food Systems PRISM Lab. To all my friends and colleagues, thank you for your friendship, guidance, encouragement, and laughter: these great times have helped me to keep a good work/life balance. I also owe special thanks to Dr. Daryoush Allaei for the motivational and meaningful conversations and for sharing his experiences and tips and tricks to stay focused on what matters and to work hard toward achieving my dreams. Last and most importantly, I would like to thank members of my family. Without their continuous support and sacrifices, my work would have taken much longer to complete. I owe special thanks to my mom and dad, who have been exceptional parents and the greatest role models. Despite being so far away, their positive energies have always been around me and flowing into my body with each breath. Thanks to my brother for his exceptional xxxiii intelligence and astounding questions. His interest has motivated me to study harder to find the answers to his questions. xxxiv Dedication To the Children of Planet Earth! 1 Chapter 1: Introduction 1.1 Synopsis This chapter discusses the importance of nitrogen use efficiency, and describes both the nitrogen cycle and the factors affecting this cycle. It also considers the major sources of nitrogen emissions, the available tools for evaluating emissions, and the sources of reactive nitrogen emitted from crop-livestock production supply chains. In addition, the research objectives and an overview of the thesis structure are provided. 1.2 The relevance of nitrogen use efficiency What merits a focus on nitrogen use efficiency (NUE)? The answer is simple… and complex. Nothing can survive without nitrogen (N). It is one of the key building blocks of life. It is also very abundant as dinitrogen gas (N2) - a chemically unreactive form of N that constitutes 78% of all gases in the atmosphere. Yet, most organisms are unable to access it. In order for N2 to become biologically available, the stable N2 molecule must be split to produce a reactive form of nitrogen (Nr). Nr refers to a variety of reactive molecules, including ammonia (NH3), ammonium (NH4+), nitrate (NO3-), nitrite (NO2-), nitrous oxide (N2O), nitrogen oxides (NOx), nitric oxide (NO) and nitrogen dioxide (NO2) (Erisman et al. 2018). In nature, Nr availability largely depends on a variety of microorganisms that can perform N conversion (Kuypers et al. 2018). Microorganisms that carry the nitrogenase 2 metalloenzyme convert N2 to reactive forms that can subsequently be transferred through food webs (Hirsch and Mauchline 2015). Furthermore, these microorganisms can convert N to nitrous oxide (N2O), ammonia (NH3), ammonium (NH4), nitrate (NO3-) and nitrite (NO2-), which are reactive and toxic forms of N, or back to N2, an inert and harmless form (Kuypers et al. 2018). To clarify the process of N2 conversion to Nr, the microbial N cycling groups and major pathways are explained in detail in Section 1.1.1. Historically, Nr was rare and hence limiting in most ecosystems. However, the advent of the Haber-Bosch process in 1913 - an energy-intensive process that uses natural gas and dinitrogen under high temperature and pressure in the presence of a catalyst to synthesize ammonia - enabled humanity to massively increase the availability of Nr. It also leads to a rapid increase in the amount of Nr in ecosystems. Despite its biological value, Nr may build up and persist in the environment, with a variety of potentially detrimental effects. A single molecule of Nr can cascade through the environment, variously contributing to water quality issues (nitrates), air quality (smog, particulate matter, ground-level ozone) impacts, freshwater eutrophication, biodiversity losses, stratospheric ozone depletion, climate change and degradation of coastal ecosystems (dead zones) (Erisman et al. 2013). Efforts to improve NUE are therefore both vital and challenging due to our dependence on N (in particular, for food production), the inherent leakiness of agriculture (the source of most N lost to the environment), the impacts of excess N, and N cycle complexity (EU Nitrogen Expert Panel 2015; Erisman et al. 2018). 3 1.2.1 The microbial nitrogen cycling groups and major pathways The N conversions characteristic of the N cycle consist of fixation, mineralization, immobilization, nitrification, and denitrification (Barakat et al. 2016). N fixation N fixation refers to the conversion of a molecule of N2 into ammonia (which is, in turn, assimilated into organic N that is available for biosynthesis) (Barakat et al. 2016; Kuypers et al. 2018). There are three pathways for N fixation and migration into terrestrial ecosystems: 1. Symbiotic fixation by nitrogenase enzymes produced by Rhizobium bacteria that are active in the nodules of Fabaceae/Leguminosae plant roots. Leguminous plants with symbiotic association with nitrogen-fixing bacteria are the primary vector for moving atmospheric nitrogen into terrestrial food webs. 2. Geochemical fixation when lightning splits N2 molecules into Nr atoms, which subsequently combine with atmospheric oxygen to produce NOx ions. 3. Industrial fixation via the Haber-Bosch process, which results in the production of ammonia (NH3) (Barakat et al. 2016; Castner et al. 2017). N mineralization N mineralization is a complex process that produces a simple product, nitrate. N mineralization is carried out by various microorganisms, including bacteria (for example, Actinomyces) and fungi. These microorganisms function on a wide range of substrates, 4 among which are crop residues, soil humus, dead microbial tissue, and manure (Kitchen and Goulding 2001). During mineralization proteins, amino sugars, and amino acids are converted into ammonium (NH4+) by ammonification and then to nitrate (NO3-) by nitrification (Barakat et al. 2016). N immobilization Immobilization is the process opposite to mineralization. It is the conversion of inorganic N compounds to organic compounds by soil microorganisms, which compete with plants for available N. Microorganisms uptake mineral N to synthesize their tissues (Barakat et al. 2016). Once “immobilized”, these organic compounds are hence not bioavailable to plants. Immobilization is the dominant process in soils with a high C/N ratio (Barakat et al. 2016). Nitrification Nitrification, an aerobic process, is the biological oxidation of ammonium (NH4+) via nitrite (NO2-) to nitrates (NO3-) by soil microorganisms. It can occur in two ways. The first is through a mutualistic symbiosis between ammonia-oxidizing bacteria (AOB), ammonia-oxidizing archaea (AOA) and nitrite-oxidizing bacteria (NOB). The second is direct nitrification by comammox bacteria (Barakat et al. 2016; Beeckman et al. 2018). Comammox, which is an abbreviation of COMplete AMMonia OXidiser, refers to an organism that converts ammonia into nitrite and then into nitrate. Although nitrification makes N available for plants and microbes, it can also lead to leaching or denitrification losses and nitrous oxide (N2O) production. Nitrification can be either autotrophic or 5 heterotrophic, depending on the soil’s specific bacteria and fungi (Barakat et al. 2016). Autotrophic nitrification consists of two pathways: ammonia oxidation and nitrifier denitrification (Braker and Conrad 2011). In nitrifier denitrification, nitrite may be totally converted into N2; however, the more common final product is N2O. In heterotrophic nitrification, N2O is produced by a variety of phylogenetically unrelated bacteria and fungi (Braker and Conrad 2011). Denitrification Denitrification, an anaerobic process, completes the N cycle by converting nitrate or nitrite to N2, with NO and N2O as intermediates, hence returning fixed N to the atmosphere. In the denitrification process, N2O is an obligatory intermediate and may sometimes be the final product. However, denitrification may act as a sink when N2O reduces to N2. The final product of the denitrification process depends on the denitrifier. Some denitrifiers completely denitrify NO3- to N2, while others leave it as N2O (Braker and Conrad 2011). Nitrous oxide (N2O) reductase (N2OR) is the only known dominant enzyme that reduces N2O to N2, while there are several enzymological pathways in fungi and bacteria that produce N2O (Pretty et al. 2012). Hence, denitrification is the dominant cause of fertilizer N losses from soil and one of the main routes of N loss that results in GHG emissions (N2O is a potent greenhouse gas) (Hirsch and Mauchline 2015). Other routes of N losses are soil erosion, leaching, ammonia volatilization and ammonia oxidation (Pretty et al. 2012). Most non-anthropogenic N input into the soil is in the form of polymers (e.g. protein, chitin, peptidoglycan), which first must be transformed into small, soluble organic molecules 6 by extracellular enzymes. These small organic molecules can then be taken up directly by plants. Otherwise, they can be broken down further and taken up as ammonium (NH4+) via the mineralization-immobilization-turnover (MIT) route. The relative importance of these two pathways during the decomposition of residues depends on the form of N available, the source of carbon (C), availability of N relative to C, as well as on ammonium concentration in the soil (Geisseler et al. 2010). 1.2.2 Factors affecting the nitrogen cycle N cycling is influenced by various factors that may be related to environmental or agricultural practices, techniques, and management (Barakat et al. 2016). Environmental factors The environmental factors include soil variables, such as C/N ratio, pH, salinity, texture, temperature, water content and types of N molecules, which are site-specific and control N fixation (Barakat et al. 2016). Among these factors pH of the soil is comparatively manageable. Maintaining the correct pH is essential for plant health, the soil microbial community, and for managing the amount of N fertilizers required. The loss of NH3 gas via volatilization increases at a soil pH greater than 7.3. Liming has been a practical method to moderate acidic soil since Roman times (Hirsch and Mauchline 2015). In addition, atmospheric temperature and carbon dioxide (CO2) levels may influence biochemical N cycling, especially in dryland ecosystems (Hu et al. 2016). 7 Other environmental factors influencing the N cycle are soil freeze-thaw (FT) and dry-wet (DW) cycles. A substantial portion of annual N2O emissions from agricultural systems are caused by FT and DW cycles (Congreves et al. 2018). A variety of factors influence FT and DW processes, including soil temperature, soil texture, soil water conditions and solute concentration. Congreves et al. (2018) suggested that the risk of N2O emissions is higher for FT cycles in comparison with DW cycles for the following reasons: (1) microbes that survive in frozen conditions might still cause production of N2O; (2) periods of anaerobiosis may be longer for FT compared to DW; (3) substrates that play a role in N2O production might accumulate; (4) at cold temperatures, nitrification and denitrification influence enzyme temperature sensitivity; and (5) when snow melts, N2O is physically released. In the meantime, denitrification is often restricted by organic carbon (C) at soil temperatures above 0°C. At temperatures below 0°C, microbial activities can be limited by soil organic C as well. The availability of C required for anaerobic respiration may be enhanced by thawing via the collapse of ice crystals and spread of the substrate to anoxic microsites. Following freeze-thaw cycles, greater microbial respiration linked to soil organic C may be observed (Phillips 2009). Most N2O is generated via the activity of the microbial community in the rhizosphere of plant roots. Heterotrophic activity is increased by the release of plant-derived low molecular weight organic compounds into the soil through denitrifiers and nitrate ammonifiers. Nitrate ammonifiers are microorganisms that reduce nitrate to ammonium. Therefore, N2O 8 production and reduction rates are related to the availability of total carbon or soluble organic carbon (Pretty et al. 2012). Agricultural practices On a global scale, population growth depends on the availability of food. The main resources for food production are land, soil, water, and some basic nutrient elements, including nitrogen. N, therefore, plays a crucial role in both food production and security, and environmental sustainability (EU Nitrogen Expert Panel 2015). Agricultural practices and techniques aim to maintain or increase crop yields. However, they also influence the diversity and function of soil microorganisms, and subsequently, N cycling. One of the common practices to increase soil fertility is the application of N fertilizers that improve crop growth. However, surplus fertilizer is often lost in the form of leaching to soil and water, which also results in gaseous N emissions. An ancient strategy to enrich the soil with N is based on including leguminous crops, such as soya beans, peas, lentils, clovers and Lucerne in crop rotations. Leguminous plants remain important in many agricultural systems, in particular when chemical fertilizer costs are prohibitive (Hirsch and Mauchline 2015). An agricultural technique for improving soil fertility by mixing fertilizers, manure and crop residues into the soil is tilling or plowing. However, excessive tillage can cause damage and erosion in susceptible soils. Soil compaction leads to anaerobic conditions, which cause denitrification and N2O emissions. Some strategies to eliminate the negative impacts of 9 tilling and decrease N2O emissions are subsoiling and applying N fertilizers at least 5 cm below the soil surface (Hirsch and Mauchline 2015). Irrigation techniques such as flood irrigation, sprinkler irrigation, surface and subsurface drip irrigation, and associated fertilization strategies, influence N transformations and fluxes. Each of these techniques has specific effects on soil water content and temperature, hence influencing microbial activities that control nitrification and mineralization rates, as well as N fixation (Barakat et al. 2016). Barakat et al. (2016) showed that the effect of irrigation on soil N cycling could be minimized by using subsurface drip irrigation techniques. On the other hand, furrow irrigation and sprinkler irrigation have shown the strongest and least controlled effects on N transformation and fluxes (Barakat et al. 2016). 1.2.3 Major sources of reactive nitrogen emissions into the environment Prior to the development of the Haber-Bosch process in 1913, which enabled the production of synthetic N-fertilizers, Nr availability was often limited in both natural and human-mediated systems. The Haber-Bosch process converts atmospheric N to ammonia via reaction with hydrogen under high temperatures and pressures (Barakat et al. 2016). The discovery of this process supported a massive increase in agricultural and livestock production which, in turn, fueled a growing human population (Erisman et al. 2018). However, too much Nr poses a variety of potential threats to ecosystems (Erisman et al. 2018). Nr is often limited in developing countries, where synthetic fertilizers may be prohibitively expensive. On the other hand, excess amounts of Nr are often found in 10 developed countries, causing a variety of negative environmental impacts (EU Nitrogen Expert Panel 2015). Agronomic production and, predominantly, livestock systems are responsible for the majority of Nr mobilization and subsequent loss to ecosystems. Livestock systems are not only N demanding but also inherently leaky. Animals consume plants, which require either mineral fertilizers or biological nitrogen fixation to grow. Fractions of this N are lost to air, soil, and water. Not all of the N present in plant tissues is converted to animal products when consumed but is rather excreted in urine and manure, with further N losses. For example, only 25% of N consumed by poultry is converted to edible protein (Garnett and Little 2015). In order to diminish anthropogenic Nr pollution, a variety of potential mitigation options are available. Four priority areas for reducing Nr creation and loss are suggested by (Galloway et al. 2008). These are: (1) NOx emissions control from fossil-fuel combustion (decrease Nr creation from 25 Tg N to 7 Tg N annually); (2) increasing nitrogen-uptake efficiency of crops (decrease Nr creation by about 15 Tg N annually), (3) improved animal management strategies (decrease Nr creation by about 15 Tg N annually), and (4) sewage treatment (converting 5 Tg N to N2 annually). Efforts to reduce N pollution must be supported by first developing rigorous estimates of the amount of Nr mobilization and loss characteristic of specific activities, clear understanding of the relative efficiency of different activities in the utilization of N resources, and subsequent selection and implementation of N efficient technologies and management strategies. 11 1.2.4 Nitrogen footprint tools Two measures of particular salience are N balance and NUE. N balance is defined as the difference between the N inputs and outputs of a system. For food production activities, the principal sources of N inputs are fertilizers and biological nitrogen fixation, as well as the fractions thereof that are cycled back into crop production in livestock manure. The outputs are crop and livestock products containing embodied N. A nitrogen deficit, which is a negative N balance, points towards potential soil fertility reduction over time, which may be problematic in terms of long-term productivity. A nutrient surplus, represented by a positive value in an N balance calculation, suggests a potential risk of soil, water or air pollution due to excess N. The N balance for Canada was 1 kilogram per hectare in 2017 (OECD 2018), suggesting overuse of N and, in consequence, potential negative environmental effects. An example of an N input-output mass balance in a representative crop-livestock production system is illustrated in Figure 1-1Figure 1-1. N inputs(1) Inorganic fertilizer(2) Organic fertilizer(3) Atmospheric deposition(4) Biological N fixation(5) Seeds and planting materials(6) Livestock manureCrop ProductionN outputs(1) Grass and fodder crop production(2) Harvested crop productionN balanceSurplus/deficit into air, soil, and water (NH3, N2O, Nox, N2, NH4+, NO3-)Livestock ProductionFeedManureN inputs(1) Feed(2) Synthetic amino acidN outputs(1) Meat(2) Egg(3) Milk(4) HoneyAnd etc. Figure 1-1 N input-output mass balance in the system of crop and livestock production. 12 The Oxford English Dictionary defines efficiency as “The ratio of the useful work performed by a machine or in a process to the total energy expended or heat taken in.” Efficiency is, therefore, the quality of the relationship between inputs and outputs (Garnett and Little 2015). Improving efficiency has been proposed as critical to improving environmental sustainability outcomes, generally (Gerber et al. 2014). NUE is a measure of the ratio between the amount of N outputs and N inputs associated with provision of a good or service (Brentrup and Palliere 2008). The goal of improved NUE is critical to improving the environmental sustainability of agri-food systems. NUE provides useful information about the relative utilization of N and is hence a measure of N productivity rather than a quantification of N balance (Brentrup and Palliere 2008). Indicators play an important role in N management, and NUE is a key indicator for managing N in agriculture. The data and information which are required for NUE estimation include: (1) the total N input-output balance of the system; (2) the kind of system (for example crop system, livestock housing system, etc.) and its boundaries; (3) the temporal boundary; and (4) possible fluctuations in the stock of N within the systems considered (EU Nitrogen Expert Panel 2015). The definitions and example calculations of NUE are provided in Table 1-1 (Huang et al. 2018). Table 1-1 Definitions and calculations of NUE Abbreviation Term Definitions and calculations NUE N use efficiency NUpE × NUtE = yield/N available NUpE N uptake efficiency NUp/Nav (soil + fertilizer) = acquired N/N available NUtE N utilization efficiency Yield/NUp (assimilation of plant N to produce grain) 13 For crop and livestock production systems, NUE depends on several factors. NUE in crop production may be influenced by the type of crop, soil, specific N fertilizers, application strategies, and crop management practices. NUE in livestock production depends on the animal category, feed quality and quantity, manure management (manure handling, manure storage, etc.), and other factors. Environmental conditions influence NUE in both crop and livestock production systems. In mixed systems, NUE depends on a combination of these factors (EU Nitrogen Expert Panel 2015). Improving NUE can be supported by a strategic plan for the implementation of beneficial technologies and management strategies. For example, the “4Rs” approach (Right Source @ Right Rate @ Right Time @ Right Place®) is an approach designed by Fertilizer Canada based on recognized best management practices (BMPs) for nutrient management in crop production (Fertilizer Canada 2016). There are currently several methods to assess NUE, including but not limited to, N-footprint, N-budget, and life cycle assessment (LCA). Each of these approaches has specific strengths and weaknesses. The N-footprint method uses N input-output (IO) balances, considers N losses at each step, and captures total N losses to the environment. This method is data-intensive and does not let practitioners link N footprint estimations for products to other potentially related environmental and social impacts (Erisman et al. 2018). The N-budget method utilizes N IO balances like the N-footprint method. However, it is less data-intensive than formal N-footprint calculations. An N-budget calculation is based on a simple mass balance approach to model and quantify internal N fluxes (Erisman et al. 2018). 14 Positive and negative budgets indicate a surplus and deficit, respectively. For example, for agronomic soil, the N budget (Nbudget) is calculated as follows: Nbudget = Nfix + Ndep + Nfert + Nman − Nwithdr (1) N inputs include biological N fixation (Nfix), atmospheric N deposition (Ndep), synthetic N fertilizer utilization (Nfert) and animal manure (Nman). The output consists of N withdrawal from the field through crop harvesting (Nwithdr) (Bouwman et al. 2017). The third method is LCA, which provides a framework to calculate the N flows and losses in each step of the supply chain associated with producing a particular good or service. Similar to N footprint calculations, LCA also requires considerable data (Erisman et al. 2018). What is distinct is that LCA is a multi-criteria assessment – meaning that it enables quantification of NUE in parallel to the quantification of a variety of other relevant resource and emissions related environmental indicators. For this reason, it likely represents the best available method to evaluate and to identify optimum strategies to increase NUE at a systems level, since it enables identification and management of potential synergies and trade-offs across environmental management objectives. 1.3 Background information on N emissions from crop-livestock production During the last century, the world has witnessed rapid population growth and associated rising demand for food. Food production has hence increased considerably (Bouwman et al. 2009), along with increased concerns about the resource consumption and environmental 15 impacts associated with agriculture– including, in particular, N emissions (Leinonen et al. 2012b). It is estimated that, in 2000, the livestock sector contributed 14 and 63 percent of anthropogenic GHG emissions and Nr mobilization, respectively (Pelletier and Tyedmers 2010). Pelletier and Tyedmers (2010) estimated that livestock production in 2050 may increase GHG emissions and Nr mobilization by 39 and 36 percent above reported 2000 levels. Generally, crop-based products are more N efficient to produce compared to animal-based products due to the inefficiencies inherent to biological feed conversion. Reported N footprints for meat range from 64 to 134 g N per kg of pork and beef, respectively and, for vegetable products, from 5 to 22 g N per kg of potatoes and legumes (Pierer et al. 2014). A variety of potential supply chain stages/activities may influence the amount of Nr emissions released via crop-livestock production. The stages and activities that are suggested to have the most pronounced impact on the amount of emissions include feed production, feed formulation and manure management. The latter includes: technologies and management strategies that may be applied in housing facilities such as manure belts and dryers; manure storage; and manure land application (Grant and Wu 2008; Gourley et al. 2012a; Bell 2014; Das and Harikrishnan 2015; Mandal and Bhanja 2017; Atwill et al. 2018; Huang et al. 2018; Srivastava et al. 2018). 16 1.3.1 Feed production Feed production is known as a crucial hotspot in terms of environmental emissions and energy consumption in the livestock industry (González-García et al. 2014). It is estimated that the global poultry industry utilized 294 million tonnes of feed in 2004. This feed consisted of approximately 190, 103 and 1.6 million tonnes of cereal, soybean and fishmeal, respectively (Gerber et al. 2007). Among poultry feed inputs, those derived from livestock cause the highest environmental emissions. In contrast, crops used as feed inputs have lower associated resource requirements and environmental emissions - especially leguminous crops, because they depend on biological N fixation rather than N fertilizers (Pelletier 2017). 1.3.2 Feed formulation A nutritionally adequate diet is essential for optimizing poultry growth and performance, as well as for reducing the quantity of N that is excreted, along with associated emissions (Carew 2010; Abd El-Hack et al. 2019). In particular, Nr emissions may increase if the level of protein is not managed properly (Abd El-Hack et al. 2019). Diets that are unnecessarily high in protein may potentially exacerbate the environmental impacts of poultry manure, including eutrophication, acidification, water and air pollution (Kim et al. 2013). One suggested strategy to decrease the concentration of protein in a diet and, consequently, N emissions is adding protease Ronozyme® ProAct to poultry feed. Protease is a specific enzyme that improves protein use efficiency. An LCA study was conducted to quantify the environmental impacts of using protease Ronozyme® ProAct in poultry feed. LCA 17 modelling was applied to two types of diets: standard soya-based poultry diets and reduced-protein diets with added protease. The results showed fewer N emissions from manure and consequently, a reduction in environmental emissions in all impact categories for both poultry and feed production when reduced-protein diets with added protease were adopted (Leinonen and Williams 2015). 1.3.3 Manure management Poultry manure consists of various nutrients and substances, among which are nitrogen and phosphorus, and, in some contexts, hormones, antibiotics and heavy metals. The amount of substances in excreted manure that are lost to the air, water and soil as emissions, depends not only on feed composition (Steinfeld et al., 2006) but also manure storage duration, whether the manure is covered or uncovered, as well as how and in what form (i.e. liquid or solid) manure is applied to land (Pelletier 2017). N emissions from manure take four main forms: ammonia (NH3), dinitrogen (N2), nitrous oxide (N2O) and nitrate (NO3-) (Gerber et al. 2007). Excessive amounts of each form of Nr lead to a cascade of effects (De Vries et al. 2003). Suggested technologies and strategies within housing facilities that can diminish Nr emissions and losses on poultry farms include:  Ventilation systems (using filtration, biofiltration, acid scrubbers, and bio trickling filters) that reduce NH3 emissions. NH3 removal efficiency by acid scrubbers and bio 18 trickling filters in poultry housing is reported at around 70% (Carew 2010; Aguirre-Villegas and Larson 2017).  Use of a grooved floor compared to a traditional slatted floor may result in up to 46% less NH3 emissions (Swierstra et al. 2001).  Manure conversion through anaerobic digestion systems results in production of unionized ammonia and ammonium ions. Subsequent land application of digestate has lower associated nitrous oxide emissions in comparison with application of raw manure (Cherubini et al. 2015). In addition, specific manure land application techniques for feed production have differing impacts, in particular with respect to NH3 emissions. For example, subsurface slurry application (SSA) can decrease NH3 losses and increase both forage yield and N uptake compared to other manure application methods such as surface banding and conventional broadcasting (Carew 2010). Despite the numerous studies focused on reducing emissions of specific N compounds at specific stages of crop and/or livestock production using particular technologies or management strategies, relatively few have considered their efficacy in the context of the full life cycle of crop/livestock products (Xia and Yan 2011; Uwizeye et al. 2014, 2016; Joensuu et al. 2019), or how they might impact emissions of other N compounds. Nor have the potential trade-offs across other relevant resource use or emissions-related indicators that may be associated with any such strategies been satisfactorily considered. 19 1.4 Egg production in Canada In recent decades, the poultry industry has both expanded and intensified in order to meet the increased demand for poultry products – including both eggs and meat - with a commensurate increase in waste flows and associated potential environmental emissions (Leinonen and Williams 2015). In Canada, 774.5 million dozen eggs were produced in 2017, which equates to a total production of roughly 557.6 thousand tonnes of eggs (Statistics Canada 2018). Over the last five years, availability increased by 3.3 percent per annum (AAFC 2017). Egg production is currently common in all ten provinces of Canada, although the largest concentration is found in Ontario and Quebec, which respectively hold 37% and 19% of the national quota in the supply-managed industry. Egg farming in Canada employs a variety of housing systems for hens, including conventional cage, “enriched” cage, free run, free-range and organic production systems. Characteristic environmental pressures and supply chain resource demands differ slightly by housing system type, as well as by farm (Pelletier 2017). The chief feed inputs in Canadian egg production are corn, wheat, soybeans, barley, peas and canola. The majority of Canadian corn (70%) is produced in Ontario, whereas wheat is primarily produced in Manitoba, Saskatchewan and Alberta. Most (i.e. 98%) of Canadian canola is produced in Alberta, Manitoba and Saskatchewan (Pelletier 2017). Compared to other livestock production systems, egg production is relatively resources efficient and has lower associated emissions – mainly because of the low feed conversion ratio. However, N emissions in the forms of ammonia (NH3), nitrous oxide (N2O), and nitrate 20 (NO3-) leaching associated with manure, along with the production of the crops required as feed inputs, may nonetheless be considerable for poultry (Leinonen and Williams 2015). Improving NUE in the poultry industry hence represents a priority sustainability research area for this sector. 1.5 Research objectives Identifying appropriate and effective NUE strategies requires a life cycle approach in order to ensure that the intended benefits are realized taking into account potential interactions between supply chain stages. For example, it might not make sense to source feed inputs that are N efficient to produce if this results in higher levels of N excretion in poultry houses. Other relevant resource use and emissions-related indicators need also be considered to ensure that NUE strategies do not create unacceptable trade-offs with respect to other aspects of sustainability. Available studies mainly focus on NUE improvement in isolation, without considering potential environmental trade-offs. In addition, few studies consider the influence of NUE measures on other supply chain stages (i.e utilizing life cycle thinking). The current study is unique both in considering all major reactive nitrogen species in agriculture as well as utilizing life cycle thinking and assessment to consider NUE strategies and technologies for the entire egg production supply chain. On this basis, the overarching objective of this research was to determine priority strategies (i.e. management or technology options) for optimizing life cycle NUE while generally maintaining or improving overall environmental performance (i.e. resource use and 21 emissions reduction) in the Canadian egg industry. This objective was achieved via the completion of the following sub-objectives: 1. Using a previously published inventory model of the Canadian egg supply chain (Pelletier 2017) and a dedicated life cycle impact assessment method for N footprinting (Pelletier and Leip 2014), determine the distribution and magnitude of Nr losses along the Canadian supply chain, key contributing activities, and the proportion of N lost in different forms to air, soil and water. 2. Review available literature to: (a) identify specific technologies and management strategies for improving NUE at each of the key supply chain stages where nitrogen losses are greatest, and assess their relative efficacy/feasibility based on technical, environmental and economic criteria in order to identify a subset for detailed life cycle assessment. (b) identify, characterize, and compare models for simulating Nr emissions in agricultural contexts in order to identify those best suited for use in process-based life cycle assessments of livestock products. 3. Develop a series of life cycle inventory models for each of the selected technologies/management strategies (2a) and integrate them into the Canadian egg industry model from Pelletier (2017) in order to assess their comparative efficacy in improving the life cycle NUE of egg production while also minimizing other resource and environmental impacts (using the emissions models identified in 2b). 22 1.6 Thesis structure This thesis consists of the following chapters: Chapter 1 includes an overview of the nitrogen cycle, human perturbation of this cycle, and the relevance of the livestock sector in this context. A discussion of NUE as a sustainability management concept and metric, and a review of factors that influence NUE in the livestock and, more specifically, the poultry industry is provided. Finally, the specific research objectives and the scope of the thesis are described. Chapter 2 includes an introduction to and overview of life cycle assessment, and its relevance for sustainability management. Chapter 3 reports a literature review of potential technologies and management strategies for improving NUE in crop and livestock production, including (and contrasting) both life cycle and non-life cycle based research. Chapter 4 reports a literature review of N flows in crop-livestock systems, and best available models for estimating emissions of N species to air, soil, and water in agricultural contexts using process-based life cycle assessment. Chapter 5 presents and compares the life cycle inventory models and assessments NUE technologies and management strategies for egg supply chains. Chapter 6 summarizes and concludes. 23 Chapter 2: The Methodology of Life Cycle Assessment 2.1 Synopsis This chapter provides a description of each phase of the life cycle assessment methodology. An overview of the methodology, including each phase and the relationships between them, are illustrated in Figure 2-1. Life Cycle Assessment FrameworkGoal and scope definitionLife cycle impact assessmentLife cycle inventory analysisInterpretationIdentification of significant issuesEvaluation of resultsConclusions, recommendations, and reporting Figure 2-1 Research methodology: life cycle assessment 2.2 Life Cycle Assessment The story of LCA began with the straightforward question, “Is product A better than product B?” This question was motivated by the emerging recognition of various environmental issues such as solid waste, pollution management, and energy and material efficiency concerns. LCA has since become a major global tool in developing environmental 24 strategies due to its comprehensive nature and incorporation of a range of environmental parameters (Guinee et al. 2011). LCA involves the "compilation and evaluation of inputs, outputs and potential environmental impacts (EI) of a product throughout its life cycle" (ISO 2006a, b). LCA hence has a comprehensive scope, which is crucial in terms of avoiding problem-shifting from one phase of the life cycle to another or from one environmental concern to another (Finnveden et al. 2009). LCA has evolved considerably in recent years. In the early 2000s, important limitations of LCA, such as the inability to accommodate many kinds of localized impacts, its static nature, and its inattention to potential market-mediated effects, as well as exclusion of other economic and social aspects, were recognized (Guinée 2002). However, some of these weaknesses have since been resolved through significant methodological development. As an example, consequential life cycle assessment (CLCA), social life cycle assessment (SLCA), and life cycle costing (LCC) now enable a dynamic modelling approach and the consideration of social and economic attributes, respectively (Woodward 1997; Ahlgren et al. 2012). While LCA had evolved continuously for over five decades, its development is presented here in terms of three distinct eras (Guinee et al. 2011) First era (1970-2000) LCA originated in the 1970s when the global scientific community was seeking a new theoretical framework for resource management and pollution prevention. The last decade of the first era was marked by a significant number of scientific workshops, the development of new guidelines for life cycle assessment, and the publication of numerous related scientific papers and handbooks. During this time, both the Society of Environmental Toxicology and 25 Chemistry (SETAC) and the International Organization for Standardization (ISO) assumed an active role in the development and standardization of methods for LCA. The latter culminated in the development of an ISO standard for LCA (ISO 2006a). However, diverse methods for conducting LCA persisted (Guinee et al. 2011). Second era (2000-2010) In 2002, SETAC and United Nations Environment Program (UNEP) launched the UNEP/SETAC Life Cycle Initiative to cooperatively develop improved LCA tools. Moreover, several organizations actively pursued LCA-related initiatives. Examples of agencies with active programs were the Commission of the European Communities (CEC) in Europe, the Environmental Protection Agency (EPA) in the USA, the Australian LCA Network, the American Center for LCA, and the Thai LCA network (Guinee et al. 2011). Third era (2010-2020) This era might be best defined as the Life Cycle Sustainability Assessment (LCSA) decade. LCSA expands and deepens the scope of LCA by covering product, meso, or economy-scale interactions and by including biophysical, social and economic sustainability assessments. LCA continues to be developed and refined, ranging from the framework itself through life cycle inventory databases and impact assessment methods to uncertainty analysis. Up to now, some of the most important issues have been about making decisions regarding appropriate modelling approaches (i.e. ALCA, CLCA or scenario-based 26 modelling), which should be supported through further development of existing guidelines and handbooks (Guinee et al. 2011). The LCA framework consists of four distinct methodological phases; (1) Goal and scope definition, (2) Life cycle inventory (LCI), (3) Life cycle impact assessment (LCIA), and (4) Life cycle interpretation (ISO 2006a, b). 2.2.1 Goal and scope Stating the goal(s) for an LCA project is the first step in the goal and scope phase. This includes justifying the precise objective(s) of the study, the target audience, the intended application of the results and any notable limitations, as well as identifying the commissioners of the study (ISO. 2008). All elements should be described in an unambiguous and transparent way in terms of what is to be studied and why a study is to be carried out (Guinée 2002; European Commission 2010). According to the objectives of a study, the attributional or consequential life cycle assessment methodology is chosen. If the purpose of a project is to characterize the resource and environmental profile of a specific product at a particular time and place, attributional life cycle assessment (ALCA) is the best choice. Likewise, consequential life cycle assessment (CLCA) is an appropriate method when seeking to understand how various kinds of decisions may influence environmental burdens at broader scales through market-mediated effects (Guinée et al. 2018). This choice will, in turn, influence the identification of appropriate system boundaries for the study (Finnveden et al. 2009). Similarities and differences between ALCA and CLCA are 27 presented in Table 2-1 (Brander et al. 2009; Finnveden et al. 2009; Pelletier et al. 2015; Weidema et al. 2018). Table 2-1 Similarities and differences between ACLCA and CLCA. Attributional Consequential Similarities Data-intensive Multi-criteria Systems analytical tools Differences Retrospective Prospective Static/ Status-quo/ Descriptive/ Accounting Change-oriented/ Effect-oriented Using average data Using marginal data Estimating absolute impacts Quantify how burdens change as a result of a specific decision Allocation to solve multi-functionally problems Using system expansion and substitution to solve multi-functionally problems Relevant physical flows Changes in relevant flows base on decisions Does not consider market effects Considers market effects The second step of this phase is defining the scope of the study. The scope defines the depth, breadth, and detail of the study along with decisions around unit processes and data collection (ISO 2006a, b). According to ISO 14040, “a unit process is the smallest element considered in the life cycle inventory analysis for which input and output data are 28 quantified”. The central aspects of the scope definition are describing the temporal (time boundary), geographical (spatial boundary), technological coverage, and the breadth/depth of the study (as required in order to achieve the previously defined goals) (Guinée 2002). These aspects collectively define the “system boundaries” for the study. The scope definition also includes definitions of the function(s) of the product system considered, the functional unit (FU), the reference flow, strategies for solving multi-functionality problems, and the impact categories and methods to be applied. In general, the scope definition phase describes all methodological choices and assumptions for the life cycle inventory, impact assessment, and interpretation phases. This includes the development of a system flow diagram. A sufficiently detailed description of scope is important for ensuring that the study can address the objectives (Guinée 2002). Function(s), functional unit, and reference flow “Function” refers to the role of a product and/or system for its consumers. In LCA, impacts of products/systems are modelled and expressed in terms of a functional unit (FU) (Guinée 2002). The functional unit (FU) is a quantitative description of the function(s) provided (Rebitzer et al. 2004). The selection of an appropriate FU is essential for characterizing the resource/environmental performance of a product/system, and also to enable comparisons among products on a functionally-equivalent basis (Reap et al. 2008). The most common FU applied in LCAs of agricultural products is a simple mass-based measure, such as 1 kilogram or 1 tonne of the raw material/product or 1 hectare of the surface area farmed. However, some authors advocate use of multiple functional units because this 29 may support a more nuanced analysis (Skowrońska and Filipek 2014). For instance, one might use 1 tonne of a crop as an FU to determine the system efficiency per unit of output, and a hectare of surface area farmed to analyze production intensity per unit area (Charles et al. 2006). The reference flow is called the declared unit and is the amount of product/service required to provide the defined FU (Smith Cooper 2003). All inventory data are collected and scaled relative to the reference flow (European Commission 2010). For example, a FU for wall painting could be the covered area (e.g. 20 m2 of wall), wall type (thermal residence of 2 m2 K/W), paint quality to cover the underlying surface (a coloured surface of 98% opacity) and paint service-free life (5 years). A specific amount of paint, which is required for this purpose, is expressed as a reference flow (Guinée 2002). System boundary System boundaries define and demarcate what is to be included and excluded within a model of the product system of interest. This requires identifying the various unit processes and associated flows that fall within the system boundary (ISO 2006a). The choice of data should be justified and be relevant based on the technological, temporal and geographical system boundaries. Towards this end, the target system under study has to be separated from the rest of the world. The types of boundaries include: (1) the boundary between the product system and environment; (2) the boundary between important and unimportant processes considered within the techno-economic system (i.e. cut-offs); and (3) the boundary between target products and other co-products in the system (allocation) (Guinée 2002). 30 Allocation Allocation is required when modelling multifunctional systems (i.e. systems providing co-products/services). Allocation refers to partitioning input and output flows among co-products on a principled basis. This includes all materials and energy inputs and emissions linked to the co-production of each product (Ayer et al. 2007). There is a specific hierarchy for solving multi-functionality problems that is specified by the ISO standard, which will be discussed in the “sources of difficulties in LCA” section. 2.2.2 Life cycle inventory The second stage of LCA is the life cycle inventory (LCI) stage. The purpose of the LCI stage is to identify and quantify resources, emissions, and waste flows that are utilized or generated throughout the product system, per FU delivered (Rebitzer et al. 2004). The inventory is, essentially, an input/output mass balance (ISO 2006a), taking into account the methodological decisions articulated during the goal and scope definition stage. In instances of co-production, allocation may be used to determine relationships between inputs and outputs (Ayer et al. 2007). The iterative nature of LCA allows practitioners to make changes/refinement to LCI data collection and model development, so long as these are justifiable relative to the goal and scope of the study (ISO 2006a). System boundaries, defined during the scope stage, can hence be refined and finalized following initial data collection and life cycle inventory modelling efforts (Jensen et al. 1997). Data collection and LCI model development are generally considered to be the most time and labour-intensive steps of LCA (Rebitzer et al. 2004). 31 There are a variety of LCI methods, each of which has its pros and cons. Therefore, it is imperative to select an appropriate method for the target application. This is defined during the goal and scope stage. Available LCI methods are: (1) process-based (either attributional or consequential); (2) matrix inversion; (3) IO-based LCI; (4) tiered hybrid analysis; (5) IO-based hybrid analysis; and (6) integrated hybrid analysis. The most popular method amongst practitioners is process-based LCI, which uses discrete unit process data sets that are directly representative of each of the modelled sub-processes to build up a representative LCI model of the product system. This model may be illustrated by a process flow diagram, with boxes and arrows used to represent the relationships between constituent processes and flows (Suh and Huppes 2009). In order to be useful, an LCI must be technically supportable, unbiased, quantitative, replicable, comprehensive, detailed, and peer-reviewed (Ciambrone 2018). The LCI consists of multiple steps, including preparation for data collection, data collection, data quality assessment, resolving multi-functionality problems, data aggregation, and data calculation (ISO 2006a, b). Streamlined procedures for LCI analysis are displayed in Figure 2-2. 32 Calculated inventoryGoal and scope definitionPreparation for data collectionData collectionValidation of dataRelating data to unit processRelating data to FUData aggregationRefining the system boundaryCompleted inventoryAllocation includes reuse and recyclingAdditional data or unit processes requiredValidated data per FUValidated data per unit processValidated dataCollected dataData collection sheetRevised data collection sheet Figure 2-2 Streamlined procedures for LCI analysis (ISO 2006b). Preparation for data collection During initial preparation for data collection, a specific process flow diagram should be drawn (Hauschild et al. 2017) to identify and determine relationships between all unit processes within the defined system boundaries for the product system. This includes flows between the product system and the ecosphere (i.e. elementary and waste flows) and within the technosphere (i.e. product flows between unit processes) (Guinée 2002). It should be noted that the ecosphere and technosphere respectively refer to the environment and human technological activities (European Commission 2010). Each unit process should be described precisely, based on an accepted list of data categories and units of measurement. Techniques 33 for collecting and calculating data should also be described. Finally, the method of reporting any issues should be provided (ISO 2008). The type and quality of the required LCI data vary depending on the purpose of conducting the LCA, the specific processes to be represented, and their importance in the context of the modelled system. The first-hand information gathered by researchers is case-specific “primary data” and is typically applied for modelling “foreground processes,” which are often unique to the product system of concern. Secondary data, which have often been collected by other researchers and may be sourced from literature, databases, industry surveys and reports, etc., are used to model “background processes” (Ciambrone 2018). Background processes are processes that are common across many product systems – for example, primary energy inputs. Some of the prominent life cycle inventory databases from which background unit process data/models may be sourced include SPINE@CPM, the German PROBAS, the Japanese JEMAI, the US NREL, the Australian LCI, the Swiss EcoInvent, and the European Reference Life Cycle Database (ELCD) (Finnveden et al. 2009). The use of proxy data or modified data sets, based on justifiable assumptions, may be required in the case that reliable, directly representative data cannot be obtained (Eriksson et al. 2016). Methods of data collection for background process data sets may differ depending on modelling context, and be influenced both by the capability of contributors and by requirements to respect the privacy and confidentiality concerns of data providers (ISO 2006a). Data may be collected for single or groups of companies, sectors, and countries, and may come from specific (i.e. individual companies) or generic sources (i.e. trade 34 organizations and public surveys). In the case of conducting a detailed LCA, specific data must usually be applied because average data will often not adequately represent the required degree of technological, geographical and/or temporal specification required. Tabulating data for detailed LCIs is hence typically considerably more complicated than for simplified life cycle inventories (Jensen et al. 1997). Collected data may be classified in correspondence with the following main categories: (1) inputs, including raw materials, energy, and ancillary inputs; (2) products, co-products, and wastes; (3) emissions to air, water, and soil; and (4) others (ISO 2006a). These categories follow the transparent Society for the Promotion of Life-Cycle Analysis (SPOLD) format, which was a common format for LCI data reporting (Guinée 2002). Currently, modified versions of SPOLD are more common. EcoSpold is a modified version of the SPOLD format that was developed by Ecoinvent. Another modified version is the International Reference Life Cycle Data System (ILCD) format developed by the European Commission DG Environment in 2005. These formats are employed by most available LCA software tools (Meinshausen et al. 2016). Data quality Data quality is a feature of data that reflects the extent to which they satisfy the stated requirements as defined in the goal definition stage of the study. Data quality must be documented during the data collection process (ISO 2006b). Key criteria for data quality include validity, relevance, measurement methods, completeness, and uncertainty (UNEP et al. 2009). Uncertainty refers to lack of knowledge whereas variability refers to diversity 35 (Edelen and Ingwersen 2016). To systematically assess data quality, a pedigree matrix can be applied. A pedigree matrix is a data quality scoring tool that allows one to score the data across a variety of quality attributes in order to identify priorities for improvement in data quality (Guinée 2002). Making a record of each unit process is necessary to avoid double counting or gaps. The sources of any secondary data coming from literature must be referenced. Instances of missing data must be documented (ISO 2006a). Dealing with missing unit processes and their interventions may be accommodated through the implementation of cut-off criteria, which should be defined during the goal and scope phase. Cut-offs may be necessary not only due to missing data but also as a result of limited time or monetary resources (Guinée 2002). Relating data to unit processes and the functional unit For each unit process, a suitable output product flow should be determined, and the quantitative input-output (IO) of the unit process, related to this flow, should be calculated. The calculation should result in all system input and output data being scaled relative to the FU. Data aggregation The inputs and outputs in the product system should be aggregated carefully, and the aggregation level should be consistent with the stated goals of the study. Data should only be aggregated if they are related to similar environmental impacts and equivalent substances (ISO 2006b). 