Arts and Sciences, Irving K. Barber School of (Okanagan)
Biology, Department of (Okanagan)
DSpace
UBCO
Bamber, Nicole
Master of Science - MSc
University of British Columbia
Food systems contribute substantially to environmental impacts, including 19-29% of greenhouse gas (GHG) emissions. The use of wood and bark chip mulch as soil cover has been proposed to reduce GHG emissions, in addition to water use and herbicide use on apple orchards. The current study expanded the scope of this prior investigation of mulch use to include all “upstream” processes for the production and transportation of the mulch used on orchards, as well as all other industrial systems that use mulch and would be impacted (i.e. through decreased availability) if mulch use on orchards were to increase. This was done using life cycle assessment (LCA), a sustainability management tool that accounts for all environmental inputs and outputs along the supply chain, or life cycle, of a product. The differences in water and herbicide use with and without mulch were not accounted for due to the experimental study design. When the orchard life cycle was considered, apples produced using mulch had higher GHG emissions than those without mulch, as well as higher impacts in all of the other impact categories considered (including acidification, eutrophication, toxicity, land and energy use). When the systems that would likely be affected by an increase in mulch use were also considered (i.e. bioenergy co-generation and paper production, the primary alternative uses of bark and wood chips in the region), the results were mixed. Some impact categories showed a benefit for mulch use on orchards and some showed a negative impact. These results also depended on whether the co-generation processes were assumed to take place in British Columbia or Washington (locations where biomass is used for co-generation). These results do not support the recommendation of mulch application on orchards as a sustainability management practice, based on the GHG mitigation benefits alone (other mulch benefits were not included). This study also demonstrates the importance of including LCA in sustainability assessment, and highlights the need for improved availability of high quality data for LCA in Canada in order to reduce uncertainty and improve the robustness of results.
ATTRIBUTIONAL AND CONSEQUENTIAL LIFE CYCLE ASSESSMENT OF WOOD CHIP AND BARK MULCH USE ON OKANAGAN APPLE ORCHARDS, INCLUDING UNCERTAINTY ASSESSMENT AND BEST PRACTICES RECOMMENDATIONS by Nicole Bamber B.Sc., University of Toronto, 2017 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE COLLEGE OF GRADUATE STUDIES (Biology) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) December 2019 © Nicole Bamber, 2019 ii The following individuals certify that they have read, and recommend to the College of Graduate Studies for acceptance, a thesis/dissertation entitled: Attributional and consequential life cycle assessment of wood chip and bark mulch use on Okanagan apple orchards, including uncertainty assessment and best practices recommendations submitted by Nicole Bamber in partial fulfillment of the requirements of the degree of Master of Science . Dr. Nathan Pelletier, Biology Department, Irving K. Barber School of Arts and Sciences, and Faculty of Management Supervisor Dr. Melanie Jones, Biology Department, Irving K. Barber School of Arts and Sciences Supervisory Committee Member Dr. Louise Nelson, Biology Department, Irving K. Barber School of Arts and Sciences Supervisory Committee Member Dr. Mike Chiasson, Faculty of Management Supervisory Committee Member Dr. Steven Young, School of Environment, Enterprise and Development, University of Waterloo External Examiner iii Abstract Food systems contribute substantially to environmental impacts, including 19-29% of greenhouse gas (GHG) emissions. The use of wood and bark chip mulch as soil cover has been proposed to reduce GHG emissions, in addition to water use and herbicide use on apple orchards. The current study expanded the scope of this prior investigation of mulch use to include all “upstream” processes for the production and transportation of the mulch used on orchards, as well as all other industrial systems that use mulch and would be impacted (i.e. through decreased availability) if mulch use on orchards were to increase. This was done using life cycle assessment (LCA), a sustainability management tool that accounts for all environmental inputs and outputs along the supply chain, or life cycle, of a product. The differences in water and herbicide use with and without mulch were not accounted for due to the experimental study design. When the orchard life cycle was considered, apples produced using mulch had higher GHG emissions than those without mulch, as well as higher impacts in all of the other impact categories considered (including acidification, eutrophication, toxicity, land and energy use). When the systems that would likely be affected by an increase in mulch use were also considered (i.e. bioenergy co-generation and paper production, the primary alternative uses of bark and wood chips in the region), the results were mixed. Some impact categories showed a benefit for mulch use on orchards and some showed a negative impact. These results also depended on whether the co-generation processes were assumed to take place in British Columbia or Washington (locations where biomass is used for co-generation). These results do not support the recommendation of mulch application on orchards as a sustainability management practice, based on the GHG mitigation benefits alone (other mulch benefits were not included). This study also demonstrates the importance of including LCA in sustainability assessment, and highlights the need for improved availability of high quality data for LCA in Canada in order to reduce uncertainty and improve the robustness of results. iv Lay Summary Wood chip and bark mulch use on apple orchard soils can reduce greenhouse gas emissions from the orchard. However, mulch production, transportation and application result in greenhouse gas emissions that outweigh this benefit, as well as other emissions and resource use. In addition, if mulch use is increased on orchards, it is less available for other uses (electricity, heat and paper production) which, in turn, must be produced in other ways. Overall, when taking all these uses of mulch into account, some types of environmental impacts are higher when apples are produced with mulch, and some are lower. Therefore, mulch use cannot be recommended as a way of reducing emissions and other environmental impacts. v Preface The review presented in Chapter 3, reprinted with permission of the authors, has been published in the International Journal of Life Cycle Assessment. It was researched by Nicole Bamber and other graduate student colleagues, analyzed and written by Nicole Bamber and edited by Nathan Pelletier. Bamber, N. et al. 2019 “Comparing sources and analysis of uncertainty in consequential and attributional life cycle assessment: Review of current practice and recommendations.” International Journal of Life Cycle Assessment. https://doi.org/10.1007/s11367-019-01663-1 A version of the content of Chapters 4 and 5 has been submitted to the Journal of Cleaner Production. These chapters were researched and written by Nicole Bamber, with edits from Nathan Pelletier, Melanie Jones, and Louise Nelson, and data and edits from Kirsten Hannam and Craig Nichol. Bamber, N. et al. “Life cycle assessment of bark mulch use on Okanagan apple orchards – Part 1: Attributional.” Journal of Cleaner production (submitted September 2019); Bamber, N. et al. “Life cycle assessment of bark mulch use on Okanagan apple orchards – Part 2: Consequential.” Journal of Cleaner production (submitted September 2019). vi Table of Contents Abstract .................................................................................................................................... iii Lay Summary ........................................................................................................................... iv Preface ....................................................................................................................................... v Table of Contents ..................................................................................................................... vi List of Tables ............................................................................................................................ xi List of Figures ...................................................................................................................... xviii Acknowledgements .............................................................................................................. xxv Dedication ............................................................................................................................ xxvi Chapter 1: Introduction ............................................................................................................ 1 1.1 Rationale: Environmental impacts of mulch use in apple production ......................... 1 1.2 Life cycle assessment ............................................................................................... 2 1.3 Research objectives ................................................................................................. 3 1.4 Scope of this thesis ................................................................................................... 4 Chapter 2: Review of life cycle assessment in horticulture and bioenergy .......................... 5 2.1 Life cycle assessment ............................................................................................... 5 2.1.1 Goal and scope ................................................................................................... 5 2.1.2 Life cycle inventory ............................................................................................. 6 2.1.3 Life cycle impact assessment.............................................................................. 7 2.1.4 Life cycle interpretation ......................................................................................10 2.1.5 Attributional and consequential LCA ..................................................................11 2.2 Horticultural LCA .....................................................................................................12 2.3 Bioenergy LCA ........................................................................................................13 Chapter 3: Comparing sources and analysis of uncertainty in consequential and attributional life cycle assessment: Review of current practice and recommendations ... 15 vii 3.1 Synopsis ..................................................................................................................15 3.2 Uncertainty in life cycle assessment ........................................................................16 3.3 Methods ...................................................................................................................18 3.3.1 Reporting of uncertainty .....................................................................................19 3.3.2 Sources and quantification of uncertainty ...........................................................19 3.3.3 Uncertainty analysis/propagation specific to A- and CLCA .................................20 3.3.4 Uncertainty analysis by sector............................................................................20 3.4 Results and discussion ............................................................................................20 3.4.1 Reporting of uncertainty .....................................................................................20 3.4.2 Sources and quantification of uncertainty ...........................................................23 3.4.2.1 Sources common to both A- and CLCA, their importance, and their rates of consideration .............................................................................................................23 3.4.2.2 Uncertainty specific to ALCA ......................................................................27 3.4.2.3 Uncertainty specific to CLCA ......................................................................27 3.4.3 Methods for uncertainty propagation in LCA ......................................................28 3.4.3.1 Uncertainty propagation in ALCA studies ...................................................31 3.4.3.2 Uncertainty propagation in CLCA studies ...................................................32 3.4.4 Uncertainty analysis by sector............................................................................34 3.5 Discussion ...............................................................................................................35 Chapter 4: Attributional life cycle assessment of mulch use on Okanagan apple orchards .................................................................................................................................. 38 4.1 Synopsis ..................................................................................................................38 4.2 Methods ...................................................................................................................39 4.2.1 Goal and scope ..................................................................................................39 4.2.2 Life cycle inventory ............................................................................................42 viii 4.2.2.1 Flow diagram and system boundaries ........................................................42 4.2.2.2 Foreground data collection .........................................................................42 4.2.2.3 Background data ........................................................................................42 4.2.2.4 Model .........................................................................................................42 4.2.2.4.1 Fertilizer application .................................................................................43 4.2.2.4.2 Irrigation ...................................................................................................45 4.2.2.4.3 Plant protection ........................................................................................46 4.2.2.4.4 Mulch .......................................................................................................52 4.2.2.4.5 Energy use ...............................................................................................53 4.2.2.4.6 Measured emissions ................................................................................54 4.2.2.4.7 Uncertainty ...............................................................................................54 4.2.3 Impact assessment ........................................................................................55 4.2.4 Interpretation ..................................................................................................57 4.3 Results ....................................................................................................................57 4.3.1 Inventory ........................................................................................................57 4.3.2 Impact assessment ........................................................................................60 4.3.2.1 Sensitivity analysis .....................................................................................75 4.3.2.1.1 Carbon sequestration ...............................................................................75 4.3.2.1.2 LCIA methods ..........................................................................................76 4.3.2.1.3 Transportation method .............................................................................79 4.3.2.2 Uncertainty analysis ...................................................................................83 4.3.3 Interpretation ..................................................................................................88 4.3.3.1 Completeness, sensitivity and consistency check ......................................88 4.3.3.2 Discussion .....................................................................................................90 ix Chapter 5: Consequential life cycle assessment of increased mulch use on Okanagan apple orchards, including displaced alternative uses .......................................................... 92 5.1 Synopsis ..................................................................................................................92 5.2 Methods ...................................................................................................................93 5.2.1 Goal and scope ..................................................................................................94 5.2.2 Life cycle inventory ............................................................................................96 5.2.2.1 Flow diagram and system boundaries ........................................................96 5.2.2.2 Primary data collection ...............................................................................96 5.2.2.3 Background data ........................................................................................96 5.2.2.4 Model .........................................................................................................97 5.2.2.4.1 Mulch ....................................................................................................97 5.2.2.4.2 Pulp and paper .....................................................................................98 5.2.2.4.3 Bioenergy co-generation ..................................................................... 102 5.2.2.5 Alternative functional units........................................................................ 102 5.2.2.6 Scenario 1: No mulch used on orchards, only for other uses .................... 103 5.2.2.7 Scenario 2: Increase of mulch use on orchards ........................................ 103 5.2.3 Impact assessment .......................................................................................... 104 5.2.4 Interpretation .................................................................................................... 106 5.3 Results .................................................................................................................. 107 5.3.1 Life cycle impact assessment........................................................................... 107 5.3.1.1 Scenario 1 vs scenario 2: no mulch use on orchards vs increased mulch use on orchards to produce 1 kg apples ......................................................................... 107 5.3.1.2 Sensitivity analysis ....................................................................................... 115 5.3.1.3 Uncertainty analysis ..................................................................................... 122 5.3.2 Interpretation .................................................................................................... 127 x 5.3.2.1 Completeness, sensitivity and consistency check ........................................ 127 5.3.2.2 Discussion ................................................................................................... 128 Chapter 6: Conclusions ....................................................................................................... 131 6.1 Mulch sustainability ................................................................................................. 131 6.2 Uncertainty .............................................................................................................. 132 6.3 The importance of life cycle thinking ........................................................................ 133 Bibliography ..................................................................................................................... 135 Appendices ........................................................................................................................... 143 Appendix A – Raw data and sample calculations ................................................................ 143 Appendix B – Biomass producer survey .............................................................................. 152 Appendix C – Monte Carlo results ....................................................................................... 153 Appendix D – Uncertainty Literature Review Supplementary Tables ................................... 177 xi List of Tables Table 1. Numbers of life cycle assessment studies reviewed from January 2014- August 2018 in International Journal of Life Cycle Assessment, Journal of Cleaner Production, Sustainability, Applied Energy, Science of the Total Environment, Resources, Conservation and Recycling, and Journal of Industrial Ecology, categorised by LCA type. .....................................................21 Table 2. Sources of uncertainty in LCA in general and specific to ALCA and CLCA from LCA studies in the International Journal of Life Cycle Assessment, Journal of Cleaner Production, Sustainability, Applied Energy, Science of the Total Environment, Resources, Conservation and Recycling, and Journal of Industrial Ecology reviewed from January 2014-August 2018. ..........24 Table 3. Sources of uncertainty and analysis type, categorized by parameter, scenario or model uncertainty, in attributional LCA studies. ...................................................................................25 Table 4. Sources of uncertainty and analysis type, categorized by parameter, scenario or model uncertainty, in consequential LCA studies. ................................................................................25 Table 5. Sources of uncertainty and analysis type, categorized by parameter, scenario or model uncertainty, in combination attributional and consequential LCA studies in the International Journal of Life Cycle Assessment, Journal of Cleaner Production, Sustainability, Applied Energy, Science of the Total Environment, Resources, Conservation and Recycling, and Journal of Industrial Ecology reviewed from January 2014-August 2018. ..................................26 Table 6. Number of studies in the International Journal of Life Cycle Assessment, Journal of Cleaner Production, Sustainability, Applied Energy, Science of the Total Environment, Resources, Conservation and Recycling, and Journal of Industrial Ecology that either did or did not perform uncertainty analysis, categorized by the major industrial sectors with defined base uncertainty values (Frischknecht et al 2005). ............................................................................35 Table 7. LCI processes and databases used to model inputs related to fertilizer application for apple production in the Okanagan Valley, including any modifications made. ...........................44 xii Table 8. LCI processes and databases used to model inputs related to irrigation for apple production in the Okanagan Valley, including any modifications made. .....................................46 Table 9. LCI processes and databases used to model inputs related to plant protection products for apple production in the Okanagan Valley, including any modifications made. ......................46 Table 10. Processes and databases used to model mulch inputs to the Okanagan Valley apple orchard LCA model, including any modifications. ......................................................................53 Table 11. Processes used to model energy inputs to the Okanagan Valley apple orchard LCA model. .......................................................................................................................................54 Table 12. Inventory table of life cycle inputs and outputs for the production of 1 kg apples on an Okanagan Valley orchard. .........................................................................................................58 Table 13. Life cycle impact assessment results for the production of 1kg apples on an Okanagan Valley orchard under four different production treatments, separated by orchard process. ....................................................................................................................................70 Table 14. Relative ranking of the contribution of each orchard process to the life cycle impacts of apple production in the Okanagan Valley for each impact category. .....................................74 Table 15. Greenhouse gas emissions (kg CO2 equivalent) for 1 kg apples produced on an Okanagan Valley apple orchard with four different production treatments, calculated using CML (baseline) impact assessment methods, separated by orchard process. ...................................77 Table 16. Greenhouse gas emissions (kg CO2 equivalent) for 1 kg apples produced on an Okanagan Valley apple orchard with four different production treatments, calculated using ReCiPe Midpoint (H) methods, separated by orchard process. .................................................77 Table 17. Comparison of LCIA results for the production of 1 kg apples on an Okanagan Valley apple orchard with four different production treatments, calculated using CML (baseline) and ReCiPe Midpoint (H) impact assessment methods. ..................................................................78 xiii Table 18. Comparison of LCIA results for the production of 1 kg apples on an Okanagan Valley apple orchard with four different production treatments with all transportation flows modelled as either truck or train transportation..............................................................................................80 Table 19. Aggregated survey results for percentage distribution of wood chip and bark mulch from sawmills in the Okanagan Valley. The current distribution is presented, as well as a scenario where mulch is not used in agriculture. .......................................................................98 Table 20. Uses of wood chips and bark mulch produced by sawmills in British Columbia, and the processes and databases used to model them, including all modifications..........................98 Table 21. Functional units of alternative uses of wood and bark chips based on the distribution of mulch use in the Okanagan. Numbers were calculated relative to the amount of mulch used to produce 1 kg apples on an Okanagan apple orchard. ......................................................... 103 Table 22. Modelled processes for each product in scenario 1: no wood chips or bark chips used on orchards, instead all used for paper and electricity. ............................................................ 103 Table 23. Modelled processes of apples, paper, and electricity produced (with or without wood/bark chips) for scenario 2: increased mulch use on orchards. ........................................ 104 Table 24. Processes modelled for electricity production without wood/bark chips for sensitivity analysis, including all modifications made to original process from database indicated. .......... 106 Table 25. Life cycle impact assessment results for scenario 1 where no mulch is used on the orchard (only used for other processes) and scenario 2 where mulch use on orchards is increased by the amount necessary to produce 1 kg of apples. Impacts are broken down by each functional unit for each process that uses bark or wood chip mulch. ............................... 111 Table 26. Sensitivity analysis results for scenario 1 (no mulch on orchard), and scenario 2 (increased mulch on orchard). The co-generation process with bark chips was changed to occur in Washington and without mulch was changed to the Washington grid mix and natural gas. . 119 xiv Table 27. Amounts and types of plant protection products applied to the apple orchard at Summerland Research and Development Centre (data provided by researchers at the research centre). ................................................................................................................................... 143 Table 28. Uncertainty analysis results of a Monte Carlo simulation (1000 runs) for the production of 1 kg apples, including parameter uncertainty only. .............................................................. 153 Table 29. Uncertainty analysis results of a Monte Carlo simulation (1000 runs) for the production of 1 kg apples, including parameter and impact assessment uncertainty. ............................... 155 Table 30. Uncertainty analysis results of a Monte Carlo simulation (1000 runs) for the production of 1 kg apples, including foreground parameter uncertainty only. ............................................ 156 Table 31. Uncertainty analysis results of a Monte Carlo simulation (1000 runs) for the production of 1 kg apples, including background parameter uncertainty only. .......................................... 159 Table 32. Uncertainty analysis results of a Monte Carlo simulation (1000 runs) for the production of 1 kg apples, including foreground parameter uncertainty from experimentally measured orchard-level emissions of N2O and CO2 only. ........................................................................ 161 Table 33. Uncertainty analysis results of a Monte Carlo simulation (1000 runs) for the production of 1 kg apples, including foreground parameter uncertainty from experimental mulch inputs only. ............................................................................................................................................... 164 Table 34. Uncertainty analysis results of a Monte Carlo simulation (1000 runs) for the production of 1 kg apples, including only the relative uncertainty that differs between treatments. ........... 166 Table 35. Monte Carlo simulation results for the production of 1 kg apples, 0.653 kWh electricity + 0.653 MJ heat co-generation and 0.144 kg paper for all impact categories with parameter uncertainty. ............................................................................................................................. 169 Table 36. Monte Carlo simulation results for the production of 1 kg apples, 0.653 kWh electricity + 0.653 MJ heat co-generation and 0.144 kg paper for select impact categories with parameter and impact assessment uncertainty. ....................................................................................... 170 xv Table 37. Monte Carlo simulation results for the production of 1 kg apples, 0.653 kWh electricity + 0.653 MJ heat co-generation and 0.144 kg paper for all impact categories with parameter uncertainty from the orchard product systems only. ................................................................ 171 Table 38. Monte Carlo simulation results for the production of 1 kg apples, 0.653 kWh electricity + 0.653 MJ heat co-generation and 0.144 kg paper for all impact categories with parameter uncertainty from the paper product systems only. ................................................................... 173 Table 39. Monte Carlo simulation results for the production of 1 kg apples, 0.653 kWh electricity + 0.653 MJ heat co-generation and 0.144 kg paper for all impact categories with parameter uncertainty from the co-generation product systems only. ....................................................... 175 Table 40. Types of LCA studies and reporting of uncertainty in the International Journal of Life Cycle Assessment from January 2014-August 2018. .............................................................. 177 Table 41. Types of LCA studies and reporting of uncertainty in Journal of Cleaner Production from January 2014-August 2018. ............................................................................................ 177 Table 42. Types of LCA studies and reporting of uncertainty in Sustainability from January 2014-August 2018. .................................................................................................................. 177 Table 43. Types of LCA studies and reporting of uncertainty in Applied Energy from January 2014-August 2018. .................................................................................................................. 177 Table 44. Types of LCA studies and reporting of uncertainty in Science of the Total Environment from January 2014-August 2018. ............................................................................................ 178 Table 45. Types of LCA studies and reporting of uncertainty in Resources, Conservation and Recycling from January 2014-August 2018. ............................................................................ 178 Table 46. Types of LCA studies and reporting of uncertainty in Journal of Industrial Ecology from January 2014-August 2018. ............................................................................................ 178 Table 47. Reported uncertainty analysis in LCA case studies in the International Journal of Life Cycle Assessment from 2014-2018. ........................................................................................ 178 xvi Table 48. Reported uncertainty analysis in LCA case studies in Journal of Cleaner Production from 2014-2018. ...................................................................................................................... 179 Table 49. Reported uncertainty analysis in LCA case studies in Sustainability from 2014-2018. ............................................................................................................................................... 179 Table 50. Reported uncertainty analysis in LCA case studies in Applied Energy from 2014-2018. ............................................................................................................................................... 179 Table 51. Reported uncertainty analysis in LCA case studies in Science of the Total Environment from 2014-2018. ................................................................................................. 179 Table 52. Reported uncertainty analysis in LCA case studies in Resources, Conservation and Recycling from 2014-2018. ..................................................................................................... 180 Table 53. Reported uncertainty analysis in LCA case studies in Journal of Industrial Ecology from 2014-2018. ...................................................................................................................... 180 Table 54. Types of uncertainty analysis performed in ALCA and CLCA studies in the International Journal of Life Cycle Assessment from 2014-2018. ............................................ 180 Table 55. Types of uncertainty analysis performed in ALCA and CLCA studies in Journal of Cleaner Production from 2014-2018. ...................................................................................... 181 Table 56. Types of uncertainty analysis performed in ALCA and CLCA studies in Sustainability from 2014-2018. ...................................................................................................................... 181 Table 57. Types of uncertainty analysis performed in ALCA and CLCA studies in Applied Energy from 2014-2018. ......................................................................................................... 181 Table 58. Types of uncertainty analysis performed in ALCA and CLCA studies in Science of the Total Environment from 2014-2018. ........................................................................................ 182 Table 59. Types of uncertainty analysis performed in ALCA and CLCA studies in Resources, Conservation and Recycling from 2014-2018. ......................................................................... 182 xvii Table 60. Types of uncertainty analysis performed in ALCA and CLCA studies in Journal of Industrial Ecology from 2014-2018. ......................................................................................... 182 Table 71. Number of studies in the International Journal of Life Cycle Assessment that either did or did not perform uncertainty analysis, categorized by the major industrial sectors with defined base uncertainty values (Frischknecht et al 2005). .................................................................. 183 Table 62. Number of studies in Journal of Cleaner Production that either did or did not perform uncertainty analysis, categorized by the major industrial sectors with defined base uncertainty values (Frischknecht et al 2005). ............................................................................................ 184 Table 63. Number of studies in Sustainability that either did or did not perform uncertainty analysis, categorized by the major industrial sectors with defined base uncertainty values (Frischknecht et al 2005). ........................................................................................................ 185 Table 64. Number of studies in Applied Energy that either did or did not perform uncertainty analysis, categorized by the major industrial sectors with defined base uncertainty values (Frischknecht et al 2005). ........................................................................................................ 186 Table 65. Number of studies in Science of the Total Environment that either did or did not perform uncertainty analysis, categorized by the major industrial sectors with defined base uncertainty values (Frischknecht et al 2005). .......................................................................... 187 Table 66. Number of studies in Resources, Conservation and Recycling that either did or did not perform uncertainty analysis, categorized by the major industrial sectors with defined base uncertainty values (Frischknecht et al 2005). .......................................................................... 188 Table 67. Number of studies in Journal of Industrial Ecology that either did or did not perform uncertainty analysis, categorized by the major industrial sectors with defined base uncertainty values (Frischknecht et al 2005). ............................................................................................ 189 xviii List of Figures Figure 1. Types of LCA studies and reporting of uncertainty in International Journal of Life Cycle Assessment, Journal of Cleaner Production, Sustainability, Applied Energy, Science of the Total Environment, Resources, Conservation and Recycling, and Journal of Industrial Ecology from January 2014-August 2018. a) Attributional studies (n = 2522 ALCAs). b) Consequential studies (n = size 136 CLCAs). c) Combination attributional and consequential studies (n = 32 LCAs). .22 Figure 2. Reported uncertainty analysis in LCA case studies in the International Journal of Life Cycle Assessment, Journal of Cleaner Production, Sustainability, Applied Energy, Science of the Total Environment, Resources, Conservation and Recycling, and Journal of Industrial Ecology from 2014-2017. ..........................................................................................................23 Figure 3. Sources of uncertainty included in uncertainty analysis in International Journal of Life Cycle Assessment, Journal of Cleaner Production, Sustainability, Applied Energy, Science of the Total Environment, Resources, Conservation and Recycling, and Journal of Industrial Ecology from January 2014-August 2018. a) Attributional studies (n = 470). b) Consequential studies (n = 19). ........................................................................................................................26 Figure 4. Types of uncertainty analysis performed in ALCA and CLCA studies in International Journal of Life Cycle Assessment, Journal of Cleaner Production, Sustainability, Applied Energy, Science of the Total Environment, Resources, Conservation and Recycling, and Journal of Industrial Ecology from January 2014-August 2018. a) Attributional studies (n = 482). b) Consequential studies (n = 20). c) Combination attributional and consequential studies (n = 7). .............................................................................................................................................31 Figure 5. Flow diagram of apple production life cycle from cradle-to-orchard gate, showing system boundary between technosphere (human activity) and ecosphere (nature), as well as the foreground (site specific) and background (generic) systems. .............................................41 xix Figure 6. Climate change potential (kg CO2 equivalent) of the cradle-to-gate life cycle of the production of 1 kg of apples on an Okanagan Valley ochard with four different orchard production treatments – Mulch (bark and wood chip), low nitrogen (20g tree-1 year-1); Mulch (bark and wood chip), high nitrogen (40g tree-1 year-1); Clean (no mulch applied), low nitrogen (20g tree-1 year-1); Clean (no mulch applied), high nitrogen (40g tree-1 year-1). ..........................61 Figure 7. Climate change potential (kg CO2 equivalent) of the cradle-to-gate life cycle of the production of 1 kg of apples on an Okanagan apple orchard with four different orchard production treatments, separated by orchard process. ..............................................................61 Figure 8. Contribution analysis of processes contributing to the global warming potential (kg CO2 eq) of the life cycle of mulch from cradle-to-application on the apple orchard. ...........................62 Figure 9. Eutrophication potential (kg PO4 equivalent) of the cradle-to-gate life cycle of the production of 1 kg of apples on an Okanagan apple orchard with four different orchard production treatments, broken down by orchard process. .........................................................63 Figure 10. Contribution analysis of processes contributing to the eutrophication potential (kg PO4 eq) of the life cycle of mulch from cradle-to-application on the apple orchard. ...................63 Figure 11. Acidification potential (kg SO2 equivalent) of the cradle-to-gate life cycle of the production of 1 kg of apples on an Okanagan apple orchard with four different orchard production treatments, broken down by orchard process. .........................................................64 Figure 12. Contribution analysis of processes contributing to the acidification potential (kg PO4 eq) of the life cycle of mulch from cradle-to-application on the apple orchard. ...........................65 Figure 13. Human toxicity potential (kg 1,4-dichlorobenzene equivalent) of the cradle-to-gate life cycle of the production of 1 kg of apples on an Okanagan apple orchard with four different orchard production treatments, broken down by orchard process. ............................................66 xx Figure 14. Contribution analysis of processes contributing to the human toxicity potential (kg 1,4-dichlorobenzene eq) of the life cycle of mulch from cradle-to-application on the apple orchard......................................................................................................................................66 Figure 15. Land use – land competition (m2*yr) of the cradle-to-gate life cycle of the production of 1 kg of apples on an Okanagan apple orchard with four different orchard production treatments, broken down by orchard process. ...........................................................................68 Figure 16. Contribution analysis of processes contributing to the land use (m2) of the life cycle of mulch from cradle-to-application on the apple orchard. .............................................................69 Figure 17. Cumulative energy demand (MJ) of the cradle-to-gate life cycle of the production of 1 kg of apples on an Okanagan apple orchard with four different orchard production treatments, broken down by energy source. ................................................................................................69 Figure 18. Contribution analysis of processes contributing to the biomass energy use (MJ) of the life cycle of mulch from cradle-to-application on the apple orchard............................................69 Figure 19. GHG emissions of apple production on an Okanagan apple orchard, including soil carbon sequestration at a rate of 4.85%, 9.7% and 19.4%. .......................................................76 Figure 20. Climate change potential of apple production on an Okanagan apple orchard, using impact assessment methods CML (baseline) and ReCiPe Midpoint (H). ...................................77 Figure 21. Freshwater ecotoxicity of apple production on an Okanagan apple orchard, using impact assessment methods CML (baseline) and ReCiPe Midpoint (H). ...................................78 Figure 22. Uncertainty ranges for climate change potential (kg CO2 equivalent) of apple production treatments, accounting for relative uncertainty only (p < 0.001). ..............................85 Figure 23. Uncertainty ranges for eutrophication potential (kg PO4 equivalent) of apple production treatments, accounting for relative uncertainty only (p < 0.001). ..............................86 Figure 24. Uncertainty ranges for acidification potential (kg SO2 equivalent) of apple production treatments, accounting for relative uncertainty only (p < 0.001). ................................................86 xxi Figure 25. Uncertainty ranges for human toxicity potential (kg 1,4-dichlorobenzene equivalent) of apple production treatments, accounting for relative uncertainty only (p < 0.001). .................87 Figure 26. Uncertainty ranges for land use (m2*yr) of apple production treatments, accounting for relative uncertainty only (p < 0.001). ....................................................................................87 Figure 27. Uncertainty ranges for (renewable) biomass energy use (MJ) of apple production treatments, accounting for relative uncertainty only (p < 0.001). ................................................88 Figure 28. Flow diagram of apple production (and alternative uses of mulch: pulp & paper and bioenergy production) life cycle from cradle-to-gate. Shows system boundary between technosphere (human activity) and ecosphere (nature), as well as the foreground (site specific) and background (generic) systems. ..........................................................................................95 Figure 29. Climate change potential (kg CO2 equivalent) of scenario 1 where no mulch is used on the orchard (only used for other processes) and scenario 2 where mulch use on orchards is increased by the amount necessary to produce 1 kg of apples. Impacts are broken down by each functional unit for each process that uses bark or wood chip mulch. ............................... 108 Figure 30. Eutrophication potential (kg PO4 equivalent) of scenario 1 where no mulch is used on the orchard (only used for other processes) and scenario 2 where mulch use on orchards is increased by the amount necessary to produce 1 kg of apples. Impacts are broken down by each functional unit for each process that uses bark or wood chip mulch. ............................... 108 Figure 31. Acidification potential (kg SO2 equivalent) of scenario 1 where no mulch is used on the orchard (only used for other processes) and scenario 2 where mulch use on orchards is increased by the amount necessary to produce 1 kg of apples. Impacts are broken down by each functional unit for each process that uses bark or wood chip mulch. ............................... 109 Figure 32. Human toxicity potential (kg 1,4-dichlorobenzene equivalent) of scenario 1 where no mulch is used on the orchard (only used for other processes) and scenario 2 where mulch use xxii on orchards is increased by the amount necessary to produce 1 kg of apples. Impacts are broken down by each functional unit for each process that uses bark or wood chip mulch. ..... 109 Figure 33. Land use (m2*yr) of scenario 1 where no mulch is used on the orchard (only used for other processes) and scenario 2 where mulch use on orchards is increased by the amount necessary to produce 1 kg of apples. Impacts are broken down by each functional unit for each process that uses bark or wood chip mulch. ............................................................................ 110 Figure 34. Cumulative energy demand (MJ) of scenario 1 where no mulch is used on the orchard (only used for other processes) and scenario 2 where mulch use on orchards is increased by the amount necessary to produce 1 kg of apples. Impacts are broken down by each functional unit for each process that uses bark or wood chip mulch. ............................... 111 Figure 35. Climate change potential (kg CO2 equivalent) for scenario 1 (no mulch on orchard) and scenario 2 (mulch on orchard), where the marginal market for co-generation using bark chips is assumed to be Washington rather than British Columbia. .......................................... 116 Figure 36. Eutrophication potential (kg PO4 equivalent) for scenario 1 (no mulch on orchard) and scenario 2 (mulch on orchard), where the marginal market for co-generation using bark chips is assumed to be Washington rather than British Columbia. ....................................................... 116 Figure 37. Acidification potential (kg SO2 equivalent) for scenario 1 (no mulch on orchard) and scenario 2 (mulch on orchard), where the marginal market for co-generation using bark chips is assumed to be Washington rather than British Columbia. ....................................................... 117 Figure 38. Human toxicity equivalent (kg 1,4-dichlorobenzene equivalent) for scenario 1 (no mulch on orchard) and scenario 2 (mulch on orchard), where the marginal market for co-generation using bark chips is assumed to be Washington rather than British Columbia. ....... 117 Figure 39. Land use (m2*yr) for scenario 1 (no mulch on orchard) and scenario 2 (mulch on orchard), where the marginal market for co-generation using bark chips is assumed to be Washington rather than British Columbia. ............................................................................... 118 xxiii Figure 40. Cumulative energy demand (MJ) for scenario 1 (no mulch on orchard) and scenario 2 (mulch on orchard), where the marginal market for co-generation using bark chips is assumed to be Washington rather than British Columbia. ...................................................................... 118 Figure 41. Monte Carlo simulation results, including parameter uncertainty only, for the climate change potential (kg CO2 equivalent) of scenario 1: no mulch on orchards, and scenario 2: mulch on orchard (p < 0.001). Results include the life cycle emissions for the production of 1 kg apples, 0.653 kWh electricity + 0.653 MJ heat co-generation and 0.144 kg paper. ................. 