36 Data calculation Data calculation consists of the validation of data and the relation of data for each unit process to the reference flow and FU in order to create the LCI model and results (ISO, 2006a). It should be considered that the LCI results comprise all inputs and outputs for the product system, expressed as elementary flows, but do not refer to calculated environmental impacts. Hence, the results cannot be employed for comparisons of impacts, but are rather an input for the impact assessment phase. 2.2.3 Life cycle impact assessment The third stage of LCA is life cycle impact assessment (LCIA), which focuses on calculating the actual environmental impacts of a product throughout its life cycle. It translates the results of the LCI analysis into impacts using formalized impact assessment models/methods, as per the impact categories that were chosen during the goal and scope definition stage (Guinée 2002). Impact types may be grouped according to areas of protection (AoPs), including human health, the natural environment, natural resources, and the man-made environment (Finnveden et al. 2009). LCIA results are the major input to the subsequent life cycle interpretation phase. The most problematic issue in LCIA originates from trying to link environmental burdens with actual, spatially and temporally resolved impacts (ISO 2006a). The LCIA phase includes both mandatory and optional elements. The mandatory components are classification and characterization, which provide the LCIA results. Classification involves assigning LCI data to appropriate impact categories. Characterization is carried out to determine the amount of contribution of an LCI result to an 37 impact category (e.g. climate change impacts in kg CO2 equivalent per FU) (Hauschild et al. 2017), and to aggregate all flows using a common reference unit so as to arrive at a single impact estimate for each category. The results can subsequently be normalized, grouped, and weighted, which are optional steps (ISO 2003; Skowrońska and Filipek 2014). Optional steps may be implemented to answer questions such as “Is that a lot?” and “Is it important?” (Hauschild et al. 2017). In addition, ISO refers to data quality analysis as an optional element. Data quality scores are typically assigned during the LCI phase and interpreted during the interpretation phase, including formal uncertainty analysis. Describing the impact assessment in a clear and transparent way is important to avoid potential bias. Additionally, results may be affected by methods, assumptions, and value-choices (ISO 2006a). Impact categories In order to describe the impacts of resource inputs and emissions, logical groupings of LCA results are defined in terms of “impact categories” (UNEP et al. 2009). In turn, impact categories may be variously considered to be “baseline”, “study-specific”, or “other” (Guinée 2002). The impact categories should be carefully chosen based on their relevance to the product system. Default impact categories (i.e. baseline) should be considered for inclusion in all LCIAs. Additionally, some study-specific categories should be included if justifiable (ISO 2006b). It should be noted that including baseline impact categories is preferred even if calculated 38 impacts are negligible. It is recommended to include additional impact categories based on the specific requirements articulated in the goal and scope. However, only relevant categories should be selected (and justified) according to the study goals (Guinée 2002). SETAC has defined a suite of baseline methods at both the midpoint and endpoint of the cause and effect chain. The midpoint refers to the basis for environmental problems, whereas the endpoint refers to actual damage (Guinée 2002). Midpoint methods are science-based and subject to lower uncertainties, but endpoint methods provide information that may be easier to interpret in decision support contexts (Bare 2010). For this reason, both midpoint and endpoint impact categories have their respective merits and weaknesses (Reap et al. 2008). Category indicator The category indicator provides a quantitative reference unit for an impact category (e.g. infrared radiative forcing for climate change, as expressed in CO2-equivalent emissions) (ISO 2003). It enables aggregating contributions to an impact category into a single category indicator (Guinée 2002). Characterization model(s) The characterization model is applied to define characterization factors for inventory flows that contribute to an impact category. It is used to illustrate relevant environmental processes by defining relationships amongst LCI results, category indicators, and category endpoints (ISO 2006b). Characterization factors are often available in the literature, 39 databases, and LCA tools (Pennington et al. 2004). Different elementary flows can be compared quantitatively using characterization factors (Hauschild et al. 2013). Classification In the classification step, the environmental interventions in the LCI analysis are qualitatively assigned to the pre-selected impact categories (Guinée 2002). This assignment must consider if the LCI results are related to more than one impact category, appropriately apportion such flows between parallel mechanisms, and classify any serial mechanisms (ISO 2006b). Characterization In the characterization step, the environmental interventions that were assigned qualitatively to each impact category during classification are quantified using characterization factors (i.e. relative to the category indicator reference unit) and aggregated in order to arrive at a single category indicator result (Guinée 2002; ISO 2006b). Normalization Normalization is defined as a calculation of the importance of indicator results relative to reference information (for example, total emissions for a country or per capita during a defined period of time) (Guinée 2002). Two main purposes for applying normalization are (1) for supporting interpretation; (2) for the quantification of cut-off criteria. The cut-off, which should be quantified, provides a threshold for the exclusion of life cycle steps, 40 activities, processes, products, or elementary flows of minor importance for the system model. The cut-off quantification is based on the percentage share of environmental impacts. For example, a 95% cut-off threshold means that minor processes collectively contributing up to 5% of the total environmental impact, may be excluded from the model. The benefit of quantifying cut-off criteria following normalization is that it allows basing the cut-off on the most relevant impact categories (European Commission 2010). Grouping The grouping step aggregates impact categories into one or several sets via sorting or ranking. The former sorts the category indicators on a nominal basis while the latter ranks them on an ordinal scale (Guinée 2002). Sorting may be performed based on a spatial scale, the area of protection, and the amount of science/value-choice in the impact category model. Ranking may be based on the degree of reversibility of impacts, certainty, or priorities among impact types (ISO 2003). Weighting The purpose of weighting is to make impact assessment results easier to interpret. Weighting employs numerical factors to convert the indicator results based on value-choices as to the relative importance of different impact categories and may consist of an aggregation of weighted indicator results as well. The outcome may be a single number representing the environmental performance of a product. It can then be compared to weighted results for other products (ISO 2003). However, since different practitioners may have different 41 preferences, which can lead to divergent weighted results, this optional element is controversial (ISO 2006b). Methods for weighting include monetary, distance-to-target, and social panel approaches (ISO 2003). Weighting must not be applied in the case of comparative assertions disclosed to the public (ISO 2006b). 2.2.4 Life cycle interpretation The life cycle interpretation, which is a crucial element for decision-making and communication of the results (Sala and Andreasson 2018), is the last phase of the LCA study (Guinée 2002). At this stage, results, as well as methodological choices and assumptions, are evaluated for reliability and robustness, vis-à-vis the goal and scope of the study. The interpretation stage consists of three core components: (1) evaluation of results, (2) analysis of results, and (3) formulating the conclusions, limitations, and recommendations of the study. In order to ensure the authoritativeness of the results, peer-review is fundamental (Guinée 2002; ISO 2006a, b). In line with the iterative nature of LCA, the scope and quality of data are reviewed and revised (if necessary) at this stage (ISO 2006a). Based on the defined goal and scope of the study, limitations associated with the definition of system boundaries, FU, functions, LCI and LCIA should be identified (ISO 2006b). It should be noted that the procedures for dealing with the interpretation of LCA have to be presented systematically (Guinée 2002) as well as the study-specific requirements as described in the goal and scope stage (ISO 2000; Sala and Andreasson 2018). To ensure the reliability of the LCA results, the interpretation phase includes several distinct steps (ISO 2006b), as described in the following sections. 42 Consistency check To verify the consistency of data assumptions, methods, and models with the goal and scope of the study, the consistency check is applied (ISO 2006b). First, the consistency check is conducted because all other analyses will be fruitless unless the assumptions, methods, models and data are consistent with the goal and scope of the study. If inconsistencies are found, they should be remedied or justified, and their influence on the results should be evaluated. Previous LCA stages should be repeated to remove the inconsistencies. Finally, the remaining inconsistencies should be reported clearly (Guinée 2002). Completeness check The completeness check is required to make sure that all information and data necessary for the interpretation stage are accessible, complete and adequate to support the results of the study (ISO 2006b). An error check is used to identify potential errors, incomplete data, false assumptions, and model choices. One or more LCA or technical expert(s) may also be consulted to evaluate model completeness. Comparing the study results with results from other relevant studies is another suitable technique for identifying flawed data or assumptions. In the case of finding errors, the solutions are the same as consistency check solutions (Guinée 2002). Contribution analysis To assess the contribution of the various modelled flows and processes to the overall results, the contribution analysis is required. Once again, the results of this analysis may 43 indicate where collecting more or better data may be required - in particular, if it is found that processes modelled using estimated data make large contributions to estimated impacts (Guinée 2002). Professional LCA tools and the LCI & LCIA result tables in spreadsheet software can be utilized for the contribution analysis (European Commission 2010). Perturbation analysis The effects of minor changes in the modelled system on the LCA results can be evaluated by perturbation analysis. This analysis can be done at various levels, such as the inventory table, the environmental profile, normalized indicator, and weighting results. Both environmental and economic flows are considered in the perturbation analysis. If small changes in model variables lead to large effects, gathering more or better data may be required. It is recommended that the results of perturbation analysis are presented in tables and graphs. Conducting perturbation analysis is rarely reported in practice due to the lack of dedicated software (Guinée 2002). Sensitivity analysis and uncertainty analysis Sensitivity analysis (SA) and uncertainty analysis (UA) are described as central components of the interpretation stage (Hauschild et al. 2017). The results of the SA and UA together comprise the sensitivity check (ISO 2006b). These results should be reported alongside LCIA results in tables or graphs. The SA evaluates the effects of changes in key process data, model choices and other variables on the results (Guinée 2002). SA can be performed at the level of the goal and scope, LCI, and LCIA. UA assesses input uncertainties 44 and their influence on overall uncertainty for the results (Saisana et al. 2005). The chief types of uncertainty are related to data quality (data inaccuracy & gaps, unrepresentative data, etc.), LCI model uncertainty, and LCIA model uncertainty (Reap et al. 2008). All sources of uncertainty should be identified and considered. Methodological decisions made during the goal and scope stage may also strongly influence the level of uncertainty associated with the LCA results. These include how functions, FU, and system boundaries are defined, as well as choices regarding allocating reference flows. As such, careful treatment of all components in the goal and scope stage is extremely important (Smith Cooper 2003; ISO 2006b; Reap et al. 2008). The estimation of uncertainties around results requires a predefined null hypothesis and a propagation method, such as Monte Carlo analysis, analytical error propagation, fuzzy logic, or a hypothesis-based significance test. In addition, a practical approach is Numeral Unit Spread Assessment Pedigree (NUSAP), which has been used as a quantitative tool to provide an uncertainty analysis (Guinée et al. 2018). Depending on the kind and amount of data, creating a spreadsheet/worksheet for calculation in a program such as EXCEL or Lotus is recommended (Jensen et al. 1997). SA and UA are required by ISO for comparative studies disclosed to the public and relevant tools are available in all major LCA software packages. Conclusions and recommendations Conclusions and recommendations are the last steps of the interpretation stage and require gathering all the information from previous stages for reporting to the target audience. Conclusions and recommendations should be aligned with the goal and scope of the study 45 and all checks and limitations should be considered alongside the conclusions. If possible, the results of the study should be compared with those of other, similar studies. It is recommended that analysis and opinions be separated for reasons of transparency. This can be achieved by summarizing the key issues and justifying the conclusions (Guinée 2002). Regarding comparisons, it should be reported whether the differences between the results of the product and its alternative are significant. The results of the interpretation phase hence include information on the validity and quality of the LCA as well as the final conclusions and recommendations, which may include, for example, an indication of preference among products or specific ways to improve products (Guinée 2002). It should be noted that unbiased interpretation of results and conclusions have to be reported, and three factors have to be considered according to the levels and forms of reporting; type of deliverables, intended applications, and target audience of the study (European Commission 2010). The stakeholders involved in the study have to be reported, especially in the case of specific studies (UNEP et al. 2009). 2.3 Source of difficulties in life cycle assessment Practitioners may face various problems in different phases of LCA, in particular, the LCI phase. Difficulties in the LCI phase may variously come from the material flow, energy flow, and process modelling components. These are related to representing modelling parameters. In order of importance, the problems include allocation, local technical uniqueness, data quantity, and data quality (Reap et al. 2008). 46 The most controversial issue in LCA is allocation, which is required in the inventory model wherever multifunctional (i.e. providing co-products/services) processes are encountered. Here, the elementary flows, product flows, and emissions must be apportioned between co-products/services perspicuously (ISO 2006a). ISO (2006b) provides the following hierarchy for the resolution of multi-functionality problems. First, if the allocation can be avoided through sub-division or expanding the product system, these solutions are preferred. If it cannot, inputs and outputs should first be allocated amongst products in a way that reflects the physical relationships between them. If using physical relationships is impossible, inputs should be allocated based on other relationships, such as economic value. In the case where outputs are a combination of co-products and wastes, this ratio must be ascertained. Each procedure has its problems. For instance, subdivision results will be accurate only if sub-processes are physically and economically independent of each other. With system expansions, either the direct system enlargement or avoided burden approaches might be chosen, depending on the existence of alternative systems and the reliability of the required inventory data (Reap et al. 2008). Furthermore, these procedures to avoid allocation often require more data, which may increase the time required and costs, and which may also be subject to non-trivial uncertainty. With respect to physical allocation solutions, practitioners should possess adequate scientific knowledge and information necessary to perform a reliable allocation. Allocation based on non-casual relationships may lack both precision and logic. Obtaining reliable results from LCA requires systematic and justifiable allocation solutions. Using pre- 47 allocated data from third-party databases must be avoided in detailed LCAs if default allocations are not consistent with the goal and scope of the study (Guinée 2002). The differentiation between coproducts and waste has high importance since the inputs and outputs should be allocated to the coproducts only (ISO 2006a). The next difficulties in the LCI phase relate to local technical uniqueness, which refers to the context-specific, characteristic features of the implemented technologies, including extraction, production, distribution, and end-of-life processes, all of which may make a substantial difference in terms of resources demanded and environmental emissions (Reap et al. 2008; Michalski and Krueger 2015). Another difficulty is data collection, the most challenging step of the LCI phase. Data quality will have a strong influence on the results so the age, representativeness, accuracy, precision, and completeness of the data have to be considered. Accuracy refers to the correctness of a data point, whereas precision refers to the relationships amongst data (Hauschild et al. 2017). Issues related to primary data collection include cost, representativeness, and potential bias in the questionnaire used to collect data, respondent and non-respondent bias, and statistical interpretations. Issues related to secondary data are cost, bias, and precision (Ciambrone 2018). Difficulties in LCIA may arise because of limitations associated with the selection of impact categories, characterization models, sensitivity and uncertainty analysis, system boundaries, and insufficient LCI data quality that compromise the rigour of LCIA results. In general, all LCI phase issues will also affect LCIA (ISO 2006b). 48 Chapter 3: Improving Nitrogen Use Efficiency in Crop-Livestock Systems: A Review of Mitigation Technologies and Management Strategies, and Their Potential Applicability for Egg Supply Chains 3.1 Synopsis One of the main challenges in crop-livestock systems is an inefficient use of nitrogen, which precipitates a variety of negative economic, human/animal health, and environmental impacts. This review aims to identify and compare potentially efficacious technologies and management strategies that reduce nitrogen emissions and improve nitrogen use efficiency along crop-livestock supply chains, with an emphasis on the egg sector. Studies addressing the mitigation of reactive nitrogen emissions at key supply chain stages, including feed input production, feed formulation, and manure management were reviewed. The assessment was based on a combination of environmental, technical and economic criteria. Several priority mitigation options were identified. This included implementation of “4Rs” practices and biochar addition to cropland at the feed input production stage, and reduced crude protein diets supplemented with synthetic amino acids or enzymes at the feed formulation stage. For manure management, three substages were considered. Promising strategies identified for these substages were: implementation of mechanical ventilation and bedded systems, along with use of manure belts or scrapers and ammonia scrubbers in poultry housing; covering and adding biochar to manure at the manure storage; and incorporating manure at the land application stage. We also underscore that nitrogen use efficiency efforts should consider all 49 major reactive nitrogen species as well as potential interactions between mitigation options across supply chain stages, while simultaneously considering other potential resources and environmental impacts so as to prevent unintended burden shifting. Towards this end, nitrogen use efficiency initiatives should be supported by detailed life cycle assessment research to elucidate potential interactions, minimize burden-shifting, and maximize systems-level efficacy. 3.2 Nitrogen use efficiency in crop-livestock systems Reactive nitrogen (Nr) is essential for plant and animal growth and hence plays a critical role in agriculture and food security (Fowler et al. 2013). Nr enters food supply chains through nitrogen fertility management in croplands (i.e., via biological nitrogen fixation or application of synthetic N fertilizers). Nitrogen embodied in plant protein may be directly consumed by humans, or fed to livestock and incorporated into animal proteins for human consumption (Fowler et al. 2013). The fraction of fed nitrogen that is not assimilated by livestock is excreted in urine and manure, much of which will be cycled back into crop production through manure land application. Nr is lost in varying forms and amounts as it enters and cycles between crop and livestock systems (Benoit et al. 2015; Leip et al. 2015). Globally, approximately 50% of N fertilizer and manure N is lost to the environment, principally as emissions of ammonia (NH3) and nitrous oxide (N2O) to air or nitrate leaching (NO3-) to soil or water (Benoit et al. 2015; Coskun et al. 2017). Nitrogen oxide (NOx) emissions, albeit in small amounts, also occur as a result of fossil fuel combustion in agricultural machinery and from the application of N 50 fertilizers (Janulevičius et al., 2017). The subsequent migration, transformation, and cycling of these compounds in ecosystems are of particular concern (Galloway and Cowling 2002; Galloway et al. 2006; Gruber and Galloway 2008). Potential environmental impacts from Nr emissions, which are known to cascade through ecosystems (Leach et al. 2012; Erisman et al. 2015, 2018), include eutrophication, acidification, soil degradation, air quality issues, ecosystem simplification, biodiversity loss, and climate change (Prapaspongsa et al. 2010; Galloway and Leach 2016; Galloway et al. 2018; Liang et al. 2018). In light of this multiplicity of impacts, reducing Nr losses and improving nitrogen use efficiency (NUE) is critical (Galloway et al. 2017) to improving myriad sustainability outcomes. Indeed, improved nitrogen management has been recognized as an important factor for achieving several of the UN Sustainable Development Goals (Zhang et al. 2015; Liang et al. 2018). With respect to climate change, specifically, agriculture is both a key contributor as well as a sector that will be profoundly impacted by climate change (Robertson et al. 2013). Minimizing Nr emissions is therefore recognized as important to slowing the rate of climate change over the next century (Townsend et al. 2012; Suddick et al. 2013). However, many climate change impacts, mitigation, and adaptation efforts are both influenced by Nr dynamics (Suddick et al. 2013) and subject to multiple potential interaction effects. These connections between nitrogen emissions and climate change are not limited to the net effects on changing temperatures and precipitation patterns, and challenge business as usual practices (Townsend et al. 2012). For example, efforts to reduce fossil fuel consumption or N 51 fertilizer application may influence tillage practices, yields, land use requirements, and different kinds and amounts of Nr emissions. Among the prevalent forms of Nr emissions in agricultural contexts, ammonia is the most volatile and susceptible to loss (El-Hack et al., 2019). Intensive livestock production is responsible for the majority (about 70%) of global ammonia emissions (Ilea 2009), presenting risks with respect to air pollution that may impact both human and livestock health, as well as eutrophication and acidification (Ilea 2009; Prapaspongsa et al. 2010). Nitrous oxide is a potent greenhouse gas and also transforms into nitrogen oxide (NOx) compounds, which adversely affect the stratospheric ozone layer (Kanter et al. 2016; Sharma and Bali 2017). Urea and ammonium-based fertilizers and livestock manure are the main sources of nitrous oxide emissions in agriculture (van der Weerden et al., 2016), which is responsible for 63% of global nitrous oxide emissions (Arriaga et al., 2010; Gerber et al., 2007). Nitrate leaching, principally from fertilizers and animal manures, leads to water quality issues, eutrophication, toxic algal blooms, ecosystem simplification, and biodiversity loss (Beeckman et al., 2018). It is also an indirect source of nitrous oxide emissions. A large and growing body of literature has investigated the distribution and drivers of N emissions along crop-livestock supply chains (Philippe et al. 2011; Liu et al. 2014; Bacenetti et al. 2016; Leinonen and Kyriazakis 2016; Pelletier 2017; Asem-Hiablie et al. 2019). Taken together, these studies indicate that feed crop production, feed formulation (and associated impacts on feed conversion efficiency), and manure management have the most significant influence on nitrogen emissions. Much literature has also been published on individual mitigation technologies and strategies to reduce Nr emissions associated with various aspects 52 of crop and livestock production (Carew 2010; Pelletier et al. 2013; Wang et al. 2014a; Regueiro et al. 2016). For example, Pelletier et al. (2013) suggested that the selection of nitrogen-efficient crops and manure management strategies could improve NUE and reduce N losses along egg supply chains. A variety of other studies have similarly considered the use of nitrification inhibitors during crop production (Zaman et al. 2009; Kim et al. 2012; Soares et al. 2012; Di and Cameron 2016; Guardia et al. 2017; Kong et al. 2018), reduced crude protein in diets ( Liu et al., 2017; Osada et al., 2011), manure acidification (Ottosen et al., 2009; Regueiro et al., 2016; Wang et al., 2014), anaerobic digestion (Chantigny et al., 2009; Holly et al., 2017), covered manure storage (Hou et al., 2015; Wang et al., 2018), and manure injection (Nyord et al., 2012; Sadeghpour et al., 2018) as potential options to reduce Nr emissions. Despite this plethora of literature, the relative efficacy of available technologies and strategies to reduce nitrogen emissions and improve NUE, whether concerning single or multiple Nr species or supply chain activities, remains unclear, as do their comparative technical and economic feasibility. In addition, few studies have considered potential mitigation options across nitrogen species, interactions between different stages of the crop-livestock system, nor other resource/environmental sustainability synergies or trade-offs that may arise as a result of particular technology/management interventions. The egg industry, with current production of more than 70 million tonnes of eggs annually worldwide, is among the fastest-growing livestock sectors and is projected to remain so for the foreseeable future (FAO, 2018). While being relatively efficient converters of feed to food, poultry diets are high in protein, and poultry manure is typically nitrogen rich (Pelletier 53 2010, 2017). For these reasons, Nr emissions in the form of ammonia, nitrous oxide, and nitrate leaching associated with poultry manure, along with the production of the crops required as feed inputs, may nonetheless be considerable (Leinonen and Williams 2015). Improving NUE hence represents a priority sustainability research area for this sector (Pelletier 2018). Despite increased attention to the diverse mitigation technologies and strategies to reduce Nr losses along crop-livestock supply chains, a comprehensive review of their potential comparative efficacy is lacking. The purpose of the current review is, therefore, to identify and evaluate the potential comparative efficacy of available technologies and strategies to reduce Nr losses and improve NUE along crop-livestock supply chains, while also considering (to the extent possible) technical and economic aspects. The goal is to identify a priority subset (i.e., taking into account both efficacy and feasibility considerations) that should be subject to further, detailed systems-level modelling using tools like process-based environmental Life Cycle Assessment (LCA) in order to provide rigorous decision support to stakeholders regarding the net benefits and trade-offs associated with NUE strategies for the egg industry. 3.3 Methods 3.3.1 Search strategy and literature selection To ensure currency of the technologies/strategies considered, the temporal scope of this review includes papers/reports published between 2009 and May 2019. Relevant literature was identified through structured searches of the Web of Science Core Collection, Science 54 Direct, and Google Scholar using combinations of keywords related to consideration of nitrogen emissions for key activities along the supply chain (Table 3-1). Table 3-1 Search terms Supply chain stage Crop production wheat, maize, soybean, canola, barley, inhibitors, cover crops, intercrops, biochar Feed formulation diet, diet manipulation, enzyme supplementation, synthetic amino acids Manure management collection, treatment, storage, ventilation, application, incorporation, scrubber, digestion, scraper, belt, biochar Nitrogen emission types ammonia, ammonium, nitrous oxide, nitrogen oxides, nitrogen dioxide, nitric oxide, nitrate, nitrite Studies reporting management strategies and technologies that have the potential to mitigate nitrogen emissions (ammonia, ammonium, nitrous oxide, nitrogen oxides (nitrogen dioxide and nitric oxide), nitrite, and nitrate) along crop-livestock supply chains were first classified in terms of the supply chain activity to which they refer (i.e. (1) feed input production, (2) feed formulation, and (3) manure management). Manure management was further differentiated in terms of the following sub-stages: manure management in housing, manure storage, and manure land application. Following this initial classification, papers were selected for further, detailed review if they (a) considered at least one mitigation technology/strategy for emissions reduction at one or more of these supply chain stages and (b) referred to emissions of at least one of these reactive nitrogen species. 55 3.3.2 Detailed review criteria More detailed criteria were subsequently applied in order to further characterize and compare available technologies and strategies to reduce nitrogen emissions at each focal stage of the crop-livestock supply chain, with a particular emphasis on poultry production. Specifically, a set of technical, environmental and economic criteria and sub-criteria were identified based on analysis of multiple literature sources (Samah et al. 2010; Amer and Daim 2011; Ahmad and Tahar 2014; Zaini et al. 2015; Aydiner et al. 2016; Lijó et al. 2018). Together, they comprise “must-be-considered criteria” for improving the performance of supply chains as per Govindan et al. (2015). The criteria and sub-criteria are described in Table 3-2. Table 3-2 Criteria, sub-criteria and indicators selected to characterize and compare reported technologies and strategies to mitigate nitrogen emissions along crop-livestock supply chains. Criteria Sub-Criteria Acronym Description Indicator Technical Fit to context TFC The technology/strategy is applicable for the considered stages along the poultry supply chain Yes No Ease of implementation TEI The technology/strategy can be implemented without cumbersome infrastructure burdens Easy Moderate Difficult NA1 56 Ease of operation and maintenance TEO The technology/strategy is relatively easy to implement/ operate, and maintenance requirements are low Easy Moderate Difficult NA Environmental Considered nitrogen species ECN The technology/strategy is intended to reduce one or more of NH3, NH4+, N2O, NOx (NO2, NO), NO3-, NO2- - + ± ? NS2 Mitigation potential EMP The technology/strategy has the potential to reduce emissions 0-25% 25-50 % 50-75 % 75-100% N excretion3 ENE Decrease in nitrogen excretion rate as influenced by feed formulation 0-25% 25-50 % 50-75 % 75-100% Economic Cost of investment ECI The technology/strategy is not cost-prohibitive to initiate Inexpensive Moderate Expensive NA Cost of operation and maintenance ECO Operation and maintenance cost for the technology/strategy is low Inexpensive Moderate Expensive NA Color code: green= reduction, red= increase, yellow= either reduction or increase (i.e. conflicting reports in the literature). 1 Not Applicable, 2 Not Significant, 3 Only populated for the feed formulation section. 57 With respect to the environmental criteria, the technology/strategy must have a positive net reduction to be considered as an effective mitigation option. It is well established that farmers are more likely to adopt cost-effective N mitigation technologies and strategies that are easy to implement, maintain, and operate (Alemu et al. 2017a). For the economic criteria, the technology/strategy must be ranked as inexpensive to moderate in order to be considered feasible, based on information available in the reviewed literature. To determine if the mitigation option is technically feasible for application to egg supply chains, it has to be currently applied in the egg industry or have a clear potential for implementation in the egg industry. The ease of implementation, operation, and maintenance sub-criteria have to be of easy to moderate rank in order for a given technology to be considered acceptable as a mitigation option. These sub-criteria do not apply to some strategies because they do not have tools or machinery that require maintenance. The differentiation among easy, moderate, and difficult is based on specific statements provided by authors in the considered literature sources. Once all of the technologies and strategies for each supply chain stage considered were screened against these criteria, those that met all specified thresholds were short-listed for priority consideration for application as NUE measures in the poultry industry, subject to further life cycle-based evaluation and comparison. The potential respective merits and weaknesses of each are discussed. 3.4 Results and Discussion A considerable number of technologies and strategies to reduce emissions of particular nitrogen species and to improve NUE at key points along livestock supply chains have been 58 described in the literature. However, little information is available regarding (a) their potential applicability for the poultry industry, (b) coverage of different nitrogen emission species, as well as (c) their comparative efficacy and feasibility. As a first step towards resolving this gap, Tables 3-3 to 3-7 compare each strategy/technology in terms of their emissions reduction potential along with technical and economic criteria in order to arrive at a subset that might potentially be considered most relevant and efficacious for poultry supply chains. It should be noted that the majority of studies addressed ammonia and/or nitrous oxide emissions only. Nitrate emissions were only considered in a subset of studies addressing crop production, manure storage, and manure land application. None of the reviewed studies considered nitrogen dioxide or nitrite. 3.4.1 Feed input production Recommended strategies for reducing nitrogen emissions during feed input production variously refer to fertilization practices, type of fertilizer, use of inhibitors, irrigation systems, use of cover crops or intercrops, and addition of biochar to soil. The comparative efficacy of these strategies in terms of the criteria considered are presented in (Table 3-3) and explained in the following sections. 59 Table 3-3 Potential mitigation options for Nr emissions in feed input production systems. Stage Technology/Strategy Technical Criteria Environmental Criteria Economic Criteria References Fit to Context Ease of Implementation Ease of Operation and Maintenance NH3 NH4+ N2O NO3- NO Cost of Investment Cost of Operation and Maintenance Feed input production Fertilization 4Rs1 Yes Moderate Easy - NI - - NI $$ $ (Alhajj Ali et al., 2013; Alhajj Ali et al., 2017; Ali et al., 2015; Bell et al., 2016; Bouwman et al., 2002; Fertilizer Canada, 2016; Gan et al., 2014; Oenema et al., 2014; Pelzer et al., 2012; Tedone et al., 2018; Xue et al., 2016) Fertigation Yes Difficult Moderate -25-50% NI -25-50% NI +0-25% $$$ $$ (Carew 2010; Shah et al. 2012b; Tian et al. 2017a; Sanz-Cobena et al. 2017; Ma et al. 2018) Fertilizer type Urea Yes NA NA + NI + + + NI NI (Mikkelsen 2009; Sanz-Cobena et al. 2012; Turner et al. 2012; Linquist et al. 2012) Ammonium sulphate Yes NA NA + NI + + NI NI NI (Mikkelsen 2009; Chien et al. 2011; Turner et al. 2012; Linquist et al. 2012) Calcium ammonium nitrate Yes NA NA + NI + NI NI NI NI (Forrestal et al. 2016; Harty et al. 2016) 60 Stage Technology/Strategy Technical Criteria Environmental Criteria Economic Criteria References Fit to Context Ease of Implementation Ease of Operation and Maintenance NH3 NH4+ N2O NO3- NO Cost of Investment Cost of Operation and Maintenance Feed input production Inhibitors Nitrification inhibitor (NI) Yes NA NA +50-75% + -50-75% -25-50% - $$$ $$$ (VanderZaag et al. 2011a; Vogeler et al. 2013a; Gerber et al. 2013; Monaghan et al. 2013; Li et al. 2014; Misselbrook et al. 2014; Di and Cameron 2016; Minet et al. 2016; Pan et al. 2016; Sanz-Cobena et al. 2017; Beeckman et al. 2018; Kong et al. 2018; Recio et al. 2018) Urease inhibitor (UI) Yes NA NA -0-25% NI -0-25% -25-50% NI $$$ $$$ (Patra and Chand 2009; Zaman et al. 2009; Luo et al. 2010; Gerber et al. 2013; Li et al. 2014; Snyder 2017; Sanz-Cobena et al. 2017; Drury et al. 2017; Sigurdarson et al. 2018) Double inhibitor (DI) Yes NA NA -0-25% + -50-75% -50-75% + $$$ $$$ (Zaman et al. 2009; Luo et al. 2010; Soares et al. 2012; Abalos et al. 2016; Drury et al. 2017) Irrigation Flood irrigation Yes Easy Difficult NI NI NI + NI $ $ (Bandyopadhyay et al., 2010; Sauer et al., 2009) Sprinkler irrigation Yes Easy Moderate NI NI + NI NI $$ $$$ ((Bandyopadhyay et al. 2010; Sauer et al. 2010a; Barakat et al. 2016; Cayuela et al. 2017) 61 Stage Technology/Strategy Technical Criteria Environmental Criteria Economic Criteria References Fit to Context Ease of Implementation Ease of Operation and Maintenance NH3 NH4+ N2O NO3- NO Cost of Investment Cost of Operation and Maintenance Feed input production Irrigation Drip irrigation Yes Easy Moderate NI - -25-50% NI ± $$$ $$$ (Barakat et al. 2016; Guardia et al. 2017b; Tian et al. 2017; Zhang et al. 2019b) Cover crops Yes Moderate NA NI NI ± - NI $$ $$ (Bergtold et al. 2012; Basche et al. 2014; Roesch-Mcnally et al. 2017; Sanz-Cobena et al. 2017 Shelton et al. 2018; Davis et al. 2019) Intercrops Yes Moderate NA NI NI ± - NI $$$ $$$ (Pappa et al. 2011; Dyer et al. 2012; Huang et al. 2014; Loïc et al. 2018; Yang et al. 2018) Biochar addition Yes Easy Easy NI NS -25-50% -0-25% -50-75% $$- $$$ $ - $$ (Roberts et al. 2010; Shackley et al. 2011; Draper and Tomlinson 2012; Kulyk 2012; Mekuria and Noble 2013; Sánchez GarcÃ-a et al. 2014; Nelissen et al. 2014; Sánchez-García et al. 2015; Haider et al. 2017; Oo et al. 2018; Borchard et al. 2019; Fidel et al. 2019) Color code: green= reduction, red= increase, orange= either reduction or increase, NS: no significant reduction, NA: not applicable, NI: no information, $: inexpensive, $$: moderate, $$$: expensive 62 3.4.1.1 Fertilization Nitrogen use efficiency improvement through Nr emissions reduction can be achieved via the 4Rs (Right Source @ Right Rate @ Right Time @ Right Place®) nutrient stewardship approach (Oenema et al. 2014; Barakat et al. 2016; Snyder 2016). 4Rs was designed by Fertilizer Canada and is promoted as integral to best management practices (BMPs) in crop production (Fertilizer Canada 2016). 4Rs practices consist of precision agricultural activities including GPS mapping, soil sampling, yield monitors, and variable-rate and/or split nutrient applications (Snyder 2016; Bruulsema 2017). According to the characteristics of each site, suitable 4Rs practices can be implemented that can improve yields, decrease nutrient losses, and provide return on investment (Sposari and Flis 2017). Numerous studies have demonstrated that N land application practices (rate, timing, splitting, and sources) influence emissions levels (Bouwman et al. 2002; Pelzer et al. 2012; Alhajj Ali et al. 2013, 2017; Gan et al. 2014; Ali et al. 2015; Xue et al. 2016). For example, Tedone et al. (2018) investigated rates, source, and application timing of nitrogen for rain-fed durum wheat. The sources of N were urea, Entec® 26, and Sulfammo 23 applied at N rates of 45, 90, and 135 kg/ha during sowing, tillering, and stem elongation. The best nitrogen application with respect to grain yield and quality was 90 kg/ha nitrogen split amongst sowing (40%), tillering (10%), and stem elongation (40%), respectively. Application of this strategy may decrease the amount of nitrogen fertilizer required for wheat production. With respect to the economic criteria, 4Rs have primary costs to obtain the technologies like GPS; 63 however, it is considered as a cost-effective strategy over time, especially due to reduced fertilizer application (Snyder 2017). Fertigation, which is a combination of fertilization with irrigation, is also a comparatively new strategy. It is reported that fertigation may decrease ammonia and nitrous oxide emissions up to 50% compared to separate irrigation and fertilizer treatments; however, it may also increase nitric oxide emissions by ~ 20% (Tian et al. 2017a; Sanz-Cobena et al. 2017). 3.4.1.2 Fertilizer type Using different types of nitrogen fertilizer influences nitrogen cycling processes and associated emissions. Ammonia is volatilized from all ammonium-based fertilizers. However, depending on fertilizer properties, the fertilizer reactions after application and the location of application, the amount of losses might vary (Mikkelsen 2009; Turner et al. 2012). The three most common types of N fertilizers are urea, ammonium sulphate, and calcium ammonium nitrate. Comparisons among them show trade-offs with respect to losses of different Nr species. In studies comparing the effects of ammonium sulphate and urea fertilizer on nitrous oxide emissions, Linquist et al. (2012) and Snyder et al. (2009) demonstrated that ammonium sulphate tended to increase nitrous oxide emissions compared to urea, but that there was no nitrogen loss through ammonia volatilization in the case of surface application to acid or neutral soils. Ammonium sulphate has less nitrate leaching to groundwater compared to ammonium nitrate and less ammonia, nitrate, and nitric oxide emissions compared to urea (Chien et al. 2011; Turner et al. 2012). 64 A comparison of calcium ammonium nitrate and urea showed higher nitrous oxide and ammonia emissions for the former (Forrestal et al. 2016; Harty et al. 2016). Ammonia volatilization from urea fertilizer was reported to vary from zero to 100% depending on various conditions including soil type, environment, time and place of application (Mikkelsen 2009). Sufficient literature was not available to enable consideration of other N fertilizers, nor did the available literature sources support making comparisons on the basis of technical or economic attributes. 3.4.1.3 Inhibitors Use of inhibitors including nitrification inhibitors (NIs) and urease inhibitors (UIs), as well as combined application of UIs and NIs (called double inhibitors (DI)) (Zaman et al. 2009), has received substantial attention as a potential nitrogen loss mitigation strategy in crop production (Abalos et al., 2016b; Di & Cameron, 2016; Guardia et al.,et al., 2017; Hube et al., 2017; Kong et al., 2018; Luo et al., 2010; Vogeler, Beukes, et al., 2013). Depending on the type, inhibitors can be injected directly into the soil, coated onto solid N fertilizers, mixed with manures, or supplied pre-blended with fertilizers (IPNI 2019). Nitrification inhibitors include dicyandiamide (DCD), nitrapyrin (N-serve) or DMPP, 3, 4-dimethylpyrazole phosphate (ENTEC) and 3,4 dimethylpyrazole succinic acid (DMPSA) (Misselbrook et al. 2014; Beeckman et al. 2018; Recio et al. 2018). Depending on the soil conditions and type of N input, the reported impact on ammonia losses has been variable. Based on a meta-analysis by Kim et al. (2012) and others, NIs appear to generally increase ammonia emissions but 65 reduce nitrous oxide and nitrate emissions (Di & Cameron, 2016; Monaghan et al., 2013; Pan et al., 2016; Recio et al., 2018; Vogeler et al., 2013). Urease inhibitors such as N-(n-butyl) thiophosphoric triamide (NBTPT) are applied to reduce ammonia volatilization ( Li et al., 2015). Zaman et al. (2009) showed that application of NBTPT decreased NH3 emissions over urine application on a dairy-grazed pasture site, but also had a slight effect on nitrous oxide emissions reduction. Double inhibitors have been shown to reduce ammonia, nitrous oxide, and nitrate emissions (Luo et al. 2010; Kim et al. 2012; Abalos et al. 2016). Available information regarding the affordability of inhibitors is mixed. Use of inhibitors is considered to be a high cost strategy by Misselbrook et al. (2014). In contrast, Minet et al. (2016) suggested that mixing inhibitors with stored manure offers a cost-effective and practical N loss mitigation option for farmers. 3.4.1.4 Irrigation Irrigation techniques such as flood irrigation, sprinkler irrigation, drip irrigation, and associated fertilization strategies also influence nitrogen transformations and fluxes. Each of these techniques has specific effects on soil water content and temperature, which in turn influences the microbial activities that control nitrification and mineralization rates, as well as N fixation (Barakat et al. 2016). The type of irrigation technique (if any) that can be used in feed crop production depends on crop type and production context. Flood irrigation is often used for annual crop production (e.g., barley, maize, rapeseed, soybeans, and wheat) in some regions. Sprinkler and drip irrigation are typically used for high-value cash crops (e.g., flowers, herbs, and 66 ornamental grasses) because of the high cost of implementation, operation, and maintenance (Sauer et al. 2010a), but sprinkler irrigation (for example, circle pivot irrigation) may also be used for feed crops in some contexts. Flood irrigation and sprinkler irrigation appear to have the strongest and least controlled effects on nitrogen transformation and fluxes, while subsurface drip irrigation techniques have the least effect on soil N cycling (Barakat et al. 2016). Sanz-Cobena et al. (2017) reported a ~ 80% reduction in nitrous oxide emissions in drip-irrigated compared to sprinkler-irrigated systems. However, capital costs of flood and sprinkler irrigation were inexpensive to moderate, respectively, while drip irrigation was expensive (Sauer et al. 2010). 3.4.1.5 Cover crops Cover crops, which are off-season crops planted after harvesting the main crop, are used for managing soil quality, soil erosion, and soil fertility. Due to diverse cover crop characteristics and management, their impact on nitrogen emissions varies (Davis et al. 2019). In a review of studies on the impacts of cover crops on nitrous oxide emissions, Basche et al. (2014) found that 60% of treatments (n=126 over 26 studies) reported an increase in nitrous oxide emissions, while 40% reported a reduction. This appears to be due to interactions with other factors. For example, Davis et al. (2019) evaluated the effects of cover crops (i.e. cereal rye, hairy vetch or mixture of both species) on nitrous oxide emissions and their interactions with “4Rs” under a range of fertility treatments (i.e. subsurface banded poultry litter (low, moderate, high application rates); surface broadcast 67 mineral fertilizer; and surface broadcast and tilled mineral fertilizer). It was found that the combination of cover crop residues and subsurface-banded poultry litter increased nitrous oxide emissions, while the application of cereal rye resulted in lower nitrous oxide emissions compared to hairy vetch. The integration of cover crops requires that farmers adopt additional farming practices, which may increase costs and reduce feasibility. However, an economic incentive may be found in the form of reduced need for exogenous nitrogen fertilizers as well as increased yields (Bergtold et al. 2012; Roesch-Mcnally et al. 2017). 3.4.1.6 Intercrops Intercropping refers to growing more than one crop in proximity in order to increase yield and improve use of land, resources, and ecological processes that may not be utilized by a single crop (Pappa et al. 2011; Dyer et al. 2012; Brooker et al. 2015). Having complementary rather than competing traits among cultivated crops can result in improved resource use efficiency (Pappa et al. 2011; Brooker et al. 2015). For example, intercropping cereals and legumes provides an opportunity to fix nitrogen and hence reduce use of nitrogen fertilizers while potentially providing two marketable crop outputs (Pappa et al. 2011; Yang et al. 2018). However, intercropping systems require more labour and are a moderately expensive strategy (Yang et al. 2018). Pappa et al. (2011) investigated the effects of cereal-legume intercropping on nitrous oxide emissions and nitrate leaching compared to monocrops of barley, oat, pea or clover. Generally, nitrate leaching was higher from crop rotations with legumes compared to 68 rotations without legumes (Pappa et al. 2011). However, considerable differences in nitrous oxide emissions reduction were observed depending on the specific intercrop treatment. Furthermore, nitrous oxide emissions were also influenced by previous year treatments (Pappa et al. 2011). Nevertheless, intercropping with maize-soybeans compared to mono-cropping over two field seasons showed that intercropping had lowest nitrous oxide emissions (Dyer et al. 2012) and that it decreased nitrous oxide emissions by almost 50% in the second year of cultivation (Huang et al. 2014). 3.4.1.7 Biochar addition to soil Biochar, a stable carbon, is made from exposing biomass to extremely high temperatures under limited oxygen conditions (Agyarko-Mintah et al. 2017). Biochar addition to soil not only boosts soil fertility and crop productivity but may also reduce nitrous oxide emissions and nitrate leaching (Kulyk 2012; Haider et al. 2017). Recently, Fidel et al. (2019) investigated the impact of biochar on nitrous oxide emissions at both the laboratory and field scale. Biochar addition to corn reduced nitrous oxide emissions by nearly 30%. This finding is consistent with the results of a meta-analysis of 56 studies by (Cayuela et al., 2015). However, Sanchez Garcia et al. (2014) found that biochar addition may either reduce or increase emissions, depending on the type of soil, due to differences in the dominant nitrous oxide formation pathway. Additionally, emission reductions may be influenced by the type of material from which the biochar is made. For example, Oo et al. (2018) found that biochar from bamboo reduced cumulative nitrous oxide emissions by increasing soil pH compared to biochar from sawdust or rice husk. 69 Despite these potential benefits, use of biochar may be cost prohibitive, depending on feedstock and context. Kulyk (2012) conducted a cost-benefit analysis of biochar application based on a literature review. The results suggested that biochar cost relies on several parameters such as feedstock, operational scale, transportation, distribution, and handling. The cost of biochar production might be high but can nonetheless be profitable due to a high return on investment. Wood chips, Miscanthus (a genus of woody, flowering ornamental grass used as an energy crop), and short rotation forestry products are among the most expensive feedstock, while straw is moderately expensive, and wood waste and green waste are comparatively much cheaper (Shackley et al. 2011). 3.4.1.8 Comparative efficacy and feasibility of NUE strategies/technologies for feed crop production Implementing the 4Rs significantly reduces Nr emissions including ammonia, nitrous oxide, and nitrate, but requires considerable training, labour, specialized equipment and, potentially, new products. This may make it both difficult and expensive to implement. Such costs should, however, be weighed against potential savings on fertilizer costs as well as improved yields. 4Rs is considered as a suitable Nr mitigation option for farms that already are in possession of the necessary equipment. Fertigation also requires specific, costly agricultural machinery, and has non-trivial implementation, operation, and maintenance requirements. For farmers who already possess suitable irrigation infrastructure, it may enable reducing ammonia and nitrous oxide emissions but may increase nitric oxide emissions from soil. In general, the application of synthetic fertilizers increases Nr emissions, 70 but emission types and amounts differ depending on the specific fertilizer. Application of inhibitors (UIs, NIs, and DIs) also offers a significant Nr reduction potential, but their high cost of procurement and application may limit their use. DIs application has shown higher ammonia, nitrous oxide, and nitrate emissions reduction potential compared to application of UIs and NIs alone. Among considered irrigation systems, the operation and maintenance of flood irrigation are more difficult than sprinkler and drip irrigation systems. Flood irrigation is inexpensive but increases nitrate leaching (little information was found with respect to other nitrogen emissions). Sprinkler irrigation and drip irrigation are more expensive and may be less applicable for feed crop production. For example, despite that drip irrigation appears to confer Nr emissions reduction compared to flood or sprinkler irrigation, only a single literature source documenting its use for feed crop production was identified. Application of cover crops and intercrops requires that farmers adopt additional farming practices. Cover crops and intercrops may either reduce or increase nitrous oxide emissions in different situations. No information was found regarding the effects on other nitrogen species. Costs for intercrops are generally higher than for cover crops due to the need for special equipment for planting, fertilizing, and harvesting. Biochar addition may be a promising strategy for reducing nitrogen emissions in feed input production. Application of biochar is technically relatively easy and may reduce nitrous oxide, nitrate, and nitric oxide emissions by 25-50%, 0-25%, and 50-75%, respectively. Costs will vary depending on context. 71 Among these mitigation options, DIs and NIs showed the highest reductions in nitrous oxide emissions (50-75%) while DIs resulted in a significant reduction in nitrate leaching (50-75%). Biochar addition to the soil showed the highest amount of nitric oxide reduction. Biochar is also considered as the easiest mitigation option for implementation and maintenance. Regarding economic criteria, 4Rs and, in some cases, biochar addition to the soil can be considered as affordable to moderately expensive strategies. 3.4.2 Feed formulation An appropriate diet is essential for optimizing animal performance as well as reducing the quantity of Nr released from the resulting manure (Carew 2010; Abd El-Hack et al. 2019). In addition, the amount, form, and ratio of Nr excretion between urine and faeces are all affected by diet (Gerber et al., 2013). Nr management through feed formulation is particularly relevant for poultry, which has N-rich manure compared to other livestock species. Liu et al. (2014) considered feed formulation as a best management practice as it facilitates an effective reduction of ammonia emissions at low cost. There are three strategies/technologies at the feed formulation stage (i.e., reduced crude protein, synthetic amino acids, and enzyme supplementation) that merit particular attention as nitrogen emission mitigation options (Table 3-4). 72 Table 3-4 Potential mitigation options for Nr emissions through feed formulation. Stage Technology/Strategy Technical Criteria Environmental Criteria Economic Criteria References Fit to Context Ease of Implementation Ease of Operation and Maintenance NH3 NH4+ N2O NO3- NO N excretion Cost of Investment Cost of Operation and Maintenance Feed formulation Reduced crude protein Yes NA NA -50-75% NI NS NI NS -25-50% $ NA (Arriaga et al. 2010; Carew 2010; Jarret et al. 2011; Rochette and McGinn 2011; Hernández et al. 2011; Burley et al. 2013; Wu et al. 2013; Liu et al. 2014; Sajeev et al. 2018b) Synthetic amino acids Yes Easy Easy - NI NI NI NI -25-50% $ $ (Nahm 2007; Yin and Tan 2010; Sirri and Meluzzi 2012; Kim et al. 2013; Liu et al. 2014; Chalova et al. 2016; Vieira et al. 2016) Enzyme supplementing Yes Easy Easy -0-25% NI - NI NI - $$ NA (Philippe et al. 2011; Sirri and Meluzzi 2012; Munir and Maqsood 2013; Ravindran 2013; Leinonen and Williams 2015) 73 3.4.2.1 Reduced crude protein Diets that are unnecessarily high in protein may potentially exacerbate the environmental impacts of poultry manure, including eutrophication, acidification, water, and air pollution (Kim et al. 2013). Conversely, reduced crude protein (CP) results in lower feed costs, which can increase overall farm revenue (Burley et al. 2013). Numerous studies have pointed toward reduced CP as an effective strategy to decrease ammonia emissions (Arriaga et al. 2010; Jarret et al. 2011; Philippe et al. 2011; Hernández et al. 2011; Liu et al. 2014). Specifically, this strategy tends to reduce total ammoniacal nitrogen (TAN) excretion (Philippe et al. 2011; Liu et al. 2014). For example, Liu et al. (2014) reported that a 10% reduction in TAN and 10-12.5% reduction in ammonia emissions may be achieved for every percent reduction in CP in pig diets. Similarly, Jarret et al. (2011) concluded that lowering dietary CP from 17.5% to 14.0% reduced ammonia volatilization from pig manure by 30%. Although increasing crude protein contents in three cow diets (14.1%, 15.9%, and 16.9% protein, respectively) resulted in a reduction of NUE from 23.9% to 22.6% and an increase in ammonia emissions by 13% per unit of crude protein, it did not influence nitrous oxide emission levels (Arriaga et al. 2010). Other dietary strategies, such as dietary fiber inclusion, can also diminish ammonia emissions by ~ 40% (Philippe et al. 2011). 3.4.2.2 Synthetic amino acids One potential strategy to reduce nitrogen emissions is combining both digestible (i.e., protein) and synthetic amino acids in order to deliver an optimal amino acid profile in the 74 diet. This can enable meeting nutritional requirements while avoiding excessive excretion of amino acids that may be present in feeds at levels exceeding those required by poultry (Liu et al. 2014). Reduction in nitrogen excretion by up to 40% via supplementation with synthetic amino acids has been reported (Nahm, 2007; Wu et al., 2013; Yin & Tan, 2010), along with reduced feed cost (Vieira et al. 2016). 3.4.2.3 Enzyme supplementation Enzymes have been used in poultry diets to enhance nutrient availability since 1926 (Munir and Maqsood 2013) and are considered as a practical strategy for achieving lower cost diets (Ravindran 2013). Use of enzymes such as protease improve the efficiency of protein digestion, enabling decreased protein levels in the diet and, consequently, lower nitrogen emissions (Sirri and Meluzzi 2012; Leinonen and Williams 2015). An LCA of the use of the protease Ronozyme® ProAct in poultry feed showed a reduction in eutrophication and acidification impacts due to reduced nitrogen excretion (Leinonen and Williams 2015). Supplementation with non-starch polysaccharides enzymes has been shown to influence digestibility, animal performance and ammonia emissions, but effects on emissions vary according to diet type. For example, Philippe et al. (2011) reported increased ammonia emissions (30%) for barley-based diets for pigs but decreased emissions (15–20%) for wheat-based diets. Nahm (2007) and Yin and Tan (2010) reported a 25% increase in nitrogen digestibility in broiler diets. 75 3.4.2.4 Comparative efficacy and feasibility of NUE strategies/technologies for feed formulation At the feed formulation stage, reducing crude protein levels along with enzyme supplementation and synthetic amino acids all appear to be efficacious strategies in terms of reducing levels of excreted nitrogen and, consequently, nitrogen emissions from manure. Reducing crude protein may be more cost-effective than the other strategies, and also result in greater reductions in ammonia emissions and nitrogen excretion; however, it must be ensured that protein levels remain adequate to meet the animals’ nutritional requirements. Here, the addition of synthetic amino acids or enzyme supplementation to reduced crude protein diets may be vital. From a “life cycle perspective,” feed formulation should consider both the comparative “upstream” NUE of substitutable feed inputs, as well as their direct impact on nitrogen excretion rates (Pelletier 2017). 3.4.3 Manure management Poultry manure, a major co-product of poultry (meat or egg) production, is typically cycled back into crop production as a nitrogen-rich fertilizer (AAFC 1990). Poultry manure improves the physical, chemical, and biological characteristics of soil, thereby increasing crop yields (Ranadheera et al. 2017). How manure is managed, from the time of excretion through land application, strongly influences the kinds and amounts of nitrogen losses that may occur. Typical manure management activities include manure management in housing systems, during storage, and at time of land application (Oenema et al. 2014; De Vries et al. 76 2015). A variety of technologies and management strategies may be applied concerning each of these activities in order to reduce Nr emissions. 3.4.3.1 Manure management in housing Careful manure management in poultry housing can have significant Nr emissions reduction effects. The following factors may all influence emissions rates: (1) the time of year, (2) temperature, (3) depth of the litter, (4) floor space per bird, (5) feeding practices, (6) disease, (7) ventilation, (8) the type of floor, and (9) litter amount (Broucek 2018). The primary options for emissions reduction are related to ventilation improvements, flooring systems and manure collection systems (Table 3-5). 77 Table 3-5 Potential mitigation options for Nr emissions in poultry housing. Stage Technology/Strategy Technical Criteria Environmental Criteria Economic Criteria References Fit to Context Ease of Implementation Ease of Operation & Maintenance NH3 NH4+ N2O NO3- NO Cost of Investment Cost of Operation & Maintenance Manure management in housing Ventilation Yes Easy Easy - NI + NI NI $$ $$ (Osorio et al. 2009; Alloui et al. 2013; Wang et al. 2014b; David et al. 2015; Lima et al. 2017; Broucek 2018) Mechanical ventilation Scrubbers Acid scrubber Yes Moderate Moderate -50-75% NI NI NI NI $$$ $$$ (Carew 2010; Lin et al. 2014; Hadlocon et al. 2015; Wood and Van Heyst 2016; Tian et al. 2017b) Air scrubber Yes NI NI -75-100% NI NI NI NI $$$ $$$ (Philippe et al. 2011; Van der Heyden et al. 2016; Sajeev et al. 2018a) Bio scrubber Yes Difficult Difficult -75-100% NI NI NI NI $$$ $$ (Hartung et al. 2011; Girard et al. 2013; Liu et al. 2017a; Blázquez et al. 2017) Biofilter Yes Moderate Moderate -50-75% NI +25-50% NI NI $$ $ (Belzile et al. 2010; Carew 2010; Akdeniz and Janni 2012; Liu et al. 2014; Van der Heyden et al. 2016) Floor systems and manure collection Yes Difficult NI - NI + NI NI $$$ NI (Cabaraux et al. 2009; Wang et al. 2011; Fournel et al. 2012; Liu et al. 2014; Jugowar et al. 2017; Broucek 2018) Slatted floor over deep-pit 78 Stage Technology/Strategy Technical Criteria Environmental Criteria Economic Criteria References Fit to Context Ease of Implementation Ease of Operation & Maintenance NH3 NH4+ N2O NO3- NO Cost of Investment Cost of Operation & Maintenance Manure management in housing Bedded systems Straw Yes Easy Easy -75-100% -0-25% +0-25% NI NI $ $$ (Gilhespy et al. 2009; Philippe et al. 2012; van der Weerden et al. 2014; Hou et al. 2015) Wood shavings Yes Easy Easy - - + NI NI $$$ $$$ (Miles et al. 2011; Van Harn et al. 2012; Le Riche et al. 2017; Smith et al. 2017) Sawdust Yes Difficult Difficult -25-50% -75-100% +0-25% + NI $$$ $$$ (Philippe et al. 2012; E. M. Shane et al. 2013; van der Weerden et al. 2014; Broucek 2018) Scraper Yes Easy Moderate -0-25% NI -25-50% NI NI $$ $$ (Mukhtar 2005; Philippe and Nicks 2013; Lenkaitis 2014; Cai et al. 2015; Lagadec et al. 2016; Kai et al. 2017) Belt Yes Easy Moderate -50-75% NI - NI NI $$ $$ (Xin et al. 2011; Ni et al. 2012; Fournel et al. 2012; H. Li and H. Xin 2013; Brouček and Čermák 2015; Loyon et al. 2016; Jugowar et al. 2017; Li et al. 2017; Loyon 2018; Naseem and King 2018) 79 3.4.3.1.1 Ventilation Mechanical ventilation Mechanical ventilation in poultry housing reduces ammonia concentration and emissions compared to natural ventilation (Osorio et al. 2009; Alloui et al. 2013) but may increase nitrous oxide emissions (Broucek 2018). Ventilation rate is a crucial factor for emission rates from poultry housing (Lima et al. 2017). The rate of ventilation should be within a range that considers not only animal welfare but also energy efficiency (Wang et al. 2014b; David et al. 2015). Initial costs of mechanical ventilation, as well as operation and maintenance costs, can be reduced by using the right size of energy efficient fans (Harris 2012). Scrubbers Scrubbers can be used to decrease ammonia losses from air ventilated from livestock housing (Carew 2010). A variety of scrubber types have been developed, including air scrubbers, acid scrubbers, and bio scrubbers. Air scrubbers include chemical, biological, and two-stage systems. Among these, biological air scrubbers with nitrification tanks have been shown to remove up to 90% of ammonia from air at pig housing facilities. In contrast, two-stage biological air scrubbers with nitrifying bacteria inhibition may reduce ammonia removal efficiency. High pH values in chemical air scrubbers similarly result in lower ammonia removal efficiency (Van der Heyden et al. 2016). The simplest scrubbers circulate water. In acid scrubbers, acid is added to enhance ammonia removal (Liu et al., 2014). Carew (2010) and Liu et al. (2014) reported that acid scrubbers remove at least 70% of ammonia from treated air. The amount of acid that is 80 applied and the contact time between air and liquid have an impact on the amount of ammonia reduction (Liu et al. 2014; Wood and Van Heyst 2016). Scrubbers are, however, considered expensive to implement and maintain (Carew 2010). Biofilters Biofilter systems include flat-bed biofilters with vertical airflow, vertical biofilters with horizontal airflow, and deep-bed biofilters (Akdeniz and Janni 2012). Akdeniz & Janni (2012) reported a reduction of 53% to 86% in ammonia emissions and 4% to 29% for nitrous oxide emissions when various biofilters were applied in dairy and swine housing. Van der Heyden et al. (2015) reported an approximately 50% reduction in ammonia emissions from poultry housing when using biofilters. Biofilters provide a lower cost option for ammonia removal from ventilation air compared to scrubbers (Carew, 2010). Comparative efficacy and feasibility of NUE strategies/technologies for ventilation Mechanical ventilation is a common, easy to be implement and operate, but moderately expensive means of reducing ammonia emissions and levels inside poultry housing. It is essential to maintaining conditions conducive to animal health. The amount of ammonia reduction depends on the ventilation rate. Scrubbers and biofilters can both be used to reduce ammonia emissions from livestock housing. Biofilters are technically easier than scrubbers in terms of implementation, operation, and maintenance. Acid scrubbers and biofilters can reduce ammonia emissions by a similar amount. Air scrubbers and bio scrubbers may reduce ammonia emissions (0-25%) more than biofilters. Biofilters may, however, increase nitrous oxide emissions. None of the 81 reviewed literature mentioned whether or not scrubbers affect nitrous oxide emissions. Biofilters reportedly have lower capital costs as well as lower costs for operation and maintenance compared to scrubbers. 3.4.3.1.2 Floor systems and manure collection Floor systems that collect manure have the potential to reduce gaseous nitrogen emissions. Slatted floor and bedded floor systems are two common floor types used in broiler and pig housing (Philippe et al. 2011; Çavuşoğlu et al. 2018) but are less common in egg production. Emission levels vary depending on specific housing conditions, which makes it challenging to differentiate characteristic ammonia emissions by floor type (Philippe et al. 2011). For egg production, active manure collection systems may be applied in conventional cage and enriched colony cage housing systems and in parts of free-run aviary systems, but are unlikely to be applied in floor-based free run housing systems (Vonk et al. 2016). Each type of floor and collection system has characteristic benefits and drawbacks. Slatted floor over deep-pit Slatted floor over deep-pit systems are sometimes used in cage-based egg production. In this system, manure is usually removed once or several times per year (Broucek 2018). Deep-pit systems can reduce odor and insect problems. Jugowar et al. (2017) found low ammonia emissions for deep pit systems. However, nitrous oxide emissions may be higher compared to housing systems that use manure belts (Fournel et al. 2012). Partly slatted floor systems have been reported to have lower ammonia emissions compared to fully slatted floor systems 82 in pig housing. For example, Sun et al. (2008) reported a 40% reduction in ammonia emissions for partially slatted floor systems. Bedded systems Bedded systems, in which organic material is used as a substrate in livestock housing, may be associated with both improved welfare outcomes for livestock (van de Weerd and Day 2009; Philippe et al. 2012) and also have a significant influence on nitrogen emissions (Philippe et al., 2011; Van Harn et al., 2012). Examples of low-cost organic materials that are applied as common bedding materials include straw, wood shavings, and sawdust (Broucek 2018). In pig housing, straw-based deep litter may have higher ammonia emissions compared to sawdust-based deep litter ( Philippe et al., 2011). In cattle housing, the application of sawdust reduces ammonia emissions by up to 26%; however, it enhances nitrous oxide emissions to 0.7% of the initial slurry-N content, as does straw (Van der Weerden et al., 2014). Wood shavings bedding may effectively reduce ammonia emissions and has been a popular approach around the globe (Miles et al. 2011). Van Harn et al. (2012) investigated the influence of bedding material (e.g., wood shavings, wheat straw, rapeseed straw and maize silage) on ammonia emissions in a broiler production system. Application of maize silage reduced ammonia emissions more than the other materials (35-45%) and was hence recommended as a potential bedding system alternative (Van Harn et al., 2012). It is worth mentioning that the availability and cost of bedding materials depend upon time and location (Le Riche et al. 2017), and that use of bedding may not be feasible in some contexts (for example, cage-based egg production). 83 Scrapers A mechanical scraper is a common technology to remove solid manure from poultry houses (Mukhtar 2005). Manure scrapers can help reduce Nr emissions in poultry housing by enabling removal of manure on a frequent basis (Lenkaitis 2014). In pig housing, increasing the frequency of manure removal with a scraper reduced ammonia emissions by 23% (Lagadec et al. 2016). Scrapers utilized in poultry housing are typically either tractor-mounted scrapers or belt scrapers (Mukhtar 2005). Scraper robots have been reported in dairy and beef production (Kai et al., 2017). Tractor-mounted scrapers can be used below elevated cages or in open barns. Belt scrapers can be used to remove poultry manure under cage systems (Mukhtar 2005). Cai et al. (2015) report varying efficacy with respect to reducing ammonia emissions, depending on the scraper type in pig housing. V-shaped scrapers were found to reduce ammonia emissions, whereas standard flat scrapers were ineffective in ammonia reduction (Cai et al. 2015). Manure scrapers are considered as a moderate-cost technology with respect to capital, operation, and maintenance costs. Belts Manure belts can significantly reduce nitrogen emissions, but come at a higher cost of investment compared to other manure collection systems (Brouček and Čermák 2015). However, they reduce labour requirements and associated costs (Xin et al. 2011). There has been a significant shift from the use of deep-pit litter collection to use of manure belts in egg production systems in recent decades (Fournel et al. 2012; H. Li and H. Xin 2013; Brouček 84 and Čermák 2015). Fresh manure (75% moisture) dropped on a conveyor belt dries more easily - whether naturally or by forced ventilation (Xin et al. 2011; Loyon 2018). The moisture content of dried manure on belts can vary from 30 to 60 %, depending on the drying method and climate. Manure drying (80% dry matter content) significantly reduces ammonia and nitrous oxide emissions compared to deep-pit systems (Brouček and Čermák 2015; Loyon et al. 2016; Jugowar et al. 2017; Li et al. 2017). Naseem and King (2018) report that daily manure removal in poultry houses by belt reduces ammonia emissions by 74% compared to twice weekly manure removal. Comparative efficacy and feasibility of NUE strategies/technologies for floor systems and manure collection Slatted floor over deep-pit is a common manure management system in cage layer housing, but does not offer a significant nitrogen emissions reduction compared to other systems. Among bedded systems, application of straw has been shown to enable a significant reduction in ammonia and ammonium emissions (75-100% and 0-25%, respectively) in cattle and pig housing, but may increase direct nitrous oxide emissions by 0-25%. Moreover, the “upstream” nitrogen emissions associated with producing straw also need to be considered. Straw bedding is considered to be an easy strategy to implement. However, it is likely only feasible in free run and free-range poultry housing systems rather than in cage-based housing. Use of wood shavings, which also reduces nitrogen emissions, can be considered where it is available at an affordable price. 85 Use of manure belts appears to be more efficacious than scrapers in reducing Nr emissions (50-75%). Manure belt installation and operation are also relatively straightforward. In general, manure collection systems have substantial costs for capital, operation, and maintenance, but these are relatively low for manure belts. 3.4.3.2 Manure storage Manure or litter (i.e. also including bedding, feathers, wasted feed, etc.) storage allows flexibility in the timing of land application (Moore et al. 1995). Storage systems may be categorized as either solid, slurry or liquid storage systems. They include permanently roofed storage, open storage, or temporary stockpiling systems (Ogejo and Junior. 2009) and, if well managed, may lead to reduced emissions. Poor management of manure storage may, however, increase emissions of ammonia, nitrous oxide, and methane, along with nitrate leaching (Cederberg et al. 2009; Abouelenien et al. 2010; Amanullah et al. 2010). Manure stored as slurry or liquid generally emits less nitrous oxide emissions than solid manure (Li et al. 2013). Table 3-6 summarizes and compares potential mitigation options for Nr emissions at the manure storage stage. 86 Table 3-6 Potential mitigation options for Nr emissions during manure storage stage. Stage Technology/Strategy Technical Criteria Environmental Criteria Economic Criteria References Fit to Context Ease of Implementation Ease of Operation and Maintenance NH3 NH4+ N2O NO3- NO Cost of Investment Cost of Operation and Maintenance Manure storage Type of storage Yes Easy Easy + NI - + NI $$ $ (Chaudhry et al. 2009; Ogejo and Junior. 2009) Open storage Bunker-type Stockpile Yes Easy Easy - NI + + NI $ $ (Ogejo and Junior. 2009; Bollwahn 2014; Kelly and Westendorf 2014; Ding et al. 2016; Naylor et al. 2016) Lagoon Yes Easy NI + NI NS + NI $$$ $ (Blanes-Vidal et al. 2009; Harper et al. 2009; Nash 2012; Lupis et al. 2017; Wang et al. 2017) Covered Roofed building Yes Easy Easy NI NI NI - NI $$ $ (Ogejo and Junior. 2009; Tittonell et al. 2010; Shah et al. 2012a; Hilborn et al. 2013; Admin 2019) Straw Yes NI NI -25-50% NI +0-25% NI NI $ $$ (Montes et al. 2013; Hou et al. 2015; VanderZaag et al. 2015; Awasthi et al. 2017; Wang et al. 2018) Zeolite Yes NI NI -75-100% NI NI NI NI NI NI (Carew 2010; Szogi et al. 2015) Tarpaulin (Plastic) Yes Easy Easy -75-100% NI NS NI NI $ $$ (Predotova et al. 2010; Montes et al. 2013; Naylor et al. 2016; Zhang et al. 2019a) 87 Stag Technology/Strategy Technical Criteria Environmental Criteria Economic Criteria References Fit to Context Ease of Implementation Ease of Operation and Maintenance NH3 NH4+ N2O NO3- NO Cost of Investment Cost of Operation and Maintenance Manure storage Treatment Yes NI NI -75-100% NI - NI NI $$$ $$$ (Iria et al. 2013; Fangueiro et al. 2015a) Acidification Aluminum sulphate Sulphuric acid Yes NI NI -50-75% NI NS NI NI $$$ $$$ (Liu et al. 2014; Fangueiro et al. 2015a; ○ et al. 2017) Digestion Yes Difficult Difficult -0-25% +50-75% ± NI NI $$$ $$$ (Bernet and Béline 2009; Gerber et al. 2013; Montes et al. 2013; Liu et al. 2014; Holly et al. 2017) Compost Yes Moderate Moderate +50-75% NI -25-50% + NI $$$ $$$ (Bernal et al. 2009; Szogi et al. 2015; Hou et al. 2017) Crust formation Yes NA NA -50-75% NI + NI NI $ $ (Petersen and Sommer 2011; Hou et al. 2015; VanderZaag et al. 2015; Holly et al. 2017) Biochar addition Yes Easy Easy -50-75% NI -75-100% NI NI $$ $ (Steiner et al. 2010; Gómez-Muñoz et al. 2016; Wang et al. 2018) 88 3.4.3.2.1 Type of storage Open storage Bunker-type storage offers the benefits of deeper manure piling and compaction, hence requiring less area for storage (Ogejo and Junior. 2009). Open bunker manure storage has been reported to result in lower nitrous oxide emissions in comparison with storing manure as deep litter in livestock housing, but may result in significant ammonia losses (Van Der Weerden et al. 2014). Open stockpile is a common and inexpensive strategy to store manure (Ogejo and Junior. 2009; Kelly and Westendorf 2014; Bollwahn 2014) but may result in significant nitrogen losses through nitrous oxide and ammonia emissions (Ogejo and Junior. 2009; Ranadheera et al. 2017). Field application of stockpiled manure has been reported to have higher ammonia emissions compared to composted manure by (Hou et al. 2015), whereas Alemu et al. (2017) reported 23% lower nitrogen losses for stockpiling compared to composting (46%) in the manure storage stage. Covering the stockpiled manure (for example, with plastic sheeting) can help reduce nitrogen losses, odors, and flies (Ogejo and Junior. 2009; Kelly and Westendorf 2014). Use of a lagoon is a more common strategy for the storage of cattle and swine slurry, but can be applied in the poultry industry as well (Mukhtar et al. 2013; Broucek 2018). Liquid poultry manure stored in an anaerobic lagoon prior to land application will generate high levels of ammonia emissions (Key et al. 2011). Moreover, the protection of groundwater from nitrate leaching is of particular concern. Therefore, sealed bottom and sides, and 89 construction with concrete are recommended to prevent potential leaching (Van der Meer et al., 2008). Covered storage Covered manure storage is already a relatively common practice among poultry farmers (Tittonell et al. 2010). There are various types of covers that may be applied in manure storage such as floating materials (vegetable oil, chopped wheat straw, wood chips) for liquid manure, impermeable covers (geotextile, synthetic) for solid/slurry manure, or a simple roof for solid manure (Carew 2010; Montes et al. 2013; Petersen et al. 2013). Such covering materials typically reduce gaseous emissions (Carew 2010; Montes et al. 2013). Impermeable covers (i.e. plastic, concrete, wood, etc.) limit or eliminate ammonia volatilization (Montes et al. 2013). Specifically, plastic covers have been shown to be effective in maintaining a low carbon/nitrogen (C/N) ratio in stored manure as a result of overall reduced nitrogen emissions (Predotova et al. 2010; Chaudhry et al. 2013). However, Petersen et al. (2013) reported that plastic covering for solid poultry manure may either reduce or increase nitrous oxide emissions depending on conditions such as temperature, moisture content, and pH of the manure. Among potential alternative covering materials (i.e., straw, zeolite, and tarpaulin), zeolite and tarpaulins reduce ammonia emissions by an average of 62.5%. The advantages of covering manure by plastic are ease of implementation, simplicity of operation, low labour requirement and low cost (Zhang et al. 2019). Semipermeable covers reduce ammonia emissions significantly but increase nitrous oxide emissions. This is due to creating aerobic conditions for nitrification at the cover surface and 90 simultaneously creating a low oxygen environment just below the cover favorable for denitrification and the production of nitrous oxide (Hansen et al. 2009; Nielsen et al. 2010; Montes et al. 2013). Semipermeable covering is often applied in composting worldwide due to ease of operation, adaptability and energy savings (Sun et al. 2018). Roofed storage typically includes a concrete floor, partial sidewalls, and a roof structure, which serves to keep the manure dry, reduces or eliminates runoff (especially during rainfall) and hence reduces the risk of nitrate leaching (Ogejo and Junior. 2009; Tittonell et al. 2010; Shah et al. 2012; Hilborn et al. 2013). A simple roof requires an initial investment but is considered to be an affordable method to improve manure storage conditions (Tittonell et al. 2010). Comparative efficacy and feasibility of NUE strategies/technologies for types of manure storage Though bunker type storage has a moderate cost of investment, operation and maintenance are requirements are low. This type of storage also results in significant ammonia and nitrate emissions. Open stockpiling is least expensive, but increases the risk of leaching and run off. Lagoons require coverage and management to reduce nitrogen emissions. They are also an expensive option due to the high cost of construction. Among potential alternative covering materials (i.e., straw, zeolite, and tarpaulin), zeolite and tarpaulins reduce ammonia emissions by an average of 62.5%. Tarpaulins are inexpensive and easy to implement. No information was found to assess zeolite in terms of the technical and economic criteria, but this would appear to be relatively inexpensive and easy to 91 implement as well. Storing the manure inside a roofed building is among the most common strategies. Roofed storage has up-front costs associated with constructing storage buildings, but is effect in reducing leaching and run off. In addition, roofed storage lasts for years compared to covered material such as straw and plastic that have to be changed in each cycle, which makes this option less labour intensive as well. 3.4.3.2.2 Manure treatment in storage There are several manure treatment strategies to reduce nitrogen emissions during manure storage. This includes the addition of biological (e.g. biochar), chemical (e.g. acidifying agents), adsorbent or masking agents (Z. Liu et al., 2014), as well as digestion of manure prior to land application. Acidification Manure acidification, which involves the addition of amendments to manure in order to reduce pH, may be an effective means to mitigate ammonia emissions (Shin et al. 2019). There are a variety of acidifying amendments such as ferrous sulphate, alum, calcium carbonate, and sulphuric acid. Each type has shown different effects on Nr emissions. For example, ferrous sulphate (FeSO4·7H2O) reduces ammonia losses from poultry manure by 11-58% (Hunde et al. 2012). Applying alum and calcium carbonate (CaCO3) or alum alone decreases ammonia volatilization from 24% to almost 100% (Hunde et al. 2012). Applying alum to poultry litter at high rates could double the nitrogen retained in the litter and also support improved poultry health and performance (Pierzynski and Gehl 2005). Addition of sulphuric acid to manure and/or slurry, reduces ammonia emissions by 70-80% (Kai et al. 92 2008; Dai and Blanes-Vidal 2013). However, it does not influence nitrous oxide emission levels significantly (Liu et al., 2014). Despite a significant reduction in ammonia emissions, the limitations of acidifying amendments are their relatively high costs, corrosiveness, and potential hazards to animal and human health (Fangueiro et al. 2015a). Digestion In anaerobic digestion, microorganisms break down the organic material (Bernet and Béline 2009; Massé et al. 2011), producing biogas while at the same time reducing environmental risks (Holm-Nielsen et al. 2009; Nasir et al. 2012). Anaerobic digestion prior to surface application of manure has been shown to reduce ammonia volatilization by 22% compared to untreated manure due to its lower dry matter content (Chantigny et al. 2009; Bernet and Béline 2009; VanderZaag et al. 2011; Liu et al. 2014). Manure with low dry matter (less than 8%) soaks into the soil quickly, reducing nitrogen losses during periods of rain or irrigation. However, anaerobic digestion of poultry manure results in increased nitrous oxide emissions following manure land application because of its low C/N ratio (Montes et al. 2013). Anaerobic digesters are expensive to implement and are technically challenging to manage (Liu et al. 2014; Ranadheera et al. 2017). Composting Composting is a method to handle solid manure (Cherubini et al., 2015). It reduces manure mass and hence costs associated with transporting manure (Alemu et al. 2017a). However, storage costs may be higher due to the duration of time required (Bernal et al. 2009; Ranadheera et al. 2017). In addition, composting requires cautious management to 93 produce the final product without any fire hazard (Chen et al. 2011; Bollwahn 2014). However, a considerable reduction in manure nitrogen content may occur during composting due to losses of nitrogen as ammonia (Van der Meer et al. 2008). This is influenced by factors such as moisture, temperature, aeration/turning and particle size (Bernal et al. 2009). There is limited literature considering Nr emissions as a result of composting. Crust formation Allowing natural crust formation on stored manure may decrease ammonia emissions substantially (50-75%) (Aguerre et al. 2012; Petersen and Sommer 2011; Hou et al. 2015; VanderZaag et al. 2015). Factors that influence crusting include gasification and the dry matter content of manure. The former enhances crusting and the latter limits crusting (less dry matter, less crusting). Despite its effect on lowering ammonia emissions, Holly et al. (2017) reported that natural crust formation increases nitrous oxide emissions. Allowing crust formation is an inexpensive strategy. Biochar addition Biochar absorbs ammonia and water-soluble ammonium, hence, it is considered as a potential amendment to reduce Nr emissions from stored manure (Steiner et al., 2010). When added to poultry manure during composting, it decreased ammonia emissions by approximately 64% (Steiner et al. 2010; Draper and Tomlinson 2012). Agyarko-Mintah et al. (2017) reported a 38% reduction in ammonia emissions from biochar addition. While prevention of ammonia volatilization during manure storage is desirable, it should be noted that this might result in increased direct nitrous oxide emissions from the soil when the 94 manure is land-applied due to its higher residual N content (Søren O Petersen & Sommer, 2011). The cost of biochar production varies depending on the feedstock. Biochar from waste (e.g., manure, wood, and straw) is much cheaper than biochar from imported wood chips (Shackley, Hammond, Gaunt, & Ibarrola, 2011). The use of biochar as a manure additive during composting may also reduce the time required and hence reduce the cost of production (Sánchez-García et al., 2015). Comparative efficacy and feasibility of NUE strategies/technologies for manure treatment during storage Potential technologies and strategies, for reducing nitrogen losses during manure storage, as well as their relative efficacy and feasibility, vary widely. Manure acidification reduces ammonia emissions considerably. It is considered an expensive strategy, but the improved nitrogen value of the manure for crop production should also be considered. The effect of acidification on soil pH is not considered in this study. Manure digestion is also difficult and expensive to implement, operate, and maintain. Composting reduces manure mass and makes manure transportation easier, but is more labor and land intensive and hence potentially costlier than alternative strategies. Crust formation is an easy and inexpensive strategy for implementation, operation, and maintenance that reduces ammonia emissions significantly while increasing nitrous oxide emissions. Addition of biochar to stored manure may be promising, as it has been shown to reduce ammonia and nitrous oxide emissions by an average of 62.5% and 87.5%, respectively. Furthermore, the costs of investment, operation, and maintenance are relatively low provided biochar is readily available. It should be noted, 95 however, that the potential “upstream” nitrogen emissions and other environmental aspects of biochar production are unclear. 3.4.3.3 Manure land application practices Manure land application strategy has a significant impact on the amount of nitrous oxide emissions that may arise through both nitrification and denitrification (Afreh et al. 2018). Improper application may also lead to considerable eutrophication effects (Abouelenien et al. 2010). In addition, the type of manure storage influences the type and amount of emitted Nr. For example, liquid manure land application from lagoons has significant volatilization losses, which can be reduced by mechanical incorporation. Common techniques for manure application include injection (deep and shallow), incorporation via tillage and surface spreading (Carew 2010; Lovanh et al. 2010), each of which has benefits and drawbacks. Table 3-7 lists the potential mitigation options for Nr emissions during manure application, and their comparative performance based on the technical, environmental and economic criteria considered. 96 Table 3-7 Potential mitigation options for Nr emissions in manure land application. Stage Technology/Strategy Technical Criteria Environmental Criteria Economic Criteria References Fit to Context Ease of Implementation Ease of Operation and Maintenance NH3 NH4+ N2O NO3- NO Cost of Investment Cost of Operation and Maintenance Manure land application Injection Deep Yes Difficult Moderate -75-100% NI +75-100% NI NI $$$ $$$ (Carew 2010; Kitchen et al. 2012; Carozzi et al. 2013; Wang et al. 2018; Nicholson et al. 2018) Shallow Yes Difficult Moderate -50-75% NI + + NI $$$ $$$ (Carew 2010; Dell et al. 2012; Kitchen et al. 2012; Montes et al. 2013) Incorporation Yes Easy Moderate -50-75% NI +75-100% + NI $$ $$ (Carew 2010; Dell et al. 2012; Carozzi et al. 2013; Gerber et al. 2013; Hou et al. 2015; Viguria et al. 2015) Spreading Yes Easy Easy + + + + NI $ $$ (Chadwick et al. 2011; Dell et al. 2012; Pote and Meisinger 2014; Hou et al. 2015) 97 3.4.3.3.1 Injection Manure injection involves injection of liquid manure into furrows/trenches created by coulters, chisels or other means. These are subsequently closed in order to reduce exposure of the wet manure to air and, consequently, reduce gaseous emissions. Manure injection leads to better nutrient management but increases both capital costs and energy consumption compared to other manure application techniques (Carew 2010; Fangueiro et al. 2015b). Direct injection may reduce ammonia emissions by up to 95 % compared to surface spreading (Dell et al. 2012; Carozzi et al. 2013). Manure injection is categorized as either deep injection (150-200 mm) or shallow injection (50-100 mm). Deep injection is reported to reduce ammonia emissions approximately 20% more than shallow injection (Kitchen et al. 2012). 3.4.3.3.2 Incorporation Manure incorporation via tillage has a positive impact on the reduction of ammonia losses (approximately 70%) when compared to spreading manure on the soil surface (Hou et al. 2015). Research for beef cattle manure applied to farmland in Alberta, Canada showed a 76-85% reduction in ammonia losses by incorporation of manure through tillage (Carew 2010). Carozzi et al. (2013) and Viguria et al. (2015) agree that manure incorporation results in lower ammonia emissions compared to surface spreading. It also reduces indirect nitrous oxide emissions from ammonia losses. However, manure incorporation has higher direct nitrous oxide emissions (98%) compared with spreading (Gerber et al. 2013; Hou et al. 2015). Dell et al. (2012) found that manure incorporation may also increase nitrate leaching. 98 In addition, incorporating manure on land requires more machinery than spreading, which adds to cost. 3.4.3.3.3 Spreading Manure surface application (spreading) is recognized as the main source of ammonia emissions from agricultural activities on farms. In addition, nitrous oxide is emitted following manure spreading (Smith et al. 2009). Spreading the manure on snow/frozen ground increases leaching and ammonia losses (Kitchen et al. 2012). Smith et al. (2009) reported that the timing of dairy and swine manure spreading could influence the amount of emissions significantly. Spreading manure on land after 8 am reduced ammonia emissions (1.5 kg/ha per extra hour). Fangueiro et al. (2017) compared ammonia and nitrous oxide emissions associated with eight cattle manure application techniques. Among these, acidified slurry surface application followed by soil incorporation (AS-tillage) and acidified slurry surface application only (AS-surface) decreased ammonia emissions by 89% and 58%, respectively. 3.4.3.3.4 Comparative efficacy and feasibility of NUE strategies/technologies for manure land application There are clear trade-offs with respect to the comparative efficacy of manure land application techniques in reducing nitrogen emissions vis-à-vis cost. Considering the technical criteria, injection is found to have moderate to difficult ease of implementation, operation, and maintenance. Spreading and incorporation require less infrastructure and maintenance. Spreading is a low-cost land application strategy; however, it increases Nr 99 emissions significantly. Injection and incorporating manure are more capital intensive. Both techniques significantly reduce ammonia emissions but increase nitrous oxide emissions. Injection (both deep and shallow) is also a more expensive option. Although deep injection decreases ammonia emissions significantly, it may increase nitrous oxide emissions. 3.5 Conclusions and recommendations This review provides information to better understand and compare available mitigation measures for the reduction of reactive nitrogen emissions at key stages along crop-livestock production supply chains, and their anticipated relative efficacy and feasibility (from both technical and economic perspectives) in poultry industry contexts. Trade-offs across criteria are evident for most of the strategies/technologies considered. For example, some strategies/technologies may have high emissions reduction potential but are technically challenging to implement or cost prohibitive. Others may be “low hanging fruit” in terms of ease of implementation and affordability, but offer limited mitigation potential or simply shift emissions among types of reactive nitrogen species. Moreover, combinations of strategies and technologies across supply chain stages should be considered in order to achieve optimized, systems-level NUE. Of the numerous strategies/technologies considered, a small subset emerges as potentially most efficacious with respect to feed crop production, feed formulation, and manure management. These include, for example; 4Rs practices and biochar addition during crop production; reduced crude protein via synthetic amino acid or enzyme supplementation during feed formulation; mechanical ventilation, and use of manure belts along with scrapers; 100 covered manure storage, and biochar addition to stored manure; and manure incorporation at the time of land application. However, it should be underscored that available information for characterizing the performance of the various technologies/strategies according to the criteria considered were often limited – in particular with respect to technical and economic feasibility aspects. Several other important limitations and areas for future research should also be noted: 1. Emission rates may be significantly influenced by climate, including seasonal precipitation and temperature. The extent to which the specific study contexts overlap with major poultry producing regions (or feed crop-producing regions supporting poultry production) was not here considered but should be the subject of future work. 2. Some strategies and technologies may be applicable to specific crops, soil, fertilizer, housing and manure types only. In some cases, they may not be broadly relevant for poultry supply chains. 3. The impact of the considered technologies and strategies largely addressed a subset of reactive nitrogen species because these are the most important species in terms of volume of losses or environmental, animal, or human health relevance. Future work might consider interactions with other reactive nitrogen species. 4. Based on the literature reviewed, consideration of the impacts of the technologies and strategies were largely restricted to specific supply chain stages and activities. Potential interactions between stages and activities were not addressed. This is potentially problematic if technologies/strategies implemented at one supply chain 101 stage simply result in shifting emissions to another supply chain stage. Further research that takes into account such potential system-level interactions and outcomes based on life cycle thinking and assessment is critical in order to ensure net benefits with respect to NUE initiatives. As importantly, any such research should also take a multi-criteria approach to ensure that recommended strategies do not create unanticipated and unacceptable trade-offs with respect to other important sustainability outcomes. 102 Chapter 4: A Review of Simulation Models to Estimate Reactive Nitrogen Emissions in Crop-Livestock Supply Chains for Process-based Life Cycle Assessment 4.1 Synopsis There is no apparent consensus in the LCA community regarding best fit models for estimating reactive N (Nr) emissions in agricultural production systems, and literature comparing available Nr emission models for use in LCA is lacking. The objective of this review is to identify and compare Nr emission models that are available for use in process-based LCAs of crop and/or livestock products. A literature review was undertaken to characterize and compare available Nr emission models for agricultural contexts. Models reported in literature published over the last five decades (1970- 2019) that meet the following two conditions were considered: first, the model must simulate at least one kind of Nr emission; and, second, it must be applicable to crop and/or livestock production. A set of detailed criteria were applied in order to evaluate the models. Forty eight models for simulating Nr emissions in crop production (28 models), livestock production (6 models), and combined crop-livestock production systems (14 models) were identified. Models for crop production systems mostly focused on simulation of nitrate emissions, whereas models addressing combined crop-livestock production systems largely addressed nitrous oxide and ammonia emissions. Among the forty eight models, only thirty-three were publicly available. Model documentation and instructions were often not accessible. Information regarding model accuracy was also frequently lacking and minimal information was found regarding 103 model uncertainty. Only five of the models included uncertainty assessment. The results suggested that model utility can only be determined taking into account the specific goals/scope and intended application of an LCA study. To increase the reliability of agricultural LCA results, practitioners should make efforts to identify best-fit Nr emissions models rather than simply applying well-known models by default. Further research/development is required in order to improve the accuracy of Nr emissions models, and to enable uncertainty assessment of model results. 4.2 The importance of nitrogen emission models in crop-livestock systems Agricultural intensification and expansion have enabled significant productivity growth, which is critical for global food security (Vogeler et al. 2013b). This has, however, been accompanied by increased resource consumption, environmental emissions, and sustainability issues (Pelletier et al. 2018). In particular, nitrogen (N) plays a central role in both the successes and challenges of modern agriculture (Wang et al. 2019). N fertilizer is a key input for food production (EU Nitrogen Expert Panel 2015). Its use has enabled the provision of food for a growing global population. However, much of the N applied in crop and livestock production is lost in various reactive forms (Nr), including ammonia (NH3), ammonium (NH4+), nitrous oxide (N2O), nitric oxide (NO), nitrogen dioxide (NO2), nitrogen oxides (NOx), nitrate (NO3-), and nitrite (NO2-) (Eickhout et al. 2006; Franqueville et al. 2018). Once released into the environment, Nr molecules are known to “cascade” through a variety of these forms, resulting in multiple potential impacts including contributions to climate change, acidification, eutrophication, human health issues, and biodiversity losses 104 (Galloway et al. 2006; Vogeler et al. 2013b; Franqueville et al. 2018). For example, excessive nitrate leaching into freshwater increases nutrient availability and water acidity, and often results in anoxic conditions, while ammonia emitted into the atmosphere precipitates and contributes to both acidification and eutrophication. Crop production and manure management are the most significant sources of Nr in agricultural systems (Philippe et al. 2011; Liu et al. 2014; Pelletier 2017). In order to reduce losses of Nr from crop-livestock systems, identification of emission sources, types, and intensities is crucial (Giltrap, Li, and Saggar 2010). Monitoring programs/field measurements can be used to investigate the impacts of management practices on Nr emissions (Necpálová et al. 2015b; Congreves et al. 2016). However, Nr emissions are highly episodic, which makes them difficult to measure directly. For example, nitrous oxide emissions from agricultural soil have considerable temporal and spatial variability that often makes their direct measurement impractical (Giltrap, Li, and Saggar 2010). This is because N2O emissions depend on processes such as denitrification, which is in turn influenced by environmental factors including soil temperature and precipitation. Direct field measurements are also often labour, time, and cost-intensive (Congreves et al. 2016a; Nemecek 2013). Modelling Nr emissions is an alternative option to direct measurement in agricultural systems (Yeluripati et al. 2015). Models have been applied widely in agricultural contexts to estimate environmental emissions to atmospheric, terrestrial, and marine ecosystems (Abdalla et al. 2009). However, available models differ widely in their structure, functionality, and level of detail (Vogeler et al. 2013b). 