124 Figure 42. Monte Carlo simulation results, including parameter uncertainty only, for the eutrophication potential (kg PO4 equivalent) of scenario 1: no mulch on orchard, and scenario 2: mulch on orchards (p = 0.102). Results include the life cycle emissions for the production of 1 kg apples, 0.653 kWh electricity + 0.653 MJ heat co-generation and 0.144 kg paper. ............. 125 Figure 43. Monte Carlo simulation results, including parameter uncertainty only, for the acidification potential (kg SO2 equivalent) of scenario 1: no mulch on orchard, and scenario 2: mulch on orchard (p = 0.059). Results include the life cycle emissions for the production of 1 kg apples, 0.653 kWh electricity + 0.653 MJ heat co-generation and 0.144 kg paper. ................. 125 Figure 44. Monte Carlo simulation results, including parameter uncertainty only, for the human toxicity potential (kg 1,4-dichlorobenzene equivalent) of scenario 1: no mulch on orchard, and scenario 2: mulch on orchard (p = 0.059). Results include the life cycle emissions for the production of 1 kg apples, 0.653 kWh electricity + 0.653 MJ heat co-generation and 0.144 kg paper. ..................................................................................................................................... 126 Figure 45. Monte Carlo simulation results, including parameter uncertainty only, for the land use (m2*yr) of scenario 1: no mulch on orchard, and scenario 2: mulch on orchard (p < 0.001). Results include the life cycle resource use for the production of 1 kg apples, 0.653 kWh electricity + 0.653 MJ heat co-generation and 0.144 kg paper. ............................................... 126 xxiv Figure 46. Monte Carlo simulation results, including parameter uncertainty only, for the renewable resource – biomass use (MJ) of scenario 1: no mulch on orchard, and scenario 2: mulch on orchard (p = 0.392). Results include the life cycle resource use for the production of 1 kg apples, 0.653 kWh electricity + 0.653 MJ heat co-generation and 0.144 kg paper. ............. 127 xxv Acknowledgements I would like to thank the following people for their support, without which the work presented in this thesis would not have been possible. First, I thank my supervisor Dr. Nathan Pelletier for his expertise, mentorship, and countless hours of work. His passion, wisdom, and patience not only made my research possible and enjoyable, but inspired me to continue on in this field. I would like to thank my committee members, Dr. Melanie Jones, Dr. Louise Nelson, and Dr. Mike Chiasson, for lending their additional perspectives and expertise to my work, and giving it a broader meaning and purpose. I also appreciate the time they dedicated to providing feedback on my work. I acknowledge the help and support of my colleagues Victoria Sanderson, Alyssa Smart, Farah Kanani, Yang Li, Shiva Zargar, Vivek Arulnathan, Cecilia Wang, Dr. Suryo Gandasasmita, Matt Fritter and Dr. Davoud Heidari, both in and out of the lab. I especially thank Ian Turner for his endless support, patience, and hours of help. I also acknowledge the assistance and access to data provided by Dr. Kirsten Hannam and her colleagues at Agriculture and Agri-Food Canada’s Summerland Research and Development Centre, as well as from Dr. Craig Nichol and Dr. Mesfin Fentabil. Their generosity and willingness to provide me with both time and data made this project possible. xxvi Dedication To my mother and father for their unconditional love and support. 1 Chapter 1: Introduction 1.1 Rationale: Environmental impacts of mulch use in apple production Agriculture is responsible for 10-12% of total direct global anthropogenic GHG emissions (i.e. at the field level) (Yue et al. 2017), and from 19-29% when taking into account the entire food system (Vermeulen et al. 2012). This includes 84% of emissions of nitrous oxide (N2O) (Smith et al. 2008), a greenhouse gas with a global warming potential 298 times that of CO2 (IPCC 2014). Agricultural soils are also an important potential carbon sink, meaning that they have the potential to remove carbon from the atmosphere and store it, thus inhibiting its ability to contribute to global warming via the greenhouse effect. Launched at the COP21 meeting in Paris in 2015, the “4 per 1000” initiative aims to increase global agricultural soil organic carbon (SOC) stocks by 0.4% annually, which would effectively halt current increases in atmospheric CO2 levels (4p1000 2017). Mulch has been used in agriculture to increase yield and a variety of soil properties (Neilsen et al. 2003b). High C:N ratio mulches, such as shredded paper or wood, have been shown to improve water retention by improving the water-holding capacity of the soil and reducing evaporation from the surface (Neilsen et al. 2003b) thus reducing the amount of water required. Mulch has also been shown to reduce weeds (Neilsen et al. 2003b) and other parasites like root-lesion nematodes (Forge et al. 2008), thus reducing the amount of pesticides applied. Mulch is also a source of nutrients which can increase soil fertility (Neilsen et al. 2003b) and reduce the amount of fertilizer necessary. It has also been shown to increase long-term soil carbon stocks (López et al. 2014). Due to these and other benefits of mulch, the addition of wood-based mulch can increase the yield and vigor of crops (Neilsen et al. 2003a). In addition to these benefits of mulch application, recent work by Fentabil et al. (2016) has shown that using wood chip and bark mulch (referred to hereafter as mulch, or bark mulch) as a soil amendment on an apple orchard in the semi-arid Okanagan Valley region of western Canada reduced direct N2O emissions by 19%, while increasing salt-extractable SOC content by 68% (Fentabil et al. 2016). Although this represents a potentially significant reduction in GHG emissions at the orchard scale, additional research is required prior to recommendation of bark mulch use for climate-friendly agriculture. This is because other processes related to the life cycle of bark mulch, including its production, transportation and management activities, may result in GHG emissions and other environmental impacts that offset these benefits (Jäppinen et 2 al. 2014). As well, the addition of bark mulch may affect other environmentally significant emissions, such as CO2. The use of bark mulch in horticulture may also displace alternative uses regionally, notably in bioenergy production (IFS 2015). Since bioenergy can replace traditional sources of energy such as the burning of fossil fuels (IFS 2015), if the supply of bark mulch to bioenergy production was decreased due to competition from horticultural uses, this reduction in energy may be substituted with fossil fuel-based energy, potentially also resulting in a net increase in GHG emissions (Ministry of Energy Mines and Petroleum Resources 2008). It is therefore important to examine not only the life cycle of apple production with or without mulch, but also the other production systems that may be affected by an increase in mulch use on orchards so as to determine the net impacts/benefits. The results of these investigations can inform decisions around the sustainability of using bark mulch as a soil amendment on Okanagan orchards. This research was carried out using a systems-level sustainability management tool known as life cycle assessment, which was designed to quantitatively assess the impacts of the inputs and outputs associated with a product’s supply chain, or life cycle. 1.2 Life cycle assessment Life cycle assessment (LCA) is a systems-level decision support tool that examines the environmental impact of a product throughout its entire life cycle (Guinee et al. 2002). The goal of LCA is to characterize all of the environmental inputs and outputs that occur as a result of activities linked to the life cycle of the product being studied, to use these values to perform an environmental impact assessment, and to subsequently support conclusions and recommendations about the use of the product. It is widely applicable to such purposes as hot-spot identification (identifying the areas within a life cycle with the largest environmental impacts), product improvement or creation, and supporting comparative assertions between different product alternatives (Guinee et al. 2002). There are two main types of LCA that can be used, depending on the question to be answered. The first is attributional LCA (ALCA), which examines a snapshot of the current or past state of affairs to determine the environmental impact that can be attributed to the product studied, using average data (Weidema 2003), for example the net environmental impact attributable to the production of one kilogram of fruit. The other is consequential LCA (CLCA) which instead looks at a future scenario to determine the impacts that may occur as a consequence of a change in the use of the product, or other technological or management changes, taking into account market-mediated effects (Weidema 2003), for example the 3 environmental impact as a consequence of a change in fruit consumption, or manner of production. CLCA uses marginal data, or data regarding what technologies would be affected by a change, instead of average data. Both attributional and consequential LCA have been selected as the tools to carry out this investigation of the environmental impacts of bark mulch use on Okanagan orchards. Attributional LCA can be used to determine the static impacts of the orchard life cycle, and consequential LCA can expand the system to include the life cycle of displaced alternative uses of mulch. 1.3 Research objectives The overarching goal of this research was to determine the net impacts of current and potentially expanded use of bark mulch as a soil amendment on orchards in the Okanagan Valley. A combination of generic and site-specific data were used, as well as both attributional and consequential LCA modelling. The specific research goals were: 1) The first research goal was to determine the net environmental impact attributable to the current use of bark mulch as a soil amendment on Okanagan valley apple orchards, from cradle to orchard gate, per kilogram of apples produced. This built upon previous research that has shown a field-level decrease in N2O emissions and an increase in SOC associated with bark mulch use (Fentabil et al. 2016). By performing an ALCA on this system, it can be determined whether or not there is a net environmental benefit when the entire life cycle of the bark mulch is included in the analysis. This provides novel information regarding bark mulch use that can be used by horticulturalists and policy-makers to improve horticultural sustainability. 2) The second research goal was to determine the net environmental impacts that would occur as a consequence of increasing bark mulch use on Okanagan orchards and vineyards, including the potentially displaced current alternative economic uses, such as bioenergy production. This system was studied using CLCA from cradle to gate, again with a functional unit of 1 kg apples produced, assuming the mulch is used in the production of apples. This builds upon the first research goal to determine whether or not the field-level environmental benefits of bark mulch use, specifically in the horticultural system, are outweighed by environmental effects associated with this change in bark mulch use elsewhere in the regional economic system. For example, a decrease in bark mulch availability for bioenergy production may have a negative impact if fossil fuels are burned to replace the marginal decrease in energy 4 production from bark mulch. The results of this study can be used by managers and policy-makers in not only horticulture, but also bioenergy production and other affected industries. 1.4 Scope of this thesis This thesis contains six chapters that explore the environmental impacts of apple production using LCA, as well as LCA best practices in terms of uncertainty assessment. Chapter 1 presents the background rationale and research goals for this study, focusing on the environmental impacts of apple production, both with and without the use of mulch as a soil amendment. Previous research by Fentabil et al. (2016) that show orchard-level benefits to mulch use are presented in order to set the stage for the current investigation. Chapter 2 is a review of LCA literature. The structure and methodology of an LCA is presented, as well as key insights from previous LCA publications of horticulture and bioenergy production, as these are both relevant sectors for the current study. Chapter 3 is a review of uncertainty analysis in LCA case studies published in the last 5 years. The incidence of reporting of uncertainty analysis, sources of uncertainty, and methods of uncertainty analysis are discussed. The results of this review are used to both inform the best practices to be used for uncertainty analysis in the current LCA study, and to make conclusions and recommendations to the LCA community. Chapter 4 presents the methods and results of the attributional LCA carried out on apple production in the Okanagan with and without the addition of mulch. This includes a description of the sources of all data and how they were used in the LCA model, as well as the environmental impact assessment results. It also includes the conclusions that can be drawn from these results. Chapter 5 presents the methods and results of the consequential LCA carried out to determine the environmental impacts of increasing mulch use on Okanagan apple orchards, including the alternative uses of that mulch that would be displaced. This chapter includes a description of the sources and modelling of all data used, as well as the environmental impact assessment results and conclusions that can be drawn from them. Chapter 6 presents a summary of the conclusions made from all previous chapters regarding the sustainability of mulch use and uncertainty assessment best practices in life cycle assessment. 5 Chapter 2: Review of life cycle assessment in horticulture and bioenergy 2.1 Life cycle assessment Life cycle assessment (LCA) is the systems-level environmental decision support tool chosen to undertake this analysis of the environmental impacts of bark mulch use in Okanagan apple production. Both attributional (ALCA) and consequential (CLCA) life cycle assessment are used to determine first the impact of the orchard life cycle with and without mulch, then of the life cycles of the systems impacted by a potential change (i.e. increase) in mulch use on orchards. As defined by ISO 14044 (ISO 2006), the main steps in an LCA are the goal and scope definition, life cycle inventory analysis, life cycle impact assessment and interpretation. Although ISO does not refer specifically to ALCA or CLCA, these guidelines were developed to support ALCA and there is no methodological document with international consensus for CLCA methodology. There are many methodological choices throughout the four steps outlined by ISO, which can have a great impact on the results of the LCA study. These choices, as well as the four steps of LCA, will be detailed in the following sections. 2.1.1 Goal and scope The goal and scope definition phase is the first of the four phases of LCA as defined by ISO 14044 (ISO 2006). The aim of the goal definition is to define and justify the questions addressed by the LCA as well as other parameters such as the intended application, reasons for carrying out the study, stakeholders and target audience. It also must include a statement disclosing whether the study is intended for use in comparative assertions to be disclosed to the public, which is defined as an assertion of superiority of one product over another (ISO 2006). The scope definition defines and justifies methodological choices and assumptions. These include the system to be studied including its temporal, geographical, technological and methodological boundaries (Rebitzer et al. 2004). In this phase, the LCA practitioner must also define the functional unit (the amount of product/service produced, to which all inputs and outputs are scaled) and reference flows to be used as a point of comparison (Guinee et al. 2002). The reference flow is the amount of output from a process that is required for the functional unit (ISO 2006). In later stages, the life cycle inventory model is structured, and impact assessment results expressed, in terms of the reference flow. When a process considered within the system boundaries of an LCA study has more than one function, it is referred to as a multi-functional process. Often, it is necessary to 6 determine how much of this process is attributable to the product being studied (Weidema 2003), for example if both mulch and lumber are produced at a sawmill. These so-called “multi-functionality” problems may be solved in a variety of ways depending on the type of LCA being performed (Weidema 2003). ISO 14044 (ISO 2006) presents a decision hierarchy for the best methods to solve multi-functionality problems. The first choice is to avoid allocation (dividing the input and output flows of the process between the multiple product systems to which it relates) by either subdividing the processes to have individual inputs and outputs, or expanding the boundaries to take into account all co-products. If allocation must be done, then the best method is to partition the inputs and outputs to the different product systems based on a physical relationship (such as mass or energy); if this is not possible it should be based on a different type of relationship, usually economical (ISO 2006). The choice of allocation or system expansion method is made and reported in the scope definition stage. The impact assessment methodology and categories, described in further detail in the life cycle impact assessment section, must also be defined in the goal and scope section. The same is true for the interpretation of methods and limitations of the study (described in detail in the interpretation section). The requirements for and assumptions about the data which are collected in the inventory stage are also defined in the goal and scope. It is also in this stage that the LCA practitioner must decide whether or not to include social (social LCA) and/or economic (life cycle costing) impacts in the LCA (Reap et al. 2008). These impacts are based on a subjective value judgement, changes in which could impact the results of the study (Reap et al. 2008). There is no agreed-upon best method for integrating LCA with social impact assessment and/or life cycle costing (Hunkeler and Rebitzer 2005). The style of report and any critical review of the study are also defined in this stage. 2.1.2 Life cycle inventory Life cycle inventory (LCI) is often the largest, most time and resource consuming component of LCA (Rebitzer et al. 2004). It is composed of multiple steps including data collection and validation, relating the data to the unit processes and functional unit, data aggregation, refining the system boundaries, and resolving multi-functionality issues as described in the goal and scope phase (ISO 2006). Before data collection, the LCA practitioner must define the product system by setting the boundaries between the ecosphere (nature – outside of the system) and the technosphere (the system of human activity being studied), as well as between different product systems (Guinee et al. 2002). Cut-off criteria are used to determine what is included, i.e. the lowest threshold for physical or economic flows that will be 7 included in the model are defined (Guinee et al. 2002). This information can be outlined in a flow diagram, which is a visual interpretation of the major unit processes, with arrows for flows between processes (Guinee et al. 2002). These flows can be characterized as elementary flows, product flows or waste flows (ISO 2006). An elementary flow refers to energy or material that enters or exits the system directly from, or to, the ecosphere, without human transformation; a product flow is any product that is transferred from one unit process to another within the boundary of the technosphere; and a waste flow is any material output from a unit process that needs to be disposed of or otherwise managed within the technosphere (ISO 2006). There are different types of data that may be collected, according to the specific needs of the LCA. Primary data are directly collected from the specific field or industry in question, and are used for foreground processes, which are the processes of highest concern that are unique to the provision of the functional unit. Secondary data are not specific, and can be drawn from the literature or databases for background processes, to represent flows that feed into the foreground processes (Guinee et al. 2002). Background processes may also be processes for which primary data cannot be collected and management interventions cannot be made, for example those shared with other supply chains. Generic data and site-specific data can yield very different LCA results, depending on the impact category being considered (Lasvaux et al. 2015), so the choice of data to use is an important step in LCI. For this reason, any relevant information about the data selection and collection process should be reported for full transparency to the intended audience (ISO 2006). The categories of data that need to be collected include energy and raw material inputs, products/co-products, waste, and emissions to air, water and soil (ISO 2006). The data are then organized as an aggregated input and output of a unit process, in relation to one unit of the reference flow (Guinee et al. 2002). Using cut-off criteria to determine which processes are included can save time and money, but can also have a large effect on the outcome of the study (Guinee et al. 2002), and setting a specific lower bound for inclusion of a mass or energy flow can be problematic as small flows can be summed together to have a significant effect (Reap et al. 2008). The quality and availability of the data collected in this phase can also have a large impact on the results of the study. 2.1.3 Life cycle impact assessment In the life cycle impact assessment (LCIA) phase of life cycle assessment, the information gained in the life cycle inventory phase is classified and interpreted according to environmental impacts (Guinee et al. 2002). The following are the mandatory steps and 8 components of LCIA (ISO 2006). First, the environmental impact categories considered in the LCA need to be defined, generally during the goal and scope definition phase of the LCA. Guinee et al. (2002) defined a list of default impact categories that should be included in almost all LCIAs, as well as some study-specific categories that should be included when relevant, and other categories that can be included if justified. Determining which categories to include can have a significant impact on the results of the study, and is an easy place within the methodology where bias can be purposefully introduced. ISO 14044 (ISO 2006) sets guidelines for which categories to include; however, these categories are not always reported in published LCA studies. The baseline impact categories are depletion of abiotic resources, impacts of land use (land competition), climate change, stratospheric ozone depletion, human toxicity, ecotoxicity (freshwater aquatic, marine aquatic and terrestrial), photo-oxidant formation, acidification, and eutrophication. Guinee et al. (2002) have defined these impact categories in their guidebook to ISO, as follows. Human toxicity refers to negative impacts on human health due to toxic emissions emitted to air, water or soil, scaled to the toxic compound 1,4-dichlorobenzene. Ecotoxicity is the negative impacts to ecosystems (freshwater, marine or terrestrial) due to toxic emissions to air, water or soil, also scaled to 1,4-dichlorobenzene equivalence. Depletion of abiotic resources refers to the use of non-living natural resources. “Elements, ultimate reserves” refers to the extraction of minerals (scaled to the mineral antimony), and “fossil fuels” are measured in MJ energy that come from fossil fuels. Photochemical oxidation is the formation of reactive compounds (smog) in the atmosphere from Volatile Organic Compounds (VOC) and carbon monoxide (CO) in the presence of nitrogen oxides (NOx) and sunlight. These compounds can damage human health and ecosystems. Ozone depletion is the thinning of the ozone layer due to anthropogenic emissions of ozone-depleting gases, scaled to CFC-11. Acidification refers to the negative impact on ecosystems from acidifying emissions such as SO2, NOx and NH4 (scaled to SO2 equivalence). Climate change refers to the radiative forcing (contributing to the greenhouse effect) of anthropogenic GHG emissions, scaled relative to the radiative forcing of CO2. Eutrophication is the release of excessive amounts of nutrients, predominantly nitrogen and phosphorus, which can have negative impacts on aquatic ecosystems due to lowered oxygen levels, measured as phosphate (PO4) equivalence. Land use – land competition refers to the use of land that is removed as an available resource for other uses (it does not focus on any further ecological impacts of land use, only on land as a human available resource). 9 Once the impact categories are chosen, the human interventions (i.e. inputs/outputs) need to be converted to category indicators (e.g. infrared radiative forcing) for each of the impact categories chosen (e.g. climate change), using characterization factors (e.g. the global warming potential for each greenhouse gas). ISO 14044 (ISO 2006) suggests choosing mid-point (problem-based, or a potential for an adverse effect) category indicators due to their lower uncertainty levels; however, some end-point (damage-based, or the adverse effect itself) indicators are possible as well, as they give a clearer indication of the actually anticipated environmental impact. Characterization factors are used as numerical factors that multiply the inventory results according to their relative intensity for their chosen impact category (e.g. a global warming potential of 298 for N2O relative to CO2, which is the reference species for this impact category) (Guinee et al. 2002). These numbers are aggregated and listed for each impact category. The characterization model, category indicators, and characterization factors must all be chosen by the LCA practitioner, and that choice may influence the results of the study. LCIA is usually implemented on life cycle inventory models that have been structured using LCA software packages. There are also optional components of LCIA, some of which are strongly recommended, and others that are not allowed by ISO (ISO 2006) for certain types of LCAs. The first optional step is normalization, which is strongly recommended (ISO 2006). Normalization scales the results in all the impact categories to a specific geographical area and time, to give relevance to the results (Guinee et al. 2002). It is also possible to give each category a different numerical weighting factor and to aggregate results to a single value (Guinee et al. 2002). Since weighting is inherently subjective, this can introduce bias and can be used to manipulate the results. ISO (ISO 2006) does not allow it for comparative assertions between different products on the market. Similarly, grouping is an allowed, but optional, step that involves categorizing impact categories either nominally or based on an importance hierarchy. Data quality analysis can be carried out in this stage, and can include a gravity analysis, uncertainty analysis and sensitivity analysis. A gravity analysis is used to determine which processes contribute the most to the indicator results. An uncertainty analysis is used to determine how uncertainties in the data/modelling assumptions affect the uncertainty of the results, and a sensitivity analysis determines how changes in data/modelling affect the results.= A common modelling technique for uncertainty analysis is Monte Carlo sampling, which performs repeated random samples using the uncertainty of the input data to calculate a confidence interval for the output (Groen et al. 2014). However, this is not the only possible 10 technique. Other fairly common techniques include Latin Hypercube or Quasi-Monte Carlo sampling, which may be more effective than Monte Carlo sampling depending on the data (Groen et al. 2014). This step is especially important since there is a large amount of uncertainty in LCAs, hence representing the results as point values would be misleading (Björklund 2002). There are many different suggestions in the literature regarding uncertainty analysis (ex. Benetto et al. 2008; Egilmez et al. 2016; Ewertowska et al. 2017) but no standardized procedure, and the type of analysis chosen may have a significant impact on the apparent uncertainty of the LCA results (Groen et al. 2014). Also, uncertainty analysis methods were designed largely for ALCA. This means there is a need to define methods appropriate for uncertainty analysis in CLCA to address the different sources of uncertainty associated with market-mediated scenario modelling. 2.1.4 Life cycle interpretation The interpretation phase of LCA involves analyzing the consistency and quality of the data and methods for robustness, evaluating the impact assessment results, and subsequently using this information to make conclusions and recommendations to the intended audience in line with the goals and scope of the study (ISO 2006). There are several steps that need to be taken to evaluate the data, models and assumptions used. According to Guinee et al (2002) the first step is to check the consistency of all assumptions, models, data, etc. with the goal and scope of the LCA. Next one must check for the completeness and availability of all data. The results of the uncertainty and sensitivity analysis are also interpreted here. The results of the impact assessment phase are used to identify hot spots and compare between different scenarios or products. Based on these results, and the evaluation of data quality, consistency and completeness, the LCA practitioner can make conclusions and recommendations, and identify the limitations of the study. Since it is in the life cycle interpretation stage that the quality and consistency of all data, models and other methodological choices are analyzed, it may have one of the largest impacts on the outcome of the study. It is of utmost importance to develop an appropriate analytical method for determining the robustness of the study. Only if the intended audience and stakeholders in the LCA believe the results to be reliable will the LCA be a useful tool in decision support regarding managing the environmental impacts of the product considered. 11 2.1.5 Attributional and consequential LCA Since ALCA and CLCA have different purposes (as described in the previous chapter), several of their methods are different, including how multi-functionality problems are solved, and the choice of processes to include. These differences are examined in detail below. In the goal and scope section, the multiple ways of solving multi-functionality issues, where a process produces multiple products, are discussed. In ALCA, the strategy for solving these multi-functionality issues is allocation. System expansion is the method to be used for CLCA studies, as their purpose is to consider the market-mediated effect of changes in products or their consumption (Weidema 2003). Allocation is considered one of the most controversial issues in LCA practice (Rebitzer et al. 2004). Since there is no robust set of guidelines beyond the ISO standard for how best to allocate the environmental impact of a multi-functional process, the outcome of an LCA can be greatly influenced by the allocation choices made, including the potential for misleading recommendations (Reap et al. 2008). Many solutions to this problem have been suggested, but as yet none have been proven to be generalizable (Curran 2007). System expansion is prioritized over allocation in ISO 14044 (ISO 2006), but these guidelines are not well defined. Although system expansion avoids the need for allocation, it leads to more complicated models that require more data. More standardized guidelines are required (Curran 2007). The definition of the functional unit is also dependent on the type of LCA being performed (Weidema 2003). The functional unit is the product, or the function served by a product, to which all the environmental inputs and outputs of the LCA are scaled (ISO 2006). According to Weidema (2003), for ALCAs the functional unit may be chosen at will, since it is not involved in comparisons between alternative uses and systems. However, ISO 14044 (ISO 2006) argues that ALCA can be used for comparisons, thus the functional unit must be chosen to be comparable between the different systems. Since CLCAs always involve comparisons, the functional unit must be chosen to reflect the comparisons between systems, and may impact the outcome of the study (Weidema 2003). Functional units are chosen differently depending on the temporal and geographical boundaries of the study. The choice of a functional unit is critical to the LCA outcome since it is the reference for all environmental inputs and outputs included (Guinee et al. 2002). In addition to the functional unit, the related processes to be included in the LCA are dependent on the type of LCA chosen (Weidema 2003). This is a key difference between ALCA and CLCA. In ALCA, only processes that contribute to the provision of the functional unit at the 12 time of the study are included; in CLCA, any process that would be affected by a change in the functional unit needs to be included (Weidema 2003). This leads to the issue of deciding which processes to include, and creating cut-off criteria for these processes. This is necessary because it is not feasible to include all processes that relate to a system. Commonly, processes are excluded once they drop below a threshold of contribution (physical or economic) to the functional unit (Reap et al. 2008). However, many insignificant process flows, when combined, can make up a significant contribution, and their exclusion may influence the overall results of the LCA. The choice of data to include can greatly influence the results of the study. Taken together, the choice between an attributional and consequential LCA approach can make a large difference on the study outcomes. 2.2 Horticultural LCA The above LCA methods have been applied to many sectors, including agriculture. Within agriculture, there have been horticultural LCAs on apple production, which will be the focus of the following discussion. Most LCAs that have been performed to examine the environmental impact of apple production are attributional LCAs (ex. Milà I Canals et al. 2006; Mouron et al. 2006a; Vinyes et al. 2017), since they are aiming to determine the current system-level environmental impact associated with the production of apples. The main audience for these LCA studies are the farmers, and managers and policy-makers associated with the horticultural systems (Milà I Canals et al. 2006). Most systems defined the production of the apple in weight (Milà I Canals et al. 2006; Vinyes et al. 2017), area, or sales (Mouron et al. 2006a) as the functional unit, and included all life cycle stages from cradle to farm gate (Milà I Canals et al. 2006; Mouron et al. 2006a). Some studies examined the life cycle from cradle to grave and found that the agricultural stage had one of the highest contributions in terms of environmental impacts, with the retail stage also making an equally large contribution in one study (Vinyes et al. 2017). The type of primary data collected includes agricultural inputs (fertilizers, pesticides, water, machinery, etc.), practices (Milà I Canals et al. 2006; Vinyes et al. 2017), and emissions (Mouron et al. 2006a). Data were typically collected using questionnaires, consultation with industry experts, telephone interviews, (Milà I Canals et al. 2006), field work (Mouron et al. 2006a), and from research institutes (Vinyes et al. 2017). Background data were collected for agro-chemical production, fertilizer production, machinery production, energy use, and transportation (Milà I Canals et al. 2006) from published literature (Milà I Canals et al. 2006) and LCI databases (Mouron et al. 2006a). 13 For the impact assessment phase, impact categories commonly included in the literature are climate change, photochemical oxidant formation, acidification, eutrophication, human toxicity, ecological toxicity (aquatic and terrestrial) (Milà I Canals et al. 2006), energy use (Mouron et al. 2006a), particulate matter formation, ionizing radiation, land use, water depletion, and metal depletion (Vinyes et al. 2017). Emission factors are taken from the literature (Milà I Canals et al. 2006) and databases (Mouron et al. 2006b). Uncertainty analysis is not often performed in apple LCAs. Fuels, fertilizers, pesticides (Milà I Canals et al. 2006; Mouron et al. 2006a), and machinery (Mouron et al. 2006a) were found to be important factors influencing the environmental impact of a horticultural system. Interestingly, higher yields are not always associated with higher use of pesticides, fertilizer and machinery, and environmental impacts do not always increase with farm income (Mouron et al. 2006b). 2.3 Bioenergy LCA LCA methodology has also been applied in the field of bioenergy. Bioenergy production refers to the production of energy from biomass sources that derive their energy from the sun, such as wood waste or crops (IFS 2015). This energy can be used to replace traditional energy sources, such as the burning of fossil fuels, potentially resulting in a lower carbon footprint (IFS 2015). There are many sources of biomass that can be used for bioenergy production; however, the focus of this section is on woody biomass as it is the most relevant to the research questions. Both attributional and consequential LCA have been used to study woody biomass use in bioenergy production, with goals that include determining the environmental impact attributable to the system, the processes within the system that have the largest impact (Boschiero et al. 2016), and the difference between the bioenergy system and the reference system of conventional fossil fuel use (Boschiero et al. 2016; Homagain et al. 2015). Lu and El Hanandeh (2017) combined LCA and life cycle costing (LCC) methodologies. LCC accounts for all of the costs associated with each stage of the life cycle, including the cost of the environmental impacts (Lu and El Hanandeh 2017). Some sources of woody biomass studied using LCA include wood waste from apple orchards (Boschiero et al. 2016), short rotation woody crops such as willow (Heller et al. 2003), forest harvest residue, sawmill residue, and unutilized trees (Homagain et al. 2015). The functional unit for bioenergy LCA studies has been defined as one year of electricity and heat produced (Boschiero et al. 2016), or one unit of biomass by weight (Homagain et al. 2015). In a case where multiple final products were considered, the functional unit of the 14 treatment of 1 Mg of hardwood logs was chosen (Lu and El Hanandeh 2017). The system boundaries have typically been defined as cradle-to-gate, which means it begins with chipping and harvesting, or collecting (Homagain et al. 2015) the woody biomass, and includes transport to the energy facility and conversion of the biomass to heat and power (Boschiero et al. 2016). Some studies also followed the life cycle from cradle-to-grave (Lu and El Hanandeh 2017), which includes the end of life processes and waste management. Although the woody waste biomass used for bioenergy is often considered a co-product, it can be considered a by-product of the industry it came from since it would otherwise go to waste (Boschiero et al. 2016). Environmental impacts were allocated to the production of heat or energy according to energy, exergy (available energy) and economic flows (Boschiero et al. 2016). Field measurements (Boschiero et al. 2016), database data (Homagain et al. 2015) and published literature (Lu and El Hanandeh 2017) were typically used for the foreground processes such as harvesting and chipping (Boschiero et al. 2016), input materials and emissions (Homagain et al. 2015). Published literature and databases (Boschiero et al. 2016; Lu and El Hanandeh 2017) were used for background data. The impact categories included in the literature are climate change, photochemical oxidation, ozone depletion, particulate matter formation, terrestrial acidification, freshwater eutrophication, freshwater and terrestrial ecotoxicity, human toxicity, fossil depletion (Boschiero et al. 2016), and land use (Cherubini and Strømman 2011). According to a review of bioenergy LCAs (Cherubini and Strømman 2011), about half the studies reviewed only included greenhouse gas emissions and energy balance, and no other impact categories in their assessment, supported by the fact that climate change and fossil fuel consumption are the main categories impacted by bioenergy production. Several studies weighted (Cherubini and Strømman 2011) and normalized (Homagain et al. 2015) the LCIA results. Sensitivity analyses were also conducted to determine the effect of small changes in the system model on the outcome of the study (Homagain et al. 2015). Using woody biomass for bioenergy is generally considered environmentally friendly and sustainable as compared to fossil fuel use (Heller et al. 2003; Lu and El Hanandeh 2017), although some trade-offs exist (Boschiero et al. 2016). Climate change and fossil fuel consumption are the main categories impacted (Cherubini and Strømman 2011). Although the carbon burned by the combustion of bioenergy is offset by the carbon sequestered by the biomass, there is still a net emission of greenhouse gases due to the harvesting, processing, transportation and transformation stages (Heller et al. 2003). 15 Chapter 3: Comparing sources and analysis of uncertainty in consequential and attributional life cycle assessment: Review of current practice and recommendations 3.1 Synopsis Life cycle assessment (LCA) is intended as a quantitative decision support tool. However, the large amount of uncertainty characteristic of LCA studies reduces confidence in results. To date, little research has been reported regarding the comparative sources of uncertainty (and their relative importance) and how (or how commonly) they are quantified in attributional and consequential LCA. This chapter answers these questions based on a review of recent LCA studies and methods papers, and advances recommendations for improved practice. All relevant LCA methods papers and case studies (amounting to 2,687 journal articles) published from 2014-2018 in the top 7 journals publishing LCA studies were reviewed. Common sources and methods for analysis of uncertainty in both attributional and consequential LCA, as well as their frequency of application, were identified. Observed practices were compared to best practice recommendations from methods papers, and additional recommendations were advanced. Less than 20% of LCA studies published in the past five years reported any kind of uncertainty analysis. There are many different sources of uncertainty in LCA, which can be classified as parameter, scenario or model uncertainties. Parameter uncertainty is most often reported, although the other types are considered equally important. There are also sources of uncertainty specific to each kind of LCA – in particular related to the resolution of multi-functionality problems (i.e. allocation in attributional LCA versus the definition of market-mediated substitution scenarios in consequential LCA). However, there are currently no widely applied methods to specifically account for these sources of uncertainty other than sensitivity analysis. Monte Carlo sampling was the most popular method used for propagating uncertainty results, regardless of LCA type. Data quality scores and inherent (i.e. stochastic) uncertainty data are widely available in LCI databases and researchers should generally be able to define comparable uncertainty information for their primary data. Moreover, uncertainty propagation for parameter uncertainty is supported by LCA modeling software. There are hence no obvious barriers to quantifying parameter uncertainty in LCA studies. More standardized methods based upon context-specific data, that strike the right balance between comprehensiveness and usability are, however, necessary in order to better account for both the shared and unique sources of uncertainty in attributional and consequential LCAs. More frequent and comprehensive reporting of 16 uncertainty analysis is strongly recommended for published LCA studies. Improved practices should be encouraged and supported by peer-reviewers, editors, LCI databases and LCA software developers. 3.2 Uncertainty in life cycle assessment Life cycle assessment (LCA) is intended as a quantitative decision support tool. However, it is not fully accepted as such due to a perceived lack of robustness (Herrmann et al. 2014). One of the main issues impacting the reliability of LCA results is uncertainty (McKone et al. 2011), which may be very high, underestimated or, very often, simply ignored (Weidema 2009). The frequent lack of quantified uncertainties in LCA studies means that it is not possible to determine confidence levels for results, which is fundamental in most branches of empirical science. Identifying, quantifying and communicating uncertainty in LCA studies remain important for both methodological development and, in particular, increased implementation by practitioners (Herrmann et al. 2014). The sources of uncertainty in LCA are numerous, and have been categorized in different ways. Lloyd and Ries (2007) divide uncertainty into three main categories: model, scenario and parameter uncertainty. Here, parameter uncertainty (also called stochastic, or data uncertainty) is described as referring to uncertainty in observed or measured values, due to inherent variability in the sampled population, as well as uncertainty related to data quality. Scenario uncertainty refers to uncertainty due to normative choices made in constructing scenarios, including the choice of functional unit, time horizon, geographical scale and other methodological choices. This can also be called uncertainty due to choices. Model uncertainty comes from the structure of, and the mathematical relationships defining the models themselves (including models for deriving emissions and characterization factors used in impact assessment models). Many kinds of uncertainties influence LCA results. However, some types of uncertainties may be more pronounced depending on the type of study being conducted. The two main types of LCA are attributional (ALCA) and consequential (CLCA). ALCA examines a snapshot of the current or past state of affairs, to determine the environmental impacts that can be attributed to the product studied, assuming a static system. CLCA considers future scenarios to determine the impacts that may occur as a consequence of a change in the use, method of production, production level, etc. of a product. Weidema (2003) defines attributional life cycle assessments as “retrospective” studies suitable for hot-spot identification. Consequential life cycle assessments are defined as “prospective” studies to evaluate the consequences of future 17 changes. These changes take into account multiple product systems, which interact dynamically via assumed market-mediated product substitution effects. These differences in modelling approach mean that different processes will be included within the system boundary, depending on whether an attributional or consequential LCA is being performed. All processes related to the functional unit are included in an ALCA, and those processes affected by a change in the product are included in a CLCA (Weidema 2003). Methods for solving multi-functionality problems in each type are also different – allocation for ALCA and system expansion for CLCA (Weidema 2003). ALCA and CLCA may also use different data. Ekvall et al. (2016) describe ALCA as using average data and CLCA as utilizing marginal (change-based) data for modelling changes. These differences can affect the types and relative importance of uncertainties associated with each type of LCA, and how this uncertainty should be analysed. Weidema (2003) states that uncertainty in CLCA may be quite large but is able to be estimated and controlled, whereas the uncertainty in ALCA (i.e. arising from not considering market interactions) is not knowable or controllable. In order to quantify uncertainty due to data quality in LCA, Weidema and Wesnaes (1996) proposed a semi-quantitative pedigree matrix, adapted from Functowicz and Ravetz (1990), which includes measures for data reliability, completeness, temporal correlation, geographical correlation, and further technological correlation. These indicators are intended to be sufficient to describe the data quality in terms of consistency with the study in which they are being used, and to be used in conjunction with measures of inherent uncertainty (Weidema and Wesnaes 1996). However, the pedigree matrix only addresses uncertainty due to data quality (parameter uncertainty), not model or scenario uncertainty. Moreover, it should be remembered that the pedigree matrix categories, score levels, and associated standard deviations are highly generic and merit continued development and sector-specific adaptation by the LCA community in order to improve their utility for contributing to uncertainty quantification and assessment. Uncertainty analysis in LCA involves calculating the total uncertainty of the outcome of a study based on the uncertainty of all the parameters and model choices of the modelled product system, which enables estimation of a confidence interval for the results. This is especially important since there is often a large amount of uncertainty in LCA models, such that representing the results as point values is misleading (Björklund 2002). There are many possible mathematical approaches for propagating uncertainty parameters in unit process data and characterisation factors, the most common approach being Monte Carlo sampling (Groen et al. 2014). Other approaches include Taylor series expansion and fuzzy interval arithmetic. In 18 essence these methods all perform the same function, which is to propagate the uncertainty of the parameters to the final outcome. However, they have different practical efficiencies depending on the situation. Given the differences in LCA methodologies and sources of uncertainty, it is worth considering which methods of uncertainty analysis are appropriate for each type of LCA as well as the relative importance of different sources of uncertainty that are either common or unique to ALCA and CLCA. There has been research on uncertainty analysis and the types of uncertainty in ALCA (for example, see Björklund 2002), which primarily considers certain aspects of uncertainty, variability or unrepresentativeness. However, literature addressing uncertainty in CLCA and the differences between ALCA and CLCA with respect to uncertainty is almost non-existent. Due to the methodological differences between these two types of LCA, it is possible that the major sources of uncertainty will differ and also that different methods may be required to address them. The relative importance of these sources of uncertainty, and how they are best quantified, is presently unclear. The aims of this paper are hence two-fold. First is to identify and evaluate the frequency of uncertainty reporting in LCA studies (including ALCA, CLCA and any combinations of the two). The common sources and methods of quantification of uncertainty are also reviewed, both those that are common to all LCA studies, as well as unique to either ALCA or CLCA. This includes identifying any current issues in uncertainty analysis methods and reporting. Second is to determine whether or not the methods that are commonly applied for uncertainty assessment in attributional LCA, and the kinds of uncertainty that are considered, are similarly applicable in consequential life cycle modelling contexts. I also discuss whether additional methodological development may be necessary to support improved quantification and communication of both shared and unique sources of uncertainty with respect to both ALCA and CLCA model results. 3.3 Methods A broad suite of relevant LCA methods and theoretical literature that focused on uncertainty were reviewed in order to determine current recommendations, best practices and challenges. Publications discussing uncertainty in ALCA and/or CLCA were identified using Web of Science and Google Scholar and the keywords “uncertainty” OR “error” OR “variability” OR “consequential” AND “life cycle assessment”. Papers were selected based on their relevance to answering research questions regarding sources, analysis or communication of uncertainty/error/variability in either ALCA, CLCA, or both. 19 In order to determine the current state of uncertainty analysis and reporting in LCA, a review was subsequently performed of all relevant papers in the journals publishing the greatest number of LCA studies from January 2014 to August 2018. The journals included were the International Journal of Life Cycle Assessment (IJLCA), the Journal of Cleaner Production, Sustainability, Applied Energy, Science of the Total Environment, Resources, Conservation and Recycling and the Journal of Industrial Ecology, each of which returned at least 150 results using keywords “life cycle assessment” in the Web of Science Core Collection for the years 2014-2018. 3.3.1 Reporting of uncertainty The type of LCA study (attributional or consequential) was recorded, as well as (1) whether or not uncertainty analysis was performed, or (2) if uncertainty was even mentioned in the publication. The type of study was first determined based on any indications of LCA study type (ALCA or CLCA) by the authors, and second based on the research questions and methods if no LCA type was indicated. If the authors said that an uncertainty analysis was performed, the paper was categorized as “performed”, regardless of whether or not the results of the uncertainty analysis were presented in the paper. This included all types of uncertainty analysis, both quantitative and qualitative parameter uncertainty analysis as well as analysis of model and scenario uncertainty. However, simply a “sensitivity analysis” with no mention of uncertainty was not included as uncertainty analysis since these two analyses have fundamentally different goals. In the case of IJLCA, as it is an LCA-focused journal, any discussion of uncertainty analysis was also recorded for non-LCA case studies, including review papers and methodological papers. However, for all other (non LCA-specific) journals, no analysis was performed of non-LCA studies since those studies are not relevant to the research questions. The total number of each type of paper was tabulated and graphed, along with the number and percentage of each for each category. The rate of reporting of uncertainty (i.e. % of papers) annually over this interval was also calculated. 3.3.2 Sources and quantification of uncertainty Both the LCA case studies and theoretical/methodological publications regarding uncertainty in LCA were reviewed in order to identify the methods for quantification of uncertainty in either ALCA or CLCA. The broad category sources of uncertainty (parameter, scenario and model) mentioned in the LCA case studies were tabulated, as well as any specific sources of uncertainty (within the broad categories) mentioned. Since not all studies reported 20 specific sources of uncertainty, methods documents discussing uncertainty in LCA were reviewed in order to gain broader insight into sources of uncertainty common to all LCA studies or unique to ALCA or CLCA. Similarly, the methods of propagation and quantification of uncertainty were identified from the LCA case studies (categorized by LCA type). If any paper utilized more than one uncertainty analysis method, this was categorized as one instance of each method employed. Based on this review, current limitations and potential for improvement of the availability/suitability of methods to address uncertainty in ALCA and CLCA were evaluated. 3.3.3 Uncertainty analysis/propagation specific to A- and CLCA A review of methods/guidance documents discussing uncertainty in LCA was also conducted to determine the current state-of-the-art with regards to best practices, general areas of agreement or disagreement, and any gaps in the literature or outstanding questions related to uncertainty propagation in both ALCA and CLCA, or common to both types. The aforementioned LCA case studies published from 2014-2018 were assessed against these best practices in order to evaluate the current quality of uncertainty analysis in published LCA research and to make recommendations for improvement, if necessary. 3.3.4 Uncertainty analysis by sector Finally, the LCA case studies from 2014-2018 were sorted according to ISIC economic sector (United Nations 2008). These sectors were then mapped to the different sectors for which a standard base uncertainty (for use in data quality assessment) is provided in (Frischknecht et al. 2005). This was done to compare the size of the base uncertainty for a given sector (representative of inherent stochasticity) to the relative proportion of studies within that sector that performed uncertainty analysis. 3.4 Results and discussion 3.4.1 Reporting of uncertainty A total of 2,687 LCA case studies published in the International Journal of Life Cycle Assessment (731 studies), Journal of Cleaner Production (1426 studies), Sustainability (147 studies), Applied Energy (231 studies), Science of the Total Environment (174 studies), Resources, Conservation and Recycling (148 studies), and Journal of Industrial Ecology (140 studies) from January 2014 to August 2018 were identified for review. The overwhelming majority (94%) of these were strictly attributional LCA studies (Table 1). Only 6% were either 21 CLCA (5%) or a combination of ALCA and CLCA (1%). Among these, 19% of ALCA studies included some type of uncertainty analysis (including quantitative and qualitative analysis of parameter uncertainty, as well as scenario/sensitivity analysis of scenario/model uncertainty) (Figure 1). In comparison, 15% of CLCA studies reported performing uncertainty analysis, as did 22% of combined ALCA/CLCA studies. There were also 2% of ALCA studies and 1% of CLCA studies that reported that uncertainty analysis was not performed due to methodological/data limitations and rather suggested it as a future step. Overall, the majority of LCA studies (79%) did not perform any kind of uncertainty analysis, and a large fraction did not even mention uncertainty (43%). In publications in IJLCA that were not LCA case studies, including reviews, methodological proposals, etc., the proportion that addressed the topic of uncertainty was lower still (14%) (Appendix D - Table 40). Moreover, there is a slight decrease (21 to 17%) over time in the proportion of LCA studies that include an uncertainty analysis despite the increase in total LCA studies (Figure 2). Only aggregated data (all journals together) for uncertainty reporting are presented in the current document. Detailed data are available in Appendix D Tables 40-46 for individual journals. Table 1. Numbers of life cycle assessment studies reviewed from January 2014- August 2018 in International Journal of Life Cycle Assessment, Journal of Cleaner Production, Sustainability, Applied Energy, Science of the Total Environment, Resources, Conservation and Recycling, and Journal of Industrial Ecology, categorised by LCA type. Type of LCA Number of studies reviewed Attributional 2519 (94%) Consequential 136 (5%) Combination attributional and consequential 32 (1%) Total 2687 22 a) b) c) Figure 1. Types of LCA studies and reporting of uncertainty in International Journal of Life Cycle Assessment, Journal of Cleaner Production, Sustainability, Applied Energy, Science of the Total Environment, Resources, Conservation and Recycling, and Journal of Industrial Ecology from January 2014-August 2018. Papers were either defined as having mentioned uncertainty but not performed an assessment, stated that they performed an uncertainty assessment, stated that they did not perform an uncertainty assessment, or not mentioned uncertainty at all. a) Attributional studies (n = 2522 ALCAs). b) Consequential studies (n = size 136 CLCAs). c) Combination attributional and consequential studies (n = 32 LCAs). 23 Figure 2. Reported uncertainty analysis in LCA case studies in the International Journal of Life Cycle Assessment, Journal of Cleaner Production, Sustainability, Applied Energy, Science of the Total Environment, Resources, Conservation and Recycling, and Journal of Industrial Ecology from 2014-2017. Note that 2018 was excluded because it only included 8 months. 3.4.2 Sources and quantification of uncertainty 3.4.2.1 Sources common to both A- and CLCA, their importance, and their rates of consideration Most sources of uncertainty are common to both types of LCA (Table 2). These sources include data variability and quality (parameter uncertainty), many kinds of methodological choices (scenario uncertainty) and impact assessment methods (model uncertainty). 24 Table 2. Sources of uncertainty in LCA in general and specific to ALCA and CLCA from LCA studies in the International Journal of Life Cycle Assessment, Journal of Cleaner Production, Sustainability, Applied Energy, Science of the Total Environment, Resources, Conservation and Recycling, and Journal of Industrial Ecology reviewed from January 2014-August 2018. Several studies have evaluated the relative importance of the different types of uncertainty in LCA, with contradicting results. As reviewed by Lloyd and Ries (2007), some suggested parameter uncertainty is the most important (for example Huijbregts et al. 2003), some studies concluded uncertainty in characterization and valuation factors (or model and scenario uncertainty) are more important (Steen 1997), and others claimed all were important (Huijbregts et al. 2005). In reality, their relative importance will likely often be context-specific depending, for example, on which LCIA methods and impact factors are considered (Henriksson 2015). That said, no LCA study identified for this review included analysis of all sources of uncertainty in a single, quantitative uncertainty assessment; it is unlikely that this is possible because there will always be unknown unknowns. (Lloyd and Ries 2007) found that parameter uncertainty was most commonly reported, and that important sources of uncertainty may often be overlooked. This is an important issue since including some, but not all, sources of uncertainty can lead to incorrect and misleading results (Uusitalo et al. 2015). Uncertainty from the impact assessment stage of LCA can sometimes (but not always) be more subjective than inventory uncertainty, due to the uncertain nature of actual environmental impacts as well as individual preferences, which leads to more assumptions and value judgements being required (Lloyd and Ries 2007). This includes the choice of characterization factors, category indicators, etc. and their representativeness. Weidema et al. Source of Uncertainty Common to A- and CLCA  System boundaries; mathematical relationships in model; data variability, availability, accuracy, representativeness; time horizon of study/timing of emissions; stakeholder interpretation of results; transformative technology; human behavior; methodological choices (functional unit, multi-functionality, impact assessment methods); normalization; weighting; lack of knowledge of biological processes (e.g. soil carbon, land change, ecosystem services); assumptions of linearity and fixed relationships ALCA-specific Uncertainty  Co-product allocation CLCA-specific Uncertainty  Behavioural-economic impacts: change over time (technical change or input substitution), horizontal competition for intermediate goods, impact on consumption (rebound), strategic competitive behaviour, impact of technology, future production costs, future demand; system expansion/substitution 25 (2013) found that uncertainty data are more often reported for LCI data than LCIA, but that the latter source of uncertainty may be just as important. Only including those studies that were previously identified as having performed an uncertainty analysis, many studies accounted for more than one source of uncertainty (Figure 3). Among ALCAs in which some form of uncertainty analysis was reported, 410 out of 470 studies (87%) accounted for parameter uncertainty, 77 (16%) for scenario uncertainty and 54 (11%) for model uncertainty (Fig. 3a). Only 8 studies (less than 2%) included all 3 sources of uncertainty in their analysis. Ninety studies (18%) included a combination of 2 different sources. Twenty-seven studies (6%) did not indicate the sources of uncertainty but still indicated that they performed an uncertainty assessment. Others stated that they did not analyze any uncertainty but parameter uncertainty, citing lack of suitable methods (Romero-Gámez et al. 2017). Table 3. Sources of uncertainty and analysis type, categorized by parameter, scenario or model uncertainty, in attributional LCA studies in the International Journal of Life Cycle Assessment, Journal of Cleaner Production, Sustainability, Applied Energy, Science of the Total Environment, Resources, Conservation and Recycling, and Journal of Industrial Ecology reviewed from January 2014-August 2018. Parameter only Scenario only Model only Parameter and scenario Parameter and model Scenario and model Parameter, scenario and model Not indicated Monte Carlo 217 3 6 12 23 0 4 22 Fuzzy 3 0 0 1 0 0 0 0 Not indicated/other 21 2 1 3 2 0 0 3 Probabilistic (other than Monte Carlo) 11 0 1 0 2 0 1 0 Qualitative 4 0 0 1 1 0 1 0 Regression 1 0 0 0 0 0 0 0 Scenario 7 6 1 12 1 0 0 2 Sensitivity 55 9 1 17 4 3 2 0 Taylor series 4 0 0 0 0 0 0 0 Table 4. Sources of uncertainty and analysis type, categorized by parameter, scenario or model uncertainty, in consequential LCA studies in the International Journal of Life Cycle Assessment, Journal of Cleaner Production, Sustainability, Applied Energy, Science of the Total Environment, Resources, Conservation and Recycling, and Journal of Industrial Ecology reviewed from January 2014-August 2018. Parameter only Scenario only Model only Parameter and scenario Parameter and model Scenario and model Parameter, scenario and model Not indicated Monte Carlo 7 0 0 2 3 0 0 0 Fuzzy 0 0 0 0 0 0 0 0 Not indicated/other 0 0 0 0 0 0 0 0 Probabilistic other 2 0 0 0 0 0 0 0 Qualitative 0 0 0 0 0 0 0 0 Regression 0 0 0 0 0 0 0 0 Scenario 1 0 0 1 0 0 0 0 Sensitivity 0 1 0 2 0 0 0 0 Taylor series 0 0 0 0 0 0 0 0 26 Table 5. Sources of uncertainty and analysis type, categorized by parameter, scenario or model uncertainty, in combination attributional and consequential LCA studies in the International Journal of Life Cycle Assessment, Journal of Cleaner Production, Sustainability, Applied Energy, Science of the Total Environment, Resources, Conservation and Recycling, and Journal of Industrial Ecology reviewed from January 2014-August 2018. Parameter only Scenario only Model only Parameter and scenario Parameter and model Scenario and model Parameter, scenario and model Not indicated Monte Carlo 2 0 0 0 0 0 0 0 Fuzzy 0 0 0 0 0 0 0 0 Not indicated/other 1 0 0 0 0 0 0 0 Probabilistic other 0 0 0 0 0 0 0 0 Qualitative 1 0 0 0 0 0 0 0 Regression 0 0 0 0 0 0 0 0 Scenario 0 1 0 0 0 0 0 0 Sensitivity 1 0 0 0 0 0 0 0 Taylor series 0 0 0 0 0 0 0 0 a) b) Figure 3. Sources of uncertainty included in uncertainty analysis in International Journal of Life Cycle Assessment, Journal of Cleaner Production, Sustainability, Applied Energy, Science of the Total Environment, Resources, Conservation and Recycling, and Journal of Industrial Ecology from January 2014-August 2018. Parameter uncertainty refers to variability of data points and data quality uncertainty, scenario uncertainty refers to uncertainty from any scenario or modelling choices made in the LCA, and model uncertainty refers to uncertainty in mathematical relationships in the model such as impact assessment methods. a) Attributional studies (n = 470). b) Consequential studies (n = 19). 27 In CLCAs, 18 out of 19 studies (95%) that performed uncertainty analysis included parameter uncertainty as a source of uncertainty in their analysis, 6 (32%) included scenario uncertainty, and 3 (16%) included model uncertainty (Fig. 3b). None of these studies included all 3 types of uncertainty, and 8 (42%) included 2 types. In combination ALCA and CLCA studies, 5 out of 6 studies (83%) listed parameter uncertainty as the only source of uncertainty analyzed, and 1 (17%) listed scenario uncertainty. No combination ALCA/CLCA studies accounted for more than one type of uncertainty. Common sources of model uncertainty were characterisation factors and emission factors (ex. Dong and Ng 2014; Aubin et al. 2018). Sources of scenario uncertainty were from modelling choices including the definition of the functional unit, system boundary, and handling of co-products (ex. Tu et al. 2018). Parameter uncertainty was by far the most common source of uncertainty considered, from both inherent data variability and data quality uncertainty quantified using the data quality indicators of the pedigree matrix (ex. Galle et al. 2017). 3.4.2.2 Uncertainty specific to ALCA The main source of uncertainty specific to ALCA comes from solving multi-functionality problems through allocation, which is not used in CLCA (Pelletier et al. 2015). Scenario uncertainty from choice of allocation method was indicated as a source of uncertainty in many of the ALCA studies reviewed, but not in the CLCA studies (Table 2). For example, Van Stappen et al. (2018) found a 10-26% change in results depending on the type of allocation used (economic or mass). It should be noted that the uncertainty that comes from the choice of allocation method is separate from the inherent variability related to the specific factors used for a particular allocation method (for example, price differences over time and space when economic allocation is used). This second type of variability is additive to LCI variability, as it is a type of data uncertainty. The uncertainty due to allocation choice, like other sources of scenario or choice uncertainty, was often analyzed using sensitivity analysis. Of the 77 ALCA studies that accounted for scenario uncertainty, 49 (64%) used scenario or sensitivity analysis to assess it (Table 3). However only 16% of studies included scenario uncertainty in their uncertainty analysis, whereas 87% included parameter uncertainty. 3.4.2.3 Uncertainty specific to CLCA CLCA differs from ALCA in that it considers the market-mediated changes and associated environmental impacts in the broader economic system that may arise due to a 28 change in a given product system. However, the identification of marginal technologies and the pathways and magnitude of the assumed substitution is quite difficult in practice (Ekvall and Weidema 2004). This is the main source of uncertainty specific to CLCA (Table 2). Further uncertainty is also added with respect to the definition of the system boundaries (Mathiesen et al. 2009). Overall, the inclusion of such assumed economic effects may introduce considerable uncertainty into CLCA due to the uncertainty of both economic activity and future predictions in general (Ekvall and Weidema 2004). Process-based CLCA is most simply modeled as a linear process which has an associated set of assumptions. It assumes fixed input/output coefficients, infinite elasticity of inputs, and an adequate market capacity for co-products (Yang and Heijungs 2018). In other words, it creates a linear extrapolation of the original system to model a change in the system. As these assumptions do not accurately reflect reality in many cases, this is a source of uncertainty that can impede CLCA as a decision support tool (Pelletier et al. 2015). The problem is not limited to CLCA, but is also a well-recognized problem of econometric policy evaluation in economics (Lucas 1976). However, some CLCA methodologies include more sophisticated systems based on marginal markets (e.g. Schmidt 2008). To help avoid erroneous conclusions, Yang and Heijungs (2018) suggest using process-based models in conjunction with other types of models such as general equilibrium models, linear optimisation models, system dynamics and integrated assessment models when making decisions, especially for economically and environmentally important policies. All of these methods will have their own characteristic types and degrees of uncertainty. In particular, they require assumptions regarding the preferences and objectives governing the decision making of economic agents. Most CLCA studies that were identified as having performed an uncertainty analysis accounted for parameter uncertainty, with only 32% of CLCAs and 17% of combination ALCA/CLCAs accounting for scenario uncertainty, which is the type of uncertainty introduced by the identification of marginal technologies (Tables 4-5). 3.4.3 Methods for uncertainty propagation in LCA Parameter uncertainty is the most commonly analysed source of uncertainty in LCA studies and is most often undertaken using stochastic modelling. Monte Carlo is the most available and used stochastic modelling method in LCA software but requires a large number of samples and thus considerable computing time. Other than MC, alternative approaches for quantifying uncertainty in LCA include fuzzy data sets, analytical uncertainty propagation, probabilistic simulation, and Bayesian statistics (Björklund 2002). However, the most 29 appropriate method of uncertainty propagation will differ depending upon the parameter data available as well as the goal and scope of the study, in terms of necessary specificity and amount of data. It will also depend on the availability and cost-effectiveness of software. Currently, OpenLCA (available at http://www.openlca.org/) is an example of a free and open source LCA software with uncertainty analysis capability (using MC), although only data variability, uncertainty due to data quality, and uncertainty due to characterization factors can be included in the analysis. There is also code within open source statistics software such as R that can be used for uncertainty analysis. According to Lloyd and Ries (2007), analytical uncertainty propagation, interval calculations, and fuzzy data sets can be less accurate than stochastic sampling such as Monte Carlo and Latin Hypercube. Groen and Heijungs (2017) found that both analytical and sampling approaches (including analytical uncertainty propagation, Monte Carlo, etc.) can yield approximately equal variance and sensitivity indices, depending on the study. Heijungs and Lenzen (2014) found that either analytical or sampling techniques may be preferable, depending on the situation, and that analytical approaches are faster but can be less reliable for large uncertainties. Groen et al. (2014) tested multiple methods of uncertainty propagation and found that sampling methods yielded more usable data than fuzzy interval or analytical uncertainty propagation. They also found that Latin Hypercube and quasi Monte Carlo sampling were more accurate and sometimes even faster than Monte Carlo sampling. Di Maria et al. (2016) proposed a new probabilistic method of uncertainty propagation based on variance and geometric mean, which produced comparable results to MC. Only some of these propagation methods allow for dependent sampling (Heijungs et al. 2017), something that is desirable for considering only relative results and paired-sampled statistics (Henriksson et al. 2015). Despite the various options, Monte Carlo remains the standard uncertainty propagation method most commonly available and used in LCA software. Monte Carlo simulation, or other Monte-Carlo type propagation techniques such as Latin Hypercube, were the most commonly applied methods of stochastic modeling in the LCA studies reviewed. Of the 502 LCA studies reviewed which reported uncertainty analysis, 301 (60%) employed MC (Figure 4, Appendix D - Tables 54-60). Several studies applied MC to propagate model uncertainty from characterization factors or emission factors, or scenario uncertainty due to choices in addition to just parameter uncertainty (ex. Meinrenken and Lackner 2015; Usack et al. 2018). Some studies combined MC simulation with other uncertainty analysis methods such as semi-quantitative analysis based on expert judgement, since MC is 30 generally used to account for data uncertainty, not uncertainty from the models or impact assessment methods (Van Stappen et al. 2018). However, it is most commonly used only for parameter uncertainty, (82% of studies that used MC). Other methods of quantitative stochastic uncertainty analysis performed in the LCA studies reviewed included Taylor series (1%). Taylor series analysis has been suggested as another method to account for uncertainty from both data and other sources, and can also be used in conjunction with data quality indicator scores (numerical representation of the quality of the data, in terms of completeness, reliability, and geographical, temporal and technological representativeness) to propagate individual parameter uncertainties when these input parameters do not have a known range of variability (Cordes et al. 2016). A small number of probabilistic analyses (4%), fuzzy logic (1%), and regression analysis (<1%) were also reported. Overall, of the studies that reported having performed an uncertainty analysis, 2% performed a purely qualitative analysis. Seven percent performed scenario analysis, and 19% performed a sensitivity analysis. An additional 7% did not report how the uncertainty analysis was performed. 31 a) b) c) Figure 4. Types of uncertainty analysis performed in ALCA and CLCA studies in International Journal of Life Cycle Assessment, Journal of Cleaner Production, Sustainability, Applied Energy, Science of the Total Environment, Resources, Conservation and Recycling, and Journal of Industrial Ecology from January 2014-August 2018. Studies that employed a combination of uncertainty analysis methods are counted under each method. The percentages are calculated based on the sum of all the categories, including these studies that employed multiple methods that are included in more than one category. a) Attributional studies (n = 482). b) Consequential studies (n = 20). c) Combination attributional and consequential studies (n = 7). 3.4.3.1 Uncertainty propagation in ALCA studies Mendoza Beltran et al. (2016) developed a methodology for simultaneously propagating data uncertainty and uncertainty due to methodological choices, specifically allocation method. MC simulations are used for the propagation of unit process data uncertainty, as well as the choice of allocation method. This is more representative of the overall uncertainty and can be quicker to perform than modelling each allocation method as a separate scenario, including a 32 MC analysis of each scenario. Using a more efficient statistical propagation method such as Latin Hypercube sampling is also possible with this method. The method can also be applied to other methodological choices, not only allocation. Among the reviewed studies, Monte Carlo is the most common method of uncertainty analysis in ALCA, accounting for 287 out of 475 (60%) ALCA studies that reported uncertainty analysis (Figure 4a, Appendix D - Tables 54-60). Other methods of uncertainty analysis employed in the ALCA studies reviewed were Taylor series (1%), probabilistic (other than MC) (3%), fuzzy logic (1%) and regression analysis (<1%). A small number (2%) of studies used qualitative methods. Scenario modeling accounted for 6%, and sensitivity analysis 19%. An additional 7% of studies did not report the type of uncertainty analysis used, or used some other kind of uncertainty analysis. 3.4.3.2 Uncertainty propagation in CLCA studies Larrea-Gallegos et al. (2018) performed uncertainty analysis in their CLCA of pisco grape production. The marginal effects of the change in pisco production were modelled using the stochastic technology-of-choice model (STCM), in which the uncertainty of any element of the model can be assessed using its probability density function and, combined with stochastic aspects of parameters, MC simulation. Plevin et al. (2010) studied bioenergy production using CLCA. To determine the potential range of the impact of indirect land use change associated with bioenergy production, they subjectively created uncertainty distributions for each parameter and propagated the uncertainty using MC simulation, and also determined the contribution of each parameter to the overall uncertainty. This resulted in large ranges - larger than reported in previous literature on the subject since this study combined the uncertainties of all the input parameters (Plevin et al. 2010). Much of this variance is from uncertainty in modeling choices and inherent stochasticity of parameters. The newest version of the ecoinvent database, version 3 (available for download at https://nexus.openlca.org/databases) now includes consequential LCI models (Wernet et al. 2016). The consequential models use substitution instead of allocation, and are intended to be used in CLCAs. Products are attributed all benefits or burdens associated with their resource inputs and emissions but are then credited for any co-products that substitute other products. However, there is no change in the pedigree matrix traditionally used in ecoinvent for the consequential system model, meaning that there are no additional categories to account for unique sources of uncertainty specific to CLCA. Practitioners of consequential modelling should look to other fields, such as economics, to determine how best to quantify and analyze the 33 unique and potentially very large sources of uncertainty in CLCA that arise from the identification of the market-mediated marginal technologies, the affected markets, and the magnitude of the assumed substitution effect. There are also limitations when using the attributional framework for consequential modelling (Yang 2016). Real life scenarios are not likely to scale linearly due to such factors as physical constraints to expansion and market-mediated human behaviours. However, the computable general equilibrium models often used in CLCA (Earles and Halog 2011) have their own assumptions such as rational expectations which can also increase the uncertainty of the result. Yang (2016) proposes a two-step approach to conducting a CLCA using the more familiar ALCA framework. The first step is to conduct an ALCA to determine the status quo and any hot-spots. The second step is to conduct scenarios (and likely collect more data) based on the change to be studied in a consequential framework. In the end, this yields a range of scenario output values. This process requires marginal input/output coefficients to replace the average coefficients used in ALCA. However, Weidema (2017) argues that the attributional framework is not appropriate to answer consequential questions and contributes a large amount of uncertainty if used. They compared the same systems in the consequential and attributional models in ecoinvent version 3 and found that in most cases there was at least a 10% difference in impact between the two systems, and up to an order of magnitude difference. The sources of these differences came from the following five categories: marginal suppliers that are very different from the average; specialty products; multiple determining products; by-products from treatment activities; and determining products heavily influenced by by-products. On this basis, Weidema (2017) suggests that using the consequential models in ecoinvent for CLCA studies removes an important source of error from CLCA. Modelling many different scenarios can help alleviate the risk of under-representing uncertainty and presenting results as too certain (Ekvall and Weidema 2004). Ekvall and Weidema (2004) also suggest cutting off the system boundaries where the uncertainty becomes too large to support any realistic decision-making, and that short-term fluctuations in marginal technology can be ignored in favour of long-term effects. Mathiesen et al. (2009) suggest performing a sensitivity analysis to determine the importance of switching between different marginal technologies. In some cases in the literature this can have a significant impact on the outcome of the study (Mathiesen et al. 2009). They also suggest to test the LCA results against several fundamentally different marginal technology options in order for the results to not become too sensitive to the uncertainty introduced by marginal technology changes. 34 Overall, fourteen out of 27 (52%) CLCA (or combination ALCA/CLCA) studies published in the past 5 years that included uncertainty analysis used MC (Figure 4b-c, Appendix D - Tables 47-53). Two studies (7%) used other probabilistic methods. One (4%) used qualitative analysis, 4 (15%) used scenario analysis, 5 (19%) sensitivity analysis and 1 (4%) did not report the method of uncertainty analysis. Roux et al. (2017) used MC analysis to propagate uncertainty based on the nine most sensitive parameters related to the marginal electricity mix. However, the majority of studies did not perform uncertainty analysis nor attempt to account for the uncertainty associated with market scenarios. 3.4.4 Uncertainty analysis by sector All LCA case studies considered in the review were classified by ISIC sector, then mapped to the appropriate category according to the generic basic uncertainty values given by Frischknecht et al. (2005). For process flows, the basic uncertainty values (geometric standard deviations) are as follows: Thermal energy, electricity, semi-finished products, working material, waste treatment services: 1.05; Transport services: 2.00; Infrastructure: 3.00; and Primary energy carriers, metals, salts: 1.05. This implies that the highest amount of inherent uncertainty is in the infrastructure sector, followed by transport services, thermal energy, electricity, semi-finished products, working material, waste treatment services and primary energy carriers, metals, and salts. In terms of performance of uncertainty analysis, results are split between attributional and consequential (including combination attributional and consequential) LCAs (Table 6, Appendix D – Tables 61-67). For ALCA, sector-specific studies with the highest level of reporting of uncertainty analysis focused on primary energy carriers, metals, and salts (26%), followed by infrastructure and transport studies (21%) and finally thermal energy, electricity, semi-finished products, working material, waste treatment services studies (18%). For CLCA, the studies in the transport sector had the highest percentage of uncertainty analysis (30%), followed by thermal energy, electricity, semi-finished products, working material, waste treatment services (17%), infrastructure (11%) and primary energy carriers, metals, and salts (8%). Aggregated results are presented in Table 6, and individual journal results in Appendix D - Tables 61-67. In theory, one would anticipate that the sectors with the largest basic uncertainty should have the most studies that perform uncertainty analysis, in order to accurately quantify the large inherent uncertainty in that sector. However, this is not seen in the reporting of uncertainty analysis by sector. There are also differences between ALCA and CLCA in terms of overall percentages of reporting as well as the order of most to least reporting by sector. 35 Table 6. Number of studies in the International Journal of Life Cycle Assessment, Journal of Cleaner Production, Sustainability, Applied Energy, Science of the Total Environment, Resources, Conservation and Recycling, and Journal of Industrial Ecology that either did or did not perform uncertainty analysis, categorized by the major industrial sectors with defined base uncertainty values (Frischknecht et al 2005). Percentages refer to how much of the total of each sector either performed or did not perform uncertainty analysis. Within each sector, papers are further subdivided by LCA type; CLCA in this case refers to all LCA studies that employed consequential modeling, including combination ALCA/CLCA studies. Studies with uncertainty analysis Studies with no uncertainty analysis Total number of studies Thermal energy, electricity, semi-finished products, working material, waste treatment services (uncertainty factor 1.05) ALCA 18% 82% 1841 CLCA 17% 83% 101 Transport (uncertainty factor 2.00) ALCA 21% 79% 157 CLCA 30% 70% 10 Infrastructure (uncertainty factor 3.00) ALCA 21% 79% 256 CLCA 11% 89% 18 Primary energy carriers, metals, salts (uncertainty factor 1.05) ALCA 26% 74% 292 CLCA 8% 92% 48 3.5 Discussion Despite the generally recognized importance (and widespread practice) of quantifying and communicating uncertainty associated with the results of scientific studies, uncertainty reporting within the LCA community is not yet commonplace. Less than 20% of the LCA studies published since 2014 report uncertainty analysis of any kind, and the rate of reporting does not appear to be increasing over time. In light of the potentially high levels of uncertainty in LCA studies, improved methods and practice with respect to uncertainty analysis are clearly critical to ensuring the utility of LCA as a decision-support tool. Of the many sources of uncertainty in LCA, parameter uncertainty is the main type that is accounted for with any degree of regularity among the subset of published LCA studies considered in this review. This is likely due to the lack of commonly accepted methods for quantitative analysis of model or scenario uncertainty. 36 There are important sources of uncertainty that are unique to either ALCA or CLCA, largely related to how multi-functionality problems are resolved (i.e. choice of allocation criterion in ALCA, and market-mediated substitutions based on identification of marginal technologies in CLCA). For the latter, uncertainty arises with respect to the identification of marginal technologies, general market trends (Mathiesen et al. 2009), assumptions regarding elasticities, and the uncertainty of future predictions in general (Ekvall and Weidema 2004). However, this review of recent literature suggests that uncertainty in CLCA is currently analyzed in the same manner as is prevalent for ALCA, with little apparent consideration of how to account for these unique and potentially very large sources of uncertainty. Monte Carlo-type analysis is by far the most common method of uncertainty analysis employed in the reviewed studies, despite the potential that other methods may be as accurate and less time-consuming. The current prevalence of Monte Carlo sampling likely reflects its general applicability and the fact that it is commonly supported in commercial LCA software. The frequency of uncertainty reporting among LCA studies for products within specific economic sectors does not align with sector group “base uncertainties”, as are commonly applied in the popular ecoinvent database. In general, one might expect that studies within sectors whose LCI data are characterized by the highest parameter uncertainty would have the highest level of uncertainty analysis, but this does not seem to be the case. Based on this review of the literature, the following specific recommendations for the LCA community with respect to uncertainty assessment methods and practice have been identified: 1) Comprehensive uncertainty analysis should be included in all published LCA studies, unless it can be justifiably excluded with regards to the goal and scope of the study. This is particularly pertinent for studies that claim ISO-14044 compliance and that advance comparative assertions. Journal editors and reviewers should begin to enforce this requirement, and should include it in their author guidelines. IJLCA, as the flagship disciplinary journal, should take the lead in “raising the bar” in this regard. 2) More attention should be paid to uncertainty analysis in studies specific to the transport and infrastructure sectors, due to their higher basic uncertainty values (if, indeed, these values are subject to and stand up to critical scrutiny). 3) Practitioners should consider different uncertainty propagation methods in order to select the most practical and efficient method for the study, rather than using Monte Carlo 37 sampling by default. LCA software developers should provide a range of options for uncertainty assessment in addition to Monte Carlo sampling. 4) Development of consensus methodologies for quantifying model and scenario uncertainties is urgently required. These are currently not available in most major LCA software. Such methodologies would allow practitioners to consider all relevant types of uncertainty and variability related to LCA studies, not just parameter uncertainty. 5) When performing an attributional or consequential LCA, attention should be paid to the types of uncertainty specific to the type of LCA. Methods should be agreed upon for accounting for the variety of sources of uncertainty present in both ALCA and CLCA. In particular, appropriate methods must be identified/developed for quantifying the unique and potentially very large uncertainties characteristic of CLCA. Methods for sensitivity analysis in consequential modelling could be borrowed from other fields, such as economics. 38 Chapter 4: Attributional life cycle assessment of mulch use on Okanagan apple orchards 4.1 Synopsis An attributional life cycle assessment was conducted to determine the life cycle environmental impacts of the production of apples on an Okanagan orchard, focusing on the relative contribution of bark and wood chip mulch used as a soil amendment, both compared to other life cycle stages and processes, and to a system producing apples without the use of mulch. This research focused on the orchard-level reductions in GHG emissions associated with mulch use but there are many other benefits of mulch that were not accounted for in the current study, due to data limitations from the experimental design, such as improved nutrient and water retention, increased soil organic matter, and suppression of weeds. A variety of environmental impact categories were examined including human toxicity, freshwater aquatic ecotoxicity, depletion of abiotic resources (elements, ultimate reserves), photochemical oxidation, ozone layer depletion, terrestrial ecotoxicity, acidification potential, climate change, eutrophication, land use – land competition, and energy use (including non-renewable: fossil, nuclear, primary forest; and renewable: biomass, geothermal, solar, water and wind). While the application of bark/wood chip mulch previously showed orchard-level benefits in terms of reducing nitrous oxide emissions by 19% and increasing soil carbon by 68% compared to a control (i.e. no mulch) treatment, these benefits were outweighed by the environmental impacts of the production, transportation and application of the mulch. The greenhouse gas emissions were 30% higher for the mulched than unmulched systems, taking into account both the reduction in orchard-level emissions and increase in upstream life cycle emissions. For other impact categories, such as acidification, the impact of the mulch system was 23% higher since the production of the mulch increased the impacts but gave no benefits elsewhere in the life cycle. In addition to increased emissions, the use of mulch also contributed to increased resource use, notably 98% higher cumulative energy demand, with 73% higher non-renewable energy use. It should be noted that there may be other orchard-level benefits associated with the use of bark mulch, such as nutrient and water retention, and suppression of weeds. However, these were not accounted for due to the design of the original study and data limitations. This may have led to an over-estimation of the impacts of mulch use. There was also a large amount of uncertainty in the model, from measured data variability, data quality, and characterization factor uncertainty. When all sources of uncertainty were included in the uncertainty analysis, many differences between treatments were still significant. Toxicity impacts were not significantly different, largely as a result of high characterization factor uncertainty. However, all differences 39 were significant when only the relative uncertainty that is unique to each treatment was considered, excluding all uncertainty sources that are shared between treatments. 4.2 Methods To address the research goal of determining the share of the net environmental impacts of apple production in the Okanagan Valley of British Columbia that are attributable to the use of wood chip/bark mulch (referred to hereafter as bark mulch or simply mulch) as a soil amendment, as compared to all other life cycle processes necessary for apple production, an ISO compliant (ISO 2006) attributional LCA (ALCA) was performed. The entire cradle-to-orchard gate life cycle of Okanagan apple production was examined, from the production of all inputs to their use on orchards. Impacts were assessed in terms of the production of 1 kg of apples. The data used comprised a combination of field-level site-specific data, survey results, and information from published literature, models and databases that was adapted as appropriate to best approximate regional conditions. The details of the ALCA methods, which correspond to the requirements of the international reference standard for life cycle assessment, ISO 14044 (2006), are as follows. 4.2.1 Goal and scope Based on previous site-specific (non-life cycle) research, there is evidence that adding wood chip/bark mulch as a soil amendment to an apple orchard provided environmental benefits by reducing nitrous oxide (N2O) emissions by 19% and increasing soil organic carbon (SOC) by 68% (Fentabil et al. 2016) compared to a control treatment. The current study is intended as a continuation of this research by examining the life cycle environmental implications of the addition of bark mulch. Specifically, the primary goal of the ALCA is to determine the net environmental impacts of apple production in the Okanagan Valley with, compared to without, the use of bark mulch as a soil amendment. A secondary goal is to determine the relative impacts of the mulch compared to all other life cycle stages and processes, and to determine the most important contributors to the environmental impacts of apple production. The intended audience includes agricultural policy makers, orchard owners, academics, as well as (potentially) the public. Some of the key stakeholders are the orchard managers and the suppliers of the mulch. This LCA will not be used to make comparative assertions (defined by ISO (2006) as an environmental claim asserting superiority of one product over a competing product) because it considers the addition of a soil amendment to an orchard, which would 40 otherwise not have any soil amendments added, producing the same product in either case: apples. The overall scope of the ALCA is cradle-to-orchard gate, meaning it included all stages from the extraction of raw materials, to the production of the apples on an orchard in the Okanagan Valley, but not the consumption and end-of-life stages. This included the production and application of mulch and all other inputs to the orchard, as well as all associated resource use and emissions. The orchard inputs considered are fertilizer, irrigation, plant protection products, energy, and bark mulch (Figure 5). However, it should be noted that due to the study design, the irrigation and plant protection inputs were identical across all treatments. The system also included the transportation of all orchard inputs and their associated emissions, as well as experimentally measured and modelled orchard-level emissions. As is common practice in the literature, the functional unit was defined as the production, in weight, of the apples (Milà I Canals et al. 2006; Vinyes et al. 2017). In this LCA the functional unit, as well as the reference flow, is one kilogram of apple yield. The temporal and geographical bounds included the most recent available data for the Okanagan Valley (for background systems that feed into orchard inputs), with all site-specific input and output data sourced from within the last 7 years from Agriculture and Agri-Food Canada’s Summerland Research and Development Centre (SuRDC) located near Summerland, British Columbia. The technological boundary included the average technology present in orchards in the Okanagan Valley, as this boundary will suffice for most LCAs (Guinee et al. 2002). By collecting the most temporally, geographically and technologically relevant data, the uncertainty of the model was reduced. In the case of any multi-functionality issues (i.e. when a process produces more than one product), co-products were allocated a portion of the environmental burden based on relative gross energy content or mass. This allocation took place in the upstream datasets used to feed into the orchard inputs modelled. To aggregate the life cycle inventory data into impact categories, several life cycle impact assessment (LCIA) methodologies are used in this LCA. These methods include CML baseline and non baseline [v4.4, January 2015], Cumulative Energy Demand [v1.0.1, January 2015] and ReCiPe Midpoint (H) [v1.11, December 2014]. The impact categories considered are human toxicity, freshwater aquatic ecotoxicity, depletion of abiotic resources, photochemical oxidation, ozone layer depletion, terrestrial ecotoxicity, acidification potential, climate change, eutrophication, land use – land competition, and energy use (including non-renewable: fossil, nuclear, primary forest; and renewable: biomass, geothermal, solar, water and wind). These were chosen since they were the most commonly 41 included impact categories in other apple LCA studies (see Chapter 2), and are in line with the categories suggested for inclusion by Guinee et al. (2002). Orchard input data, as well as orchard-level soil carbon, soil nitrogen, and N2O emissions data came from site-specific measurements as reported by Fentabil et al. (2016). Upstream production data for these inputs (and their associated resource use and emissions) came from average background datasets, described in detail in the following sections (Tables 7-11). With the exception of N2O, orchard-level emissions from fertilizer (i.e. NH3, NOx, NO3, PO4) were calculated based on the IPCC (2006) Tier 1 protocol and Dalgaard et al. (2008), and for plant protection product (pesticide, herbicide and fungicide) application based on the model developed by Margni et al. (2002). Additional energy use for any orchard processes not accounted for by SuRDC or in Fentabil et al (2016) was modelled based on recent apple LCA studies (Annaert et al. 2017; Goossens et al. 2017; Longo et al. 2017; Vinyes et al. 2018; Zhu et al. 2018). These datasets and models are assumed to include all relevant inputs and outputs for the processes modelled. Figure 5. Flow diagram of apple production life cycle from cradle-to-orchard gate, showing system boundary between technosphere (human activity) and ecosphere (nature), as well as the foreground (site specific) and background (generic) systems. 