105 Models for estimating agricultural emissions include empirical, conceptual, and process-based model types. Each type is characterized by different degrees of complexity with respect to the physical processes simulated as well as the mitigation measures represented (Bouraoui and Grizzetti 2014a). Empirical and conceptual models are cause and effect models that transform input data into output data. Empirical models typically have fewer input variables than conceptual and process-based models (Bouraoui and Grizzetti 2014a; Hafner et al. 2019), which makes them simpler and easier to use (Hafner et al. 2019). However, the empirical modelling approach typically lacks a full description of system interactions and behaviours and also often relies on correlations between datasets (Adams et al. 2013). Process-based models consider each single process that occurs on a farm (Zifei Liu et al. 2013) with a detailed simulation that explicitly describes the system behaviour. Empirical models depend on correlative relationships based on mechanistic understandings, without fully describing system behaviours and interactions (Adams et al. 2013). Process-based models are better able to simulate complex processes, taking into account spatial and temporal conditions, including relevant ecological processes (Hafner et al. 2019). However, due to model complexity, the uncertainty of the outputs may actually be higher than in empirical models (Adams et al. 2013). Despite these differences between process-based and empirical models (e.g., model uncertainty, data requirements, and relative comprehensiveness), the demarcation between types is not clear cut. All process-based models utilize empirical information; and the 106 correlative relationships utilized in empirical models are based on research of ecological and biogeochemical processes (Mäkelä et al. 2000; Adams et al. 2013). Emission models are used to represent a wide range of application contexts with different management practices. A large number of models are available in the literature for simulating Nr emissions in agricultural contexts, which vary widely in scope, resolution, and intended application (IPCC 2006; Abdalla et al. 2010; Giltrap et al. 2010; Kröbel et al. 2012; Wu and Zhang 2014). However, there is a lack of literature elaborating and comparing the features and limitations of these models. In addition, very little guidance is available regarding the suitability of such models to simulate Nr emissions in life cycle assessment (LCA) studies, despite the widespread use of LCA to study agricultural systems (Cederberg et al. 2013). In light of the important role of Nr emissions in determining the life cycle impacts of agricultural products, this seemingly represents a significant knowledge gap. In the current study, we address this lack of information by reviewing emission models considering Nr species in three application contexts - crop production, livestock production, and combined crop-livestock production systems. On this basis, the review seeks to answer the following questions:  What are the prevalent approaches for simulating Nr emissions in agricultural systems?  What are the main differences and similarities among models for simulating Nr emissions in crop and/or livestock production systems?  What are the required input data, their degree of accessibility, and the performance of the models? 107  Which models are more or less suitable for use in process-based LCA studies, taking into account the specific product systems of interest, the goals/scope of the study (including the intended scale of geographic representation), and the considered N species?  What are the limits of existing Nr emission models for use in LCA? 4.3 Methods 4.3.1 Search strategy The first necessary step was to identify available models for simulating agricultural Nr emissions by using keyword searches in the Web of Science Core Collection, Science Direct, World Wide Science, and Google Scholar. Search terms included “nitrogen”, “ammonia”, “ammonium”, “nitrous oxide”, “nitrogen dioxide”, “nitric oxide”, “nitrate”, and “nitrite” and process-based, empirical, and conceptual models. Crop and livestock terms were also used in conjunction with LCA terms to capture LCA-specific studies. This process was iterative and included canvassing the reference lists of identified literature sources. 4.3.2 Criteria for model selection During the screening process, in order to be considered for detailed review, the candidate models were required to: (1) address at least one species of Nr; and (2) be intended for estimating emissions in the context of crop and/or livestock production. Following this preliminary screening, a set of more detailed criteria were used to characterize and compare the selected models in order to enable LCA practitioners to identify best-suited models for use in LCA studies of crop and livestock production systems. Specifically, two criteria sets 108 comprising (a) informative (i.e. providing detailed information about the model) and (b) comparative (i.e. to compare the features of the selected models) were applied (Table 4-1). Table 4-1 Criteria and sub-criteria for identifying the best-suited models for considering Nr emissions in LCA. Criteria Sub criteria Description Scale Informative Model type Process-based, empirical, or conceptual - Process-based - Empirical - Conceptual Considered N species Different species of Nr emissions addressed - NH3 - NH4+ - N2O - NOx (NO2, NO) - NO3- - NO2- Spatial scale The geographical scale at which the model is applicable - Farm - Regional - National - Global Country The country of origin in which the model and any associated data sets are developed Cost Price of model - Free - Payment required Comparative Accessibility and availability of supporting information Ease of access to tool and manual - Easy - Difficult - Unavailable Robustness To what extent the model is recognized - Accurate ~ - Inaccurate × Required input data The data required to be gathered by LCA practitioners to run the tool/model and gain a reliable result - Accessible - Inaccessible Ease of application User-friendliness and ease of simulation - User-friendly - Not mentioned Capacity to quantify uncertainty The model includes uncertainty assessment - Present - Not present 109 4.3.2.1 Informative Criteria The informative criteria, which include model type (empirical or process-based), N species (e.g., ammonia, nitrous oxide, nitrate, and nitric oxide), spatial scale (e.g., farm, regional, national, and global), the country, and cost were assessed based on available documentation for the models as well as related literature. 4.3.2.2 Comparative Criteria In order to be deemed potentially suitable for application in LCAs of crop-livestock production systems, Nr emissions models should be supported by publicly available manuals, model documentation, and/or other forms of supporting information. To evaluate the robustness of models, the R-squared (R2) value for the model, as reported in the literature, was employed. R2 is a measure of how close the data are to the fitted regression line. Models with an R-squared of more than 0.5 (R2 > 0.5) were considered to be robust. Required input data refers to the parameters needed to run the model. Required input data were rated as accessible or inaccessible. Here, “accessible” indicates that the model’s input data are publicly and easily accessible to LCA practitioners, whereas “inaccessible” refers to data that are not easily available or that require context-specific experimental measurement (such information is typically not collected by LCA practitioners). Preferred models are those that utilize readily accessible input data and have high accuracy based on R2 value. User friendliness was not independently evaluated, but drawn from commentary in the literature; when ease of use was noted, this was recorded. While not crucial, lack of ease of application 110 represents a barrier to use. Finally, Nr emissions models deemed suitable for use in LCA should have the capacity to quantify uncertainty. 4.4 Results Forty-eight models were short-listed for consideration based on the preliminary screening. Of these 48 models, 28, 6, and 14 models are respectively applicable to crop production, livestock production, and combined crop-livestock production systems. A summary of these Nr emission models is provided in Table 4-2 to 4-4 and a detailed description of each model is provided in Appendix A. 111 Table 4-2 Summary of Nr emission models for crop production systems (presented alphabetically). F: Farm, R: Regional, N: National, G: Global. Criteria for which no information was found are indicated by; NA: Not Available. Model Informative criteria Comparative criteria Model type Considered Nr species Spatial scale Country Cost Accessibility & availability of supporting information Robustness Required input data Ease of application Capacity to quantify uncertainty AGRIFLUX Process-based (Bernard et al. 2016; El Khattabi et al. 2018) NO3- (Bernard et al. 2016; El Khattabi et al. 2018) F (Larocque et al. 2002; Baran et al. 2007) Canada (Larocque et al. 2002; Baran et al. 2007) NA Difficult (Banton and Larocque 1997) NA Inaccessible (Larocque et al. 2002; Baran et al. 2007) User-friendly (Bernard et al. 2016) Present (Larocque et al. 2002) ANIMO Process-based (Wolf et al. 2005; Farmaha 2014) NO3- (Wolf et al. 2005; Farmaha 2014) F, R, N (Rijtema and Kroes 1991; Wolf et al. 2005) Netherlands (Wolf et al. 2005; Renaud et al. 2006) Free (Renaud et al. 2006) Easy (Renaud et al. 2006; ANIMO 2019) Accurate (Farmaha 2014) Inaccessible (Renaud et al. 2006) NA NA CANDY Process-based (Rode et al. 2009; Franko et al. 2015) NO3- (Rode et al. 2009; Franko et al. 2015) F, R (Rode et al. 2009) Germany (Ramsbeck et al. 1997; Franko et al. 2007) Free (Helmholtz 2018) Easy (Franko et al. 2015) Accurate (Smith et al. 1997a; Franko et al. 2007; Haberle and Káš 2012) Accessible (Ramsbeck et al. 1997; Franko et al. 2007) NA NA CENTURY Process-based (CENTURY 2012) N2O (Frolking et al. 1997) R, G (Del Grosso et al. 2012) U.S (Metherell et al. 1993) Free (CENTURY 2012) Easy (CENTURY 2012) Accurate (Chilcott et al. 2007; Smith et al. 2012) Inaccessible (Qian et al. 2003) NA NA CERES Semi-empirical (Gabrielle et al. 2006) N2O (Gabrielle et al. 2006; Chen et al. 2008) NA NA NA Difficult NA NA NA NA CREAMS NA NO3- (Sapek and Sapek 1993) F (Jamieson and Clausen 1988) U.S NA Unavailable Accurate (Jamieson and Clausen 1988) Accessible (Jamieson and Clausen 1988) NA NA DAISY Process-based (Abrahamsen and Hansen 2000; Olesen et al. 2004; Müller et al. 2006) NH4+, NO3- (Hansen et al. 2012) F (Hansen et al. 2012) Denmark (Hansen et al. 2012) Free (Abrahamsen and Hansen 2000; Hansen et al. 2012) Easy (Abrahamsen and Hansen 2000) Accurate (Hansen et al. 1991; Diekkriiger Av L et al. 1995; Smith et al. 1997b; Kröbel et al. 2010) Inaccessible (Abrahamsen and Hansen 2000) NA NA 112 Model Informative criteria Comparative criteria Model type Considered Nr species Spatial scale Country Cost Accessibility & availability of supporting information Robustness Required input data Ease of application Capacity to quantify uncertainty DAYCENT Process-based (Álvaro-Fuentes et al. 2017) NH4+, N2O, NOx, NO3- (Necpálová et al. 2015a; Álvaro-Fuentes et al. 2017) F, N, G (DayCent 2012) U.S (DayCent 2012) Free (Necpálová et al. 2015a) Difficult (DayCent 2012) Accurate (Parton et al. 2001; Smith et al. 2012; Cheng et al. 2014) Inaccessible (DayCent 2012; Cheng et al. 2014) NA NA ECOSYS Process-based (Grant 1997; Grant and Pattey 2008; Metivier et al. 2009) N2O (Grant 1997; Grant and Pattey 2008; Metivier et al. 2009) F, R, N (Grant 2019) U.S., Europe, Canada (Grant 2019) Free (Grant 2019) Easy (Grant 2019) Inaccurate (Metivier et al. 2009) Inaccessible (Metivier et al. 2009) NA NA EPIC Process-based (Williams and Sharpley 1989; Izaurralde et al. 2017) NO3- (Williams and Sharpley 1989) F, R, N (Williams and Sharpley 1989; Izaurralde et al. 2006, 2017; Arunrat and Pumijumnong 2014) U.S Williams and Sharpley 1989) Free (Williams and Sharpley 1989) Easy (Williams and Sharpley 1989) Accurate (Izaurralde et al. 2006; Arunrat and Pumijumnong 2014) Inaccessible (Arunrat and Pumijumnong 2014) User-friendly (Williams and Sharpley 1989; Izaurralde et al. 2017) NA INCA Process-based (Whitehead et al. 1998; Wade et al. 2002; Granlund et al. 2004; INCA 2019) NH4+, NO3- (INCA 2019) F (Whitehead et al. 1998) Europe (INCA 2019) Free (INCA 2019) Easy (INCA 2019) Inaccurate (Kaste 2004) Inaccessible (Kaste 2004; Ranzini et al. 2007) User-friendly (Whitehead et al. 1998; Wade et al. 2002; Granlund et al. 2004) NA LEACHN Process-based (Ramos and Carbonell 1991) NO3- (Ramos and Carbonell 1991) F (Mahmood 2018) NA NA NA Inaccurate (Ramos and Carbonell 1991; Mahmood and Tillman 2015) Inaccessible (Mahmood and Tillman 2015) User-friendly (Asada et al. 2013) NA LIXIM NA NO3- (Rakotovololona et al. 2019) F (Rakotovololona et al. 2019) France (Rakotovololona et al. 2019) Free (Rakotovololona et al. 2019) Easy (Rakotovololona et al. 2019) NA Inaccessible (Mary et al. 1999a) NA NA NCYCLE Empirical (Cuttle and Jarvis 2005; Prado et al. 2006) NH3, NO3- (Prado et al. 2006) F (Prado et al. 2006) UK (Prado et al. 2006) NA Unavailable NA Accessible (Prado et al. 2006) User-friendly (Prado et al. 2006) NA 113 Model Informative criteria Comparative criteria Comparative criteria Model type Considered Nr species Spatial scale Country Cost Accessibility & availability of supporting information Robustness Required input data Ease of application Capacity to quantify uncertainty NGAS Process-based (Parton et al. 1996) N2O (Mummey et al. 2000) R, G (Parton et al. 1996) U.S Mid-West (Mummey et al. 2000) NA Unavailable Inaccurate (Del Grosso et al. 2000) NA NA NA NLEAP NA N2O, NO3- (Shaffer et al. 1995, 2001; Karaman et al. 2005) F (Shaffer et al. 1995, 2001; Karaman et al. 2005) U.S Free (Delgado et al. 2010) Easy (Delgado et al. 2010) Accurate (Delgado 2001) Inaccessible (Karaman et al. 2005; Palmer and Angstadt 2012) User-friendly (Delgado et al. 2010) NA RZWQM Process-based (Hanson et al. 1998; Anapalli et al. 2005; Gillette et al. 2017) N2O, NO3- (Hanson et al. 1998; Anapalli et al. 2005; Gillette et al. 2017) F (Hanson et al. 1998; Anapalli et al. 2005; Gillette et al. 2017) U.S (RZWQM 2018) Free (RZWQM 2018) Easy (RZWQM 2018) Accurate (Ma et al. 2007; Malone et al. 2007) Inaccessible (Hanson et al. 1998; Ma et al. 2001; Gillette et al. 2017) User-friendly (Hanson et al. 1998; Anapalli et al. 2005) NA SOILN Process-based (Eckersten and Beier 1998) NO3- (Hoffmann and Johnsson 2000; Jabro et al. 2001) F (Bergström et al. 1991; Jabro et al. 2001) Sweden (Hoffmann and Johnsson 2000) NA Unavailable Accurate (Jabro et al. 2001) Inaccessible (Hoffmann and Johnsson 2000) NA NA SOILNDB Process-based (Johnsson et al. 2002; Kyllmar et al. 2005; Bouraoui and Grizzetti 2014b) NO3- (Johnsson et al. 2002; Kyllmar et al. 2005; Bouraoui and Grizzetti 2014b) F (Kyllmar et al. 2005) Sweden (Johnsson et al. 2002) NA Difficult NA Inaccessible (Johnsson et al. 2002) NA NA STICS Process-based (Brisson et al. 1998; Sierra et al. 2003; STICS 2014; Bernard et al. 2016) NO3-(Brisson et al. 2003) F, R (Brisson et al. 2003) France (Brisson et al. 1998, 2003) Free (STICS 2014) Easy (STICS 2014) Accurate (Jégo et al. 2012; Coucheney et al. 2015) Accessible (Brisson et al. 2003) User-friendly (STICS 2014) NA STONE Process-based (Wolf et al. 2003; Velthof and Mosquera 2011) NH3, NO3-(Velthof and Mosquera 2011) R, N (Overbeek et al. 2001; Wolf et al. 2003; Velthof and Mosquera 2011) Netherlands (Velthof and Mosquera 2011) NA Unavailable NA NA NA NA SWAT Process-based (Arnold et al. 2012; Sullivan 2018) NO3- (Moriasi et al. 2013) F (Gitau et al. 2008) U.S (Arnold et al. 2012) Free (Arnold et al. 2012; Zhang et al. 2016b; SWAT 2019) Easy (Arnold et al. 2012; Zhang et al. 2016b; SWAT 2019) Inaccurate (Akhavan et al. 2010; Moriasi et al. 2013) Inaccessible (Akhavan et al. 2010; Arnold et al. 2012) NA NA 114 Model Informative criteria Comparative criteria Model type Considered Nr species Spatial scale Country Cost Accessibility & availability of supporting information Robustness Required input data Ease of application Capacity to quantify uncertainty Thinkstep NA NH3, NH4+, N2O, NO, NO3- (Shah et al. 2018; Deimling et al. 2019) NA Germany (Deimling et al. 2019) Free (Deimling et al. 2019) Easy (Deimling et al. 2019) NA NA NA NA TNT2 Process-based (Beaujouan et al. 2002; Ferrant et al. 2011; Moreau et al. 2013) NO3- (Beaujouan et al. 2002; Ferrant et al. 2011; Moreau et al. 2013) F (Ferrant et al. 2011) North-western Europe (Ferrant et al. 2011) NA Unavailable Accurate (Ferrant et al. 2013) Inaccessible (Beaujouan et al. 2002; Moreau et al. 2013) NA NA TOUGHREACT-N Process-based (Maggi et al. 2008; Gu et al. 2009) NH3, N2O, NO, NO3- (Maggi et al. 2008; Gu et al. 2009) F (Xu et al. 2008) U.S (Xu et al. 2008) Free (Xu et al. 2008) Easy (Xu et al. 2008) Accurate (Gu et al. 2009; Gu and Riley 2010) NA NA NA VOLT’AIR Process-based (Hamaoui-Laguel et al. 2014; Ramanantenasoa et al. 2018) NH3 (Hamaoui-Laguel et al. 2014; VOLT’AIR 2015; Ramanantenasoa et al. 2018) F, N (Hamaoui-Laguel et al. 2014; Ramanantenasoa et al. 2018) France (VOLT’AIR 2015) NA Unavailable NA Inaccessible (Garcia et al. 2011; INCA 2019) NA NA WHNSIM NA NO3- (Huwe and van der Ploeg 1991) F (Huwe and Totsche 1995) Southern Germany (Huwe and van der Ploeg 1991) NA Easy (Huwe and van der Ploeg 1991) NA Accessible (Huwe and van der Ploeg 1991) Difficult (Huwe and Totsche 1995) NA WNMM Process-based (Li et al. 2007; Wei et al. 2009) NH3, N2O, NO, NO3- (Li et al. 2007; Wei et al. 2009; Ahrens et al. 2010) F, R, N (Li et al. 2007) NA NA Unavailable NA NA NA NA 115 Table 4-3 Summary of Nr models for livestock production systems (presented alphabetically). F: Farm, R: Regional, P: Province, N: National, C: Continental, G: Global. The criteria for which no information was found through reviewing the open literature is indicated by NA: Not Available. Model Informative criteria Comparative criteria Model type Considered Nr species Spatial scale Country Cost Accessibility & availability of supporting information Robustness Required input data Ease of application Capacity to quantify uncertainty AGRAMMON NA NH3 (Kupper et al. 2010; Nemecek and Schnetzer 2011; Agrammon 2019) F, R (Agrammon 2019) Switzerland (Agrammon 2019) Free (Agrammon 2019) Easy (Menzi et al. 2008; Agrammon 2019) Inaccurate (Kupper et al. 2010) Accessible (Agrammon 2019) User-friendly (Kupper et al. 2010) NA ALFAM Semi-empirical (Søgaard et al. 2002; Reidy et al. 2008; Hafner et al. 2019) NH3 (Søgaard et al. 2002; Reidy et al. 2008; Hafner et al. 2019) N (European) (Søgaard et al. 2002) Europe, UK (Søgaard et al. 2002) Free (Søgaard et al. 2002) Easy (Søgaard et al. 2002) Accurate (Søgaard et al. 2002) Accessible (Søgaard et al. 2002; Reidy et al. 2008; Thorman et al. 2008) User-friendly (Søgaard et al. 2002) NA GNL NA NO3- (Børgesen et al. 2001) R (Børgesen et al. 2001) NA NA Unavailable NA Accessible (Schroder and Neeteson 2005) NA NA MANNER-NPK NA NH3, N2O, NO3- (Webb et al. 2006; Chambers et al. 2006; Nicholson et al. 2008, 2013) F (MANNER 2019) England, Wales, Scotland, Northern Ireland (MANNER 2019) Free (Nicholson et al. 2013; MANNER 2019) Easy (Nicholson et al. 2013; MANNER 2019) Accurate (Nicholson et al. 2013; MANNER 2019) Accessible (MANNER 2019) User-friendly (Nicholson et al. 2013; MANNER 2019) NA MITERRA NA NH3, N2O, NOx, NO3-(Velthof et al. 2007; Lesschen et al. 2011) R, N (Velthof et al. 2007) Europe (Velthof et al. 2007) Free (Velthof et al. 2007) Easy (Velthof et al. 2007) NA Accessible (Lesschen et al. 2011; Leip et al. 2013) NA NA TAN-mass balance model NA NH3 (Chai et al. 2014; Legesse et al. 2018) R (Chai et al. 2014; Legesse et al. 2018) Canada (Chai et al. 2014; Legesse et al. 2018) Free (Chai et al. 2014) Easy (Chai et al. 2014) NA Accessible (Chai et al. 2014) User-friendly (Chai et al. 2014) NA 116 Table 4-4 Summary of Nr models for combined crop-livestock production systems (presented alphabetically). F: Farm, R: Regional, P: Province, N: National, C: Continental, G: Global. The criteria for which no information was found through reviewing the open literature is indicated by NA: Not Available. Model Informative criteria Comparative criteria Model type Considered Nr species Spatial scale Country Cost Accessibility & availability of supporting information Robustness Required input data Ease of application Capacity to quantify uncertainty APEX Process-based (Ford et al. 2015) NH3, N2O (Gassman et al. 2010) F, R (Tuppad et al. 2010; Williams et al. 2010) U.S (APEX 2019) Free (APEX 2019) Easy (APEX 2019) Accurate (Senaviratne et al. 2014) Inaccessible (Gassman et al. 2010) User-friendly (Gassman et al. 2010) NA CAPRI Process-based NH3, N2O, NO (Mittenzwei et al. 2007; Weiss and Leip 2012; Britz et al. 2014) R, N (Mittenzwei et al. 2007) Europe (Mittenzwei et al. 2007; Lesschen et al. 2011) Free (CAPRI 2019) Easy (CAPRI 2019) Accurate (Mittenzwei et al. 2007) Inaccessible (Britz et al. 2014) NA NA DNDC Process-based (Kröbel et al. 2011; Dutta et al. 2016a) NH3, NH4+, N2O, NO, NO3- (Giltrap et al. 2010; Congreves et al. 2016) F, R (Giltrap et al. 2010) US, Canada, Europe (Giltrap et al. 2010; Congreves et al. 2016; Goglio et al. 2018; Dutta et al. 2018) Free (Hampshire 2007) Easy (Hampshire 2007) Accurate (Congreves et al. 2016; Dutta et al. 2016b, a) Inaccessible (Smith et al. 2010; Gilhespy et al. 2014; Uzoma et al. 2015) User-friendly (Hampshire 2007) NA FarmN Empirical (FARM-N 2019) NH3, N2O, NO3- (Jorgensen et al. 2005) F (Jorgensen et al. 2005; FARM-N 2019) Denmark (Jorgensen et al. 2005) Free (Jorgensen et al. 2005; FARM-N 2019) Easy (FARM-N 2019) NA Accessible (Jorgensen et al. 2005) NA NA GLEAM Process-based (FAO 2018a; MacLeod et al. 2018; Leinonen et al. 2019; Kiggundu et al. 2019) N2O (FAO 2018a) R, N, G (FAO 2018a) UN-FAO (Global) Free (FAO 2018a; Kiggundu et al. 2019) Easy (FAO 2018a) NA Accessible (FAO 2018a; MacLeod et al. 2018; Kiggundu et al. 2019) User-friendly (FAO 2018a) NA HOLOS Empirical (Little et al. 2008; Amadi et al. 2016; Guest et al. 2018) N2O (Little et al. 2008; Amadi et al. 2016; Guest et al. 2018) F (Amadi et al. 2016; Alemu et al. 2017b; Kroebel et al. 2017) Canada (Alemu et al. 2017b) Free (AAFC 2019) Easy (AAFC 2019) NA Accessible (Kröbel et al. 2012) User-friendly Present (Little et al. 2008) IFSM Process-based (Rotz et al. 2011; IFSM 2017) NH3, N2O, NO3- (Rotz et al. 2011) F (Rotz et al. 2011) U.S and Canada (Rotz et al. 2011) Free (IFSM 2017) Easy (Rotz et al. 2011) NA Accessible (Rotz et al. 2011) User-friendly (Rotz et al. 2011) NA 117 Model Informative criteria Comparative criteria Model type Considered Nr species Spatial scale Country Cost Accessibility & availability of supporting information Robustness Required input data Ease of application Capacity to quantify uncertainty IPCC Tier 1 NA N (IPCC 2006) NA Free (IPCC 2006) Easy (IPCC 1996, 2006) Inaccurate (IPCC 1996) Accessible (IPCC 2006) User-friendly (IPCC 1996) NA Tier 2 Empirical (Smith et al. 2010) N2O (IPCC 1996, 2006) Accurate (IPCC 1996) Accessible (IPCC 2006) NA Present (IPCC 1996) Tier 3 Process-based (Eve et al. 2014) Accurate (IPCC 1996) Accessible (IPCC 2006) NA NA IULIA Process-based (Bretscher 2012) N2O (Bretscher 2012; Heldstab et al. 2017) N (Bretscher 2012; Heldstab et al. 2017) Switzerland (Bretscher 2012; Heldstab et al. 2017) Free (Bretscher 2012) Easy (Bretscher 2012) NA Accessible (Bretscher 2012) User-friendly (Bretscher 2012) NA NARSES Process-based (Misselbrook et al. 2004; Webb and Misselbrook 2004; Reidy et al. 2009) NH3 (Misselbrook et al. 2004; Webb and Misselbrook 2004; Reidy et al. 2009) F, N (Misselbrook et al. 2004; Webb and Misselbrook 2004; Reidy et al. 2009) U.K (Misselbrook et al. 2004; Webb and Misselbrook 2004; Reidy et al. 2009) Free (Webb and Misselbrook 2004) Easy (Webb and Misselbrook 2004) NA Accessible (Misselbrook et al. 2004) NA NA NEMA NA NH3, N2O, NOx (Groenestein and Bruggen 2012) N (Groenestein and Bruggen 2012) Netherlands (Groenestein and Bruggen 2012; Velthof et al. 2012; Vonk et al. 2016) Free (Groenestein and Bruggen 2012) Easy (Groenestein and Bruggen 2012) NA NA User-friendly (Groenestein and Bruggen 2012) Present (Vonk et al. 2016) NitroScape Process-based (Drouet et al. 2012) NH3, N2O, NOx, NO3- (Drouet et al. 2012; Ferrer Savall et al. 2019) F (Duretz et al. 2011; Drouet et al. 2012; Franqueville et al. 2018) Europe (France) (Duretz et al. 2011) Free (Franqueville et al. 2018) Easy (Franqueville et al. 2018) NA Inaccessible (Duretz et al. 2011) User-friendly (Franqueville et al. 2018) NA OVERSEER Empirical (Vogeler et al. 2013a) N2O (Vogeler et al. 2013a; Watkins and Selbie 2015(Wheeler et al. 2008, 2011)) F (Vogeler et al. 2013a) New Zealand (Dunbier et al. 2013) Free (Overseer 2019) Easy (Overseer 2019) NA Accessible (Wheeler et al. 2008) User-friendly (Overseer 2019) Present (Dunbier et al. 2013) SALCA NA NH3, N2O, NO3- (Weisskopf et al. 2013; Nemecek et al. 2016) F (Weisskopf et al. 2013) Switzerland Free (Weisskopf et al. 2013) Easy (Weisskopf et al. 2013) NA Accessible (Nemecek and Kagi 2007) NA NA 118 The proportions of models addressing crop, livestock, or combined crop-livestock systems, as well as each of the major Nr species, are depicted in Figure 4-1. More than half of the models (58%) simulate Nr emissions in crop production, while 13% and 29% simulate emissions in livestock and combined crop-livestock production systems, respectively. Figure 4-1 The number of models simulating emissions of various Nr species in agricultural systems. 4.4.1 Model characteristics 4.4.1.1 Model type Process-based models are most represented (30 of 48) among model types, followed by empirical models (5). Process-based models incorporate explicit mechanisms, whereas the empirical models are much simpler (i.e. typically relying on emission factors) (Adams et al. 2013). Most of the models for crop production (21 of 28), and for crop-livestock production 6 4101 323521391132 2505101520253035NH3 NH4+ N2O NOx NO NO3-Number of modelsNitrogen speciesCrop-livestock productionLivestock productionCrop proudction 119 systems (9 of 14) were process-based. Few models were empirical or semi-empirical. For ten of the models, information about the type was not available. 4.4.1.2 Considered nitrogen species Among the types of Nr species, nitrate emissions are simulated by the majority share of models (31) (Figure 4-1). Nitrous oxide is the second most considered Nr species (25) followed by ammonia (20 models). Models simulating emissions from crop production systems largely focus on nitrate. Nitrous oxide and ammonia emissions are predominantly addressed by models for combined crop-livestock production systems. Other Nr species such as nitrogen oxides, nitrite and ammonium are rarely considered. This is likely because ammonia, nitrous oxide and nitrate are the most environmentally relevant nitrogen emissions in agriculture (Brentrup et al. 2000), whereas nitrogen oxides, nitrite and ammonium emissions are relatively negligible in magnitude and importance. 4.4.1.3 Spatial scale Among the spatial scales represented by the models, field scale was most often represented (34 of 48), followed by regional scale (18 of 48). For crop production, 23 and 9 models were respectively intended for simulating emissions at farm or regional scales. For livestock production systems, 4 out of the 6 models were for simulating emissions at the regional scale. Some models are able to simulate emissions at more than one scale, such as DNDC, CAPRI, CENTURY, DayCent, and ECOSYS. The numbers of models considering different spatial scales in each agricultural system context are listed in Table 4-5. 120 Table 4-5 Numbers of models considering different spatial scales for each agricultural production system context Crop production systems Livestock production systems Combined crop-livestock production systems Farm 22 2 9 Regional 9 4 4 National 7 2 6 Global 3 0 1 4.4.1.4 Country Many of the models are specifically intended for application in particular countries. However, some models (for example, the IPCC emissions models) can be applied in multiple countries. Such models typically require attention to country-specific variables including climate, temperature, soil characteristics, etc. The proportion of models for simulating Nr emissions in different countries are shown in Figure 4-2. The largest share of models is specific to the United States (13), followed by Europe (7) and Canada (6). 121 Figure 4-2 Numbers of models to simulate Nr emissions in different countries 4.4.1.5 Cost Most of the models considered (34) are publicly available at no cost. Some are available as simple equations, whereas others require use of a specific tool that can obtained without cost. 4.4.1.6 Accessibility and availability of supporting information Of immediate salience to the current review is the limited accessibility of model tools as well as the unavailability of supporting information such as manuals. While the majority of the models considered (33) were publicly available, many of them had little to no supporting information available, effectively rendering them inaccessible to non-expert users. Canada, 6Netherlands, 3Germany, 3US, 13Europe, 7France, 3UK, 4Sweden, 2Global, 1Switzerland, 3New Zealand, 1 Denmark, 2 122 4.4.1.7 Robustness Also of note is the dearth of information regarding the accuracy of simulation provided by most models. This may constrain their potential for application in LCA studies where the goal/scope may specify a required level of model accuracy, or where data quality and uncertainty assessment is intended and/or required for ISO 14044 compliance (for example, in the case of any study intending to supporting a comparative assertion). The R2 values as reported in literature for the models are provided in Table 6. Nineteen models simulate Nr emissions accurately in their intended application context and spatial scale (R2 > 0.5). Six models were found to be inaccurate (i.e. R2 < 0.5), and no information was found for the remaining models. Table 4-6 Robustness of Nr emission models Model R2 Reference Model R2 Reference Crop production systems ANIMO > 0.76 (Farmaha 2014) NGAS 0.47-0.50 (Del Grosso et al. 2000) CANDY 0.95 (Haberle and Káš 2012) NLEAP 0.86-0.97 (Karaman et al. 2005) CENTURY 0.71-0.82 (Chilcott et al. 2007; Smith et al. 2012) RZWQM 0.71-1.0 (Ma et al. 2007; Malone et al. 2007) CREAMS 0.69-0.90 (Jamieson and Clausen 1988) SOILN 0.64-0.99 (Jabro et al. 2001) DAISY 0.53-0.75 (Kröbel et al. 2010) STICS 0.81-0.92 (Jégo et al. 2012) DAYCENT 0.71-0.85 (Smith et al. 2012; Cheng et al. 2014) SWAT 0.21-0.49 (Akhavan et al. 2010; Moriasi et al. 2013) ECOSYS 0.46 (Metivier et al. 2009) TNT2 0.67 (Ferrant et al. 2013) 123 Model R2 Reference Model R2 Reference EPIC 0.69-0.91 (Izaurralde et al. 2006; Arunrat and Pumijumnong 2014) TOUGHREAC-N 0.56-0.63 (Gu et al. 2009; Gu and Riley 2010) INCA 0.26-0.68 (Kaste 2004) Livestock production systems AGRAMMON 0.24-0.28 (Kupper et al. 2015) MANNER 0.39-0.54 (Nicholson et al. 2013) ALFAM 0.8 (Søgaard et al. 2002) Crop-livestock production systems APEX 0.53-0.86 (Senaviratne et al. 2014) NitroScape 0.10-0.77 (Franqueville et al. 2018) DNDC 0.53-0.95 (Congreves et al. 2016; Dutta et al. 2016b) 4.4.1.8 Required input data Detailed information regarding the required input data for each model is provided in the supplementary information. Forty percent of models (20 of 48) require data that are easily accessible to LCA practitioners. Among these, 9, 6, and 5 models are respectively applicable to estimating Nr emissions in crop-livestock systems, livestock production systems, and crop production systems. Among models requiring input data that are not easily accessible, 17 and 4 are applicable to crop production systems and combined crop-livestock systems, respectively. No information was found regarding the required input data for 15% of the models considered. 124 4.4.1.9 Ease of application With respect to ease of application, almost half of the models were explicitly reported by authors to be user-friendly. No clear indication with respect to ease of use was provided for the remaining models. 4.4.1.10 Capacity to quantify uncertainty Minimal information was found regarding how uncertainty was addressed and quantified for most of the models. For only a small fraction of models (5) was some form of uncertainty assessment provided. 4.5 Discussion Emission models for Nr have been developed for use under a wide range of application contexts representing different management practices. However, there is a distinct lack of comprehensive literature/reports elaborating on the respective features, strengths, and limitations of such models. In the current study, we address this lack of information by reviewing available emission models for simulating Nr emissions in three application contexts - crop production, livestock production, and combined crop-livestock production systems. Literature is particularly lacking with respect to Nr emission models for livestock production systems – which is notable considering that livestock production is among the major sources of Nr emissions globally (Gerber et al. 2014; Leip et al. 2015; Zargar et al. 2020). A first important observation is the limited availability and accessibility of model tools and/or supporting information such as manuals for the models considered. While the majority 125 of the models (33) were publicly available, many had little or no publicly available supporting information. A second important issue is that, often, only limited information is available regarding the accuracy of simulation provided by the models. No literature was identified that discussed and compared the accuracy of different models and, for many, no information regarding model accuracy was available. This is of clear relevance for LCA practitioners who, in most research contexts, are required to understand and communicate with respect to the accuracy and suitability of utilized data, taking into account conditions and context. A third key issue is uncertainty. One critical source of uncertainty in LCA studies is model uncertainty, in particular the uncertainty associated with characterization factors in LCIA methods (Chen and Corson 2014). In general, minimal information was found regarding the degree of uncertainty associated with the simulation models considered. Despite the diversity and heterogeneity of available models for simulating Nr emissions from crop and/or livestock production systems, what is clear is that each model has unique benefits and features and may potentially be useful for LCA practitioners depending on intended application context. Many models were developed for application in specific countries, which obviously limits their potential application. For example, a farm scale model that requires context-specific weather data and soil data (organic N and C contents) in a specific location (e.g. in Europe), regardless of robustness, will be of limited utility to LCA practitioners considering agricultural production systems elsewhere. Other models are, however, variously suitable for simulating emissions at farm scale, regional, national, and/or global levels. A model that represents emissions at the farm level in Europe might not be appropriate for a regional or national scale application. It is hence incumbent on LCA 126 practitioners to carefully consider and choose models that are most appropriate for simulating Nr emissions in the specific region and spatial/temporal scale of concern. Table 4-7 lists characterizes the Nr emission models that meet the defined thresholds for each of the comparative criteria for use in process-based LCA, as employed in the current analysis. Table 4-7 Comparative evaluation of Nr simulation models based on the intended application context (only those models satisfying the defined threshold for each criterion for use in process-based LCA are indicated). Comparative criteria Application context Crop production Livestock production Combined crop-livestock production Accessibility and availability of supporting information ANIMO, CANDY, CENTURY, DAISY, ECOSYS, EPIC, LIXIM, NLEAP, RZWQM, STICS, SWAT, Thinkstep, TOUGHREAC-N, WHNSIM AGRAMMON, ALFAM, MANNER-NPK, MITERRA, TAN-mass balance APEX, CAPRI, DNDC, FarmN, GLEAM, HOLOS, IFSM, IPCC, IULIA, NEMA, OVERSEER, SALCA Robustness ANIMO, CANDY, CENTURY, CREAMS, DAISY, DAYCENT, EPIC, NLEAP, RZWQM, SOILN, STICS, TNT2, TOUGHREACT-N ALFAM, MANNER APEX, CAPRI, DNDC, IPCC (Tier 2&3) Required input data CANDY, CREAMS, NCYCLE, STICS, WHNSIM AGRAMMON, ALFAM , MANNER-NPK, MITERRA, TAN-mass balance FarmN, GLEAM, HOLOS, IFSM, IPCC (Tier 1 & 2), IULIA, NARSES, OVERSEER, SALCA Ease of application AGRIFLUX, EPIC, INCA, LEACHN, NCYCLE, NLEAP, RZWQM, STICS AGRAMMON, ALFAM, MANNER, TAN-mass balance APEX, DNDC, GLEAM, HOLOS, IFSM, IPCC, IULIA, NEMA, NitroScape, OVERSEER, Capacity to quantify uncertainty AGRIFLUX NA APEX, HOLOS, IPCC (Tier 2), NEMA, OVERSEER 127 For a model to be suitable for use in LCA, it should be readily available, robust, user friendly, allow for qualification of uncertainty, and not require input data that will typically not easily accessible to LCA practitioners. According to Table 7, the IPCC Tier 2 models are the only models considered that satisfy all of the considered comparative criteria. Practitioners wishing to apply other, more context-appropriate models should hence carefully consider the specific limitations associated with candidate models and the degree to which these might be remedied within the study of concern. The detailed analysis of models respectively addressing crop, livestock, or combined crop-livestock production produced the following results. Among models for crop production systems, CANDY and STICS were recognized as robust models that require generally accessible input data. CANDY is a farm and regional scale model originating from Germany. STICS, which was developed in France, simulates NO3- emission at the farm and regional scales. According to Wolf et al. (2003), the accuracy of input data for regional and national scales is lower than for farm-scale studies; additionally regional and national input data are often scarce. STICS was reported to be a user friendly model. No information was found about the capacity to quantify uncertainty in any of these models. Among models addressing emissions in livestock production systems, the ALFAM and MANNER-NPK models for livestock production systems and the IPCC Tier 2 models for combined crop-livestock production systems were found to be robust, user-friendly, and requiring accessible input data. Considering both informative and comparative criteria, none of the emission models is universally satisfactory for all situations. Emissions model applicability is highly dependent 128 on intended application context, spatial scale, geography, required input data, and model accuracy. For example, robustly simulating emissions at the farm level will likely be better served by different models than will simulating emissions at the country scale. At the farm level, a model requiring context specific data may offer better assessment while, at the national level, non-context specific data may suffice. Similarly, a model that has been generated with data specific to a certain country might not be suitable for application in a different national context. To enable selecting suitable emission models for simulating Nr emissions in the context of specific studies, we recommend LCA practitioners to consider the comparisons of models presented in Tables 4-2 to 4-4 of this review. 4.6 Conclusions There is currently a lack of consensus in the LCA community regarding the most appropriate models to estimate Nr emissions in agricultural production systems. Hence, there is a need to clarify the scope of application, resolution, limitations, and strengths of available models for particular application contexts. A set of evaluation criteria, including informative and comparative scopes, were defined to identify, evaluate, and compare available models for simulating Nr emission. Forty-eight models were identified to simulate Nr emissions in three agricultural contexts (crop production, livestock production, and combined crop-livestock production systems). There is considerable heterogeneity among available models with respect to accuracy, the nitrogen species considered, and the intended geographical scale of application. Moreover, 129 attributes such as accessibility, transparency and accuracy of the simulation, as well as accessibly of required input data and availability of a comprehensive user manual are clearly all desirable features of models that might be selected for use by LCA practitioners. In addition, any such model should also enable uncertainty assessment. Each of the models considered could, to varying degrees, be potentially useful in LCA, depending on the intended application and goals of the study, as well as the scope of resources to which the LCA practitioner has access. In some cases, despite the limitations identified for particular models in this analysis, a given model may nonetheless be suitable. For example, despite the relative inaccessibility of required input data, DNDC might be the most suitable model for a crop-livestock LCA study at the farm scale provided that the practitioner is able to access the required input data. However, this model would likely never be suitable for application at the national level due to the nature of the data required. Similarly, IPCC Tier 2 models may be most suitable for simulating N2O emission at the national scale accurately, but would not be as accurate as DNDC for farm scale research. There are notable limitations associated with most available models for simulating Nr emissions. These include robustness of the results, accessibility of required input data, and the common inability to support uncertainty assessment. These limitations/drawbacks hence present opportunities for future research to develop accurate and suitable Nr emission models for use in LCA. 130 Chapter 5: Environmental Life Cycle Assessment of Mitigation Technologies and Management Strategies to Improve Nitrogen Use Efficiency along Industrial Egg Supply Chains 5.1 Synopsis A non-trivial challenge along egg supply chains is inefficient use of nitrogen, which may have a combination of negative economic, human/animal health, and environmental implications. A variety of technologies and management strategies have been proposed to improve nitrogen use efficiency and reduce emissions at key supply chain stages. Evaluation of the comparative effects on nitrogen use efficiency and life cycle impacts associated with each can facilitate identification and adoption of a priority subset of suitable mitigation options. The present study considered seven scenarios representing alternative NUE strategies compared to a (1) baseline model, specifically: (2) biochar addition to the soil and (3) application of the “4Rs” approach in feed input production; (4) a reduced crude protein supplemented with synthetic amino acids diet; (5) use of ammonia scrubbers in poultry barns; (6) biochar addition to the stored manure; (7) manure incorporation at the time of application to land; and (8) joint application of all strategies. In order to evaluate, analyze, and compare the impacts of these mitigation technologies and management strategies, an ISO 14044 compliant environmental life cycle assessment was performed. In addition to calculating nitrogen use efficiency, CML (baseline) [v 4.4, January 2015] and cumulative energy demand [v1.0.1, January 2015] impact assessment methods were applied to consider the following impact categories: acidification, climate change, depletion of abiotic resources, 131 energy use, eutrophication, ozone layer depletion, and photochemical oxidation. The functional unit was one tonne of egg production at the farm gate. At the crop production stage, implementation of the 4Rs strategy resulted in slightly lower impacts compared to the baseline for all impact categories. It also had higher NUE (about 4%) depending on the crop type, and lower N footprint by 20% to 22% compared to the baseline. Biochar addition to soil increased emissions in all impact categories, except GWP, when an energy allocation approach was applied. However, biochar addition resulted in substantial net benefits across impact categories if a system expansion modelling approach was used. At the feed formulation stage, the reduced crude protein diet scenario resulted in higher impacts than the baseline with respect to acidification (13%), GWP (1%), abiotic resource depletion (18%), energy use (39%), and eutrophication (19%). However, it had an 8% higher NUE and a 28% lower N footprint compared to the control diet. At the barn stage of manure management, acidification, eutrophication, and ozone layer depletion were reduced by 71%, 69% and 56% when an acid scrubber was applied. It also had a 19 % higher NUE and 67% lower N footprint compared to the baseline. By contrast, energy use and depletion of abiotic resources increased substantially due to the acid and electricity consumption. At the manure storage stage, the addition of biochar resulted in lower impacts for acidification, GWP, and eutrophication. It also had a 16% higher NUE and 57% lower N footprint compared to the baseline. At the field application stage of manure management, manure incorporation resulted in lower emissions all across impact categories, except GWP. Lower emission levels were mainly due to the higher N credit from lower loss of N for incorporation compared to spreading. However, it increased GWP due to fossil fuel consumption for machinery, as well 132 as increasing N2O emissions. Incorporating manure to the land showed a 4% higher NUE and 18% lower N footprint compared to manure spreading. Assessing the effects of alternative strategies with respect to the life cycle impacts of egg production showed that the 4Rs strategy had slightly lower impacts (in all categories) compared to the reference scenario. The most significant increase in NUE per tonne of eggs produced (13%) resulted from the application of the scrubber in the layer barn. The scrubber also significantly lowered acidifying (21%) and eutrophying (16%) emissions compared to the baseline. The combined application of all strategies increased NUE by 15% compared to the reference scenario and reduced acidification, GWP, and eutrophication potential per tonne of eggs produced, but at the cost of a large increase in life cycle energy consumption. The results indicated that each strategy might be considered more or less suitable depending on the considered environmental impacts, as well as NUE outcomes. Future research should focus on determining the effectiveness of the considered NUE strategies from economic and social perspectives. 5.2 Efficiency and environmental impacts in the egg sector Food production is responsible for a substantial share of anthropogenic resource consumption and environmental emissions (Pelletier and Tyedmers 2010; Pelletier et al. 2018), including reactive nitrogen (Nr) emissions (Leinonen et al. 2012a). Various Nr species, including ammonia (NH3), nitrous oxide (N2O), nitrogen oxides (NOx), and nitrate (NO3-), are lost from agricultural systems – in particular, from livestock production and the production of feeds consumed by livestock. These emissions variously contribute to GHG 133 emissions, acidification, eutrophication, biodiversity losses, and soil, water, and air pollution (Groenestein et al. 2019). The egg industry is the fastest-growing livestock sector. Global egg production increased from 51 million tonnes of eggs in 2000 to 68 million tonnes of eggs in 2013 (FAO 2016). Like other livestock sectors, the egg industry is under increasing pressure to demonstrate continued progress with respect to sustainability objectives. Improving nitrogen use efficiency (NUE) has been identified as one of the top sustainability priorities for this sector (Gerber et al. 2014; Pelletier 2017; Pelletier et al. 2018). Nitrogen use efficiency (NUE) is a measure of the ratio between the amount of N outputs and N inputs associated with provision of a good or service (Brentrap and Palliere 2010). NUE plays a key role in determining the egg industry’s net nitrogen footprint balance, as well as impacts on air quality, human and animal health, and profitability (Pelletier et al. 2018). NUE is influenced by the production and composition of feeds, feed conversion efficiency, and manure management, including manure management in barns, during storage and at time of manure land application (Pelletier et al. 2018; Zargar et al. 2020) A variety of technological and management strategies are available that may enable reducing nitrogen losses at key points along egg supply chains, and therefore, improve overall NUE (Pelletier et al. 2018, Zargar et al., 2020). For example, based on a combination of environmental, technical, and economic criteria, Zargar et al. (2020) identified the following priority subset: implementation of the “4Rs” (Right Source @ Right Rate @ Right Time @ Right Place®) nutrient stewardship and biochar addition to cropland for the feed input production stage; reduced crude protein content in formulated feeds supplemented with 134 synthetic amino acids for the feed formulation stage; use of ammonia scrubbers in barns; biochar addition to the stored manure; and manure incorporation at the time of field application for the manure management stage. However, it is imperative that the implementation of any such strategies be informed by a broader analysis and comparison of their relative efficacy with respect to NUE, as well as impacts on the overall environmental sustainability of egg production (Lemmen and Warren 2004; Pelletier et al. 2018). Life cycle assessment (LCA) provides an effective means to consider the relative efficacy of each Nr emissions mitigation option and to quantify NUE in parallel to a variety of other resource and environmental impact categories (Roy et al. 2009; Erisman et al. 2018). Such an approach is essential in order to understand and prevent burden-shifting among different kinds of impacts, supply chain stages, and different stakeholders (Williams et al. 2011; Pelletier 2015). Several studies have used the LCA methodology to evaluate the environmental burdens of egg supply chains (Mollenhorst et al. 2006; Cederberg et al. 2009; Vergé et al. 2009; Wiedemann and McGahan 2011; Dekker et al. 2011; Leinonen et al. 2012b; Leinonen and Kyriazakis 2013; Pelletier et al. 2013; Pelletier 2017, 2018; Abín et al. 2018). For example, Abín et al. (2018) evaluated the environmental emissions of intensive egg production in Spain and found that feed input production has the highest environmental impacts. In Canada, Pelletier (2017) assessed the environmental emissions of egg production considering different types of hen housing. It was found that feed formulation, feed conversion efficiency, and manure management are the most critical determinants of emissions. 135 However, none of these studies assessed the comparative efficacy of alternative technologies and management strategies for nitrogen emissions reduction along egg supply chains. On this basis, the objective of the current study was to provide information that will enable improving NUE in the egg sector whilst simultaneously considering other resource and environmental impacts. Towards the end, a series of life cycle inventory (LCI) models were developed for the priority subset of mitigation technologies and management strategies identified by Zargar et al. (2020). These models were integrated into an LCI model of the Canadian egg industry (Pelletier 2017), in order to evaluate their comparative efficacy. 5.3 Methods 5.3.1 Nitrogen use efficiency To calculate nitrogen emissions for the baseline models and each alternative scenario, as well as to calculate any associated difference in NUE for the activity and egg production as a whole, Equation 5-1 and 5-2 were applied. 𝑁𝑈𝐸 = 𝑁 𝑖𝑛𝑝𝑢𝑡 − 𝑁 𝑜𝑢𝑡𝑝𝑢𝑡𝑁 𝑖𝑛𝑝𝑢𝑡 × 100 Equation 5-1 𝑁𝑖𝑡𝑟𝑜𝑔𝑒𝑛 𝑓𝑜𝑜𝑡𝑝𝑟𝑖𝑛𝑡 = 𝑁 𝑜𝑢𝑡𝑝𝑢𝑡emissions Equation 5-2 At the crop production stage, NUE was calculated based on one tonne of produced crop. For corn and wheat production, the total amount of nitrogen fertilizer applied per tonne of crop was considered as the N input. For soybean production, the N input comprised of a 136 small amount of nitrogen fertilizer, as well as biological N2 fixation. An N2 fixation rate was calculated at 202 kg N he-1 based on (Ciampitti and Salvagiotti 2018). The N output was the N content of all N emissions, including ammonia, nitrous oxide, nitrate, and nitric oxide. N footprints included all N emissions, expressed in kg Nr. For the feed formulation stage, NUE was calculated based on two tonnes of feed consumed (i.e. per one tonne of eggs produced). N inputs consisted of total N in the feed. Outputs were N resident in manure/mortalities and spent hens (here, emissions are the difference between N inputs in feeds and N retained in the co-products of egg production). For the manure management stages, the first step was calculating the amount of N excreted in the barn, which was considered as the N input for the barn stage of manure management. The N output consists of N remaining in the manure, following Nr emissions in the barn. The remaining N in the manure was the N input to the subsequent manure storage stage and similarly through manure land application. For the entire egg production supply chain, NUE was calculated for one tonne of egg production. N inputs and outputs were calculated as follows: N input: N fertilizer applied to produce corn, wheat, soymeal, and canola oil as well as N2 fixed by soybeans and N contained in synthetic amino acids. N output = N input – N retained N retained: N in 1 tonne eggs + N in manure at the time of field application after emissions + N in spent hens + N credit from the ammonia captured by the acid scrubber (assuming it is utilized as a fertilizer product). 