42 4.2.2 Life cycle inventory 4.2.2.1 Flow diagram and system boundaries The inventory phase of LCA involves the collection of data, either from primary research, databases, published literature and reports, or expert judgement (ISO 2006) and the construction of the system model. All data necessary to model the orchard system were represented in a flow diagram (Figure 5). The foreground processes of this system occur directly on the orchard, including fertilizer application, irrigation, mulch application, and plant protection product application, as well as experimentally measured orchard-level emissions of N2O, and SOC. The background processes are those that feed into these orchard processes, such as the production of the fertilizer, plant protection products, etc., and all related processes, resource use and emissions. 4.2.2.2 Foreground data collection Site-specific primary field data were collected from SuRDC regarding inputs of mulch, fertilizer, irrigation, and plant protection products, emissions of N2O, and apple yield (Fentabil et al. 2016). Details regarding the orchard-level foreground data collection process are documented in Fentabil et al. (2016), along with all relevant technological, geographical, temporal and climatic data. These data were used to create a unit process model of the orchard. Orchard-level emissions (other than N2O) from fertilizer and plant protection were modelled using IPCC (2006), Dalgaard et al. (2008) and Margni et al. (2002) methods, and are described in detail below in the fertilizer and plant protection sections. 4.2.2.3 Background data Data for background processes were taken from life cycle inventory (LCI) databases, including ecoinvent v.3.4 Allocation at the point of substitution (APOS), USDA crop data v.1.1 and Agri-footprint v.3.0 (available for download at https://nexus.openlca.org/databases). These data sets were altered as appropriate to be better representative of the region (described in detail below). All data were scaled to relate to the unit process flows and, ultimately, the functional unit. 4.2.2.4 Model The apple orchard system was modelled in OpenLCA 1.7.4, an open-source LCA modelling software created by Green Delta (available at http://www.openlca.org/download). The main process modelled was “Apple production on orchard”, with an output of 1 kg of apples. 43 Most foreground inputs and outputs were originally given in terms of one experimental plot which had a yield of 37.53 kg of apples per year, so all inputs and outputs were scaled accordingly. This was to ensure comparability with other apple LCA studies, where a functional unit of 1 kg apples is common (Milà I Canals et al. 2006; Vinyes et al. 2017). This apple production process was duplicated to compare the orchard plots that had bark mulch applied to the soil, and those that had no mulch added (also referred to as “clean”). These processes were duplicated again to compare the two different nitrogen fertilizer treatments in the experimental set-up: high nitrogen (40 g per tree) and low nitrogen (20 g per tree). Most inputs to each apple production process were the same, except for an additional input of mulch on the mulch systems, and the differing amounts of nitrogen application. The modelled greenhouse gas emissions for the four different processes varied according to the field-level research carried out by Fentabil et al. (2016), and the different amounts of upstream emissions and resource use associated with nitrogen fertilizer and bark mulch. Processes involved in orchard setup and teardown were not included in the model for several reasons. First, the orchard setup and teardown stages have been shown to have only a small impact compared to other life cycle stages (Sanderson et al. 2019). Second, the goal of this study, as outlined above, is to determine the impact of mulch use on the orchard, so the setup and teardown of the orchard are not part of the foreground system. 4.2.2.4.1 Fertilizer application Fertilizer input types and amounts were derived from SuRDC fertilizer application logs. All fertilizer product flows were modelled using LCIs from the ecoinvent v.3.4 Allocation at the Point Of Substitution (APOS) model database. SuRDC gave target amounts of nitrogen (N), phosphorus (P), potassium (K) and boron (B) applied per tree per year, and these numbers were scaled to the functional unit. The species LCI models for nitrogen, phosphorus, potassium and boron fertilizers were chosen to best represent the forms of these fertilizers indicated by SuRDC indicated (Table 7). Fertilizer was applied via fertigation, so any necessary equipment/machinery was included in the irrigation model. Ecoinvent background processes were modified, wherever possible, to best represent production in the appropriate area (for these and all other background processes mentioned). For potassium chloride production, an ecoinvent process already existed that is representative of production in Saskatchewan, Canada (where potassium fertilizer is produced), therefore this process was used and not altered (Table 7). 44 The modelling of transportation of fertilizer by truck from the sites of production to the orchard was based on the Okanagan cherry orchard LCA by Sanderson et al. (2019). Nitrogen fertilizer came from Carseland, Alberta (666 km). Phosphate rock sourced from northern Ontario (2887 km) was used in phosphorus fertilizer. Potassium fertilizer came from Saskatchewan (1315 km), and Solubor (boron fertilizer) from Boron, California (2096 km). Emissions of ammonia (NH3), nitrate (NO3), nitrogen oxides (NOx), and phosphorus (P) from fertilizer were estimated using the IPCC (2006) and Dalgaard et al. (2008) models (Appendix A). N2O emissions were not calculated here since site-specific N2O emissions were measured and provided from the SuRDC experiments. Table 7. LCI processes and databases used to model inputs related to fertilizer application for apple production in the Okanagan Valley, including any modifications made. Fertilizer input Process/flow Database Modifications Boron boron carbide production | boron carbide | APOS, U - GLO ecoinvent v.3.4 APOS  Electricity provider changed to “market for electricity, medium voltage | electricity, medium voltage | APOS, U - WECC, US only” Nitrogen calcium nitrate production | calcium nitrate | APOS, U - RoW ecoinvent v.3.4 APOS Phosphorus purification of wet-process phosphoric acid to industrial grade, product in 85% solution state | phosphoric acid, industrial grade, without water, in 85% solution state | APOS, U - RoW ecoinvent v.3.4 APOS  Heat providers changed to “heat and power co-generation, natural gas, conventional power plant, 100MW electrical | heat, district or industrial, natural gas | APOS, U - CA-ON”, and “heat, from municipal waste incineration to generic market for heat district or industrial, other than natural gas | heat, district or industrial, other than natural gas | APOS, U - CA-ON”  Provider for phosphoric acid changed to “phosphoric acid production, dihydrate process | phosphoric acid, fertiliser grade, without water, in 70% solution state | APOS, U – US”  Provider for quicklime changed to “quicklime production, milled, loose | quicklime, milled, loose | APOS, U - CA-QC”  Provider for sodium hydroxide changed to “chlor-alkali electrolysis, membrane cell | sodium hydroxide, without water, in 50% solution state | APOS, U - CA-QC” 45 Fertilizer input Process/flow Database Modifications  Provider for sulfuric acid changed to “primary zinc production from concentrate | sulfuric acid | APOS, U - CA-QC” Potassium potassium chloride production | potassium chloride, as K2O | APOS, U - CA-SK ecoinvent v.3.4 APOS Transportation of fertilizer from location of production to orchard Transport, truck 10-20t, EURO2, 100%LF, default Mass - GLO ecoinvent v.3.4 APOS Ammonia (NH3) Ammonia – Emission to air/low population density Nitrate (NO3-) Nitrate – Emission to water/ground water Phosphorus (P) Phosphorus – Emission to water/ground water Nitrogen oxide (NOx) Nitrogen oxides – Emission to air/low population density 4.2.2.4.2 Irrigation Fentabil et al. (2016) described two different irrigation frequency treatments: every day (high) or every second day (low). The values used in this model were taken from the high frequency treatments, although the total amount is the same between the two treatments and this model only accounts for total amount, not frequency. The average monthly water usage was given by SuRDC and converted to be representative of the functional unit, then modelled using the “irrigation” process from ecoinvent v.3.4 APOS, which contains LCI flows for both the irrigation water, and the process of application (Table 8). The process is representative of irrigation in Quebec, which was chosen as the most technologically representative process, and modified to include lake water as the source of water. 46 Table 8. LCI processes and databases used to model inputs related to irrigation for apple production in the Okanagan Valley, including any modifications made. Irrigation input Process Database Modifications Irrigation (water and application) irrigation | irrigation | APOS, U - CA-QC ecoinvent v.3.4 APOS Water input changed to lake water 4.2.2.4.3 Plant protection Total amounts of plant protection products, including herbicides, fungicides, and insecticides, applied from 2013 to 2014 were supplied by SuRDC (Table 9). These were converted to amounts per kg of apples produced and averaged over the 2 years (Appendix A). Plant protection products for which there was an exact match in the databases used were modelled accordingly. Those products include 2,4-D, carbaryl, paraquat, pendimethalin and glyphosate. All others were modelled by the most representative process available. Transportation by truck of plant protection products was included, based on the distance from the Alberta location of Bayer CropScience, a leading supplier of plant protection products for Western Canada (609 km). All transport modelled included only the distance to transport the product, not an empty return distance. Orchard-level emissions from plant protection product application were modelled based on the guidelines in Margni et al. (2002), as implemented in the Okanagan cherry industry LCA by Sanderson et al. (2019), assuming that 76.5% of the active ingredient of each agricultural plant protection product is emitted to soil, 8.5% to water, and 10% to air. The last 5% is taken up by the plant and is therefore not considered as an emission in this model. Table 9. LCI processes and databases used to model inputs related to plant protection products for apple production in the Okanagan Valley, including any modifications made. Plant protection product Process/Flow Database Modifications Fungicides Kumulus DF (wettable sulphur) Sulphur, from crude oil, consumption mix, at refinery, elemental sulphur - EU-15 Agri-footprint v.3.0 Nova (myclobutanil) Fungicide, at plant Energy - RER Agri-footprint v.3.0  Electricity provider changed to “electricity production, hydro, pumped storage | electricity, high voltage | APOS, S - CA-AB” Herbicides Gramoxone (paraquat) Paraquat, at plant Mass - RER Agri-footprint v.3.0  Electricity provider changed to “electricity production, hydro, 47 Plant protection product Process/Flow Database Modifications pumped storage | electricity, high voltage | APOS, S - CA-AB” Prowl (pendimethalin) pendimethalin production | pendimethalin | APOS, U - RoW ecoinvent v.3.4 APOS  Electricity provider changed to ”electricity production, hydro, pumped storage | electricity, high voltage | APOS, S - CA-AB”  Heat provider changed to “market for heat, district or industrial, natural gas | heat, district or industrial, natural gas | APOS, U - CA-QC, market for heat, district or industrial, other than natural gas | heat, district or industrial, other than natural gas | APOS, U - CA-QC”  Liquid hydrogen provider changed to “chlor-alkali electrolysis, membrane cell | hydrogen, liquid | APOS, U - CA-QC”  Sodium hydroxide provider changed to “chlor-alkali electrolysis, membrane cell | sodium hydroxide, without water, in 50% solution state | APOS, U - CA-QC” Simazine Herbicide, at plant Mass - RER Agri-footprint v.3.0  Electricity provider changed to “electricity production, hydro, pumped storage | electricity, high voltage | APOS, S - CA-AB” Glyphosate glyphosate production | glyphosate | APOS, U - RoW ecoinvent v.3.4 APOS  Chlorine provider changed to “chlor-alkali electrolysis, membrane cell | chlorine, gaseous | APOS, U - CA-QC”  Electricity provider changed to “market for electricity, medium voltage | electricity, medium voltage | APOS, U - CA-AB”  Heat provider changed to “market for heat, district or industrial, natural gas | heat, district or industrial, natural gas | APOS, U - CA-QC, market for heat, district or industrial, other than natural gas | 48 Plant protection product Process/Flow Database Modifications heat, district or industrial, other than natural gas | APOS, U - CA-QC”  Sodium hydroxide provider changed to “chlor-alkali electrolysis, membrane cell | sodium hydroxide, without water, in 50% solution state | APOS, U - CA-QC” 2,4-D 2,4-D, at plant Mass - RER Agri-footprint v.3.0  Electricity provider changed to “electricity production, hydro, pumped storage | electricity, high voltage | APOS, S - CA-AB” Insecticides Movento (spirotetramat) Insecticide, at plant Mass - RER Agri-footprint v.3.0  Electricity provider changed to ”electricity production, hydro, pumped storage | electricity, high voltage | APOS, S - CA-AB” Sevin (carbaryl) Carbaryl, at plant Mass - RER Agri-footprint v.3.0  Electricity provider changed to “electricity production, hydro, pumped storage | electricity, high voltage | APOS, S - CA-AB” DiPel (Bacillus thuringiensis subspecies kurstaki) Insecticide, at plant Mass - RER Agri-footprint v.3.0  Electricity provider changed to ”electricity production, hydro, pumped storage | electricity, high voltage | APOS, S - CA-AB” Diazinon diazine-compound production | diazine-compound | APOS, U - RoW ecoinvent v.3.4 APOS  Chlorine provider changed to “chlor-alkali electrolysis, membrane cell | chlorine, gaseous | APOS, U - CA-QC”  Electricity provider changed to “market for electricity, medium voltage | electricity, medium voltage | APOS, U - CA-AB”  Heat provider changed to “market for heat, district or industrial, natural gas | heat, district or industrial, natural gas | APOS, U - CA-QC, market for heat, district or industrial, other than natural gas | heat, district or industrial, other than natural gas | APOS, U - CA-QC” 49 Plant protection product Process/Flow Database Modifications  Hydrochloric acid provider changed to “hydrochloric acid production, from the reaction of hydrogen with chlorine | hydrochloric acid, without water, in 30% solution state | APOS, U - CA-QC”  Liquid hydrogen provider changed to “chlor-alkali electrolysis, membrane cell | hydrogen, liquid | APOS, U - CA-QC”  Natural gas provider changed to “natural gas production | natural gas, high pressure | APOS, U - CA-AB”  Liquid nitrogen provider changed to “air separation, cryogenic | nitrogen, liquid | APOS, U - CA-QC”  Liquid oxygen provider changed to “air separation, cryogenic | nitrogen, liquid | APOS, U - CA-QC”  Sodium hydroxide provider changed to “chlor-alkali electrolysis, membrane cell | sodium hydroxide, without water, in 50% solution state | APOS, U - CA-QC”  Sulfur provider changed to “natural gas production | sulfur | APOS, U - CA-AB”  Sulfur dioxide provider changed to “primary zinc production from concentrate | sulfur dioxide, liquid | APOS, U - CA-QC”  Sulfuric acid provider changed to “primary zinc production from concentrate | sulfuric acid, liquid | APOS, U - CA-QC” Dormant oil market for vegetable oil, refined | vegetable oil, refined | APOS, U - GLO ecoinvent v.3.4 APOS Other 50 Plant protection product Process/Flow Database Modifications Calcium market for calcium chloride | calcium chloride | APOS, U - GLO ecoinvent v.3.4 APOS  For “epichlorohydrin production from allyl chloride | calcium chloride | APOS, U” and “soda production, solvay process | calcium chloride | APOS, U”:  Electricity provider changed to “market for electricity, medium voltage | electricity, medium voltage | APOS, U - CA-AB”  Heat provider changed to “market for heat, district or industrial, natural gas | heat, district or industrial, natural gas | APOS, U - CA-QC, market for heat, district or industrial, other than natural gas | heat, district or industrial, other than natural gas | APOS, U - CA-QC” Application Plant protection application by sprayer application of plant protection product, by field sprayer | application of plant protection product, by field sprayer | APOS, U - CA-QC ecoinvent v.3.4 APOS Transportation Transport, truck 10-20t, EURO2, 100%LF, default Mass - GLO Agri-footprint v.3.0 Emissions 2,4-D 2,4-D - Emission to air/low population density, Emission to soil/agricultural, Emission to water/ground water DiPel Bacillus thuringiensis subspecies kurstaki - Emission to soil/agricultural  Only soil emissions included (no elementary flows for emissions to water or air) Calcium Calcium - Emission to air/low population density, Emission to soil. agricultural, Emission to water/ground water 51 Plant protection product Process/Flow Database Modifications Sevin (carbaryl) Carbaryl - Emission to soil/agricultural, Emission to water/ ground water  Only soil and water emissions included (no elementary flow for emission to air) Diazinon Diazinon - Emission to air/low population density, Emission to soil/agricultural, Emission to water/ground water Glyphosate Glyphosate - Emission to air/low population density, Emission to soil/agricultural, Emission to water/ground water Movento (spirotetramat) Insecticides, unspecified - Emission to soil/agricultural, Emission to water/ground water  Generic insecticide flows used because no spirotetramat flows exist, and only soil and water emissions included (no elementary flow for emission to air) Nova (myclobutanil) Myclobutanil - Emission to air/low population density, Emission to soil/agricultural, Emission to water/ground water Dormant oil Oils, unspecified - Emission to soil/agricultural, Emission to water/ground water  Generic oil flows used because no agricultural dormant oil flows exist, and only soil and water emissions included (no elementary flow for emission to air) Gramoxon (paraquat) Paraquat - Emission to air/low population density, Emission to soil/agricultural, Emission to water/ground water Prowl (pendimethalin) Pendimethalin - Emission to air/low population density, Emission to soil/agricultural, Emission to water/ground water Simazine Simazine - Emission to air/low population density, Emission to 52 Plant protection product Process/Flow Database Modifications soil/agricultural, Emission to water/ground water Kumulus (sulphur) Sulphur trioxide - Emission to air/low population density, Emission to water/fresh water  Sulphur trioxide flow used because no sulfur emission elementary flows exist, and only ait and water emissions included (no elementary flow for emission to soil) 4.2.2.4.4 Mulch Wood chip/bark mulch application was modelled based on the “Mulch” soil amendment treatment in Fentabil et al. (2016). SuRDC reported that the mulch applied was mostly bark and partly wood chips. This was hence assumed to be 80% bark and 20% wood chips. In order to model wood chip and bark mulch production as co-products of lumber production at sawmills, alterations were made to the ecoinvent v.3.4 APOS processes for these flows in order to allocate, based on mass, the proportionate amount of emissions and resource use to both the lumber and the mulch (Table 10). For bark mulch, the process “bark chips production, softwood, at sawmill | bark chips, wet, measured as dry mass | APOS, U” was used, and altered to reflect the physical relationship between the mass of lumber produced and the mass of bark chips produced (Natural Resources Canada and Forest Products Association of Canada 2004) (See Appendix A for detailed calculations). Similarly, the process “wood chips production, softwood, at sawmill | wood chips, wet, measured as dry mass | APOS, U” was modified to reflect the mass ratio between lumber production and wood chip production. The ecoinvent v.3.4 APOS processes used were modified by removing the inputs related to the setup of the sawmill, since these processes are outside the scope of the study. The transport of the mulch to the orchard was modelled using the generic Agrifootprint v.3.0 truck transport flow LCI. The distance travelled was estimated as 50 km, an approximate average of different possible sources of mulch (Tolko, Superior Peat, etc.). The ecoinvent process “mulching” was included to model the process of mulch application. A percentage of 9.7% of carbon added to the orchard floor in the mulch was modelled as sequestered on a long-term basis (Petersen et al. 2013). This was modelled as an input of CO2 as a resource from the air into the orchard model (Table 10). 53 Table 10. Processes and databases used to model mulch inputs to the Okanagan Valley apple orchard LCA model, including any modifications. Mulch input Process Database Modifications Bark chips bark chips production, softwood, at sawmill | bark chips, wet, measured as dry mass | APOS, U - CA-QC ecoinvent v.3.4 APOS  Inputs related to setup of sawmill removed  Electricity provider changed to “market for electricity, medium voltage | electricity, medium voltage | APOS, U - CA-BC”  Heat providers changed to “heat and power co-generation, natural gas, conventional power plant, 100MW electrical | heat, district or industrial, natural gas | APOS, U - CA-BC”, and “heat and power co-generation, wood chips, 6667 kW, state-of-the-art 2014 | heat, district or industrial, other than natural gas | APOS, U - CA-BC” Wood chips wood chips production, softwood, at sawmill | wood chips, wet, measured as dry mass | APOS, U - CA-QC ecoinvent v.3.4 APOS  Inputs related to setup of sawmill removed  Electricity provider changed to “market for electricity, medium voltage | electricity, medium voltage | APOS, U - CA-BC” Transportation of bark/wood chips to orchard transport, freight, lorry, all sizes, EURO3 to generic market for transport, freight, lorry, unspecified | transport, freight, lorry, unspecified | APOS, U - RoW Agri-footprint v.3.0 Application of bark/wood chips on orchard mulching | mulching | APOS, U - RoW ecoinvent v.3.4 APOS Soil carbon sequestration Carbon dioxide – resource from air 4.2.2.4.5 Energy use Based on the energy inputs of recent apple production LCA studies (Annaert et al. 2017; Goossens et al. 2017; Longo et al. 2017; Vinyes et al. 2018; Zhu et al. 2018), average inputs of 0.309 MJ diesel per kg apple, and 0.001638 kWh electricity per kg apple were calculated for on-54 orchard processes. In order to add this amount of energy use, but avoid double counting, the existing energy use from fertigation and plant protection application was removed, and the remaining amounts of electricity and diesel use were added to the model (Table 11). See Appendix A for sample calculations. Table 11. Processes used to model energy inputs to the Okanagan Valley apple orchard LCA model. Energy input Process Database Diesel diesel, burned in agricultural machinery | diesel, burned in agricultural machinery | APOS, U - GLO ecoinvent v.3.4 APOS Electricity market for electricity, high voltage | electricity, high voltage | APOS, U - CA-BC ecoinvent v.3.4 APOS 4.2.2.4.6 Measured emissions Field-level emissions to air of N2O for each treatment reported in Fentabil et al. (2016) were modelled. All upstream emissions were embedded in the upstream processes in the LCA databases used, or modelled as described above in the fertilizer and plant protection sections. 4.2.2.4.7 Uncertainty Parameter uncertainty data were entered for all foreground processes in the model. When possible, a lognormal uncertainty distribution was calculated directly from the raw data (multiple replications of the same measurement for the same treatment). Where ranges of data were given, an uncertainty distribution was interpolated and represented as a lognormal distribution. This was used in combination with uncertainty related to data quality as estimated using a standard pedigree matrix. The pedigree matrix is a matrix of 5 different data quality indicators (DQIs) – reliability, completeness, temporal correlation, geographical correlation, further technological correlation – with five possible data quality scores, ranging from best quality (1) to worst (5). Each of these scores corresponds with a specific uncertainty value, depending on the specific pedigree matrix used, that is represented as the geometric standard deviation of a lognormal distribution (Frischknecht et al. 2005). When the stochastic uncertainty distribution of the data was not known, the generic basic sectoral uncertainty factors from Frischknecht et al. (2005) were used, again in combination with the uncertainty from the DQIs. These basic uncertainty factors are meant to represent the amount of inherent uncertainty for LCI data from the sector represented. 55 4.2.3 Impact assessment The following methods are the mandatory elements of life cycle impact assessment (LCIA) as defined by ISO 14044 (ISO 2006). In general, this phase includes the selection of impact categories and characterization models, the classification of LCI results into impact categories and the calculation of impact category indicator results. The impact assessment methods used were the CML (baseline) [v4.4, January 2015], CML (non baseline) [v4.4, January 2015], and Cumulative Energy Demand [v1.0.1, January 2015] LCIA methods. The following impact categories in the CML (baseline) method were included: human toxicity, freshwater aquatic ecotoxicity, depletion of abiotic resources (elements, ultimate reserves), photochemical oxidation, ozone layer depletion, terrestrial ecotoxicity, acidification potential, climate change and eutrophication, as well as land use – land competition from CML non baseline. From the Cumulative Energy Demand method, all categories were included: non-renewable resources – fossil, nuclear, and primary forest; and renewable resources – geothermal, solar, water, and wind. These were chosen since they were the most commonly included impact categories in other apple LCA studies (Milà I Canals et al. 2006; Mouron et al. 2006; Vinyes et al. 2017), and are in line with the categories suggested for inclusion by Guinee et al. (2002). The CML (baseline) impact category climate change was altered to remove all characterization factors for biogenic CO2 flows, meaning that any CO2 directly produced or taken up by living organisms in the short term did not contribute to the total GHG emissions of any product’s life cycle. The openLCA software platform was used for the classification and characterization steps of the LCIA, which consist of assigning LCI data to the aforementioned impact categories (including multiplication by the characterization factor) and calculating the aggregated results for each impact category. Uncertainty factors (geometric standard deviation) for the characterization models chosen were entered into the impact assessment methods in openLCA. These uncertainty factors were based on the generic uncertainty factors for impact categories presented in the supplementary information of Huijbregts et al. (2003). There are also optional elements of LCIA, which include normalization, grouping, weighting and data quality analysis (ISO 2006). Normalization refers to calculating impact assessment results relative to a specific reference, generally geographical and temporal, representing the product system under study. Normalization was not performed due to the lack of representative normalization factors for the temporal and geographical aspects of the system. Since the aim of this study was to compare different methods of apple production, the relative 56 difference between treatments is more important than the relevance to the time and geography of the system. Grouping is the sorting of impact categories into predefined sets, either based on characteristics of the impact categories or based on a hierarchy of priority. Since this is inherently based on value choices, grouping was not performed. Instead, all impact category results were presented on their own to give an objective representation of the results. Weighting involves numerically multiplying impact category indicator results by weighting factors that represent the importance of the impact category. Similarly, weighting was not performed due to its subjective, non-scientific nature. Finally, data quality analysis was performed to better understand the LCIA results (ISO 2006). This included gravity analysis, uncertainty analysis and sensitivity analysis. A gravity analysis was conducted, to determine the processes or flows with the highest contribution to the impact category indicators. An uncertainty analysis was performed to determine the overall uncertainty of the impact assessment results based on both data uncertainty and characterization factor uncertainty (described above). This was done using Monte Carlo simulations (1000 runs). An uncertainty contribution analysis was also performed to determine the relative amounts of uncertainty that come from foreground data variability, background data quality and impact assessment uncertainty. Due to software limitations, it was not possible to do an uncertainty contribution analysis in one Monte Carlo simulation; therefore, individual Monte Carlo simulations were performed that accounted for each important source of uncertainty: parameter uncertainty, parameter and impact assessment uncertainty, foreground data uncertainty, background data uncertainty, measured emissions uncertainty and mulch uncertainty. According to Henriksson et al. (2015), when life cycle assessments are used to compare between multiple options, it is more important to compare the relative uncertainty of the options, rather than the absolute uncertainty of each. This is because there are large amounts of uncertainty in the background processes in LCA that are common to the compared processes; therefore, only looking at relative uncertainty can help to better discern between processes. For this reason, a scenario was also created where only relative uncertainty was included in the uncertainty analysis. The results of these Monte Carlo simulations were used to determine the statistical significance of the differences between treatments using single factor ANOVA. A sensitivity analysis was performed to determine the effect of methodological choices, including impact assessment methods, transportation modelling and the assumed soil carbon sequestration rate, on the LCIA results. For impact assessment methods, in addition to the CML 57 baseline impact assessment methods, the ReCiPe Midpoint (H) [v.1.11, December 2014] methods were applied to compare LCIA results for all comparable impact categories in the CML baseline methods, since these are the most commonly utilized LCIA methods available in openLCA. For transportation modelling, all transportation flows modelled above for transportation of orchard input materials within Canada/US were assumed to be truck transport. A sensitivity analysis was conducted by replacing these truck transportation flows with train transportation, which has lower environmental impacts than truck transportation. Since soil carbon sequestration was estimated, the estimated percent of carbon sequestered (9.7%) was varied by 100% - i.e. ranging from half the value (4.85%) to double (19.4%) – as a final sensitivity analysis. 4.2.4 Interpretation The life cycle interpretation phase included the evaluation of the completeness/consistency of the results (according to the goal and scope of the study) and the explanation of conclusions and recommendations, as well as limitations (ISO 2006). A completeness check was carried out to ensure all data were available and complete. A sensitivity check was performed to assess the sensitivity of results to uncertainty, data quality and methodological choices, and a consistency check was performed to assess the consistency of methods and assumptions with the goal and scope. Finally, conclusions and recommendations were made based on the impact assessment and completeness/sensitivity/consistency check, and limitations of the study were identified. 4.3 Results 4.3.1 Inventory Most inputs and outputs to and from the production of apples on the SuRDC apple orchard were the same for all treatments (mulch, low nitrogen; mulch, high nitrogen; clean, low nitrogen; clean, high nitrogen), and are indicated for all instances in which they vary (Table 12). A more detailed inventory of all elementary flows for each orchard model, as a part of the entire LCA model are available online at https://osf.io/vz9ef/. 58 Table 12. Inventory table of life cycle inputs and outputs for the production of 1 kg apples on an Okanagan Valley orchard. Inputs Amount per 1 kg apple yield Plant protection products Calcium 0.269 g Gramoxon (paraquat) 0.055 g Movento (spirotetramat) 0.003 g Sevin (carbaryl) 0.017 g Kumulus DF (wettable sulphur) 0.041 g DiPel (Bacillus thuringiensis subspecies kurstaki) 0.005 g Nova (myclobutanil) 0.779 g Dormant Oil 0.317 g Prowl (pendimethalin)/Simazine 4.200 x 10-5 g pendimethalin; 4.670 x 10-5 g simazine Crush’R Plus (glyphosate) 0.061 g 2,4-D/glyphosate 1.380 x 10-3 g 2,4-D; 1.560 x 10-3 g glyphosate Diazinon 6.890 x 10-3 g Application of plant protection products 0.144 m2 Transportation of plant protection products 0.948 kg*km Irrigation Irrigation application including water 0.096 m3 Mulch Bark/hog fuel 0.575 kg Wood chips 0.144 kg Application of mulch 0.144 m2 Transportation of mulch 35.950 kg*km Fertilizer Calcium nitrate (high N) 18.698 g Calcium nitrate (low N) 9.349 g Boron carbide 0.017 g Phosphoric acid 5.06 g Potassium chloride 3.051 g Transport of fertilizer (high N) 31.107 kg*km Transport of fertilizer (low N) 24.881 kg*km Energy Diesel 0.121 MJ Electricity 1.638 x 10-3 kWh Soil carbon CO2 from atmosphere (Mulch, low nitrogen) 0.0013 kg CO2 from atmosphere (Mulch, high nitrogen) 0.0013 kg CO2 from atmosphere (Clean, low nitrogen) 0.000 kg CO2 from atmosphere (Clean, high nitrogen) 0.000 kg 59 Outputs Amount per 1 kg apples Measured nitrous oxide emissions N2O emitted (Mulch, low nitrogen) 10.649 mg N2O emitted (Mulch, high nitrogen) 10.649 mg N2O emitted (Clean, low nitrogen) 14.246 mg N2O emitted (Clean, high nitrogen) 14.246 mg Modelled fertilizer emissions Ammonia - Emission to air/low population density (high N) 0.289 g Ammonia - Emission to air/low population density (low N) 0.145 g Nitrate - Emission to water/ground water (high N) 0.768 g Nitrate - Emission to water/ground water (low N) 0.384 g Nitrogen oxides - Emission to air/low population density (high N) 0.032 g Nitrogen oxides - Emissions to air/low population density (low N) 0.016 g Phosphorus - Emission to water/ground water 0.046 g Modelled plant protection emissions 2,4-D - Emission to air/low population density 8.280 x 10-5 g 2,4-D - Emission to soil/agricultural 6.334 x 10-4 g 2,4-D - Emission to water/ground water 7.038 x 10-5 g Bacillus thuringiensis subspecies kurstaki - Emission to soil/agricultural 2.622 x 10-3 g Calcium - Emission to air/low population density 1.075 x 10-3 g Calcium - Emission to soil/agricultural 8.223 x 10-3 g Calcium - Emission to water/ground water 9.136 x 10-4 g Carbaryl - Emission to soil/agricultural 2.624 x 10-4 g Carbaryl - Emission to water/ground water 2.917 x 10-5 g Diazinon - Emission to air/low population density 1.380 x 10-4 g Diazinon - Emission to soil/agricultural 1.054 x 10-3 g Diazinon - emission to water/ground water 1.171 x 10-4 g Glyphosate - Emission to air/low population density 3.136 x 10-3 g Glyphosate - Emission to soil/agricultural 2.399 x 10-2 g Glyphosate - Emission to water/ground water 2.666 x 10-3 g Insecticides, unspecified - Emission to soil/agricultural 6.630 x 10-4 g Insecticides, unspecified - Emission to water/ground water 1.100 x 10-4 g Myclobutanil - Emission to air/low population density 3.114 x 10-2 g Myclobutanil - Emission to soil/agricultural 2.382 x 10-1 g Myclobutanil - Emission to water/ground water 2.647 x 10-2 g Oils, unspecified - Emission to soil/agricultural 2.583 x 10-1 g Oils, unspecified - Emission to water/ground water 4.279 x 10-2 g Paraquat - Emission to air/low population density 1.096 x 10-3 g Paraquat - Emission to soil/agricultural 8.386 x 10-3 g Paraquat - Emission to water/ground water 9.318 x 10-4 g Pendimethalin - Emission to air/low population density 1.930 x 10-6 g Pendimethalin - Emission to soil/agricultural 1.478 x 10-5 g Pendimethalin - Emission to water/ground water 1.642 x 10-6 g Simazine - Emission to air/low population density 2.340 x 10-6 g 60 Outputs Amount per 1 kg apples Simazine - Emission to soil/agricultural 1.786 x 10-5 g Simazine - Emission to water/ground 1.985 x 10-6 g Sulphur trioxide - Emission to air/low population density 1.596 x 10-2 g Sulphur trioxide - Emissions to water/fresh water 1.546 x 10-2 g 4.3.2 Impact assessment Looking at the entire cradle-to-orchard gate life cycle, the apples produced using mulch emitted around 30% more GHGs than without mulch (Figure 6). All the GHG benefits of mulch were outweighed by GHG emissions arising from the mulch production, transportation, and application processes (Figure 7). These mulch production GHG emissions largely came from GHG emissions from electricity and fuel production for the production of the wood/bark mulch (logging and sawmills) and from its transportation. 58.99% of the life cycle GHG emissions of mulch production were from the transportation, fuel and electricity use associated with the production of wood chips at the sawmill, and 29.63% came from the production of bark chips at the sawmill (predominantly from electricity use) (Figure 8). Smaller amounts came from transportation of the wood and bark chips from the sawmill to the orchard (10.78%) and from the machinery and fuel use associated with mulch application (0.6%). Diesel use from all sources accounted for 13.85% of the GHG emissions of mulch, electricity for 38.3%, and transport for 15.05%. Doubling the amount of nitrogen fertilizer increased the overall GHG emissions by around 20%, regardless of wood chip/bark mulch application (Figure 6). The modelled impacts from irrigation, plant protection and energy use were all the same in each treatment, only the mulch, fertilizer and orchard-level N2O and soil carbon varied (Figure 7). 61 Figure 6. Climate change potential (kg CO2 equivalent) of the cradle-to-gate life cycle of the production of 1 kg of apples on an Okanagan Valley ochard with four different orchard production treatments – Mulch (bark and wood chip), low nitrogen (20g tree-1 year-1); Mulch (bark and wood chip), high nitrogen (40g tree-1 year-1); Clean (no mulch applied), low nitrogen (20g tree-1 year-1); Clean (no mulch applied), high nitrogen (40g tree-1 year-1). Figure 7. Climate change potential (kg CO2 equivalent) of the cradle-to-gate life cycle of the production of 1 kg of apples on an Okanagan apple orchard with four different orchard production treatments, separated by orchard process. 00.050.10.150.2Mulch, lownitrogenClean, lownitrogenMulch, highnitrogenClean, highnitrogenkg CO2eq-0.0200.020.040.060.08Mulch, lownitrogenClean, lownitrogenMulch, highnitrogenClean, highnitrogenkg CO2eqFertilizerIrrigationPlant protectionEnergy useMulchOrchard level N₂OSoil carbon62 Figure 8. Contribution analysis of processes contributing to the global warming potential (kg CO2 eq) of the life cycle of mulch from cradle-to-application on the apple orchard. Only processes contributing over 5% were included. The amounts of CO2 equivalent emissions for each process represent the emissions from that process and all “upstream” processes from the supply chain that feed into that process. The percentages refer to the percent of the total mulch emissions contributed by the process and all upstream processes feeding into it. Eutrophication is another impact category where the orchard-level emissions reductions associated with use of mulch as a soil amendment were counteracted by the life-cycle emissions from the mulch production/application stage, yielding around 15% higher eutrophication potential for the mulch treatments than for the no mulch treatments (Figure 9). The addition of mulch resulted in reduced N2O emitted to the air and NO3- to the soil. 52.37% of the life cycle eutrophying emissions of mulch production were from the transportation, fuel and electricity use associated with the production of wood chips at the sawmill, and 40.59% came from the production of bark chips at the sawmill (predominantly from electricity use) (Figure 10). Smaller amounts came from transportation of the wood and bark chips from the sawmill to the orchard (6.46%) and from the machinery and fuel use associated with mulch application (0.58%). Diesel use from all sources accounted for 12.8% of the eutrophication potential of mulch, electricity for 62.3%, and transport for 5.1%. However, the production and application of the mulch results in emissions of phosphate, nitrogen oxides (0.13 kg PO4 eq) and nitrate (0.1 kg PO4 eq) – largely from the electricity for the 63 production of the wood/bark mulch, as well as nitrogen oxides from the transportation of the mulch. In comparison, high nitrogen fertilizer application increased eutrophying emissions by 32%. Figure 9. Eutrophication potential (kg PO4 equivalent) of the cradle-to-gate life cycle of the production of 1 kg of apples on an Okanagan apple orchard with four different orchard production treatments, broken down by orchard process. Figure 10. Contribution analysis of processes contributing to the eutrophication potential (kg PO4 eq) of the life cycle of mulch from cradle-to-application on the apple orchard. Only processes contributing over 5% were included. The amounts of PO4 equivalent emissions for each process represent the emissions from that process and all “upstream” processes from the supply chain that feed into that process. The percentages refer to the percent of the total mulch emissions contributed by the process and all upstream processes feeding into it. 0.00E+002.00E-044.00E-046.00E-048.00E-041.00E-03Mulch, lownitrogenClean, lownitrogenMulch, highnitrogenClean, highnitrogenkg PO4eqOrchard level N₂OMulchEnergy usePlant protectionIrrigationFertilizer64 For some impact categories - for example acidification potential - addition of mulch resulted in increased life cycle impacts that were not offset by orchard-level effects (Figure 11). Mulch production/application resulted in SO2 and NOx (0.5 kg SO2 eq) emissions from electricity for bark mulch production, resulting in 23% higher acidification potential in the mulch treatments compared to no mulch. 65.65% of the life cycle acidifying emissions of mulch production were from the transportation, fuel and electricity use associated with the production of wood chips at the sawmill, and 19.93% came from the production of bark chips at the sawmill (predominantly from electricity use) (Figure 12). Smaller amounts came from the transportation of the wood and bark chips to the orchard (13.43%) and from the fuel and machinery associated with the application of the mulch on the orchard (0.99%). Diesel use from all sources accounted for 24.78% of the acidification potential of mulch, transport for 14.8%, electricity for 10.2%, and natural gas for 11.1%. However, the main contributor to the acidification potential of apple production was fertilizer production, mainly due to NH3 (1.6 kg SO2 eq) and nitrogen oxides emissions associated with nitric acid production for nitrogen fertilizer. High nitrogen fertilizer application increased acidifying emissions by 38%. Figure 11. Acidification potential (kg SO2 equivalent) of the cradle-to-gate life cycle of the production of 1 kg of apples on an Okanagan apple orchard with four different orchard production treatments, broken down by orchard process. 00.00020.00040.00060.00080.0010.00120.00140.00160.0018Mulch, lownitrogenClean, lownitrogenMulch, highnitrogenClean, highnitrogenkg SO2eq MulchEnergy usePlant protectionIrrigationFertilizer65 Figure 12. Contribution analysis of processes contributing to the acidification potential (kg PO4 eq) of the life cycle of mulch from cradle-to-application on the apple orchard. Only processes contributing over 5% were included. The amounts of PO4 equivalent emissions for each process represent the emissions from that process and all “upstream” processes from the supply chain that feed into that process. The percentages refer to the percent of the total mulch emissions contributed by the process and all upstream processes feeding into it. Mulch production, transportation and application added an additional 33% to the life cycle human toxicity (kg 1,4-dichlorobenzene equivalent) emissions of apple production (Figure 13). This is due to toxic emissions such as selenium (5.6 kg 1,4-dichlorobenzene equivalent) and nickel (331 kg 1,4-dichlorobenzene equivalent) from the process of softwood transformation at the sawmill, and electricity use for this process. In comparison, high nitrogen fertilizer application increased human toxicity by 21%. 55.09% of the life cycle toxic emissions of mulch production were from the transportation, building materials, fuel and electricity use associated with the production of wood chips at the sawmill, and 35.68% came from the production of bark chips at the sawmill (predominantly from electricity use) (Figure 14). Smaller amounts came from the transportation of the wood and bark chips to the orchard (8.23%) and from the fuel and machinery associated with the application of the mulch on the orchard (1%). Production of metal material throughout 66 the supply chain accounted for 29.4% of the human toxicity potential of mulch, electricity production for 29.2%, transportation for 13.2%, and natural gas for 5.4%. Figure 13. Human toxicity potential (kg 1,4-dichlorobenzene equivalent) of the cradle-to-gate life cycle of the production of 1 kg of apples on an Okanagan apple orchard with four different orchard production treatments, broken down by orchard process. Figure 14. Contribution analysis of processes contributing to the human toxicity potential (kg 1,4-dichlorobenzene eq) of the life cycle of mulch from cradle-to-application on the apple orchard. Only processes contributing over 5% were included. The amounts of 1,4-DB equivalent emissions for each process represent the emissions from that process and all “upstream” processes from the supply chain that feed into that process. The percentages refer to the percent of the total mulch emissions contributed by the process and all upstream processes feeding into it. 0.000.020.040.060.080.100.120.140.16Mulch, lownitrogenClean, lownitrogenMulch, highnitrogenClean, highnitrogenkg 1,4-dichlorobenzene eqMulchEnergy usePlant protectionIrrigationFertilizer67 In general, resource use was higher with mulch due to the resources required for the production and transformation of wood products, including wood chip and bark mulch as co-products. Land use was 139 times higher with mulch than without, due to the land needed to grow and produce the mulch (Figure 15). 83.93% of the land use of mulch production was from the production of bark chips at the sawmill (predominantly from forestry for lumber production), and 16.05% came from the production of wood chips at the sawmill (predominantly from forestry for lumber production) (Figure 16). Almost none (0.02%) came from the transportation of the wood and bark chips to the orchard. Softwood forestry from all sources accounted for 99% of the land use of mulch. The overall energy demand for apple production with mulch was 35 times higher than without mulch (Figure 17). There are many different sources of energy accounted for in the cumulative energy demand, most of which were fairly similar between treatments. The largest difference was due to the 766 times larger demand for (renewable) biomass energy for the mulch - this was mostly the mulch biomass itself. 84.11% of the demand for biomass energy came from the forestry demand for softwood lumber producing bark chips as a co-product, and 15.89% came from softwood lumber producing wood chips as a co-product (Figure 18). Softwood forestry accounted for 99.9% of the biomass energy use of mulch. The demand for other sources of renewable energy was lower overall but still almost double for mulched apple production, indicating the increased demand for these sources of energy in the production, transportation and application of the mulch. The total renewable energy demand of the mulched production systems is 470 times higher than non-mulched, and the non-renewable energy demand was 61% higher (Figure 17). 