137 5.3.2 Life cycle assessment In order to assess the comparative efficacy of the priority subset of Nr emissions mitigation options identified by Zargar et al. (2020) with respect to NUE as well as resource/environmental criteria, an ISO 14044-compliant life cycle assessment was conducted. ISO 14044 describes the principles and guidelines for the four phases of LCA (ISO 2006a, b). The research methods are described in correspondence with these four phases of LCA, as per the ISO 14044 methodological standard. 5.3.2.1 Goal and scope definition The overarching goal of this study was to evaluate and compare a subset of NUE technologies and management strategies that might be applied along egg supply chains in order to determine their comparative efficacy in simultaneously reducing N emissions, resource use, and other environmental impacts, as well as to elucidate potential trade-offs. Towards the end, a series of ISO 14044-compliant life cycle inventory models were developed and integrated into the Canadian egg industry supply chain model developed by Pelletier (2017). Attributional life cycle assessment (ALCA) modelling was applied. The use of ALCA supports the understanding of the current distribution of potential environmental burdens along product supply chains. This may include, for example, how the life cycle NUE and other resource/environmental burdens are influenced by variables such as feed composition, feed conversion efficiency, and manure management strategies. The intended audience includes egg farmers and egg industry associations, producers of feed inputs for the poultry 138 industry, government agencies tasked with regulating agriculture, and agroecosystem modelling experts. 5.3.2.1.1 Description of the conventional layer supply chain in Canada The baseline conventional cage Canadian egg production and supply chain model described by Pelletier (2017) has been adopted for this study as this currently represents the majority of egg production in Canada. Conventional cage systems for layer hens typically include automatic drinking, feeding, egg collection, and manure removal systems in tunnel-ventilated barns. Manure falls through the wire cage floor onto a belt, or into a pit for disposal. Hens are moved into the cages as pullets, at roughly 20 weeks of age, and spend 50 weeks in the cages before being cycled out of production. During this time, they are fed a diet of formulated feed, composed of mostly grains and oilseeds, with small amounts of animal by-product meals and fats, as well as minerals and other micronutrients. Feed inputs are largely domestically produced. The laying hens will produce roughly 300 eggs each during their lay cycle. 5.3.2.1.2 System boundary The system boundary is defined to clarify what is included/excluded within the model (ISO 2006a, b). In this study, the system boundary for the egg supply chains followed that defined by Pelletier (2017) up to the farm gate, including inputs (materials, energy, and transportation) and emissions characteristic of industry average breeder flock, hatchery, pullet, and layer facilities. This boundary was subsequently expanded to include the inventories and upstream supply chains of each of the NUE technology/management 139 strategies considered. The considered technologies at different stages of the supply chain are listed in Table 5-1. Table 5-1 Mitigation technologies and management strategies for NUE at key supply chain stages Stage Mitigation technologies and strategies Feed input production  4Rs (Right Source @ Right Rate @ Right Time @ Right Place®)  Biochar addition to the soil Feed formulation  Reduced crude protein supplemented with synthetic amino acids Manure management (barn)  Scrubber (ammonia) Manure management (storage)  Biochar addition to the manure Manure management (field)  Manure incorporation The system boundaries and flow diagram are illustrated in Figure 5-1. 140 EcosphereTechnosphereInputsFertilizers PesticidesEnergy CarriersTransport ModesMachinery BiocharAgricultural Crop ProductionCrop ProcessingLivestock ProductionProcessing Poultry Feed (Diet Fromulation)Livestock Processing and RenderingLayer Facilities Pullet FacilitiesSalt, Limestone, etc. ProductionBreeder Flock FacilitiesEgg ProductionManure Management and TreatmentHatchery FacilitiesManureBackground – From Pelletier (2017) modelForeground – Using collected data, data from literature, databases, reports, etc.Biochar AdditionRoofed StorageManure incorporationLand ApplicationProduct or waste flowElementary flowTreatmentBarnScrubberStorageCornWheatSoybean Figure 5-1 Diagram and system boundaries for the cradle-to-farm gate environmental life cycle assessment of NUE technologies and management strategies for the Canadian egg industry 141 5.3.2.1.3 Functional unit The selection of an appropriate functional unit (FU) to which the life cycle inventory is scaled is essential to characterize the resource and environmental performance of a product system (Reap et al. 2008). The most common FU applied in LCAs of agricultural products is a simple mass-based measure (Skowrońska and Filipek 2014). Therefore, the FU/reference flow for this study was one tonne of eggs produced at the farm gate. 5.3.2.2 Life cycle inventory analysis Foreground LCI model inputs and outputs, including direct, and indirect emissions considered in this study included those attributable to the production and processing of feed inputs and other raw materials, energy carriers, and manure management from the baseline model, along with those specific to each of the mitigation options considered. 5.3.2.2.1 Data acquisition, assumptions, and limitations Required data to develop LCI models for each mitigation strategy were collected from literature, databases, industry publications, catalogues, consultancy reports, and via personal consultation with domain experts in related companies and are summarized in the following sub sections. Processes and scenarios are listed in Table 5-2. . 142 Table 5-2 Processes and scenarios for the considered technologies and management strategies at each supply chain stage Process Process Description At the feed input production stage FIP1 No treatment (baseline) FIP2 Application of 4Rs strategy FIP3 Biochar addition to the soil At the feed formulation stage FFO1 Control diet (baseline) FFO2 Reduced crude protein diet with synthetic amino acids At the manure management stage: Barn MB1 Mechanical ventilation and manure belt (baseline) MB2 Acid scrubber At the manure management stage: Storage MS1 Roofed storage (baseline) MS2 Biochar addition to manure At the manure management stage: Field MF1 Spreading (baseline) MF2 Incorporation Scenario Application of processes at the egg production stage S1 (Reference scenario) FIP1 + FFO1 + MB1 + MS1 + MF1 S2 FIP2 + FFO1 + MB1 + MS1 + MF1 S3 FIP3 + FFO1 + MB1 + MS1 + MF1 S4 FIP1 + FFO2 + MB1 + MS1 + MF1 S5 FIP1 + FFO1 + MB2 + MS1 + MF1 S6 FIP1 + FFO1 + MB1 + MS2 + MF1 S7 FIP1 + FFO1 + MB1 + MS1 + MF2 S8 (All strategies) FIP2 + FIP3 + FFO2 + MB2 + MS2 + MF2 143 Feed input production The primary inventory data for feed input production (corn, soybean, and wheat, which comprise the majority share of layer feed in Canada) were based on Pelletier (2017). The mitigation options considered for feed input production were the application of 4Rs fertilizer management and biochar addition to the soil. 4Rs fertilizer management The inventory data for fertilizer application based on the 4Rs strategy were derived from Li et al. (2016). Li et al. (2016) indicated that an 11% N reduction in fertilizer use is achievable while maintaining corn yield via a sensor-based variable-rate N application in Lincoln County, MO. The amount of fertilizer flow in the baseline model (Pelletier 2017) was modified based on Li et al. (2016) to represent the 4Rs approach. The amount of elementary flows, including ammonia, carbon dioxide, nitrous oxide, nitrate, and nitric oxide emissions for the baseline scenario, were derived from Pelletier (2017), which were simulated by applying IPCC tier 2 protocols and default emission factors. The 4Rs technique may reduce the emissions of nitrous oxide, ammonia, and nitrate emissions in corn production by 10, 23, and 16 percent, respectively (Li et al. 2016). There was no literature found reporting the amount of fertilizer and emissions reduction associated with the application of the 4Rs approach in wheat and soybean production. Hence, the amount of N fertilizer and emissions reduction for wheat and soybean production were assumed to be the same as for corn production. The calculation of emissions reduction utilized Equation 5-3: 144 𝑋 = −(𝑌 × 𝑍%) + 𝑌 Equation 5-3 X= the amount of emission after application of mitigation option Y= the amount of emission before application of mitigation option Z= the percentage of reduction Biochar addition to soil The primary data for biochar production were derived from Gu and Bergman (2016), who evaluated the co-production of biochar and syngas using a modular advanced biomass pyrolysis system in the US. The data were modified to best reflect Canadian conditions (i.e. the electricity flow in Gu and Bergman (2016) was “Electricity, at eGrid, NWPP, 2008 – RNA”, which was changed to “Electricity, medium voltage” representing Canada, British Columbia (BC) electricity consumption). The feedstock was woodchips from BC forests or mill residues. It was assumed that the biochar was produced in BC and shipped to the provinces producing the considered crops (corn, wheat, and soybean). Prior research has shown that the choice of system boundaries for biochar production models can significantly influence estimated life cycle impacts – in particular with respect to allocation between syngas and biochar co-products. Consensus as to best practice has not yet coalesced (Matuštík et al., 2020). Two scenarios were hence implemented in which biochar production was modeled using either physical allocation based on the energy content of biochar and syngas, or using a system expansion in which syngas was assumed to displace propane at a conversion rate of 78% (Ge et al. 2008). 145 The application rate of biochar to agricultural soils depends on soil and crop type. According to literature, biochar application rates may vary from 5 to 50 tonnes per hectare (Chan et al. 2007; Major 2009, 2010). Some studies suggest that higher rates of biochar negatively affected crop yield. In the current study, a sensitivity analysis was conducted, and three rates of biochar addition (5, 7.5, and 10 tonnes per hectare) were modelled based on Dennis (2019). Due to the dependency of N emissions on application rate, type of soil, type of crop etc. (He et al. 2017) as well as lack of clarity with respect to nature of this relationship (linear or otherwise), it was assumed that the lowest rate of biochar application modelled in this research (5 tonne ha-1) has the lowest percentage of emission reduction from the range reported in Zargar et al. (2020). For example, biochar addition to the soil has been reported to reduce N2O emissions by 25-50% (Zargar et al., 2020). Therefore, the following assumptions were made: 5, 7.5 and 10 tonne ha-1 biochar addition to the soil reduced N2O by 25%, 37.5%, and 50%, respectively. NO3- (0-25%) and NO (50-75%) emissions were reduced based on the same assumptions. The amount of N2O, NO, and NO3 in the baseline model, simulated using IPCC tier 2 protocols, were modified based on the above assumptions to reflect the anticipated impact of biochar application on Nr emissions. It was assumed biochar was incorporated into agricultural soils since the majority of biochar field trials reported used this application method (Major 2010). Biochar effects on soil and crop productivity cannot be generalized, as they depend on type of biochar and plant as well as the site characteristics (Lorenz and Lal 2014). Jeffery et al. (2011) demonstrated that there is no correlation between the biochar application rate and the impacts on crop productivity. However, Gryze and Cullen (2010) found that biochar 146 application reduced fertilizer application by 7% as a result of increased nutrient availability. Hence, a 7% fertilizer application rate reduction was considered when biochar was applied. Biochar is also advantageous in its potential for carbon sequestration. Carbon sequestration refers to the process of storing carbon in soil organic matter, thus removing carbon dioxide from the atmosphere. Windeatt et al. (2014) reported a potential of 373 Mt of biochar to sequester 0.55 Pg CO2 per year in soils over long-time periods. On this basis, it was assumed that 36 kg of biochar has an associated 53.01 kg annual carbon sequestration potential. Another key feature of biochar is its resistance to decomposition in soils. This allows for a single application of biochar to be useful over several growing seasons (Major 2010). However, there is no recommended application frequency since research on biochar is relatively new/limited (Dennis 2019). The few studies that have been conducted to determine biochar application frequency best practices suggest application every three years (Backer et al. 2016; Bieser and Thomas 2019). Therefore, the frequency of application was considered three years in current study as well. The life cycle inventory data utilized to model feed input production with the application of the 4Rs approach and biochar addition to the soil are provided in Table 5-3 to 5-5 for corn, soybean, and wheat production, respectively. 147 Table 5-3 LCI processes and databases used to model 1 tonne of corn production (baseline vs. alternative strategies) Process/ Flow Baseline 4Rs Biochar 5 tonnes/ha Biochar 7.5 tonnes/ha Biochar 10 tonnes/ha Unit Provider Database Inputs Diesel, burned in agricultural machinery 9.02 9.02 9.02 9.02 9.02 l Market for diesel, burned in agricultural machinery | APOS, U - GLO EcoInvent v.3.5 APOS Unit Processes Biochar - - 187.00 281.00 375.00 kg Biochar production Gu and Bergman (2016) Corn - seed 23.70 23.70 23.70 23.70 23.70 kg Corn production Created as a system process from the corn production model Electricity, medium voltage 43.70 43.70 43.70 43.70 43.70 kWh Market for electricity, medium voltage | electricity, medium voltage | APOS, U – CA – ON EcoInvent v.3.5 APOS Unit Processes Natural Gas 498.00 498.00 498.00 498.00 498.00 MJ Natural gas, burned in gas motor, for storage | natural gas, burned in gas motor, for storage | APOS, U EcoInvent v.3.5 APOS Unit Processes Nitrogen fertilizers, as N 17.60 15.70 16.40 16.40 16.40 kg Nitrogen fertilizer mix- ON- CA EcoInvent v.3.5 APOS Unit Processes 148 Process/ Flow Baseline 4Rs Biochar 5 tonnes/ha Biochar 7.5 tonnes/ha Biochar 10 tonnes/ha Unit Provider Database Occupation, agriculture 1120.00 1120.00 1120.00 1120.00 1120.00 m2*a Pesticide, unspecified 1.89 1.89 1.89 1.89 1.89 kg Pesticide production, unspecified | pesticide, unspecified | APOS, U – RER EcoInvent v.3.5 APOS Unit Processes Phosphate fertilizer, as P2O5 5.10 5.10 5.10 5.10 5.10 kg Monoammonium phosphate production | phosphate fertilizer, as P2O5 | APOS, U – RER EcoInvent v.3.5 APOS Unit Processes Potassium chloride, as K2O 5.45 5.45 5.45 5.45 5.45 kg Potassium chloride production | potassium chloride, as K2O | APOS, U – CA – SK EcoInvent v.3.5 APOS Unit Processes Pesticide transport, freight, lorry 16-32 metric ton, EURO6 1.89 1.89 1.89 1.89 1.89 t*km Transport, freight, lorry 16-32 metric ton, EURO6 | transport, freight, lorry 16-32 metric ton, EURO6 | APOS, U – RER EcoInvent v.3.5 APOS Unit Processes Fertilizer transport, freight, lorry 16-32 metric ton, EURO6 5.10 5.10 5.10 5.10 5.10 t*km Transport, freight, lorry 16-32 metric ton, EURO6 | transport, freight, lorry 16-32 metric ton, EURO6 | APOS, U – RER EcoInvent v.3.5 APOS Unit Processes 149 Process/ Flow Baseline 4Rs Biochar 5 tonnes/ha Biochar 7.5 tonnes/ha Biochar 10 tonnes/ha Unit Provider Database Seed transport, freight, lorry 16-32 metric ton, EURO6 2.37 2.37 2.37 2.37 2.37 t*km Transport, freight, lorry 16-32 metric ton, EURO6 | transport, freight, lorry 16-32 metric ton, EURO6 | APOS, U – RER EcoInvent v.3.5 APOS Unit Processes Biochar application, broadcasting - - 1120.00 1120.00 1120.00 m2 Fertilising, by broadcasting | fertilising, by broadcaster | APOS, U – CA - QC EcoInvent v.3.5 APOS Unit Processes Biochar incorporation - tillage, cultivating, chiselling - - 0.11 0.11 0.11 ha Tillage, cultivating, chiselling | tillage, cultivating, chiselling | APOS, U- CA- QC EcoInvent v.3.5 APOS Unit Processes Biochar transport, freight, lorry 16-32 metric ton, EURO6 - - 531.00 796.00 1060.00 t*km Transport, freight, lorry 16-32 metric ton, EURO6 | APOS, U – RER EcoInvent v.3.5 APOS Unit Processes Outputs Ammonia 2.60 2.00 2.60 2.60 2.60 kg Carbon dioxide 6.16 6.16 6.16 6.16 6.16 kg Corn 1000.00 1000.00 1000.00 1000.00 1000.00 kg Nitrous oxide 0.49 0.44 0.37 0.31 0.24 kg Nitrate 3.21 2.70 3.21 2.81 2.41 kg Nitric oxide 0.38 0.38 0.19 0.14 0.10 kg 150 Table 5-4 LCI processes and databases used to model 1 tonne of soybean production (baseline vs. alternative strategies) Process/Flow Baseline 4Rs Biochar 5 tonnes/ha Biochar 7.5 tonnes/ha Biochar 10 tonnes/ha Unit Provider Database Inputs Diesel, burned in agricultural machinery 12.30 12.30 12.30 12.30 12.30 l Market for diesel, burned in agricultural machinery | APOS, U - GLO EcoInvent v.3.5 APOS Biochar - - 575.00 862.00 1150.00 kg Biochar production Gu and Bergman (2016) Electricity, medium voltage 6.18 6.18 6.18 6.18 6.18 kWh Market for electricity, medium voltage | electricity, medium voltage | APOS, U – CA – ON EcoInvent v.3.5 APOS Unit Processes Natural Gas 45.20 45.20 45.20 45.20 45.20 MJ Natural gas, burned in gas motor, for storage | APOS, U EcoInvent v.3.5 APOS Unit Processes Nitrogen fertilizers, as N 2.24 1.99 2.08 2.08 2.08 kg Nitrogen fertilizer mix- ON- CA EcoInvent v.3.5 APOS Unit Processes Occupation, agriculture 3450.00 3450.00 3450.00 3450.00 3450.00 m2*a Pesticide, unspecified 0.51 0.51 0.51 0.51 0.51 kg Pesticide production, unspecified | pesticide, unspecified | APOS, U – RER EcoInvent v.3.5 APOS Unit Processes 151 Process/Flow Baseline 4Rs Biochar 5 tonnes/ha Biochar 7.5 tonnes/ha Biochar 10 tonnes/ha Unit Provider Database Petrol, unleaded, burned in machinery 3.87 3.87 3.87 3.87 3.87 l Petrol, unleaded, burned in machinery | petrol, unleaded, burned in machinery | APOS, U- GLO EcoInvent v.3.5 APOS Unit Processes Phosphate fertilizer, as P2O5 11.60 11.60 11.60 11.60 11.60 kg Monoammonium phosphate production | phosphate fertilizer, as P2O5 | APOS, U – RER EcoInvent v.3.5 APOS Unit Processes Potassium chloride, as K2O 6.94 6.94 6.94 6.94 6.94 kg Potassium chloride production | potassium chloride, as K2O | APOS, U – CA – SK EcoInvent v.3.5 APOS Unit Processes Soybean-seed 41.70 41.70 41.70 41.70 41.70 kg Soybean production Created as a system process from the soybean production model Sulfur 1.23 1.23 1.23 1.23 1.23 kg Market for sulfur | sulfur | APOS, U- GLO EcoInvent v.3.5 APOS Unit Processes Pesticide transport, freight, lorry 16-32 metric ton, EURO6 0.51 0.51 0.51 0.51 0.51 t*km Transport, freight, lorry 16-32 metric ton, EURO6 | APOS, U – RER EcoInvent v.3.5 APOS Unit Processes 152 Process/Flow Baseline 4Rs Biochar 5 tonnes/ha Biochar 7.5 tonnes/ha Biochar 10 tonnes/ha Unit Provider Database Fertilizer transport, freight, lorry 16-32 metric ton, EURO6 11.60 11.60 11.60 11.60 11.60 t*km Transport, freight, lorry 16-32 metric ton, EURO6 | transport, freight, lorry 16-32 metric ton, EURO6 | APOS, U – RER EcoInvent v.3.5 APOS Unit Processes Seed transport, freight, lorry 16-32 metric ton, EURO6 4.17 4.17 4.17 4.17 4.17 t*km Transport, freight, lorry 16-32 metric ton, EURO6 | APOS, U – RER EcoInvent v.3.5 APOS Unit Processes Biochar application, broadcasting - - 3450.00 3450.00 3450.00 m2 Fertilising, by broadcasting | fertilising, by broadcaster | APOS, U – CA - QC EcoInvent v.3.5 APOS Unit Processes Biochar incorporation - tillage, cultivating, chiselling - - 0.35 0.35 0.35 ha Tillage, cultivating, chiselling | tillage, cultivating, chiselling | APOS, U- CA- QC EcoInvent v.3.5 APOS Unit Processes Biochar transport, freight, lorry 16-32 metric ton, EURO6 - - 1630.00 2440.00 3260.00 t*km Transport, freight, lorry 16-32 metric ton, EURO6 | APOS, U – RER EcoInvent v.3.5 APOS Unit Processes Outputs Ammonia 2.33 1.79 2.33 2.33 2.33 kg Carbon dioxide 0.79 0.79 0.79 0.79 0.79 kg Nitrous oxide 0.27 0.24 0.20 0.17 0.14 kg Nitrate 0.00 0.00 0.00 0.00 0.00 kg Nitric oxide 0.05 0.05 0.03 0.02 0.01 kg Soybean 1000.00 1000.00 1000.00 1000.00 1000.00 kg 153 Table 5-5 LCI processes and databases used to model 1 tonne of wheat production (baseline vs. alternative strategies) Process/Flow Baseline 4Rs Biochar 5 tonnes/ha Biochar 7.5 tonnes/ha Biochar 10 tonnes/ha Unit Provider Database Inputs Diesel, burned in agricultural machinery 7.05 7.05 7.05 7.05 7.05 l Market for diesel, burned in agricultural machinery | diesel, burned in agricultural machinery | APOS, U - GLO EcoInvent v.3.5 APOS Unit Processes Biochar - - 555.00 833.00 1110.00 kg Biochar production Gu and Bergman (2016) Electricity, medium voltage 5.00×10-4 5.00×10-4 5.00×10-4 5.00×10-4 5.00×10-4 kWh Market for electricity, medium voltage | electricity, medium voltage | APOS, U – CA – SK EcoInvent v.3.5 APOS Unit Processes Nitrogen fertilizers, as N 23.30 20.70 21.70 21.70 21.70 kg Nitrogen fertilizer mix- SK- CA EcoInvent v.3.5 APOS Unit Processes Occupation, agriculture 3330.00 3330.00 3330.00 3330.00 3330.00 m2*a Pesticide, unspecified 0.33 0.33 0.33 0.33 0.33 kg Pesticide production, unspecified | pesticide, unspecified | APOS, U – RER EcoInvent v.3.5 APOS Unit Processes Phosphate fertilizer, as P2O5 7.69 7.69 7.69 7.69 7.69 kg Monoammonium phosphate production | phosphate fertilizer, as P2O5 | APOS, U – RER EcoInvent v.3.5 APOS Unit Processes 154 Process/Flow Baseline 4Rs Biochar 5 tonnes/ha Biochar 7.5 tonnes/ha Biochar 10 tonnes/ha Unit Provider Database Potassium chloride, as K2O 2.77 2.77 2.77 2.77 2.77 kg Potassium chloride production | potassium chloride, as K2O | APOS, U – CA – SK EcoInvent v.3.5 APOS Unit Processes Sulfur 0.30 0.30 0.30 0.30 0.30 kg Market for sulfur | sulfur | APOS, U- GLO EcoInvent v.3.5 APOS Unit Processes Pesticide transport, freight, lorry 16-32 metric ton, EURO6 0.33 0.33 0.33 0.33 0.33 t*km Transport, freight, lorry 16-32 metric ton, EURO6 | transport, freight, lorry 16-32 metric ton, EURO6 | APOS, U – RER EcoInvent v.3.5 APOS Unit Processes Fertilizer transport, freight, lorry 16-32 metric ton, EURO6 7.69 7.69 7.69 7.69 7.69 t*km Transport, freight, lorry 16-32 metric ton, EURO6 | transport, freight, lorry 16-32 metric ton, EURO6 | APOS, U – RER EcoInvent v.3.5 APOS Unit Processes Seed transport, freight, lorry 16-32 metric ton, EURO6 4.00 4.00 4.00 4.00 4.00 t*km Transport, freight, lorry 16-32 metric ton, EURO6 | transport, freight, lorry 16-32 metric ton, EURO6 | APOS, U – RER EcoInvent v.3.5 APOS Unit Processes Biochar application, broadcasting - - 3330.00 3330.00 3330.00 m2 Fertilising, by broadcasting | fertilising, by broadcaster | APOS, U – CA - QC EcoInvent v.3.5 APOS Unit Processes 155 Process/Flow Baseline 4Rs Biochar 5 tonnes/ha Biochar 7.5 tonnes/ha Biochar 10 tonnes/ha Unit Provider Database Biochar incorporation - tillage, cultivating, chiselling - - 0.33 0.33 0.33 ha Tillage, cultivating, chiselling | tillage, cultivating, chiselling | APOS, U- CA- QC EcoInvent v.3.5 APOS Unit Processes Biochar transport, freight, lorry 16-32 metric ton, EURO6 - - 783.00 1170.00 1570.00 t*km Transport, freight, lorry 16-32 metric ton, EURO6 | transport, freight, lorry 16-32 metric ton, EURO6 | APOS, U – RER EcoInvent v.3.5 APOS Unit Processes Wheat -seed 40.00 40.00 40.00 40.00 40.00 kg Wheat production Created as a system process from the wheat production model Outputs Ammonia 4.55 3.50 4.55 4.55 4.55 kg Carbon dioxide 8.14 8.14 8.14 8.14 8.14 kg Wheat 1000.00 1000.00 1000.00 1000.00 1000.00 kg Nitrous oxide 0.61 0.55 0.46 0.38 0.31 kg Nitrate 0.43 0.36 0.43 0.38 0.32 kg Nitric oxide 0.50 0.50 0.25 0.19 0.13 kg Phosphate 0.01 0.01 0.01 0.01 0.01 kg 156 Feed formulation Feed formulation is a critical and complex process that requires the nutritional requirements of laying hens to be met to ensure high egg quality, and thus provide cost-effective feeds (Moe et al. 2014). To evaluate the potential of improving NUE through feed formulation, a feed with reduced crude protein and supplemental amino acids was compared to a typical layer feed in Canada. Here, two formulations (a baseline feed formulation and an alternative, reduced crude protein feed formulation with synthetic amino acids supplementation) were solicited from a western Canadian feed milling company (pers. comm, V. Alizadeh, Otter Coop) (Table 5-6). Table 5-6 Formulations of the baseline and reduced crude protein with synthetic amino acids supplementation layer feeds (per tonne of feed). Ingredients Unit Baseline diet Reduced crude protein diet Canola Oil kg 45.00 40.60 Coarse Lime kg 69.70 74.70 Corn kg 467.00 367.00 DL-Methionine kg - 1.27 Fine Lime kg 29.90 32.10 Layer Premix kg 26.10 6.90 Liquid Choline kg 0.75 0.75 L-Lysine kg - 0.82 L-Threonine kg - 16.40 Monocalcium phosphate kg 9.40 10.40 Salt kg 2.52 2.74 Sodium Bicarbonate kg 1.76 1.40 Soya Meal kg 248.00 150.00 Wheat kg 100.00 295.00 157 LCI models and source databases used to model ingredients in the feed formulations that were not otherwise provided in Pelletier (2017) are provided in Table 5-7. Life cycle inventory data for DL-Methionine, L-lysine, L-threonine, monocalcium phosphate, and sodium bicarbonate were derived from AGRIBALYSE V1.3 (April 2016), based on the project ECOALIM. Premix was modelled based on information provided by Otter Coop (pers. comm, V. Alizadeh, Otter Coop). Liquid choline was excluded from the analysis due to lack of reliable inventory data for the production process. As the ingredients are included at the same rate in both formulations, this omission is not anticipated to unduly influence the analysis. Table 5-7 LCI processes and databases used to model the production of 1 tonne of baseline layer feed (baseline vs. reduced crude protein diet supplemented with synthetic amino acids) Process/Flow Baseline Reduced crude protein diet Unit Provider Database Inputs Canola oil (Crude rapeseed oil), from crushing (solvent), at plant/AT Energy 45.00 40.60 kg Canola oil milling, Canada at plant Energy - AT Pelletier (2017), EcoInvent v.3.5 APOS Unit Processes Corn grain, feed 467.00 367.00 kg Corn production Pelletier (2017) Diesel, burned in agricultural machinery 1.29 1.29 l Market for diesel, burned in agricultural machinery | diesel, burned in agricultural machinery | APOS, U - GLO EcoInvent v.3.5 APOS Unit Processes 158 Process/Flow Baseline Reduced crude protein diet Unit Provider Database DL-Methionine, processing - RER - 1.28 kg DL-Methionine, processing - RER AGRIBALYSE V1.3 (April 2016) - supporting LCI (based on the project ECOALIM) Electricity, medium voltage 4.39 4.39 kWh Market for electricity, medium voltage | electricity, medium voltage | APOS, U - CA-BC EcoInvent v.3.5 APOS Unit Processes L-Lysine, processing - FR - 0.82 kg L-Lysine hcl, processing - FR AGRIBALYSE V1.3 (April 2016) - supporting LCI (based on the project ECOALIM) L-Threonine, processing - FR - 16.40 kg L-Threonine, processing - FR AGRIBALYSE V1.3 (April 2016) - supporting LCI (based on the project ECOALIM) Limestone, crushed, for mill 99.60 107.00 kg Limestone production, crushed, for mill | limestone, crushed, for mill | APOS, U - CA-QC EcoInvent v.3.5 APOS Unit Processes Monocalcium phosphate, processing - FR 9.40 10.40 kg Monocalcium phosphate, processing - FR AGRIBALYSE V1.3 (April 2016) - supporting LCI (based on the project ECOALIM) Petrol, unleaded, burned in machinery 0.04 0.04 l Petrol, unleaded, burned in machinery | petrol, unleaded, burned in machinery | APOS, U - GLO EcoInvent v.3.5 APOS Unit Processes 159 Process/Flow Baseline Reduced crude protein diet Unit Provider Database Premix 26.10 6.91 kg Premix (pers. comm., V. Alizadeh, Otter Coop), EcoInvent v.3.5 APOS Unit Processes Sodium bicarbonate, animal feed, at retailer gate - RER 1.76 1.40 kg Sodium bicarbonate, production -RER AGRIBALYSE V1.3 (April 2016) - supporting LCI (based on the project ECOALIM) Sodium chloride, powder 2.52 2.74 kg Sodium chloride production, powder | sodium chloride, powder | APOS, U - row EcoInvent v.3.5 APOS Unit Processes Soybean expeller, from crushing (pressing), at plant/DE Energy 248.00 150.00 kg Canadian Soybean expeller, from crushing (pressing), at plant Energy - DE Pelletier (2017), EcoInvent v.3.5 APOS Wheat grain, feed 100.00 295.00 kg Wheat production Pelletier (2017) Output Feed - layer 1000.00 1000.00 kg 160 Manure management The manure management stage consists of three sub-stages: including manure management in the barn, during storage, and at the time of land application. The baseline model (Pelletier, 2017) consists of the technologies and management strategies that are already widely applied in the Canadian egg industry, some of which were shown in Zargar et al. (2020) to be effective in reducing Nr emissions compared to alternative strategies. These mitigation options include mechanical ventilation and use of manure belts in barns, use of roofed manure storage, and spreading the manure on land. However, other strategies were identified by Zargar et al. (2020), such as use of ammonia scrubbers at the barn stage, biochar addition to the stored manure, and incorporating manure at the time of land application. All seen as potentially efficacious options to reduce further Nr emissions. Hence, a comparison among baseline, versus each of these alternative mitigation options were conducted at each sub stage of manure management. Specifically, for manure management in the barn, application of a manure belt and mechanical ventilation was considered as the baseline model, and an ammonia scrubber was applied as an additional, alternative option. Life cycle inventory data for manure management in the barn with and without the application of scrubber are provided in Table 5-8. Direct energy inputs for the baseline scenario follow Pelletier (2017). Reported amounts of acid, electricity and water used in acid scrubbers, as well as the type of acid employed, vary considerably. As a result, a sensitivity analysis was conducted to evaluate and compare the extent to which the amount of inputs influences the estimated life 161 cycle impacts. The most common type of acid used in the acid scrubber is sulfuric acid, which was considered in all models. Separate LCIs for acid scrubbers were considered based on (Hadlocon et al. 2015a), (Zhao et al. 2013), and Zhang et al. (2016a), as well an average of these three studies. The results of the sensitivity analysis are provided in section 5.4.7.4. It was assumed that the scrubber removed 75% of ammonia from ventilation air based on the low end of the range for ammonia removal efficiency (75-100%) reported in the literature (Carew 2010; Lin et al. 2014; Hadlocon et al. 2015; Wood and Van Heyst 2016). Based on an estimated N excretion rate by hens of 32.18 kg N/tonne of eggs and assuming 50% of ammonia emissions from the barn and storage occur in the barn, the total amount of N scrubbed was calculated accordingly. The captured NH3 was assumed to be made available as a fertilizer input to crop production, thereby displacing production of an equivalent amount of N fertilizer. 162 Table 5-8 LCI models and source databases used to model manure management in barns (baseline vs. application of an acid scrubber) Process/ Flow Baseline Scrubber (Average data) Scrubber Hadlocon et al. (2015) Scrubber Zhao (2013) Scrubber Zhang et al. (2016a) Unit Provider Database Inputs Mechanical ventilation and belt electricity, medium voltage 217.00 217.00 217.00 217.00 217.00 kWh Market group for electricity, medium voltage | electricity, medium voltage | APOS, U - CA EcoInvent v.3.5 APOS Unit Processes Acid scrubber electricity, medium voltage - 106.00 2.02 0.06 316.00 kWh Market group for electricity, medium voltage | electricity, medium voltage | APOS, U - CA EcoInvent v.3.5 APOS Unit Processes N credit as nitrogen fertiliser, as N - -5.94 -5.94 -5.94 -5.94 kg Nitrogen fertilizer mix- ON- CA EcoInvent v.3.5 APOS System Processes Poultry manure 1140.00 1140.00 1140.00 1140.00 1140.00 kg Pelletier (2017) Sulfuric acid - 36.60 0.23 0.32 109.00 kg Sulfuric acid production | sulfuric acid | APOS, U - row EcoInvent v.3.5 APOS Unit Processes 163 Process/ Flow Baseline Scrubber (Average data) Scrubber Hadlocon et al. (2015) Scrubber Zhao (2013) Scrubber Zhang et al. (2016a) Unit Provider Database Tap water - 0.56 2.75×10-3 3.70×10-3 1.66 m3 Market for tap water | tap water | APOS, U - CA-QC EcoInvent v.3.5 APOS Unit Processes Outputs Ammonia 9.64 2.41 2.41 2.41 2.41 kg Nitrous oxide 0.16 0.16 0.16 0.16 0.16 kg Nitric oxide 1.90 1.90 1.90 1.90 1.90 kg At the manure storage stage, roofed storage was considered as the baseline model. Biochar addition to stored manure was considered as an alternative strategy. For biochar addition to manure, the same life cycle inventory data for biochar production, as previously described for crop production, was employed. The rate of biochar application varies according to the manure properties and feedstock materials (Steiner et al. 2010; Kalus et al. 2019). For the current analysis, a rate of 5% of poultry litter by mass was assumed based on (Steiner et al. 2010). Life cycle inventory data for manure management in storage is provided in Table 5-9. Biochar addition to manure reduced emissions, resulting in more N-rich manure at the time of land application. 164 Table 5-9 LCI models and source databases used to model manure management in storage (baseline vs. biochar addition) Process/Flow Baseline Biochar addition Unit Provider Database Inputs Biochar - 57.00 kg Biochar production Gu and Bergman (2016) Poultry Manure 1140.00 1140.00 kg Pelletier (2017) Outputs Ammonia 6.97 2.61 kg Nitrous oxide 0.12 0.01 kg Methane 14.80 14.80 kg Nitric oxide 1.37 1.37 kg At the manure land application stage, manure spreading was applied in the baseline model. Incorporating manure was considered as an alternative strategy. Life cycle inventory data for manure land application are provided in Table 5-10. Incorporation was assumed to be via chisel plow based on (Hanna et al. 2014). 165 Table 5-10 LCI models and source databases used to model manure land application (baseline vs. incorporation) Process/Flow Baseline Incorporation Unit Provider Database Inputs N credit as nitrogen fertiliser, as N -11.50 -12.10 kg Nitrogen fertilizer mix- ON- CA EcoInvent v.3.5 APOS System Processes Poultry manure 1140.00 1140.00 kg Pelletier (2017) Tillage, cultivating, chiselling - 0.08 ha Tillage, cultivating, chiselling | tillage, cultivating, chiselling | APOS, U - CA-QC EcoInvent v.3.5 APOS Unit Processes Solid manure loading and spreading, by hydraulic loader and spreader 1140.00 1140.00 kg Solid manure loading and spreading, by hydraulic loader and spreader | solid manure loading and spreading, by hydraulic loader and spreader | APOS, U - CA-QC EcoInvent v.3.5 APOS Unit Processes Transport, freight, lorry 16-32 metric ton, EURO6 18.00 18.00 t*km Transport, freight, lorry 16-32 metric ton, EURO6 | transport, freight, lorry 16-32 metric ton, EURO6 | APOS, U - RER EcoInvent v.3.5 APOS Unit Processes Outputs Ammonia 1.63 0.61 kg Nitrous oxide 0.28 0.53 kg Nitrate 9.91 9.91 kg Nitric oxide 0.32 0.32 kg Phosphate 0.27 0.27 kg 166 The previously described life cycle inventories for the manure management stage were subsequently used in combination to model the potential combined effects of applying the strategies (Table 5-11). Life cycle inventories, including all background inventories, as well as data quality scores, are available as a Zolca file in the supplementary information. 167 Table 5-11 LCI models and source databases used to model the manure management stage assuming combined application of all strategies Process/Flow Baseline Reduced crude protein Acid scrubber Biochar to the manure Incorporation All strategies Unit Provider Database Inputs Biochar - - - 57.00 - 57.00 kg Biochar production Gu and Bergman (2016) Mechanical ventilation and belt electricity, medium voltage 217.00 217.00 217.00 217.00 217.00 217.00 kWh Market group for electricity, medium voltage | electricity, medium voltage | APOS, U - CA EcoInvent v.3.5 APOS Unit Processes Scrubber electricity, medium voltage - - 106.00 - - 76.20 kWh Market group for electricity, medium voltage | electricity, medium voltage | APOS, U - CA EcoInvent v.3.5 APOS Unit Processes N credit as nitrogen fertiliser, as N – from land application -11.50 -8.26 -11.50 -14.00 -12.10 -10.60 kg Nitrogen fertilizer mix- ON- CA EcoInvent v.3.5 APOS System Processes 168 Process/Flow Baseline Reduced crude protein Acid scrubber Biochar to the manure Incorporation All strategies Unit Provider Database N credit as nitrogen fertiliser, as N – from scrubber - - -5.94 - - -4.27 kg Nitrogen fertilizer mix- ON- CA EcoInvent v.3.5 APOS Unit Processes Poultry manure 1140.00 1140.00 1140.00 1140.00 1140.00 1140.00 kg Pelletier (2017) Solid manure loading and spreading, by hydraulic loader and spreader 1140.00 1140.00 1140.00 1140.00 1140.00 1140.00 kg Solid manure loading and spreading, by hydraulic loader and spreader | solid manure loading and spreading, by hydraulic loader and spreader | APOS, U - CA-QC EcoInvent v.3.5 APOS Unit Processes Process/Flow Baseline Reduced crude protein Acid scrubber Biochar to the manure Incorporation All strategies Unit Provider Database Sulfuric acid - - 36.60 - - 26.30 kg Sulfuric acid production | sulfuric acid | APOS, U - ROW EcoInvent v.3.5 APOS Unit Processes Tap water - - 0.56 - - 0.40 m3 169 Process/Flow Baseline Reduced crude protein Acid scrubber Biochar to the manure Incorporation All strategies Unit Provider Database Tillage, cultivating, chiselling - - - - 0.08 0.09 ha Tillage, cultivating, chiselling | tillage, cultivating, chiselling | APOS, U - CA-QC EcoInvent v.3.5 APOS Unit Processes Transport, freight, lorry 16-32 metric ton, EURO6 18.00 18.00 18.00 18.00 18.00 18.00 t*km Transport, freight, lorry 16-32 metric ton, EURO6 | transport, freight, lorry 16-32 metric ton, EURO6 | APOS, U - RER EcoInvent v.3.5 APOS Unit Processes Outputs Ammonia 18.20 13.10 11.00 14.20 17.20 4.14 kg Nitrous oxide 0.56 0.41 0.56 0.52 0.81 0.59 kg Methane 14.80 14.80 14.80 14.80 14.80 14.80 kg Nitrate 9.91 7.12 9.91 12.10 9.91 8.67 kg Nitric oxide 3.59 2.58 3.58 3.61 3.59 2.63 kg Phosphate 0.27 0.23 0.27 0.27 0.27 0.23 kg 170 Egg production Life cycle inventory data, models and source databases used to model production of one tonne of eggs assuming application of all mitigation strategies are provided in Table 5-12. Table 5-12 Life cycle inventory data, models, and source databases for the production of one tonne of eggs assuming application of all mitigation strategies Process/Flow Amount Unit Provider Database Inputs Diesel, burned in agricultural machinery 0.58 l Market for diesel, burned in agricultural machinery | diesel, burned in agricultural machinery | APOS, U - GLO EcoInvent v.3.5 APOS Unit Processes Feed - layer 2000.00 kg This process was modified for each strategy. For example, for all strategies, reduced crude protein diet supplemented with synthetic amino acids was considered, and feed crops were produced with the application of 4Rs approach and 5 tonnes/ha biochar addition to the soil. Heat, central or small-scale, natural gas 215.00 MJ Market for heat, central or small-scale, natural gas | heat, central or small-scale, natural gas | APOS, U - row EcoInvent v.3.5 APOS Unit Processes Heat, district or industrial, other than natural gas 1.57 MJ Heat production, light fuel oil, at industrial furnace 1MW | heat, district or industrial, other than natural gas | APOS, U - CA-QC EcoInvent v.3.5 APOS Unit Processes Petrol, unleaded, burned in machinery 0.60 l Market for petrol, unleaded, burned in machinery | petrol, unleaded, burned in machinery | APOS, U - GLO EcoInvent v.3.5 APOS Unit Processes 171 Process/Flow Amount Unit Provider Database Propane, burned in building machine 28.40 MJ Market for propane, burned in building machine | propane, burned in building machine | APOS, U - GLO EcoInvent v.3.5 APOS Unit Processes Pullets-conventional 56.00 Item(s) Pullets-conventional Pelletier (2017) Tap water 3.12 m3 Market for tap water | tap water | APOS, U - CA-QC EcoInvent v.3.5 APOS Unit Processes Flock mortality transport, freight, lorry 16-32 metric ton, EURO6 0.08 t*km Transport, freight, lorry 16-32 metric ton, EURO6 | APOS, U - RER EcoInvent v.3.5 APOS Unit Processes Feed transport, freight, lorry 16-32 metric ton, EURO6 110.00 t*km Transport, freight, lorry 16-32 metric ton, EURO6 | APOS, U - RER EcoInvent v.3.5 APOS Unit Processes Pullets transport, freight, lorry 16-32 metric ton, EURO6 11.20 t*km Transport, freight, lorry 16-32 metric ton, EURO6 | APOS, U - RER EcoInvent v.3.5 APOS Unit Processes Flock mortality transport, tractor and trailer, agricultural 1.00×10-3 t*km Transport, tractor and trailer, agricultural | transport, tractor and trailer, agricultural | APOS, S - row EcoInvent v.3.5 APOS Unit Processes Outputs Egg-conventional cage layer 1000.00 kg Mortality-conventional cage layer 1.78 Item(s) Poultry manure 1140.00 kg This process was modified for each strategy. For example, for application of all strategies, the application of an acid scrubber at the barn stage, as well as biochar addition to the stored manure were applied. At the land application stage, manure incorporation was applied. Spent hens 93.70 kg 172 5.3.2.2.2 Modeling Nr, P, and methane emissions from poultry manure in the barn, storage, and land application stages The application of suitable emissions models is crucial to increase the reliability of LCIA results (Zargar et al., 2020). Here, suitable emissions models for estimating Nr emissions were determined based on Zargar et al. (Chapter 4), which reviewed, characterized, and compared publicly available Nr emission models. Among the 50 models reviewed, IPCC Tier 2 emission models were identified as the most suitable option for the current study, due to their capacity to support simulating nitrous oxide, ammonia, nitrate, and nitric oxide emissions at the national scale (which is consistent with the application context of the current study). IPCC Tier 2 methods provide a series of equations for estimating emissions for both crop and livestock production systems. The equations are freely and easily accessible. They enable simulating emissions with an acceptable degree of accuracy, as well as using data that is generally available to LCA practitioners. The rate of nitrogen excretion by poultry is required to calculate the amount of emissions in the barn, storage, and land application stages of manure management. Nitrogen excretion rates were calculated as described in Pelletier (2017) via a nutrient mass balance model, with feed input nitrogen contents derived from www.feedipedia.org. The proportion of emitted nitrous oxide, ammonia and nitric oxide from the nitrogen excreted in barn vs. storage stages is unclear. IPCC Tier 2 provides emission factors for the combination of these two stages only. In order to consider barn and manure storage stages separately, it was assumed that emissions were divided equally between these two stages (i.e. 50% of total emissions were attributed to the barn stage and the other 50% to the storage stage). A sensitivity analysis was 173 thus conducted to evaluate the influence of this assumption on model results, compared to assuming a 25%/75% split, and vice versa. Emissions from manure land application were also assessed using IPCC (2006) Tier 2 default emission factors. The phosphorus excretion rate was calculated via a nutrient mass balance. Phosphorus emissions were calculated based on an assumed 2.9% run-off rate at the time of manure application following (Dalgaard et al. 2008). Methane emissions were assumed to be emitted 100% in manure storage, and were calculated following IPCC (2006) Tier 2 methods. 5.3.2.2.3 Uncertainty assessment In order to quantify uncertainty related to stochastic variability and data quality, a pedigree matrix was applied using the Ecoinvent data quality system in openLCA. Pedigree scores were applied to all foreground and background systems life cycle inventory data points. Monte Carlo (MC) simulations (1000 runs) were subsequently performed using openLCA software to quantify uncertainties for the LCIA results (95% confidence intervals). 5.3.2.3 Life cycle impact assessment The life cycle impact assessment (LCIA) stage analyzes and interprets LCI data in terms of environmental impacts across a relevant suite of impact categories (ISO 2006b, a). LCIA was implemented in openLCA software version 1.10.0, an open-source LCA modelling software created by Green Delta (available at http://www.openlca.org/download). Impact categories and models were selected based on a review of relevant published work on manure, fertilizer, N management, as well as LCA of livestock and poultry industries (Hasler 174 et al. 2015; Leinonen and Williams 2015; Eriksson et al. 2016; Mackenzie et al. 2016; Paolotti et al. 2016; Putman et al. 2017; Pelletier 2017, 2018). Table 5-13 Table 5-13 lists the considered impact categories. CML (baseline) [v4.4, January 2015], as well as the cumulative energy demand (CED) [v 1.0.0, January 2015] method, were applied. The climate change category in CML (baseline) [v4.4, January 2015] method, was changed to exclude characterization factors for biogenic carbon dioxide flows. This exclusion means that CO2 directly produced or taken up by living organisms in the short term did not contribute to the total GHG emissions balance of any product’s life cycle. Table 5-13 Selected life cycle impact assessment categories and category indicators Impact category Symbol Category indicator Acidification AC Kg SO2 equivalent Climate change – GWP100 GWP Kg CO2 equivalent Depletion of abiotic resources (fossil fuels) ADF MJ Energy use EnergyU MJ Eutrophication EU Kg PO4-3 equivalent Ozone layer depletion OLD Kg CFC-11 equivalent Photochemical oxidant formation PO Kg ethylene eq. 5.4 Results and discussion The comparative impacts of the baseline and alternative strategies on the nitrogen footprint and NUE outcomes at each stage of the egg production supply chain and per tonne of eggs produced are described in the following sections. In addition, the life cycle impact assessment results for NUE interventions at each stage of the Canadian egg supply chain are 175 provided. Section 5.4.1 reports the N footprint, NUE, and LCIA results at the feed input production stage for three scenarios, including no treatment (baseline) (FIP1), application of the 4Rs strategy (FIP2), and biochar addition to soil (FIP3). Section 5.4.2 reports the N footprint, NUE, and LCIA results at the feed formulation stage for two scenarios, including the baseline diet (FFO1) and a reduced crude protein with synthetic amino acids diet (FFO2). Section 5.4.3 reports the N footprint, NUE, and LCIA results at the manure management in the barn stage for two scenarios including the application of mechanical ventilation and belt (baseline) (MB1) and the addition of an acid scrubber to the baseline model (MB2). Section 5.4.4 reports the N footprint, NUE, and LCIA results at the manure storage stage for two scenarios, including the application of roofed storage (baseline) (MS1), and biochar addition to the stored manure (MS2). Section 5.4.5 reports the N footprint, NUE, and LCIA results at the manure management at the field application stage for two scenarios including manure spreading on land (baseline) (MF1), and manure incorporation following spreading (MF2). Section 5.4.6 reports the N footprint, NUE, and LCIA results for the entire egg production supply chains (i.e. per tonne of eggs produced) for eight scenarios as described in Table 5-2 5.4.1 Life cycle NUE and N footprint for feed input production NUE, N footprint, and N input and outputs were calculated per tonne of crop production. Table 5-14 shows the estimated NUE and N footprints for corn, soybean, and wheat production under the baseline and alternative scenarios. There were significant differences in NUE between the baseline and the 4Rs scenario, which are in general agreement with prior insights from literature (Herrera et al. 2016; Venterea et al. 2016). In corn production, 4Rs 176 application resulted in 3% higher NUE compared to the baseline. NUE with biochar incorporation to the soil showed no increase or increase of 1% compared to the baseline, depending on the rate of biochar application. Wheat production showed a range of NUEs similar to corn production. However, soybean production had a significantly higher NUE for both the baseline and the alternative strategies, mainly due to its potential for N fixation. Differences in NUE for the baseline versus alternative strategies were minor for soybean production since little exogenous N is applied in soy cultivation. Clearly, optimizing NUE for different crops will require different management strategies. The 4Rs approach cannot be simply prescribed for all cropping systems (Norton, 2014). Zheng et al. (2013) and Wang et al. (2017b) found that biochar addition to soil reduced N leaching and increased N availability in soils, thus, reducing the demand for N fertilizers for corn growth. Similar to these studies, we found that biochar application resulted in slightly different N footprints, depending on the crop, but these differences were very small. 177 Table 5-14 NUE and N footprint associated with 1 tonne of crop production (baseline vs. alternative strategies) N input* (kg) N output (kg) N footprint (kg) NUE for the strategy (%) NH3 N2O NO3- NO Corn production (Baseline) 17.60 2.60 0.49 3.21 0.38 3.35 81.00 Corn production (4Rs) 15.70 2.00 0.44 2.70 0.38 2.71 83.00 Corn production (Biochar 5 tonne/ha) 16.40 2.60 0.37 3.21 0.19 3.19 81.00 Corn production (Biochar 7.5 tonne/ha) 16.40 2.60 0.31 2.81 0.14 3.03 81.00 Corn production (Biochar 10 tonne/ha) 16.40 2.60 0.25 2.41 0.10 2.88 82.00 Soybean production (Baseline) 71.90 2.33 0.27 0.00 0.05 2.11 97.10 Soybean production (4Rs) 71.60 1.79 0.24 0.00 0.05 1.65 97.70 Soybean production (Biochar 5 tonne/ha) 71.70 2.33 0.20 0.00 0.03 2.06 97.10 Soybean production (Biochar 7.5 tonne/ha) 71.70 2.33 0.17 0.00 0.02 2.03 97.20 Soybean production (Biochar 10 tonne/ha) 71.70 2.33 0.14 0.00 0.01 2.01 97.20 178 N input* (kg) N output (kg) N footprint (kg) NUE for the strategy (%) NH3 N2O NO3- NO Wheat production (Baseline) 23.30 4.55 0.61 0.43 0.50 4.46 80.90 Wheat production (4Rs) 20.70 3.50 0.55 0.36 0.50 3.55 83.00 Wheat production (Biochar 5 tonne/ha) 21.70 4.55 0.46 0.43 0.25 4.25 80.40 Wheat production (Biochar 7.5 tonne/ha) 21.70 4.55 0.38 0.38 0.19 4.16 80.80 Wheat production (Biochar 10 tonne/ha) 21.70 4.55 0.31 0.32 0.13 4.07 81.00 * N input for corn and wheat production includes N fertilizer and for soybean production includes N fertilizer as well as biological N fixation (202 kg N he-1) To evaluate and compare the potential efficacy of each mitigation option on the basis of a broader suite of environmental indicators, LCIA results were considered alongside NUE and N footprints. The absolute LCIA results for each impact category for crop production with and without application of the alternative strategies are shown in Table 5-15 as well as in Figures 5-2 to 5-4. 179 Table 5-15 Life cycle impact assessment results for feed input production (per tonne of crop) applying the 4Rs strategy and biochar addition to the soil compared to the baseline Acidification potential Climate change - GWP100 Depletion of abiotic resources - fossil fuels Energy use Eutrophication Ozone Layer Depletion Photochemical Oxidation kg SO2 eq. kg CO2 eq. MJ MJ kg PO4-3 eq. mg CFC-11 eq. g ethylene eq. Corn production Baseline 5.34 347.00 2780.00 3810.00 1.72 60.00 58.00 4Rs approach 4.35 325.00 2680.00 3690.00 1.44 59.00 57.00 Biochar addition (allocation) 6.50 -3.60 7790.00 18000.00 1.97 120.00 160.00 Biochar addition (system expansion) 2.90 -1550.00 -12000.00 -3260.00 1.20 -120.00 -270.00 Soybean production Baseline 4.86 228.00 2010.00 2480.00 1.23 28.30 110.00 4Rs approach 3.99 219.00 2000.00 2460.00 1.03 28.20 110.00 Biochar addition (allocation) 8.48 -743.00 17600.00 46300.00 2.10 210.00 423.00 Biochar addition (system expansion) -2.70 -5490.00 -43100.00 -19000.00 -0.10 -530.00 -901.00 Wheat production 180 Acidification potential Climate change - GWP100 Depletion of abiotic resources - fossil fuels Energy use Eutrophication Ozone Layer Depletion Photochemical Oxidation kg SO2 eq. kg CO2 eq. MJ MJ kg PO4-3 eq. mg CFC-11 eq. g ethylene eq. Baseline 8.58 364.00 2260.00 2700.00 2.20 27.00 47.00 4Rs approach 6.85 336.00 2130.00 2500.00 1.79 26.00 45.00 Biochar addition (allocation) 11.80 -733.00 15300.00 43000.00 2.94 180.00 330.00 Biochar addition (system expansion) 0.94 -5320.00 -43300.00 -20000.00 0.79 -530.00 -950.00 181 For corn production, the application of 4Rs resulted in lower impacts compared to the baseline in all categories. Differences ranged from a 9% reduction (acidification category) to a 2% reduction (ozone layer depletion category). These reductions were due to the decrease in fertilizer rate (i.e. less fertilizer required) as well as associated emissions reductions. The results were similar to (Fallahpour et al. 2012), which showed lower environmental impacts with respect to global warming, acidification, and eutrophication potential in wheat and barley production under low consumption of N fertilizer. Additionally, Pelletier et al. (2008) found that organic crop production, which does not utilize synthetic N fertilizer, generated less global warming, ozone-depleting, and acidifying emissions than conventional production in Canada (Pelletier et al. 2008). Biochar addition to soil resulted in higher impacts compared to the baseline for some categories due to energy use for producing and transporting the biochar. However, it resulted in significantly lower impact for GWP (a reduction from 100% to -1%) compared to the baseline scenario. This was due to the effect of biochar on reducing nitrous oxide, nitric oxide, and nitrate emissions, as well as the biogenic carbon sequestered in the biochar itself. Nelissen et al. (2014) also reported that biochar application reduces nitrous oxide emissions by 52%-84% and nitric oxide emissions by 47%-67%. Biochar application is hence an effective mitigation option for reducing GHG emissions at the field level. However, other life cycle impacts, including with respect to emissions that may adversely affect human, may be negative when considered over the whole life cycle (Sparrevik et al. 2013). Choice of allocation strategy made a very large difference with respect to the estimated impacts of biochar production. Corn produced with biochar application had higher impacts in 182 all categories except GWP when allocation was used to partition the impacts of biochar production between biochar and syngas. In contrast, application of biochar produced substantial net benefits across all impact categories when the system expansion approach was employed. 