68 Figure 15. Land use – land competition (m2*yr) of the cradle-to-gate life cycle of the production of 1 kg of apples on an Okanagan apple orchard with four different orchard production treatments, broken down by orchard process. Figure 16. Contribution analysis of processes contributing to the land use (m2) of the life cycle of mulch from cradle-to-application on the apple orchard. Only processes contributing over 5% were included. The areas of land use for each process represent the emissions from that process and all “upstream” processes from the supply chain that feed into that process. The percentages refer to the percent of the total mulch emissions contributed by the process and all upstream processes feeding into it. 0.000.501.001.502.002.50Mulch, lownitrogenClean, lownitrogenMulch, highnitrogenClean, highnitrogenm2*yrMulchEnergy usePlant protectionIrrigationFertilizer69 Figure 18. Contribution analysis of processes contributing to the biomass energy use (MJ) of the life cycle of mulch from cradle-to-application on the apple orchard. Only processes contributing over 5% were included. The amounts of biomass energy use for each process represent the emissions from that process and all “upstream” processes from the supply chain that feed into that process. The percentages refer to the percent of the total mulch emissions contributed by the process and all upstream processes feeding into it. 0204060Mulch, lownitrogenClean, lownitrogenMulch, highnitrogenClean, highnitrogenMJRenewable resources - wind Renewable resources - waterRenewable resources - solar Renewable resources - geothermalRenewable resources - biomass Non-renewable resources - primary forestNon-renewable resources - nuclear Non-renewable resources - fossilFigure 17. Cumulative energy demand (MJ) of the cradle-to-gate life cycle of the production of 1 kg of apples on an Okanagan apple orchard with four different orchard production treatments, broken down by energy source. 70 Overall, in all impact categories, apples produced using mulch had higher environmental impacts (generally ranging from 10% to 75% higher) than those produced without mulch. For land use and nuclear, biomass, water, wind and geothermal energy use, the impacts were orders of magnitude higher, up to 766 times (Table 13). Table 13. Life cycle impact assessment results for the production of 1kg apples on an Okanagan Valley orchard under four different production treatments, separated by orchard process. Climate change (kg CO2 eq) Fertilizer Irrigation Plant protection Mulch Energy use Orchard level N2O Soil carbon Total Mulch, low nitrogen 0.044 0.031 0.020 0.056 0.023 3.170 x 10-3 -0.0179 0.160 Clean, low nitrogen 0.044 0.031 0.020 0.023 4.250 x 10-3 0 0.122 Mulch, high nitrogen 0.076 0.031 0.020 0.056 0.023 3.170 x 10-3 -0.0179 0.192 Clean, high nitrogen 0.076 0.031 0.020 0.023 4.250 x 10-3 0 0.155 Acidification potential (kg SO2 eq) Fertilizer Irrigation Plant protection Mulch Energy use Total Mulch, low nitrogen 4.900 x 10-4 2.100 x 10-4 8.938 x 10-5 2.300 x 10-4 1.500 x 10-4 1.169 x 10-3 Clean, low nitrogen 4.900 x 10-4 2.100 x 10-4 8.938 x 10-5 1.500 x 10-4 9.300 x 10-4 Mulch, high nitrogen 8.500 x 10-4 2.100 x 10-4 8.938 x 10-5 2.300 x 10-4 1.500 x 10-4 1.520 x 10-3 Clean, high nitrogen 8.500 x 10-4 2.100 x 10-4 8.938 x 10-5 1.500 x 10-4 1.290 x 10-3 Depletion of abiotic resources - elements, ultimate reserves (kg antimony eq) Fertilizer Irrigation Plant protection Mulch Energy use Total Mulch, low nitrogen 7.024 x 10-7 9.913 x 10-8 4.848 x 10-8 1.122 x 10-7 1.263 x 10-7 1.088 x 10-6 Clean, low nitrogen 7.024 x 10-7 9.913 x 10-8 4.848 x 10-8 1.263 x 10-7 9.762 x 10-7 Mulch, high nitrogen 9.012 x 10-7 9.913 x 10-8 4.848 x 10-8 1.122 x 10-7 1.263 x 10-7 1.287 x 10-6 Clean, high nitrogen 9.012 x 10-7 9.913 x 10-8 4.848 x 10-8 1.263 x 10-7 1.175 x 10-6 Eutrophication (kg PO4--- eq) Fertilizer Irrigation Plant protection Mulch Energy use Orchard level N2O Total Mulch, low nitrogen 3.900 x 10-4 5.548 x 10-5 9.426 x 10-5 1.100 x 10-4 4.146 x 10-5 2.875 x 10-6 6.900 x 10-4 Clean, low nitrogen 3.900 x 10-4 5.548 x 10-5 9.426 x 10-5 4.146 x 10-5 3.85 x 10-6 5.800 x 10-4 Mulch, high nitrogen 5.800 x 10-4 5.548 x 10-5 9.426 x 10-5 1.100 x 10-4 4.146 x 10-5 2.875 x 10-6 8.900 x 10-4 Clean, high nitrogen 5.800 x 10-4 5.548 x 10-5 9.426 x 10-5 4.146 x 10-5 3.85 x 10-6 7.800 x 10-4 Freshwater aquatic ecotoxicity (kg Fertilizer Irrigation Plant protection Mulch application Energy use Total 71 1,4-dichlorobenzene eq) Mulch, low nitrogen 0.023 7.430 x 10-3 0.032 0.017 8.080 x 10-3 0.088 Clean, low nitrogen 0.023 7.430 x 10-3 0.032 8.080 x 10-3 0.071 Mulch, high nitrogen 0.029 7.430 x 10-3 0.032 0.017 8.080 x 10-3 0.094 Clean, high nitrogen 0.029 7.430 x 10-3 0.032 8.080 x 10-3 0.077 Human toxicity (kg 1,4-dichlorobenzene eq) Fertilizer Irrigation Plant protection Mulch Energy use Total Mulch, low nitrogen 0.034 0.020 0.010 0.027 0.018 0.110 Clean, low nitrogen 0.034 0.020 0.010 0.018 0.083 Mulch, high nitrogen 0.052 0.020 0.010 0.027 0.018 0.128 Clean, high nitrogen 0.052 0.020 0.010 0.018 0.100 Marine aquatic ecotoxicity (kg 1,4-dichlorobenzene eq) Fertilizer Irrigation Plant protection Mulch Energy use Total Mulch, low nitrogen 58.427 19.776 35.463 50.696 69.479 233.84 Clean, low nitrogen 58.427 19.776 35.463 69.479 183.15 Mulch, high nitrogen 78.009 19.776 35.463 50.696 69.479 253.42 Clean, high nitrogen 78.009 19.776 35.463 69.479 202.73 Ozone layer depletion (kg CFC-11 eq) Fertilizer Irrigation Plant protection Mulch Energy use Total Mulch, low nitrogen 3.176 x 10-9 3.547 x 10-9 6.468 x 10-10 7.325 x 10-9 2.609 x 10-9 1.730 x 10-8 Clean, low nitrogen 3.176 x 10-9 3.547 x 10-9 6.468 x 10-10 2.609 x 10-9 9.978 x 10-9 Mulch, high nitrogen 4.722 x 10-9 3.547 x 10-9 6.468 x 10-10 7.325 x 10-9 2.609 x 10-9 1.885 x 10-8 Clean, high nitrogen 4.722 x 10-9 3.547 x 10-9 6.468 x 10-10 2.609 x 10-9 1.153 x 10-8 Photochemical oxidation (kg ethylene eq.) Fertilizer Irrigation Plant protection Mulch Energy use Total Mulch, low nitrogen 1.209 x 10-5 1.208 x 10-5 4.688 x 10-6 1.035 x 10-5 8.859 x 10-6 4.806 x 10-5 Clean, low nitrogen 1.209 x 10-5 1.208 x 10-5 4.688 x 10-6 8.859 x 10-6 3.771 x 10-5 Mulch, high nitrogen 1.575 x 10-5 1.208 x 10-5 4.688 x 10-6 1.035 x 10-5 8.859 x 10-6 5.172 x 10-5 Clean, high nitrogen 1.575 x 10-5 1.208 x 10-5 4.688 x 10-6 8.859 x 10-6 4.137 x 10-5 72 Terrestrial ecotoxicity (kg 1,4-dichlorobenzene eq.) Fertilizer Irrigation Plant protection Mulch Energy use Total Mulch, low nitrogen 3.600 x 10-4 4.400 x 10-4 2.900 x 10-4 3.600 x 10-4 2.300 x 10-4 1.690 x 10-3 Clean, low nitrogen 3.600 x 10-4 4.400 x 10-4 2.900 x 10-4 2.300 x 10-4 1.330 x 10-3 Mulch, high nitrogen 5.700 x 10-4 4.400 x 10-4 2.900 x 10-4 3.600 x 10-4 2.300 x 10-4 1.900 x 10-3 Clean, high nitrogen 5.700 x 10-4 4.400 x 10-4 2.900 x 10-4 2.300 x 10-4 1.530 x 10-3 Land use - land competition (m2*yr) Fertilizer Irrigation Plant protection Mulch Energy use Total Mulch, low nitrogen 4.380 x 10-3 4.540 x 10-3 3.530 x 10-3 2.277 3.880 x 10-3 2.293 Clean, low nitrogen 4.380 x 10-3 4.540 x 10-3 3.530 x 10-3 3.880 x 10-3 0.016 Mulch, high nitrogen 5.190 x 10-3 4.540 x 10-3 3.530 x 10-3 2.277 3.880 x 10-3 2.294 Clean, high nitrogen 5.190 x 10-3 4.540 x 10-3 3.530 x 10-3 3.880 x 10-3 0.017 Non-renewable resources - fossil (MJ) Fertilizer Irrigation Plant protection Mulch Energy use Total Mulch, low nitrogen 0.379 0.506 0.172 0.822 0.307 2.186 Clean, low nitrogen 0.379 0.506 0.172 0.307 1.364 Mulch, high nitrogen 0.565 0.506 0.172 0.822 0.307 2.373 Clean, high nitrogen 0.565 0.506 0.172 0.307 1.551 Non-renewable resources - nuclear (MJ) Fertilizer Irrigation Plant protection Mulch Energy use Total Mulch, low nitrogen 0.016 2.491 x 10-2 2.330 x 10-3 0.053 0.012 0.108 Clean, low nitrogen 0.016 2.491 x 10-2 2.330 x 10-3 0.012 0.055 Mulch, high nitrogen 0.023 2.491 x 10-2 2.330 x 10-3 0.053 0.012 0.115 Clean, high nitrogen 0.023 2.491 x 10-2 2.330 x 10-3 0.012 0.062 Non-renewable resources – primary forest (MJ) Fertilizer Irrigation Plant protection Mulch Energy use Total Mulch, low nitrogen 8.774 x 10-5 7.690 x 10-5 5.910 x 10-3 2.581 x 10-5 6.646 x 10-5 6.160 x 10-3 Clean, low nitrogen 8.774 x 10-5 7.690 x 10-5 5.910 x 10-3 6.646 x 10-5 6.140 x 10-3 Mulch, high nitrogen 9.447 x 10-5 7.690 x 10-5 5.910 x 10-3 2.581 x 10-5 6.646 x 10-5 6.170 x 10-3 Clean, high nitrogen 9.447 x 10-5 7.690 x 10-5 5.910 x 10-3 6.646 x 10-5 6.140 x 10-3 73 Renewable resources - biomass (MJ) Fertilizer Irrigation Plant protection Mulch Energy use Total Mulch, low nitrogen 7.180 x 10-3 0.020 0.023 52.834 0.018 52.903 Clean, low nitrogen 7.180 x 10-3 0.020 0.023 0.018 0.069 Mulch, high nitrogen 0.011 0.020 0.023 52.834 0.018 52.906 Clean, high nitrogen 0.011 0.020 0.023 0.018 0.073 Renewable resources - geothermal (MJ) Fertilizer Irrigation Plant protection Mulch Energy use Total Mulch, low nitrogen 1.900 x 10-4 1.700 x 10-4 6.962 x 10-5 5.570 x 10-3 1.600 x 10-4 6.150 x 10-3 Clean, low nitrogen 1.900 x 10-4 1.700 x 10-4 6.962 x 10-5 1.600 x 10-4 5.800 x 10-4 Mulch, high nitrogen 2.600 x 10-4 1.700 x 10-4 6.962 x 10-5 5.570 x 10-3 1.600 x 10-4 6.220 x 10-3 Clean, high nitrogen 2.600 x 10-4 1.700 x 10-4 6.962 x 10-5 1.600 x 10-4 6.500 x 10-4 Renewable resources – solar (MJ) Fertilizer Irrigation Plant protection Mulch Energy use Total Mulch, low nitrogen 2.498 x 10-5 6.526 x 10-6 2.434 x 10-5 1.631 x 10-5 5.433 x 10-6 7.759 x 10-5 Clean, low nitrogen 2.498 x 10-5 6.526 x 10-6 2.434 x 10-5 5.433 x 10-6 6.128 x 10-5 Mulch, high nitrogen 2.884 x 10-5 6.526 x 10-6 2.434 x 10-5 1.631 x 10-5 5.433 x 10-6 8.145 x 10-5 Clean, high nitrogen 2.884 x 10-5 6.526 x 10-6 2.434 x 10-5 5.433 x 10-6 6.513 x 10-5 Renewable resources - water (MJ) Fertilizer Irrigation Plant protection Mulch Energy use Total Mulch, low nitrogen 8.580 x 10-3 8.310 x 10-3 4.610 x 10-3 0.909 0.017 0.947 Clean, low nitrogen 8.580 x 10-3 8.310 x 10-3 4.610 x 10-3 0 0.017 0.038 Mulch, high nitrogen 0.012 8.310 x 10-3 4.610 x 10-3 0.909 0.017 0.950 Clean, high nitrogen 0.012 8.310 x 10-3 4.610 x 10-3 0 0.017 0.042 Renewable resources - wind (MJ) Fertilizer Irrigation Plant protection Mulch Energy use Total Mulch, low nitrogen 1.140 x 10-3 1.030 x 10-3 3.580 x 10-3 0.025 8.500 x 10-4 0.031 Clean, low nitrogen 1.140 x 10-3 1.030 x 10-3 3.580 x 10-3 8.500 x 10-4 6.610 x 10-3 Mulch, high nitrogen 1.580 x 10-3 1.030 x 10-3 3.580 x 10-3 0.025 8.500 x 10-4 0.032 Clean, high nitrogen 1.580 x 10-3 1.030 x 10-3 3.580 x 10-3 8.500 x 10-4 0.007 74 Fertilizer production/transportation/application was the highest contributor to 9 out of 19 impact categories, and second highest to 7 more (Table 14). The measured orchard-level emissions only impacted climate change (4th highest contributor) and eutrophication (lowest contributor). The production/transportation/application of mulch was the highest contributor to 8 categories as well, most of which are energy use categories. As shown above, this highlights the fact that, although there are orchard-level reductions in GHG emissions due to the addition of mulch to the orchard, these emissions were outweighed by the mulch production impacts, and had a relatively small impact compared to the upstream impacts of other orchard inputs, most importantly fertilizer production. Table 14. Relative ranking of the contribution of each orchard process to the life cycle impacts of apple production in the Okanagan Valley for each impact category. Fertilizer Irrigation Plant protection Mulch Energy use Measured Orchard-level GHGs Climate change 1 3 6 2 5 4 Acidification 1 3 5 2 4 - Depletion of abiotic resources – elements, ultimate reserves 1 4 5 3 2 - Eutrophication 1 4 3 2 5 6 Freshwater aquatic ecotoxicity 2 5 1 3 4 - Human toxicity 1 3 5 2 4 - Marine aquatic ecotoxixcity 1 5 4 3 2 - Ozone layer depletion 2 3 5 1 4 - Photochemical oxidation 1 2 5 3 4 - Terrestrial ecotoxicity 1 2 4 3 5 - Land use – land competition 2 3 5 1 4 - Non renewable resources – fossil 2 3 5 1 4 - Non-renewable resources - nuclear 3 2 5 1 4 - 75 Fertilizer Irrigation Plant protection Mulch Energy use Measured Orchard-level GHGs Non-renewable resources - primary forest 2 3 1 5 4 - Renewable resources - biomass 5 3 2 1 4 - Renewable resources - geothermal 2 3 5 1 4 - Renewable resources - solar 1 4 2 3 5 - Renewable resources - water 3 4 5 1 2 - Renewable resources - wind 3 4 2 1 5 - 4.3.2.1 Sensitivity analysis 4.3.2.1.1 Carbon sequestration As part of the sensitivity analysis, the change in GHG emissions (kg CO2 equivalent) for each apple production system was determined when soil carbon is assumed to be sequestered at a rate of 4.85%, 9.7% and 19.4% (Figure 19). In the impact assessment above, a rate of 9.7% was assumed based on the methods for integration of soil carbon into life cycle assessment in Petersen et al. (2013). However, biophysical interactions such as soil carbon sequestration are dependent on many other factors; therefore, a sensitivity analysis was performed to determine the change in results if this assumed C sequestration rate were halved or doubled. Regardless of C sequestration rate (4.85%, 9.7% or 19.4%), the impact of the mulch systems is higher than the no mulch systems (30.9%, 30.6%, and 30.1%, respectively) (Figure 19). The GHG emissions of the no mulch systems were the same for all assumed C sequestration rates because there is assumed to be no C sequestration taking place without the presence of mulch as a soil amendment. 76 Figure 19. GHG emissions of apple production on an Okanagan apple orchard, including soil carbon sequestration at a rate of 4.85%, 9.7% and 19.4%. 4.3.2.1.2 LCIA methods Different life cycle impact assessment methods use different models to calculate and aggregate the environmental impacts based on the life cycle inventory data. Two of the most commonly used impact assessment methods are CML (baseline) and ReCiPe Midpoint (H). Results for all comparable impact categories between the two methods were calculated (Table 17). Not all impact categories from the impact assessment above are included in this sensitivity analysis if either the category did not exist in the ReCiPe Midpoint (H) methods, or the reference units were not the same. Some impact categories had almost identical results (only 1% difference) between the two impact assessment methods, for example ozone depletion (Table 17). However, most impact categories did not yield identical results, for example climate change (Figure 20). For the no mulch orchard systems, the results were only 4% different, however the ReCiPe results were 14-24% higher for the mulch systems. This is because the ReCiPe methods did not account for the negative CO2 emissions (modelled as an input of CO2 from the air) from soil carbon sequestration in the mulch systems that were accounted for by CML (Tables 15-16). This means that in the ReCiPe Midpoint (H) impact assessment methods, there is no characterization factor for an input of CO2 from the atmosphere, as there is in CML (baseline). 00.050.10.150.2Mulch, lownitrogenClean, lownitrogenMulch, highnitrogenClean, highnitrogenkg CO2eq4.85% C sequestration9.7% C sequestration19.4% C seqestration77 Figure 20. Climate change potential of apple production on an Okanagan apple orchard, using impact assessment methods CML (baseline) and ReCiPe Midpoint (H). Table 15. Greenhouse gas emissions (kg CO2 equivalent) for 1 kg apples produced on an Okanagan Valley apple orchard with four different production treatments, calculated using CML (baseline) impact assessment methods, separated by orchard process. kg CO2 eq CML (baseline) Fertilizer Irrigation Plant protection Mulch Energy use Orchard- level N2O Soil carbon Mulch, low nitrogen 0.044 0.031 0.020 0.056 0.023 3.170 x 10-3 -0.0013 Clean, low nitrogen 0.044 0.031 0.020 0.000 0.023 4.250 x 10-3 0.000 Mulch, high nitrogen 0.076 0.031 0.020 0.056 0.023 3.170 x 10-3 -0.0013 Clean, high nitrogen 0.076 0.031 0.020 0.000 0.023 4.250 x 10-3 0.000 Table 16. Greenhouse gas emissions (kg CO2 equivalent) for 1 kg apples produced on an Okanagan Valley apple orchard with four different production treatments, calculated using ReCiPe Midpoint (H) methods, separated by orchard process. kg CO2 eq CML (baseline) Fertilizer Irrigation Plant protection Mulch Energy use Orchard- level N2O Soil carbon Mulch, low nitrogen 0.044 0.033 0.021 0.060 0.025 3.170 x 10-3 0.000 Clean, low nitrogen 0.044 0.033 0.021 0.000 0.025 4.250 x 10-3 0.000 Mulch, high nitrogen 0.077 0.033 0.021 0.060 0.025 3.170 x 10-3 0.000 Clean, high nitrogen 0.077 0.033 0.021 0.000 0.025 4.250 x 10-3 0.000 For some impact categories, such as all toxicity categories (freshwater, marine, terrestrial and human) the ReCiPe results were much lower (up to 99%) than the CML results (Figure 21). This is likely because ReCiPe does not have characterization factors (to aggregate all emissions of one type into a single impact category) for many emissions in this category that CML does (for the nomenclature used in the model). This is a current issue in LCA data reporting, since there are many ways to refer to a single flow or chemical, and not all nomenclature is mapped and accounted for in all impact assessment methods, due to multiple commonly used data reporting formats that are not entirely consistent with each other 00.050.10.150.20.25Mulch, lownitrogenClean, lownitrogenMulch,highnitrogenClean,highnitrogenkg CO2eqCML (baseline)ReCiPe Midpoint (H)78 (Ingwersen et al. 2015). However, although the magnitudes of the impacts are quite different, the relative differences between the four orchard systems remain the same in both impact assessment methods. Therefore, the same conclusions can be made using either method: toxicity is higher in mulched compared to non-mulched systems, and in high nitrogen compared to low nitrogen systems. Figure 21. Freshwater ecotoxicity of apple production on an Okanagan apple orchard, using impact assessment methods CML (baseline) and ReCiPe Midpoint (H). Overall, the same conclusions can be made about the differences between the four orchard systems using either CML (baseline) or ReCiPe Midpoint (H). However, ReCiPe classified fewer flows into many of the impact categories, therefore the results are less reliable than CML, which classified more flows and thus gave a better representation of the life cycle environmental impacts of each orchard system (Table 17). Table 17. Comparison of LCIA results for the production of 1 kg apples on an Okanagan Valley apple orchard with four different production treatments, calculated using CML (baseline) and ReCiPe Midpoint (H) impact assessment methods. Acidification potential (kg SO2 eq) CML (baseline) ReCiPe Midpoint (H) Mulch, low nitrogen 1.169 x 10-3 1.260 x 10-3 Clean, low nitrogen 9.300 x 10-4 1.030 x 10-3 Mulch, high nitrogen 1.520 x 10-3 1.760 x 10-3 Clean, high nitrogen 1.290 x 10-3 1.520 x 10-3 Climate change (kg CO2 eq) CML (baseline) ReCiPe Midpoint (H) Mulch, low nitrogen 0.176 0.187 Clean, low nitrogen 0.122 0.128 Mulch, high nitrogen 0.209 0.220 Clean, high nitrogen 0.155 0.161 00.050.10.150.20.250.3Mulch, lownitrogenClean, lownitrogenMulch,highnitrogenClean,highnitrogenkg 1,4-dichlorobenzene eqCML (baseline)ReCiPe Midpoint (H)79 Fossil depletion (MJ) CML (baseline) ReCiPe Midpoint (H) Mulch, low nitrogen 2.122 0.049 Clean, low nitrogen 1.340 0.031 Mulch, high nitrogen 2.296 0.053 Clean, high nitrogen 1.513 0.035 Freshwater ecotoxicity (kg 1,4-dichlorobenzene eq) CML (baseline) ReCiPe Midpoint (H) Mulch, low nitrogen 0.088 2.610 x 10-3 Clean, low nitrogen 0.071 2.010 x 10-3 Mulch, high nitrogen 0.094 2.830 x 10-3 Clean, high nitrogen 0.077 2.230 x 10-3 Human toxicity (kg 1,4-dichlorobenzene eq) CML (baseline) ReCiPe Midpoint (H) Mulch, low nitrogen 0.110 0.074 Clean, low nitrogen 0.083 0.053 Mulch, high nitrogen 0.128 0.084 Clean, high nitrogen 0.100 0.063 Marine ecotoxicity (kg 1,4-dichlorobenzene eq) CML (baseline) ReCiPe Midpoint (H) Mulch, low nitrogen 233.84 2.010 x 10-3 Clean, low nitrogen 183.15 1.420 x 10-3 Mulch, high nitrogen 253.42 2.230 x 10-3 Clean, high nitrogen 202.73 1.640 x 10-3 Ozone depletion (kg CFC-11 eq) CML (baseline) ReCiPe Midpoint (H) Mulch, low nitrogen 1.730 x 10-8 1.746 x 10-8 Clean, low nitrogen 9.978 x 10-9 1.012 x 10-8 Mulch, high nitrogen 1.885 x 10-8 1.905 x 10-8 Clean, high nitrogen 1.153 x 10-8 1.171 x 10-8 Terrestrial ecotoxicity (kg 1,4-dichlorobenzene eq) CML (baseline) ReCiPe Midpoint (H) Mulch, low nitrogen 1.690 x 10-3 5.400 x 10-4 Clean, low nitrogen 1.330 x 10-3 5.300 x 10-4 Mulch, high nitrogen 1.900 x 10-3 5.500 x 10-4 Clean, high nitrogen 1.530 x 10-3 5.400 x 10-4 4.3.2.1.3 Transportation method For the transportation method sensitivity analysis, all transportation flows that were added into the model as truck transportation were replaced by train transportation. In most impact categories (climate change, depletion of abiotic resources, human toxicity, ozone layer depletion, land use, fossil energy use, primary forest energy use and solar energy use), this reduced the estimated impact, but only slightly (Table 18). Other categories (acidification, 80 eutrophication, freshwater ecotoxiciry, marine aquatic ecotoxicity, photochemical oxidation, terrestrial ecotoxicity, nuclear energy use, biomass energy use, water energy use and wind energy use) were higher with train transport for some of the apple production treatments and lower for others. However, no differences were larger than 6%. These results show that the choice of transportation method is not important for the overall impacts of the model, which is consistent with the general consensus in LCA literature that transportation typically contributes only a small portion of the life cycle impacts of a food product (Weber and Matthews 2008). This sensitivity analysis was performed since the actual transportation method for many of these flows is unknown. Table 18. Comparison of LCIA results for the production of 1 kg apples on an Okanagan Valley apple orchard with four different production treatments with all transportation flows modelled as either truck or train transportation. Acidification potential (kg SO2 eq) Truck transport Train transport Mulch, low nitrogen 1.169 x 10-3 1.170 x 10-3 Clean, low nitrogen 9.300 x 10-4 9.200 x 10-4 Mulch, high nitrogen 1.520 x 10-3 1.530 x 10-3 Clean, high nitrogen 1.290 x 10-3 1.280 x 10-3 Climate change potential (kg CO2 eq) Truck transport Train transport Mulch, low nitrogen 0.176 0.166 Clean, low nitrogen 0.122 0.119 Mulch, high nitrogen 0.209 0.207 Clean, high nitrogen 0.155 0.151 Depletion of abiotic resources - elements, ultimate reserves (kg antimony eq) Truck transport Train transport Mulch, low nitrogen 1.088 x 10-6 1.082 x 10-6 Clean, low nitrogen 9.762 x 10-7 9.666 x 10-7 Mulch, high nitrogen 1.287 x 10-6 1.280 x 10-6 Clean, high nitrogen 1.175 x 10-6 1.163 x 10-6 Eutrophication (kg PO4--- eq) Truck transport Train transport Mulch, low nitrogen 6.900 x 10-4 6.900 x 10-4 Clean, low nitrogen 5.800 x 10-4 5.800 x 10-4 Mulch, high nitrogen 8.900 x 10-4 8.900 x 10-4 Clean, high nitrogen 7.800 x 10-4 7.800 x 10-4 Freshwater aquatic ecotoxicity (kg 1,4-dichlorobenzene eq.) Truck transport Train transport Mulch, low nitrogen 0.088 0.088 Clean, low nitrogen 0.071 0.071 Mulch, high nitrogen 0.094 0.094 Clean, high nitrogen 0.077 0.077 81 Human toxicity (kg 1,4-dichlorobenzene eq.) Truck transport Train transport Mulch, low nitrogen 0.110 0.110 Clean, low nitrogen 0.083 0.082 Mulch, high nitrogen 0.128 0.127 Clean, high nitrogen 0.100 0.099 Marine aquatic ecotoxicity (kg 1,4-dichlorobenzene eq.) Truck transport Train transport Mulch, low nitrogen 233.84 234.66 Clean, low nitrogen 183.15 182.63 Mulch, high nitrogen 253.42 254.12 Clean, high nitrogen 202.73 202.09 Ozone layer depletion (kg CFC-11 eq.) Truck transport Train transport Mulch, low nitrogen 1.730 x 10-8 1.704 x 10-8 Clean, low nitrogen 9.978 x 10-9 9.408 x 10-9 Mulch, high nitrogen 1.885 x 10-8 1.845 x 10-8 Clean, high nitrogen 1.153 x 10-8 1.082 x 10-8 Photochemical oxidation (kg ethylene eq.) Truck transport Train transport Mulch, low nitrogen 4.806 x 10-5 4.819 x 10-5 Clean, low nitrogen 3.771 x 10-5 3.729 x 10-5 Mulch, high nitrogen 5.172 x 10-5 5.175 x 10-5 Clean, high nitrogen 4.137 x 10-5 4.085 x 10-5 Terrestrial ecotoxicity (kg 1,4-dichlorobenzene eq.) Truck transport Train transport Mulch, low nitrogen 1.690 x 10-3 1.700 x 10-3 Clean, low nitrogen 1.330 x 10-3 1.320 x 10-3 Mulch, high nitrogen 1.900 x 10-3 1.910 x 10-3 Clean, high nitrogen 1.530 x 10-3 1.530 x 10-3 Land use – land competition (m2*yr) Truck transport Train transport Mulch, low nitrogen 2.293 2.293 Clean, low nitrogen 0.016 0.016 Mulch, high nitrogen 2.294 2.294 Clean, high nitrogen 0.017 0.017 Non-renewable resources - fossil (MJ) Truck transport Train transport Mulch, low nitrogen 2.186 2.167 Clean, low nitrogen 1.364 1.315 Mulch, high nitrogen 2.373 2.342 Clean, high nitrogen 1.551 1.491 82 Non-renewable resources - nuclear (MJ) Truck transport Train transport Mulch, low nitrogen 0.108 0.109 Clean, low nitrogen 0.055 0.055 Mulch, high nitrogen 0.115 0.116 Clean, high nitrogen 0.062 0.062 Non-renewable resources - primary forest (MJ) Truck transport Train transport Mulch, low nitrogen 6.160 x 10-3 6.160 x 10-3 Clean, low nitrogen 6.140 x 10-3 6.140 x 10-3 Mulch, high nitrogen 6.170 x 10-3 6.170 x 10-3 Clean, high nitrogen 6.140 x 10-3 6.140 x 10-3 Renewable resources - biomass Truck transport Train transport Mulch, low nitrogen 52.903 52.903 Clean, low nitrogen 0.069 0.069 Mulch, high nitrogen 52.906 52.907 Clean, high nitrogen 0.073 0.073 Renewable resources - geothermal (MJ) Truck transport Train transport Mulch, low nitrogen 6.150 x 10-3 6.160 x 10-3 Clean, low nitrogen 5.800 x 10-4 5.800 x 10-4 Mulch, high nitrogen 6.220 x 10-3 6.230 x 10-3 Clean, high nitrogen 6.500 x 10-4 6.400 x 10-4 Renewable resources - solar (MJ) Truck transport Train transport Mulch, low nitrogen 7.759 x 10-5 7.592 x 10-5 Clean, low nitrogen 6.128 x 10-5 5.926 x 10-5 Mulch, high nitrogen 8.145 x 10-5 7.929 x 10-5 Clean, high nitrogen 6.513 x 10-5 6.263 x 10-5 Renewable resources - water (MJ) Truck transport Train transport Mulch, low nitrogen 0.947 0.947 Clean, low nitrogen 0.038 0.038 Mulch, high nitrogen 0.950 0.951 Clean, high nitrogen 0.042 0.042 Renewable resources - wind Truck transport Train transport Mulch, low nitrogen 0.031 0.031 Clean, low nitrogen 6.610 x 10-3 6.590 x 10-3 Mulch, high nitrogen 0.032 0.032 Clean, high nitrogen 0.007 7.020 x 10-3 83 4.3.2.2 Uncertainty analysis When only parameter uncertainty was accounted for, most impact category results were significantly higher for mulched systems than non-mulched. There were no significant differences in the depletion of abiotic resources between mulched and non-mulched treatments, as well as eutrophication and primary forest energy use between mulch, low nitrogen and clean, low nitrogen. In all other impact categories, impacts for apple production with mulch were significantly higher than without mulch (Appendix C - Table 28). The impacts of the high nitrogen treatments were significantly higher for acidification, climate change, depletion of abiotic resources, photochemical oxidation, terrestrial ecotoxicity, land use (non-mulched treatments only), fossil energy use (non-mulched treatments only), nuclear energy use (mulched treatments only), geothermal energy use (non-mulched treatments only) and wind energy use (non-mulched treatments only). All other impact categories had no significant differences between low and high nitrogen treatments. The uncertainty of the results (measured as the standard deviation) greatly increased (up to 108 times higher), when both parameter and impact assessment uncertainty were included in the Monte Carlo simulation (Appendix C – Table 29). This was particularly true for the toxicity categories results, since these categories have high levels of uncertainty in the characterization models used for impact assessment (Huijbregts et al. (2003)). Due to this increased uncertainty, the impacts of the different mulch treatments for freshwater aquatic ecotoxicity, human toxicity, marine aquatic ecotoxicity and terrestrial ecotoxicity were no longer statistically significantly different (for both the effect of nitrogen and mulch). The differences between low and high nitrogen treatments were also no longer significant for photochemical oxidation. Thus, there were differences between mulched treatments for acidification, climate change, eutrophication, ozone layer depletion, and photochemical oxidation. There were significant differences between low and high nitrogen treatments for acidification, climate change, eutrophication, freshwater aquatic ecotoxicity, human toxicity, marine aquatic ecotoxicity, ozone layer depletion and terrestrial ecotoxicity. An uncertainty contribution analysis was conducted to determine the amount of the parameter uncertainty that came from background processes that were common to all treatments (and feed into many agricultural systems), and how much came from foreground data uncertainty unique to this orchard system and that varied between each treatment. Since it was not possible in the openLCA software to determine the relative contributions of each source of uncertainty in one Monte Carlo simulation, multiple Monte Carlo simulations were performed. 84 To approximate these relative contributions, the standard deviation of the Monte Carlo simulations with only background or foreground uncertainty were compared to the standard deviations of the Monte Carlo simulations with all parameter uncertainty included. Since these simulations were all independent, the amounts of uncertainty in each impact category were variable. Due to the high amount of uncertainty from both the background and foreground data, sometimes the standard deviation for certain impact categories was higher in the Monte Carlo simulation with only background or foreground uncertainty than in the simulation with all parameter uncertainty. Therefore, this approximation of an uncertainty contribution analysis can only give an indication of which sources of uncertainty may have been the most important contributors to the overall uncertainty, but it is not a robust assessment. Foreground uncertainty was 4-106% of total parameter uncertainty (percentages larger than 100% mean that the standard deviation for the Monte Carlo simulation with background or foreground uncertainty only was higher than that for all parameter uncertainty, which happens as a result of the random nature of Monte Carlo sampling), averaging 57% (Appendix C – Table 30). Background uncertainty was 5-1564% of parameter uncertainty, averaging 102% (Appendix C – Table 31). These results indicate that a large amount of uncertainty came from both foreground and background data uncertainty, but background uncertainty accounted for a larger share of total uncertainty than foreground. Background parameter uncertainty in this model came from data quality indicators – both those provided in the ecoinvent v.3.4 APOS processes used, as well as those modified according to the representativeness of these ecoinvent processes in the current application context. The data quality for these processes could be improved by the improved availability of temporally, geographically and technologically representative LCI datasets, specifically for the Canadian agri-food sector in the case of this model. In general, foreground parameter uncertainty for emissions-based impact categories was higher in high nitrogen treatments than low nitrogen treatments, with the standard deviation ranging up to 192% higher (Appendix C – Table 30). This is in part due to the differences in measured N2O emissions, and higher variability in emissions when more nitrogen fertilizer is applied. This uncertainty in orchard-level emissions accounted for <0.001-208% of the total foreground parameter uncertainty for emissions categories, averaging 26%. With only the uncertainty in orchard-level emissions accounted for, the high nitrogen treatment standard deviations range from 94% lower to 9.06 x 104 times higher than low nitrogen (Appendix C – Table 32). For resource use-based impact categories, the uncertainty was similar between 85 nitrogen treatments, but the mulch treatments have higher uncertainty than the no mulch treatments (up to 3.5 x 105 times higher). This can be explained by the fact that mulch use contributes to the majority of land and energy use impacts. The uncertainty from mulch processes was 0.05-10294% of foreground parameter uncertainty, averaging 297% (Appendix C – Table 33). Based on the logic that the relative uncertainty of the different treatments is more important than the overall uncertainty (Henriksson et al. 2015), the impact assessment results with only the uncertainty that was not common between treatments had much lower uncertainty, averaging 9% of the total uncertainty (Figures 22-27; Appendix C - Table 34). Due to this lower uncertainty, all differences between mulch treatments were statistically significant except for primary forest energy use (low nitrogen treatments only). Most differences between nitrogen treatments were statistically significant except for freshwater ecotoxicity (mulched treatments only), human toxicity (non-mulched treatments only), terrestrial ecotoxicity (non-mulched treatments only), land use (mulched treatments only), primary forest energy use (mulched treatments only), biomass energy use (mulched treatments only), geothermal energy use (mulched treatments only), solar energy use (mulched treatments only), water energy use (mulched treatments only) and wind energy use (mulched treatments only). Figure 22. Uncertainty ranges for climate change potential (kg CO2 equivalent) of apple production treatments, accounting for relative uncertainty only (p < 0.001). 86 Figure 23. Uncertainty ranges for eutrophication potential (kg PO4 equivalent) of apple production treatments, accounting for relative uncertainty only (p < 0.001). Figure 24. Uncertainty ranges for acidification potential (kg SO2 equivalent) of apple production treatments, accounting for relative uncertainty only (p < 0.001). 87 Figure 25. Uncertainty ranges for human toxicity potential (kg 1,4-dichlorobenzene equivalent) of apple production treatments, accounting for relative uncertainty only (p < 0.001). Figure 26. Uncertainty ranges for land use (m2*yr) of apple production treatments, accounting for relative uncertainty only (p < 0.001). 88 Figure 27. Uncertainty ranges for (renewable) biomass energy use (MJ) of apple production treatments, accounting for relative uncertainty only (p < 0.001). 4.3.3 Interpretation 4.3.3.1 Completeness, sensitivity and consistency check As per the ISO 14044 LCA standard (ISO 2006), a completeness, sensitivity and consistency check were carried out in order to evaluate the results and determine their reliability and any significant issues. Most relevant data for the construction and interpretation of this LCA model were available and complete (either from direct measurement or from existing LCI datasets); however, some important data were estimated using models instead, due to a lack of data availability. These included the amount of soil carbon sequestered long-term, the orchard-level emissions due to plant protection and fertilizer application, and the inputs of some specific fertilizer and plant protection products. Exact LCI datasets for the inputs of most plant protection products (Kumulus, myclobutanil, Simazine, spirotetramat, DiPel, Diazinon, dormant oil and calcium) were not found, so generic inputs for “insecticide” or “fungicide”, or alternative inputs similar to the actual product were used. This approximation was sufficient for this LCA model since, according to the goal and scope of the study, the aim was to determine the difference in environmental impacts between apples produced with mulch and without mulch. Since the fertilizer and plant production inputs were not the main focus of the study, and were the same between mulch and no mulch treatments, using non-specific data is sufficient to support the goal of the study. This is 89 true for both the generic product inputs and upstream processes, as well as modelled emissions. The soil carbon assumed to be sequestered, and thus removed from the atmosphere in the form of CO2, was modelled based on an estimate of the amount of C that would be sequestered long-term, therefore a sensitivity analysis was conducted to determine the sensitivity of the results to the choice of the amount of C sequestered. The relative ranking of alternatives remained the same in all cases: apples produced with mulch produced more life cycle GHG emissions than without mulch. However, improved accuracy of soil carbon sequestration modelling would be beneficial. This is an area for continued development in agricultural LCA methodology. Some data used in this LCA, while directly measured and relevant to the field-level experiments carried out by Fentabil et al. (2016) at the SuRDC experimental plot, were not necessarily representative of orchards in the region. The amount of irrigation, fertilizer and herbicides applied to the orchard plots were the same regardless of mulch application, due to the experimental design. However, mulch application could improve water retention and reduce weeds (Neilsen et al. 2003b), which could reduce the need for water and herbicide inputs. Therefore, it would be necessary to design an experiment that took these changes in inputs into account and use those experimental results in an LCA study in order to model more representative impacts of adding mulch to apple orchards. Irrigation inputs have been shown to decrease by around 10% with the application of organic mulch such as straw and paper (Yin et al. 2012; Neilsen et al. 2017). Straw mulch was also shown to be even more effective than glyphosate at suppressing weed growth (DeVetter et al. 2015), indicating that herbicide use could be completely eliminated from mulched plots. These reductions in inputs would directly decrease the impacts associated with the application of these products and the upstream processes associated with their production and transportation. In addition, these reductions in inputs would influence orchard-level emissions. If herbicide application was eliminated, all herbicide emissions would be eliminated. Decreasing water application could indirectly affect orchard-level emissions. Reduced irrigation was shown to decrease CO2 emissions (Zornoza et al. 2016) and N2O emissions (Zornoza et al. 2018). However, these studies did not account for the interaction of reduced irrigation and added mulch application, which could have different impacts on emissions. Increased soil moisture is associated with higher CO2 and N2O emissions due to higher microbial breakdown of soil organic matter, and increased denitrification (Zornoza et al. 2018); therefore, increased moisture retention due to mulch may also increase CO2 and N2O emissions. In order to study the life cycle impacts of mulch use in terms of water and 90 herbicide use on Okanagan apple orchards, experiments would first need to be carried out to determine, in that climate, how much water and herbicide application could be reduced with the application of mulch. Then, similar experiments to Fentabil et al. (2016) would need to be carried out to determine the relative emissions and soil nutrient levels of mulched and non-mulched systems, including the different inputs of water and herbicides. These input and output data could then be included in a similar LCA model to this study in order to determine the net life cycle impacts of mulch application. A sensitivity analysis was also carried out to determine the sensitivity of the result to changes in transportation method choice (truck vs. train for transportation of orchard inputs within Canada), and impact assessment method choice (CML baseline vs. ReCiPe Midpoint H). The LCA results proved to not be sensitive to changes in the choice of transportation method, only changing up to 6%. This lack of significant change supports the reliability of the results. However, some results were very sensitive to the change in impact assessment methods, changing by up to 99% when ReCiPe was used instead of CML. This is because, with the nomenclature used in the model, ReCiPe had characterization factors for fewer flows than CML. Therefore, although the results were quite sensitive to this change, CML is the more accurate impact assessment method in this case, so the results using this method are more reliable. 4.3.3.2 Discussion The life cycle GHG emissions of apple production using mulch as a soil amendment are 25% higher than without mulch, despite the observed reduction in orchard-level N2O emissions and increase in soil carbon (Fentabil et al. 2016). This is because the upstream GHG emissions associated with the production, transportation and application of the mulch outweighed these benefits. In fact, all life cycle emissions were 13-50% higher for apple production using mulch because the production of the mulch increased upstream emissions without an equal reduction of emissions anywhere in the life cycle. Resource use was even higher (80-99% higher for mulched treatments compared to non-mulched) with the addition of mulch due to the land and energy needed to create the mulch, as a co-product of lumber production. Instead of considering mulch a burden-free by-product, all emissions and resource use were allocated on a mass ratio basis to both the lumber and mulch products produced at the sawmill. However,mulch production impacts were not the main contributor to most life cycle emissions impact categories. These were dominated by fertilizer production, transportation, application, and modelled emissions of ammonia, nitrate, nitrogen oxides and phosphorus, which accounted for the largest share of impacts in 7 out of 9 emissions impact categories. For resource use 91 impact categories, however, mulch production was the highest contributor for 7 out of 10 categories. Long-term soil carbon sequestration is a proposed benefit of mulch, and was modelled based on the amount of carbon added to the system in the mulched treatments. This model assumption was examined using a sensitivity analysis to vary the assumed amount of carbon sequestered by 100% (halving and doubling the amount). This only altered the life cycle GHG emissions by 8-17%, and in all cases the mulch systems were still 7-29% higher than no mulch. There is a high amount of uncertainty in this LCA model, from stochastic variability of measured data, data quality, methodological choices, and impact assessment methods. When all sources of uncertainty were accounted for, all toxicity impact categories (kg 1,4-dichlorobenzene equivalent emissions) had no significant differences between treatments. However, most of those sources of uncertainty were common between all apple production treatments. When only considering the foreground parameter uncertainty that is unique to each treatment, all differences between treatments were statistically significant, meaning that impacts in all categories were higher for mulched compared to non-mulched treatments. Taken together, these results suggest that, on the basis of life cycle emissions and resource use, and assuming all other orchard inputs remain the same, it is not beneficial to use bark and wood chip mulch as a soil amendment on apple orchards in the Okanagan Valley of British Columbia. However, the other potential orchard-level benefits associated with the use of bark mulch, such as nutrient and water retention, increase in soil organic matter and the many associated benefits, and suppression of weeds were not accounted for in this study. Future research should focus on the collection of data for water use, herbicide use, and soil nutrients associated with the use of mulch, and inclusion of these data into life cycle modelling. 92 Chapter 5: Consequential life cycle assessment of increased mulch use on Okanagan apple orchards, including displaced alternative uses 5.1 Synopsis A consequential life cycle assessment was conducted to determine the life cycle environmental impacts of increasing wood chip/bark mulch use in the production of apples on Okanagan orchards. This assessment included the impacts of the orchard system as well as other current alternative uses of wood chip/bark mulch which include bioenergy production and pulp & paper manufacturing. The data for the orchard system were based on the previous attributional assessment, which modelled different mulch and nitrogen application rates but kept water, pesticide, and energy inputs constant for all models. Many environmental impact categories were examined including human toxicity, freshwater aquatic ecotoxicity, depletion of abiotic resources (elements, ultimate reserves), photochemical oxidation, ozone layer depletion, terrestrial ecotoxicity, acidification potential, climate change, eutrophication, land use – land competition, and energy use (including non-renewable: fossil, nuclear, primary forest; and renewable: biomass, geothermal, solar, water and wind). One scenario was modelled to represent the case in which no mulch was used on orchards (only used for alternative products). A second model was created to represent the marginal impacts of adding the amount of mulch to an apple orchard necessary to produce 1 kg of apples (0.575 kg bark and 0.144 kg wood chips). These amounts of bark and wood chips were assumed to be taken away from their current alternative uses (co-generation for bark and paper production for wood chips), thereby decreasing the amount of electricity and heat produced by bark by 0.653 kWh electricity and 0.653 MJ heat, and the amount of paper produced by wood chips by 0.144 kg paper. In turn, these amounts of electricity, heat and paper were assumed to be produced by their marginal production technologies – hydro-electric generation for electricity, natural gas for heat, and recycled paper for paper production. Finally, the scenarios were modelled using different electricity production processes (assuming the marginal market for co-generation from bark is in Washington, rather than the Okanagan) as a sensitivity analysis. The results did not show a clear environmental benefit to either using or not using mulch on orchards. In the scenario in which bark mulch was assumed to be used either on apple orchards or for co-generation in British Columbia, impacts in 14 categories (including climate change, eutrophication, acidification, all toxicities, land use and some renewable energy use) were lower when mulch was used on the orchard, and results for 5 categories (including some non-renewable and renewable resources/energy use) were higher. When bark mulch was 93 assumed to be used either on orchards or for co-generation in Washington rather than British Columbia, terrestrial ecotoxicity, land use, biomass and solar energy use were lower when mulch was used on the orchard, and all others (15 of categories) were higher. There was a large amount of uncertainty in the model, coming from data variability, data quality and impact assessment uncertainty. When all sources of uncertainty were included, all impact categories except the toxicity categories (known for being highly uncertain) had significant differences between the mulched and non-mulched apple production systems. Eutrophication and photochemical oxidation were lower when mulch was used on the orchard, and acidification, climate change and ozone depletion were higher when mulch was used. When only parameter uncertainty was considered (since the impact assessment uncertainty is common to both scenarios), terrestrial ecotoxicity and solar energy use were lower when mulch was used on the orchard, and climate change, abiotic resource use, photochemical oxidation and land use were higher when mulch was used. Overall, the orchard system played a significant role in the impact assessment results (averaging 41% of the total scenario impacts without uncertainty), and was the main contributor to the overall uncertainty. These results do not support the recommendation to use mulch on apple orchards, but the marginal impacts of mulch use warrant further investigation, 5.2 Methods To determine the net life cycle environmental impacts that would occur as a consequence of increasing wood chip/bark mulch use on Okanagan orchards in the place of current alternative economic uses, a change-based consequential LCA (CLCA) was performed. The potential market-mediated impacts of this change in mulch use throughout its cradle-to-gate life cycle were examined, from production to application in horticulture, bioenergy, and paper production. Impacts were assessed in terms of the production of each product with the amount of wood or bark mulch used to produce 1 kg of apples on an Okanagan orchard. The data used to model these impacts included a combination of data from field-level site-specific research, survey results, published literature and databases that were adapted as appropriate to best approximate regional conditions. The details of the CLCA methods, which correspond to the requirements of the international reference standard for life cycle assessment, ISO 14044 (2006), are as follows. 94 5.2.1 Goal and scope The goal of the CLCA was to determine the net life cycle environmental impacts of increasing the use of bark mulch as a soil amendment on orchards in the Okanagan, including the displacement of alternative economic uses. This study was intended to build on the results of the ALCA conducted on the apple orchard system (described in the previous chapter) which found that, at the orchard life cycle scale, any of the site-specific GHG reduction benefits of mulch use (Fentabil et al. 2016) were outweighed by the life cycle impacts of the production, transportation and application of the mulch, assuming no reduction in irrigation or herbicide use accompanies mulch application. This CLCA expanded the system boundary to also account for the impacts of the other uses of bark mulch, and the substituted processes that might increase as a result of an increase of mulch use on orchards (and thus a decrease in availability of mulch for other uses). The most common current uses of bark mulch in the Okanagan were identified via surveys administered to local sawmills that produce mulch as a co-product of lumber production. The major uses in the Okanagan are predominantly pulp & paper and bioenergy co-generation, as well as a small amount of animal bedding and agriculture. The intended audience of this CLCA included agricultural policy makers and orchard owners, as well as renewable or bioenergy policy makers and local industry such as sawmills. The key stakeholders were the orchard owners, the suppliers of the bark mulch and the other organisations who would otherwise make use of the wood or bark chips (pulp & paper mills, bioenergy producers, etc.). This LCA will not be used to make comparative assertions (defined by ISO (2006) as an environmental claim asserting superiority of one product over a competing product) because it considers the addition of a soil amendment to an orchard, which would otherwise not have any soil amendments added, producing the same product in either case: apples. The overall scope of the CLCA is cradle-to-gate, meaning it included all life cycle stages from the extraction of raw materials, to the production of a finished product (apples on an orchard in the Okanagan Valley, or other products produced using mulch). The details of each production system are described below. Modelling these systems allowed for the comparison of the impacts of bark mulch use on orchards with the impacts of displaced alternative uses. As a CLCA, the relevant processes that would change as a result of increased bark mulch use on orchards (other than a potential decrease in water and herbicide inputs) were included, in or out of the direct orchard supply chain (Weidema 2003). This included potential market-mediated changes (Figure 28). 95 Apple LCA studies from the literature defined their functional unit as the amount of apples produced, in weight (Milà I Canals et al. 2006; Vinyes et al. 2017). Although this CLCA differed from these studies in that it compared horticultural and other uses of bark mulch, the functional unit was still defined as the weight of fruit product, since the study ultimately related to the use of bark mulch on orchards. Therefore, the functional unit and reference flow for this CLCA was defined as one kilogram of usable apple yield, as they were for the ALCA study. Additional functional units are also defined for the predominant alternative mulch uses, relative to the amount of mulch used for 1 kg of apples. These additional functional units were 0.144 kg paper, and 0.653 kWh electricity + 0.653 MJ heat co-generation (see Appendix A for calculations of these functional units). The temporal and geographical bounds included the most recent available data for the Okanagan Valley, with site specific data for the orchard system sourced from the last 7 years from Agriculture and Agri-Food Canada’s Summerland Research and Development Centre (SuRDC) located near Summerland, British Columbia, and data regarding uses of mulch sourced directly from local mulch producers. The technological Figure 28. Flow diagram of apple production (and alternative uses of mulch: pulp & paper and bioenergy production) life cycle from cradle-to-gate. Shows system boundary between technosphere (human activity) and ecosphere (nature), as well as the foreground (site specific) and background (generic) systems. 96 boundary includes the average technology present in the Okanagan, as this boundary will suffice for most LCAs (Guinee et al. 2002). By collecting the most temporally, geographically and technologically relevant data, the uncertainty of the model was minimized. Any multi-functionality issues were solved by system expansion, as is required for CLCA (Rebitzer et al. 2004). 5.2.2 Life cycle inventory 5.2.2.1 Flow diagram and system boundaries The life cycle inventory phase of LCA involves the collection of data, either from primary research, databases, published literature and reports or expert judgement, and the construction of the system model (ISO 2006). The foreground processes of this CLCA were based on (and added to) those of the ALCA: processes that occurred directly on the orchard, including fertilizer application, irrigation, mulch application, plant protection product application, as well as experimentally measured and modelled orchard-level emissions (Figure 28). In addition, the foreground data for this model also included the alternative uses of wood chips/bark mulch that might be displaced as a result of increased use of mulch on orchards. These uses included bioenergy production, and pulp & paper production. The background processes were those that feed into these orchard processes (fertilizer, mulch, etc.) as well as into the other processes using mulch (pulp & paper, bioenergy). 5.2.2.2 Primary data collection Primary orchard data came from research by Fentabil et al. (2016), unpublished data from Hannam, Fentabil and Nichol, and information supplied by SuRDC and were described in the previous ALCA chapter. Primary data regarding alternate uses of mulch were collected via survey (see Appendix B). Local sawmills were contacted regarding the production of mulch to determine the amount and type of biomass by-products (including mulch) produced, where it is used, and for what purposes. All data were aggregated into a production-weighted average. 5.2.2.3 Background data Data for background processes were taken from life cycle inventory (LCI) databases, including ecoinvent v.3.4 Allocation at the Point of Substitution (APOS), USDA crop data v.1.1 and Agri-footprint v.3.0 (available for download at https://nexus.openlca.org/databases). Flows were altered as appropriate to better represent the region (described in detail below). All data were scaled to relate to the unit process flows and, ultimately, the functional unit. 97 5.2.2.4 Model The systems for apple production and all other uses of mulch were modelled in OpenLCA 1.7.4 (available for download at http://www.openlca.org/download/). The attributional apple model was used as a baseline for the consequential model, and is described in the previous chapter. Only the low (20 g N per tree) nitrogen fertilizer input (both with and without mulch) systems are used in this study since this was indicated by SuRDC that it better reflects average Okanagan orchard conditions. The orchard inputs/outputs were described in detail in the previous chapter. Each alternative use of bark or wood chips, as modelled, is described in detail below. 5.2.2.4.1 Mulch Very little wood and bark chip production from the Okanagan is currently used as an agricultural soil amendment (Table 19). Instead, the majority is used for electricity and heat co-generation and paper production, and a small amount is used for animal bedding. Since the mulch used on the SuRDC orchard was assumed to be a combination of 20% wood and 80% bark chips (coming from local sawmills), this ratio was maintained in the distribution of flows in the model. In light of the very small percentage of wood and bark chips used for animal bedding, this process was omitted from the LCA model, leaving paper production as the only alternative use for wood chips and bioenergy co-generation as the only alternative use for bark chips that were considered. In order to model wood chip and bark mulch production as co-products of lumber production at sawmills, alterations were made to the ecoinvent v.3.4 APOS processes for these flows, based on the mass relationships between wood/bark chips and lumber (Natural Resources Canada and Forest Products Association of Canada 2004). The bark chip and wood chip flows produced by these processes formed the inputs of the process “Mulch for orchard”, which produced the output of bark/wood chip mulch (used as the input to all processes that utilize mulch). The mulch processes modeled, including all modifications, are described in detail in the previous chapter. 98 Table 19. Aggregated survey results for percentage distribution of wood chip and bark mulch from sawmills in the Okanagan Valley. The current distribution is presented, as well as a scenario where mulch is not used in agriculture. Activity Percent (%) CURRENT DISTRIBUTION Wood chips Pulp & paper 99.5 Agricultural soil amendment 0.5 Bark chips Bioenergy co-generation 99.5 Agricultural soil amendment 0.5 SCENARIO: NO MULCH USED IN AGRICULTURE Wood chips Pulp & paper 100 Agricultural soil amendment 0.0 Bark chips Bioenergy co-generation 100 Agricultural soil amendment 0.0 5.2.2.4.2 Pulp and paper Paper production was modelled in two different ways. The first way assumed an input of wood chips as the fibrous material for paper production. The second modelled the marginal technology that would be substituted if wood chips were taken away from paper production for use on orchards, which is a recycled paper process (Table 20). This was identified as the marginal technology based on industry information from Forestry Innovation Investment Ltd. (2019). A truck transportation flow was added to the paper production process (and all other processes modelled) to represent local transport. An estimated distance of 50 km was assumed for all local transportation (estimated as the distance of transportation for the product, not including an empty return trip). Providers for flows were modified, when possible, to reflect production in British Columbia, or as representative a context as possible (for this and all other systems listed below that use wood or bark chips) (Table 20). Table 20. Uses of wood chips and bark mulch produced by sawmills in British Columbia, and the processes and databases used to model them, including all modifications. Biomass use Process Database Modifications Activities modelled WITH the use of wood chips/bark chips Pulp & paper (wood chips) paper production, woodcontaining, lightweight coated | paper, woodcontaining, ecoinvent v.3.4 APOS  “pulpwood, softwood, measured as solid wood under bark” flow replaced by “wood chips, wet, measured as dry mass” flow from modified process “wood chips production, softwood, at sawmill | wood chips, wet, 99 Biomass use Process Database Modifications lightweight coated | APOS, U measured as dry mass | APOS, U – CA-QC” (modifications described in previous chapter)  “transport, freight, lorry, unspecified” flow added to represent transport to local BC market  electricity provider changed to “market for electricity, medium voltage | electricity, medium voltage | APOS, U - CA-BC”  provider for chlorine gas was changed to “chlor-alkali electrolysis, membrane cell | chlorine, gaseous | APOS, U - CA-QC”  providers for heat changed to “heat and power co-generation, biogas, gas engine | heat, central or small-scale, other than natural gas | APOS, U - CA-AB”, “heat and power co-generation, natural gas, conventional power plant, 100MW electrical | heat, district or industrial, natural gas | APOS, U - CA-BC” and “heat, from municipal waste incineration to generic market for heat district or industrial, other than natural gas | heat, district or industrial, other than natural gas | APOS, U - CA-AB”  provider for municipal solid waste (treatment) changed to “treatment of municipal solid waste, incineration | municipal solid waste | APOS, U - CA-AB”  provider for sludge from pulp and paper mil (treatment) changed to “treatment, sludge from pulp and paper production, landfarming | sludge from pulp and paper production | APOS, U - CA-QC”  provider for sodium hydroxide changed to “chlor-alkali electrolysis, membrane cell | sodium hydroxide, without water, in 50% solution state | APOS, U - CA-QC”  provider for sulfur dioxide changed to “primary zinc production from concentrate | sulfur dioxide, liquid | APOS, U - CA-QC”  provider for sulfuric acid changed to “primary zinc production from concentrate | sulfuric acid | APOS, U - CA-QC”  provider for lime changed to “lime production, milled, loose | lime | APOS, U - CA-QC” 100 Biomass use Process Database Modifications Agricultural soil amendment (wood chips and bark chips) Apple production on orchard  Process created by Nicole Bamber (described in detail in previous chapter) Electricity production (bark chips) heat and power co-generation, wood chips, 6667 kW, state-of-the-art 2014 | electricity, high voltage | APOS, U – CA-BC ecoinvent v.3.4 APOS  “wood chips, wet, measured as dry mass” input flow replaced by “bark chips, wet, measured as dry mass” from modified process “bark chips production, softwood, at sawmill | bark chips, wet, measured as dry mass | APOS, U – CA-QC”  “transport, freight, lorry, unspecified” flow added to represent transport to local BC market  provider for chlorine gas changed to “chlor-alkali electrolysis, membrane cell | chlorine, gaseous | APOS, U - CA-QC”  provider for municipal solid waste (treatment) changed to “treatment of municipal solid waste, incineration | municipal solid waste | APOS, U - CA-AB”  provider for wastewater (treatment) changed to “treatment of wastewater, average, capacity 1E9l/year | wastewater, average | APOS, U - CA-QC” Heat production (bark chips) heat and power co-generation, wood chips, 6667 kW, state-of-the-art 2014 | heat, district or industrial, other than natural gas | APOS, U – CA-BC ecoinvent v.3.4 APOS  “wood chips, wet, measured as dry mass” input flow replaced by “bark chips, wet, measured as dry mass” from modified process “bark chips production, softwood, at sawmill | bark chips, wet, measured as dry mass | APOS, U – CA-QC”  “transport, freight, lorry, unspecified” flow added to represent transport to local BC market  provider for chlorine gas changed to “chlor-alkali electrolysis, membrane cell | chlorine, gaseous | APOS, U - CA-QC”  provider for municipal solid waste (treatment) changed to “treatment of municipal solid waste, incineration | municipal solid waste | APOS, U - CA-AB”  provider for wastewater (treatment) changed to “treatment of wastewater, average, 101 Biomass use Process Database Modifications capacity 1E9l/year | wastewater, average | APOS, U - CA-QC” Activities modelled WITHOUT the use of wood chips/bark mulch Recycled paper production paper production, woodfree, uncoated, 100% recycled content, at non-integrated mill | paper, woodfree, uncoated | APOS, U ecoinvent v.3.4 APOS  maize starch flow deleted and amount added to input of deinked pulp (recycled paper)  transport amount changed to 1.9845 t*km  provider for deinked pulp changed to “treatment of waste paper to pulp, wet lap, totally chlorine free bleached | deinked pulp, wet lap | APOS, U - CA-QC”  provider for electricity changed to “market for electricity, medium voltage | electricity, medium voltage | APOS, U - CA-BC”  providers for heat changed to “heat and power co-generation, biogas, gas engine | heat, central or small-scale, other than natural gas | APOS, U - CA-AB”, “heat and power co-generation, natural gas, conventional power plant, 100MW electrical | heat, district or industrial, natural gas | APOS, U - CA-BC” and “heat, from municipal waste incineration to generic market for heat district or industrial, other than natural gas | heat, district or industrial, other than natural gas | APOS, U - CA-AB”  provider for lime changed to “lime production, milled, loose | lime | APOS, U - CA-QC”  provider for municipal solid waste (treatment) changed to “treatment of municipal solid waste, sanitary landfill | municipal solid waste | APOS, U - CA-QC”  flow for construction of paper mill deleted  provider for sludge from pulp and paper production (treatment) changed to “treatment, sludge from pulp and paper production, landfarming | sludge from pulp and paper production | APOS, U - CA-QC”  provider for sodium hypochlorate changed to “sodium hypochlorite production, product in 15% solution state | sodium hypochlorite, without water, in 15% solution state | APOS, U - CA-QC” Electricity production electricity production, ecoinvent v.3.4 APOS 102 Biomass use Process Database Modifications hydro, run-of-river | electricity, high voltage | APOS, U Heat production heat production, natural gas, at industrial furnace >100kW | heat, district or industrial, natural gas | APOS, U ecoinvent v.3.4 APOS  Electricity provider changed to “market for electricity, low voltage | electricity, low voltage | APOS, U - CA-BC”  Natural gas provider changed to “market for natural gas, high pressure | natural gas, high pressure | APOS, U - CA-AB” 5.2.2.4.3 Bioenergy co-generation Electricity and heat co-generation using bark chips was modelled by replacing the input of wood chips with bark chips in the ecoinvent processes for co-generation “heat and power co-generation, wood chips, 6667 kW, state-of-the-art 2014 | electricity, high voltage | APOS, U – CA-BC” and “heat and power co-generation, wood chips, 6667 kW, state-of-the-art 2014 | heat, district or industrial, other than natural gas | APOS, U – CA-BC”. Both ecoinvent co-generation processes for British Columbia, Canada produce a single product, electricity and heat, respectively. Therefore, both processes together make up the entire co-generation process. Electricity production without bark chips was modelled as hydroelectric electricity generation (Table 20). This was identified as the marginal technology for electricity production in BC since it was indicated by Sopinka and Kooten (2010) that additional hydro facilities are being constructed to keep up with demand, to meet the BC Clean Energy Act’s objective of generating electricity from renewable sources (BC Legislature 2010). This was confirmed in the recent BC Hydro Service Plan which indicated that all new projects would be clean energy, and that the Site C hydroelectric dam was planned to supply long-term renewable energy (BC Hydro 2017). The marginal market provider for heat production in BC is natural gas, as it was identified by Fortis BC as the most affordable option, and they indicated that efforts are being made to lower its carbon footprint, in line with the Clean Energy Act (Fortis BC 2019). 5.2.2.5 Alternative functional units In order to calculate the environmental impacts of wood/bark chip distribution scenarios, one functional unit was selected for each different use of biomass. Amounts for each functional 103 unit were calculated based on the amount of each alternative product that would be produced using the amount of wood chips or bark mulch used to produce 1 kg of apples (0.144 kg wood chips and 0.575 kg bark chips) (Table 21). See Appendix A for sample calculations. All LCA scenarios were modelled to produce these functional units (0.144 kg paper, and 0.653 kWh electricity + 0.653 MJ heat co-generation), plus 1 kg of apples. Table 21. Functional units of alternative uses of wood and bark chips based on the distribution of mulch use in the Okanagan. Numbers were calculated relative to the amount of mulch used to produce 1 kg apples on an Okanagan apple orchard. Activity Amount of biomass input (kg) Functional unit Wood chips Pulp & paper 0.144 0.144 kg paper Bark chips Co-generation 0.575 0.653 kWh electricity 0.653 MJ heat 5.2.2.6 Scenario 1: No mulch used on orchards, only for other uses The first scenario was modelled to represent apple production without the use of mulch. Therefore, the wood chips and bark chips that could be used to produce apples were instead used for their alternative uses according to the processes identified by survey. In scenario 1, all 0.144 kg paper and 0.653 kWh electricity + 0.653 MJ heat came from wood chips and bark chips, and 1 kg of apples were produced without mulch (Table 22). This was, effectively, the baseline scenario representing current regional market conditions and resource allocations. Table 22. Modelled processes for each product in scenario 1: no wood chips or bark chips used on orchards, instead all used for paper and electricity. Product Process modelled Apples (1 kg) Apple production on orchard (clean, low nitrogen) Paper (0.144 kg) paper production, woodcontaining, lightweight coated | paper, woodcontaining, lightweight coated | APOS, U Electricity (0.653 kWh) heat and power co-generation, wood chips, 6667 kW, state-of-the-art 2014 | electricity, high voltage | APOS, U – CA-BC Heat (0.653 MJ) heat and power co-generation, wood chips, 6667 kW, state-of-the-art 2014 | heat, district or industrial, other than natural gas | APOS, U – CA-BC 5.2.2.7 Scenario 2: Increase of mulch use on orchards This scenario modelled the use of an additional 0.144 kg wood chips and 0.575 kg bark chips to produce 1 kg apples. This means that those amounts of wood and bark chips were no 104 longer available for co-generation and paper production. In scenario 2, all 0.144 kg paper came from recycled paper, all 0.653 kWh electricity came from hydro-electric generation, and all 0.653 MJ heat came from natural gas, and 1 kg apples were produced with the 0.144 kg wood chips and 0.575 kg bark chips (Table 23). Table 23. Modelled processes of apples, paper, and electricity produced (with or without wood/bark chips) for scenario 2: increased mulch use on orchards. Product Process modelled Apples (1 kg) Apple production on orchard (mulch, low nitrogen) Paper (0.144 kg) paper production, woodfree, uncoated, 100% recycled content, at non-integrated mill | paper, woodfree, uncoated | APOS, U Electricity (0.653 kWh) electricity production, hydro, run-of-river | electricity, high voltage | APOS, U Heat (0.653 MJ) heat production, natural gas, at industrial furnace >100kW | heat, district or industrial, natural gas | APOS, U 5.2.3 Impact assessment A life cycle impact assessment was conducted for each of the scenarios described above. For each scenario, all functional units were produced: 1 kg apples, 0.144 kg paper, and 0.653 kWh electricity and 0.653 MJ heat. This was done so all environmental impact results could be comparable between scenarios producing the same products/services. The impact assessment methods used were CML (baseline) [v4.4, January 2015], CML (non-baseline) [v4.4, January 2015], and Cumulative Energy Demand (available for use in openLCA at http://www.openlca.org/download/). The following CML (baseline) impact categories were included: human toxicity, freshwater aquatic ecotoxicity, depletion of abiotic resources (elements, ultimate reserves), photochemical oxidation, ozone layer depletion, terrestrial ecotoxicity, acidification potential, climate change, eutrophication, plus land use – land competition from CML (non-baseine) and all categories from Cumulative Energy Demand: non-renewable resources – fossil, nuclear, primary forest, and renewable resources – biomass, geothermal, solar, water, and wind. These impact categories were chosen since they were most commonly used by previous LCA studies of horticulture (see Chapter 2) and were suggested for use by Guinee et al. (2002). The details of each impact category are described in Chapter 2. The CML (baseline) category climate change was altered to not include characterization factors for biogenic carbon dioxide flows, meaning that any CO2 directly produced or taken up by living organisms in the short term did not contribute to the total GHG emissions of any product’s life cycle. Uncertainty factors (geometric standard deviation) for the characterization models chosen 105 were entered into the impact assessment methods in openLCA. These uncertainty factors were based on the generic uncertainty factors for impact categories presented in the supplementary information of Huijbregts et al. (2003). A gravity analysis was conducted to determine the processes or flows with the highest contribution to the impact category indicators. An uncertainty analysis was performed to determine the overall uncertainty of the impact assessment results based on both data uncertainty and characterization factor uncertainty (described above). This was done using Monte Carlo simulation (1000 runs), plus a single factor ANOVA to determine the statistical significance of the Monte Carlo results. An uncertainty contribution analysis was also performed to determine the relative amounts of uncertainty that came from orchard, co-generation and paper data, and impact assessment uncertainty. A sensitivity analysis was conducted on the choice of processes to replace electricity and heat production using bark chips. BC hydro and natural gas were originally chosen as the electricity and heat production processes to replace co-generation from bark chips. The sensitivity analysis compared this model choice to the choice of electricity production in Washington, USA, since there is evidence that Okanagan wood pellets are being sent to the US for energy production (American Biomass Corporation Inc. 2016), hence it can be assumed that bark chips could be sent for energy production in the US as well (Table 24). Transportation of 50km by truck was added to represent local Okanagan (BC) transport and 500km by rail to represent transport to Washington, and LCI models were changed to best represent production in BC or Washington. These new electricity and heat production processes were assessed in the same manner as the previous processes in scenario 1 and 2 (above). 106 Table 24. Processes modelled for electricity production without wood/bark chips for sensitivity analysis, including all modifications made to original process from database indicated. Activity Process modelled Database Modifications Electricity production using Okanagan bark chips in Washington, USA heat and power co-generation, wood chips, 6667 kW, state-of-the-art 2014 | electricity, high voltage | APOS, U - WECC, US only ecoinvent v.3.4 APOS  added process “transport, freight train” for transport of bark chips from the Okanagan to Washington  provider for municipal solid waste (treatment) changed to “treatment of municipal solid waste, incineration | municipal solid waste | APOS, U - CA-AB”  provider for wastewater (treatment) changed to “treatment of wastewater, average, capacity 1E9l/year | wastewater, average | APOS, U - CA-QC” Electricity grid mix in Washinton, USA electricity, high voltage, production mix | electricity, high voltage | APOS, U ecoinvent v.3.4 APOS Heat production using Okanagan bark chips in Washington, USA heat and power co-generation, wood chips, 6667 kW, state-of-the-art 2014 | electricity, high voltage | APOS, U - WECC, US only ecoinvent v.3.4 APOS  added process “transport, freight train” for transport of bark chips from the Okanagan to Washington  provider for municipal solid waste (treatment) changed to “treatment of municipal solid waste, incineration | municipal solid waste | APOS, U - CA-AB”  provider for wastewater (treatment) changed to “treatment of wastewater, average, capacity 1E9l/year | wastewater, average | APOS, U - CA-QC” Natural gas production in Washington, USA heat production, natural gas, at industrial furnace >100kW | heat, district or industrial, natural gas | APOS, U ecoinvent v.3.4 APOS  electricity provider changed to “market for electricity, low voltage | electricity, low voltage | APOS, U - WECC, US only”  natural gas provider changed to “market for natural gas, high pressure | natural gas, high pressure | APOS, U – US” 5.2.4 Interpretation The life cycle interpretation phase includes the evaluation of completeness/consistency of results (according to the goal and scope of the study) and the explanation of conclusions and recommendations, as well as limitations (ISO 2006). A completeness check was carried out to 107 ensure all data are available and complete. A sensitivity check was completed to assess the sensitivity of results to uncertainty, data quality and methodological choices, and a consistency check was performed to assess the consistency of methods and assumptions with the goal and scope. Finally, conclusions and recommendations were made based on the impact assessment and completeness/sensitivity/consistency check, and limitations of the study were identified. 5.3 Results 5.3.1 Life cycle impact assessment 5.3.1.1 Scenario 1 vs scenario 2: no mulch use on orchards vs increased mulch use on orchards to produce 1 kg apples The differences were analyzed between scenario 1, where no mulch was used for apple production, and scenario 2, in which 1 kg of apples was produced using mulch, taking away a relative amount of wood and bark chips from their other uses. The assumption was that the increase of wood and bark chip mulch needed for orchard use came from the alternative uses of the wood/bark chips, and that these alternative products had to be produced in another way (without wood/bark chips) to keep the supply constant. The differences between scenarios ranged from small (2% difference for fossil energy use) to large (1384% for solar energy use), depending on the impact category (Table 25). For climate change (Figure 29), eutrophication (Figure 30), acidification (Figure 31), freshwater ecotoxicity, human toxicity (Figure 32), marine aquatic ecotoxicity, ozone layer depletion, photochemical oxidation, terrestrial ecotoxicity, land use (Figure 33), and biomass, geothermal, solar, and wind energy use, scenario 1 had 8-1384% higher impacts than scenario 2. For depletion of abiotic resources, fossil, nuclear, primary forest and water energy use, the impacts of scenario 2 were 5-280% higher than scenario 1. Taking all energy use categories together, the cumulative energy demand of scenario 1 was 20% higher than scenario 2 (Figure 34). 108 Figure 29. Climate change potential (kg CO2 equivalent) of scenario 1 where no mulch is used on the orchard (only used for other processes) and scenario 2 where mulch use on orchards is increased by the amount necessary to produce 1 kg of apples. Impacts are broken down by each functional unit for each process that uses bark or wood chip mulch. Figure 30. Eutrophication potential (kg PO4 equivalent) of scenario 1 where no mulch is used on the orchard (only used for other processes) and scenario 2 where mulch use on orchards is increased by the amount necessary to produce 1 kg of apples. Impacts are broken down by each functional unit for each process that uses bark or wood chip mulch. 0.000.050.100.150.200.250.30Scenario 1: no mulch onorchardScenario 2: increased mulchuse to produce 1 kg appleskg CO2eq0.144 kg paperco-generation: 0.653 kWh electricity + 0.653 MJ heat1 kg apples0.00E+002.00E-044.00E-046.00E-048.00E-041.00E-031.20E-031.40E-03Scenario 1: no mulch onorchardScenario 2: increasedmulch use to produce 1 kgappleskg PO4eq0.144 kg paperco-generation: 0.653 kWh electricity + 0.653 MJ heat1 kg apples109 Figure 31. Acidification potential (kg SO2 equivalent) of scenario 1 where no mulch is used on the orchard (only used for other processes) and scenario 2 where mulch use on orchards is increased by the amount necessary to produce 1 kg of apples. Impacts are broken down by each functional unit for each process that uses bark or wood chip mulch. Figure 32. Human toxicity potential (kg 1,4-dichlorobenzene equivalent) of scenario 1 where no mulch is used on the orchard (only used for other processes) and scenario 2 where mulch use on orchards is increased by the amount necessary to produce 1 kg of apples. Impacts are broken down by each functional unit for each process that uses bark or wood chip mulch. 00.00050.0010.00150.0020.00250.003Scenario 1: no mulch onorchardScenario 2: increasedmulch use to produce 1 kgappleskg SO2eq0.144 kg paperco-generation: 0.653 kWh electricity + 0.653 MJ heat1 kg apples00.050.10.150.20.25Scenario 1: no mulch onorchardScenario 2: increased mulchuse to produce 1 kg appleskg 1,4-dichlorobenzene eq0.144 kg paperco-generation: 0.653 kWh electricity + 0.653 MJ heat1 kg apples110 Figure 33. Land use (m2*yr) of scenario 1 where no mulch is used on the orchard (only used for other processes) and scenario 2 where mulch use on orchards is increased by the amount necessary to produce 1 kg of apples. Impacts are broken down by each functional unit for each process that uses bark or wood chip mulch. Note that an equivalent amount of direct land use is attributed to the production of the wood and bark chips in either scenario, therefore the differences come from indirect land use change associated with the alternative production systems making use of those wood and bark chips. 0.000.501.001.502.002.503.003.50Scenario 1: no mulch onorchardScenario 2: increased mulchuse to produce 1 kg applesm2*yr0.144 kg paperco-generation: 0.653 kWh electricity + 0.653 MJ heat1 kg apples111 Figure 34. Cumulative energy demand (MJ) of scenario 1 where no mulch is used on the orchard (only used for other processes) and scenario 2 where mulch use on orchards is increased by the amount necessary to produce 1 kg of apples. Impacts are broken down by each functional unit for each process that uses bark or wood chip mulch. The life cycle of apple production accounted for a variable amount of these total impacts, ranging from 0.1% to 99.9%, depending on the impact category. This spread was large for both scenarios, with orchard mulch use accounting for 0.1-65% of impacts (across all categories) for scenario 1, and 3-99.9% for scenario 2. The impact categories with the highest proportion of impacts from apple production were (in descending order) biomass energy use, land use, eutrophication, marine aquatic ecotoxicity and wind energy use (Table 25). Table 25. Life cycle impact assessment results for scenario 1 where no mulch is used on the orchard (only used for other processes) and scenario 2 where mulch use on orchards is increased by the amount necessary to produce 1 kg of apples. Impacts are broken down by each functional unit for each process that uses bark or wood chip mulch. 01020304050607080Scenario 1: no mulch on orchard Scenario 2: increased mulch use toproduce 1 kg applesMJNon-renewable resources - fossil Non-renewable resources - nuclearNon-renewable resources - primary forest Renewable resources - biomassRenewable resources - geothermal Renewable resources - solarRenewable resources - water Renewable resources - windAcidification potential (kg SO2 eq) 1 kg apples co-generation: 0.653 kWh electricity + 0.653 MJ heat 0.144 kg paper Total Scenario 1: no mulch on orchard 9.300 x 10-4 8.627 x 10-4 6.800 x 10-4 2.473 x 10-3 Scenario 2: increased mulch use to produce 1 kg apples 1.160 x 10-3 8.437 x 10-5 6.600 x 10-4 1.904 x 10-3 112 Climate change (kg CO2 eq) 1 kg apples co-generation: 0.653 kWh electricity + 0.653 MJ heat 0.144 kg paper Total Scenario 1: no mulch on orchard 0.122 0.035 0.118 0.275 Scenario 2: increased mulch use to produce 1 kg apples 0.155 0.045 0.059 0.253 Depletion of abiotic resources - elements, ultimate reserves (kg antimony eq) 1 kg apples co-generation: 0.653 kWh electricity + 0.653 MJ heat 0.144 kg paper Total Scenario 1: no mulch on orchard 9.762 x 10-7 7.302 x 10-8 1.790 x 10-6 2.840 x 10-6 Scenario 2: increased mulch use to produce 1 kg apples 1.088 x 10-6 1.350 x 10-8 1.069 x 10-5 1.179 x 10-5 Eutrophication (kg PO4 eq) 1 kg apples co-generation: 0.653 kWh electricity + 0.653 MJ heat 0.144 kg paper Total Scenario 1: no mulch on orchard 5.800 x 10-4 3.278 x 10-4 3.600 x 10-4 1.268 x 10-3 Scenario 2: increased mulch use to produce 1 kg apples 6.900 x 10-4 1.387 x 10-5 2.700 x 10-4 9.739 x 10-4 Freshwater aquatic ecotoxicity (kg 1,4-dichlorobenzene eq) 1 kg apples co-generation: 0.653 kWh electricity + 0.653 MJ heat 0.144 kg paper Total Scenario 1: no mulch on orchard 0.071 0.018 0.067 0.155 Scenario 2: increased mulch use to produce 1 kg apples 0.0882 2.530 x 10-3 0.047 0.138 Human toxicity (kg 1,4-dichlorobenzene eq) 1 kg apples co-generation: 0.653 kWh electricity + 0.653 MJ heat 0.144 kg paper Total Scenario 1: no mulch on orchard 0.083 0.059 0.084 0.225 Scenario 2: increased mulch use to produce 1 kg apples 0.110 8.850 x 10-3 0.073 0.192 Marine aquatic ecotoxicity (kg 1,4-dichlorobenzene eq) 1 kg apples co-generation: 0.653 kWh electricity + 0.653 MJ heat 0.144 kg paper Total Scenario 1: no mulch on orchard 183.15 44.168 216.37 443.69 Scenario 2: increased mulch use to produce 1 kg apples 233.84 5.674 105.25 344.76 113 Ozone layer depletion (kg CFC-11 eq) 1 kg apples co-generation: 0.653 kWh electricity + 0.653 MJ heat 0.144 kg paper Total Scenario 1: no mulch on orchard 9.978 x 10-9 9.930 x 10-9 2.105 x 10-8 4.096 x 10-8 Scenario 2: increased mulch use to produce 1 kg apples 1.730 x 10-8 4.890 x 10-9 1.044 x 10-8 3.263 x 10-8 Photochemical oxidation (kg ethylene eq) 1 kg apples co-generation: 0.653 kWh electricity + 0.653 MJ heat 0.144 kg paper Total Scenario 1: no mulch on orchard 3.771 x 10-5 1.393 x 10-5 3.381 x 10-5 8.546 x 10-5 Scenario 2: increased mulch use to produce 1 kg apples 4.806 x 10-5 8.081 x 10-6 2.176 x 10-5 7.790 x 10-5 Terrestrial ecotoxicity (kg 1,4-dichlorobenzene eq) 1 kg apples co-generation: 0.653 kWh electricity + 0.653 MJ heat 0.144 kg paper Total Scenario 1: no mulch on orchard 1.330 x 10-3 3.710 x 10-3 3.540 x 10-3 8.570 x 10-3 Scenario 2: increased mulch use to produce 1 kg apples 1.690 x 10-3 1.508 x 10-4 1.930 x 10-3 3.771 x 10-3 Land use - land competition (m2*yr) 1 kg apples co-generation: 0.653 kWh electricity + 0.653 MJ heat 0.144 kg paper Total Scenario 1: no mulch on orchard 0.016 2.673 0.414 3.103 Scenario 2: increased mulch use to produce 1 kg apples 2.293 3.134 x 10-3 0.014 2.310 Non-renewable resources - fossil (MJ) 1 kg apples co-generation: 0.653 kWh electricity + 0.653 MJ heat 0.144 kg paper Total Scenario 1: no mulch on orchard 1.364 0.420 2.166 3.950 Scenario 2: increased mulch use to produce 1 kg apples 2.186 0.731 1.099 4.015 Non-renewable resources - nuclear (MJ) 1 kg apples co-generation: 0.653 kWh electricity + 0.653 MJ heat 0.144 kg paper Total Scenario 1: no mulch on orchard 0.055 0.033 0.301 0.390 Scenario 2: increased mulch use to produce 1 kg apples 0.108 2.350 x 10-3 3.077 3.188 114 The change from scenario 1 to scenario 2 assumed an additional 1 kg of apples is produced using mulch. The implications of this change, in terms of substituted marginal processes and the resulting environmental impacts, were assumed to scale linearly if more Non-renewable resources - primary forest (MJ) 1 kg apples co-generation: 0.653 kWh electricity + 0.653 MJ heat 0.144 kg paper Total Scenario 1: no mulch on orchard 6.140 x 10-3 1.693 x 10-5 3.330 x 10-3 9.487 x 10-3 Scenario 2: increased mulch use to produce 1 kg apples 6.160 x 10-3 2.110 x 10-6 7.870 x 10-3 0.014 Renewable resources - biomass (MJ) 1 kg apples co-generation: 0.653 kWh electricity + 0.653 MJ heat 0.144 kg paper Total Scenario 1: no mulch on orchard 0.069 62.202 9.032 71.303 Scenario 2: increased mulch use to produce 1 kg apples 52.903 9.000 x 10-4 0.073 52.976 Renewable resources - geothermal (MJ) 1 kg apples co-generation: 0.653 kWh electricity + 0.653 MJ heat 0.144 kg paper Total Scenario 1: no mulch on orchard 5.800 x 10-4 3.420 x 10-3 8.440 x 10-3 0.012 Scenario 2: increased mulch use to produce 1 kg apples 6.150 x 10-3 4.587 x 10-5 3.490 x 10-3 9.686 x 10-3 Renewable resources - solar (MJ) 1 kg apples co-generation: 0.653 kWh electricity + 0.653 MJ heat 0.144 kg paper Total Scenario 1: no mulch on orchard 6.128 x 10-5 1.412 x 10-5 4.070 x 10-3 4.145 x 10-3 Scenario 2: increased mulch use to produce 1 kg apples 7.759 x 10-5 1.823 x 10-6 2.000 x 10-4 2.794 x 10-4 Renewable resources - water (MJ) 1 kg apples co-generation: 0.653 kWh electricity + 0.653 MJ heat 0.144 kg paper Total Scenario 1: no mulch on orchard 0.038 0.540 1.217 1.795 Scenario 2: increased mulch use to produce 1 kg apples 0.947 2.479 0.514 3.940 Renewable resources - wind (MJ) 1 kg apples co-generation: 0.653 kWh electricity + 0.653 MJ heat 0.144 kg paper Total Scenario 1: no mulch on orchard 6.610 x 10-3 0.015 0.036 0.057 Scenario 2: increased mulch use to produce 1 kg apples 0.0313 3.300 x 10-4 0.015 0.046 115 apples are produced with mulch. However, it is interesting to note that even if all orchards in the province used mulch at the same rate as the SuRDC orchard, only 6.4% of the provincial wood chip and 9.8% of bark chip supply would be displaced (see Appendix A for calculations). 5.3.1.2 Sensitivity analysis The electricity and heat production sensitivity analysis assumed that bark chips were sent to Washington for electricity and heat co-generation rather than to the Okanagan. This means that in scenario 1 when bark chips were used for co-generation (as opposed to apple production), this process was modelled in Washington rather than BC, and in scenario 2 when bark chips were used for apple production and not for co-generation, the electricity instead came from the Washington grid mix and heat from natural gas in Washington. For climate change (Figure 35), eutrophication (Figure 36), depletion of abiotic resources, freshwater aquatic ecotoxicity, human toxicity (Figure 38), marine aquatic ecotoxicity, ozone layer depletion, photochemical oxidation, and fossil, nuclear, primary forest, geothermal, water and wind energy use, scenario 2 had 11-91% higher impacts than scenario 1. For acidification (Figure 37), terrestrial ecotoxicity, land use (Figure 39), and biomass and solar energy use, scenario 1 had 3-1014% higher impacts. This is in contrast with the LCIA results above which assumed co-generation (and substitution) in British Columbia, in which only 5 categories were higher for scenario 2 than scenario 1 with (relatively) small differences, and 14 were higher in scenario 1 with larger differences (Table 25). However for Washington co-generation, 14 categories were higher for scenario 2 (with relatively small differences) and only 5 for scenario 1 (with larger differences) (Table 26). With all the energy sources considered together, the cumulative energy demand of scenario 1 was 15% higher than scenario 2 (Figure 40), compared to 20% above (Figure 34). These differences were due to the differences in the environmental impacts of electricity and heat production in British Columbia, which were lower compared to Washington. In both cases there were trade-offs to choosing to either produce apples with or without mulch, in terms of the marginal technologies displaced. However, when the displaced technologies were BC heating and electricity, there may be an overall benefit to using mulch for apple production rather than co-generation, and the opposite trend is seen when Washington heating and electricity were displaced. 116 Figure 35. Climate change potential (kg CO2 equivalent) for scenario 1 (no mulch on orchard) and scenario 2 (mulch on orchard), where the marginal market for co-generation using bark chips is assumed to be Washington rather than British Columbia. Figure 36. Eutrophication potential (kg PO4 equivalent) for scenario 1 (no mulch on orchard) and scenario 2 (mulch on orchard), where the marginal market for co-generation using bark chips is assumed to be Washington rather than British Columbia. 0.000.100.200.300.400.500.60Scenario 1: co-generation inWashingtonScenario 2: Washingtonnatural gas and electricitygridkg CO2eq0.144 kg paperco-generation: 0.653 kWh electricity + 0.653 MJ heat1 kg apples0.00E+005.00E-041.00E-031.50E-032.00E-032.50E-03Scenario 1: co-generationin WashingtonScenario 2: Washingtonnatural gas and electricitygridkg PO4eq0.144 kg paperco-generation: 0.653 kWh electricity + 0.653 MJ heat1 kg apples117 Figure 37. Acidification potential (kg SO2 equivalent) for scenario 1 (no mulch on orchard) and scenario 2 (mulch on orchard), where the marginal market for co-generation using bark chips is assumed to be Washington rather than British Columbia. Figure 38. Human toxicity equivalent (kg 1,4-dichlorobenzene equivalent) for scenario 1 (no mulch on orchard) and scenario 2 (mulch on orchard), where the marginal market for co-generation using bark chips is assumed to be Washington rather than British Columbia. 00.00050.0010.00150.0020.00250.003Scenario 1: co-generation inWashingtonScenario 2: Washingtonnatural gas and electricitygridkg SO2eq0.144 kg paperco-generation: 0.653 kWh electricity + 0.653 MJ heat1 kg apples00.050.10.150.20.250.30.350.4Scenario 1: co-generation inWashingtonScenario 2: Washingtonnatural gas and electricitygridkg 1,4-dichlorobenzene eq0.144 kg paperco-generation: 0.653 kWh electricity + 0.653 MJ heat1 kg apples118 Figure 39. Land use (m2*yr) for scenario 1 (no mulch on orchard) and scenario 2 (mulch on orchard), where the marginal market for co-generation using bark chips is assumed to be Washington rather than British Columbia. Figure 40. Cumulative energy demand (MJ) for scenario 1 (no mulch on orchard) and scenario 2 (mulch on orchard), where the marginal market for co-generation using bark chips is assumed to be Washington rather than British Columbia. 0.000.501.001.502.002.503.003.50Scenario 1: co-generation inWashingtonScenario 2: Washingtonnatural gas and electricitygridm2*yr0.144 kg paperco-generation: 0.653 kWh electricity + 0.653 MJ heat1 kg apples01020304050607080Scenario 1: co-generation in Washington Scenario 2: Washington natural gas andelectricity gridMJNon-renewable resources - fossil Non-renewable resources - nuclearNon-renewable resources - primary forest Renewable resources - biomassRenewable resources - geothermal Renewable resources - solarRenewable resources - water Renewable resources - wind119 Table 26. Sensitivity analysis results for scenario 1 (no mulch on orchard), and scenario 2 (increased mulch on orchard). The co-generation process with bark chips was changed to occur in Washington and without mulch was changed to the Washington grid mix and natural gas. Acidification potential (kg SO2 eq) 1 kg apples co-generation: 0.653 kWh electricity + 0.653 MJ heat 0.144 kg paper Total Scenario 1: co-generation in Washington 9.300 x 10-4 9.600 x 10-4 6.800 x 10-4 2.570 x 10-3 Scenario 2: Washington natural gas and electricity grid 1.160 x 10-3 6.351 x 10-3 6.600 x 10-4 2.510 x 10-3 Climate change (kg CO2 eq) 1 kg apples co-generation: 0.653 kWh electricity + 0.653 MJ heat 0.144 kg paper Total Scenario 1: co-generation in Washington 0.122 0.053 0.118 0.293 Scenario 2: Washington natural gas and electricity grid 0.150 0.354 0.059 0.563 Depletion of abiotic resources - elements, ultimate reserves (kg antimony eq) 1 kg apples co-generation: 0.653 kWh electricity + 0.653 MJ heat 0.144 kg paper Total Scenario 1: co-generation in Washington 9.762 x 10-7 1.001 x 10-7 1.790 x 10-6 2.866 x 10-6 Scenario 2: Washington natural gas and electricity grid 1.088 x 10-6 6.748 x 10-8 1.069 x 10-5 1.184 x 10-5 Eutrophication (kg PO4 eq) 1 kg apples co-generation: 0.653 kWh electricity + 0.653 MJ heat 0.144 kg paper Total Scenario 1: co-generation in Washington 5.800 x 10-4 3.687 x 10-4 3.600 x 10-4 1.309 x 10-3 Scenario 2: Washington natural gas and electricity grid 6.900 x 10-4 1.087 x 10-3 2.700 x 10-4 2.047 x 10-3 Freshwater aquatic ecotoxicity (kg 1,4-dichlorobenzene eq) 1 kg apples co-generation: 0.653 kWh electricity + 0.653 MJ heat 0.144 kg paper Total Scenario 1: co-generation in Washington 0.071 0.022 0.067 0.160 Scenario 2: Washington natural gas and electricity grid 0.0882 0.186 0.047 0.321 120 Human toxicity (kg 1,4-dichlorobenzene eq) 1 kg apples co-generation: 0.653 kWh electricity + 0.653 MJ heat 0.144 kg paper Total Scenario 1: co-generation in Washington 0.083 0.073 0.084 0.240 Scenario 2: Washington natural gas and electricity grid 0.110 0.169 0.073 0.352 Marine aquatic ecotoxicity (kg 1,4-dichlorobenzene eq) 1 kg apples co-generation: 0.653 kWh electricity + 0.653 MJ heat 0.144 kg paper Total Scenario 1: co-generation in Washington 183.15 81.429 216.37 480.95 Scenario 2: Washington natural gas and electricity grid 233.84 581.67 105.25 920.76 Ozone layer depletion (kg CFC-11 eq) 1 kg apples co-generation: 0.653 kWh electricity + 0.653 MJ heat 0.144 kg paper Total Scenario 1: co-generation in Washington 9.978 x 10-9 1.019 x 10-8 2.105 x 10-8 4.122 x 10-8 Scenario 2: Washington natural gas and electricity grid 1.730 x 10-8 2.388 x 10-8 1.044 x 10-8 5.162 x 10-8 Photochemical oxidation (kg ethylene eq) 1 kg apples co-generation: 0.653 kWh electricity + 0.653 MJ heat 0.144 kg paper Total Scenario 1: co-generation in Washington 3.771 x 10-5 1.893 x 10-5 3.381 x 10-5 9.046 x 10-5 Scenario 2: Washington natural gas and electricity grid 4.806 x 10-5 4.732 x 10-5 2.176 x 10-5 1.170 x 10-4 Terrestrial ecotoxicity (kg 1,4-dichlorobenzene eq) 1 kg apples co-generation: 0.653 kWh electricity + 0.653 MJ heat 0.144 kg paper Total Scenario 1: co-generation in Washington 1.330 x 10-3 3.950 x 10-3 3.540 x 10-3 8.810 x 10-3 Scenario 2: Washington natural gas and electricity grid 1.690 x 10-3 6.525 x 10-4 1.930 x 10-3 4.273 x 10-3 Land use - land competition (m2*yr) 1 kg apples co-generation: 0.653 kWh electricity + 0.653 MJ heat 0.144 kg paper Total Scenario 1: co-generation in Washington 0.016 2.674 0.414 3.105 Scenario 2: Washington natural gas and electricity grid 2.293 0.012 0.014 2.319 121 Non-renewable resources - fossil (MJ) 1 kg apples co-generation: 0.653 kWh electricity + 0.653 MJ heat 0.144 kg paper Total Scenario 1: co-generation in Washington 1.364 0.599 2.166 4.129 Scenario 2: Washington natural gas and electricity grid 2.186 4.843 1.099 8.128 Non-renewable resources - nuclear (MJ) 1 kg apples co-generation: 0.653 kWh electricity + 0.653 MJ heat 0.144 kg paper Total Scenario 1: co-generation in Washington 0.055 0.076 0.301 0.432 Scenario 2: Washington natural gas and electricity grid 0.108 0.753 3.077 3.939 Non-renewable resources - primary forest (MJ) 1 kg apples co-generation: 0.653 kWh electricity + 0.653 MJ heat 0.144 kg paper Total Scenario 1: co-generation in Washington 6.140 x 10-3 4.207 x 10-5 3.330 x 10-3 9.512 x 10-3 Scenario 2: Washington natural gas and electricity grid 6.160 x 10-3 9.894 x 10-5 7.870 x 10-3 0.014 Renewable resources - biomass (MJ) 1 kg apples co-generation: 0.653 kWh electricity + 0.653 MJ heat 0.144 kg paper Total Scenario 1: co-generation in Washington 0.069 62.208 9.032 71.309 Scenario 2: Washington natural gas and electricity grid 52.903 0.069 0.073 53.045 Renewable resources - geothermal (MJ) 1 kg apples co-generation: 0.653 kWh electricity + 0.653 MJ heat 0.144 kg paper Total Scenario 1: co-generation in Washington 5.800 x 10-4 3.980 x 10-3 8.440 x 10-3 0.013 Scenario 2: Washington natural gas and electricity grid 6.150 x 10-3 0.103 3.490 x 10-3 0.113 Renewable resources - solar (MJ) 1 kg apples co-generation: 0.653 kWh electricity + 0.653 MJ heat 0.144 kg paper Total Scenario 1: co-generation in Washington 6.128 x 10-5 1.372 x 10-5 4.070 x 10-3 4.145 x 10-3 Scenario 2: Washington natural gas and electricity grid 7.759 x 10-5 9.435 x 10-5 2.000 x 10-4 3.720 x 10-4 122 Renewable resources - water (MJ) 1 kg apples co-generation: 0.653 kWh electricity + 0.653 MJ heat 0.144 kg paper Total Scenario 1: co-generation in Washington 0.038 0.561 1.217 1.816 Scenario 2: Washington natural gas and electricity grid 0.947 0.578 0.514 2.039 Renewable resources - wind (MJ) 1 kg apples co-generation: 0.653 kWh electricity + 0.653 MJ heat 0.144 kg paper Total Scenario 1: co-generation in Washington 6.610 x 10-3 0.018 0.036 0.061 Scenario 2: Washington natural gas and electricity grid 0.0313 0.173 0.015 0.219 5.3.1.3 Uncertainty analysis Monte Carlo simulations (1000 runs) were performed for all impact categories, including parameter (data) uncertainty, both from ranges of experimental data, and data quality indicators scored using a pedigree matrix. This involved sampling from the distribution of this uncertainty 1000 times to give an uncertainty range of output values. This was also done with both parameter and impact assessment uncertainty for a subset of categories: acidification, climate change, eutrophication, freshwater aquatic ecotoxicity, human toxicity, marine aquatic ecotoxicity, ozone layer depletion, photochemical oxidation and terrestrial ecotoxicity. In this case, the Monte Carlo simulation sampled both the uncertainty range for parameter uncertainty, and the uncertainty range for the characterization factors used in impact assessment. When only parameter uncertainty was accounted for, some of the relationships seen in the impact assessment above were still seen, but many changed (Appendix C - Table 35). Despite the fairly large spread of data, there were still significant differences (p < 0.05) between scenario 1 and 2 for climate change, depletion of abiotic resources, ozone layer depletion, photochemical oxidation, terrestrial ecotoxicity, land use, and solar energy use. However, while the results without uncertainty indicated that climate change, ozone layer depletion, photochemical oxidation, land use, terrestrial ecotoxicity, and solar energy use were higher for scenario 1 and depletion of abiotic resources were higher for scenario 2, with parameter uncertainty included the results for climate change, ozone layer depletion, photochemical oxidation, and land use changed to be higher for scenario 2 rather than scenario 1. This was due to the large amount of log-normally distributed uncertainty which gave sampled means 123 which were different and higher than the means calculated without uncertainty. Generally the higher the uncertainty, the higher the sampled mean, which is the case for log-normal distributions with large sample sizes (Ricciardi et al. 2005). With parameter uncertainty assessment there were no significant differences between scenarios for eutrophication, acidification, freshwater ecotoxicity, human toxicity, marine ecotoxicity, non-renewable energy use (fossil, nuclear and primary forest), and renewable energy (biomass, geothermal, water and wind). When both parameter and impact assessment uncertainty were accounted for, there were no significant differences between scenarios for any toxicity impacts (freshwater, human, marine and terrestrial). Interestingly, eutrophication and acidification, which had no significant differences between scenarios when only parameter uncertainty was included in the Monte Carlo simulation, were higher for scenario 2 once impact assessment uncertainty was also included (as opposed to higher for scenario 1 without uncertainty). Overall, the uncertainty of the impact assessment results ranged from 1.5 times to up to 107 times higher with both parameter and impact assessment uncertainty included in the assessment compared to only parameter uncertainty (Appendix C – Table 36). An uncertainty contribution analysis was conducted to determine the amount of the parameter uncertainty that comes from each of the product systems modelled (with or without mulch). As described in the previous chapter, due to software limitations it was not possible to perform an uncertainty contribution analysis in one Monte Carlo simulation. Therefore, an independent simulation was performed for each source of uncertainty and the standard deviations for each impact category were compared to the total amount of uncertainty. Due to the random nature of this sampling and the high amounts of uncertainty, this sometimes led to higher standard deviations in the simulations with only uncertainty from one product system compared to the simulation with all uncertainty from all product systems together. Therefore, this was not a robust uncertainty contribution analysis and can only be used to indicate potentially which product systems contributed the most to the overall uncertainty. Uncertainty from the orchard systems ranged from 0.001% to 87840 times the magnitude of parameter uncertainty, averaging 2243 times (Appendix C - Table 37), paper uncertainty was <0.001-334% (average 29%) (Appendix C - Table 38) and co-generation was <0.001-98% (average 7%) (Appendix C - Table 39). This indicated that the majority of parameter uncertainty came from the orchard systems, followed by the paper systems, and the least from the co-generation systems. 