183 Figure 5-2 Comparative LCIA results for corn production under baseline and alternative NUE strategies. For soybean production, the application of the 4Rs resulted in slightly lower impacts compared to the baseline for acidification (18%), climate change (4%), and eutrophication (16%). These reductions were mainly due to the decreased amount of ammonia, nitrous oxide, and nitrate emissions (10%, 23%, and 16%). Most impact category results were significantly lower compared to the biochar addition to soil scenario (allocation), except for 184 GWP, which was considerably higher. For the biochar addition to soil (system expansion) scenario, all impact assessment results were much lower. The greater differences between the baseline and biochar addition to soil scenarios for corn production compared to soybean production reflects both biological characteristics of soybeans and differences in yield. Soy is a leguminous plant able to fix atmospheric nitrogen; therefore, it requires only small amounts of synthetic N fertilizer. This decreases energy and material consumption associated with soybean production compared to corn production. However, the production of one tonne of soybean requires roughly three times as much land. Based on the assumed rate of biochar application (5 tonne ha-1), soybean production consumed three times more biochar than corn production. 185 186 Figure 5-3 Comparative LCIA results for soybean production under baseline and alternative NUE strategies. For wheat production, the application of 4Rs also resulted in lower impacts, compared to the baseline scenario in all categories. This ranged from a 20% reduction for acidification to a 4% reduction for photochemical oxidation. The effects were more significant for wheat production compared to soybean and corn production. This was primarily due to differences in the amount of fertilizer applied. Producing one tonne of wheat requires 23.3 kg of N fertilizer, whereas corn requires 17.6 kg N fertilizer. The 4Rs strategy was assumed to reduce N fertilizer by 11%, which translated to a 2.56 kg reduction in N fertilizer use for wheat production compared to 1.94 kg for corn production. This resulted in fewer upstream impacts from fertilizer production for wheat as well as a corresponding decrease in emissions. The effect of biochar addition to soil compared to the baseline for wheat production was similar to that observed for corn and soybean production. 187 188 Figure 5-4 Comparative LCIA results for wheat production under baseline and alternative NUE strategies. 5.4.2 Life cycle NUE and N footprint for feed formulation For the purpose of estimating changes in NUE for the baseline compared to the reduced crude protein with synthetic amino acids feed scenario, the N content of feed was considered as the N input and the amount of N excreted by hens per tonne of eggs produced was the N output. Each tonne of eggs requires two tonnes of feed as input. NUE, N footprint, N input and outputs per tonne of eggs are provided in Table 5-16. Dietary crude protein reduction reduces N excretion; consequently, the N footprint was lower for the alternative scenario. Arriaga et al. (2010), Ji et al. (2014), Hou et al. (2015) and Naseem and King (2018) found similar results. In this study, the reduced crude protein feed improved NUE by 8% and reduced the N footprint by 9.05 kg per tonne of eggs produced. 189 Table 5-16 NUE and N footprint of feed production for the baseline and reduced crude protein feeds (based on the amount of feed required to produce 1 tonne of eggs, and the associated level of N excretion) N input (kg) N output (kg) N footprint (kg) NUE (%) Control diet (Baseline) 49.10 32.20 32.20 34.00 Reduced crude protein diet with synthetic amino acids 40.10 23.10 23.10 42.00 Life cycle energy use and emissions vary widely between the inputs sourced for layer hen feed in the Canadian egg supply chain 190 Table 5-17. In general, the production of amino acids, including DL-Methionine, L-Lysine, and L-Threonine, required a high amount of energy compared to other feed inputs on a mass basis. Inputs levels for synthetic amino acids included in the alternative feed varied from very low (i.e. DL-Methionine – 1.24 kg and L-Lysine – 0.82 kg) to quite high (L-Threonine – 16.43). 191 Table 5-17 Life cycle impact assessment results at the feed mill gate per tonne of feed input sourced for layer feeds Acidification potential Climate change Depletion of abiotic resources Energy use Eutrophication Ozone Layer Depletion Photochemical Oxidation kg SO2 eq. kg CO2 eq. MJ MJ kg PO4-3 eq. mg CFC-11 eq. g ethylene eq. Canola oil 21.30 1010.00 7990.00 7660.00 7.48 69.30 570.00 Corn 5.34 347.00 2780.00 3810.00 1.72 59.80 60.00 DL-Methionine 7.45 3010.00 76200.00 89400.00 1.77 170.00 550.00 L-Lysine 22.30 388.00 50700.00 118000.00 14.80 360.00 570.00 L-Threonine 22.30 388.00 50700.00 118000.00 14.80 360.00 570.00 Limestone 0.03 3.85 52.30 57.50 0.01 0.63 0.00 Monocalcium phosphate 18.00 1430.00 22600.00 26700.00 8.15 180.00 800.00 Premix 15.40 960.00 19600.00 23500.00 2.07 120.00 650.00 Sodium Bicarbonate 13.30 1410.00 13200.00 17100.00 4.10 63.70 470.00 Salt 1.39 242.00 2510.00 3410.00 0.76 13.60 60.00 Soya meal 4.04 204.00 1710.00 2310.00 1.08 23.80 90.00 Wheat 8.58 364.00 2260.00 2690.00 2.20 27.40 50.00 192 The contribution of different feed ingredients to the life cycle impacts per tonne of feed produced for the baseline diet and reduced crude protein with synthetic amino acids feed is presented in Table 5-18. The reduced crude protein feed had higher impacts in all impact categories except for photochemical oxidation. This is primarily attributable to the high upstream emissions from amino acid production (despite their low inclusion rate), and the larger amount of wheat in the diet (which has a higher impact than corn and soy). The proportion of wheat in the reduced CP diet is almost three times higher than in the baseline diet. These results align with previous work by Mosnier et al. (2011) and Garcia-Launay et al. (2014), who evaluated the effects of amino acids in pig/broiler and pig diets, respectively. Amino acids contributed strongly to the acidification, global warming, eutrophication, and cumulative energy demand impact categories (Mosnier et al. 2011; Garcia-Launay et al. 2014). In other research conducted by Ogino et al. (2013), crude protein was reduced from 171 kg to 140 kg for growing pigs through use of amino acids. This increased energy demand by 39%. Mosnier et al. (2011) noted that, despite the high impact associated with amino acid production, they have a low contribution to impact categories for broiler and pig feed due to their low inclusion rate. Inclusion rates were higher in the reduced crude protein diet modelled in the current analysis; therefore, the relative contribution of the synthetic amino acids to estimated impacts was correspondingly higher. 193 Table 5-18 Life cycle impact assessment results (per tonne of feed produced) for the baseline and reduced crude protein supplemented with synthetic amino acids (RCP) feeds. Acidification potential Climate change - GWP100 Depletion of abiotic resources - fossil fuels Energy use Eutrophication Ozone Layer Depletion Photochemical Oxidation kg SO2 eq. kg CO2 eq. MJ MJ kg PO4-3 eq. mg CFC-11 eq. g ethylene eq. Control RCP Control RCP Control RCP Control RCP Control RCP Control RCP Control RCP Canola oil 0.96 0.87 45.50 41.00 360.00 324.00 345.00 311.00 0.34 0.30 3.12 2.81 25.60 23.00 Corn 2.50 1.96 162.00 127.00 1300.00 1020.00 1780.00 1400.00 0.80 0.63 28.00 22.00 27.20 21.40 DL-Methionine NA 0.01 NA 3.85 NA 97.40 NA 114.00 NA 0.00 NA 0.21 NA 0.70 L-Lysine NA 0.02 NA 0.32 NA 41.60 NA 96.60 NA 0.01 NA 0.30 NA 0.46 L-Threonine NA 0.37 NA 6.38 NA 833.00 NA 1930.00 NA 0.24 NA 5.96 NA 9.30 Limestone 0.00 0.00 0.38 0.41 5.21 5.59 5.73 6.14 0.00 0.00 0.06 0.07 0.13 0.14 Monocalcium phosphate 0.17 0.19 13.50 14.90 212.00 235.00 251.00 278.00 0.08 0.08 1.70 1.88 7.49 8.29 Premix 0.40 0.11 25.10 6.63 512.00 135.00 615.00 163.00 0.05 0.01 3.17 0.84 16.90 4.47 Sodium Bicarbonate 0.02 0.02 2.49 1.98 23.30 18.60 30.10 24.00 0.01 0.01 0.11 0.09 0.83 0.66 Salt 0.00 0.00 0.61 0.66 6.32 6.88 8.59 9.34 0.00 0.00 0.03 0.04 0.16 0.17 Soya meal 1.00 0.61 50.50 30.60 424.00 257.00 573.00 347.00 0.27 0.16 5.88 3.56 23.40 14.20 Wheat 0.86 2.53 36.40 107.00 226.00 667.00 269.00 792.00 0.22 0.65 2.74 8.07 4.68 13.80 194 The comparative life cycle impact assessment results for the control diet and reduced crude protein diet supplemented with synthetic amino acids are shown in Figure 5-5. The error bars represent the standard error. 195 Figure 5-5 Comparative LCIA results for control diet and reduced crude protein diet supplemented with synthetic amino acids 5.4.3 Life cycle NUE and N footprint for manure management: Barn N input at the barn stage was equal to the N excreted, and N output included all N emissions emitted into the air. NUE, N footprint, N input and outputs per tonne of egg production are provided in Table 5-19. The scrubber application increased NUE at this stage by 19%, relative to the baseline by capturing ammonia from the exhaust (subsequently available as fertilizer) (Hadlocon et al. 2015b). This strategy also decreased the N footprint in the barn by 66%. Table 5-19 NUE and N footprint associated with manure management in the barn under the baseline and alternative NUE (scrubber) scenario N input (kg) N output (kg) N footprint (kg) NUE (%) NH3 N2O NO3- NO Control diet Ventilation and belt 32.20 9.63 0.16 0.00 1.89 8.90 72.00 Scrubber 32.20 2.40 0.16 0.00 1.89 2.97 91.00 Reduced crude protein diet Ventilation and belt 23.10 6.92 0.12 0.00 1.36 6.39 72.00 Scrubber 1.73 0.12 0.00 1.36 2.14 2.14 91.00 The LCIA results for the baseline and alternative strategy at the barn stage of manure management are provided in Table 5-20. Compared to the baseline strategy, the application of the acid scrubber reduced acidification, eutrophication, and ozone layer depletion impacts 196 by 72%, 69%, and 46%, respectively. However, it considerably increased photochemical oxidation, energy use, depletion of abiotic resources – fossil fuel, and global warming impacts – largely due to electricity consumption and, to a lesser extent, sulfuric acid consumption by the scrubber. However, the nitrogen credit associated with ammonia captured by the scrubber (assumed to be made available as fertilizer) offset the majority of emissions associated with the electricity and sulfuric acid consumption. The use of the acid scrubber has previously been recognized as a promising strategy to reduce ammonia emissions as well as to recover ammonia for fertilizer (Hadlocon et al. 2015b; Szogi et al. 2015). However, this creates trade-offs with respect to material/energy use and associated emissions. Table 5-20 Comparative LCIA results for the baseline and alternative (an acid scrubber) manure management strategy in the layer hen barn per tonne of eggs produced (1140 kg manure). Acidification potential Climate change Depletion of abiotic resources Energy use Eutrophication Ozone Layer Depletion Photochemical Oxidation kg SO2 eq. kg CO2 eq. MJ MJ kg PO4-3 eq. mg CFC-11 eq. g ethylene eq. Mechanical ventilation and belt (baseline) 15.60 91.90 446.00 1620.00 3.59 1.73 7.40 Scrubber (average data) 4.31 95.80 590.00 2320.00 1.13 0.92 19.00 197 Figure 5-6 shows the comparative life cycle impact assessment results for manure management in the barn for the baseline and alternative NUE strategy (acid scrubber). 198 Figure 5-6 Comparative LCIA results for manure management in the barn for the baseline and alternative (acid scrubber) NUE strategy 5.4.4 Life cycle NUE and N footprint for manure management: Storage N footprint, N input and outputs for manure storage per tonne of eggs produced under the baseline (roofed storage) and alternative (biochar addition to stored manure) scenarios are provided in Table 5-21. Here, biochar addition increased NUE by 16% relative to the baseline and reduced the N footprint at this stage by 57%. This was mainly due to reduced ammonia and nitrous oxide emissions (Steiner et al. 2010; Chowdhury et al. 2014; Jia et al. 2015). 199 Table 5-21 NUE and N footprint during manure storage for the baseline and alternative NUE strategies N input (kg) N output (kg) N footprint (kg) NUE (%) NH3 N2O NO3- NO Control diet Roofed storage 23.30 6.97 0.12 0.00 1.37 6.44 72.00 BC addition to manure 23.30 2.61 0.01 0.00 1.37 2.79 88.00 Reduced crude protein diet Roofed storage 16.70 5.01 0.08 0.00 0.98 4.63 72.00 BC addition to manure 16.70 1.87 0.01 0.00 0.98 2.01 88.00 The LCIA results for the baseline and alternative strategy at the manure storage stage are provided in Table 5-22 and Figure 5-7 Table 5-22. Compared to the baseline strategy (roofed storage), biochar addition to the stored manure resulted in lower impacts for acidification (from 60% to 70% lower), climate change (from 447% to 1775% lower) and eutrophication (from 60% to 70% lower) depending on the allocation approach in the biochar production model. The lower impacts were because of the effect of biochar on reducing ammonia and nitrous oxide emissions as well as the biogenic C sequestered in the biochar. The upstream impacts of producing biochar (allocation) substantially increased impacts with respect to the depletion of abiotic resources – fossil fuels, energy use, ozone layer depletion, and photochemical oxidation in energy allocation scenario, but reduced impacts in the system expansion scenario. The reference scenario (roofed storage) had zero emissions for the depletion of abiotic resources, 200 energy use, ozone layer depletion, and photochemical oxidation, due to having no associated energy and fuel consumption. Table 5-22 Comparative LCIA results for the baseline and alternative (biochar addition to manure) manure management strategies at the storage per tonne of eggs produced (1140 kg manure). Acidification potential Climate change Depletion of abiotic resources Energy use Eutrophication Ozone Layer Depletion Photochemical Oxidation kg SO2 eq. kg CO2 eq. MJ MJ kg PO4-3 eq. mg CFC-11 eq. g ethylene eq. Roofed storage (baseline) 11.20 35.20 0.00 0.00 2.47 0.00 0.00 Biochar addition (allocation) 4.46 -118.00 1110.00 3880.00 0.99 0.01 26.2 Biochar addition (system expansion) 3.36 -589.00 -4900.00 -2600.00 0.77 -0.06 -105 201 Figure 5-7 Comparative LCIA results for manure storage for the baseline and alternative (biochar addition to manure) NUE strategy 202 5.4.5 Life cycle NUE and N footprint for manure management: Field application N footprint, N input and outputs associated with manure land application per tonne of eggs produced under the baseline (surface spreading) and alternative (incorporation following spreading) NUE strategies are presented in Table 5-23. Incorporation had 4% higher NUE and reduced the N footprint by 17% compared to the baseline scenario. The results were mainly due to the effects of incorporation on ammonia emissions. This finding is consistent with that of Smith et al. (2009) and (Webb et al. 2005), who indicated incorporation was the best ammonia mitigation option compared to surface application. However, nitrous oxide emissions increased with this strategy. The reported effects of manure incorporation on N2O loss are mixed. In conditions with intense denitrification, incorporation reduced N2O emissions and vice versa (Thorman and Chadwick 2008; Chadwick et al. 2011). Table 5-23 NUE and N footprint per tonne of eggs produced associated with manure management at time of land application under the baseline (surface spreading) and alternative (incorporation following spreading) scenarios. N input (kg) N output (kg) N footprint (kg) NUE (%) NH3 N2O NO3- NO Control diet Spreading (MF1) 16.80 1.63 0.28 9.91 0.32 3.91 77.00 Incorporation (MF2) 16.80 0.61 0.53 9.91 0.32 3.23 81.00 Reduced crude protein diet Spreading (MF1) 12.10 1.17 0.20 7.13 0.23 2.81 77.00 Incorporation (MF2) 12.10 0.43 0.38 7.13 0.23 2.32 81.00 203 The LCIA results for the baseline and alternative strategy (incorporation) at the field application stage are provided in Table 5-24 and Figure 5-8Table 5-24. Compared to the baseline strategy (surface application only), manure incorporation resulted in lower impacts in the acidification and eutrophication categories (65%, and 15%). However, it considerably increased global warming potential (138%) and slightly increased energy consumption (5%) compared to manure spreading due to higher nitrous oxide emissions as well as additional fuel consumption. Manure land application was found to be one of the main contributors to global warming emissions from swine manure management (Zhong et al. 2013). No information was found regarding the effects of different manure land application techniques from a life cycle perspective. Table 5-24 Comparative LCIA results for the baseline and alternative manure land application techniques (incorporation) per tonne of eggs produced (1140 kg manure). Acidification potential Climate change Depletion of abiotic resources Energy use Eutrophication Ozone Layer Depletion Photochemical Oxidation kg SO2 eq. kg CO2 eq. MJ MJ kg PO4-3 eq. mg CFC-11 eq. g ethylene eq. Manure spreading (baseline) 2.47 55.30 -446.00 -530.00 1.87 -3.96 -4.97 Manure incorporation 0.86 131.00 -424.00 -505.00 1.58 -3.68 -3.92 204 Figure 5-8 Comparative LCIA results for manure management at time of field application for the baseline and alternative (incorporation) strategies 205 5.4.6 Life cycle NUE and N footprint for egg production under baseline and alternative scenarios The life cycle NUE and N footprints per tonne of eggs produced under the baseline and alternative scenarios are shown in Table 5-25. In this study, life cycle NUE for egg production for the baseline scenario was 67%, and ranged from 59% to 82%, depending on the alternative strategy applied. According to Hristov et al. (2006), Powell et al. (2010), and Gourley et al. (2012b), whole- dairy farm NUE varied from 8% to 64%, and is impacted by a range of farming practices. It is worth noting that NUE for egg and chicken production is generally higher than for milk production (Groenestein et al. 2019). The largest gain in NUE among scenarios was achieved by the application of all strategies combined (82%), followed by the use of the scrubber (80%). The application of all strategies and scrubber application in the barn showed 15% and 13% higher NUE compared to the reference scenario. Implementation of all strategies also resulted in the lowest N footprint, followed by scrubber use. The reduced crude protein diet supplemented with synthetic amino acids decreased life cycle NUE to 59% per tonne of eggs as well as increased the N footprint considerably compared to the baseline scenario, despite generating significant improvement in NUE at the feed formulation stage. This was mainly due to the increased amount of wheat in the diet, which had lower NUE compared to the soy and corn in the baseline feed. Wheat production requires more N fertilizer per tonne of crop produced. 206 Table 5-25 Life cycle NUE and N footprints for the baseline and alternative scenarios per tonne of eggs produced. Negg: N content in 1 tonne egg, NM: N content in manure on field after emissions, Ns: N in spent hens. Scenario # N input (kg) N retained (kg) N footprint (kg) NUE (%) Negg NM NS Ncredit S1. Baseline/reference scenario 47.40 17.00 12.90 2.06 0.00 24.60 67.00 S2. 4Rs approach 45.00 17.00 12.90 2.06 0.00 23.60 71.00 S3. Biochar addition to the soil 45.90 17.00 12.90 2.06 0.00 24.40 70.00 S4. Reduced crude protein diet supplemented with amino acids 47.70 17.00 9.28 2.06 0.00 19.90 59.00 S5. An scrubber application in the barn 47.40 17.00 12.90 2.06 5.94 18.60 80.00 S6. Biochar addition to the stored manure 47.40 17.00 15.70 2.06 0.00 21.80 73.00 S7. Manure land incorporation 47.40 17.00 13.60 2.06 0.00 23.90 69.00 S8. All strategies 43.10 17.00 11.90 2.06 4.27 11.70 82.00 207 LCIA results for the baseline scenario compared to the alternative scenarios are provided in Table 5-26 as well as in Figure 5-9. 208 Table 5-26 Comparative LCIA results for the baseline and alternative NUE strategies for 1 tonne of egg production. Acidification potential Climate change Depletion of abiotic resources Energy use Eutrophication Ozone Layer Depletion Photochemical Oxidation kg SO2 eq. kg CO2 eq. MJ MJ kg PO4-3 eq. mg CFC-11 eq. g ethylene eq. S1. Baseline/ reference scenario 48.80 1530.00 8280.00 12000.00 14.10 110.00 360.00 S2. 4Rs approach 47.30 1500.00 8170.00 11900.00 13.70 110.00 350.00 S3. Biochar addition to the soil – allocation 51.80 662.00 20800.00 48200.00 14.80 260.00 610.00 S3. Biochar addition to the soil – system expansion 42.40 -3300.00 -29800.00 -6360.00 12.90 -360.00 -490.00 S4. Reduced crude protein diet supplemented with amino acids 42.70 1510.00 9490.00 15200.00 12.80 120.00 340.00 S5. An scrubber application in the barn 38.50 1540.00 8420.00 12700.00 11.90 110.00 370.00 S6. Biochar addition to the stored manure (allocation) 43.10 1400.00 9190.00 15500.00 13.10 120.00 380.00 209 Acidification potential Climate change Depletion of abiotic resources Energy use Eutrophication Ozone Layer Depletion Photochemical Oxidation kg SO2 eq. kg CO2 eq. MJ MJ kg PO4-3 eq. mg CFC-11 eq. g ethylene eq. S6. Biochar addition to the stored manure (system expansion) 42.10 967.00 3650.00 9520.00 12.90 57.10 260.00 S7. Manure land incorporation 47.30 1600.00 8300.00 12100.00 13.90 110.00 360.00 S8. All strategies (allocation) 31.50 366.00 24400.00 60400.00 10.50 290.00 660.00 S8. All strategies (system expansion) 19.60 -4680.00 -40100.00 -9100.00 8.13 -490.00 -750.00 210 For acidification potential, the application of all strategies (S8) reduced impacts by 60% and 35% when considering biochar production modelled using energy allocation and system expansion, respectively. Application of an acid scrubber (S5) was observed to have the second-largest SO2 reduction (21%) followed by biochar addition to the stored manure (S6). The latter resulted in a 12% to 14% reduction based on biochar addition (allocation) and (system expansion). From the detailed inventory analysis, the most significant contributor to this impact category was ammonia emissions, followed by sulfur dioxide emissions. Therefore, to reduce acidification impacts in the egg supply chain, the main focus should be to control ammonia emissions, which is also consistent with efforts to improve NUE. For global warming potential, the application of all strategies (S8) showed a large reduction in GWP, regardless of whether allocation or system expansion were applied in the biochar model. However, the reduction was more prominent for system expansion (406% lower than the baseline) as compared to allocation (76% lower than the baseline). Both biochar addition to the soil (S3) and to the stored manure (S6) showed a significant reduction in estimated GHG emissions. In contrast, incorporating manure to the land (S7) increased CO2 emissions by 5% compared to the baseline. This is because of the higher fuel consumption needed to incorporate manure as well as increased N2O emissions. Carbon dioxide, nitrous oxide and methane were responsible for the majority of global warming potential impacts. From the detailed inventory analysis, the main contributors to these results were nitrogen fertilizer, diesel combusted in machinery and electricity use. Overall, the reference scenario (S1) had 37% lower CO2 emissions compared to estimated emissions for the conventional hen housing model in Pelletier (2017), largely due to differences in feed 211 formulations. In addition, the global warming potential (1531.87 kg CO2 eq. per tonne of eggs) was also found to be much lower than has been reported in other studies such as Ghasempour and Ahmadi (2016) (4070 kg CO2 eq. per tonne of eggs in Iran), and Dekker et al. (2008) (4000 kg CO2 eq. per tonne of organic eggs and 2470 kg CO2 eq. per tonne of conventional eggs in the Netherlands.) For depletion of abiotic resources, application of the 4Rs approach (S2) made only a small difference (1% reduction) compared with the reference scenario (S1) when considered per tonne of eggs produced. However, since the application of each of the other mitigation options increased life cycle fossil fuel consumption, the alternative scenarios (S2-S8) resulted in higher abiotic depletion compared to the reference scenario (S1). Only biochar application showed marked benefits with respect to net abiotic depletion potential under the system expansion scenario. For the energy use category, results were similar to those for abiotic resources depletion potential. The 4Rs strategy (S2) reduced life cycle energy consumption by only 1% due to the lower N fertilizer input. The alternative scenarios all (S2-S8) resulted in higher energy use. Strategies depending on biochar application including (S8), (S3) and (S6), there was beneficial energy production, while a system expansion approach was considered. Biochar production was either the main source of energy consumption (allocation) or net cumulative energy demand benefits (system expansion) in scenarios (S3), (S6), and (S8). The reduced crude protein diet also increased the cumulative energy demand of egg production considerably (i.e. by 26% compared to the control diet), while application of the scrubber (S5) increased life cycle energy use by 5%. Comparison of the energy use with Pelletier 212 (2017), showed a 7% increase in energy consumption in the reference scenario due to the different diet formulation. For eutrophication potential, application of all strategies (S8) resulted in the lowest impact, regardless of whether energy allocation or system expansion was applied in the biochar model. The second highest eutrophication reduction was associated with use of the acid scrubber in the barn. Scrubber application reduced the life cycle eutrophication potential of egg production by 16% compared to the baseline. The main contributors to this impact category were ammonia and nitrate emissions, followed by phosphate emissions. The reduced crude protein diet scenario (S4) reduced eutrophying impacts by 10% compared to S1. The reference scenario had 42% lower eutrophication potential then was reported in Pelletier (2017), largely because animal products were not included in the feed formulations in the current study. For ozone layer depletion potential, the amount of CFC-11 equivalent was very small for the baseline scenario (1.10E-04 kg CFC-11 eq.). Hence, no differences were observed when the 4Rs approach, acid scrubber, and manure incorporation were implemented. The emissions were more significant in scenarios using gas and petroleum, which resulted in methane emissions. The reduced crude protein diet scenario (S4) increased life cycle ozone depleting emissions by 9% compared to the reference scenario (S1), mainly due to the greater use of pesticides and machinery for the production of wheat in the diet, as well as the production of the synthetic amino acids. Agricultural machinery, electricity, and pesticides accounted for the largest share of ozone layer depletion impacts. The application of biochar showed the same trends for the energy use and depletion of abiotic resource impact 213 categories. The reference scenario had 1.10E-04 kg CFC-11 eq. emissions per tonne of eggs, which was quite similar to Ghasempour and Ahmadi (2016) (1.22E-04 kg CFC-11 eq. emissions). For the photochemical oxidation impact category, the impacts of the various strategies on emissions were insignificant. Application of the reduced crude protein diet scenario (S4) showed a 5% emissions reduction. On the other hand, the application of scrubbers in the barn (S5) increased emissions by 3%. The most relevant contributors were emissions of carbon monoxide (CO), sulphur dioxide (SO2), nitrogen oxide (NO), ammonium and non-methane volatile organic compounds. 214 48.81 47.3151.7642.40 42.7138.4343.14 42.1247.3331.4919.580.0010.0020.0030.0040.0050.0060.00S1.ReferencescenarioS2 S3(allocation)S3 (systemexpansion)S4 S5 S6(allocation)S6 (systemexpansion)S7 S8. Allstrategies(allocation)S8. Allstrategies(systemexpansion)kg SO2 eq.Acidification potential 1,531.87 1,504.33661.86-3,301.621,507.69 1,535.63 1,399.51966.531,601.92365.53-4,681.75-5,000.00-4,000.00-3,000.00-2,000.00-1,000.000.001,000.002,000.00S1.ReferencescenarioS2 S3(allocation)S3 (systemexpansion)S4 S5 S6(allocation)S6 (systemexpansion)S7 S8. Allstrategies(allocation)S8. Allstrategies(systemexpansion)kg CO2 eq.Climate change - GWP1008,283.63 8,166.9020,847.50-29,832.709,493.90 8,417.72 9,189.383,652.958,304.2824,414.70-40,123.80-45,000.00-30,000.00-15,000.000.0015,000.0030,000.0045,000.00S1.ReferencescenarioS2 S3(allocation)S3 (systemexpansion)S4 S5 S6(allocation)S6 (systemexpansion)S7 S8. Allstrategies(allocation)S8. Allstrategies(systemexpansion)MJDepletion of abiotic resources - fossil fuels 215 12,048.40 11,912.3048,199.50-6,358.5215,164.50 12,693.50 15,480.70 9,520.68 12,071.2060,372.40-9,104.30-20,000.000.0020,000.0040,000.0060,000.0080,000.00S1.ReferencescenarioS2 S3(allocation)S3 (systemexpansion)S4 S5 S6(allocation)S6 (systemexpansion)S7 S8. Allstrategies(allocation)S8. Allstrategies(systemexpansion)MJEnergy use14.11 13.7214.7912.93 12.7711.8513.06 12.8613.8510.508.130.004.008.0012.0016.0020.00S1.ReferencescenarioS2 S3(allocation)S3 (systemexpansion)S4 S5 S6(allocation)S6 (systemexpansion)S7 S8. Allstrategies(allocation)S8. Allstrategies(systemexpansion)kg PO4-3 eq.Eutrophication110.00 110.00260.00-360.00120.00 110.00 120.0057.09110.00290.00-490.00-600.00-400.00-200.000.00200.00400.00600.00S1.ReferencescenarioS2 S3(allocation)S3 (systemexpansion)S4 S5 S6(allocation)S6 (systemexpansion)S7 S8. Allstrategies(allocation)S8. Allstrategies(systemexpansion)mg CFC-11 eqOzone layer depletion 216 Figure 5-9 Comparative LCIA results for 1 tonne of egg production for the baseline and alternative NUE strategies. Scenario 2: application of 4Rs strategy; Scenario 3: Biochar addition to soil; Scenario 4: Reduced crude protein diet; Scenario 5: application of scrubbers in the barn; Scenario 6: Biochar addition to the stored manure; Scenario 7: Manure incorporation; Scenario 8: Application of all strategies. A general insight is that most scenarios improve NUE, and reduce acidification and eutrophication impacts to varying degrees, which is consistent with the goal of improved nitrogen management. However, while some strategies also reduce GHG emissions, they are typically associated with higher depletion of abiotic resources, ozone layer depletion, and photochemical oxidation impacts (all of these are related to the use of more energy in order to reduce nitrogen losses). As shown, there are clear trade-offs among impact categories when applying all or various strategies. This is demonstrated, for example, when considering application of all scenarios simultaneously, where acidifying, eutrophying, and GHG emissions were reduced substantially, but impacts were higher in other categories. Application of scrubbers in the barn (S5) demonstrated the highest reduction in acidifying and eutrophying potential (21% and 16%, respectively) with respect to single strategies. 355.93 354.41612.33-492.98339.91 366.82 378.52257.77356.89662.40-745.15-1000.00-500.000.00500.001000.001500.00S1.ReferencescenarioS2 S3(allocation)S3 (systemexpansion)S4 S5 S6(allocation)S6 (systemexpansion)S7 S8. Allstrategies(allocation)S8. Allstrategies(systemexpansion)g ethylene eq.Photochemical oxidation 217 Simultaneous application of all strategies (S8) resulted in 76%, 35%, and 26% reductions in GHG, acidifying and eutrophying potential. On the other hand, this resulted in 195%, 401% and 164% increase in abiotic depletion, energy use, and ozone layer depletion, respectively. It is worth mentioning the importance of integrating renewable energy systems to support these strategies along the supply chain, since this would likely create mutually beneficial outcomes across all impact categories. Figure 5-10 shows the comparative LCIA of one tonne of eggs produced, including reference and alternative scenarios. All impact category results are scaled relative to the reference scenario results. 218 Figure 5-10 Comparative LCIA results for 1 tonne of eggs produced relative to the baseline. Scenario 2: application of 4Rs strategy; Scenario 3: Biochar addition to soil; Scenario 4: Reduced crude protein diet; Scenario 5: application of scrubbers in the barn; Scenario 6: Biochar addition to the stored manure; Scenario 7: Manure incorporation; Scenario 8: Application of all strategies. -500%-400%-300%-200%-100%0%100%200%300%400%500%S1. Baseline S2.Applicationof 4RsstrategyS3. Biocharaddition tothe soil(allocation)S3. Biocharaddition tothe soil(systemexpansion)S4. Reducedcrude proteindietS5.Applicationof an acidscrubber inthe barnS6. Biocharaddition tothe storedmanure(allocation)S6. Biocharaddition tothe storedmanure(systemexpansion)S7. ManurelandincorporationS8.Applicationof allstrategies(allocation)S8.Applicationof allstrategies(systemexpansion)PercentAcidification potential Climate change - GWP100 Depletion of abiotic resources - fossil fuelsEnergy use Eutrophication Ozone layer depletionPhotochemical oxidation 219 5.4.7 Sensitivity analyses 5.4.7.1 Rate of biochar addition to soil The literature indicates a wide range of potential biochar addition rates to agricultural soil. LCIA results for feed input production assuming biochar application rates of 5, 7.5, and 10 tonnes ha-1 are provided in Table 5-27. The lowest impacts derived from the application of 5 tonnes ha-1 in all impact categories, under the energy allocation scenario. Higher application rates affected all impact categories negatively, except GWP. The impact was most pronounced in the energy use and climate change category. In corn production, energy use was 39% and 79% higher for 7.5 and 10 tonnes ha-1 biochar addition compared to the 5 tonne ha-1 biochar addition scenario. Impacts were similarly higher for ozone layer depletion (25% and 50%) and photochemical oxidation (31% and 62%). The higher rates of application did not increase emissions in the acidification and eutrophication impact categories substantially, mainly due to the potential of biochar to reduce N emissions at the higher rates. The effects of greater application rates were more significant in soybean and wheat production compared to corn production, mainly due to the larger land area that was requiring more biochar and energy consumption for biochar land application. 220 Table 5-27 LCIA results for feed input production under different assumed rates of biochar application (5, 7.5 and 10 tonnes biochar per ha) Acidification potential Climate change Depletion of abiotic resources Energy use Eutrophication Ozone Layer Depletion Photochemical Oxidation kg SO2 eq. kg CO2 eq. MJ MJ kg PO4-3 eq. mg CFC-11 eq. g ethylene eq. Corn production 5 tonne/ha 6.50 -3.60 7790.00 18000.00 1.97 120.00 160.00 7.5 tonne/ha 7.10 -181.00 10300.00 25000.00 2.10 150.00 210.00 10 tonne/ha 7.60 -358.00 12800.00 32000.00 2.10 180.00 260.00 Soybean production 5 tonne/ha 8.48 -743.00 17600.00 46300.00 2.10 210.00 423.00 7.5 tonne/ha 10.20 -1240.00 25200.00 68100.00 2.50 300.00 574.00 10 tonne/ha 12.00 -1740.00 32800.00 89800.00 2.90 390.00 726.00 Wheat production 5 tonne/ha 11.80 -733.00 15300.00 43000.00 2.94 180.00 330.00 7.5 tonne/ha 13.30 -1290.00 21600.00 63000.00 3.28 250.00 470.00 10 tonne/ha 14.80 -1850.00 28000.00 83000.00 3.63 330.00 600.00 221 Figure 5-11 Comparative LCIA results for corn production under different assumed rates of biochar application (5, 7.5 and 10 tonne biochar per ha) 0.001.002.003.004.005.006.007.008.005tonnes/ha7.5tonnes/ha10tonnes/hakg SO2 eq.Acidification potential-400.00-350.00-300.00-250.00-200.00-150.00-100.00-50.000.005tonnes/ha7.5tonnes/ha10tonnes/hakg CO2 eq.Climate change - GWP1000.002,000.004,000.006,000.008,000.0010,000.0012,000.0014,000.00MJDepletion of abiotic resources -fossil fuels0.005,000.0010,000.0015,000.0020,000.0025,000.0030,000.0035,000.00MJEnergy use0.000.501.001.502.002.505tonnes/ha7.5tonnes/ha10tonnes/hakg PO4-3 eq.Eutrophication0.0040.0080.00120.00160.00200.005tonnes/ha7.5tonnes/ha10tonnes/hamg CFC-11 eq.Ozone layer depletion0.0050.00100.00150.00200.00250.00300.005tonnes/ha7.5tonnes/ha10tonnes/hag ethylene eq.Photochemical oxidation 222 5.4.7.2 Emission factors applied in the barn and manure storage stages The IPCC Tier 2 emissions models provide combined EFs for manure in barn and storage. To estimate emissions at each stage separately, it was assumed that 50% of emissions occur at each of the barn and storage stages. LCIA results for egg production assuming a 25%/75% and 75%/25% split in emissions between the barn and storage stages compared to the baseline assumption (50% barn, 50% storage) are provided in Table 5-28. Differences in the assumed distribution of emissions between the barn and storage stages resulted in no differences in estimated impacts for some impact categories such as the depletion of fossil fuels, and ozone layer depletion. The result showed relatively minor differences, (2% to 3%), in acidification, climate change, energy use, and eutrophication categories. Assuming 25% of emissions occur in the barn increased the above emissions by a maximum of 3%. Moreover, assuming 75% of emissions in the barn reduced emissions by 2%. Assuming more emissions in the barn supported more emission reduction via the scrubber, as well as a larger nitrogen credit since more ammonia is captured. 223 Table 5-28 LCIA results per 1 tonne of eggs produced, assuming that the combined emissions from manure in the barn and during storage are distributed at rates of 50% barn/50% storage, 25% barn/75% storage, and 75% barn/25% storage. Acidification potential Climate change Depletion of abiotic resources Energy use Eutrophication Ozone Layer Depletion Photochemical Oxidation kg SO2 eq. kg CO2 eq. MJ MJ kg PO4-3 eq. mg CFC-11 eq. g ethylene eq. EF: 50%barn-50% storage 31.50 366.00 24400.00 60400.00 10.50 290.00 660.00 EF: 25% barn-75% storage 32.40 360.00 24300.00 60100.00 10.80 290.00 660.00 EF: 75% barn-25% storage 31.10 369.00 24500.00 60700.00 10.30 290.00 670.00 5.4.7.3 Effects of choice of emissions model IPCC Tier 2 emission models were used to simulate N emissions in the current study. To test the sensitivity of the study results to choice of emissions model, a sensitivity analysis was conducted where the GLEAM model was used in place of the IPCC Tier 2 approach. Among the 48 models identified (Chapter 4), the GLEAM model was found to also be potentially suitable for the current study due to the fact that it simulates N2O emissions at the national scale, as well as being easy to use, easily accessible, and requiring input data that is reasonably accessible to LCA practitioners. However, some deficiencies with respect to the 224 requirements of the current study are apparent. For example, it calculates the emissions from crop production as a whole at the feed formulation stage, rather than calculating the emissions from each crop separately. In addition, it only simulates nitrous oxide emissions among the various Nr emissions considered in this study. For the purpose of the sensitivity analysis, N2O emissions, as calculated at the feed formulation stage for the baseline diet were estimated using GLEAM. This value was used to replace the feed input production-level N2O emissions that were calculated using IPCC Tier 2 in the life cycle inventory models. Each was assessed using CML (baseline) 2015 and CED 2015. GWP results were 14% higher when N2O emissions were calculated using GLEAM. 5.4.7.4 Acid scrubber: Acid, water, and electricity usage The reported amount of acid, electricity, and water required per unit of ammonia scrubbed varies widely in the literature. In order to evaluate and compare the extent to which the amount of inputs influence estimated emissions, a sensitivity analysis was conducted. According to Zhang et al. (2016a), for every kg of ammonia scrubbed, the average water, sodium bisulfate and electricity consumption were 0.23 m3, 15.10 kg and 43.74 kWh, respectively. However, Hadlocon et al. (2015) reported much lower inputs at 3.80E-04 m3 water, 3.10E-02 kg sulfuric acid and 0.28 kWh electricity per kg of ammonia scrubbed. Based on data from Zhao (2013), an average of 5.10E-04 m3 water, 4.40E-02 kg sulfuric acid and 8.80E-03 kWh electricity are required. LCIA results based on each of these input levels, as well as the average data, used in the previously described results are shown in Table 5-29. 225 Table 5-29 LCIA results for the manure management at the barn using an acid scrubber for production of 1 tonne of eggs under different rates of acid consumption as well as water and energy use Acidification potential Climate change Depletion of abiotic resources Energy use Eutrophication Ozone Layer Depletion Photochemical Oxidation kg SO2 eq. kg CO2 eq. MJ MJ kg PO4-3 eq. mg CFC-11 eq. g ethylene eq. Hadlocon et al. (2015) 3.93 71.50 141.00 1280.00 1.03 -1.23 3.55 Zhao (2013) 3.93 71.10 138.00 1260.00 1.03 -1.24 3.52 Zhang et al. (2016a) 5.07 145.00 1490.00 4420.00 1.32 5.24 50.40 Average data 4.31 95.80 590.00 2320.00 1.13 0.92 19.20 The results indicated substantially lower emissions all across impact categories when using data from both Hadlocon et al. (2015) and Zhao (2013) compared to those calculated using the Zhang et al. (2016a) data. The lower impacts in the abiotic depletion resources – fossil fuels, energy use and global warming categories were mainly due to lower electricity consumption. The decreased ozone layer depletion and photochemical oxidation impacts were influenced by lesser acid consumption. In light of the large differences in estimated impacts, and the potential utility of acid scrubbers as an NUE efficiency strategy for egg supply chains, development of improved life cycle inventory data for acid scrubbers should be considered a research priority in the context of egg industry sustainability research. 226 5.4.8 Uncertainty analyses The uncertainty analysis results for all estimated life cycle impacts in each scenario are provided in Appendix C. In addition, Figures in section 5.4 include error bars (standard error). At the feed input production stage, the differences between the baseline and the alternative scenarios (4Rs approach and biochar addition to the soil) were statistically significant (p < 0.05) for all impact categories for corn production. However, for climate change impacts, the difference was not statistically significant between the baseline and biochar addition scenario, when the system expansion approach was adopted. For soybean production, the differences between the baseline and 4Rs application were not statistically significant across impact categories. However, the differences were statistically significant between the baseline and biochar addition, regardless of whether energy allocation or system expansion was used in the biochar production model. For wheat production, most differences between the baseline and alternative strategies were statistically significant except for climate change, eutrophication, and photochemical oxidation when the allocation approach was applied. At the feed formulation stage, as well as the manure management in the barn and on the field, the differences between the baseline scenario and NUE strategies were statistically significant for all impact categories. At the manure storage stage, the differences between the baseline and biochar addition to the stored manure (both allocation and system expansion approaches) were not statistically significant for the acidification and eutrophication impact categories. The climate change impact category results were also not significantly different when the system expansion approach was applied in the biochar production model. 227 With respect to the life cycle impacts of egg production, the differences between the reference scenario and 4Rs application were not statistically significant for the acidification, eutrophication, and photochemical oxidation categories. Uncertainty analysis showed significant differences in all impact categories between the reference and biochar addition to soil scenarios when system expansion was utilized, but not when energy allocation was utilized. Differences were statistically significant for most impact categories (except photochemical oxidation) between the reference scenario and the following NUE mitigation strategies: reduced crude protein diet, application of an acid scrubber in the barn, and the biochar addition to the stored manure (except for ozone layer depletion when system expansion was considered). At the manure land application stage, the differences between spreading (reference scenario) and incorporation (the alternative strategy) were statistically significant for all categories. Simultaneous application of all strategies had statistically significant differences in results compared to the reference scenario in all categories when system expansion for biochar addition was considered. However, the differences were not statistically significant for climate change, eutrophication, or photochemical oxidation when energy allocation was applied in the biochar production model. These uncertainties were largely associated with the background data quality indicators as quantified using the pedigree matrix. The background inventories applied in this study were mostly from Ecoinvent, USDA, or were created based on the literature. Many of these inventories are representative of European and American rather than Canadian conditions, 228 which, in turn decreases the certainty of model outcomes. On-going development of the Canadian Agri-food Life Cycle Data Centre (CALDC) will reduce such model uncertainties in the future. 5.4.9 Interpretation The top three strategies that might be applied along egg supply chains in terms of reducing environmental impacts and nitrogen footprints while increasing NUE compared to the baseline are summarized in Table 5-31 (energy allocation in biochar models). 229 Table 5-30 Top strategies for egg supply chains for reducing environmental impacts and nitrogen footprints and improving NUE. Impact category First Second Third Acidification potential (kg SO2 eq.) (S5) An acid scrubber application in the barn (S6) Biochar addition to the stored manure (S4) Reduced crude protein diet Climate change (kg CO2 eq.) (S3) Biochar addition to the soil (S6) Biochar addition to the stored manure (S2) Application of 4Rs strategy Depletion of abiotic resources (MJ) (S2) Application of 4Rs strategy (S1) Baseline None Energy use (MJ) (S2) Application of 4Rs strategy (S1) Baseline None Eutrophication (kg PO4-3 eq.) (S5) An acid scrubber application in the barn (S6) Biochar addition to the stored manure (S2) Application of 4Rs strategy Ozone Layer Depletion (kg CFC-11 eq.) (S1) Baseline (S2), (S5) or (S7) None Photochemical Oxidation (kg ethylene eq.) (S4) Reduced crude protein diet (S1) Baseline (S2) or (S7) NUE (%) (S5) An acid scrubber application (S6) Biochar addition to the stored manure (S2) Application of 4Rs strategy N footprint (kg N) (S5) An acid scrubber application (S4) Reduced crude protein diet (S6) Biochar addition to the stored manure 230 The results clearly indicate that each strategy can be considered more or less suitable depending on the environmental impacts of concern, as well as NUE outcomes. For example, the application of scrubbers in the barn (S5) showed a 13% higher NUE and reduced the N footprint by 24% compared to the reference scenario (S1). It also resulted in lower acidification and eutrophication impacts compared to the baseline (21% and 16%). However, this scenario produced higher impacts (2% – 5%) with respect to abiotic depletion, energy use, and photochemical oxidation. The 4Rs strategy (S2) was the only option that improved outcomes in all impact categories, but these improvements were quite small. In addition, improvements in NUE (4%) and the nitrogen footprint (4%) were relatively modest. Combined application of all strategies (S8) achieved the best outcomes with respect to GWP, acidification, and eutrophication potential (76%, 35% and 26% less impact than S1, respectively), but at the expense of a large increase in energy use (by 401%), ozone layer depletion (by 164%), and abiotic depletion (by 195%), compared to the baseline. However, when using system expansion in the biochar production model, biochar addition to the soil and the stored manure, as well as the application of all strategies, had the lowest emissions in most impact categories. 5.5 Conclusions and recommendations This study presented and compared the efficacy of six alternative strategies to improve nitrogen use efficiency at key stages along egg supply chains, considering both supply chain stage-specific benefits and impacts, as well as the life cycle impacts of egg production as a 231 whole across a range of relevant resource and environmental indicators. The combined application of life cycle assessment with NUE and N footprint calculations enabled the understanding of both opportunities and constraints for improving outcomes via each technology/strategy. What can be concluded from this analysis is that NUE and N footprints should not be considered in isolation when choosing amongst alternative technologies and management strategies. Rather, it is imperative to consider such interventions using life cycle models as well and taking into account multiple relevant resources and environmental indicators so as to be aware of and manage for trade-offs. Application of the mitigation options considered at each stage of the egg supply chain generally improved NUE with respect to the specific activities considered. However, this did not always hold true when considering nitrogen efficiency for the entire egg production supply chain. For example, the reduced crude protein diet scenario decreased nitrogen excretion per tonne of eggs produced, but also decreased systems-level N efficiency due to the lower NUE efficiencies associated with inputs to the reduced crude protein diet. Reductions in life cycle acidification, GWP and eutrophication impacts were found for three of the NUE strategies, including the application of the 4Rs approach, the reduced crude protein diet supplemented with synthetic amino acids and use of the acid scrubber. Among these, only the 4Rs approach did not result in increased energy consumption. Although biochar addition to the soil and the stored manure reduced climate change impacts substantially, it was a double-edged sword for the other categories under the energy allocation scenario (but not under the system expansion scenario). 232 Overall, application of all strategies (S8) and use of scrubbers (S5) appear to provide the most efficacious options with respect to NUE improvement (15% and 13%, respectively). These strategies also reduce acidification (35% and 21%) and eutrophication potential (26% and 16%), but increase energy consumption, abiotic depletion and ozone layer depletion. Use of renewable energy to support these activities would result in win-win outcomes across impact categories. The 4Rs strategy was also found to present a promising option for improving supply chain NUE, while slightly reducing the environmental footprint of egg production. It should be noted that the models utilized herein were based on average national data and the specific characteristics of soil, climate, and geographical location were not considered. Future research should focus on improving the regional representativeness of both the inventory models and LCIA characterization factors. 