124 Since the product systems modelled varied between scenario 1 and 2, the total parameter uncertainty was the best indicator of the relative uncertainty of the two scenarios (Appendix C – Table 35). The results of those Monte Carlo simulations had quite large uncertainty ranges due to the variability of measured inputs and emissions (in the orchard system) and due to the quality of the representativeness of LCI processes used to model foreground processes in all systems (Figures 41-46). Despite this uncertainty, the differences between apple production treatments (and their market-mediated impacts) for most impact categories were statistically significant, with 10 categories higher in scenario 1 (apples produced without mulch), and 2 higher in scenario 2 (apples produced with mulch). Figure 41. Monte Carlo simulation results, including parameter uncertainty only, for the climate change potential (kg CO2 equivalent) of scenario 1: no mulch on orchards, and scenario 2: mulch on orchard (p < 0.001). Results include the life cycle emissions for the production of 1 kg apples, 0.653 kWh electricity + 0.653 MJ heat co-generation and 0.144 kg paper. 125 Figure 42. Monte Carlo simulation results, including parameter uncertainty only, for the eutrophication potential (kg PO4 equivalent) of scenario 1: no mulch on orchard, and scenario 2: mulch on orchards (p = 0.102). Results include the life cycle emissions for the production of 1 kg apples, 0.653 kWh electricity + 0.653 MJ heat co-generation and 0.144 kg paper. Figure 43. Monte Carlo simulation results, including parameter uncertainty only, for the acidification potential (kg SO2 equivalent) of scenario 1: no mulch on orchard, and scenario 2: mulch on orchard (p = 0.059). Results include the life cycle emissions for the production of 1 kg apples, 0.653 kWh electricity + 0.653 MJ heat co-generation and 0.144 kg paper. 126 Figure 44. Monte Carlo simulation results, including parameter uncertainty only, for the human toxicity potential (kg 1,4-dichlorobenzene equivalent) of scenario 1: no mulch on orchard, and scenario 2: mulch on orchard (p = 0.059). Results include the life cycle emissions for the production of 1 kg apples, 0.653 kWh electricity + 0.653 MJ heat co-generation and 0.144 kg paper. Figure 45. Monte Carlo simulation results, including parameter uncertainty only, for the land use (m2*yr) of scenario 1: no mulch on orchard, and scenario 2: mulch on orchard (p < 0.001). Results include the life cycle resource use for the production of 1 kg apples, 0.653 kWh electricity + 0.653 MJ heat co-generation and 0.144 kg paper. 127 Figure 46. Monte Carlo simulation results, including parameter uncertainty only, for the renewable resource – biomass use (MJ) of scenario 1: no mulch on orchard, and scenario 2: mulch on orchard (p = 0.392). Results include the life cycle resource use for the production of 1 kg apples, 0.653 kWh electricity + 0.653 MJ heat co-generation and 0.144 kg paper. 5.3.2 Interpretation 5.3.2.1 Completeness, sensitivity and consistency check As per the ISO 14044 LCA standard (ISO 2006), a completeness, sensitivity and consistency check were carried out in order to evaluate the results and determine the reliability and any significant issues. These checks were performed for the attributional apple production models that form the basis of this consequential LCA in the previous chapter. The most important issue with the attributional model was the assumption that the water and herbicide inputs were identical between mulch and no mulch treatments. This was due to the original experimental design (Fentabil et al. 2016) but likely does not reflect real orchard inputs. It is likely that both irrigation and glyphosate inputs could be decreased with the application of bark mulch (Yin et al. 2012; Neilsen et al. 2017, DeVetter et al. 2015). Most relevant data for the construction and interpretation of this CLCA model (building on the previous ALCA model) were available and complete (either from survey results or from existing LCI datasets); however, the survey results were only representative of a subset of biomass production, representing only about 20% of the total capacity of the Okanagan region (Ghafghazi et al. 2017). Given the nature of the scenario modelling done in this CLCA, there were many assumptions made in the construction of the model. The main assumption that is key to consequential modelling, is that when a change is made in the use/management of the functional unit, there will be a linearly scaled change in the marginal technologies affected 128 (Weidema 2003). In this case, that means that for every 1 kg of apples produced with mulch, 0.575 kg of bark chips and 0.144 kg of wood chips were modelled as used on the orchard and removed from use in the production of 0.653 kWh electricity/MJ heat and 0.144 kg of paper, which then were replaced with an equal amount of electricity produced by hydro, heat from natural gas, and paper from recycled paper. In reality, the market may not respond in a fully elastic, linear manner (Dale and Kim 2014). Another major assumption was the choice of marginal technologies for co-generation and paper production. The chosen technologies were based on evidence from the local industries (BC Legislature 2010; BC Hydro 2017; Forestry Innovation Investment Ltd. 2019a; Fortis BC 2019). However, the results of the study were quite sensitive to the choice of marginal technology. When the substituted electricity and heat production processes were assumed to occur in Washington rather than British Columbia, the results changed to indicate that most impact categories had higher impacts when mulch was used on the orchard, compared to most categories being higher when mulch was not used for BC co-generation. This indicates that the relative sustainability of mulch use is highly dependent on the market-mediated substitutions for biomass-based products. 5.3.2.2 Discussion The results of this study were inconclusive as to the relative sustainability of apple production with or without mulch, including the broader market-mediated impact on substituted marginal technologies. When the impact assessment results were considered without uncertainty, most categories had a higher impact for apple production without mulch use compared to production with mulch. For climate change, eutrophication, acidification, freshwater ecotoxicity, human toxicity, marine aquatic ecotoxicity, ozone layer depletion, photochemical oxidation, terrestrial ecotoxicity, land use, and biomass, geothermal, solar, and wind energy use, the impacts were 8-1384% higher with no mulch use on the orchard compared to with mulch. For depletion of abiotic resources, fossil, nuclear, primary forest and water energy use, the impacts of the mulch use scenario were 5-280% higher than for the no mulch use scenario. These scenarios assumed that the substituted marginal technologies for co-generation with bark chips were hydro-electric electricity production and heat production from natural gas in British Columbia when bark chips were not used for co-generation. However, when the substituted co-generation processes were assumed to take place in Washington rather than BC, the majority of impact categories had higher impacts when mulch was used on orchards. For climate change, 129 eutrophication, acidification, depletion of abiotic resources, freshwater aquatic ecotoxicity, human toxicity, marine aquatic ecotoxicity, ozone layer depletion, photochemical oxidation, and fossil, nuclear, primary forest, geothermal, water and wind energy use, the mulch scenario had 11-91% higher impacts than no mulch. For terrestrial ecotoxicity, land use, and biomass and solar energy use, the no mulch scenario had 34-1014% higher impacts than the mulch scenario. Once uncertainty was introduced into the results, the apparent relative sustainability of apple production with or without mulch changed again. There was a large amount of uncertainty in the results, especially for the mulched orchard treatments. When only parameter uncertainty (from data measurement variability and data quality) was included, the results for climate change, ozone layer depletion, photochemical oxidation, and land use changed to be higher for the mulch scenario rather than no mulch. This happened as a result of the large amount of log-normally distributed uncertainty used to calculate the sampled means in these impact categories, which can result in higher mean values with larger sample sizes. When parameter and impact assessment uncertainty were included, the eutrophication and acidification categories added to the list of impact categories that changed to be higher when mulch use was increased on orchards. This large amount of uncertainty also resulted in many non-significant differences between scenarios, particularly for the toxicity impact categories which are known to have higher levels of uncertainty (Huijbregts et al. 2003b). The orchard system contributed the most to the overall impacts of the systems affected by the increase of mulch use on orchards. Taken together, these results indicate that if the bark mulch used for Okanagan Valley apple orchards was taken away from bioenergy co-generation in BC, it may be beneficial to increase mulch use on orchards since the lower combined impacts of electricity, heat and paper production without wood/bark chips outweighed the increased life cycle impacts of apple production with mulch. However, if that bark mulch was taken away from co-generation in Washington, it may be beneficial not to use it on orchards since electricity and heat production in Washington had larger impacts than BC production, which adds to the higher impacts of apple production with mulch. These results were not very robust due to the large amounts of uncertainty which made many differences non-significant, and changed the relative ranking of the two scenarios for some impact categories. This indicates that the quality of the data was not sufficient to determine the impact of increased mulch use on orchards, which could be greatly improved with up-to-date, regionally-specific LCI data for agricultural processes. Based on these results, on the basis of the GHG reduction benefits of mulch, it cannot currently be 130 recommended to use wood/bark chip mulch as an orchard management practice to reduce environmental emissions and resource use on Okanagan Valley apple orchards. 131 Chapter 6: Conclusions 6.1 Mulch sustainability Based on the results of the research presented in Chapters 4-5, there is no clear conclusion as to the environmental sustainability of using wood and bark mulch as a soil amendment on Okanagan apple orchards. In previous research by Fentabil et al. (2016), when only the on-orchard impacts were considered (not including the life cycle), there were reductions in N2O emissions and increases in soil carbon with the application of bark mulch yielding an overall reduction in GHG emissions. However, when the cradle-to-gate life cycle of apple production was considered, this reduction in GHGs at the farm level was counteracted by a larger increase in GHG emissions and other impacts from all the upstream processes supporting mulch use on the orchard, including the production, transportation and application of the mulch. The models only included the GHG-reducing benefits of mulch, not any of the other benefits such as water retention, increased soil microbial activity and weed suppression. This is due to the original experimental design which kept all orchard inputs constant, other than mulch and nitrogen fertilizer. There was also a large amount of uncertainty in this model. However, when only the relative uncertainty of the treatments was assessed (excluding all uncertainty that was common to all four apple production treatments), there were significantly higher impacts in all categories when mulch was applied as a soil amendment. These impacts included climate change, acidification, eutrophication, toxicity, land, energy and resource use. When the broader market-mediated impacts of increased mulch use on orchards were examined, the results were less clear. In Chapter 5, a consequential LCA was conducted to determine the impacts of all systems that would be impacted by increasing wood and bark chip use on orchards. This assumed that if 0.575 kg bark and 0.144 kg wood chips were used to produce 1 kg of apples on an Okanagan orchard, that same amount of bark would be taken away from the co-generation of 0.653 kWh electricity and 0.653 MJ heat and wood from the production of 0.144 kg paper for wood chips. This electricity, heat and paper must then be produced by the marginal technologies, which were assumed to be hydro for electricity, natural gas for heat, and recycled paper for paper production (all occurring in British Columbia). Without uncertainty analysis, most impact category results were lower with the addition of mulch to orchards, because the lower impacts of electricity, heat and paper production without mulch outweighed the higher impacts of apple production with mulch. Due to the high amounts of uncertainty in the model, especially for the mulched orchard treatments, more impact category results were higher with mulch on the orchard than without, once uncertainty 132 analysis was introduced. Also, when the marginal technology for co-generation was assumed to be in Washington rather than BC, the impacts of apple production with mulch were almost all higher than without mulch. Overall, based on the life cycle emissions and resource use associated with mulch use, including displaced alternative uses, it is not clear whether or not it is environmentally beneficial to use mulch on Okanagan apple orchards. Therefore, it cannot be recommended to horticulturalists as a management practice with the goal of reducing emissions and resource use. However, there are other potential benefits associated with mulch use such as nutrient and water retention, increased soil microbial activity, and suppression of weeds, which were not included in this model. In the future, these aspects could be integrated into the LCA framework and used to expand this research to better understand a broader suite of the potential impacts and benefits of adding mulch as an orchard soil amendment. 6.2 Uncertainty There was a large amount of uncertainty in the LCA models which, especially in the case of the CLCA model, greatly reduced the robustness of the results. Many differences were not statistically significant due to the large variability in the Monte Carlo results. In addition to this, the relative ranking of the two scenarios changed for many impact categories depending on what uncertainty (if any) was included in the analysis. This uncertainty came largely from background data quality indicators quantified using the pedigree matrix. In this system, the lower the representativeness of the data, the larger the data quality score, which corresponds to a larger uncertainty factor (geometric standard deviation) (Muller et al. 2016). Therefore, the uncertainty of the model could be greatly reduced with the availability of more representative data, particularly with regards to the geographical and temporal scope of the model. The LCI database most used in this model is ecoinvent, which is one of the most commonly used databases in LCA (ecoinvent 2019). However, the ecoinvent database largely represents Europe, with a small amount of Canada-specific data and an even smaller amount that is BC-specific. The Canadian Agri-food Life-cycle Data Centre (CALDC) is a Canada-specific, regionalized agri-food LCI database initiative currently in development in the Food Systems PRISM Lab at the University of British Columbia, Okanagan. Development of this resource will enable improved regionalized LCI modelling in Canada and hence also improve life cycle-based decision support. Another way to improve the uncertainty analysis in this study would be to diversify both the methods available for uncertainty analysis, and the types of uncertainty that can be included 133 into this model. Monte Carlo simulation was chosen as the method of uncertainty analysis for this study because it is the only method available in the openLCA software, and it is also the most commonly applied method in other LCA studies (see Chapter 3). However, as was also discussed in Chapter 3, it may not be the most efficient method for all studies. OpenLCA also allows the inclusion of both parameter uncertainty and impact assessment uncertainty into the Monte Carlo simulation. This is not possible in all software, and there is still no method of including all sources of uncertainty (including scenario uncertainty, or uncertainty due to choices) into one analysis. In this, and many other studies, the uncertainty due to choices and assumptions made in the creation of the LCA model was analyzed using a sensitivity analysis to determine the impact of those choices on the outcome of the study. Moving forward with respect to uncertainty analysis in LCA, it would be beneficial for LCA software to offer options of different analytical methods (not limited to Monte Carlo simulation), and methods to include all sources of uncertainty in the analysis, either separately or combined. The development and implementation of consensus methods for uncertainty assessment in the LCA community is strongly recommended. 6.3 The importance of life cycle thinking This study clearly demonstrates the importance of life cycle thinking in sustainability research and decision support. When trying to determine the environmental impacts of adding mulch to apple orchards, three different scales were considered. First, the (non-life cycle) farm-level impacts, then the life cycle impacts of the orchard system only, and finally the systems-level life cycle impacts of all systems potentially impacted by an increase in mulch use on orchards. All three levels of analysis yielded different conclusions about the relative sustainability of adding mulch. It is therefore necessary to look at not only the (attributional) life cycle of a product’s environmental impacts, but also the consequential life cycle, including all systems impacted by a change in the product system. The uncertainty associated with the attributional and consequential models was also different. Although there was a large amount of relative uncertainty in the attributional model, from data quality and measurement variability, all the results seen without uncertainty were still the same in the uncertainty analysis, and were all statistically significant. However, in the consequential model, there was added parameter uncertainty from the other systems involved, as well as uncertainty from the modelling choices. The high levels of uncertainty in this model made many results statistically non-significant, and changed the relative ranking of the mulched 134 and non-mulched scenarios in many impact categories. 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Amounts and types of plant protection products applied to the apple orchard at Summerland Research and Development Centre (data provided by researchers at the research centre). Calculations are based on an average spray rate of 100 gal/acre (standard spray rate for Summerland research centre), as well as a specific application rate of 470L/ha for Gramoxone, TradeName AppRate Amount (g) per 100L Amount (mL) per 100L Amount per 1L Amount (g or mL, respectively) per plot Combining multiple applications per year Averaged over 2 years Calcium 400g/100L 400 4 0.05512 0.33072 0.26871 Calcium 400g/100L 400 4 0.05512 Calcium 400g/100L 400 4 0.05512 Calcium 400g/100L 400 4 0.05512 Calcium 400g/100L 400 4 0.05512 Calcium 400gms/100L 400 4 0.05512 Gramoxone 1.4 L/t or /800L 175 1.75 0.008558 0.008558 0.043851 Movento 20ml/100L 20 0.2 0.002756 0.002756 0.002756 Sevin 104ml/100L 104 1.04 0.014331 0.014331 0.014331 Kumulus DF 200g/100L 200 2 0.02756 0.02756 0.04134 Dipel 37g/100L 37 0.37 0.005099 0.005099 0.005099 Nova 11.3L/100 11300 113 1.55714 1.55714 0.77857 Dormant Oil 3L/100L 3000 30 0.4134 0.4134 0.3445 Prwl/Smz 1.7L/t-500mg/t 2.125 0.02125 0.000144 0.000144 0.0000718 Gramoxone 5.5 0.0791 0.0791 Calcium 500g/100L 500 5 0.0689 0.2067 Calcium 500g/100L 500 5 0.0689 Calcium 500g/100L 500 5 0.0689 Crush'R Plus 5 0.07195 0.07195 0.035975 Sevin 104ml/100L 104 1.04 0.014331 0.014331 Kumulus DF 200g/100L 200 2 0.02756 0.05512 Movento 20ml/100L 20 0.2 0.002756 0.002756 Dipel 37g/100L 37 0.37 0.005099 0.005099 Kumulus DF 200g/100L 200 2 0.02756 2 4-D glyphosate 600ml/t-5L/h 75 0.75 0.003668 0.003668 0.001834 Diazinon 100g/100L 100 1 0.01378 0.01378 0.00689 Dormant Oil 2L/100L 2000 20 0.2756 0.2756 144 Prowl/Simazine, and 340L/ha for glyphosate products and 2,4-D. Application was via a 3 nozzle boom at 26 PSI. The plot size was 0.1439 m2 for the production of 1kg apples. Spray rate per kg apples for 100 gal/acre: 0.1439 𝑚2 = 3.556 ∗ 10−5 𝑎𝑐𝑟𝑒𝑠 100 𝑔𝑎𝑙 = 378.541 𝐿 378.541 𝐿1 𝑎𝑐𝑟𝑒∗3.556 ∗ 10−5𝑎𝑐𝑟𝑒𝑠1 𝑘𝑔 𝑎𝑝𝑝𝑙𝑒𝑠=0.01378 𝐿1 𝑘𝑔 𝑎𝑝𝑝𝑙𝑒𝑠 Spray rate per kg apples for 470 L/ha, ex: Gramoxone (paraquat): 0.1439 𝑚2 = 1.439 ∗ 10−5ℎ𝑎 470 𝐿ℎ𝑎∗1.439 ∗ 10−5 ℎ𝑎1 𝑘𝑔 𝑎𝑝𝑝𝑙𝑒𝑠=0.00676 𝐿1 𝑘𝑔 𝑎𝑝𝑝𝑙𝑒𝑠 Spray rate per kg apples for 340 L/ha, ex: glyphosate: 340𝐿ℎ𝑎∗1.439 ∗ 10−5 ℎ𝑎1 𝑘𝑔 𝑎𝑝𝑝𝑙𝑒𝑠=0.00489 𝐿1 𝑘𝑔 𝑎𝑝𝑝𝑙𝑒𝑠 Quantities of plant protection products were scaled to the amount applied per kg of apples. If there were multiple applications in one year, these were added to get the total amount applied per year, then averaged over the 2 years (2013-2014) for which data were given. Plant protection product application rates were given in different units. A sample calculation will be given for an example of each. Application rate in g/L (multiple applications per year), ex: calcium: 4 𝑔 𝑐𝑎𝑙𝑐𝑖𝑢𝑚1 𝐿∗0.01378 𝐿1 𝑘𝑔 𝑎𝑝𝑝𝑙𝑒𝑠=0.05512 𝑔 𝑐𝑎𝑙𝑐𝑖𝑢𝑚1 𝑘𝑔 𝑎𝑝𝑝𝑙𝑒𝑠 0.05512 𝑔 𝑐𝑎𝑙𝑐𝑖𝑢𝑚 ∗ 6 𝑎𝑝𝑝𝑙𝑖𝑐𝑎𝑡𝑖𝑜𝑛𝑠 𝑖𝑛 2014 = 0.33072 𝑔 𝑐𝑎𝑙𝑐𝑖𝑢𝑚 𝑎𝑝𝑝𝑙𝑖𝑒𝑑 𝑖𝑛 2014 0.33072 𝑔 𝑐𝑎𝑙𝑐𝑖𝑢𝑚 𝑎𝑝𝑝𝑙𝑖𝑒𝑑 𝑖𝑛 2014 + 0.2067 𝑔 𝑖𝑛 20132 𝑦𝑒𝑎𝑟𝑠=0.26871 𝑔 𝑐𝑎𝑙𝑐𝑖𝑢𝑚1 𝑦𝑒𝑎𝑟 Application rate in mL/L and mL/ha (one application per year), ex: Gramoxone (paraquat): 1400 𝑚𝐿 𝑝𝑎𝑟𝑎𝑞𝑢𝑎𝑡 𝑎𝑝𝑝𝑙𝑖𝑒𝑑 𝑖𝑛 2014800 𝐿=1.75 𝑚𝐿 𝑝𝑎𝑟𝑎𝑞𝑢𝑎𝑡 𝑎𝑝𝑝𝑙𝑖𝑒𝑑 𝑖𝑛 20141 𝐿 1.75 𝑚𝐿 𝑝𝑎𝑟𝑎𝑞𝑢𝑎𝑡 𝑎𝑝𝑝𝑙𝑖𝑒𝑑 𝑖𝑛 20141 𝐿∗0.00676 𝐿1 𝑘𝑔 𝑎𝑝𝑝𝑙𝑒𝑠=0.008558 𝑚𝐿 𝑝𝑎𝑟𝑎𝑞𝑢𝑎𝑡 𝑎𝑝𝑝𝑙𝑖𝑒𝑑 𝑖𝑛 20141 𝑘𝑔 𝑎𝑝𝑝𝑙𝑒𝑠 145 5500 𝑚𝐿 𝑝𝑎𝑟𝑎𝑞𝑢𝑎𝑡 𝑎𝑝𝑝𝑙𝑖𝑒𝑑 𝑖𝑛 20131 ℎ𝑎∗0.0001 ℎ𝑎1 𝑚2∗0.1439 𝑚21 𝑘𝑔 𝑎𝑝𝑝𝑙𝑒𝑠=0.0791 𝑚𝐿 𝑝𝑎𝑟𝑎𝑞𝑢𝑎𝑡 𝑎𝑝𝑝𝑙𝑖𝑒𝑑 𝑖𝑛 20131 𝑘𝑔 𝑎𝑝𝑝𝑙𝑒𝑠 0.008558 𝑚𝐿 𝑝𝑎𝑟𝑎𝑞𝑢𝑎𝑡 𝑎𝑝𝑝𝑙𝑖𝑒𝑑 𝑖𝑛 2014 + 0.0791 𝑚𝐿 𝑖𝑛 20132𝑦𝑒𝑎𝑟𝑠=0.04385 𝑚𝐿 𝑝𝑎𝑟𝑎𝑞𝑢𝑎𝑡1 𝑦𝑒𝑎𝑟 𝐷𝑒𝑛𝑠𝑖𝑡𝑦 𝑜𝑓 𝑝𝑎𝑟𝑎𝑞𝑢𝑎𝑡 =1.25 𝑔𝑐𝑚3=1.25 𝑔 𝑚𝐿 0.04385 𝑚𝐿 𝑝𝑎𝑟𝑎𝑞𝑢𝑎𝑡1 𝑦𝑒𝑎𝑟∗1.25 𝑔1 𝑚𝐿=0.05481 𝑔 𝑝𝑎𝑟𝑎𝑞𝑢𝑎𝑡1 𝑦𝑒𝑎𝑟 Plant protection product emissions: As suggested in Margni et al. (2002), 76.5% of each plant protection product was modelled as emitted to soil, 8.5% to water, and 10% to air. These amounts were calculated by multiplying the amount of each product applied by the percent active ingredient, then by the percentage above for each emission to soil, water and air. In the cases where no elementary flow exists for an emission of a product to either soil, water or air, it is assumed that none is emitted to that area, and it is instead all emitted to the other areas. Example calculation for Sevin (carbaryl): 𝐴𝑚𝑜𝑢𝑛𝑡 𝑜𝑓 𝑆𝑒𝑣𝑖𝑛 𝑎𝑝𝑝𝑙𝑖𝑒𝑑 = 0.01716 𝑔 𝑃𝑒𝑟𝑐𝑒𝑛𝑡 𝑎𝑐𝑡𝑖𝑣𝑒 𝑖𝑛𝑔𝑟𝑒𝑑𝑖𝑒𝑛𝑡 = 2% 𝑐𝑎𝑟𝑏𝑎𝑟𝑦𝑙 𝐶𝑎𝑟𝑏𝑎𝑟𝑦𝑙 𝑒𝑚𝑖𝑠𝑠𝑖𝑜𝑛𝑠 𝑡𝑜 𝑠𝑜𝑖𝑙 = 0.01716 ∗ 0.02 ∗ 0.76 = 2.647 𝑥 10−4 𝐶𝑎𝑟𝑏𝑎𝑟𝑦𝑙 𝑒𝑚𝑖𝑠𝑠𝑖𝑜𝑛𝑠 𝑡𝑜 𝑤𝑎𝑡𝑒𝑟 = 2.917 𝑥 10−5 𝐶𝑎𝑟𝑏𝑟𝑦𝑙 𝑒𝑚𝑖𝑠𝑠𝑖𝑜𝑛𝑠 𝑡𝑜 𝑎𝑖𝑟 = 3.43 𝑥 10−5 Fertilizer emissions: The IPCC (2006) model requires inputs of N from fertilizer (kg/tonne), and N removed with crop (kg/tonne) for calculation of N emissions (kg/tonne NH3, NO, NO3). According to SuRDC: 𝑁 𝑓𝑟𝑜𝑚 𝑓𝑒𝑟𝑡𝑖𝑙𝑖𝑧𝑒𝑟 (𝑔 𝑝𝑒𝑟 𝑡𝑟𝑒𝑒) = 20 𝑔 (𝑙𝑜𝑤) 𝑜𝑟 40 𝑔 (ℎ𝑖𝑔ℎ) 𝑌𝑖𝑒𝑙𝑑 𝑝𝑒𝑟 𝑡𝑟𝑒𝑒 = 12.51 𝑘𝑔 𝑁 𝑓𝑟𝑜𝑚 𝑓𝑒𝑟𝑡𝑖𝑙𝑖𝑧𝑒𝑟 (𝑙𝑜𝑤 𝑁) =20 𝑔 𝑁12.51 𝑘𝑔 𝑎𝑝𝑝𝑙𝑒𝑠=1.599 𝑔𝑘𝑔=1.599 𝑘𝑔𝑡𝑜𝑛𝑛𝑒 𝑁 𝑓𝑟𝑜𝑚 𝑓𝑒𝑟𝑡𝑖𝑙𝑖𝑧𝑒𝑟 (ℎ𝑖𝑔ℎ 𝑁) =3.198 𝑘𝑔𝑡𝑜𝑛𝑛𝑒 146 Based on the protein content of apples (Health Canada 2008): trace amounts of protein, input as 0. Using the IPCC (2006) model, the N emissions (kg/tonne) are as follows: 0.145 kg/tonne NH3, 0.016 kg/tonne NO, 0.384 kg/tonne NO3. To represent these emissions in terms of the functional unit (1 kg apples), the units were converted to g/kg, which is equivalent to kg/tonne. For phosphorus, the model from Dalgaard et al. (2008) requires an input of total phosphate applied as fertilizer (kg/tonne) to calculate total phosphorus leaching (kg/tonne). According to SuRDC: 𝑃 𝑓𝑟𝑜𝑚 𝑓𝑒𝑟𝑡𝑖𝑙𝑖𝑧𝑒𝑟 (𝑔 𝑝𝑒𝑟 𝑡𝑟𝑒𝑒) = 20 𝑔 ∗2.2915 𝑔 𝑝ℎ𝑜𝑠𝑝ℎ𝑎𝑡𝑒𝑔 𝑃= 45.83 𝑔 𝑝ℎ𝑜𝑠𝑝ℎ𝑎𝑡𝑒 𝑝𝑒𝑟 𝑡𝑟𝑒𝑒 𝑃 𝑓𝑟𝑜𝑚 𝑓𝑒𝑟𝑡𝑖𝑙𝑖𝑧𝑒𝑟 (𝑘𝑔 𝑝𝑒𝑟 𝑡𝑜𝑛𝑛𝑒) =3.663 𝑘𝑔𝑡𝑜𝑛𝑛𝑒 Using the Dalgaard et al. (2008) model, the total P leaching is 0.046 kg/tonne or, equivalently, 0.046 g/kg. Lumber production to residue calculations: According to Natural Resources Canada and Forest Products Association of Canada, total sawmill residue production in Canada was 21228536 BDt (bone dry metric tonne = 1000kg), including 11553549 BDt bark and 4319934 BDt wood shavings. Specific to British Columbia, the total amount of residue production was 6554000 BDt, but no breakdown was given in terms of percentage of bark, sawdust and shavings produced. The total amount of lumber produced was 13994 MMfbm (million board feet = 2359.737 m3). Assuming the distribution of residue types is the same in Canada and in British Columbia specifically: 𝐵𝐷𝑡 𝑏𝑎𝑟𝑘 𝑟𝑒𝑠𝑖𝑑𝑢𝑒 𝑝𝑟𝑜𝑑𝑢𝑐𝑒𝑑 𝑖𝑛 𝐵𝑟𝑖𝑡𝑖𝑠ℎ 𝐶𝑜𝑙𝑢𝑚𝑏𝑖𝑎=11553549 𝐵𝐷𝑡 𝑏𝑎𝑟𝑘 𝑝𝑟𝑜𝑑𝑢𝑐𝑒𝑑 𝑖𝑛 𝐶𝑎𝑛𝑎𝑑𝑎21228536 𝐵𝐷𝑡 𝑡𝑜𝑡𝑎𝑙 𝑟𝑒𝑠𝑖𝑑𝑢𝑒 𝑝𝑟𝑜𝑑𝑢𝑐𝑒𝑑 𝑖𝑛 𝐶𝑎𝑛𝑎𝑑𝑎∗ 6554000 𝐵𝐷𝑡 𝑡𝑜𝑡𝑎𝑙 𝑟𝑒𝑠𝑖𝑑𝑢𝑒 𝑝𝑟𝑜𝑑𝑢𝑐𝑒𝑑 𝑖𝑛 𝐵𝑟𝑖𝑡𝑖𝑠ℎ 𝐶𝑜𝑙𝑢𝑚𝑏𝑖𝑎 = 3566989 𝐵𝐷𝑡 𝐵𝐷𝑡 𝑤𝑜𝑜𝑑 𝑠ℎ𝑎𝑣𝑖𝑛𝑔 𝑟𝑒𝑠𝑖𝑑𝑢𝑒 𝑝𝑟𝑜𝑑𝑢𝑐𝑒𝑑 𝑖𝑛 𝐵𝑟𝑖𝑡𝑖𝑠ℎ 𝐶𝑜𝑙𝑢𝑚𝑏𝑖𝑎=4319934 𝐵𝐷𝑡 𝑤𝑜𝑜𝑑 𝑠ℎ𝑎𝑣𝑖𝑛𝑔 𝑝𝑟𝑜𝑑𝑢𝑐𝑒𝑑 𝑖𝑛 𝐶𝑎𝑛𝑎𝑑𝑎21228536 𝐵𝐷𝑡 𝑡𝑜𝑡𝑎𝑙 𝑟𝑒𝑠𝑖𝑑𝑢𝑒 𝑝𝑟𝑜𝑑𝑢𝑐𝑒𝑑 𝑖𝑛 𝐶𝑎𝑛𝑎𝑑𝑎∗ 6554000 𝐵𝐷𝑡 𝑡𝑜𝑡𝑎𝑙 𝑟𝑒𝑠𝑖𝑑𝑢𝑒 𝑝𝑟𝑜𝑑𝑢𝑐𝑒𝑑 𝑖𝑛 𝐵𝑟𝑖𝑡𝑖𝑠ℎ 𝐶𝑜𝑙𝑢𝑚𝑏𝑖𝑎 = 1333716 𝐵𝐷𝑡 𝑅𝑎𝑡𝑖𝑜 𝑜𝑓𝑙𝑢𝑚𝑏𝑒𝑟 𝑡𝑜 𝑏𝑎𝑟𝑘 𝑟𝑒𝑠𝑖𝑑𝑢𝑒 𝑖𝑛 𝐵𝑟𝑖𝑡𝑖𝑠ℎ 𝐶𝑜𝑙𝑢𝑚𝑏𝑖𝑎 =13994 𝑀𝑀𝑓𝑏𝑚3566989 𝐵𝐷𝑡∗1 𝐵𝐷𝑡1000 𝑘𝑔∗2359.737 𝑚31 𝑀𝑀𝑓𝑏𝑚=0.0093 𝑚3 𝑙𝑢𝑚𝑏𝑒𝑟𝑘𝑔 𝑏𝑎𝑟𝑘 𝑐ℎ𝑖𝑝𝑠 147 𝑅𝑎𝑡𝑖𝑜 𝑜𝑓𝑙𝑢𝑚𝑏𝑒𝑟 𝑡𝑜 𝑤𝑜𝑜𝑑 𝑠ℎ𝑎𝑣𝑖𝑛𝑔 (𝑜𝑟 𝑐ℎ𝑖𝑝) 𝑟𝑒𝑠𝑖𝑑𝑢𝑒 𝑖𝑛 𝐵𝑟𝑖𝑡𝑖𝑠ℎ 𝐶𝑜𝑙𝑢𝑚𝑏𝑖𝑎=13994 𝑀𝑀𝑓𝑏𝑚1333716 𝐵𝐷𝑡∗1 𝐵𝐷𝑡1000 𝑘𝑔∗2359.737 𝑚31 𝑀𝑀𝑓𝑏𝑚=0.0248 𝑚3 𝑙𝑢𝑚𝑏𝑒𝑟𝑘𝑔 𝑤𝑜𝑜𝑑 𝑐ℎ𝑖𝑝 Wood chip and bark totals In British Columbia, there were 6,554,000 bone dry tonnes (BDt) of residue. No BC specific data were given for the percentages of that residue that were bark or wood chips/shavings, so these percentages were estimated based on Canadian data. Source: Natural Resources Canada and Forest Products Association of Canada (2004). 𝐶𝑎𝑛𝑎𝑑𝑎 𝑡𝑜𝑡𝑎𝑙 𝑟𝑒𝑠𝑖𝑑𝑢𝑒 = 21,228,536 𝐵𝐷𝑡 𝐶𝑎𝑛𝑎𝑑𝑎 𝑡𝑜𝑡𝑎𝑙 𝑏𝑎𝑟𝑘 = 11,553,549 𝐵𝐷𝑡 𝐶𝑎𝑛𝑎𝑑𝑎 𝑡𝑜𝑡𝑎𝑙 𝑤𝑜𝑜𝑑 𝑐ℎ𝑖𝑝𝑠 = 4,319,934 𝐵𝐷𝑡 𝐶𝑎𝑛𝑎𝑑𝑎 𝑡𝑜𝑡𝑎𝑙 𝑏𝑎𝑟𝑘𝐶𝑎𝑛𝑎𝑑𝑎 𝑡𝑜𝑡𝑎𝑙 𝑟𝑒𝑠𝑖𝑑𝑢𝑒=𝐵𝐶 𝑡𝑜𝑡𝑎𝑙 𝑏𝑎𝑟𝑘𝐵𝐶 𝑡𝑜𝑡𝑎𝑙 𝑟𝑒𝑠𝑖𝑑𝑢𝑒 𝐵𝐶 𝑡𝑜𝑡𝑎𝑙 𝑏𝑎𝑟𝑘 =𝐵𝐶 𝑡𝑜𝑡𝑎𝑙 𝑟𝑒𝑠𝑖𝑑𝑢𝑒 ∗ 𝐶𝑎𝑛𝑎𝑑𝑎 𝑡𝑜𝑡𝑎𝑙 𝑏𝑎𝑟𝑘𝐶𝑎𝑛𝑎𝑑𝑎 𝑡𝑜𝑡𝑎𝑙 𝑟𝑒𝑠𝑖𝑑𝑢𝑒=6,554,000 𝐵𝐷𝑡 ∗ 11,553,549 𝐵𝐷𝑡21,228,536 𝐵𝐷𝑡= 3,566,989.271 𝐵𝐷𝑡 𝐵𝐶 𝑡𝑜𝑡𝑎𝑙 𝑤𝑜𝑜𝑑 𝑐ℎ𝑖𝑝𝑠 = 1,333,716.439 𝐵𝐷𝑡 Converted to kg and m3: 1𝐵𝐷𝑡 = 1000𝑘𝑔 𝐵𝐶 𝑡𝑜𝑡𝑎𝑙 𝑏𝑎𝑟𝑘 =(3,566,989.271𝐵𝐷𝑡 ∗ 1000𝑘𝑔)1 𝐵𝐷𝑡= 3,566,989,271 𝑘𝑔 𝐵𝐶 𝑡𝑜𝑡𝑎𝑙 𝑤𝑜𝑜𝑑 𝑐ℎ𝑖𝑝𝑠 = 1,333,716,439 𝑘𝑔 𝐷𝑒𝑛𝑠𝑖𝑡𝑦 ~500 𝑘𝑔𝑚3 𝐵𝐶 𝑡𝑜𝑡𝑎𝑙 𝑏𝑎𝑟𝑘 =3,566,989,271 𝑘𝑔 ∗ 1 𝑚3500 𝑘𝑔= 7,113,978.542 𝑚3 𝐵𝐶 𝑡𝑜𝑡𝑎𝑙 𝑤𝑜𝑜𝑑 𝑐ℎ𝑖𝑝𝑠 = 2,667,432.878 𝑚3 Ratio of bark to wood chips: 𝑤𝑜𝑜𝑑 𝑐ℎ𝑖𝑝𝑠𝑏𝑎𝑟𝑘 𝑐ℎ𝑖𝑝𝑠=2,667,432.878 𝑚3 𝑤𝑜𝑜𝑑 𝑐ℎ𝑖𝑝𝑠7,113,978.542 𝑚3 𝑏𝑎𝑟𝑘 𝑐ℎ𝑖𝑝𝑠=0.375 𝑚3 𝑤𝑜𝑜𝑑 𝑐ℎ𝑖𝑝𝑠1 𝑚3 𝑏𝑎𝑟𝑘 𝑐ℎ𝑖𝑝𝑠 Wood chip and bark to lumber ratios Based on ecoinvent v.3.4 APOS processes for softwood sawn wood, 76.95 kg bark are produced as a co-product of 1 m3 sawn wood production. 148 Using the relationship between bark and wood chips: 𝑘𝑔 𝑤𝑜𝑜𝑑 𝑐ℎ𝑖𝑝𝑠 𝑝𝑟𝑜𝑑𝑢𝑐𝑒𝑑1 𝑚3 𝑠𝑎𝑤𝑛𝑤𝑜𝑜𝑑=76.95 𝑘𝑔 𝑏𝑎𝑟𝑘1 𝑚3 𝑠𝑎𝑤𝑛𝑤𝑜𝑜𝑑∗0.375 𝑚3 𝑤𝑜𝑜𝑑 𝑐ℎ𝑖𝑝𝑠1 𝑚3 𝑏𝑎𝑟𝑘=28.85 𝑘𝑔 𝑤𝑜𝑜𝑑 𝑐ℎ𝑖𝑝𝑠1 𝑚3 𝑠𝑎𝑤𝑛𝑤𝑜𝑜𝑑 Ecoinvent v.3.4 APOS processes “bark chips production, softwood, at sawmill | bark chips, wet, measured as dry mass | APOS, U” and “wood chips production, softwood, at sawmill | wood chips, wet, measured as dry mass | APOS, U” were altered according to these relationships. These alterations were meant to replace any economic allocation performed in the ecoinvent database, in order to accurately reflect biophysical relationships between biomass flows. 76.95 𝑘𝑔 𝑏𝑎𝑟𝑘 𝑐ℎ𝑖𝑝𝑠1 𝑚3 𝑠𝑎𝑤𝑛𝑤𝑜𝑜𝑑∗ 0.00035 𝑚3𝑠𝑎𝑤𝑛𝑤𝑜𝑜𝑑 = 0.0269 𝑘𝑔 𝑏𝑎𝑟𝑘 𝑐ℎ𝑖𝑝𝑠 28.85 𝑘𝑔 𝑤𝑜𝑜𝑑 𝑐ℎ𝑖𝑝𝑠1 𝑚3 𝑠𝑎𝑤𝑛𝑤𝑜𝑜𝑑∗ 1 𝑘𝑔 𝑠𝑎𝑤𝑛𝑤𝑜𝑜𝑑 ∗1 𝑚3 𝑠𝑜𝑓𝑡𝑤𝑜𝑜𝑑500 𝑘𝑔 𝑠𝑜𝑓𝑡𝑤𝑜𝑜𝑑= 0.0577 𝑘𝑔 𝑤𝑜𝑜𝑑 𝑐ℎ𝑖𝑝𝑠 Soil carbon sequestration The amount of carbon sequestered in the soil long-term was calculated based on the amount of carbon added in the mulch, and an estimate that 9.7% of that carbon stays sequestered on a 100 year basis from Petersen et al. (2013). 0.575 𝑘𝑔 𝑏𝑎𝑟𝑘 + 0.144 𝑘𝑔 𝑤𝑜𝑜𝑑 𝑐ℎ𝑖𝑝𝑠𝑘𝑔 𝑎𝑝𝑝𝑙𝑒𝑠=0.719 𝑘𝑔 𝑠𝑜𝑓𝑡𝑤𝑜𝑜𝑑 𝑏𝑖𝑜𝑚𝑎𝑠𝑠𝑘𝑔 𝑎𝑝𝑝𝑙𝑒𝑠 0.5 𝑘𝑔 𝐶𝑘𝑔 𝑠𝑜𝑓𝑡𝑤𝑜𝑜𝑑 𝑏𝑖𝑜𝑚𝑎𝑠𝑠∗0.719 𝑘𝑔 𝑠𝑜𝑓𝑡𝑤𝑜𝑜𝑑 𝑏𝑖𝑜𝑚𝑎𝑠𝑠𝑘𝑔 𝑎𝑝𝑝𝑙𝑒𝑠=0.3595 𝑘𝑔 𝐶𝑘𝑔 𝑎𝑝𝑝𝑙𝑒𝑠 0.3595 𝑘𝑔 𝐶𝑘𝑔 𝑎𝑝𝑝𝑙𝑒𝑠∗3.664 𝑘𝑔 𝐶𝑂2𝑘𝑔 𝐶=1.317 𝑘𝑔 𝐶𝑂2𝑘𝑔 𝑎𝑝𝑝𝑙𝑒𝑠 1.317 𝑘𝑔 𝐶𝑂2𝑘𝑔 𝑎𝑝𝑝𝑙𝑒𝑠∗0.097 𝑘𝑔 𝑠𝑒𝑞𝑢𝑒𝑠𝑡𝑒𝑟𝑒𝑑 𝑖𝑛 100 𝑦𝑒𝑎𝑟𝑠𝑘𝑔 𝑎𝑑𝑑𝑒𝑑 𝑖𝑛 1 𝑦𝑒𝑎𝑟∗1𝑦𝑒𝑎𝑟100 𝑦𝑒𝑎𝑟𝑠=0.0013 𝑘𝑔 𝐶𝑂2 𝑟𝑒𝑚𝑜𝑣𝑒𝑑𝑘𝑔 𝑎𝑝𝑝𝑙𝑒𝑠 Energy use on orchard Based on the energy inputs of recent apple production LCA studies (Annaert et al. 2017; Goossens et al. 2017; Longo et al. 2017; Vinyes et al. 2018; Zhu et al. 2018), average inputs for the conventional apple production systems studies of 0.309 MJ diesel per kg apple, and 149 0.001638 kWh electricity per kg apple, were calculated for on orchard processes. In order to add this amount of energy use, but avoid double counting, the existing energy use from Fertigation and plant protection application is removed. This accounts for 0.043 kg diesel per m3 irrigation, and 1.67 kg diesel per ha plant protection application (no electricity use is already accounted for as a direct orchard input). 𝐶𝑎𝑙𝑜𝑟𝑖𝑓𝑖𝑐 𝑣𝑎𝑙𝑢𝑒 𝑜𝑓 𝑑𝑖𝑒𝑠𝑒𝑙 =45.5 𝑀𝐽𝑘𝑔 𝐼𝑟𝑟𝑖𝑔𝑎𝑡𝑖𝑜𝑛 𝑟𝑎𝑡𝑒 =0.0962 𝑚3𝑘𝑔 𝑎𝑝𝑝𝑙𝑒𝑠 𝑃𝑙𝑎𝑛𝑡 𝑝𝑟𝑜𝑡𝑒𝑐𝑡𝑖𝑜𝑛 𝑎𝑝𝑝𝑙𝑖𝑐𝑎𝑡𝑖𝑜𝑛 𝑟𝑎𝑡𝑒 =1.439 𝑥 10−7ℎ𝑎𝑘𝑔 𝑎𝑝𝑝𝑙𝑒𝑠 The amount of diesel in MJ/kg apples already accounted for in the model was calculated: 0.043 𝑘𝑔 𝑑𝑖𝑒𝑠𝑒𝑙1 𝑚3 𝑖𝑟𝑟𝑖𝑔𝑎𝑡𝑖𝑜𝑛∗0.0962 𝑚3 𝑖𝑟𝑟𝑖𝑔𝑎𝑡𝑖𝑜𝑛1 𝑘𝑔 𝑎𝑝𝑝𝑙𝑒𝑠=0.00414 𝑘𝑔 𝑑𝑖𝑒𝑠𝑒𝑙𝑘𝑔 𝑎𝑝𝑝𝑙𝑒𝑠 1.67 𝑘𝑔 𝑑𝑖𝑒𝑠𝑒𝑙1 ℎ𝑎 𝑝𝑙𝑎𝑛𝑡 𝑝𝑟𝑜𝑡𝑒𝑐𝑡𝑖𝑜𝑛∗1.439 𝑥 10−7 ℎ𝑎1 𝑘𝑔 𝑎𝑝𝑝𝑙𝑒𝑠=2.403 𝑥 10−7𝑘𝑔 𝑑𝑖𝑒𝑠𝑒𝑙𝑘𝑔 𝑎𝑝𝑝𝑙𝑒𝑠 0.00414 𝑘𝑔 𝑑𝑖𝑒𝑠𝑒𝑙𝑘𝑔 𝑎𝑝𝑝𝑙𝑒𝑠+2.403 𝑥 10−7𝑘𝑔 𝑑𝑖𝑒𝑠𝑒𝑙𝑘𝑔 𝑎𝑝𝑝𝑙𝑒𝑠=0.00414 𝑘𝑔 𝑑𝑖𝑒𝑠𝑒𝑙𝑘𝑔 𝑎𝑝𝑝𝑙𝑒𝑠 0.00414 𝑘𝑔 𝑑𝑖𝑒𝑠𝑒𝑙𝑘𝑔 𝑎𝑝𝑝𝑙𝑒𝑠∗45.5 𝑀𝐽𝑘𝑔 𝑑𝑖𝑒𝑠𝑒𝑙=0.188 𝑀𝐽 𝑑𝑖𝑒𝑠𝑒𝑙𝑘𝑔 𝑎𝑝𝑝𝑙𝑒𝑠 The amount of diesel already present in the model was subtracted from the total amount of diesel use presented in Zhu et al. (2018) to determine the amount that still needs to be accounted for: 0.309 𝑀𝐽 𝑑𝑖𝑒𝑠𝑒𝑙𝑘𝑔 𝑎𝑝𝑝𝑙𝑒𝑠−0.188 𝑀𝐽 𝑑𝑖𝑒𝑠𝑒𝑙𝑘𝑔 𝑎𝑝𝑝𝑙𝑒𝑠=0.121 𝑀𝐽 𝑑𝑖𝑒𝑠𝑒𝑙𝑘𝑔 𝑎𝑝𝑝𝑙𝑒𝑠 Alternative functional units From attributional model inventory: 0.575 kg bark chips/kg apples, 0.144 kg wood chips/kg apples. In the case where no mulch is used on orchards, 100% of bark chips are assumed to be used for co-generation, and 100% of wood chips for paper production. Amounts of wood chips/bark chips needed, based on ecoinvent v.3.4 APOS processes: 1 kg wood chips/kg paper (assuming 100% conversion since wood chips contain all usable fibre), 0.84149 kg bark chips/kWh electricity, 0.03966 kg bark chips/MJ heat. 150 𝑝𝑎𝑝𝑒𝑟 𝑓𝑢𝑛𝑐𝑡𝑖𝑜𝑛𝑎𝑙 𝑢𝑛𝑖𝑡 = 0.144 𝑘𝑔 𝑤𝑜𝑜𝑑 𝑐ℎ𝑖𝑝𝑠 ∗1 𝑘𝑔 𝑝𝑎𝑝𝑒𝑟1 𝑘𝑔 𝑤𝑜𝑜𝑑 𝑐ℎ𝑖𝑝𝑠= 0.144 𝑘𝑔 𝑝𝑎𝑝𝑒𝑟 Since the power to heat ratio of biomass is around 0.25-0.3, and the ratio of kWh to MJ is 0.28, the co-generation process is assumed to produce 1 kWh electricity to 1 MJ heat. 𝑐𝑜 − 𝑔𝑒𝑛𝑒𝑟𝑎𝑡𝑖𝑜𝑛 𝑓𝑢𝑛𝑐𝑡𝑖𝑜𝑛𝑎𝑙 𝑢𝑛𝑖𝑡 = 0.575 𝑘𝑔 𝑏𝑎𝑟𝑘 𝑐ℎ𝑖𝑝𝑠 ∗1 𝑘𝑊ℎ 𝑒𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑖𝑡𝑦 + 1 𝑀𝐽 ℎ𝑒𝑎𝑡0.88115 𝑘𝑔 𝑏𝑎𝑟𝑘 𝑐ℎ𝑖𝑝𝑠= 0.653 𝑘𝑊ℎ 𝑒𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑖𝑡𝑦 + 0.653 𝑀𝐽 ℎ𝑒𝑎𝑡 Biomass percentage To determine the extent to which it would be possible to expand mulch use on orchards, the total amount of wood chips and bark chips produced in British Columbia was determined, as well as the total area of fruit tree orchards in BC, in order to determine the percent of total wood and bark chips produced that would be used if all orchards used mulch. Values for the whole province were used, rather than just the Okanagan valley, for two reasons. First because more data are available regarding biomass production and orchard capacity on a provincial level, and second because the Okanagan represents a large proportion of the provincial production of fruit (BC Ministry of Agriculture 2014), therefore using provincial data is representative. According to the BC Forest Sector Overview (Forestry Innovation Investment Ltd. 2019), BC produced 30.5 million m3 of softwood lumber in 2017. According to the lumber to wood/bark chip ratios: 76.95 𝑘𝑔 𝑏𝑎𝑟𝑘 𝑐ℎ𝑖𝑝𝑠𝑚3 𝑙𝑢𝑚𝑏𝑒𝑟∗ 30.5 𝑚𝑖𝑙𝑙𝑖𝑜𝑛 𝑚3 𝑙𝑢𝑚𝑏𝑒𝑟 = 2,347,000,000 𝑘𝑔 𝑏𝑎𝑟𝑘 𝑐ℎ𝑖𝑝𝑠 28.85 𝑘𝑔 𝑤𝑜𝑜𝑑 𝑐ℎ𝑖𝑝𝑠𝑚3𝑙𝑢𝑚𝑏𝑒𝑟∗ 30.5 𝑚𝑖𝑙𝑙𝑖𝑜𝑛 𝑚3 𝑙𝑢𝑚𝑏𝑒𝑟 = 880,000,000 𝑘𝑔 𝑤𝑜𝑜𝑑 𝑐ℎ𝑖𝑝𝑠 According to the BC Agrifood Industry Year in Review (BC Ministry of Agriculture 2014), there were 6000 ha of tree fruit orchards in BC in 2014. According to the amount of wood chips and bark chips used on the apple orchard in the model (see attributional model in previous chapter): 39972.2 𝑘𝑔 𝑏𝑎𝑟𝑘 𝑐ℎ𝑖𝑝𝑠ℎ𝑎∗ 6000 ℎ𝑎 = 239,833,200 𝑘𝑔 𝑏𝑎𝑟𝑘 𝑐ℎ𝑖𝑝𝑠 9993.05 𝑘𝑔 𝑤𝑜𝑜𝑑 𝑐ℎ𝑖𝑝𝑠ℎ𝑎∗ 6000 ℎ𝑎 = 59,958,300 𝑘𝑔 𝑤𝑜𝑜𝑑 𝑐ℎ𝑖𝑝𝑠 239,833,200 𝑘𝑔 𝑏𝑎𝑟𝑘 𝑐ℎ𝑖𝑝𝑠 𝑢𝑠𝑒𝑑 𝑜𝑛 𝑜𝑟𝑐ℎ𝑎𝑟𝑑𝑠2,347,000,000 𝑘𝑔 𝑏𝑎𝑟𝑘 𝑐ℎ𝑖𝑝𝑠 𝑝𝑟𝑜𝑑𝑢𝑐𝑒𝑑= 10.2 % 59,958,300 𝑘𝑔 𝑤𝑜𝑜𝑑 𝑐ℎ𝑖𝑝𝑠 𝑢𝑠𝑒𝑑 𝑜𝑛 𝑜𝑟𝑐ℎ𝑎𝑟𝑑𝑠880,000,000 𝑘𝑔 𝑤𝑜𝑜𝑑 𝑐ℎ𝑖𝑝𝑠 𝑝𝑟𝑜𝑑𝑢𝑐𝑒𝑑= 6.8 % 151 Assuming that these values for previous years are a good estimate for the current amounts of wood/bark chips, this means that if all tree fruit orchards used wood/bark chips as mulch at the same rate as the SuRDC apple orchard then 10.2% of all bark chips produced and 6.8% of wood chips would be used on orchards. In comparison, the survey results from sawmills indicated that currently 0.4% of wood and bark chips are used in agriculture (for simplicity assumed, in this model, to all be orchards). This requires a 230,006,000 kg (9.8%) increase in bark use and a 56,320,000 kg (6.4%) increase in wood chip use on orchards, which would come from paper production for wood chips and co-generation for bark chips. 152 Appendix B – Biomass producer survey The following information will be used for the MSc thesis of Nicole Bamber regarding the environmental sustainability of mulch use on Okanagan apple orchards at the University of British Columbia, Okanagan campus in the faculty of biology. Any data, if presented, will be aggregated and no individual data will be disclosed. Location and type of facility (sawmill, etc.): Okanagan valley aggregated results Types of products produced: hog fuel (bark), sawdust, shavings, chips, log homes, log cabins, log and timber siding Average yield of these products: variable Biomass production: Do you produce bark mulch or wood chips as a product or by-product? Yes – by-product and product If yes, how much per year? variable List any other biomass by-products produced, including amounts: sawdust and shavings Distribution: Are your bark mulch/wood chips sent to orchards/vineyards for use as a soil amendment? If so, which orchards/vineyards, and how much? 0.05% total for organic growers Where else are your bark mulch/wood chips sent to (bioenergy sector, animal bedding, etc.)? Include amounts/percentages. Bark: 99.5% bioenergy, 0.01% animal bedding. Wood: 99.5% paper production, 0.01% animal bedding Where are any other biomass by-products sent? Include amounts/percentages. Sawdust and shavings: pellet production How much biomass is used on-site as energy for the production of other wood products? Variable, some energy sold to electricity grid Additional comments: Please return this completed survey to Nicole Bamber at nbamber@mail.ubc.ca. 153 Appendix C – Monte Carlo results Table 28. Uncertainty analysis results of a Monte Carlo simulation (1000 runs) for the production of 1 kg apples, including parameter uncertainty only. Impact categories with significant differences (p < 0.05) marked with *, and n.s. for non significant. Mean Standard deviation Minimum Maximum Median 5% Percentile 95% Percentile Acidification potential (kg SO2 eq.) * Mulch, low nitrogen 2.321 x 10-3 9.132 x 10-4 5.636 x 10-4 7.567 x 10-3 2.139 x 10-3 1.173 x 10-3 4.046 x 10-3 Clean, low nitrogen 1.996 x 10-3 9.841 x 10-4 -5.818 x 10-3 8.102 x 10-3 1.755 x 10-3 9.859 x 10-4 3.962 x 10-3 Mulch, high nitrogen 2.768 x 10-3 1.148 x 10-3 -5.963 x 10-4 0.014 2.556 x 10-3 1.459 x 10-3 4.745 x 10-3 Clean, high nitrogen 2.305 x 10-3 9.494 x 10-4 0.000 6.932 x 10-3 2.130 x 10-3 1.151 x 10-3 4.183 x 10-3 Climate change (kg CO2 eq.) * Mulch, low nitrogen 0.306 0.136 0.021 1.473 0.276 0.151 0.552 Clean, low nitrogen 0.250 0.114 -0.577 0.962 0.225 0.128 0.472 Mulch, high nitrogen 0.360 0.170 -0.100 2.567 0.333 0.185 0.631 Clean, high nitrogen 0.276 0.110 -0.026 0.887 0.257 0.138 0.476 Depletion of abiotic resources – elements, ultimate reserves (kg antimony eq.) * Mulch, low nitrogen 2.131 x 10-6 9.383 x 10-7 5.824 x 10-7 8.944 x 10-6 1.960 x 10-6 1.089 x 10-6 3.716 x 10-6 Clean, low nitrogen 2.133 x 10-6 2.874 x 10-6 -7.802 x 10-5 7.555 x 10-6 1.921 x 10-6 9.985 x 10-7 4.374 x 10-6 Mulch, high nitrogen 2.352 x 10-6 1.833 x 10-6 -3.264 x 10-5 1.720 x 10-5 2.164 x 10-6 1.210 x 10-6 4.315 x 10-6 Clean, high nitrogen 2.383 x 10-6 1.323 x 10-6 -1.683 x 10-5 1.495 x 10-5 2.140 x 10-6 1.051 x 10-6 4.638 x 10-6 Eutrophication (kg PO4--- eq.) * Mulch, low nitrogen 1.195 x 10-3 5.36 x 10-4 3.666 x 10-4 7.480 x 10-3 1.090 x 10-3 6.054 x 10-4 2.088 x 10-3 Clean, low nitrogen 1.207 x 10-3 6.362 x 10-4 -2.189 x 10-4 4.400 x 10-3 1.024 x 10-3 5.327 x 10-4 2.466 x 10-3 Mulch, high nitrogen 1.460 x 10-3 5.881 x 10-4 -1.410 x 10-4 4.963 x 10-3 1.332 x 10-3 7.566 x 10-4 2.534 x 10-3 Clean, high nitrogen 1.358 x 10-3 5.907 x 10-4 0.000 4.342 x 10-3 1.242 x 10-3 6.500 x 10-4 2.536 x 10-3 Freshwater aquatic ecotoxicity (kg 1,4-dichlorobenzene eq.) * Mulch, low nitrogen 0.256 0.358 0.041 9.231 0.205 0.108 0.444 Clean, low nitrogen 0.226 0.195 -0.711 4.135 0.182 0.089 0.470 Mulch, high nitrogen 0.254 0.148 -0.197 1.734 0.220 0.113 0.479 Clean, high nitrogen 0.230 0.132 0.000 1.141 0.196 0.092 0.471 Human toxicity (kg 1,4-dichlorobenzene eq.) * Mulch, low nitrogen 0.394 0.906 0.079 25.234 0.302 0.163 0.647 Clean, low nitrogen 0.322 0.311 -1.126 5.864 0.259 0.130 0.653 Mulch, high nitrogen 0.395 0.254 -0.433 2.946 0.341 0.177 0.757 Clean, high nitrogen 0.337 0.193 -0.010 2.221 0.294 0.136 0.672 Marine aquatic ecotoxicity (kg 1,4-dichlorobenzene eq.) * Mulch, low nitrogen 800.90 1216.04 174.18 33586.38 667.78 364.73 1331.83 Clean, low nitrogen 684.20 457.72 -6067.59 4617.74 607.32 314.00 1318.64 Mulch, high nitrogen 780.39 407.94 -2019.94 4079.16 702.54 378.30 1430.67 Clean, high nitrogen 711.15 340.73 0.000 3466.44 641.81 313.65 1361.54 154 Mean Standard deviation Minimum Maximum Median 5% Percentile 95% Percentile Ozone layer depletion (kg CFC-11 eq.) * Mulch, low nitrogen 4.670 x 10-8 3.417 x 10-8 8.675 x 10-9 5.732 x 10-7 3.837 x 10-8 1.823 x 10-8 9.817 x 10-8 Clean, low nitrogen 3.272 x 10-8 1.994 x 10-8 -1.054 x 10-7 1.503 x 10-7 2.777 x 10-8 1.208 x 10-8 6.858 x 10-8 Mulch, high nitrogen 4.923 x 10-8 3.261 x 10-8 -1.870 x 10-8 2.725 x 10-7 4.033 x 10-8 1.870 x 10-8 1.088 x 10-7 Clean, high nitrogen 3.344 x 10-8 1.917 x 10-8 0.000 1.461 x 10-7 2.873 x 10-8 1.219 x 10-8 7.082 x 10-8 Photochemical oxidation (kg ethylene eq.) * Mulch, low nitrogen 1.240 x 10-4 5.234 x 10-5 3.249 x 10-5 3.849 x 10-4 1.118 x 10-4 5.855 x 10-5 2.282 x 10-4 Clean, low nitrogen 1.034 x 10-4 5.236 x 10-5 -3.754 x 10-4 4.741 x 10-4 9.181 x 10-5 5.039 x 10-5 2.061 x 10-4 Mulch, high nitrogen 1.303 x 10-4 6.059 x 10-5 -1.185 x 10-4 8.953 x 10-4 1.183 x 10-4 6.383 x 10-5 2.360 x 10-4 Clean, high nitrogen 1.098 x 10-4 5.297 x 10-5 0.000 4.738 x 10-4 9.882 x 10-5 4.916 x 10-5 2.102 x 10-4 Terrestrial ecotoxicity (kg 1,4-dichlorobenzene eq) * Mulch, low nitrogen 3.667 x 10-3 1.653 x 10-3 9.368 x 10-4 0.014 3.315 x 10-3 1.728 x 10-3 6.813 x 10-3 Clean, low nitrogen 3.233 x 10-3 1.712 x 10-3 -9.497 x 10-3 0.012 2.785 x 10-3 1.412 x 10-3 6.625 x 10-3 Mulch, high nitrogen 4.104 x 10-3 1.970 x 10-3 -2.827 x 10-3 0.020 3.699 x 10-3 1.898 x 10-3 7.596 x 10-3 Clean, high nitrogen 3.466 x 10-3 1.706 x 10-3 0.000 0.012 3.115 x 10-3 1.517 x 10-3 6.706 x 10-3 Land use – land competition (m2*yr) * Mulch, low nitrogen 4.454 3.563 0.452 29.151 3.378 1.183 11.620 Clean, low nitrogen 0.049 0.046 7.637 x 10-3 0.630 0.036 0.016 0.129 Mulch, high nitrogen 4.320 3.744 0.395 42.712 3.309 1.176 10.774 Clean, high nitrogen 0.056 0.062 5.456 x 10-3 0.841 0.038 0.016 0.154 Non-renewable resources – fossil (MJ) * Mulch, low nitrogen 4.427 2.136 0.000 20.452 3.991 2.055 8.384 Clean, low nitrogen 2.675 1.863 -49.856 7.284 2.622 1.591 4.254 Mulch, high nitrogen 4.557 1.927 1.468 17.553 4.157 2.356 8.272 Clean, high nitrogen 2.908 0.979 -6.995 6.653 2.786 1.676 4.681 Non-renewable resources – nuclear (MJ) * Mulch, low nitrogen 0.196 0.091 -0.141 1.029 0.180 0.089 0.359 Clean, low nitrogen 0.090 0.709 -22.276 0.376 0.106 0.063 0.180 Mulch, high nitrogen 0.205 0.083 0.068 0.768 0.188 0.108 0.361 Clean, high nitrogen 0.117 0.127 -3.628 0.597 0.113 0.066 0.198 Non-renewable resources – primary forest (MJ) * Mulch, low nitrogen 6.859 x 10-3 2.623 x 10-3 0.000 0.018 6.289 x 10-3 3.641 x 10-3 0.012 Clean, low nitrogen 6.679 x 10-3 3.431 x 10-3 -0.083 0.015 6.497 x 10-3 4.071 x 10-3 0.010 Mulch, high nitrogen 6.993 x 10-3 2.425 x 10-3 2.088 x 10-3 0.020 6.608 x 10-3 3.771 x 10-3 0.011 Clean, high nitrogen 6.674 x 10-3 2.036 x 10-3 -8.949 x 10-3 0.013 6.409 x 10-3 3.900 x 10-3 0.010 Renewable resources – biomass (MJ) * Mulch, low nitrogen 105.35 96.927 0.000 994.21 75.498 24.326 283.32 Clean, low nitrogen 0.231 0.557 -10.909 11.028 0.155 0.067 0.626 Mulch, high nitrogen 108.63 103.73 7.688 1404.50 81.859 25.521 286.65 Clean, high nitrogen 0.234 0.289 -3.029 4.387 0.161 0.068 0.618 155 Mean Standard deviation Minimum Maximum Median 5% Percentile 95% Percentile Renewable resources – geothermal (MJ) * Mulch, low nitrogen 8.274 x 10-3 4.626 x 10-3 0.000 0.036 7.145 x 10-3 3.124 x 10-3 0.018 Clean, low nitrogen 1.156 x 10-3 6.418 x 10-4 -0.015 3.733 x 10-3 1.114 x 10-3 6.936 x 10-4 1.861 x 10-3 Mulch, high nitrogen 8.670 x 10-3 4.716 x 10-3 1.779 x 10-3 0.041 7.516 x 10-3 3.607 x 10-3 0.017 Clean, high nitrogen 1.251 x 10-3 4.155 x 10-4 -1.713 x 10-3 3.076 x 10-3 1.197 x 10-3 7.143 x 10-4 2.006 x 10-3 Renewable resources – solar (MJ) * Mulch, low nitrogen 1.446 x 10-4 8.131 x 10-5 0.000 8.928 x 10-4 1.276 x 10-4 6.402 x 10-5 2.916 x 10-4 Clean, low nitrogen 9.333 x 10-5 1.133 x 10-4 -3.237 x 10-3 6.816 x 10-4 8.997 x 10-5 5.474 x 10-5 1.526 x 10-4 Mulch, high nitrogen 1.432 x 10-4 6.732 x 10-5 4.192 x 10-5 6.662 x 10-4 1.276 x 10-4 7.175 x 10-5 2.639 x 10-4 Clean, high nitrogen 9.979 x 10-5 3.963 x 10-5 -5.584 x 10-4 2.945 x 10-4 9.400 x 10-5 5.748 x 10-5 1.617 x 10-4 Renewable resources – water (MJ) * Mulch, low nitrogen 1.278 0.750 0.000 6.057 1.105 0.457 2.729 Clean, low nitrogen 0.085 0.180 -5.516 0.246 0.085 0.054 0.142 Mulch, high nitrogen 1.336 0.761 0.238 6.431 1.147 0.519 2.768 Clean, high nitrogen 0.094 0.043 -0.851 0.238 0.090 0.053 0.152 Renewable resources – wind (MJ) * Mulch, low nitrogen 0.044 0.023 0.000 0.193 0.039 0.018 0.088 Clean, low nitrogen 0.010 6.051 x 10-3 -0.147 0.029 9.998 x 10-3 6.099 x 10-3 0.017 Mulch, high nitrogen 0.046 0.023 0.011 0.194 0.041 0.020 0.088 Clean, high nitrogen 0.011 3.788 x 10-3 -0.018 0.030 0.011 6.263 x 10-3 0.018 Table 29. Uncertainty analysis results of a Monte Carlo simulation (1000 runs) for the production of 1 kg apples, including parameter and impact assessment uncertainty. Impact categories with significant differences (p < 0.05) marked with *, and n.s. for non significant. Mean Standard deviation Minimum Maximum Median 5% Percentile 95% Percentile Acidification potential (kg SO2 eq.) * Mulch, low nitrogen 3.412 21.390 6.317 x 10-4 227.81 2.453 x 10-3 1.116 x 10-3 7.066 x 10-3 Clean, low nitrogen 2.070 x 10-3 9.714 x 10-4 0.000 8.428 x 10-3 1.847 x 10-3 9.339 x 10-4 3.877 x 10-3 Mulch, high nitrogen 3.242 x 10-3 1.609 x 10-3 7.640 x 10-4 0.016 2.878 x 10-3 1.431 x 10-3 6.284 x 10-3 Clean, high nitrogen 2.611 x 10-3 1.201 x 10-3 0.000 9.481 x 10-3 2.311 x 10-3 1.192 x 10-3 4.869 x 10-3 Climate change (kg CO2 eq.) * Mulch, low nitrogen 0.608 1.332 -1.511 x 10-3 10.749 0.294 0.135 1.305 Clean, low nitrogen 0.246 0.111 0.000 1.078 0.224 0.121 0.453 Mulch, high nitrogen 0.381 0.214 -0.070 3.567 0.341 0.177 0.669 Clean, high nitrogen 0.288 0.119 -4.314 x 10-3 1.077 0.267 0.138 0.492 Eutrophication (kg PO4--- eq.) * Mulch, low nitrogen 2.286 x 10-3 4.214 x 10-3 1.141 x 10-4 0.044 1.272 x 10-3 5.606 x 10-4 8.578 x 10-3 Clean, low nitrogen 1.141 x 10-3 5.505 x 10-4 0.000 4.256 x 10-3 1.017 x 10-3 5.385 x 10-4 2.165 x 10-3 Mulch, high nitrogen 1.666 x 10-3 7.712 x 10-4 2.653 x 10-4 7.009 x 10-3 1.490 x 10-3 7.442 x 10-4 3.109 x 10-3 Clean, high nitrogen 1.408 x 10-3 6.652 x 10-4 0.000 6.493 x 10-3 1.266 x 10-3 6.396 x 10-4 2.564 x 10-3 156 Mean Standard deviation Minimum Maximum Median 5% Percentile 95% Percentile Freshwater aquatic ecotoxicity (kg 1,4-dichlorobenzene eq.) n.s. Mulch, low nitrogen 8194.17 7.657 x 104 0.185 2.026 x 106 155.04 8.080 1.627 x 104 Clean, low nitrogen 4.149 x 104 9.796 x 105 0.000 3.085 x 107 123.06 5.092 1.581 x 104 Mulch, high nitrogen 2.464 x 104 2.610 x 105 -46.555 6.026 x 106 170.8 7.478 2.732 x 104 Clean, high nitrogen 5.444 x 105 1.698 x 107 -123.41 5.369 x 108 132 6.206 1.539 x 104 Human toxicity (kg 1,4-dichlorobenzene eq.) n.s. Mulch, low nitrogen 5.467 x 104 8.030 x 105 4.131 2.151 x 107 612.05 43.245 4.533 x 104 Clean, low nitrogen 1.137 x 105 2.251 x 106 0.000 6.928 x 107 557.45 36.771 3.140 x 104 Mulch, high nitrogen 2.516 x 104 2.311 x 105 -81.305 4.681 x 106 781.47 40.446 3.930 x 104 Clean, high nitrogen 1.893 x 104 2.160 x 105 -0.010 5.982 x 106 573.23 37.347 3.666 x 104 Marine aquatic ecotoxicity (kg 1,4-dichlorobenzene eq.) n.s. Mulch, low nitrogen 2.281 x 108 3.242 x 109 125.69 8.751 x 1010 4.087 x 105 1.811 x 104 3.465 x 107 Clean, low nitrogen 5.393 x 107 9.939x 1010 0.000 3.072 x 1010 2.952 x 105 1.336 x 104 3.471 x 107 Mulch, high nitrogen 1.766 x 108 4.682 x 109 -2.427 x 106 1.479 x 1011 4.436 x 105 1.798 x 104 4.750 x 107 Clean, high nitrogen 1.138 x 109 3.515 x 1010 0.000 1.111 x 1012 3.275 x 105 1.356 x 104 4.490 x 107 Ozone layer depletion (kg CFC-11 eq.) * Mulch, low nitrogen 0.147 0.678 6.925 x 10-9 3.465 3.946 x 108 1.491 x 108 2.135 x 10-7 Clean, low nitrogen 2.824 x 10-8 1.723 x 10-8 0.000 1.708 x 10-7 2.399 x 10-8 1.056 x 10-8 5.987 x 10-8 Mulch, high nitrogen 5.081 x 10-8 4.034 x 10-8 -3.22 x 10-8 4.801 x 10-7 4.042 x 10-8 1.797 x 10-8 1.167 x 10-7 Clean, high nitrogen 3.036 x 10-8 1.733 x 10-8 0.000 1.541 x 10-7 2.639 x 10-8 1.139 x 10-8 6.47 x 10-8 Photochemical oxidation (kg ethylene eq.) * Mulch, low nitrogen 1.652 x 10-4 1.022 x 10-4 1.770 x 10-6 8.880 x 10-4 1.397 x 10-4 5.679 x 10-5 3.376 x 10-4 Clean, low nitrogen 1.286 x 10-4 7.882 x 10-5 0.000 8.631 x 10-4 1.074 x 10-4 5.344 x 10-5 2.832 x 10-4 Mulch, high nitrogen 1.639 x 10-4 8.371 x 10-5 -8.737 x 10-5 6.382 x 10-4 1.433 x 10-4 7.161 x 10-5 3.245 x 10-4 Clean, high nitrogen 1.327 x 10-4 7.351 x 10-5 0.000 6.088 x 10-4 1.156 x 10-4 5.311 x 10-5 2.711 x 10-4 Terrestrial ecotoxicity (kg 1,4-dichlorobenzene eq) n.s. Mulch, low nitrogen 346.30 2689.69 4.276 x 10-3 5.573 x 104 6.002 0.223 534.67 Clean, low nitrogen 528.08 7715.82 0.000 2.280 x 105 2.737 0.150 444.46 Mulch, high nitrogen 4063.26 8.783 x 104 -47.122 2.700 x 106 6.017 0.248 604.99 Clean, high nitrogen 1055.68 1.186 x 104 0.000 2.466 x 105 4.062 0.155 796.31 Table 30. Uncertainty analysis results of a Monte Carlo simulation (1000 runs) for the production of 1 kg apples, including foreground parameter uncertainty only. Impact categories with significant differences (p < 0.05) marked with *, and n.s. for non significant. Mean Standard deviation Minimum Maximum Median 5% Percentile 95% Percentile Acidification potential (kg SO2 eq.) * Mulch, low nitrogen 2.273 x 10-3 8.537 x 10-4 7.363 x 10-4 6.115 x 10-3 2.115 x 10-3 1.151 x 10-3 3.928 x 10-3 Clean, low nitrogen 1.923 x 10-3 5.935 x 10-4 0.000 4.887 x 10-3 1.842 x 10-3 1.125 x 10-3 2.996 x 10-3 Mulch, high nitrogen 2.267 x 10-3 7.499 x 10-4 6.251 x 10-4 5.901 x 10-3 2.162 x 10-3 1.259 x 10-3 3.757 x 10-3 Clean, high nitrogen 2.115 x 10-3 8.693 x 10-4 0.000 8.172 x 10-3 1.914 x 10-3 1.094 x 10-3 3.677 x 10-3 157 Mean Standard deviation Minimum Maximum Median 5% Percentile 95% Percentile Climate change (kg CO2 eq.) * Mulch, low nitrogen 0.295 0.108 0.070 0.773 0.279 0.150 0.508 Clean, low nitrogen 0.247 0.072 0.000 0.587 0.236 0.149 0.380 Mulch, high nitrogen 0.303 0.096 0.092 0.748 0.291 