233 Chapter 6: Conclusions and Future Research Direction 6.1 Main findings A key challenge with respect to the sustainability of crop-livestock production systems is inefficient use of Nr, which results in multiple resource and environmental impacts. At key stages in crop-livestock production supply chains, there are technologies and management strategies that can be used to improve nitrogen use efficiencies. These key stages include crop production, feed formulation, and manure management. However, any such efforts should take into account a range of relevant resource efficiency and environmental indicators to gain understanding regarding potential efficacy and to manage potential trade-offs. The first task accomplished in this research was to determine the potentially most suitable mitigation methods and to understand their relative efficacy and feasibility for application along egg supply chains. Towards the end, a subset of environmental, technical, and economic criteria was applied to determine potential technologies and management strategies that may be environmentally efficacious (i.e. with respect to reducing N emissions), technically feasible, and economically inexpensive. Based on the results of the review, it was observed that a given strategy/technology may significantly reduce emissions; however, it might not be technically feasible or might be cost-prohibitive. On the other hand, a strategy/technology may be easy and inexpensive to implement without offering a net positive emissions reduction benefit. According to the defined criteria, the most promising NUE technologies/strategies to be considered for implementation along egg supply chains included the 4Rs strategy and biochar addition to the soil during feed input production, 234 reduced crude protein diets supplemented with synthetic amino acids, use of an ammonia scrubber in barns, biochar addition to stored manure, and manure land incorporation. The second goal of this research was to identify and compare Nr emissions models that can potentially be used in agricultural LCAs, as a basis for selecting an appropriate emissions models for the current study. After reviewing 48 available models, it was determined that each model might be more or less suitable, depending on the specific goals, spatial scale, country, emission type, and intended application of an LCA study. Therefore, it is recommended that LCA practitioners carefully select appropriate models on this basis. This study highlights that the application of appropriate models also aids in reducing uncertainties in LCA results. For the current study, given the goal of modelling at a generic, national-average level, IPCC Tier 2 emissions models were identified as most appropriate. The last and most important objective of this research was to evaluate the subset of promising methods for improving NUE in the egg industry in order to understand their comparative benefits and trade-offs by simultaneously considering a range of relevant resource and environmental indicators and potential interactions across the supply chain. Hence, combinations of strategies and technologies, across supply chain stages, were evaluated using the life cycle assessment methodology. The results showed that each mitigation option has benefits and impacts, depending on the impact category, and that some are more efficacious when considered at a “systems level” compared to others. For example, the application of an ammonia scrubber in barns resulted in a significant life cycle NUE improvement (13%) per tonne of eggs produced and much reduced acidifying and eutrophying emissions. However, this came at the cost of increased abiotic depletion, energy 235 use, and ozone layer depletion. Simultaneous application of all strategies similarly showed a potential 15% life cycle NUE improvement for egg production compared to the baseline and considerable reduction in GWP (76%), acidification (35%) and eutrophication potential (26%), but increased energy consumption, abiotic depletion and ozone layer depletion. Choice of system expansion over energy allocation in models for biochar production resulted in distinctly more favorable estimates for scenarios involving the use of biochar. 6.2 Research Contributions The results of this study provide information that can contribute to improving the sustainability of egg production. Importantly, the work represents the first systematic application of life cycle thinking and assessment to identifying, evaluating, and comparing potential nitrogen use efficiency strategies for egg supply chains. The finding can hence aid egg farmers and their value chain partners in identifying and implementing the most suitable technologies and management strategies to reduce nitrogen emissions while remaining attentive to potential resource efficiency and environmental trade-offs, and also considering cost and technical feasibility. Such evidence-based decision making is essential to improving “systems-level” sustainability outcomes. In addition to the publication of study results, all of the life cycle models that were created in order to compare and assess NUE strategies will be made publicly available through the Canadian Agri-food Life Cycle Data Centre, such that other researchers and interested parties can access and adapt them in support of future research endeavours. Finally, this research provides the first systematice review of available models for simulating nitrogen emissions in process-based life cycle assessment research. In addition to providing LCA practitioners with in-depth information on a broad swath of 236 available models, it enables easy comparison of modelfeatures, requirements, strengths and limitations in order to facilitating selecting those that are best suited to the intended application context. 6.3 Limitations and future directions A key limitation of the current work was the lack of reliable life cycle inventory data for acid scrubbers. Current literature reports a wide range of material and energy inputs for acid scrubbers. In order to address this limitation, average data for each product flow was employed. In light of the considerable potential for acids scrubbers to improve life cycle NUE in egg production that was indicated in this study, in depth research of in-situ application of acid scrubbers on a subset of Canadian egg farms is recommended. A second important limitation was consideration of only two potential feed formulations based on data from a single feed milling company. The formulations considered did enable highlighting the importance of both feed composition and the “upstream” NUE of feed input supply chains with respect to life cycle outcomes in egg production. However, due to the limited scope of the study, readers of this work may erroneously interpret that feed formulation is of minor importance for NUE management compared to other supply chain activities. Further research incorporating a wide range of feed inputs and formulations is hence recommended. A third important limitation relates to choice of allocation strategy in the life cycle assessment of biochar production. At present, a well-established and standard method for allocating product flows and elementary flows to syngas and biochar produced from the biochar production system is lacking. Two alterative approaches were hence utilized that yielded highly divergent results. Due to the large differences in apparent impacts and 237 benefits that are indicated when utilizing these different approaches, it is challenging to advance concrete recommendations regarding use of biochar in NUE management. It is suggested that the following studies may be helpful to further support optimizing egg production supply chains for NUE and other resource/environmental objectives: 1. Conducting farm-specific LCAs would increase the rigour of such analyses, since the accuracy of results depends on the accuracy of input data. The results presented herein were based on average national data; hence, the specific characteristics of soil, climate, geographical location, and potential farm-specific efficiencies were not considered. 2. LCIA results of crop production with the addition of biochar to soil should be evaluated for biochar produced from various types of feedstock. The assessment of poultry manure biochar is recommended. Poultry manure biochar may be a cost effective option for managing manure from egg farms, while also producing net N management benefits. 3. A more detailed analysis of the reduced crude protein diet strategy is warranted, since the impacts of feed formulation depend strongly on the percentage and types of ingredients utilized. While the alternative formulation considered in this study resulted in higher life cycle impacts, this may not be the case for other formulations. What is clear, however, is that efforts to formulate feeds for NUE should simultaneously consider relevant life cycle environmental impacts of producing, 238 processing and transporting alternative feed inputs in order to ensure that net benefits are realized. 4. The current study considered the application of an acid scrubber only. Considering different types of scrubbers may yield different results. Opportunities to support scrubber operation (as well as other potential technologies/strategies along the supply chain) with renewable energy generation should be considered so as to achieve net benefits across all relevant impact categories. Development of improved life cycle inventory data for scrubbers should be prioritized. 5. 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It has been applied at the field scale in various agricultural cropping systems including corn and winter wheat in Canada (Larocque et al. 2002; Baran et al. 2007). The software and manual are provided in French only, hence use of the model is considered as challenging for non-French speaking users (Banton and Larocque 1997). For French speakers, however, it is considered to be a user-friendly model (Bernard et al. 2016). AGRIFLUX is considered as a model with a high level of required input data (Larocque et al. 2002; Baran et al. 2007). Uncertainty and other parameter variability, such as temporal and spatial variability, can be assessed using Monte Carlo simulation (Larocque et al. 2002). A.1.2 ANIMO ANIMO is a complex and detailed process-based model to calculate the amount of N emissions (nitrate-N, ammonium-N, N2O, N balance) to surface and groundwater (Wolf et al. 2005; Farmaha 2014). It has been applied at the farm, regional, and national scales 320 (Rijtema and Kroes 1991; Wolf et al. 2005). ANIMO is free for non-commercial and academic purposes (ANIMO 2019). The tool and manual are available online (Renaud et al. 2006). Reported weaknesses of ANIMO include (1) overestimating nitrate leaching and (2) lack of application for large scales (Wolf et al. 2005). Application of ANIMO requires a set of input data files including default crop data (nitrate-N, ammonium-N, N2O, N balance), soil chemical and soil physical data, data concerning additions to and tillage of the soil system, water quantity data imported from separate calculations with other models like WATBAL or SWAP, soil chemical data for phosphorus and water quantity data (Renaud et al. 2006). A.1.3 CANDY CANDY (Carbon and Nitrogen Dynamics) is a process-based model which considers soil as a one-dimensional profile consisting of different characterization of soil texture and soil physical properties. It calculates N dynamics (e.g., mineralization, immobilization, uptake, leaching, gaseous losses, and symbiotic N fixation) and nitrate leaching from agricultural soils (Rode et al. 2009; Franko et al. 2015). CANDY has been applied in studies at farm and regional scale (Rode et al. 2009). The tool is freely available to download (Helmholtz 2018), and the manual, which contains detailed explanations, is easy to access (Franko et al. 2015). Several field experiments and comparisons with other models have validated the model’s accuracy in simulation (Smith et al. 1997a; Franko et al. 2007; Haberle and Káš 2012). The required input data include daily weather, temperature, precipitation, sunshine duration, soil, and field management information (Ramsbeck et al. 1997; Franko et al. 2007). 321 A.1.4 CENTURY CENTURY is a process-based model of plant-soil nutrient cycling that simulates nutrient dynamics for various ecosystem types such as grasslands and agricultural fields (CENTURY 2012). Additionally, it simulates complex agricultural processes including fertilization, tillage and irrigation practices (Metherell et al. 1993). The tool and manual are freely and easily accessible online (CENTURY 2012). It requires a significant number of input variables including (1) average of maximum and minimum air temperature per month, (2) monthly precipitation, (3) texture of soil, (4) initial soil carbon (5) atmospheric and soil nitrogen, (6) plant nitrogen, and (7) lignin content of plant material. A.1.5 CERES CERES is a dynamic soil-crop semi-empirical simulation model containing two sub-models (NOE and NGAS) to calculate nitrous oxide emissions from nitrification and denitrification pathways (Gabrielle et al. 2006). While CERES–NOE have been reported to correctly simulate average levels of nitrous oxide emissions, CERES–NGAS overestimates nitrous oxide fluxes due to being over-responsive to the water content of the soil (Gabrielle et al. 2006; Chen et al. 2008). Both sub-models require the following input information: (1) temperature, (2) soil NO3, and NH4+ content, and (3) heterotrophic carbon respiration rate (Gabrielle et al. 2006). A.1.6 CREAMS CREAMS, Chemicals, Runoff, and Erosion from Agricultural Management System, evaluates and plans best management practices (BMPs) for controlling agricultural source 322 pollution (Jamieson and Clausen 1988). Sapek and Sapek (1993) considered CREAMS as a suitable tool to simulate N losses on grassland due to nitrate leaching. The model simulates values for runoff, sediment, and phosphorus exports from agricultural field at the farm scale (Jamieson and Clausen 1988). No further information was available for this model. A.1.7 DAISY DAISY is a one-dimensional agroecosystem process-based model that simulates N losses and N balance under various management strategies (Abrahamsen and Hansen 2000; Olesen et al. 2004; Müller et al. 2006). The specific feature of DAISY is that it can be easily incorporated into other projects. The required data is accessible, but model set-up may be time consuming (Abrahamsen and Hansen 2000). The model, documentation and a user’s manual are available on the Daisy homepage (www.Daisy-model.org). The model and documentation are provided at no charge to users (Abrahamsen and Hansen 2000). A comparison of several models indicated that the DAISY simulation is relatively accurate (Hansen et al. 1991; Diekkriiger Av L et al. 1995; Smith et al. 1997a). The DAISY model requires the following mandatory input variables: weather, Danish soil profile, management action, and simulation period (Hansen et al. 2012). A.1.8 DAYCENT DAYCENT is a terrestrial biogeochemical process-based model and is a daily version of the CENTURY model (Álvaro-Fuentes et al. 2017). DAYCENT simulates nitrous oxide emissions derived solely from nitrification and denitrification processes from various types 323 of soils such as agricultural soils and also abandoned and afforested land (Necpálová et al. 2015b; Álvaro-Fuentes et al. 2017). The DayCent tool is available free of charge (Necpálová et al. 2015b). The documentation and instructions are easily available to download; however, access to the current versions of the DayCent model must be requested (DayCent 2012). The results of Necpálová et al. (2015) showed that the DAYCENT model tends to underestimate nitrous oxide emissions in the situation of an excessive amount of fluxes. In the same vein, Parton et al. (2001) evaluated the robustness of the DAYCENT model by comparing observed and simulated parameters. The results indicated a poor correlation between observed and simulated N2O emissions; in contrast, NOx emissions are simulated reasonably. Necpálová et al. (2015), through inverse modelling, indicated that DayCent tends to underestimate the amount of ammonia, ammonium, and nitrous oxide emissions after fertilizer application. The data required to run the DayCent model is quite intensive and includes detailed soil and climate (C & N) information (e.g. minimum and maximum depth of soil layer, bulk density of soil layer, field capacity of soil layer (volumetric), percentage of roots in soil layer, saturated hydraulic conductivity of soil layer in centimeters per second, and etc.), annual wet and dry N deposition, and crop C & N parameterization, amongst others (DayCent 2012). A.1.9 ECOSYS ECOSYS, is a process-based, 3-dimensional mathematical model to estimate N2O emissions from agricultural soils (Grant 1997; Grant and Pattey 2008; Metivier et al. 2009). The program is easily and freely available by contacting the developers (Grant 2019). The model 324 processes have been tested in collaboration with field experiments in the U.S, Europe, and Canada (Grant 2019). The required input data to run the ECOSYS model include: (1) site (geographic, atmospheric and site characteristics), (2) topography (slope, aspect, surface roughness), (3) soil (depth, bulk density, water content at field capacity and wilting point, vertical and horizontal saturated hydraulic conductivity, texture, organic C, N and P, pH), (4) weather (half-hourly, hourly or daily radiation, temperature, wind-speed, humidity and precipitation), (5) soil management (tillage, fertilizer and irrigation), and (6) plant management (planting and harvest dates; crop species – plant functional type, CO2 kinetics, phenology/ morphology, root and organ characteristics and water relations) (Metivier et al. 2009). A.1.10 EPIC EPIC, Environmental Policy Integrated Climate (EPIC), or the Erosion Productivity Impact Calculator model (Izaurralde et al. 2006; Arunrat and Pumijumnong 2014), estimates the amount of NO3-N in surface runoff, NO3-N leaching, and NO3-N transport by soil water evaporation. It also simulates contributions from organic N transport by sediment, denitrification, mineralization, immobilization, and rainfall (Williams and Sharpley 1989). It can simulate at the scale of farm, region or nation (Williams and Sharpley 1989; Izaurralde et al. 2006, 2017; Arunrat and Pumijumnong 2014). EPIC was developed to consider soil erosion, hence it captures hydrology, weather, erosion, nutrients, plant growth, soil temperature, tillage, plant environment control, and economics in its simulations (Williams 325 and Sharpley 1989; Izaurralde et al. 2006). It is a process based model that is recognized as being user-friendly (Williams and Sharpley 1989; Izaurralde et al. 2017). A.1.11 INCA INCA, Integrated Nitrogen in Catchments model, is a user-friendly process-based model that simulates the N cycle in plant, soil and in-stream systems (e.g., nitrate and ammonium concentrations) (Whitehead et al. 1998; Wade et al. 2002; Granlund et al. 2004; INCA 2019). INCA was developed based on mass-balance calculations for N in the system. Hence, a wide range of temporal and spatial scales can be evaluated. The typical scale of assessment is farm-level since INCA provides N distributions at the selected sites (Whitehead et al. 1998). The required variables to run the model include nitrate and ammonium load from livestock, fertilizer and deposition, air temperature, precipitation, and soil moisture deficit (Skeffington 2002; Wade et al. 2002; Kaste 2004; Ranzini et al. 2007). INCA has a stochastic feature that evaluates parameter sensitivity (Whitehead et al. 1998). A.1.12 LEACHN LEACHN, the N module of LEACHM, is a process-based model that calculates the mineral N in the soil and describes N transport and transformations (Ramos and Carbonell 1991). Mahmood and Tillman (2015) investigated the sensitivity of LEACHN predictions to changes in rate constants of N transformation. The LEACHN model simulates N transportation under different irrigation scenarios at the field scale (Mahmood 2018). It has been used widely due to its user-friendly interface and simple structure (Asada et al. 2013). The model overestimates NO3-N concentrations, mostly due to the application of the default 326 value for N transformation. LEACHN requires a considerable amount of input data (Ramos and Carbonell 1991; Mahmood and Tillman 2015) including soil data for the different layers, the conditions of the soil surface boundary, crop data, diffusion and dispersion coefficients, urea, ammonium and nitrate adsorption coefficients, and rate constants for the nitrogen transformations considered (urea hydrolysis, ammonia volatilization, nitrification and denitrification) (Ramos and Carbonell 1991). A.1.13 LIXIM LIXIM estimates N mineralization and leaching from bare soils. The LIXIM model is used at the field scale (Rakotovololona et al. 2019). The required input data include soil characteristics, frequent measurements of mineral N contents in soil, and meteorological data (Mary et al. 1999b). A.1.14 NCYCLE NCYCLE, a mass-balance empirical model, simulates the N transformations, fluxes, and losses from grasslands in the UK (Cuttle and Jarvis 2005; Prado et al. 2006). The model is recognized as user-friendly since detailed computing knowledge is not required. The required input data include climatic zone, atmospheric deposition zone, soil texture and drainage class, land management, and age and fertilizer input (Prado et al. 2006). A.1.15 NGAS 327 NGAS is a simple process-based model used to simulate gaseous N emissions due to climate, soil properties, and management strategies (Parton et al. 1996). The required information for the NGAS model consists of surface soil temperature, NO3- and NH4+ content, and heterotrophic C respiration rate (Gabrielle et al. 2006). A.1.16 NLEAP NLEAP, Nitrate Leaching and Economic Analysis Package, estimates the potential NO3-N leaching from agricultural lands into groundwater and the movement of NO3-N below the root zone (Shaffer et al. 1995; Karaman et al. 2005). In addition, it simulates N2O emissions from organic residue decay in the recently extended version (Shaffer et al. 2001). The additional functionality of NLEAP-GIS 4.2 allows users to assess N losses due to various management practices. NLEAP-GIS 4.2 is provided free of charge to users, and the application is straightforward. The new version allows users to evaluate multiple practices over a long time scale quickly (Delgado et al. 2010). It has been characterized as an accurate simulation model (Delgado 2001). NLEAP-GIS 4.2 is a revised, improved, renamed, and advanced version of NLEAP (Delgado et al. 2010). A variety of input data is required including N transformation in soil; input data for physical, chemical and hydraulic parameters for topsoil and subsoil; weather and environmental data such as temperature and precipitation; aquifer, crop, fertilization, manure amendment and farming management information (Karaman et al. 2005; Palmer and Angstadt 2012). A.1.17 RZWQM 328 RZWQM, Root Zone Water Quality Model, is a user-friendly process-based farm scale model that simulates the effects of various farm management practices on nitrate run-off and nitrous oxide emissions (Hanson et al. 1998; Anapalli et al. 2005; Gillette et al. 2017). It considers management practices such as the application of tillage and residue, crop rotations, and fertilizer application (Hanson et al. 1998). RZWQM requires detailed input data including weather data such as daily temperature, solar radiation, wind speed, relative humidity, and rainfall; soil data like texture, bulk density, and hydraulic properties; and management practices data (Hanson et al. 1998; Ma et al. 2001; Gillette et al. 2017). A.1.18 SOILN SOILN is a mathematical field-scale model that simulates daily N fluxes, N dynamics, water transport and production of biomass in agricultural systems (Bergström et al. 1991; Jabro et al. 2001). The required variables consist of time series meteorological data (e.g. precipitation and temperature), soil properties, crop management data (e.g. crops and fertilization), and N input data (fertilizer, manure and deposition) (Hoffmann and Johnsson 2000). According to 3-years of field experiments, the model simulates nitrate leaching accurately, but not NO3-N fluxes (Jabro et al. 2001). A.1.19 SOILNDB SOILNDB is a process-based model that can be applied to a variety of simulations, including N leaching from arable land to surface water and the effects of N leaching in agricultural production (Johnsson et al. 2002; Kyllmar et al. 2005; Bouraoui and Grizzetti 2014a). It also 329 enables the evaluation of the effects of management strategies in agricultural production and land use on N leaching (Johnsson et al. 2002). It is considered as a farm-scale model (Kyllmar et al. 2005). The model requires more general, less extensive, and less detailed data for simulation compared to SOILN (Johnsson et al. 2002). SOILNDB requires soil data (e.g. texture class, organic matter content of topsoil and subsoil (%)), climate data (e.g. meteorological), data on the type of crop and manure, the amount and time of applied manure, ammonia losses from application, the amount and time of fertilizer application, sowing time, yield and time of harvest, the time of tillage in the case of application, and catch crop uptake (Johnsson et al. 2002). A.1.20 STICS STICS, Simulateur mulTIdiscplinaire pour les Cultures Standard, is a process-based model designed for simulating the soil-crop-atmosphere system (Brisson et al. 1998; Sierra et al. 2003; STICS 2014; Bernard et al. 2016). The STICS tool is easily accessible to download after registration on the website. Additionally, a mailing list and workshops are available to discuss tool development (STICS 2014). Constantin et al. (2012) showed that STICS overestimates drainage and leaching by 10-20 % and 25 %, respectively. However, Coucheney et al. (2015) concluded that robustness of the STICS model is high due to low bias, and the dependency of errors on agro-environmental conditions or crops as little. Notably, the input data required by STICS includes climate, soil (water and nitrogen initial profiles and permanent soil features) and crop management information (Brisson et al. 2003). A.1.21 STONE 330 STONE, a process-based model, is intended to evaluate the impacts of various changes in agriculture and policy measures on N leaching (Wolf et al. 2003; Velthof and Mosquera 2011). These changes include different cropping strategies and distribution of manure and inorganic fertilizers (Wolf et al. 2003, 2005). The model is applied at a regional and national scale (Overbeek et al. 2001; Wolf et al. 2003; Velthof and Mosquera 2011). It is specific to the Netherlands in terms of soil, climate, crop, and nutrient management data (Velthof and Mosquera 2011). The challenge of the STONE model application is that it overestimates the amount of nitrate leaching (Wolf et al. 2005). A.1.22 SWAT SWAT, Soil and Water Assessment Tool, is a process-based model that evaluates the effects of management decisions on water resources (Arnold et al. 2012; Sullivan 2018). Atmospheric N deposition and fixation are simulated by SWAT (Jung and Kim 2017). SWAT also evaluates the effects of N-fertilizer application rates on NO3-N losses (Moriasi et al. 2013). The software is offered as an open-access tool, and the manual is also freely available (Arnold et al. 2012; Zhang et al. 2016b; SWAT 2019). The required input data to run the model are considerable, which makes the model complicated to parameterize and calibrate (Arnold et al. 2012). SWAT is recognized as an appropriate and useful model to predict the losses of nutrients for long term simulations (Jung and Kim 2017). SWAT-CS is a revised version of SWAT for the Canadian Shield watersheds; however, the simulation of nitrate processes has not been found to be satisfactory (Zhang et al. 2016b). A.1.23 Thinkstep 331 Thinkstep simulates the nitrogen flows within, to, and from agricultural systems. It simulates a significant number of Nr emissions including ammonia, ammonium, nitrous oxide, nitric oxide, and nitrate into air, water and soil (Shah et al. 2018; Deimling et al. 2019). NH3 emissions to air are modelled from fertilizers (mineral and organic) for the cropping system. The emissions depend on the ammonium content of fertilizers, the soil pH, temperature, and rainfall. Default emission factors are applied but may be adjusted according to available information. The emission factor for ammonium nitrate, calcium ammonium nitrate, monoammonium phosphate, diammonium phosphate is 2% of applied N fertilizer. The emission factors for ammonium sulphate, urea ammonium nitrate, and urea are 10%, 8%, and 15%, respectively. N2O emissions are calculated as 1% of all N available such as applied N fertilizers, atmospheric deposition, microbial N fixation, N available from previous cropping systems and indirect emissions. NO emissions calculations are a combination of N-input from air and 0.43% of the applied N fertilizer. The calculation of NO3- emissions to groundwater is based on available N, and the fraction of this N is considered as leached N depending on the soil type and the amount of water leached (Deimling et al. 2019). A.1.24 TNT2 TNT2, Topography‐based N Transfer and Transformation, is a process-based model that explicitly addresses water quality management related to nitrogen/nitrate (Beaujouan et al. 2002; Ferrant et al. 2011, 2013; Moreau et al. 2013). TNT2 analyzes the effects of agricultural activities and changes in land use on N fluxes and storage accurately (Moreau et al. 2013). In addition, the simulation of the N cycle at the watershed scale is precise (Moreau 332 et al. 2013). Ferrant et al. (2011) showed that in TNT2 compared to SWAT more leaching and nitrogen, which flow to the aquifer, are transferred . Overall, the base flow is simulated better by TNT2 in comparison with SWAT (Ferrant et al. 2011). TNT2 requires a variety of input data (Beaujouan et al. 2002) including a digital elevation model (DEM), a landscape map, a soil map, and a climate map in raster format; daily climate data in each climate area; soil characteristics for each soil type represented on the soil map; agricultural practices such as mineral and organic fertilization, sowing date, and crop sequences in each plot (Moreau et al. 2013). A.1.25 TOUGHREACT-N TOUGHREACT-N is a process-based multiphase flow model that simulates NH3, N2O, NO, and NO3- leaching (Maggi et al. 2008; Gu et al. 2009). The TOUGHREACT-N model includes nitrification and denitrification pathways, biochemical kinetic reactions and microbial biomass dynamics for various bacterial species and soil moisture dynamics. It also includes several equilibrium and kinetic reactions that link the N and C cycles (Maggi et al. 2008). It considers soil types, alternative N fertilizers and the methods of application to simulate N emissions (Gu et al. 2009). A.1.26 VOLT’AIR VOLT’AIR, a process-based model, evaluates ammonia volatilization after various kinds of fertilizer applications such as slurry, manure, ammonia nitrate, and urea (Hamaoui-Laguel et al. 2014; Ramanantenasoa et al. 2018). It also evaluates various technologies and management strategies to reduce ammonia emissions (VOLT’AIR 2015). VOLT’AIR is a 333 farm-scale model (Ramanantenasoa et al. 2018), but it has also been applied at the national scale in various studies (Hamaoui-Laguel et al. 2014; Ramanantenasoa et al. 2018). The required input data to simulate ammonia emissions in the field consists of soil properties (e.g. pH, texture, organic carbon content, bulk density); weather conditions (e.g. air temperature, relative air humidity, incident solar radiation, wind speed, and rainfall); cultural practices (e.g. type of N fertilizer application, day and hour of N application, amount of application, type of application technique, soil management, and irrigation technique); and properties of mineral fertilizers and organic products (e.g. total ammoniacal N content, pH, density, and water transfer parameters) (Garcia et al. 2011; Hamaoui-Laguel et al. 2014; Ramanantenasoa et al. 2018). A.1.27 WHNSIM WHNSIM, Water, Heat and Nitrogen Simulation Model evaluates nitrate leaching and N mineralization from soil (Huwe and van der Ploeg 1991). In addition, it was developed to quantify N dynamics in agricultural fields and improve agricultural management practices (Huwe and Totsche 1995). WHNSIM is an equation-based model with the following input data requirements: soil matric potential, soil temperature, soil nitrate in the soil profile, dates of fertilization and amounts of N-fertilizer, the atmospheric input of nitrate, an agro-climatic weather station, mean daily temperature, mean daily air humidity, daily hours with bright sunshine, daily sums for global radiation, day of emergence, day of harvest, soil cover and root distribution (Huwe and van der Ploeg 1991; Huwe and Totsche 1995). Huwe and 334 Totsche (1995) noted that the simulation of nitrate leaching by WHNSIM is very time-consuming and labour-intensive. A.1.28 WNMM WNMM, Water and Nitrogen Management Model, is a process-based model that was developed to simulate the dynamic soil water movement and N cycle under various agricultural management strategies (Li et al. 2007; Wei et al. 2009). WNMM simulates nitrification, ammonia volatilization, denitrification, nitrous oxide emissions, mineralization of crop residue, and formation and mineralization of soil organic N (Li et al. 2007; Wei et al. 2009; Ahrens et al. 2010). The purpose of the simulation is to identify the most suitable management strategies for the application of N fertilizer and water (Li et al. 2007). A sensitivity analysis is conducted to quantify the uncertainty of WNMM. The results showed that WNMM is sensitive to changes in agricultural management, soil properties, land use and meteorological variables (Li et al. 2007). Wu and Zhang (2014) compared the nitrous oxide emissions from simulation via WNMM, DayCent, and Crop-DNDC with field observation from rice-wheat rotation systems. The result showed that the partitioning of gaseous nitrogen loss into NO, N2O, and N2 is challenging for all models; however, WNMM performed better than others (Wu and Zhang 2014). Equations to calculate emissions were provided in (Li et al. 2007). 335 A.2 Emission models for livestock production systems A.2.1 AGRAMMON AGRAMMON is a simulation model that considers various stages of agricultural systems to calculate ammonia emissions (Kupper et al. 2010; Nemecek and Schnetzer 2011; Agrammon 2019). These stages include feed, housing system, manure storage, manure land application, and grazing (Agrammon 2019). AGRAMMON is a user-friendly web application (Kupper et al. 2010), and its manual is available on the website as well (Menzi et al. 2008). For the application of AGRAMMON, various detailed data are required such as number of animals, type of housing, storage, and land application (Agrammon 2019). A.2.2 ALFAM ALFAM, a semi-empirical dynamic model, simulates ammonia volatilization from slurry land applications in the context of livestock systems (Søgaard et al. 2002; Reidy et al. 2008; Hafner et al. 2019). Using ALFAM, Søgaard et al. (2002) showed that ammonia volatilization from different types of slurry land applications, such as tailing hoses and injection systems, are overestimated and underestimated, respectively (Søgaard et al. 2002). The ALFAM model is provided free of charge, and is easily accessible. Two interfaces for using ALFAM model include R and Excel. The R interface requires some basic understanding but provides better flexibility and power. Although the Excel version is easy to use, modeling flexibility is limited (Søgaard et al. 2002). The model requires a few input variables (Thorman et al. 2008) including slurry application (t/ha), total ammoniacal nitrogen 336 (TAN) concentration (kg/t), application method, slurry dry matter (%), air temperature (°C), wind speed (m/s), rainfall rate (mm/h), incorporation, incorporation time, acidification, and duration of emission (Reidy et al. 2008; ALFAM 2019). A.2.3 GNL GNL, General Nitrogen Leaching, simulates components of livestock farms on an annual basis including water balance, N leaching, denitrification, N harvest, the changes in soil N pools, and crop yields (Børgesen et al. 2001; Schroder and Neeteson 2005). It evaluates N emissions on a regional scale such as at the municipal and country levels (Børgesen et al. 2001). The required input data for the simulation include crop and soil information, irrigation, climate data, and crop yields (Schroder and Neeteson 2005). A.2.4 MANNER MANNER, MANure Nitrogen Evaluation Routine, is recognized as a valuable tool that enables farmers and advisors to quantify manure crop available N supply (Webb et al. 2006; Nicholson et al. 2008). MANNER-NPK is only applicable in England, Wales, Scotland and Northern Ireland (MANNER 2019). MANNER-NPK is a version of MANNER that predicts the amount of nitrogen, phosphorus, potassium, magnesium, and sulphur losses following organic manure land application. It also evaluates the effects of timing and methods of manure application on ammonia volatilization, nitrate leaching, and nitrous oxide emissions (Webb et al. 2006; Chambers et al. 2006; Nicholson et al. 2008, 2013). The simulation tool and user guide of MANNER-NPK are available online, and the model is considered to be user-friendly (Nicholson et al. 2013; MANNER 2019). Nicholson et al. (2008) and Chambers 337 et al. (2006) applied MANNER to compare estimations of ammonia and nitrate emissions to the results of a field experiment. The results showed that the model simulations were in agreement with the field experiment for cattle, pig, and poultry manure land applications (Chambers et al. 2006; Nicholson et al. 2008). MANNER-NPK requires a few simple inputs including (1) farm and field details (e.g. location, soil type, and crop type), (2) manure type and nutrient content, and (3) manure application details (e.g. rate and method of application, soil incorporation) (MANNER 2019). A.2.5 MITERRA MITERRA is an emission model developed based on the -livestock emission models of CAPRI (Common Agricultural Policy Regionalized Impact) and Regional Air Pollution Information and Simulation (RAINS) models. MITERRA evaluates the amounts of ammonia, nitrous oxide, nitrogen oxides, and nitrogen leaching due to ammonia and nitrate application (Velthof et al. 2007; Lesschen et al. 2011). The model covers EU-27, country, and regional scales (Velthof et al. 2007). The following emission factors are available in this model: ammonia emission factor for housing, storage, raw manure application, and grazing; nitrous oxide emission factor for storage, raw manure application, and grazing; and nitrogen oxides emission factor for storage (Lesschen et al. 2011; Hou et al. 2017). The main input data may derive from CAPRI, GAINS, FAO statistics, and IPCC, and includes crop area, livestock distribution, animal numbers, and N2O emission factors. (Lesschen et al. 2011). A.2.6 TAN-mass balance 338 TAN-mass balance is a Canadian regionally-specific model to estimate ammonia emissions from beef production systems, including animal housing (barns and feedlots), grazing, manure storage, and manure land application (Chai et al. 2014; Legesse et al. 2018). It also considers the amount of ammonia volatilization under different air temperatures (Chai et al. 2014). It is available free of charge and is easy to access. The required input variables to run the model are (1) animal information such as numbers and distribution, body weight and weight gain, lactation and pregnancy, (2) weather condition, (3) dry matter intake (DMI), and (4) crude protein (CP) content of the diet (Chai et al. 2014). The equations to calculate ammonia emissions within the model are based on Chai et al. (2014). A.3 Emission models for combined crop-livestock production systems A.3.1 APEX APEX, Agricultural Policy/Environmental eXtender Model, is an extension of the Environmental Policy Integrated Climate (EPIC) model (Tuppad et al. 2010). APEX simulates the N cycle in the context of watershed management including atmospheric N inputs, application of N fertilizer and manure, crop N uptake, mineralization, immobilization, nitrification, denitrification, ammonia volatilization, organic N transport on sediment, and nitrate‐nitrogen (NO3‐N) losses in leaching, surface runoff, lateral subsurface flow, and tile flow (Gassman et al. 2010). APEX has been used for field and watershed scale modelling (Tuppad et al. 2010; Williams et al. 2010). It is identified as a flexible and useful tool for evaluating complex landscape and management scenarios since it can be configured for different land management strategies (Gassman et al. 2010). APEX was developed to 339 evaluate various land management practices and the effects of global climate changes as well as in the context of whole farm/small watershed management (Tuppad et al. 2010; Williams et al. 2010). The required input data to run the APEX model consist of precipitation, maximum and minimum temperature, solar radiation, wind speed, and relative humidity (Gassman et al. 2010). A.3.2 CAPRI CAPRI, Common Agricultural Policy Regionalised Impact analysis, is both a data base and a simulation model (Weiss and Leip 2012). CAPRI considers several indicators such as nutrient surpluses for nitrate, phosphate and potassium, the emissions of GHG such as methane (CH4), N2O and carbon dioxide (C2O), and ammonium output (Mittenzwei et al. 2007; Weiss and Leip 2012; Britz et al. 2014). The model, manual, and documentation are free of charge and easily available online (CAPRI 2019). The input data for the application of CAPRI is similar to DNDC and DAYCENT (Britz et al. 2014). It includes extensive information in terms of weather data, soil data, agricultural management practices. A.3.3 DNDC DNDC, DeNitrification and DeComposition, is a process-based geochemical agro-ecosystem model that was developed from the DNDC93 framework (Kröbel et al. 2011; Dutta et al. 2016b). The DNDC model predicts various Nr emissions, including ammonia, ammonium, nitrous oxide, nitrate, and nitric oxide (Giltrap et al. 2010; Congreves et al. 2016). DNDC was originally developed to simulate N2O emissions from cropped soils in the US (Giltrap et 340 al. 2010). It is easily accessible via download (Hampshire 2007). DNDC v.CAN is a Canadian version of DNDC that is improved specifically for Canadian agroecosystems to predict ammonia emissions (Congreves et al. 2016; Dutta et al. 2016a, 2018; Goglio et al. 2018). DNDC consists of six sub-models: soil-climate, crop growth, decomposition, denitrification, nitrification, and fermentation (Uzoma et al. 2015; Zhang and Niu 2016; Zhao et al. 2017). Temperature and moisture fluxes in one dimension per hour and per day are simulated by the soil climate sub-model. The crop growth sub-model calculates crop biomass accumulation and partitioning. Decomposition, nitrification, ammonia volatilization, and carbon dioxide production are measured by the decomposition sub-model while the subsequent biochemical reductions from nitrate to nitrite, nitric oxide, nitrous oxide, and dinitrogen gas are tracked by the denitrification sub-model. The required input datasets consist of soil characteristics (% sand, % silt, % clay, soil organic carbon (SOC), pH, bulk density, hydraulic conductivity, saturation, field capacity, and wilting point), daily climate (air temperature (minimum and maximum), precipitation, and solar radiation), and agronomic management (crop rotation data; planting, harvest, and tillage scheduling; and fertilizer type, method, rate, dates, and depth; manure type, method, rate, dates, and depth; irrigation) (Smith et al. 2010; Gilhespy et al. 2014). Important issues that have to be considered with the application of DNDC included reported overestimations of observed nitrous oxide fluxes and underestimations of observed above-ground biomass (Abdalla et al. 2009; Ludwig et al. 2011). The model is sensitive to inputs such as climate, size of initial soil C levels, and fertilizer application rates (Smith et al. 2010). During the past 20 years, various versions of DNDC have been developed, regionalised, and adapted for different ecosystems 341 such as Forest-DNDC, Forest-DNDC-Tropica, NZ-DNDC, UK-DNDC, and DNDC-Rice (Gilhespy et al. 2014). A.3.4 FarmN FarmN is a farm-scale web-based model for N flow management (Jorgensen et al. 2005; FARM-N 2019) that is available upon request. It calculates N losses through ammonia volatilization from livestock (cattle and pig) farms and housing, manure storage, manure and fertilizer land application, denitrification and nitrate leaching (Jorgensen et al. 2005). The required input data to run the model includes the area and crop mixture for each rotation on the farm, the number and productivity of the livestock, and the type of animal housing (FARM-N 2019). A.3.5 GLEAM GLEAM, Global Livestock Environmental Assessment Model, is a process-based model that simulates the interactions of livestock activities and the environment (FAO 2018b; MacLeod et al. 2018; Leinonen et al. 2019; Kiggundu et al. 2019). The model operates at a national, regional or global scale (FAO 2018). The tool, which is Microsoft-Excel based, and the manual are available free of charge (FAO 2018a; Kiggundu et al. 2019). The considered livestock production systems in the model include cattle (meat and milk), sheep, goats and buffalo; pigs (meat); and chickens (meat and eggs) (FAO 2018b; MacLeod et al. 2018). GLEAM uses an IPCC Tier 2 methodology in order to provide accurate information regarding the potential of mitigation options such as animal feeding, herd, and manure management (FAO 2018). GLEAM calculates the emissions based on IPCC equations (FAO 342 2018). The required input data for calculation include animal population, herd parameters, live weights, mortality, fertility, nitrogen losses rate in manure, leaching rate, herd parameters, mineral fertilizer application rates, temperature, crop yields, and energy use (FAO 2018a; MacLeod et al. 2018; Kiggundu et al. 2019). A.3.6 HOLOS HOLOS, an empirical model, is developed by Agriculture and Agri-Food Canada (AB, Canada) to estimate GHG emissions from Canadian agricultural systems based on IPCC algorithms (Little et al. 2008; Amadi et al. 2016; Guest et al. 2018). HOLOS evaluates GHG emissions by considering cropping systems, land use, and management changes (Alemu et al. 2017a). The HOLOS software and manual are available at no cost for users (AAFC 2019). The considered pathways for emissions calculations include nitrous oxide emissions from soils (crop, forage, pasture, range) and manure; nitrous oxide emissions from nitrogen leaching, runoff, and re-deposition of volatilized ammonia (Alemu et al. 2017b). According to expert opinion, a rough estimate of uncertainty for each emission category is developed. The relative level of uncertainty is demonstrated through colour-coding in the graphical output (Little et al. 2008). The input required to run HOLOS depends on the specific farm operation. For example, the user input required for crops, grassland, and land-use change includes area of annual crops and fallow, area of perennial crops, area of grassland, tillage system, area of irrigation, and herbicide usage; and for poultry production includes type of poultry, barn capacity, and wet or dry manure system (Little et al. 2008). A.3.7 IFSM 343 IFSM, Integrated Farm System Model, is a whole farm process-based model to assess the environmental impacts of agricultural production systems (Rotz et al. 2011; IFSM 2017). IFSM considers volatile N losses, nitrate loss to ground and surface water, erosion, and GHG emissions as environmental impacts related to Nr emissions. The IFSM model was originally developed for application in the temperate climate in the northern United States and southern Canada. Applications have been expanded in regions with similar climate conditions such as northern Europe as well as in other areas such as Ontario and Quebec, Canada, Midwest, Northeast, and Pacific Northwest regions of the United States, Brazil and New Zealand (Rotz et al. 2011). The IFSM software and manual are available on the website (IFSM 2017). The considered systems include crop production, milk or beef production, manure handling, and the return of manure to the field (Rotz et al. 2011). The model requires three files, including farm, machinery, and weather as input data. The data for the farm file includes crop area, soil characteristics, equipment and structures applied, number of livestock, harvest and tillage strategies, manure handling, and various farm inputs/outputs prices. The machinery parameters include machine size, initial cost, operating parameters, and repair factors. The required weather data includes daily weather at the particular location including the year and day of that year, total daily solar radiation (MJ/m²), mean temperature (°C), maximum temperature (°C), minimum temperature (°C), total precipitation (mm), and average wind speed (m/s) (Rotz et al. 2011). A.3.8 IPCC 344 IPCC, Intergovernmental Panel on Climate Change, comprises 3 tiers of models that represent the method’s level of complexity/specificity in simulating N2O emissions. The Tiers include Tier 1 (basic method), Tier 2 (intermediate), Tier 3 (the most demanding in terms of complexity and data requirements) (IPCC 1996). Tier 1 consists of basic estimation equations (Eve et al. 2014) and applies a gain-loss method (IPCC 1996). It is provided with default factors that may overestimate or underestimate the emissions depending on the simulation area (IPCC 1996). Tier 2 and 3 are more complex, though they provide more accurate simulations. Tier 2 may apply an inference approach, which is a similar approach to Tier 1 or use an empirical model; the emissions factors and data are specific to the country (IPCC 1996; Eve et al. 2014). Tier 3 is a process-based model that requires ancillary data to support the data and emissions factor selection (Eve et al. 2014). Although the uncertainty of GHG estimation is decreased from Tier 1 to Tier 3, the complexity of modelling is increased (IPCC 1996). A.3.9 IULIA IULIA is an IPCC-derived method to evaluate nitrous oxide emissions from agricultural activities (Bretscher 2012; Heldstab et al. 2017). IULIA applies the same emission factors as IPCC but with specific to Switzerland's situation, applying the equations available in Bretscher (2012). The required input data to solve the equation are (1) livestock number, (2) annual average N excretion per livestock, (3) fraction of total annual excretion for each 345 livestock that is managed, (4) N2O emission factor for manure management system, (5) type of manure management, and (6) species of livestock (Bretscher 2012). A.3.10 NARSES NARSES, National Ammonia Reduction Strategy Evaluation System, is a process-based model to assess ammonia emissions. It was developed for agricultural systems based in the United Kingdom (UK) (Misselbrook et al. 2004; Webb and Misselbrook 2004; Reidy et al. 2009). NARSES assesses the effects of changes at field and national scale (Misselbrook et al. 2004; Webb and Misselbrook 2004; Reidy et al. 2009). Emission factors in NARSES are expressed within a given emission pool such as livestock housing and slurry storage as a percentage of the total ammoniacal nitrogen (TAN). A.3.11 NEMA NEMA, National Emission Model for Agriculture for the Netherlands, evaluates ammonia, nitrous oxide and nitrogen oxides from grazing, livestock housing, manure storage, and manure land application (Groenestein and Bruggen 2012; Velthof et al. 2012; Vonk et al. 2016). In addition, it stimulates the emissions attributed to inorganic N fertilizer, compost, sludge, residues, ripening of crops, and organic soil cultivation (Vonk et al., 2016). The emission factors applied in the NEMA model are based on IPCC Tier 1 factors (2006), and the equations are reported in Vonk et al. (2016). The emission factors are expressed as a percentage of TAN present (Groenestein and Bruggen 2012). Vonk et al. (2016) noted that the uncertainty of Nr emissions is based on expert judgement and the equation is provided for “uncertainty estimate total” calculation. 346 A.3.12 NitroScape NitroScape, an integrated model, was developed and applied in Europe. It simulates Nr emissions at the landscape scale (Duretz et al. 2011). It consists of four types of models, including (1) farm model, (2) ecosystem model, (3) atmospheric model, and (4) hydrological model (Duretz et al. 2011; Drouet et al. 2012; Franqueville et al. 2018). The farm model manages the Nr cycle in the livestock production systems (e.g., housing, manure storage, and manure land application). Total indirect Nr emissions considered include ammonia, nitrate, nitrous oxide, and nitrogen oxides (Drouet et al. 2012; Savall et al. 2019). The ecosystem model considers different pathways of Nr input, such as mineral and organic fertilizer application, deposition, uptake, transformations, and losses. The atmospheric model considers atmospheric pathways for simulation of dispersion, transport and ammonia deposition. The hydrological model considers the transfer of Nr in water (Duretz et al. 2011). NitroScape input data includes meteorology (e.g. temperature, evapotranspiration over grass, global radiation, rainfall, relative humidity, wind direction, wind speed), farm management (e.g. arable: date and N in seed; date, amount, and type of mineral fertilization and manure application; harvest date; grassland: hay making date, animal density, start and end date of grazing; animals: type and number of heads, calving distribution, and type of housing), soil and vegetation conditions (e.g. digital soil map, soil depth, infiltration rates, field capacity, texture, and root mass distribution), canopy characteristics (e.g. roughness length for farm, and surface type), and localisation (e.g. latitude, and longitude) (Duretz et al. 2011). A.3.13 OVERSEER 347 OVERSEER is an empirical model based on the IPCC inventory methodology to estimate GHG emissions on farms (Vogeler et al. 2012). It evaluates nutrient losses through run-off, leaching, and GHG emissions to identify potential environmental risks, reduce nutrient losses, and optimize the process of production (Watkins and Selbie 2015). The online software, manual, support, and training are provided (Overseer 2019). The required input data to run the model depends on the intended application. For example, the required input data for the dairy farm model includes milk solids production, cow numbers, cow breed (Friesian, Jersey, Friesian × Jersey and Ayrshire), and replacement policy (whether replacements are grazed on- or off-farm) (Wheeler et al. 2008). OVERSEER estimates direct N2O losses and indirect losses from fertilizer, excreta and effluent N inputs associated with leaching and volatilization (Wheeler et al. 2003, 2008, 2011). The estimation is conducted through multiplying the amount of N in fertilizer, excreta, or indirect losses from fertilizer or excreta by the N2O emission factor provided by Wheeler et al. (2008). Uncertainties in OVERSEER estimates, reported to be ±20%, are associated with uncertainty around values for inputs and compare to measured values (Dunbier et al. 2013). A.3.14 SALCA SALCA, Swiss Agricultural Life Cycle Assessment model, simulates ammonia, nitrous oxide, and nitrate emissions among Nr elements (Weisskopf et al. 2013; Nemecek et al. 2016). The scope of application is Europe (Nemecek et al. 2016). Four emissions paths are considered to estimate ammonia emissions: (1) farm manure application, (2) mineral fertilizer application, (3) pasture emissions, and (4) stable/barn emissions. The estimation of 348 nitrous oxide emissions to air arises from (1) fertilizer, (2) symbiotic N-fixation in legumes, (3) crop residues, (4) stored manure, (5) pasture, and (6) induced emissions. For modelling nitrate emission, N mineralization of soil organic matter, the input of mineral and organic N by fertilizers, and dates of application are considered (Nemecek 2013; Weisskopf et al. 2013). 349 Appendix B Emission models required input data Table A-1 Required input data for N emissions models for crop production systems Model Required input data Reference AGRIFLUX Water input (precipitation, irrigation) (Baran et al. 2007) Water up- take (crops, evaporation, drainage) Daily precipitation Precipitation (PREC, WET, TEMP, TEMP_MIN) (Larocque et al. 2002) Irrigation (IRRIG) Snowmelt (MELT, SNOW) Soil evaporation (EVAP, EV_DEP) Percolation (HORIZ, SATK, DR_POR, WILT) Fertilizer input (FER_N, FER_REL) Net mineralization (LITK, LITEFF, LITHF, HUMK, ORG_MAT, CNORG) Nitrification (NITK) Denitrification (DENHS, DENPOT, DEN_MAX) Water and N uptake (GROWH, PL_WAT, PL_N, UPMA) Root growth (ROOTS) ANIMO Soil (Thickness of Surface layer (0–0.1 m), Thickness of intermediate layer (0.1–1.0 m), Thickness of deep layer (1.0–2.0 m), Thickness of top soil compartment, Thickness of the reservoir for additions, Diffusion coefficient (Surface layer), Diffusion coefficient (Intermediate and deeper layers), Saturated hydraulic conductivity of the root zone (Surface and intermediate layers), Dry bulk density (Surface layer), Dry bulk density (Intermediate layer), Dry bulk density (Deeper layer), Soil carbon-nitrogen ratio (Soil profile), Coefficient for organic matter transformations and nitrification, Coefficient for dissolved organic matter transformations, Soil pH (Surface layer), Soil pH (Intermediate and deeper layers), NH4 sorption coefficient (Soil profile), Reference temperature, Nitrification rate, Denitrification rate) (Pinto et al. 2017) Plant (Depth of initial root zone , Plant residues (roots), “Sowing” date, Harvesting date, Transitional data for uptake periods, Average cumulative N plant uptake, Cumulative transpiration in the first period, Cumulative transpiration in the second period, Maximum N transpiration stream concentration factor Root mass and length (Marinov et al. 2005) Time of sowing and harvesting Amount of N-uptake Cumulative transpiration during growing periods Selectivity factor for N-uptake Time after sowing when maximum N-uptake rate begins to fall Mass of harvested tubers and reduction factor for potential correction of root losses 350 Model Required input data Reference CANDY Soil physical properties (texture, wilting point, water capacity, bulk density) (Rode et al. 2009) Meteorological data (air temperature, precipitation, global radiation or sunshine duration) Management information (tillage, emergence, fertilizer, harvest) Daily weather (Haberle and Káš 2012) Types and amounts of nitrogen fertilizers, manure, straw, sowing Harvest time Time and depth of soil tillage CENTURY Soil texture (% sand, clay, and silt) (Qian et al. 2003) Monthly average maximum and minimum air temperatures Monthly precipitation and irrigation Lignin content of plant material Plant tissue C and N ratio and initial soil C CREAMS Daily precipitation (Jamieson and Clausen 1988) Average monthly temperatures Monthly solar radiation DAISY Weather data (Temperature (min, max), rainfall, solar radiation (opt.), wind Speed (opt.)) (Kröbel et al. 2010) Soil (Bulk density, clay content, soil pH, field capacity, wilting point, soil organic carbon (SOC), initial NO3− and NH4+ conc., microbial index, slope, SOC partitioning (opt.) Management (crop (planting date, optimum yield), tillage (date, method), fertilization (date, method, type), irrigation (date, method), weeding (date)) Others (Latitude, by-pass flow, N-conc. In rainfall, atmospheric NH3 conc., atmospheric CO2 conc., increase rate of atmospheric CO2 concentration DAYCENT Weather data with daily maximum/minimum temperature (°C); daily precipitation (cm) (Cheng et al. 2014) Soil data with SOC content (g m−2); C/N; sand, silt, clay, and rock fraction (0–1); bulk density (g cm−3); pH Crop rotation information with crop type; planting date; harvest date; Agricultural management practices for N fertilizer amount (g N m−2); fertilization date; irrigation amount (cm H2O, or fraction of available water holding capacity); irrigation date; tillage intensity; and tillage date. ECOSYS Site (geographic, atmospheric and site characteristics) (Metivier et al. 2009) Topography (slope, aspect, surface roughness) Soil (depth, bulk density, water content at field capacity and wilting point, vertical and horizontal saturated hydraulic conductivity, texture, organic C, N and P, pH) Weather (half-hourly, hourly or daily radiation, temperature, wind-speed, humidity and precipitation) Soil management (tillage, fertilizer and irrigation) Plant management (planting and harvest dates; crop species – plant functional type, CO2 kinetics, phenology/ morphology, root and organ characteristics and water relations) 351 Model Required input data Reference EPIC Weather inputs (Arunrat and Pumijumnong 2014) Precipitation Maximum and minimum daily temperature Solar radiation Wind speed Numerous crop parameters such as (morphology, phenology, physiology, etc.) INCA Soil moisture deficit (Ranzini et al. 2007) Hydrologically effective rainfall (the fraction of precipitation that contributes directly to the runoff Air temperature Daily potential evapotranspiration Air humidity Solar radiation Wind speed Daily time series of air temperature (Kaste 2004) Actual precipitation Soil moisture deficit Hydrologically effective rainfall Sub catchment structure (number, size, reach length) Physical properties of the selected sub catchments Inputs of N from atmospheric deposition Fertilizer application Effluent discharges LEACHN Soil properties and initial conditions for each soil layer (initial water content, hydrological constants for the soil moisture retentivity curve, chemical contents, and soil chemical properties) (Mahmood and Tillman 2015) Soil surface boundary conditions (irrigation and rainfall amounts and rates of application, mean temperature, and potential evapotranspiration data) Crop details (time of planting, root and crop maturity and harvest, and root and cover growth parameters) Other constants needed include the diffusion coefficient, dispersivity coefficient, rate constants, and adsorption co-efficient for each soil segment. NCYCLE Soil texture (Prado et al. 2006) Drainage status Land use history Climatic zone Atmospheric deposition zone NLEAP Physical, chemical and hydraulic parameters for topsoil and subsoil, soil N transformation components (Karaman et al. 2005) Monthly weather and environmental data (Daily precipitation, monthly air temperatures, monthly pan evaporations) Aquifer information data Crop and management information for the simulation site for each year 352 Model Required input data Reference RZWQM Daily or hourly weather data (maximum and minimum daily temperature, solar radiation, wind speed, relative humidity, and rainfall) Soil texture Soil bulk density Soil hydraulic properties (if known) Recorded management practices (USDA 2019) SOILN Time series of meteorological data (e.g., precipitation, temperature) (Hoffmann and Johnsson 2003) Information about soil properties Information about crop management (e.g., crops, fertilization) SOILNDB Soil (Soil texture class or specific soil, Organic matter content (topsoil, subsoil)) (Johnsson et al. 2002) Climate (Meteorological data, Deposition) Cropping system (Crop type, Type of manure, Applied amount manure (ton ha−1), Manure application time, Loss of NH3 at application, Applied amount fertilizer (ton ha−1), fertilizer application time, Sowing time, Harvest yield, Harvest time, Tillage (yes/no), Tillage time, Catch crop uptake (kg N ha−1) STICS Climatic data (Moreau et al. 2013) Land-use Agricultural practices Soil characteristics SWAT Spatial resolution of 20 m (Akhavan et al. 2010) Soil physical properties Daily rainfall and temperature Daily outflow of reservoir The location of the stream network TNT2 A DEM in raster format (Moreau et al. 2013) A landscape pattern in raster format delineating the agricultural plots, roads, hydrological network and landscape features (wetlands, hedgerows, etc.) A soil map in raster format A climate map in raster format enabling potential climate gradients within the watershed Daily climate data in each climate area 20 soil characteristics for each soil type represented on the soil map Agricultural practices (mineral and organic fertilization, sowing date, etc.) and crop sequences in each plot during the simulation period VOLT’AIR Soil - all data relative to the soil (texture, analytical properties, hydraulic characteristics) and initial water content (INRA 2019) Cultural techniques - all information concerning agricultural practices (application, incorporation, irrigation) Parcel configuration - latitude, size, albedo, roughness length Variables measured using a classic meteorological station 353 Model Required input data Reference WHNSIM Soil matric potential (Huwe and van der Ploeg 1991) Soil temperature Soil nitrate in the soil profile Dates of fertilization and amounts of N-fertilizer The atmospheric input of nitrate An agro-climatic weather station Mean daily temperature Mean daily air humidity Daily hours with bright sunshine Daily sums for global radiation Day of emergence Day of harvest Soil cover and root distribution Table A-2 Required input data for N emission models for livestock production systems Model Required input data Reference AGRAMMON Number of animals (Agrammon 2019) The housing type Details about manure stores and manure application ALFAM Soil water content (Søgaard et al. 2002) Air temperature Wind speed Slurry type Dry matter content of slurry TAN content of slurry Application method Application rate of slurry Slurry incorporation Technique for NH3 loss measurement MANNER-NPK Farm and field details, i.e. Location, soil type, crop type (Manner 2019) Manure type and nutrient content. The results of laboratory analysis can be entered if available, or alternatively MANNER-NPK will use default nutrient analysis for the selected organic manure type. Manure application details, i.e. Application rate, application method and soil incorporation. Details of up to 3 manure applications to a single crop/field can be added MITERRA Crop areas (Leip et al. 2013) Animal numbers Feed use at the regional TAN-mass balance model Animal information (numbers and distribution, body weight and weight gain, lactation and pregnancy) (Chai et al. 2014) Weather condition Dry matter intake (DMI) Crude protein (CP) content of the diet 354 Table A-3 Required input data for N emissions models for combined crop-livestock production systems Model Required input data Reference APEX Daily Precipitation, maximum and minimum temperature, and solar radiation (Gassman et al. 2010) Century model (C cycling) Atmospheric N inputs; fertilizer and manure N applications; crop N uptake Forage intake rate in kg head-1 d-1, grazing efficiency (accounts for waste by Trampling, etc.) Manure production rate in kg head-1 d-1, urine production in l head-1 d-1 Soluble and organic N and P fractions in the manure Manure production in kg head-1 d-1 and its ingredients (mineral and organic N and P) CAPRI Same as DNDC with parameterization based on EU databases (climate, soil, etc.) (Britz et al. 2014) DNDC Weather data with daily maximum/minimum temperature (°C); daily precipitation (cm) (Uzoma et al. 2015) Soil data with SOC content (g m−2); C/N; sand, silt, clay, and rock fraction (0–1); bulk density (g cm−3); pH Crop rotation information with crop type; planting date; harvest date; Agricultural management practices for N fertilizer amount (g N m−2); fertilization date; irrigation amount (cm H2O, or fraction of available water holding capacity); irrigation date; tillage intensity; and tillage date. FarmN Fertilizer information (Jorgensen et al. 2005) Manure information Fodder Livestock Seeds Atmospheric deposition and fixation GLEAM Animal numbers (MacLeod et al. 2018) Herd/flock parameters Mineral fertilizer application rates Temperature HOLOS 30-year climate normal (Kröbel et al. 2012) Farm management practices into parameter input sets (or “scenarios”). These scenarios require simple user input (e.g. Number of animals, feed quality), fertilizer application rates) fixed parameters based on Canadian averages and/or expert opinion IFSM Farm parameter files: crop areas; soil characteristics; equipment and structures used; number of animals at various ages; harvest, tillage, and manure handling strategies; and prices for various farm inputs and outputs (Rotz et al. 2011) Machinery parameter files: machine size, initial cost, operating parameters, and repair factors Weather parameter files: daily data includes the year and day of that year, total daily solar radiation (MJ/m²), mean temperature (°C), maximum temperature (°C), minimum temperature(°C), total precipitation (mm),and average wind speed (m/s) 355 Model Required input data Reference IPCC Crop type (IPCC 2006) Soil type Manure type Manure management system IULIA Livestock number (Bretscher 2012) Annual average N excretion per livestock Fraction of total annual excretion for each livestock that is managed N2O emission factor for manure management system Type of manure management Species of livestock NARSES Fertilizer type (Misselbrook et al. 2004) Soil pH Land use, Application rate, Rainfall Temperature NitroScape Meteorological data (e.g., rainfall, wind speed), (Duretz et al. 2011) Land use maps, Topographic data (e.g., altitude), Vegetation data, Soil properties (e.g., bulk density, field capacity) Farm management data OVERSEER Fuel (Wheeler et al. 2011) Electricity Fertilizers Supplements Chemicals SALCA Type of nitrogen applied (Nemecek and Kagi 2007) Crop residues to soil 356 Appendix C - Monto Carlo results C.1 Feed input production Table A-4 Uncertainty analysis results from a Monte Carlo simulation (1000 runs) for 1 tonne of corn production, including reference and alternative scenarios. Only parameter uncertainty was considered. Impact categories with significant differences (p < 0.05) marked with *, and n.s. for non-significant. Mean SD 5% Percentile 95% Percentile Median Standard Error P- value Acidification (kg SO2 eq.)* Reference scenario 5.09 0.84 3.85 6.59 5.02 0.03 0.00 Application of 4Rs strategy 4.47 0.68 3.49 5.70 4.39 0.02 Acidification (kg SO2 eq.)* Reference scenario 5.09 0.84 3.85 6.59 5.02 0.03 0.00 Biochar addition (allocation) 6.91 4.11 5.47 8.91 6.93 0.13 357 Mean SD 5% Percentile 95% Percentile Median Standard Error P- value Acidification (kg SO2 eq.)* Reference scenario 5.09 0.84 3.85 6.59 5.02 0.03 0.00 Biochar addition (system expansion) 7.50 4.24 5.69 9.35 7.22 0.13 Climate change - GWP100 (kg CO2 eq.)* Reference scenario 275.00 54.80 198.00 377.00 267.00 1.73 0.00 Application of 4Rs strategy 308.00 52.90 234.00 404.00 302.00 1.67 Climate change - GWP100 (kg CO2 eq.)* Reference scenario 275.00 54.80 198.00 377.00 267.00 1.73 0.00 Biochar addition (allocation) 68.20 652.00 -265.00 408.00 87.90 20.60 Climate change - GWP100 (kg CO2 eq.) n.s Reference scenario 275.00 54.80 198.00 377.00 267.00 1.73 0.99 Biochar addition (system expansion) 275.00 694.00 -72.80 563.00 253.00 22.00 358 Mean SD 5% Percentile 95% Percentile Median Standard Error P- value Depletion of abiotic resources - fossil fuels (MJ)* Reference scenario 1670.00 174.00 1430.00 1980.00 1650.00 5.50 0.00 Application of 4Rs strategy 2310.00 229.00 1960.00 2710.00 2290.00 7.25 Depletion of abiotic resources - fossil fuels (MJ)* Reference scenario 1670.00 174.00 1430.00 1980.00 1650.00 5.50 0.00 Biochar addition (allocation) 7530.00 6820.00 5870.00 11100.00 7340.00 216.00 Depletion of abiotic resources - fossil fuels (MJ)* Reference scenario 1670.00 174.00 1430.00 1980.00 1650.00 5.50 0.00 Biochar addition (system expansion) 10200.00 7170.00 7780.00 13300.00 9690.00 227.00 Energy use (MJ)* Reference scenario 2520.00 249.00 2160.00 2960.00 2500.00 7.87 0.00 Application of 4Rs strategy 3370.00 389.00 2810.00 4030.00 3330.00 12.30 Energy use (MJ)* Reference scenario 2520.00 249.00 2160.00 2960.00 2500.00 7.87 0.00 Biochar addition (allocation) 16300.00 11700.00 12600.00 22300.00 16300.00 370.00 359 Mean SD 5% Percentile 95% Percentile Median Standard Error P- value Energy use (MJ)* Reference scenario 2520.00 249.00 2160.00 2960.00 2500.00 7.87 0.00 Biochar addition (system expansion) 19400.00 12400.00 14700.00 24200.00 18600.00 393.00 Eutrophication (kg PO4-3 eq.)* Reference scenario 1.72 0.26 1.35 2.16 1.70 0.01 0.00 Application of 4Rs strategy 1.56 0.23 1.24 1.98 1.53 0.01 Eutrophication (kg PO4-3 eq.)* Reference scenario 1.72 0.26 1.35 2.16 1.70 0.01 0.00 Biochar addition (allocation) 2.14 1.10 1.69 2.76 2.13 0.03 Eutrophication (kg PO4-3 eq.)* Reference scenario 1.72 0.26 1.35 2.16 1.70 0.01 0.00 Biochar addition (system expansion) 2.20 1.48 1.70 2.74 2.11 0.05 360 Mean SD 5% Percentile 95% Percentile Median Standard Error P- value Ozone layer depletion (mg CFC-11 eq.)* Reference scenario 53.00 7.96 41.30 67.80 52.40 0.25 0.00 Application of 4Rs strategy 60.30 9.52 46.90 78.20 59.10 0.30 Ozone layer depletion (mg CFC-11 eq.)* Reference scenario 53.00 7.96 41.30 67.80 52.40 0.25 0.00 Biochar addition (allocation) 139.00 92.10 88.90 225.00 129.00 2.91 Ozone layer depletion (mg CFC-11 eq.)* Reference scenario 53.00 7.96 41.30 67.80 52.40 0.25 0.00 Biochar addition (system expansion) 178.00 103.00 108.00 300.00 160.00 3.26 Photochemical oxidation (g ethylene eq.)* Reference scenario 40.00 10.00 30.00 50.00 40.00 0.18 0.00 Application of 4Rs strategy 70.00 20.00 50.00 90.00 60.00 0.65 Photochemical oxidation (g ethylene eq.)* Reference scenario 40.00 10.00 30.00 50.00 40.00 0.18 0.00 Biochar addition (allocation) 200.00 270.00 140.00 320.00 190.00 8.66 361 Mean SD 5% Percentile 95% Percentile Median Standard Error P- value Photochemical oxidation (g ethylene eq.)* Reference scenario 40.00 10.00 30.00 50.00 40.00 0.18 0.00 Biochar addition (system expansion) 270.00 310.00 170.00 440.00 230.00 9.70 Table A-5 Uncertainty analysis results from a Monte Carlo simulation (1000 runs) for 1 tonne soybean of production, including reference and alternative scenarios. Only parameter uncertainty was considered. Impact categories with significant differences (p < 0.05) marked with *, and n.s. for non-significant. Mean SD 5% Percentile 95% Percentile Median Standard Error P- value Acidification (kg SO2 eq.) n.s. Reference scenario 4.87 0.79 3.74 6.26 4.79 0.02 0.23 Application of 4Rs strategy 4.91 0.84 3.73 6.39 4.79 0.03 362 Mean SD 5% Percentile 95% Percentile Median Standard Error P- value Acidification (kg SO2 eq.)* Reference scenario 4.87 0.79 3.74 6.26 4.79 0.02 0.00 Biochar addition (allocation) 9.47 3.82 7.06 12.50 9.03 0.12 Acidification (kg SO2 eq.)* Reference scenario 4.87 0.79 3.74 6.26 4.79 0.02 0.00 Biochar addition (system expansion) 11.70 3.93 8.84 15.60 11.20 0.12 Climate change - GWP100 (kg CO2 eq.) n.s. Reference scenario 237.00 44.90 179.00 315.00 230.00 1.42 0.68 Application of 4Rs strategy 238.00 106.00 178.00 318.00 228.00 3.35 Climate change - GWP100 (kg CO2 eq.)* Reference scenario 237.00 44.90 179.00 315.00 230.00 1.42 0.00 Biochar addition (allocation) -524.00 725.00 -1510.00 408.00 -518.00 22.90 Climate change - GWP100 (kg CO2 eq.)* Reference scenario 237.00 44.90 179.00 315.00 230.00 1.42 0.02 Biochar addition (system expansion) 180.00 775.00 -863.00 1130.00 186.00 24.50 363 Mean SD 5% Percentile 95% Percentile Median Standard Error P- value Depletion of abiotic resources - fossil fuels (MJ) n.s. Reference scenario 2010.00 247.00 1670.00 2450.00 1990.00 7.80 0.64 Application of 4Rs strategy 2010.00 288.00 1650.00 2400.00 1990.00 9.11 Depletion of abiotic resources - fossil fuels (MJ)* Reference scenario 2010.00 247.00 1670.00 2450.00 1990.00 7.80 0.00 Biochar addition (allocation) 18200.00 6710.00 12700.00 27500.00 17000.00 212.00 Depletion of abiotic resources - fossil fuels (MJ)* Reference scenario 2010.00 247.00 1670.00 2450.00 1990.00 7.80 0.00 Biochar addition (system expansion) 27200.00 7380.00 20500.00 36900.00 26200.00 233.00 Energy use (MJ) n.s. Reference scenario 2570.00 451.00 2070.00 3390.00 2490.00 14.30 0.93 Application of 4Rs strategy 2580.00 938.00 2040.00 3250.00 2480.00 29.70 Energy use (MJ)* Reference scenario 2570.00 451.00 2070.00 3390.00 2490.00 14.30 0.00 Biochar addition (allocation) 42900.00 11300.00 31000.00 57000.00 41500.00 359.00 364 Mean SD 5% Percentile 95% Percentile Median Standard Error P- value Energy use (MJ)* Reference scenario 2570.00 451.00 2070.00 3390.00 2490.00 14.30 0.00 Biochar addition (system expansion) 52600.00 12700.00 39900.00 68300.00 51600.00 402.00 Eutrophication (kg PO4-3 eq.) n.s. Reference scenario 1.29 0.22 0.99 1.67 1.25 0.01 0.95 Application of 4Rs strategy 1.29 0.23 1.00 1.67 1.25 0.01 Eutrophication (kg PO4-3 eq.)* Reference scenario 1.29 0.22 0.99 1.67 1.25 0.01 0.00 Biochar addition (allocation) 2.19 1.03 1.62 2.84 2.06 0.03 Eutrophication (kg PO4-3 eq.)* Reference scenario 1.29 0.22 0.99 1.67 1.25 0.01 0.00 Biochar addition (system expansion) 2.63 1.17 1.98 3.49 2.52 0.04 Ozone layer depletion (mg CFC-11 eq.) n.s. Reference scenario 31.90 8.14 22.20 46.70 30.40 0.26 0.78 Application of 4Rs strategy 31.80 7.79 22.30 45.70 30.30 0.25 365 Mean SD 5% Percentile 95% Percentile Median Standard Error P- value Ozone layer depletion (mg CFC-11 eq.)* Reference scenario 31.90 8.14 22.20 46.70 30.40 0.26 0.00 Biochar addition (allocation) 271.00 146.00 135.00 520.00 237.00 4.62 Ozone layer depletion (mg CFC-11 eq.)* Reference scenario 31.90 8.14 22.20 46.70 30.40 0.26 0.00 Biochar addition (system expansion) 409.00 188.00 210.00 727.00 369.00 5.95 Photochemical oxidation (g ethylene eq.) n.s. Reference scenario 240.00 370.00 60.00 850.00 130.00 10.00 0.15 Application of 4Rs strategy 310.00 1300.00 70.00 880.00 120.00 40.00 Photochemical oxidation (g ethylene eq.)* Reference scenario 240.00 370.00 60.00 850.00 130.00 10.00 0.00 Biochar addition (allocation) 670.00 460.00 340.00 1410.00 540.00 10.00 Photochemical oxidation (g ethylene eq.)* Reference scenario 240.00 370.00 60.00 850.00 130.00 10.00 0.00 Biochar addition (system expansion) 930.00 640.00 500.00 1750.00 800.00 20.00 366 Table A-6 Uncertainty analysis results from a Monte Carlo simulation (1000 runs) for 1 tonne of wheat production, including reference and alternative scenarios. Only parameter uncertainty was considered. Impact categories with significant differences (p < 0.05) marked with *, and n.s. for non-significant. Mean SD 5% Percentile 95% Percentile Median Standard Error P- value Acidification (kg SO2 eq.)* Reference scenario 8.87 1.49 6.74 11.60 8.75 0.05 0.00 Application of 4Rs strategy 8.47 1.43 6.31 11.10 8.38 0.05 Acidification (kg SO2 eq.)* Reference scenario 8.87 1.49 6.74 11.60 8.75 0.05 0.04 Biochar addition (allocation) 15.70 108.00 9.58 15.70 12.20 3.40 Acidification (kg SO2 eq.)* Reference scenario 8.87 1.49 6.74 11.60 8.75 0.05 0.00 Biochar addition (system expansion) 14.50 2.76 11.30 19.00 14.20 0.09 Climate change - GWP100 (kg CO2 eq.)* Reference scenario 387.00 80.50 286.00 534.00 375.00 2.54 0.00 Application of 4Rs strategy 370.00 68.90 274.00 499.00 363.00 2.18 367 Mean SD 5% Percentile 95% Percentile Median Standard Error P- value Climate change - GWP100 (kg CO2 eq.) n.s. Reference scenario 387.00 80.50 286.00 534.00 375.00 2.54 0.39 Biochar addition (allocation) -63.00 16700.00 -1460.00 165.00 -512.00 527.00 Climate change - GWP100 (kg CO2 eq.)* Reference scenario 387.00 80.50 286.00 534.00 375.00 2.54 0.00 Biochar addition (system expansion) 150.00 516.00 -718.00 853.00 200.00 16.30 Depletion of abiotic resources - fossil fuels (MJ)* Reference scenario 2390.00 408.00 2010.00 2870.00 2380.00 12.90 0.00 Application of 4Rs strategy 2310.00 251.00 1930.00 2770.00 2290.00 7.94 Depletion of abiotic resources - fossil fuels (MJ)* Reference scenario 2390.00 408.00 2010.00 2870.00 2380.00 12.90 0.00 Biochar addition (allocation) 20000.00 159000.00 11800.00 19900.00 14700.00 5030.00 Depletion of abiotic resources - fossil fuels (MJ)* Reference scenario 2390.00 408.00 2010.00 2870.00 2380.00 12.90 0.00 Biochar addition (system expansion) 23900.00 3500.00 19200.00 29700.00 23600.00 111.00 368 Mean SD 5% Percentile 95% Percentile Median Standard Error P- value Energy use (MJ)* Reference scenario 2900.00 671.00 2390.00 3530.00 2860.00 21.20 0.00 Application of 4Rs strategy 2800.00 363.00 2310.00 3400.00 2760.00 11.50 Energy use (MJ)* Reference scenario 2900.00 671.00 2390.00 3530.00 2860.00 21.20 0.00 Biochar addition (allocation) 47200.00 277000.00 29100.00 51000.00 38000.00 8750.00 Energy use (MJ)* Reference scenario 2900.00 671.00 2390.00 3530.00 2860.00 21.20 0.00 Biochar addition (system expansion) 48000.00 7790.00 37200.00 61200.00 47200.00 246.00 Eutrophication (kg PO4-3 eq.)* Reference scenario 2.34 0.36 1.83 2.98 2.30 0.01 0.00 Application of 4Rs strategy 2.23 0.36 1.70 2.85 2.20 0.01 Eutrophication (kg PO4-3 eq.) n.s. Reference scenario 2.34 0.36 1.83 2.98 2.30 0.01 0.11 Biochar addition (allocation) 4.03 33.40 2.37 3.83 2.93 1.06 369 Mean SD 5% Percentile 95% Percentile Median Standard Error P- value Eutrophication (kg PO4-3 eq.)* Reference scenario 2.34 0.36 1.83 2.98 2.30 0.01 0.00 Biochar addition (system expansion) 3.40 0.62 2.65 4.40 3.32 0.02 Ozone layer depletion (mg CFC-11 eq.)* Reference scenario 31.70 7.78 23.70 44.80 30.50 0.25 0.01 Application of 4Rs strategy 30.90 7.22 22.40 44.00 29.50 0.23 Ozone layer depletion (mg CFC-11 eq.)* Reference scenario 31.70 7.78 23.70 44.80 30.50 0.25 0.00 Biochar addition (allocation) 277.00 1900.00 122.00 402.00 197.00 60.00 Ozone layer depletion (mg CFC-11 eq.)* Reference scenario 31.70 7.78 23.70 44.80 30.50 0.25 0.00 Biochar addition (system expansion) 351.00 139.00 196.00 605.00 321.00 4.40 Photochemical oxidation (g ethylene eq.) n.s. Reference scenario 59.20 20.30 44.20 81.60 56.90 0.64 0.23 Application of 4Rs strategy 58.20 18.60 43.40 84.00 55.10 0.59 370 Mean SD 5% Percentile 95% Percentile Median Standard Error P- value Photochemical oxidation (g ethylene eq.)* Reference scenario 59.20 20.30 44.20 81.60 56.90 0.64 0.01 Biochar addition (allocation) 640.00 6870.00 269.00 719.00 398.00 217.00 Photochemical oxidation (g ethylene eq.)* Reference scenario 59.20 20.30 44.20 81.60 56.90 0.64 0.00 Biochar addition (system expansion) 692.00 294.00 410.00 1240.00 623.00 9.29 371 C.2 Feed formulation Table A-7 Uncertainty analysis results from a Monte Carlo simulation (1000 runs) for 1 tonne of feed production, including reference and alternative scenarios. Only parameter uncertainty was considered. Impact categories with significant differences (p < 0.05) marked with *, and n.s. for non-significant. Mean SD 5% Percentile 95% Percentile Median Standard Error P- value Acidification (kg SO2 eq.)* Control diet 5.62 0.65 4.65 6.75 5.60 0.02 0.00 Reduced crude protein diet 6.67 0.72 5.51 7.90 6.63 0.02 Climate change - GWP100 (kg CO2 eq.)* Control diet 297.00 37.50 241.00 361.00 293.00 1.19 0.00 Reduced crude protein diet 328.00 37.00 271.00 392.00 324.00 1.17 372 Mean SD 5% Percentile 95% Percentile Median Standard Error P- value Depletion of abiotic resources - fossil fuels (MJ)* Control diet 2260.00 227.00 1920.00 2640.00 2250.00 7.17 0.00 Reduced crude protein diet 3290.00 305.00 2840.00 3810.00 3260.00 9.66 Energy use (MJ)* Control diet 2990.00 344.00 2510.00 3570.00 2960.00 10.90 0.00 Reduced crude protein diet 5150.00 510.00 4390.00 6040.00 5100.00 16.10 Eutrophication (kg PO4-3 eq.)* Control diet 1.83 0.22 1.51 2.23 1.82 0.01 0.00 Reduced crude protein diet 2.18 0.26 1.82 2.62 2.16 0.01 Ozone layer depletion (mg CFC-11 eq.)* Control diet 41.90 6.84 32.60 54.90 41.00 0.22 0.00 Reduced crude protein diet 47.00 7.59 37.20 61.50 45.90 0.24 373 Mean SD 5% Percentile 95% Percentile Median Standard Error P- value Photochemical oxidation (g ethylene eq.)* Control diet 119.00 94.30 73.30 216.00 95.60 2.98 0.04 Reduced crude protein diet 111.00 49.50 78.80 177.00 99.30 1.57 374 C.3 Manure management: Barn Table A-8 Uncertainty analysis results from a Monte Carlo simulation (1000 runs) for manure management at the barn for 1 tonne of egg production, including reference and alternative scenario. Only parameter uncertainty was considered. Impact categories with significant differences (p < 0.05) marked with *, and n.s. for non-significant. Mean SD 5% Percentile 95% Percentile Median Standard Error P- value Acidification (kg SO2 eq.)* Reference scenario 28.90 5.58 20.70 38.30 28.60 0.18 0.00 Scrubber 28.20 5.54 20.30 37.80 27.60 0.18 Climate change - GWP100 (kg CO2 eq.)* Reference scenario 137.00 34.40 91.70 196.00 132.00 1.09 0.00 Scrubber 103.00 34.90 55.20 168.00 97.10 1.10 Depletion of abiotic resources - fossil fuels (MJ)* Reference scenario 463.00 139.00 358.00 573.00 456.00 4.40 0.00 Scrubber -62.60 80.30 -190.00 72.60 -63.30 2.54 375 Mean SD 5% Percentile 95% Percentile Median Standard Error P- value Energy use (MJ)* Reference scenario 1660.00 319.00 1280.00 2070.00 1640.00 10.10 0.00 Scrubber 1050.00 231.00 687.00 1450.00 1040.00 7.31 Eutrophication (kg PO4-3 eq.)* Reference scenario 6.60 1.24 4.78 8.78 6.51 0.04 0.00 Scrubber 6.42 1.23 4.66 8.50 6.27 0.04 Ozone layer depletion (mg CFC-11 eq.)* Reference scenario 2.24 2.18 1.22 3.92 1.93 0.07 0.00 Scrubber -3.55 1.50 -5.91 -1.46 -3.49 0.05 Photochemical oxidation (g ethylene eq.)* Reference scenario 8.28 4.89 6.21 10.40 8.06 0.16 0.00 Scrubber 0.55 1.87 -2.53 3.57 0.58 0.06 376 C.4 Manure management: Storage Table A-9 Uncertainty analysis results from a Monte Carlo simulation (1000 runs) for manure management at storage for 1 tonne of egg production, including reference and alternative scenarios. Only parameter uncertainty was considered. Impact categories with significant differences (p < 0.05) marked with *, and n.s. for non-significant. Mean SD 5% Percentile 95% Percentile Median Standard Error P- value Acidification (kg SO2 eq.) n.s Reference scenario 28.80 5.72 20.50 38.80 27.90 0.18 0.16 Biochar addition (allocation) 29.10 5.77 20.30 39.40 28.80 0.18 Acidification (kg SO2 eq.) n.s Reference scenario 28.80 5.72 20.50 38.80 27.90 0.18 0.08 Biochar addition (system expansion) 29.60 14.00 21.10 39.30 28.50 0.44 Climate change - GWP100 (kg CO2 eq.)* Reference scenario 92.70 32.80 48.50 154.00 87.60 1.04 0.00 Biochar addition (allocation) -35.90 98.90 -208.00 76.80 -16.90 3.13 377 Mean SD 5% Percentile 95% Percentile Median Standard Error P- value Climate change - GWP100 (kg CO2 eq.) n.s Reference scenario 92.70 32.80 48.50 154.00 87.60 1.04 0.83 Biochar addition (system expansion) 107.00 2090.00 -78.70 135.00 49.70 65.90 Depletion of abiotic resources - fossil fuels (MJ)* Reference scenario 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Biochar addition (allocation) 1150.00 617.00 429.00 2340.00 1020.00 19.50 Depletion of abiotic resources - fossil fuels (MJ)* Reference scenario 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Biochar addition (system expansion) 2860.00 22400.00 816.00 4430.00 1900.00 708.00 Energy use (MJ)* Reference scenario 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Biochar addition (allocation) 3780.00 2120.00 1420.00 7730.00 3320.00 67.00 378 Mean SD 5% Percentile 95% Percentile Median Standard Error P- value Energy use (MJ)* Reference scenario 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Biochar addition (system expansion) 6070.00 38500.00 1830.00 9920.00 4310.00 1220.00 Eutrophication (kg PO4-3 eq.) n.s Reference scenario 6.37 1.25 4.57 8.57 6.20 0.04 0.23 Biochar addition (allocation) 6.44 1.26 4.54 8.68 6.37 0.04 Eutrophication (kg PO4-3 eq.) n.s Reference scenario 6.37 1.25 4.57 8.57 6.20 0.04 0.18 Biochar addition (system expansion) 6.62 5.58 4.65 8.69 6.29 0.18 Ozone layer depletion (mg CFC-11 eq.)* Reference scenario 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Biochar addition (allocation) 16.90 11.80 5.17 38.80 14.10 0.00 379 Mean SD 5% Percentile 95% Percentile Median Standard Error P- value Ozone layer depletion (mg CFC-11 eq.)* Reference scenario 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Biochar addition (system expansion) 40.20 258.00 10.00 73.60 26.80 8.14 Photochemical oxidation (g ethylene eq.)* Reference scenario 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Biochar addition (allocation) 37.40 26.40 11.90 84.00 30.70 0.00 Photochemical oxidation (g ethylene eq.)* Reference scenario 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Biochar addition (system expansion) 98.60 936.00 21.00 163.00 54.90 30.00 380 C.5 Manure management: Field Table A-10 Uncertainty analysis results from a Monte Carlo simulation (1000 runs) for manure management at field for 1 tonne of egg production, including reference and alternative scenarios. Only parameter uncertainty was considered. Impact categories with significant differences (p < 0.05) marked with *, and n.s. for non-significant. Mean SD 5% Percentile 95% Percentile Median Standard Error P- value Acidification (kg SO2 eq.)* Reference scenario 9.14 1.80 6.57 12.50 8.93 0.06 0.00 Incorporation 9.68 1.76 7.12 12.90 9.49 0.06 Climate change - GWP100 (kg CO2 eq.)* Reference scenario 289.00 108.00 145.00 495.00 272.00 3.41 0.00 Incorporation 383.00 107.00 241.00 582.00 368.00 3.38 Depletion of abiotic resources - fossil fuels (MJ)* Reference scenario -358.00 70.30 -468.00 -243.00 -359.00 2.22 0.00 Incorporation 940.00 111.00 766.00 1130.00 932.00 3.50 381 Mean SD 5% Percentile 95% Percentile Median Standard Error P- value Energy use (MJ)* Reference scenario -433.00 82.90 -564.00 -303.00 -432.00 2.62 0.00 Incorporation 1070.00 127.00 875.00 1290.00 1060.00 4.03 Eutrophication (kg PO4-3 eq.)* Reference scenario 3.75 0.65 2.83 4.96 3.67 0.02 0.00 Incorporation 3.96 0.63 3.02 5.01 3.90 0.02 Ozone layer depletion (mg CFC-11 eq.)* Reference scenario -3.29 0.97 -4.77 -1.89 -3.28 0.03 0.00 Incorporation 12.70 5.03 6.83 22.70 11.60 0.16 Photochemical oxidation (g ethylene eq.)* Reference scenario -4.12 1.74 -7.12 -1.47 -4.08 0.06 0.00 Incorporation 30.10 11.60 18.60 50.30 27.50 0.37 382 C.6 Egg production supply chains Table A-11 Uncertainty analysis results from a Monte Carlo simulation (1000 runs) for the entire egg production supply chain (1 tonne egg produced), including reference scenario (S1) and application of 4Rs strategy scenario (S2). Only parameter uncertainty was considered. Impact categories with significant differences (p < 0.05) marked with *, and n.s. for non-significant. Mean SD 5% Percentile 95% Percentile Median Standard Error P- value Acidification (kg SO2 eq.) n.s. Reference scenario (S1) 88.30 18.20 62.30 121.00 86.00 0.57 0.06 4Rs application scenario (S2) 86.80 17.60 60.90 118.00 85.80 0.56 Climate change - GWP100 (kg CO2 eq.)* Reference scenario (S1) 2530.00 494.00 1950.00 3200.00 2470.00 15.60 0.04 4Rs application scenario (S2) 2580.00 586.00 1960.00 3290.00 2510.00 18.50 Depletion of abiotic resources - fossil fuels (MJ)* Reference scenario (S1) 14600.00 2020.00 11700.00 18200.00 14400.00 63.80 0.00 4Rs application scenario (S2) 15200.00 2160.00 12000.00 18900.00 15000.00 68.20 383 Mean SD 5% Percentile 95% Percentile Median Standard Error P- value Energy use (MJ)* Reference scenario (S1) 26000.00 3660.00 20600.00 32600.00 25700.00 116.00 0.00 4Rs application scenario (S2) 26700.00 3910.00 21100.00 33500.00 26400.00 124.00 Eutrophication (kg PO4-3 eq.) n.s. Reference scenario (S1) 26.60 4.82 19.70 34.80 26.00 0.15 0.08 4Rs application scenario (S2) 26.20 4.72 19.00 34.30 25.90 0.15 Ozone layer depletion (mg CFC-11 eq.)* Reference scenario (S1) 196.00 43.20 141.00 269.00 191.00 1.37 0.00 4Rs application scenario (S2) 203.00 50.10 141.00 283.00 196.00 1.58 Photochemical oxidation (g ethylene eq.) n.s. Reference scenario (S1) 1690.00 4760.00 590.00 3880.00 970.00 150.00 0.26 4Rs application scenario (S2) 1970.00 6110.00 580.00 5620.00 980.00 190.00 384 Table A-12 Uncertainty analysis results from a Monte Carlo simulation (1000 runs) for the entire egg production supply chain (1 tonne egg produced), including reference scenario (S1) and biochar addition to soil scenario (S3). Only parameter uncertainty was considered. Impact categories with significant differences (p < 0.05) marked with *, and n.s. for non-significant. Mean SD 5% Percentile 95% Percentile Median Standard Error P- value Acidification (kg SO2 eq.) n.s. Reference scenario (S1) 88.30 18.20 62.30 121.00 86.00 0.57 0.86 Biochar addition scenario (S3)- allocation 87.70 104.00 63.60 123.00 88.50 3.29 Acidification (kg SO2 eq.) * Reference scenario (S1) 88.30 18.20 62.30 121.00 86.00 0.57 0.00 Biochar addition scenario (S3)- system expansion 93.60 19.00 64.80 127.00 92.00 0.60 Climate change - GWP100 (kg CO2 eq.)* Reference scenario (S1) 2530.00 494.00 1950.00 3200.00 2470.00 15.60 0.01 Biochar addition scenario (S3)- allocation 1370.00 14500.00 909.00 2830.00 1830.00 459.00 385 Mean SD 5% Percentile 95% Percentile Median Standard Error P- value Climate change - GWP100 (kg CO2 eq.)* Reference scenario (S1) 2530.00 494.00 1950.00 3200.00 2470.00 15.60 0.00 Biochar addition scenario (S3)- system expansion 2410.00 627.00 1450.00 3470.00 2370.00 19.80 Depletion of abiotic resources - fossil fuels (MJ)* Reference scenario (S1) 14600.00 2020.00 11700.00 18200.00 14400.00 63.80 0.04 Biochar addition scenario (S3)- allocation 23700.00 141000.00 21100.00 37300.00 27800.00 4470.00 Depletion of abiotic resources - fossil fuels (MJ)* Reference scenario (S1) 14600.00 2020.00 11700.00 18200.00 14400.00 63.80 0.00 Biochar addition scenario (S3)- system expansion 35400.00 5850.00 26800.00 46000.00 34900.00 185.00 Energy use (MJ)* Reference scenario (S1) 26000.00 3660.00 20600.00 32600.00 25700.00 116.00 0.00 Biochar addition scenario (S3)- allocation 52000.00 251000.00 44500.00 80000.00 59000.00 7940.00 386 Mean SD 5% Percentile 95% Percentile Median Standard Error P- value Energy use (MJ)* Reference scenario (S1) 26000.00 3660.00 20600.00 32600.00 25700.00 116.00 0.00 Biochar addition scenario (S3)- system expansion 67800.00 11400.00 50500.00 87700.00 66800.00 360.00 Eutrophication (kg PO4-3 eq.) n.s. Reference scenario (S1) 26.60 4.82 19.70 34.80 26.00 0.15 0.50 Biochar addition scenario (S3)- allocation 25.40 51.90 19.60 36.10 26.50 1.64 Eutrophication (kg PO4-3 eq.) * Reference scenario (S1) 26.60 4.82 19.70 34.80 26.00 0.15 0.00 Biochar addition scenario (S3)- system expansion 27.60 5.13 19.70 36.80 27.20 0.16 Ozone layer depletion (mg CFC-11 eq.) n.s. Reference scenario (S1) 196.00 43.20 141.00 269.00 191.00 1.37 0.05 Biochar addition scenario (S3)- allocation 324.00 2070.00 239.00 609.00 368.00 65.40 387 Mean SD 5% Percentile 95% Percentile Median Standard Error P- value Ozone layer depletion (mg CFC-11 eq.)* Reference scenario (S1) 196.00 43.20 141.00 269.00 191.00 1.37 0.00 Biochar addition scenario (S3)- system expansion 511.00 200.00 304.00 854.00 467.00 6.33 Photochemical oxidation (g ethylene eq.) n.s. Reference scenario (S1) 1690.00 4760.00 590.00 3880.00 970.00 150.00 0.98 Biochar addition scenario (S3)- allocation 1680.00 8220.00 860.00 4880.00 1350.00 260.00 Photochemical oxidation (g ethylene eq.)* Reference scenario (S1) 1690.00 4760.00 590.00 3880.00 970.00 150.00 0.03 Biochar addition scenario (S3)- system expansion 2040.00 1980.00 1010.00 4720.00 1550.00 60.00 388 Table A-13 Uncertainty analysis results from a Monte Carlo simulation (1000 runs) for the entire egg production supply chains (1 tonne egg produced), including reference scenario (S1) and reduced crude protein diet scenario (S4). Only parameter uncertainty was considered. Impact categories with significant differences (p < 0.05) marked with *, and n.s. for non-significant. Mean SD 5% Percentile 95% Percentile Median Standard Error P- value Acidification (kg SO2 eq.)* Reference scenario (S1) 88.30 18.20 62.30 121.00 86.00 0.57 0.01 Reduced crude protein diet scenario (S4) 90.30 18.10 64.10 122.00 88.30 0.57 Climate change - GWP100 (kg CO2 eq.)* Reference scenario (S1) 2530.00 494.00 1950.00 3200.00 2470.00 15.60 0.00 Reduced crude protein diet scenario (S4) 2610.00 441.00 2000.00 3290.00 2580.00 13.90 Depletion of abiotic resources - fossil fuels (MJ)* Reference scenario (S1) 14600.00 2020.00 11700.00 18200.00 14400.00 63.80 0.00 Reduced crude protein diet scenario (S4) 17000.00 2260.00 13500.00 21200.00 17000.00 71.60 389 Mean SD 5% Percentile 95% Percentile Median Standard Error P- value Energy use (MJ)* Reference scenario (S1) 26000.00 3660.00 20600.00 32600.00 25700.00 116.00 0.00 Reduced crude protein diet scenario (S4) 30500.00 4140.00 24200.00 37900.00 30400.00 131.00 Eutrophication (kg PO4-3 eq.)* Reference scenario (S1) 26.60 4.82 19.70 34.80 26.00 0.15 0.00 Reduced crude protein diet scenario (S4) 27.30 4.86 19.80 35.60 26.70 0.15 Ozone layer depletion (mg CFC-11 eq.)* Reference scenario 196.00 43.20 141.00 269.00 191.00 1.37 0.00 Reduced crude protein diet scenario (S4) 211.00 44.60 150.00 291.00 205.00 1.41 Photochemical oxidation (g ethylene eq.) n.s. Reference scenario (S1) 1690.00 4760.00 590.00 3880.00 970.00 150.00 0.73 Reduced crude protein diet scenario (S4) 1630.00 3210.00 610.00 4430.00 980.00 100.00 390 Table A-14 Uncertainty analysis results from a Monte Carlo simulation (1000 runs) for the entire egg production supply chain (1 tonne egg produced), including reference scenario (S1) and application of scrubber in barn scenario (S5). Only parameter uncertainty was considered. Impact categories with significant differences (p < 0.05) marked with *, and n.s. for non-significant. Mean SD 5% Percentile 95% Percentile Median Standard Error P- value Acidification (kg SO2 eq.)* Reference scenario (S1) 88.30 18.20 62.30 121.00 86.00 0.57 0.00 Scrubber application scenario (S5) 51.70 9.14 38.50 68.30 50.70 0.29 Climate change - GWP100 (kg CO2 eq.)* Reference scenario (S1) 2530.00 494.00 1950.00 3200.00 2470.00 15.60 0.00 Scrubber application scenario (S5) 2180.00 343.00 1690.00 2770.00 2150.00 10.90 Depletion of abiotic resources - fossil fuels (MJ)* Reference scenario (S1) 14600.00 2020.00 11700.00 18200.00 14400.00 63.80 0.00 Scrubber application scenario (S5) 14000.00 1980.00 11100.00 17700.00 13900.00 62.70 Energy use (MJ)* Reference scenario (S1) 26000.00 3660.00 20600.00 32600.00 25700.00 116.00 0.00 Scrubber application scenario (S5) 25300.00 3640.00 19600.00 31900.00 25100.00 115.00 391 Mean SD 5% Percentile 95% Percentile Median Standard Error P- value Eutrophication (kg PO4-3 eq.)* Reference scenario (S1) 26.60 4.82 19.70 34.80 26.00 0.15 0.00 Scrubber application scenario (S5) 18.20 2.95 13.90 23.80 17.90 0.09 Ozone layer depletion (mg CFC-11 eq.)* Reference scenario (S1) 196.00 43.20 141.00 269.00 191.00 1.37 0.00 Scrubber application scenario (S5) 190.00 40.50 136.00 257.00 185.00 1.28 Photochemical oxidation (g ethylene eq.) n.s. Reference scenario 1690.00 4760.00 590.00 3880.00 970.00 150.00 0.96 Scrubber application scenario (S5) 1680.00 2350.00 560.00 5340.00 990.00 70.00 392 Table A-15 Uncertainty analysis results from a Monte Carlo simulation (1000 runs) for the entire egg production supply chain (1 tonne egg produced), including reference scenario (S1) and biochar addition to the stored manure scenario (S6). Only parameter uncertainty was considered. Impact categories with significant differences (p < 0.05) marked with *, and n.s. for non-significant. Mean SD 5% Percentile 95% Percentile Median Standard Error P- value Acidification (kg SO2 eq.)* Reference scenario (S1) 88.30 18.20 62.30 121.00 86.00 0.57 0.00 Biochar addition to the stored manure scenario (S6) - allocation 52.00 8.83 39.00 67.80 51.10 0.28 Acidification (kg SO2 eq.) * Reference scenario (S1) 88.30 18.20 62.30 121.00 86.00 0.57 0.00 Biochar addition to the stored manure scenario (S6) – system expansion 52.40 9.23 38.90 68.40 51.30 0.29 Climate change - GWP100 (kg CO2 eq.)* Reference scenario (S1) 2530.00 494.00 1950.00 3200.00 2470.00 15.60 0.00 Biochar addition to the stored manure scenario (S6) - allocation 2080.00 340.00 1600.00 2640.00 2060.00 10.80 393 Mean SD 5% Percentile 95% Percentile Median Standard Error P- value Climate change - GWP100 (kg CO2 eq.) * Reference scenario (S1) 2530.00 494.00 1950.00 3200.00 2470.00 15.60 0.00 Biochar addition to the stored manure scenario (S6) – system expansion 2170.00 391.00 1660.00 2790.00 2140.00 12.40 Depletion of abiotic resources - fossil fuels (MJ)* Reference scenario (S1) 14600.00 2020.00 11700.00 18200.00 14400.00 63.80 0.00 Biochar addition to the stored manure scenario (S6) - allocation 15600.00 2420.00 12200.00 19300.00 15300.00 76.60 Depletion of abiotic resources - fossil fuels (MJ)* Reference scenario (S1) 14600.00 2020.00 11700.00 18200.00 14400.00 63.80 0.00 Biochar addition to the stored manure scenario (S6) – system expansion 16600.00 2960.00 12700.00 21200.00 16400.00 93.60 394 Mean SD 5% Percentile 95% Percentile Median Standard Error P- value Energy use (MJ)* Reference scenario (S1) 26000.00 3660.00 20600.00 32600.00 25700.00 116.00 0.00 Biochar addition to the stored manure scenario (S6) - allocation 29200.00 4790.00 23000.00 37200.00 28700.00 151.00 Energy use (MJ)* Reference scenario (S1) 26000.00 3660.00 20600.00 32600.00 25700.00 116.00 0.00 Biochar addition to the stored manure scenario (S6) – system expansion 30400.00 5640.00 23300.00 38900.00 30000.00 178.00 Eutrophication (kg PO4-3 eq.)* Reference scenario (S1) 26.60 4.82 19.70 34.80 26.00 0.15 0.00 Biochar addition to the stored manure scenario (S6) - allocation 18.20 2.94 13.80 23.40 18.00 0.09 395 Mean SD 5% Percentile 95% Percentile Median Standard Error P- value Eutrophication (kg PO4-3 eq.) * Reference scenario (S1) 26.60 4.82 19.70 34.80 26.00 0.15 0.00 Biochar addition to the stored manure scenario (S6) – system expansion 18.40 3.10 13.70 24.10 18.10 0.10 Ozone layer depletion (mg CFC-11 eq.)* Reference scenario (S1) 196.00 43.20 141.00 269.00 191.00 1.37 0.00 Biochar addition to the stored manure scenario (S6) - allocation 211.00 55.10 149.00 300.00 203.00 1.74 Ozone layer depletion (mg CFC-11 eq.) n.s. Reference scenario (S1) 196.00 43.20 141.00 269.00 191.00 1.37 0.00 Biochar addition to the stored manure scenario (S6) – system expansion 226.00 62.00 154.00 324.00 216.00 1.96 396 Mean SD 5% Percentile 95% Percentile Median Standard Error P- value Photochemical oxidation (g ethylene eq.) n.s. Reference scenario (S1) 1690.00 4760.00 590.00 3880.00 970.00 150.00 0.54 Biochar addition to the stored manure scenario (S6) - allocation 1590.00 2100.00 600.00 4080.00 1000.00 70.00 Photochemical oxidation (g ethylene eq.) n.s. Reference scenario (S1) 1690.00 4760.00 590.00 3880.00 970.00 150.00 0.91 Biochar addition to the stored manure scenario (S6) – system expansion 1720.00 2720.00 610.00 4800.00 1030.00 90.00 397 Table A-16 Uncertainty analysis results from a Monte Carlo simulation (1000 runs) for the entire egg production supply chain (1 tonne egg produced), including reference scenario (S1) and manure incorporation scenario (S7). Only parameter uncertainty was considered. Impact categories with significant differences (p < 0.05) marked with *, and n.s. for non-significant. Mean SD 5% Percentile 95% Percentile Median Standard Error P- value Acidification (kg SO2 eq.)* Reference scenario (S1) 88.30 18.20 62.30 121.00 86.00 0.57 0.00 Manure incorporation scenario (S7) 52.10 9.19 39.10 69.20 50.90 0.29 Climate change - GWP100 (kg CO2 eq.)* Reference scenario (S1) 2530.00 494.00 1950.00 3200.00 2470.00 15.60 0.00 Manure incorporation scenario (S7) 2290.00 384.00 1780.00 2930.00 2250.00 12.20 Depletion of abiotic resources - fossil fuels (MJ)* Reference scenario (S1) 14600.00 2020.00 11700.00 18200.00 14400.00 63.80 0.00 Manure incorporation scenario (S7) 15600.00 2210.00 12300.00 19700.00 15500.00 69.90 Energy use (MJ)* Reference scenario (S1) 26000.00 3660.00 20600.00 32600.00 25700.00 116.00 0.00 Manure incorporation scenario (S7) 27000.00 3910.00 21200.00 34300.00 26700.00 124.00 398 Mean SD 5% Percentile 95% Percentile Median Standard Error P- value Eutrophication (kg PO4-3 eq.)* Reference scenario (S1) 26.60 4.82 19.70 34.80 26.00 0.15 0.00 Manure incorporation scenario (S7) 18.30 3.05 13.80 23.50 18.00 0.10 Ozone layer depletion (mg CFC-11 eq.)* Reference scenario (S1) 196.00 43.20 141.00 269.00 191.00 1.37 0.00 Manure incorporation scenario (S7) 208.00 45.90 150.00 290.00 201.00 1.45 Photochemical oxidation (g ethylene eq.)* Reference scenario (S1) 1690.00 4760.00 590.00 3880.00 970.00 150.00 0.00 Manure incorporation scenario (S7) 1750.00 3170.00 610.00 5200.00 990.00 100.00 399 Table A-17 Uncertainty analysis results from a Monte Carlo simulation (1000 runs) for the entire egg production supply chain (1 tonne egg produced), including reference scenario (S1) and application of all strategies scenario (S8). Only parameter uncertainty was considered. Impact categories with significant differences (p < 0.05) are marked with *, and n.s. for non-significant. Mean SD 5% Percentile 95% Percentile Median Standard Error P- value Acidification (kg SO2 eq.) * Reference scenario (S1) 88.30 18.20 62.30 121.00 86.00 0.57 0.00 All strategies scenario (S8) - allocation 64.90 183.00 44.00 77.90 58.20 5.80 Acidification (kg SO2 eq.) * Reference scenario (S1) 88.30 18.20 62.30 121.00 86.00 0.57 0.00 All strategies scenario (S8) – system expansion 60.90 10.90 45.20 79.50 60.20 0.34 Climate change - GWP100 (kg CO2 eq.) n.s. Reference scenario (S1) 2530.00 494.00 1950.00 3200.00 2470.00 15.60 0.76 All strategies scenario (S8) - allocation 2290.00 25000.00 538.00 2420.00 1510.00 791.00 400 Mean SD 5% Percentile 95% Percentile Median Standard Error P- value Climate change - GWP100 (kg CO2 eq.) * Reference scenario (S1) 2530.00 494.00 1950.00 3200.00 2470.00 15.60 0.00 All strategies scenario (S8) – system expansion 2210.00 625.00 1230.00 3140.00 2190.00 19.80 Depletion of abiotic resources - fossil fuels (MJ)* Reference scenario (S1) 14600.00 2020.00 11700.00 18200.00 14400.00 63.80 0.00 All strategies scenario (S8) - allocation 40300.00 264000.00 24800.00 41300.00 31500.00 8340.00 Depletion of abiotic resources - fossil fuels (MJ)* Reference scenario (S1) 14600.00 2020.00 11700.00 18200.00 14400.00 63.80 0.00 All strategies scenario (S8) – system expansion 41100.00 6710.00 31300.00 52700.00 40800.00 212.00 Energy use (MJ)* Reference scenario (S1) 26000.00 3660.00 20600.00 32600.00 25700.00 116.00 0.00 All strategies scenario (S8) - allocation 83100.00 446000.00 52100.00 89300.00 67800.00 14100.00 401 Mean SD 5% Percentile 95% Percentile Median Standard Error P- value Energy use (MJ)* Reference scenario (S1) 26000.00 3660.00 20600.00 32600.00 25700.00 116.00 0.00 All strategies scenario (S8) – system expansion 78900.00 13600.00 59200.00 103000.00 77600.00 429.00 Eutrophication (kg PO4-3 eq.) * Reference scenario (S1) 26.60 4.82 19.70 34.80 26.00 0.15 0.01 All strategies scenario (S8) - allocation 22.00 57.50 15.00 26.40 19.90 1.82 Eutrophication (kg PO4-3 eq.) n.s Reference scenario (S1) 26.60 4.82 19.70 34.80 26.00 0.15 0.00 All strategies scenario (S8) – system expansion 20.40 3.49 15.30 26.50 20.20 0.11 Ozone layer depletion (mg CFC-11 eq.)* Reference scenario (S1) 196.00 43.20 141.00 269.00 191.00 1.37 0.00 All strategies scenario (S8) - allocation 531.00 3210.00 267.00 685.00 406.00 101.00 402 Mean SD 5% Percentile 95% Percentile Median Standard Error P- value Ozone layer depletion (mg CFC-11 eq.) * Reference scenario (S1) 196.00 43.20 141.00 269.00 191.00 1.37 0.00 All strategies scenario (S8) – system expansion 564.00 207.00 338.00 912.00 528.00 6.55 Photochemical oxidation (g ethylene eq.) n.s. Reference scenario (S1) 1690.00 4760.00 590.00 3880.00 970.00 150.00 0.10 All strategies scenario (S8) - allocation 2390.00 12500.00 920.00 4830.00 1420.00 390.00 Photochemical oxidation (g ethylene eq.)* Reference scenario (S1) 1690.00 4760.00 590.00 3880.00 970.00 150.00 0.00 All strategies scenario (S8) – system expansion 2400.00 3090.00 1070.00 5440.00 1700.00 100.00
Thesis/Dissertation
eng
Biology
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Life cycle assessment of nitrogen efficiency strategies for Canadian egg supply chains
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