0.172 0.483 Clean, high nitrogen 0.271 0.110 0.000 0.963 0.247 0.144 0.475 Depletion of abiotic resources – elements, ultimate reserves (kg antimony eq.) * Mulch, low nitrogen 1.858 x 10-6 6.398 x 10-7 5.331 x 10-7 4.249 x 10-6 1.753 x 10-6 9.798 x 10-7 3.139 x 10-6 Clean, low nitrogen 1.664 x 10-6 4.619 x 10-7 0.000 3.801 x 10-6 1.603 x 10-6 1.047 x 10-6 2.541 x 10-6 Mulch, high nitrogen 1.837 x 10-6 5.428 x 10-7 5.447 x 10-7 3.854 x 10-6 1.754 x 10-6 1.082 x 10-6 2.822 x 10-6 Clean, high nitrogen 2.067 x 10-6 1.151 x 10-6 -1.324 x 10-5 1.495 x 10-5 1.797 x 10-6 1.004 x 10-6 3.973 x 10-6 Eutrophication (kg PO4--- eq.) * Mulch, low nitrogen 1.192 x 10-3 0.0004.368 x 10-4 3.666 x 10-4 3.299 x 10-3 1.120 x 10-3 6.158 x 10-4 2.047 x 10-3 Clean, low nitrogen 1.014 x 10-3 3.158 x 10-4 0.000 3.221 x 10-3 9.718 x 10-4 5.967 x 10-4 1.601 x 10-3 Mulch, high nitrogen 1.179 x 10-3 3.770 x 10-4 2.994 x 10-4 2.778 x 10-3 1.126 x 10-3 6.663 x 10-4 1.904 x 10-3 Clean, high nitrogen 1.219 x 10-3 5.494 x 10-4 0.000 4.400 x 10-3 1.082 x 10-3 5.881 x 10-4 2.250 x 10-3 Freshwater aquatic ecotoxicity (kg 1,4-dichlorobenzene eq.) * Mulch, low nitrogen 0.138 0.050 0.034 0.365 0.130 0.071 0.233 Clean, low nitrogen 0.113 0.033 0.000 0.255 0.108 0.068 0.175 Mulch, high nitrogen 0.137 0.042 0.035 0.324 0.131 0.079 0.216 Clean, high nitrogen 0.153 0.095 0.000 1.129 0.122 0.067 0.340 Human toxicity (kg 1,4-dichlorobenzene eq.) * Mulch, low nitrogen 0.210 0.075 0.054 0.512 0.198 0.109 0.357 Clean, low nitrogen 0.170 0.050 0.00 0.379 0.163 0.103 0.262 Mulch, high nitrogen 0.207 0.064 0.063 0.490 0.197 0.120 0.321 Clean, high nitrogen 0.218 0.126 -0.156 1.307 0.182 0.100 0.473 Marine aquatic ecotoxicity (kg 1,4-dichlorobenzene eq.) * Mulch, low nitrogen 539.90 196.39 133.80 1310.49 508.36 276.36 919.13 Clean, low nitrogen 457.80 137.71 0.000 1098.41 440.09 271.12 711.25 Mulch, high nitrogen 531.47 168.14 132.24 1308.02 508.71 298.22 841.60 Clean, high nitrogen 550.72 279.70 -1539.78 2390.33 482.61 268.89 1059.34 Ozone layer depletion (kg CFC-11 eq.) * Mulch, low nitrogen 3.122 x 10-8 1.161 x 10-8 7.251 x 10-9 7.808 x 10-8 2.929 x 10-8 1.590 x 10-8 5.345 x 10-8 Clean, low nitrogen 2.123 x 10-8 6.454 x 10-9 0.000 4.988 x 10-8 2.026 x 10-8 1.258 x 10-8 3.334 x 10-8 Mulch, high nitrogen 3.085 x 10-8 1.004 x 10-8 9.441 x 10-9 8.083 x 10-8 2.936 x 10-8 1.739 x 10-8 4.942 x 10-8 Clean, high nitrogen 2.740 x 10-8 1.474 x 10-8 0.000 1.328 x 10-7 2.299 x 10-8 1.241 x 10-8 5.636 x 10-8 Photochemical oxidation (kg ethylene eq.) * Mulch, low nitrogen 9.032 x 10-5 3.264 x 10-5 2.310 x 10-5 2.265 x 10-4 8.551 x 10-5 4.594 x 10-5 1.550 x 10-4 Clean, low nitrogen 7.480 x 10-5 2.251 x 10-5 0.000 1.726 x 10-4 7.128 x 10-5 4.469 x 10-5 1.174 x 10-4 Mulch, high nitrogen 8.927 x 10-5 2.785 x 10-5 2.788 x 10-5 2.167 x 10-4 8.530 x 10-5 5.136 x 10-5 1.408 x 10-4 Clean, high nitrogen 8.523 x 10-5 3.763 x 10-5 -9.446 x 10-7 3.218 x 10-4 7.649 x 10-5 4.374 x 10-5 1.534 x 10-4 158 Mean Standard deviation Minimum Maximum Median 5% Percentile 95% Percentile Terrestrial ecotoxicity (kg 1,4-dichlorobenzene eq) * Mulch, low nitrogen 2.946 x 10-3 1.052 x 10-3 7.753 x 10-4 7.410 x 10-3 2.791 x 10-3 1.525 x 10-3 5.025 x 10-3 Clean, low nitrogen 2.426 x 10-3 7.236 x 10-4 0.000 5.893 x 10-3 2.314 x 10-3 1.453 x 10-3 3.778 x 10-3 Mulch, high nitrogen 2.913 x 10-3 9.040 x 10-4 9.783 x 10-4 7.240 x 10-3 2.776 x 10-3 1.668 x 10-3 4.538 x 10-3 Clean, high nitrogen 2.851 x 10-3 1.368 x 10-3 0.000 1.085 x 10-2 2.491 x 10-3 1.422 x 10-3 5.636 x 10-3 Land use – land competition (m2*yr) * Mulch, low nitrogen 2.837 1.945 2.641 x 10-1 2.022 x 101 2.299 9.028 x 10-1 6.246 Clean, low nitrogen 3.438 x 10-2 1.025 x 10-2 1.382 x 10-2 7.965 x 10-2 3.301 x 10-2 2.044 x 10-2 5.306 x 10-2 Mulch, high nitrogen 2.807 1.731 5.327 x 10-1 1.744 x 101 2.387 1.006 6.016 Clean, high nitrogen 3.517 x 10-2 1.162 x 10-2 1.136 x 10-2 9.283 x 10-2 3.346 x 10-2 1.967 x 10-2 5.600 x 10-2 Non-renewable resources – fossil (MJ) * Mulch, low nitrogen 3.933 1.533 0.000 1.228 x 101 3.633 2.011 6.943 Clean, low nitrogen 2.771 7.921 x 10-1 1.201 7.185 2.674 1.676 4.298 Mulch, high nitrogen 3.889 1.371 1.336 1.280 x 101 3.712 2.102 6.329 Clean, high nitrogen 2.802 9.010 x 10-1 0.000 6.940 2.652 1.529 4.309 Non-renewable resources – nuclear (MJ) * Mulch, low nitrogen 1.818 x 10-1 7.429 x 10-2 0.000 6.061 x 10-1 1.661 x 10-1 8.861 x 10-2 3.280 x 10-1 Clean, low nitrogen 1.087 x 10-1 3.160 x 10-2 4.676 x 10-2 2.937 x 10-1 1.044 x 10-1 6.643 x 10-2 1.707 x 10-1 Mulch, high nitrogen 1.782 x 10-1 6.673 x 10-2 6.011 x 10-2 6.261 x 10-1 1.669 x 10-1 9.350 x 10-2 3.024 x 10-1 Clean, high nitrogen 1.096 x 10-1 3.618 x 10-2 0.000 2.787 x 10-1 1.044 x 10-1 5.914 x 10-2 1.713 x 10-1 Non-renewable resources – primary forest (MJ) * Mulch, low nitrogen 7.059 x 10-3 2.744 x 10-3 0.000 2.443 x 10-2 6.526 x 10-3 3.827 x 10-3 1.210 x 10-2 Clean, low nitrogen 6.739 x 10-3 1.884 x 10-3 2.973 x 10-3 1.607 x 10-2 6.564 x 10-3 4.162 x 10-3 1.013 x 10-2 Mulch, high nitrogen 6.774 x 10-3 2.266 x 10-3 2.329 x 10-3 2.443 x 10-2 6.529 x 10-3 3.835 x 10-3 1.078 x 10-2 Clean, high nitrogen 6.747 x 10-3 2.150 x 10-3 0.000 1.818 x 10-2 6.373 x 10-3 3.931 x 10-3 1.075 x 10-2 Renewable resources – biomass (MJ) * Mulch, low nitrogen 7.006 x 101 4.438 x 101 0.000 3.243 x 102 5.988 x 101 2.167 x 101 1.602 x 102 Clean, low nitrogen 1.429 x 10-1 4.017 x 10-2 6.096 x 10-2 3.279 x 10-1 1.386 x 10-1 8.658 x 10-2 2.182 x 10-1 Mulch, high nitrogen 6.675 x 101 4.350 x 101 1.223 x 101 3.101 x 102 5.424 x 101 2.124 x 101 1.561 x 102 Clean, high nitrogen 1.446 x 10-1 4.717 x 10-2 0.000 x 100 4.285 x 10-1 1.380 x 10-1 7.935 x 10-2 2.262 x 10-1 Renewable resources – geothermal (MJ) * Mulch, low nitrogen 8.497 x 10-3 4.457 x 10-3 0.000 3.036 x 10-2 7.525 x 10-3 3.263 x 10-3 1.773 x 10-2 Clean, low nitrogen 1.144 x 10-3 3.126 x 10-4 5.171 x 10-4 2.555 x 10-3 1.108 x 10-3 6.990 x 10-4 1.750 x 10-3 Mulch, high nitrogen 8.176 x 10-3 4.159 x 10-3 2.188 x 10-3 2.622 x 10-2 7.096 x 10-3 3.282 x 10-3 1.664 x 10-2 Clean, high nitrogen 1.156 x 10-3 3.604 x 10-4 0.000 2.899 x 10-3 1.101 x 10-3 6.457 x 10-4 1.772 x 10-3 Renewable resources – solar (MJ) * Mulch, low nitrogen 1.136 x 10-4 4.297 x 10-5 0.000 3.325 x 10-4 1.070 x 10-4 5.883 x 10-5 1.945 x 10-4 Clean, low nitrogen 8.981 x 10-5 2.471 x 10-5 4.054 x 10-5 1.927 x 10-4 8.712 x 10-5 5.547 x 10-5 1.367 x 10-4 Mulch, high nitrogen 1.120 x 10-4 3.746 x 10-5 3.972 x 10-5 2.965 x 10-4 1.084 x 10-4 6.255 x 10-5 1.838 x 10-4 Clean, high nitrogen 9.074 x 10-5 2.790 x 10-5 0.000 2.487 x 10-4 8.731 x 10-5 5.207 x 10-5 1.405 x 10-4 159 Mean Standard deviation Minimum Maximum Median 5% Percentile 95% Percentile Renewable resources – water (MJ) * Mulch, low nitrogen 1.282 7.002 x 10-1 0.000 4.764 1.137 4.611 x 10-1 2.729 Clean, low nitrogen 8.787 x 10-2 2.442 x 10-2 3.883 x 10-2 2.025 x 10-1 8.489 x 10-2 5.333 x 10-2 1.338 x 10-1 Mulch, high nitrogen 1.231 6.564 x 10-1 3.004 x 10-1 4.079 1.062 4.581 x 10-1 2.565 Clean, high nitrogen 8.867 x 10-2 2.787 x 10-2 0.000 2.192 x 10-1 8.462 x 10-2 4.968 x 10-2 1.377 x 10-1 Renewable resources – wind (MJ) * Mulch, low nitrogen 4.316 x 10-2 2.120 x 10-2 0.000 1.476 x 10-1 3.903 x 10-2 1.765 x 10-2 8.615 x 10-2 Clean, low nitrogen 1.042 x 10-2 3.099 x 10-3 4.210 x 10-3 2.387 x 10-2 9.942 x 10-3 6.191 x 10-3 1.649 x 10-2 Mulch, high nitrogen 4.165 x 10-2 1.952 x 10-2 1.168 x 10-2 1.181 x 10-1 3.705 x 10-2 1.816 x 10-2 8.029 x 10-2 Clean, high nitrogen 1.050 x 10-2 3.409 x 10-3 0.000 3.064 x 10-2 1.003 x 10-2 5.766 x 10-3 1.672 x 10-2 Table 31. Uncertainty analysis results of a Monte Carlo simulation (1000 runs) for the production of 1 kg apples, including background parameter uncertainty only. Impact categories with significant differences (p < 0.05) marked with *, and n.s. for non significant. Mean Standard deviation Minimum Maximum Median 5% Percentile 95% Percentile Acidification potential (kg SO2 eq.) * Mulch, low nitrogen 2.167 x 10-3 4.919 x 10-4 1.192 x 10-3 6.048 x 10-3 2.090 x 10-3 1.568 x 10-3 3.046 x 10-3 Clean, low nitrogen 1.649 x 10-3 3.603 x 10-4 -4.525 x 10-3 3.932 x 10-3 1.620 x 10-3 1.244 x 10-3 2.174 x 10-3 Mulch, high nitrogen 2.590 x 10-3 5.776 x 10-4 1.380 x 10-3 1.017 x 10-2 2.516 x 10-3 1.911 x 10-3 3.537 x 10-3 Clean, high nitrogen 2.073 x 10-3 3.963 x 10-4 9.458 x 10-4 5.011 x 10-3 2.035 x 10-3 1.528 x 10-3 2.788 x 10-3 Climate change (kg CO2 eq.) * Mulch, low nitrogen 2.828 x 10-1 8.009 x 10-2 3.873 x 10-2 1.133 2.682 x 10-1 1.975 x 10-1 4.107 x 10-1 Clean, low nitrogen 2.110 x 10-1 4.564 x 10-2 -5.773 x 10-1 3.555 x 10-1 2.097 x 10-1 1.623 x 10-1 2.735 x 10-1 Mulch, high nitrogen 3.323 x 10-1 8.794 x 10-2 1.459 x 10-1 1.573 3.187 x 10-1 2.436 x 10-1 4.665 x 10-1 Clean, high nitrogen 2.480 x 10-1 3.691 x 10-2 8.744 x 10-2 3.930 x 10-1 2.451 x 10-1 1.943 x 10-1 3.101 x 10-1 Depletion of abiotic resources – elements, ultimate reserves (kg antimony eq.) * Mulch, low nitrogen 1.966 x 10-6 5.508 x 10-7 1.042 x 10-6 1.261 x 10-5 1.868 x 10-6 1.410 x 10-6 2.795 x 10-6 Clean, low nitrogen 1.558 x 10-6 2.420 x 10-6 -7.103 x 10-5 3.641 x 10-6 1.603 x 10-6 1.185 x 10-6 2.248 x 10-6 Mulch, high nitrogen 2.269 x 10-6 8.018 x 10-7 1.085 x 10-6 1.683 x 10-5 2.160 x 10-6 1.557 x 10-6 3.155 x 10-6 Clean, high nitrogen 1.913 x 10-6 5.858 x 10-7 -8.309 x 10-6 3.768 x 10-6 1.854 x 10-6 1.368 x 10-6 2.772 x 10-6 Eutrophication (kg PO4--- eq.) * Mulch, low nitrogen 1.091 x 10-3 2.324 x 10-4 6.402 x 10-4 2.370 x 10-3 1.046 x 10-3 7.990 x 10-4 1.542 x 10-3 Clean, low nitrogen 8.745 x 10-4 2.663 x 10-4 -1.362 x 10-3 6.719 x 10-3 8.523 x 10-4 6.460 x 10-4 1.167 x 10-3 Mulch, high nitrogen 1.333 x 10-3 3.228 x 10-4 8.140 x 10-4 4.598 x 10-3 1.275 x 10-3 9.779 x 10-4 1.818 x 10-3 Clean, high nitrogen 1.110 x 10-3 2.291 x 10-4 7.169 x 10-4 3.553 x 10-3 1.081 x 10-3 8.353 x 10-4 1.465 x 10-3 Freshwater aquatic ecotoxicity (kg 1,4-dichlorobenzene eq.) * Mulch, low nitrogen 2.376 x 10-1 4.900 x 10-1 8.881 x 10-2 1.482 x 101 1.980 x 10-1 1.355 x 10-1 3.538 x 10-1 Clean, low nitrogen 1.751 x 10-1 1.813 x 10-1 -6.958 x 10-1 5.214 1.561 x 10-1 1.053 x 10-1 2.736 x 10-1 Mulch, high nitrogen 2.617 x 10-1 3.148 x 10-1 1.107 x 10-1 5.441 2.157 x 10-1 1.441 x 10-1 3.978 x 10-1 Clean, high nitrogen 1.851 x 10-1 8.065 x 10-2 2.028 x 10-3 1.251 1.680 x 10-1 1.127 x 10-1 2.973 x 10-1 160 Mean Standard deviation Minimum Maximum Median 5% Percentile 95% Percentile Human toxicity (kg 1,4-dichlorobenzene eq.) * Mulch, low nitrogen 4.047 x 10-1 2.155 1.527 x 10-1 6.747 x 101 2.951 x 10-1 2.016 x 10-1 5.229 x 10-1 Clean, low nitrogen 2.653 x 10-1 4.899 x 10-1 -1.231 1.421 x 101 2.245 x 10-1 1.534 x 10-1 3.828 x 10-1 Mulch, high nitrogen 4.178 x 10-1 7.108 x 10-1 1.565 x 10-1 1.423 x 101 3.333 x 10-1 2.240 x 10-1 6.446 x 10-1 Clean, high nitrogen 2.831 x 10-1 1.448 x 10-1 4.798 x 10-2 3.244 2.530 x 10-1 1.740 x 10-1 4.391 x 10-1 Marine aquatic ecotoxicity (kg 1,4-dichlorobenzene eq.) * Mulch, low nitrogen 7.082 x 102 5.575 x 102 3.219 x 102 1.453 x 104 6.569 x 102 4.591 x 102 9.948 x 102 Clean, low nitrogen 5.712 x 102 6.918 x 102 -8.141 x 103 1.891 x 104 5.279 x 102 3.790 x 102 8.029 x 102 Mulch, high nitrogen 8.095 x 102 9.556 x 102 3.602 x 102 1.894 x 104 7.015 x 102 4.856 x 102 1.152 x 103 Clean, high nitrogen 5.966 x 102 2.103 x 102 -5.296 x 102 4.419 x 103 5.667 x 102 3.933 x 102 8.664 x 102 Ozone layer depletion (kg CFC-11 eq.) * Mulch, low nitrogen 4.237 x 10-8 2.219 x 10-8 1.402 x 10-8 2.151 x 10-7 3.673 x 10-8 2.109 x 10-8 8.362 x 10-8 Clean, low nitrogen 2.475 x 10-8 1.089 x 10-8 -9.565 x 10-8 9.317 x 10-8 2.306 x 10-8 1.264 x 10-8 4.351 x 10-8 Mulch, high nitrogen 4.548 x 10-8 2.462 x 10-8 1.331 x 10-8 2.827 x 10-7 3.900 x 10-8 2.279 x 10-8 8.678 x 10-8 Clean, high nitrogen 2.721 x 10-8 1.106 x 10-8 6.112 x 10-9 1.102 x 10-7 2.486 x 10-8 1.469 x 10-8 4.740 x 10-8 Photochemical oxidation (kg ethylene eq.) * Mulch, low nitrogen 1.169 x 10-4 3.362 x 10-5 5.141 x 10-5 3.713 x 10-4 1.099 x 10-4 7.698 x 10-5 1.834 x 10-4 Clean, low nitrogen 9.291 x 10-5 3.439 x 10-5 -3.283 x 10-4 4.001 x 10-4 8.707 x 10-5 5.960 x 10-5 1.429 x 10-4 Mulch, high nitrogen 1.247 x 10-4 4.051 x 10-5 5.671 x 10-5 7.138 x 10-4 1.157 x 10-4 8.349 x 10-5 1.976 x 10-4 Clean, high nitrogen 9.788 x 10-5 2.739 x 10-5 1.556 x 10-5 2.618 x 10-4 9.276 x 10-5 6.482 x 10-5 1.482 x 10-4 Terrestrial ecotoxicity (kg 1,4-dichlorobenzene eq) * Mulch, low nitrogen 3.447 x 10-3 9.777 x 10-4 1.449 x 10-3 9.545 x 10-3 3.252 x 10-3 2.218 x 10-3 5.173 x 10-3 Clean, low nitrogen 2.672 x 10-3 8.242 x 10-4 -8.952 x 10-3 6.297 x 10-3 2.592 x 10-3 1.761 x 10-3 4.044 x 10-3 Mulch, high nitrogen 3.813 x 10-3 1.055 x 10-3 1.633 x 10-3 1.083 x 10-2 3.644 x 10-3 2.455 x 10-3 5.655 x 10-3 Clean, high nitrogen 2.989 x 10-3 8.580 x 10-4 1.041 x 10-3 7.732 x 10-3 2.844 x 10-3 1.880 x 10-3 4.535 x 10-3 Land use – land competition (m2*yr) * Mulch, low nitrogen 4.426 3.259 5.675 x 10-1 2.436 x 101 3.500 1.312 1.068 x 101 Clean, low nitrogen 5.131 x 10-2 5.666 x 10-2 1.449 x 10-2 7.286 x 10-1 3.309 x 10-2 1.797 x 10-2 1.395 x 10-1 Mulch, high nitrogen 4.267 3.339 7.451 x 10-1 4.976 x 101 3.356 1.272 1.005 x 101 Clean, high nitrogen 5.156 x 10-2 6.103 x 10-2 1.173 x 10-2 9.358 x 10-1 3.516 x 10-2 1.827 x 10-2 1.324 x 10-1 Non-renewable resources – fossil (MJ) * Mulch, low nitrogen 4.061 1.460 -2.169 x 101 1.568 x 101 3.827 2.870 6.036 Clean, low nitrogen 2.743 8.123 x 10-1 0.000 8.365 2.587 1.919 4.566 Mulch, high nitrogen 4.362 3.537 2.407 1.097 x 102 4.019 3.020 6.286 Clean, high nitrogen 2.946 8.119 x 10-1 -7.483 x 10-1 8.365 2.789 2.139 4.699 Non-renewable resources – nuclear (MJ) * Mulch, low nitrogen 1.698 x 10-1 3.409 x 10-1 -1.046 x 101 4.268 x 10-1 1.741 x 10-1 1.259 x 10-1 2.647 x 10-1 Clean, low nitrogen 1.123 x 10-1 3.795 x 10-2 -1.657 x 10-1 4.083 x 10-1 1.057 x 10-1 7.656 x 10-2 1.879 x 10-1 Mulch, high nitrogen 2.316 x 10-1 1.296 -3.874 x 10-2 4.115 x 101 1.837 x 10-1 1.348 x 10-1 2.740 x 10-1 Clean, high nitrogen 1.199 x 10-1 7.709 x 10-2 -1.887 5.434 x 10-1 1.151 x 10-1 8.166 x 10-2 1.976 x 10-1 161 Mean Standard deviation Minimum Maximum Median 5% Percentile 95% Percentile Non-renewable resources – primary forest (MJ) * Mulch, low nitrogen 6.505 x 10-3 1.501 x 10-3 -3.241 x 10-2 9.394 x 10-3 6.482 x 10-3 5.270 x 10-3 8.002 x 10-3 Clean, low nitrogen 7.057 x 10-3 1.992 x 10-3 0.000 1.552 x 10-2 6.610 x 10-3 5.245 x 10-3 1.249 x 10-2 Mulch, high nitrogen 6.750 x 10-3 5.019 x 10-3 4.091 x 10-3 1.623 x 10-1 6.541 x 10-3 5.242 x 10-3 8.089 x 10-3 Clean, high nitrogen 7.067 x 10-3 2.120 x 10-3 -1.559 x 10-3 1.662 x 10-2 6.594 x 10-3 5.149 x 10-3 1.258 x 10-2 Renewable resources – biomass (MJ) * Mulch, low nitrogen 1.010 x 102 7.568 x 101 1.286 x 101 7.572 x 102 7.986 x 101 2.861 x 101 2.445 x 102 Clean, low nitrogen 2.189 x 10-1 2.288 x 10-1 -3.173 x 10-2 3.544 1.553 x 10-1 7.323 x 10-2 5.428 x 10-1 Mulch, high nitrogen 9.796 x 101 8.390 x 101 1.095 x 101 1.265 x 103 7.726 x 101 2.905 x 101 2.357 x 102 Clean, high nitrogen 2.380 x 10-1 2.781 x 10-1 -1.135 3.544 1.572 x 10-1 7.611 x 10-2 6.591 x 10-1 Renewable resources – geothermal (MJ) * Mulch, low nitrogen 7.849 x 10-3 3.157 x 10-3 2.349 x 10-3 2.195 x 10-2 7.231 x 10-3 4.041 x 10-3 1.363 x 10-2 Clean, low nitrogen 1.151 x 10-3 2.949 x 10-4 0.000 4.242 x 10-3 1.106 x 10-3 8.291 x 10-4 1.622 x 10-3 Mulch, high nitrogen 7.886 x 10-3 3.414 x 10-3 2.185 x 10-3 4.238 x 10-2 7.111 x 10-3 4.093 x 10-3 1.398 x 10-2 Clean, high nitrogen 1.229 x 10-3 3.123 x 10-4 0.000 4.242 x 10-3 1.185 x 10-3 8.900 x 10-4 1.832 x 10-3 Renewable resources – solar (MJ) * Mulch, low nitrogen 1.288 x 10-4 7.141 x 10-5 -1.511 x 10-3 6.879 x 10-4 1.180 x 10-4 8.778 x 10-5 2.147 x 10-4 Clean, low nitrogen 9.602 x 10-5 2.821 x 10-5 0.000 2.890 x 10-4 8.985 x 10-5 6.790 x 10-5 1.546 x 10-4 Mulch, high nitrogen 1.407 x 10-4 1.901 x 10-4 7.404 x 10-5 5.979 x 10-3 1.250 x 10-4 9.356 x 10-5 2.097 x 10-4 Clean, high nitrogen 1.004 x 10-4 3.068 x 10-5 -1.965 x 10-4 2.876 x 10-4 9.343 x 10-5 7.297 x 10-5 1.660 x 10-4 Renewable resources – water (MJ) * Mulch, low nitrogen 1.216 5.270 x 10-1 -8.941 x 10-1 3.738 1.126 5.715 x 10-1 2.173 Clean, low nitrogen 8.879 x 10-2 2.371 x 10-2 0.000 3.039 x 10-1 8.551 x 10-2 6.338 x 10-2 1.283 x 10-1 Mulch, high nitrogen 1.218 6.488 x 10-1 2.847 x 10-1 1.279 x 101 1.086 5.808 x 10-1 2.260 Clean, high nitrogen 9.249 x 10-2 2.879 x 10-2 -3.918 x 10-1 3.039 x 10-1 8.942 x 10-2 6.657 x 10-2 1.362 x 10-1 Renewable resources – wind (MJ) * Mulch, low nitrogen 4.171 x 10-2 1.477 x 10-2 -1.924 x 10-2 1.128 x 10-1 3.909 x 10-2 2.368 x 10-2 6.860 x 10-2 Clean, low nitrogen 1.063 x 10-2 2.930 x 10-3 0.000 2.515 x 10-2 1.005 x 10-2 7.373 x 10-3 1.637 x 10-2 Mulch, high nitrogen 4.223 x 10-2 1.811 x 10-2 1.400 x 10-2 3.579 x 10-1 3.869 x 10-2 2.436 x 10-2 7.049 x 10-2 Clean, high nitrogen 1.106 x 10-2 2.947 x 10-3 -6.820 x 10-4 2.515 x 10-2 1.044 x 10-2 7.727 x 10-3 1.717 x 10-2 Table 32. Uncertainty analysis results of a Monte Carlo simulation (1000 runs) for the production of 1 kg apples, including foreground parameter uncertainty from experimentally measured orchard-level emissions of N2O and CO2 only. Impact categories with significant differences (p < 0.05) marked with *, and n.s. for non significant. Mean Standard deviation Minimum Maximum Median 5% Percentile 95% Percentile Acidification potential (kg SO2 eq.) * Mulch, low nitrogen 1.174 x 10-3 1.274 x 10-4 1.156 x 10-3 2.688 x 10-3 1.163 x 10-3 1.163 x 10-3 1.163 x 10-3 Clean, low nitrogen 9.420 x 10-4 1.288 x 10-4 9.320 x 10-4 2.915 x 10-3 9.329 x 10-4 9.326 x 10-4 9.334 x 10-4 Mulch, high nitrogen 1.533 x 10-3 9.829 x 10-5 1.163 x 10-3 2.688 x 10-3 1.525 x 10-3 1.524 x 10-3 1.525 x 10-3 Clean, high nitrogen 1.297 x 10-3 5.449 x 10-5 8.558 x 10-4 2.228 x 10-3 1.295 x 10-3 1.294 x 10-3 1.295 x 10-3 162 Mean Standard deviation Minimum Maximum Median 5% Percentile 95% Percentile Climate change (kg CO2 eq.) * Mulch, low nitrogen 1.529 x 10-1 1.929 x 10-2 1.256 x 10-1 3.879 x 10-1 1.508 x 10-1 1.399 x 10-1 1.649 x 10-1 Clean, low nitrogen 1.255 x 10-1 1.698 x 10-2 1.184 x 10-1 3.715 x 10-1 1.224 x 10-1 1.191 x 10-1 1.365 x 10-1 Mulch, high nitrogen 1.975 x 10-1 2.098 x 10-2 1.542 x 10-1 3.978 x 10-1 1.930 x 10-1 1.858 x 10-1 2.185 x 10-1 Clean, high nitrogen 1.582 x 10-1 1.056 x 10-2 1.166 x 10-1 2.962 x 10-1 1.556 x 10-1 1.518 x 10-1 1.735 x 10-1 Depletion of abiotic resources – elements, ultimate reserves (kg antimony eq.) * Mulch, low nitrogen 1.099 x 10-6 1.138 x 10-7 1.031 x 10-6 2.376 x 10-6 1.089 x 10-6 1.088 x 10-6 1.089 x 10-6 Clean, low nitrogen 9.905 x 10-7 1.612 x 10-7 9.697 x 10-7 3.576 x 10-6 9.764 x 10-7 9.761 x 10-7 9.769 x 10-7 Mulch, high nitrogen 1.296 x 10-6 9.612 x 10-8 1.274 x 10-6 2.376 x 10-6 1.287 x 10-6 1.287 x 10-6 1.288 x 10-6 Clean, high nitrogen 1.180 x 10-6 7.616 x 10-8 2.001 x 10-7 2.152 x 10-6 1.175 x 10-6 1.175 x 10-6 1.176 x 10-6 Eutrophication (kg PO4--- eq.) * Mulch, low nitrogen 7.028 x 10-4 7.591 x 10-5 6.861 x 10-4 1.627 x 10-3 6.938 x 10-4 6.882 x 10-4 7.115 x 10-4 Clean, low nitrogen 5.923 x 10-4 9.662 x 10-5 5.734 x 10-4 2.090 x 10-3 5.822 x 10-4 5.758 x 10-4 6.008 x 10-4 Mulch, high nitrogen 9.018 x 10-4 6.193 x 10-5 7.711 x 10-4 1.627 x 10-3 8.924 x 10-4 8.862 x 10-4 9.228 x 10-4 Clean, high nitrogen 7.843 x 10-4 3.989 x 10-5 1.428 x 10-4 1.363 x 10-3 7.807 x 10-4 7.741 x 10-4 8.002 x 10-4 Freshwater aquatic ecotoxicity (kg 1,4-dichlorobenzene eq.) * Mulch, low nitrogen 8.974 x 10-2 8.469 x 10-3 8.773 x 10-2 1.847 x 10-1 8.867 x 10-2 8.800 x 10-2 9.092 x 10-2 Clean, low nitrogen 7.436 x 10-2 4.078 x 10-2 7.050 x 10-2 1.251 7.141 x 10-2 7.079 x 10-2 7.415 x 10-2 Mulch, high nitrogen 9.567 x 10-2 8.816 x 10-3 9.358 x 10-2 2.246 x 10-1 9.446 x 10-2 9.384 x 10-2 9.697 x 10-2 Clean, high nitrogen 7.782 x 10-2 5.676 x 10-3 1.124 x 10-3 1.565 x 10-1 7.718 x 10-2 7.653 x 10-2 7.964 x 10-2 Human toxicity (kg 1,4-dichlorobenzene eq.) * Mulch, low nitrogen 1.117 x 10-1 1.060 x 10-2 1.086 x 10-1 2.405 x 10-1 1.104 x 10-1 1.099 x 10-1 1.129 x 10-1 Clean, low nitrogen 8.850 x 10-2 1.029 x 10-1 8.236 x 10-2 3.244 8.302 x 10-2 8.256 x 10-2 8.584 x 10-2 Mulch, high nitrogen 1.293 x 10-1 9.927 x 10-3 1.159 x 10-1 2.398 x 10-1 1.281 x 10-1 1.275 x 10-1 1.309 x 10-1 Clean, high nitrogen 1.013 x 10-1 6.386 x 10-3 3.185 x 10-3 1.878 x 10-1 1.006 x 10-1 1.002 x 10-1 1.033 x 10-1 Marine aquatic ecotoxicity (kg 1,4-dichlorobenzene eq.) * Mulch, low nitrogen 2.373 x 102 2.047 x 101 2.281 x 102 4.925 x 102 2.348 x 102 2.333 x 102 2.395 x 102 Clean, low nitrogen 1.931 x 102 1.431 x 102 1.820 x 102 4.419 x 103 1.841 x 102 1.827 x 102 1.894 x 102 Mulch, high nitrogen 2.571 x 102 2.079 x 101 2.498 x 102 4.937 x 102 2.545 x 102 2.530 x 102 2.598 x 102 Clean, high nitrogen 2.049 x 102 1.206 x 101 3.297 x 101 3.964 x 102 2.037 x 102 2.022 x 102 2.092 x 102 Ozone layer depletion (kg CFC-11 eq.) * Mulch, low nitrogen 1.748 x 10-8 1.681 x 10-9 1.722 x 10-8 3.622 x 10-8 1.733 x 10-8 1.731 x 10-8 1.738 x 10-8 Clean, low nitrogen 1.024 x 10-8 2.830 x 10-9 9.966 x 10-9 6.466 x 10-8 1.000 x 10-8 9.982 x 10-9 1.005 x 10-8 Mulch, high nitrogen 1.902 x 10-8 1.536 x 10-9 1.884 x 10-8 3.625 x 10-8 1.887 x 10-8 1.885 x 10-8 1.892 x 10-8 Clean, high nitrogen 1.160 x 10-8 8.253 x 10-10 3.650 x 10-10 2.159 x 10-8 1.155 x 10-8 1.153 x 10-8 1.160 x 10-8 Photochemical oxidation (kg ethylene eq.) * Mulch, low nitrogen 4.847 x 10-5 4.345 x 10-6 4.756 x 10-5 9.980 x 10-5 4.807 x 10-5 4.805 x 10-5 4.810 x 10-5 Clean, low nitrogen 3.816 x 10-5 6.557 x 10-6 3.769 x 10-5 1.960 x 10-4 3.772 x 10-5 3.771 x 10-5 3.775 x 10-5 Mulch, high nitrogen 5.211 x 10-5 4.193 x 10-6 4.833 x 10-5 9.981 x 10-5 5.173 x 10-5 5.171 x 10-5 5.176 x 10-5 Clean, high nitrogen 4.151 x 10-5 2.286 x 10-6 2.952 x 10-5 7.914 x 10-5 4.138 x 10-5 4.136 x 10-5 4.142 x 10-5 163 Mean Standard deviation Minimum Maximum Median 5% Percentile 95% Percentile Terrestrial ecotoxicity (kg 1,4-dichlorobenzene eq) * Mulch, low nitrogen 1.703 x 10-3 1.575 x 10-4 1.682 x 10-3 3.592 x 10-3 1.689 x 10-3 1.688 x 10-3 1.692 x 10-3 Clean, low nitrogen 1.347 x 10-3 2.362 x 10-4 1.325 x 10-3 4.885 x 10-3 1.327 x 10-3 1.326 x 10-3 1.331 x 10-3 Mulch, high nitrogen 1.910 x 10-3 1.500 x 10-4 1.693 x 10-3 3.592 x 10-3 1.897 x 10-3 1.896 x 10-3 1.900 x 10-3 Clean, high nitrogen 1.539 x 10-3 9.657 x 10-5 2.490 x 10-5 2.862 x 10-3 1.535 x 10-3 1.534 x 10-3 1.539 x 10-3 Land use – land competition (m2*yr) * Mulch, low nitrogen 2.294 9.828 x 10-6 2.294 2.295 2.294 2.294 2.294 Clean, low nitrogen 1.730 x 10-2 3.026 x 10-5 1.707 x 10-2 1.818 x 10-2 1.730 x 10-2 1.729 x 10-2 1.732 x 10-2 Mulch, high nitrogen 2.295 9.272 x 10-6 2.295 2.295 2.295 2.295 2.295 Clean, high nitrogen 1.811 x 10-2 9.197 x 10-6 1.809 x 10-2 1.815 x 10-2 1.811 x 10-2 1.810 x 10-2 1.813 x 10-2 Non-renewable resources – fossil (MJ) * Mulch, low nitrogen 2.187 4.770 x 10-4 2.184 2.197 2.187 2.187 2.188 Clean, low nitrogen 1.376 1.365 x 10-1 4.223 x 10-3 2.729 1.365 1.364 1.365 Mulch, high nitrogen 2.379 1.806 x 10-1 0.000 5.346 2.374 2.373 2.375 Clean, high nitrogen 1.754 7.462 x 10-1 6.876 x 10-5 5.394 1.552 1.551 4.126 Non-renewable resources – nuclear (MJ) * Mulch, low nitrogen 1.083 x 10-1 1.412 x 10-4 1.070 x 10-1 1.125 x 10-1 1.083 x 10-1 1.083 x 10-1 1.084 x 10-1 Clean, low nitrogen 5.584 x 10-2 6.253 x 10-3 -1.856 x 10-2 1.109 x 10-1 5.541 x 10-2 5.539 x 10-2 5.544 x 10-2 Mulch, high nitrogen 1.149 x 10-1 2.330 x 10-2 -5.688 x 10-1 2.376 x 10-1 1.153 x 10-1 1.153 x 10-1 1.154 x 10-1 Clean, high nitrogen 7.082 x 10-2 3.134 x 10-2 0.000 2.617 x 10-1 6.242 x 10-2 6.240 x 10-2 1.673 x 10-1 Non-renewable resources – primary forest (MJ) * Mulch, low nitrogen 6.164 x 10-3 5.200 x 10-7 6.159 x 10-3 6.179 x 10-3 6.164 x 10-3 6.164 x 10-3 6.164 x 10-3 Clean, low nitrogen 6.205 x 10-3 6.406 x 10-4 5.884 x 10-3 1.228 x 10-2 6.138 x 10-3 6.138 x 10-3 6.138 x 10-3 Mulch, high nitrogen 6.192 x 10-3 5.243 x 10-4 0.000 1.238 x 10-2 6.171 x 10-3 6.171 x 10-3 6.171 x 10-3 Clean, high nitrogen 6.627 x 10-3 1.819 x 10-3 0.000 1.481 x 10-2 6.145 x 10-3 6.145 x 10-3 1.232 x 10-2 Renewable resources – biomass (MJ) * Mulch, low nitrogen 5.290 x 101 5.522 x 10-5 5.290 x 101 5.290 x 101 5.290 x 101 5.290 x 101 5.290 x 101 Clean, low nitrogen 6.912 x 10-2 4.446 x 10-3 3.989 x 10-2 1.378 x 10-1 6.887 x 10-2 6.886 x 10-2 6.889 x 10-2 Mulch, high nitrogen 5.309 x 101 5.006 0.000 1.606 x 102 5.291 x 101 5.291 x 101 5.291 x 101 Clean, high nitrogen 8.777 x 10-2 7.421 x 10-2 0.000 1.098 7.266 x 10-2 7.265 x 10-2 1.885 x 10-1 Renewable resources – geothermal (MJ) * Mulch, low nitrogen 6.153 x 10-3 1.848 x 10-7 6.152 x 10-3 6.156 x 10-3 6.153 x 10-3 6.153 x 10-3 6.154 x 10-3 Clean, low nitrogen 5.840 x 10-4 4.858 x 10-5 0.000 1.163 x 10-3 5.811 x 10-4 5.809 x 10-4 5.815 x 10-4 Mulch, high nitrogen 6.238 x 10-3 5.229 x 10-4 0.000 1.409 x 10-2 6.221 x 10-3 6.221 x 10-3 6.222 x 10-3 Clean, high nitrogen 7.173 x 10-4 2.792 x 10-4 0.000 2.623 x 10-3 6.494 x 10-4 6.492 x 10-4 1.548 x 10-3 Renewable resources – solar (MJ) * Mulch, low nitrogen 7.758 x 10-5 5.302 x 10-8 7.740 x 10-5 7.812 x 10-5 7.757 x 10-5 7.752 x 10-5 7.768 x 10-5 Clean, low nitrogen 6.141 x 10-5 4.758 x 10-6 0.000 1.226 x 10-4 6.129 x 10-5 6.124 x 10-5 6.139 x 10-5 Mulch, high nitrogen 8.172 x 10-5 6.529 x 10-6 0.000 2.167 x 10-4 8.142 x 10-5 8.138 x 10-5 8.153 x 10-5 Clean, high nitrogen 7.078 x 10-5 2.364 x 10-5 0.000 2.734 x 10-4 6.515 x 10-5 6.511 x 10-5 1.416 x 10-4 164 Mean Standard deviation Minimum Maximum Median 5% Percentile 95% Percentile Renewable resources – water (MJ) * Mulch, low nitrogen 9.468 x 10-1 3.509 x 10-5 9.465 x 10-1 9.479 x 10-1 9.468 x 10-1 9.468 x 10-1 9.469 x 10-1 Clean, low nitrogen 3.839 x 10-2 3.240 x 10-3 0.000 7.646 x 10-2 3.822 x 10-2 3.821 x 10-2 3.823 x 10-2 Mulch, high nitrogen 9.529 x 10-1 7.903 x 10-2 0.000 2.046 9.503 x 10-1 9.503 x 10-1 9.503 x 10-1 Clean, high nitrogen 4.716 x 10-2 2.168 x 10-2 0.000 1.775 x 10-1 4.170 x 10-2 4.169 x 10-2 1.117 x 10-1 Renewable resources – wind (MJ) * Mulch, low nitrogen 3.132 x 10-2 1.303 x 10-6 3.132 x 10-2 3.135 x 10-2 3.132 x 10-2 3.132 x 10-2 3.133 x 10-2 Clean, low nitrogen 6.641 x 10-3 5.122 x 10-4 0.000 1.323 x 10-2 6.615 x 10-3 6.614 x 10-3 6.617 x 10-3 Mulch, high nitrogen 3.183 x 10-2 2.007 x 10-3 0.000 6.609 x 10-2 3.176 x 10-2 3.176 x 10-2 3.176 x 10-2 Clean, high nitrogen 7.650 x 10-3 2.498 x 10-3 0.000 2.282 x 10-2 7.052 x 10-3 7.051 x 10-3 1.521 x 10-2 Table 33. Uncertainty analysis results of a Monte Carlo simulation (1000 runs) for the production of 1 kg apples, including foreground parameter uncertainty from experimental mulch inputs only. Impact categories with significant differences (p < 0.05) marked with *, and n.s. for non significant. Mean Standard deviation Minimum Maximum Median 5% Percentile 95% Percentile Acidification potential (kg SO2 eq.) * Mulch, low nitrogen 1.264 x 10-3 5.993 x 10-3 -1.875 x 10-1 6.379 x 10-3 1.333 x 10-3 1.111 x 10-3 2.198 x 10-3 Clean, low nitrogen 9.374 x 10-4 7.223 x 10-5 9.299 x 10-4 2.228 x 10-3 9.329 x 10-4 9.326 x 10-4 9.333 x 10-4 Mulch, high nitrogen 1.573 x 10-3 1.726 x 10-4 1.380 x 10-3 3.668 x 10-3 1.542 x 10-3 1.424 x 10-3 1.790 x 10-3 Clean, high nitrogen 1.295 x 10-3 7.261 x 10-7 1.294 x 10-3 1.316 x 10-3 1.295 x 10-3 1.295 x 10-3 1.295 x 10-3 Climate change (kg CO2 eq.) * Mulch, low nitrogen 1.747 x 10-1 7.654 x 10-1 -2.386 x 101 1.036 1.754 x 10-1 1.330 x 10-1 3.300 x 10-1 Clean, low nitrogen 1.226 x 10-1 7.083 x 10-3 1.218 x 10-1 2.793 x 10-1 1.222 x 10-1 1.222 x 10-1 1.223 x 10-1 Mulch, high nitrogen 2.031 x 10-1 3.360 x 10-2 1.565 x 10-1 4.937 x 10-1 1.972 x 10-1 1.683 x 10-1 2.537 x 10-1 Clean, high nitrogen 1.550 x 10-1 6.239 x 10-5 1.549 x 10-1 1.569 x 10-1 1.550 x 10-1 1.550 x 10-1 1.550 x 10-1 Depletion of abiotic resources – elements, ultimate reserves (kg antimony eq.) n.s. Mulch, low nitrogen -7.608 x 10-7 6.586 x 10-5 -2.081 x 10-3 3.010 x 10-5 1.196 x 10-6 1.067 x 10-6 1.798 x 10-6 Clean, low nitrogen 9.841 x 10-7 9.025 x 10-8 9.324 x 10-7 2.152 x 10-6 9.764 x 10-7 9.762 x 10-7 9.770 x 10-7 Mulch, high nitrogen 1.316 x 10-6 1.359 x 10-7 1.065 x 10-6 3.169 x 10-6 1.296 x 10-6 1.238 x 10-6 1.421 x 10-6 Clean, high nitrogen 1.175 x 10-6 4.789 x 10-9 1.167 x 10-6 1.326 x 10-6 1.175 x 10-6 1.175 x 10-6 1.176 x 10-6 Eutrophication (kg PO4--- eq.) * Mulch, low nitrogen 6.952 x 10-4 3.438 x 10-3 -1.078 x 10-1 2.938 x 10-3 7.647 x 10-4 6.591 x 10-4 1.062 x 10-3 Clean, low nitrogen 5.863 x 10-4 5.167 x 10-5 5.762 x 10-4 1.364 x 10-3 5.809 x 10-4 5.775 x 10-4 5.934 x 10-4 Mulch, high nitrogen 9.172 x 10-4 1.017 x 10-4 8.155 x 10-4 2.324 x 10-3 9.020 x 10-4 8.422 x 10-4 1.019 x 10-3 Clean, high nitrogen 7.806 x 10-4 5.116 x 10-6 7.745 x 10-4 8.360 x 10-4 7.794 x 10-4 7.757 x 10-4 7.898 x 10-4 Freshwater aquatic ecotoxicity (kg 1,4-dichlorobenzene eq.) n.s. Mulch, low nitrogen 1.081 x 10-1 7.084 x 10-1 -2.182 x 101 4.177 1.121 x 10-1 8.875 x 10-2 1.922 x 10-1 Clean, low nitrogen 7.245 x 10-2 6.639 x 10-3 7.052 x 10-2 1.565 x 10-1 7.146 x 10-2 7.076 x 10-2 7.413 x 10-2 Mulch, high nitrogen 9.834 x 10-2 1.212 x 10-2 8.239 x 10-2 2.233 x 10-1 9.605 x 10-2 8.681 x 10-2 1.154 x 10-1 Clean, high nitrogen 7.770 x 10-2 2.694 x 10-3 7.625 x 10-2 1.369 x 10-1 7.718 x 10-2 7.654 x 10-2 7.975 x 10-2 165 Mean Standard deviation Minimum Maximum Median 5% Percentile 95% Percentile Human toxicity (kg 1,4-dichlorobenzene eq.) n.s. Mulch, low nitrogen 1.410 x 10-1 1.114 -3.316 x 101 1.140 x 101 1.467 x 10-1 1.094 x 10-1 2.637 x 10-1 Clean, low nitrogen 8.409 x 10-2 7.283 x 10-3 8.221 x 10-2 1.878 x 10-1 8.302 x 10-2 8.256 x 10-2 8.616 x 10-2 Mulch, high nitrogen 1.342 x 10-1 1.964 x 10-2 1.101 x 10-1 3.408 x 10-1 1.310 x 10-1 1.164 x 10-1 1.588 x 10-1 Clean, high nitrogen 1.011 x 10-1 1.617 x 10-3 9.996 x 10-2 1.213 x 10-1 1.007 x 10-1 1.002 x 10-1 1.035 x 10-1 Marine aquatic ecotoxicity (kg 1,4-dichlorobenzene eq.) n.s. Mulch, low nitrogen 1.706 x 102 5.099 x 103 -1.602 x 105 1.515 x 104 2.883 x 102 2.294 x 102 4.801 x 102 Clean, low nitrogen 1.860 x 102 1.295 x 101 1.801 x 102 3.964 x 102 1.841 x 102 1.827 x 102 1.898 x 102 Mulch, high nitrogen 2.662 x 102 4.385 x 101 2.193 x 102 8.436 x 102 2.592 x 102 2.316 x 102 3.133 x 102 Clean, high nitrogen 2.045 x 102 3.347 2.016 x 102 2.600 x 102 2.037 x 102 2.023 x 102 2.091 x 102 Ozone layer depletion (kg CFC-11 eq.) * Mulch, low nitrogen 2.547 x 10-8 1.086 x 10-7 -3.355 x 10-6 1.829 x 10-7 2.327 x 10-8 1.516 x 10-8 6.297 x 10-8 Clean, low nitrogen 1.008 x 10-8 8.729 x 10-10 9.910 x 10-9 2.159 x 10-8 1.000 x 10-8 9.982 x 10-9 1.005 x 10-8 Mulch, high nitrogen 2.031 x 10-8 4.216 x 10-9 1.428 x 10-8 5.148 x 10-8 1.950 x 10-8 1.568 x 10-8 2.743 x 10-8 Clean, high nitrogen 1.155 x 10-8 2.338 x 10-11 1.151 x 10-8 1.183 x 10-8 1.155 x 10-8 1.153 x 10-8 1.160 x 10-8 Photochemical oxidation (kg ethylene eq.) n.s. Mulch, low nitrogen 5.153 x 10-5 3.566 x 10-4 -1.119 x 10-2 3.022 x 10-4 5.713 x 10-5 4.669 x 10-5 9.765 x 10-5 Clean, low nitrogen 3.789 x 10-5 2.491 x 10-6 3.752 x 10-5 7.914 x 10-5 3.772 x 10-5 3.770 x 10-5 3.775 x 10-5 Mulch, high nitrogen 5.387 x 10-5 7.472 x 10-6 4.527 x 10-5 1.494 x 10-4 5.264 x 10-5 4.732 x 10-5 6.313 x 10-5 Clean, high nitrogen 4.139 x 10-5 4.327 x 10-8 4.132 x 10-5 4.265 x 10-5 4.138 x 10-5 4.136 x 10-5 4.142 x 10-5 Terrestrial ecotoxicity (kg 1,4-dichlorobenzene eq) * Mulch, low nitrogen 1.808 x 10-3 1.061 x 10-2 -3.328 x 10-1 6.846 x 10-3 1.983 x 10-3 1.623 x 10-3 3.093 x 10-3 Clean, low nitrogen 1.334 x 10-3 9.700 x 10-5 1.321 x 10-3 2.863 x 10-3 1.327 x 10-3 1.326 x 10-3 1.331 x 10-3 Mulch, high nitrogen 1.974 x 10-3 2.751 x 10-4 1.656 x 10-3 4.999 x 10-3 1.922 x 10-3 1.740 x 10-3 2.304 x 10-3 Clean, high nitrogen 1.536 x 10-3 1.744 x 10-6 1.533 x 10-3 1.565 x 10-3 1.535 x 10-3 1.534 x 10-3 1.538 x 10-3 Land use – land competition (m2*yr) * Mulch, low nitrogen 4.198 3.081 5.065 x 10-1 3.251 x 101 3.403 1.208 1.045 x 101 Clean, low nitrogen 1.730 x 10-2 8.904 x 10-6 1.728 x 10-2 1.735 x 10-2 1.730 x 10-2 1.729 x 10-2 1.732 x 10-2 Mulch, high nitrogen 2.626 1.389 6.152 x 10-1 1.134 x 101 2.263 1.034 5.377 Clean, high nitrogen 1.811 x 10-2 1.024 x 10-5 1.806 x 10-2 1.826 x 10-2 1.811 x 10-2 1.810 x 10-2 1.813 x 10-2 Non-renewable resources – fossil (MJ) * Mulch, low nitrogen 2.949 1.700 0.000 3.994 x 101 2.620 1.955 4.809 Clean, low nitrogen 1.376 1.364 x 10-1 4.618 x 10-3 2.730 1.365 1.364 1.365 Mulch, high nitrogen 2.550 4.325 x 10-1 1.849 6.096 2.456 2.079 3.333 Clean, high nitrogen 1.562 1.468 x 10-1 4.691 x 10-3 3.104 1.552 1.551 1.552 Non-renewable resources – nuclear (MJ) * Mulch, low nitrogen 1.333 x 10-1 5.310 x 10-2 -5.688 x 10-1 9.548 x 10-1 1.247 x 10-1 8.954 x 10-2 2.076 x 10-1 Clean, low nitrogen 5.592 x 10-2 5.795 x 10-3 4.814 x 10-7 1.109 x 10-1 5.541 x 10-2 5.539 x 10-2 5.544 x 10-2 Mulch, high nitrogen 1.261 x 10-1 2.897 x 10-2 7.978 x 10-2 3.781 x 10-1 1.202 x 10-1 9.329 x 10-2 1.759 x 10-1 Clean, high nitrogen 6.286 x 10-2 5.911 x 10-3 0.000 1.248 x 10-1 6.242 x 10-2 6.240 x 10-2 6.244 x 10-2 166 Mean Standard deviation Minimum Maximum Median 5% Percentile 95% Percentile Non-renewable resources – primary forest (MJ) n.s. Mulch, low nitrogen 6.230 x 10-3 4.933 x 10-4 0.000 1.238 x 10-2 6.183 x 10-3 6.158 x 10-3 6.301 x 10-3 Clean, low nitrogen 6.206 x 10-3 6.405 x 10-4 6.132 x 10-3 1.228 x 10-2 6.138 x 10-3 6.138 x 10-3 6.138 x 10-3 Mulch, high nitrogen 6.176 x 10-3 1.490 x 10-5 6.155 x 10-3 6.411 x 10-3 6.173 x 10-3 6.162 x 10-3 6.199 x 10-3 Clean, high nitrogen 6.206 x 10-3 6.117 x 10-4 6.126 x 10-3 1.229 x 10-2 6.145 x 10-3 6.145 x 10-3 6.145 x 10-3 Renewable resources – biomass (MJ) * Mulch, low nitrogen 9.672 x 101 6.890 x 101 0.000 6.453 x 102 7.958 x 101 2.954 x 101 2.257 x 102 Clean, low nitrogen 6.922 x 10-2 4.861 x 10-3 6.830 x 10-2 1.378 x 10-1 6.887 x 10-2 6.886 x 10-2 6.889 x 10-2 Mulch, high nitrogen 6.331 x 101 3.581 x 101 1.410 x 101 4.637 x 102 5.568 x 101 2.683 x 101 1.274 x 102 Clean, high nitrogen 7.295 x 10-2 5.624 x 10-3 0.000 1.453 x 10-1 7.266 x 10-2 7.265 x 10-2 7.268 x 10-2 Renewable resources – geothermal (MJ) * Mulch, low nitrogen 7.322 x 10-3 2.979 x 10-3 0.000 2.370 x 10-2 6.675 x 10-3 3.757 x 10-3 1.287 x 10-2 Clean, low nitrogen 5.852 x 10-4 4.847 x 10-5 5.800 x 10-4 1.163 x 10-3 5.811 x 10-4 5.809 x 10-4 5.815 x 10-4 Mulch, high nitrogen 7.335 x 10-3 3.172 x 10-3 2.370 x 10-3 3.721 x 10-2 6.707 x 10-3 3.825 x 10-3 1.279 x 10-2 Clean, high nitrogen 6.546 x 10-4 6.476 x 10-5 0.000 1.299 x 10-3 6.494 x 10-4 6.492 x 10-4 6.497 x 10-4 Renewable resources – solar (MJ) * Mulch, low nitrogen 1.053 x 10-4 6.988 x 10-5 0.000 1.549 x 10-3 8.893 x 10-5 7.187 x 10-5 1.784 x 10-4 Clean, low nitrogen 6.155 x 10-5 4.745 x 10-6 0.000 1.227 x 10-4 6.129 x 10-5 6.124 x 10-5 6.140 x 10-5 Mulch, high nitrogen 8.507 x 10-5 9.248 x 10-6 7.082 x 10-5 1.625 x 10-4 8.287 x 10-5 7.508 x 10-5 1.022 x 10-4 Clean, high nitrogen 6.548 x 10-5 5.447 x 10-6 0.000 1.305 x 10-4 6.515 x 10-5 6.511 x 10-5 6.525 x 10-5 Renewable resources – water (MJ) * Mulch, low nitrogen 1.172 4.940 x 10-1 0.000 3.464 1.077 5.559 x 10-1 2.133 Clean, low nitrogen 3.849 x 10-2 3.188 x 10-3 3.785 x 10-2 7.646 x 10-2 3.822 x 10-2 3.821 x 10-2 3.823 x 10-2 Mulch, high nitrogen 1.132 5.152 x 10-1 3.226 x 10-1 5.874 1.029 5.601 x 10-1 2.027 Clean, high nitrogen 4.195 x 10-2 3.723 x 10-3 0.000 8.341 x 10-2 4.170 x 10-2 4.169 x 10-2 4.171 x 10-2 Renewable resources – wind (MJ) * Mulch, low nitrogen 3.834 x 10-2 1.396 x 10-2 0.000 1.209 x 10-1 3.552 x 10-2 2.105 x 10-2 6.517 x 10-2 Clean, low nitrogen 6.655 x 10-3 5.111 x 10-4 6.604 x 10-3 1.323 x 10-2 6.615 x 10-3 6.614 x 10-3 6.617 x 10-3 Mulch, high nitrogen 3.671 x 10-2 1.392 x 10-2 1.476 x 10-2 1.632 x 10-1 3.394 x 10-2 2.119 x 10-2 6.084 x 10-2 Clean, high nitrogen 7.088 x 10-3 5.893 x 10-4 0.000 1.411 x 10-2 7.052 x 10-3 7.051 x 10-3 7.054 x 10-3 Table 34. Uncertainty analysis results of a Monte Carlo simulation (1000 runs) for the production of 1 kg apples, including only the relative uncertainty that differs between treatments. Impact categories with significant differences (p < 0.05) marked with *, and n.s. for non significant. Mean Standard deviation Minimum Maximum Median 5% Percentile 95% Percentile Acidification potential (kg SO2 eq.) * Mulch, low nitrogen 1.390 x 10-3 2.931 x 10-4 1.059 x 10-3 3.370 x 10-3 1.319 x 10-3 1.126 x 10-3 1.944 x 10-3 Clean, low nitrogen 1.055 x 10-3 3.529 x 10-4 9.300 x 10-4 2.458 x 10-3 9.329 x 10-4 9.326 x 10-4 2.093 x 10-3 Mulch, high nitrogen 1.751 x 10-3 2.833 x 10-4 1.215 x 10-3 4.661 x 10-3 1.678 x 10-3 1.482 x 10-3 2.257 x 10-3 Clean, high nitrogen 1.295 x 10-3 2.591 x 10-7 1.294 x 10-3 1.298 x 10-3 1.295 x 10-3 1.295 x 10-3 1.295 x 10-3 Climate change (kg CO2 eq.) * 167 Mean Standard deviation Minimum Maximum Median 5% Percentile 95% Percentile Mulch, low nitrogen 1.868 x 10-1 5.449 x 10-2 1.149 x 10-1 5.497 x 10-1 1.728 x 10-1 1.347 x 10-1 2.807 x 10-1 Clean, low nitrogen 1.390 x 10-1 4.586 x 10-2 5.159 x 10-2 3.637 x 10-1 1.232 x 10-1 1.191 x 10-1 2.690 x 10-1 Mulch, high nitrogen 2.327 x 10-1 5.464 x 10-2 1.564 x 10-1 7.628 x 10-1 2.182 x 10-1 1.804 x 10-1 3.284 x 10-1 Clean, high nitrogen 1.582 x 10-1 9.824 x 10-3 1.508 x 10-1 2.772 x 10-1 1.554 x 10-1 1.517 x 10-1 1.740 x 10-1 Depletion of abiotic resources – elements, ultimate reserves (kg antimony eq.) * Mulch, low nitrogen 1.244 x 10-6 2.303 x 10-7 -1.663 x 10-6 3.027 x 10-6 1.194 x 10-6 1.071 x 10-6 1.595 x 10-6 Clean, low nitrogen 1.136 x 10-6 4.012 x 10-7 4.416 x 10-8 2.266 x 10-6 9.764 x 10-7 9.762 x 10-7 2.214 x 10-6 Mulch, high nitrogen 1.443 x 10-6 3.328 x 10-7 -4.441 x 10-6 5.413 x 10-6 1.397 x 10-6 1.271 x 10-6 1.801 x 10-6 Clean, high nitrogen 1.175 x 10-6 7.620x 10-10 1.171 x 10-6 1.193 x 10-6 1.175 x 10-6 1.175 x 10-6 1.176 x 10-6 Eutrophication (kg PO4--- eq.) * Mulch, low nitrogen 7.812 x 10-4 1.259 x 10-4 6.238 x 10-4 2.118 x 10-3 7.504 x 10-4 6.618 x 10-4 9.951 x 10-4 Clean, low nitrogen 6.906 x 10-4 2.681 x 10-4 2.763 x 10-4 1.523 x 10-3 5.832 x 10-4 5.759 x 10-4 1.468 x 10-3 Mulch, high nitrogen 9.854 x 10-4 1.156 x 10-4 7.411 x 10-4 1.737 x 10-3 9.604 x 10-4 8.627 x 10-4 1.197 x 10-3 Clean, high nitrogen 7.830 x 10-4 1.017 x 10-5 7.716 x 10-4 8.874 x 10-4 7.803 x 10-4 7.740 x 10-4 8.002 x 10-4 Freshwater aquatic ecotoxicity (kg 1,4-dichlorobenzene eq.) * Mulch, low nitrogen 1.205 x 10-1 5.667 x 10-2 6.403 x 10-2 1.371 1.087 x 10-1 8.922 x 10-2 1.762 x 10-1 Clean, low nitrogen 8.417 x 10-2 3.107 x 10-2 1.429 x 10-3 1.815 x 10-1 7.159 x 10-2 7.080 x 10-2 1.647 x 10-1 Mulch, high nitrogen 1.254 x 10-1 4.254 x 10-2 6.769 x 10-2 7.393 x 10-1 1.162 x 10-1 9.589 x 10-2 1.790 x 10-1 Clean, high nitrogen 7.774 x 10-2 3.313 x 10-3 7.633 x 10-2 1.366 x 10-1 7.721 x 10-2 7.656 x 10-2 7.951 x 10-2 Human toxicity (kg 1,4-dichlorobenzene eq.) * Mulch, low nitrogen 1.576 x 10-1 7.637 x 10-2 7.788 x 10-2 1.441 1.415 x 10-1 1.122 x 10-1 2.381 x 10-1 Clean, low nitrogen 9.962 x 10-2 4.058 x 10-2 3.270 x 10-2 2.274 x 10-1 8.317 x 10-2 8.257 x 10-2 2.107 x 10-1 Mulch, high nitrogen 1.734 x 10-1 5.121 x 10-2 5.867 x 10-2 6.853 x 10-1 1.593 x 10-1 1.308 x 10-1 2.570 x 10-1 Clean, high nitrogen 1.010 x 10-1 1.589 x 10-3 1.000 x 10-1 1.223 x 10-1 1.006 x 10-1 1.002 x 10-1 1.029 x 10-1 Marine aquatic ecotoxicity (kg 1,4-dichlorobenzene eq.) * Mulch, low nitrogen 3.022 x 102 9.942 x 101 -2.820 x 101 1.713 x 103 2.815 x 102 2.326 x 102 4.170 x 102 Clean, low nitrogen 2.135 x 102 7.310 x 101 5.515 x 101 4.466 x 102 1.844 x 102 1.827 x 102 4.180 x 102 Mulch, high nitrogen 3.210 x 102 7.482 x 101 -1.121 x 102 9.685 x 102 3.036 x 102 2.539 x 102 4.480 x 102 Clean, high nitrogen 2.045 x 102 3.637 2.015 x 102 2.608 x 102 2.037 x 102 2.023 x 102 2.088 x 102 Ozone layer depletion (kg CFC-11 eq.) * Mulch, low nitrogen 2.654 x 10-8 1.477 x 10-8 1.325 x 10-8 2.411 x 10-7 2.244 x 10-8 1.532 x 10-8 5.242 x 10-8 Clean, low nitrogen 1.252 x 10-8 6.278 x 10-9 4.255 x 10-11 2.895 x 10-8 1.001 x 10-8 9.981 x 10-9 2.884 x 10-8 Mulch, high nitrogen 2.819 x 10-8 1.616 x 10-8 1.251 x 10-8 2.393 x 10-7 2.426 x 10-8 1.714 x 10-8 5.100 x 10-8 Clean, high nitrogen 1.155 x 10-8 2.088 x 10-11 1.152 x 10-8 1.166 x 10-8 1.155 x 10-8 1.153 x 10-8 1.159 x 10-8 Photochemical oxidation (kg ethylene eq.) * Mulch, low nitrogen 5.970 x 10-5 1.322 x 10-5 4.170 x 10-5 1.530 x 10-4 5.637 x 10-5 4.753 x 10-5 8.277 x 10-5 Clean, low nitrogen 4.229 x 10-5 1.332 x 10-5 2.464 x 10-5 8.952 x 10-5 3.773 x 10-5 3.771 x 10-5 8.548 x 10-5 Mulch, high nitrogen 6.330 x 10-5 1.281 x 10-5 2.801 x 10-5 1.921 x 10-4 5.988 x 10-5 5.127 x 10-5 8.932 x 10-5 Clean, high nitrogen 4.139 x 10-5 1.664 x 10-8 4.135 x 10-5 4.155 x 10-5 4.138 x 10-5 4.137 x 10-5 4.141 x 10-5 Terrestrial ecotoxicity (kg 1,4-dichlorobenzene eq) * 168 Mean Standard deviation Minimum Maximum Median 5% Percentile 95% Percentile Mulch, low nitrogen 2.039 x 10-3 3.839 x 10-4 1.266 x 10-3 4.603 x 10-3 1.949 x 10-3 1.637 x 10-3 2.772 x 10-3 Clean, low nitrogen 1.564 x 10-3 6.057 x 10-4 4.790 x 10-5 3.235 x 10-3 1.328 x 10-3 1.326 x 10-3 3.223 x 10-3 Mulch, high nitrogen 2.263 x 10-3 3.893 x 10-4 1.192 x 10-3 4.880 x 10-3 2.161 x 10-3 1.860 x 10-3 3.013 x 10-3 Clean, high nitrogen 1.536 x 10-3 1.626 x 10-6 1.533 x 10-3 1.550 x 10-3 1.535 x 10-3 1.534 x 10-3 1.539 x 10-3 Land use – land competition (m2*yr) * Mulch, low nitrogen 3.843 2.372 8.045 x 10-1 2.333 x 101 3.210 1.466 8.191 Clean, low nitrogen 1.730 x 10-2 9.495 x 10-6 1.722 x 10-2 1.735 x 10-2 1.730 x 10-2 1.729 x 10-2 1.732 x 10-2 Mulch, high nitrogen 4.031 2.618 7.690 x 10-1 3.539 x 101 3.436 1.545 8.682 Clean, high nitrogen 1.811 x 10-2 9.558 x 10-6 1.804 x 10-2 1.816 x 10-2 1.811 x 10-2 1.810 x 10-2 1.813 x 10-2 Non-renewable resources – fossil (MJ) * Mulch, low nitrogen 2.820 1.028 -3.320 x 10-1 1.427 x 101 2.575 2.001 4.549 Clean, low nitrogen 1.435 3.939 x 10-1 1.364 3.740 1.365 1.364 1.366 Mulch, high nitrogen 2.974 9.673 x 10-1 1.935 1.263 x 101 2.718 2.191 4.771 Clean, high nitrogen 1.562 1.468 x 10-1 0.000 3.104 1.552 1.551 1.552 Non-renewable resources – nuclear (MJ) * Mulch, low nitrogen 1.281 x 10-1 4.887 x 10-2 -9.493 x 10-1 4.882 x 10-1 1.223 x 10-1 9.482 x 10-2 1.803 x 10-1 Clean, low nitrogen 5.892 x 10-2 1.944 x 10-2 5.516 x 10-2 1.708 x 10-1 5.541 x 10-2 5.539 x 10-2 5.545 x 10-2 Mulch, high nitrogen 1.335 x 10-1 4.692 x 10-2 -6.264 x 10-1 1.001 1.264 x 10-1 1.001 x 10-1 1.861 x 10-1 Clean, high nitrogen 6.285 x 10-2 5.916 x 10-3 0.000 1.248 x 10-1 6.242 x 10-2 6.240 x 10-2 6.245 x 10-2 Non-renewable resources – primary forest (MJ) * Mulch, low nitrogen 6.257 x 10-3 6.928 x 10-4 0.000 1.248 x 10-2 6.183 x 10-3 6.160 x 10-3 6.304 x 10-3 Clean, low nitrogen 6.329 x 10-3 1.070 x 10-3 6.137 x 10-3 1.231 x 10-2 6.138 x 10-3 6.138 x 10-3 6.138 x 10-3 Mulch, high nitrogen 6.267 x 10-3 6.583 x 10-4 3.367 x 10-3 1.248 x 10-2 6.187 x 10-3 6.166 x 10-3 6.300 x 10-3 Clean, high nitrogen 6.206 x 10-3 6.707 x 10-4 0.000 1.229 x 10-2 6.145 x 10-3 6.145 x 10-3 6.145 x 10-3 Renewable resources – biomass (MJ) * Mulch, low nitrogen 8.898 x 101 5.126 x 101 0.000 4.255 x 102 7.760 x 101 3.154 x 101 1.855 x 102 Clean, low nitrogen 1.444 8.419 6.858 x 10-2 5.298 x 101 6.887 x 10-2 6.886 x 10-2 6.889 x 10-2 Mulch, high nitrogen 8.780 x 101 5.523 x 101 1.333 x 101 5.776 x 102 7.552 x 101 3.044 x 101 1.963 x 102 Clean, high nitrogen 7.295 x 10-2 5.626 x 10-3 0.000 1.453 x 10-1 7.266 x 10-2 7.265 x 10-2 7.268 x 10-2 Renewable resources – geothermal (MJ) * Mulch, low nitrogen 6.892 x 10-3 2.167 x 10-3 0.000 2.103 x 10-2 6.512 x 10-3 4.073 x 10-3 1.102 x 10-2 Clean, low nitrogen 7.359 x 10-4 9.639 x 10-4 0.000 6.803 x 10-3 5.811 x 10-4 5.809 x 10-4 5.815 x 10-4 Mulch, high nitrogen 6.821 x 10-3 2.131 x 10-3 0.000 2.103 x 10-2 6.499 x 10-3 4.077 x 10-3 1.091 x 10-2 Clean, high nitrogen 6.546 x 10-4 6.476 x 10-5 0.000 1.299 x 10-3 6.494 x 10-4 6.492 x 10-4 6.498 x 10-4 Renewable resources – solar (MJ) * Mulch, low nitrogen 1.006 x 10-4 4.352 x 10-5 -6.462 x 10-5 7.550 x 10-4 8.829 x 10-5 7.286 x 10-5 1.720 x 10-4 Clean, low nitrogen 6.299 x 10-5 1.192 x 10-5 0.000 1.428 x 10-4 6.129 x 10-5 6.125 x 10-5 6.144 x 10-5 Mulch, high nitrogen 1.035 x 10-4 4.130 x 10-5 0.000 5.283 x 10-4 9.140 x 10-5 7.686 x 10-5 1.705 x 10-4 Clean, high nitrogen 6.548 x 10-5 5.447 x 10-6 0.000 1.305 x 10-4 6.515 x 10-5 6.510 x 10-5 6.526 x 10-5 Renewable resources – water (MJ) * 169 Mean Standard deviation Minimum Maximum Median 5% Percentile 95% Percentile Mulch, low nitrogen 1.090 3.510 x 10-1 0.000 3.222 1.033 6.261 x 10-1 1.772 Clean, low nitrogen 5.974 x 10-2 1.414 x 10-1 6.679 x 10-10 9.885 x 10-1 3.822 x 10-2 3.821 x 10-2 3.823 x 10-2 Mulch, high nitrogen 1.075 3.480 x 10-1 6.676 x 10-10 3.222 1.016 6.136 x 10-1 1.731 Clean, high nitrogen 4.195 x 10-2 3.724 x 10-3 0.000 8.341 x 10-2 4.170 x 10-2 4.169 x 10-2 4.171 x 10-2 Renewable resources – wind (MJ) * Mulch, low nitrogen 3.610 x 10-2 9.885 x 10-3 0.000 9.979 x 10-2 3.440 x 10-2 2.340 x 10-2 5.518 x 10-2 Clean, low nitrogen 7.454 x 10-3 5.009 x 10-3 0.000 3.838 x 10-2 6.615 x 10-3 6.614 x 10-3 6.617 x 10-3 Mulch, high nitrogen 3.598 x 10-2 9.806 x 10-3 0.000 9.979 x 10-2 3.447 x 10-2 2.305 x 10-2 5.444 x 10-2 Clean, high nitrogen 7.088 x 10-3 5.893 x 10-4 0.000 1.411 x 10-2 7.052 x 10-3 7.051 x 10-3 7.054 x 10-3 Table 35. Monte Carlo simulation results for the production of 1 kg apples, 0.653 kWh electricity + 0.653 MJ heat co-generation and 0.144 kg paper for all impact categories with parameter uncertainty. Categories marked with * are significant at p < 0.05, n.s. are not significant. Mean Standard deviation Minimum Maximum Median 5% Percentile 95% Percentile Acidification potential (kg SO2 eq.) n.s. Scenario 1: no mulch on orchards 3.225 x 10-3 6.816 x 10-4 -8.559 x 10-3 6.895 x 10-3 3.146 x 10-3 2.471 x 10-3 4.211 x 10-3 Scenario 2: all orchards use mulch 3.151 x 10-3 1.022 x 10-3 1.384 x 10-3 1.201 x 10-2 2.945 x 10-3 1.971 x 10-3 5.062 x 10-3 Climate change (kg CO2 eq.) * Scenario 1: no mulch on orchards 3.482 x 10-1 1.380 x 10-1 -1.305 9.488 x 10-1 3.454 x 10-1 1.551 x 10-1 5.508 x 10-1 Scenario 2: all orchards use mulch 4.157 x 10-1 1.488 x 10-1 1.863 x 10-1 2.327 3.833 x 10-1 2.528 x 10-1 6.717 x 10-1 Depletion of abiotic resources – elements, ultimate reserves (kg antimony eq.) * Scenario 1: no mulch on orchards 3.637 x 10-6 4.575 x 10-6 -1.386 x 10-4 1.011 x 10-5 3.672 x 10-6 2.724 x 10-6 5.204 x 10-6 Scenario 2: all orchards use mulch 1.338 x 10-5 3.688 x 10-6 6.666 x 10-6 7.825 x 10-5 1.295 x 10-5 9.363 x 10-6 1.855 x 10-5 Eutrophication (kg PO4--- eq.) n.s. Scenario 1: no mulch on orchards 1.691 x 10-3 4.825 x 10-4 -2.536 x 10-3 8.850 x 10-3 1.620 x 10-3 1.192 x 10-3 2.432 x 10-3 Scenario 2: all orchards use mulch 1.980 x 10-3 5.551 x 10-3 6.431 x 10-4 1.705 x 10-1 1.543 x 10-3 9.455 x 10-4 3.175 x 10-3 Freshwater aquatic ecotoxicity (kg 1,4-dichlorobenzene eq.) n.s. Scenario 1: no mulch on orchards 3.175 x 10-1 1.736 x 10-1 -1.034 2.420 2.863 x 10-1 1.967 x 10-1 5.127 x 10-1 Scenario 2: all orchards use mulch 3.273 x 10-1 2.485 x 10-1 1.140 x 10-1 5.532 2.807 x 10-1 1.753 x 10-1 5.455 x 10-1 Human toxicity (kg 1,4-dichlorobenzene eq.) n.s. Scenario 1: no mulch on orchards 4.685 x 10-1 3.044 x 10-1 -2.407 5.457 4.189 x 10-1 2.983 x 10-1 7.202 x 10-1 Scenario 2: all orchards use mulch 5.064 x 10-1 5.565 x 10-1 2.002 x 10-1 1.344 x 101 4.342 x 10-1 2.751 x 10-1 8.488 x 10-1 Marine aquatic ecotoxicity (kg 1,4-dichlorobenzene eq.) n.s. Scenario 1: no mulch on orchards 9.665 x 102 6.302 x 102 -1.343 x 104 7.815 x 103 9.085 x 102 6.267 x 102 1.495 x 103 Scenario 2: all orchards use mulch 9.445 x 102 7.619 x 102 3.444 x 102 1.825 x 104 8.257 x 102 5.113 x 102 1.544 x 103 Ozone layer depletion (kg CFC-11 eq.) * Scenario 1: no mulch on orchards 5.813 x 10-8 1.883 x 10-8 -2.073 x 10-7 1.869 x 10-7 5.525 x 10-8 3.812 x 10-8 8.708 x 10-8 Scenario 2: all orchards use mulch 6.350 x 10-8 3.107 x 10-8 2.271 x 10-8 3.915 x 10-7 5.628 x 10-8 3.260 x 10-8 1.176 x 10-7 Photochemical oxidation (kg ethylene eq.) * 170 Mean Standard deviation Minimum Maximum Median 5% Percentile 95% Percentile Scenario 1: no mulch on orchards 1.472 x 10-4 5.029 x 10-5 -6.655 x 10-4 5.069 x 10-4 1.397 x 10-4 1.003 x 10-4 2.201 x 10-4 Scenario 2: all orchards use mulch 1.620 x 10-4 6.022 x 10-5 7.245 x 10-5 5.980 x 10-4 1.503 x 10-4 9.427 x 10-5 2.735 x 10-4 Terrestrial ecotoxicity (kg 1,4-dichlorobenzene eq.) * Scenario 1: no mulch on orchards 1.243 x 10-2 8.719 x 10-3 -1.551 x 10-2 2.084 x 10-1 1.059 x 10-2 6.515 x 10-3 2.369 x 10-2 Scenario 2: all orchards use mulch 6.223 x 10-3 2.002 x 10-3 2.764 x 10-3 1.801 x 10-2 5.806 x 10-3 3.823 x 10-3 1.011 x 10-2 Land use - land competition (m2*yr) * Scenario 1: no mulch on orchards 3.273 1.699 8.517 x 10-1 1.261 x 101 2.845 1.352 6.579 Scenario 2: all orchards use mulch 4.613 3.762 4.282 x 10-1 3.801 x 101 3.517 1.231 1.151 x 101 Non-renewable resources – fossil (MJ) n.s. Scenario 1: no mulch on orchards 3.233 x 104 9.936 x 105 -4.996 3.141 x 107 4.975 3.790 7.726 Scenario 2: all orchards use mulch 9.465 x 102 1.345 x 104 -1.061 3.045 x 105 5.981 3.856 7.595 x 101 Non-renewable resources – nuclear (MJ) n.s. Scenario 1: no mulch on orchards 1.826 x 101 2.750 x 102 -3.792 5.992 x 103 4.438 x 10-1 3.196 x 10-1 7.270 x 10-1 Scenario 2: all orchards use mulch 4.423 x 101 6.218 x 102 0.000 1.284 x 104 3.306 2.432 5.331 Non-renewable resources - primary forest (MJ) n.s. Scenario 1: no mulch on orchards 4.577 x 106 9.919 x 107 -3.973 x 10-3 3.050 x 109 1.043 x 10-2 6.803 x 10-3 1.827 x 10-2 Scenario 2: all orchards use mulch 6.928 x 105 1.699 x 107 0.000 5.343 x 108 1.551 x 10-2 9.960 x 10-3 3.586 x 104 Renewable resources – biomass (MJ) n.s. Scenario 1: no mulch on orchards 1.756 x 103 4.175 x 104 0.000 1.268 x 106 6.474 x 101 2.594 x 101 1.598 x 102 Scenario 2: all orchards use mulch 6.094 x 102 6.780 x 103 0.000 1.761 x 105 7.721 x 101 2.090 x 101 3.622 x 102 Renewable resources – geothermal (MJ) n.s. Scenario 1: no mulch on orchards 7.610 x 101 2.302 x 103 0.000 7.277 x 104 1.121 x 10-2 8.135 x 10-3 1.961 x 10-2 Scenario 2: all orchards use mulch 8.244 x 101 2.390 x 103 0.000 7.551 x 104 1.102 x 10-2 6.690 x 10-3 4.047 Renewable resources – solar (MJ) * Scenario 1: no mulch on orchards 5.036 x 10-3 3.488 x 10-3 0.000 2.767 x 10-2 4.299 x 10-3 2.018 x 10-3 1.014 x 10-2 Scenario 2: all orchards use mulch 1.265 x 10-3 3.907 x 10-3 -1.353 x 10-4 3.000 x 10-2 3.409 x 10-4 2.464 x 10-4 1.087 x 10-2 Renewable resources – water (MJ) n.s. Scenario 1: no mulch on orchards 1.170 x 106 3.391 x 107 0.000 1.068 x 109 1.517 1.089 2.674 Scenario 2: all orchards use mulch 7.829 x 104 1.412 x 106 0.000 4.096 x 107 4.129 3.457 1.770 x 104 Renewable resources – wind (MJ) n.s. Scenario 1: no mulch on orchards 2.526 x 101 7.165 x 102 0.000 2.262 x 104 5.252 x 10-2 3.943 x 10-2 8.190 x 10-2 Scenario 2: all orchards use mulch 1.602 1.986 x 101 0.000 5.782 x 102 5.505 x 10-2 3.286 x 10-2 5.639 Table 36. Monte Carlo simulation results for the production of 1 kg apples, 0.653 kWh electricity + 0.653 MJ heat co-generation and 0.144 kg paper for select impact categories with parameter and impact assessment uncertainty. Categories marked with * are significant at p < 0.05, n.s. are not significant. Mean Standard deviation Minimum Maximum Median 5% Percentile 95% Percentile Acidification potential (kg SO2 eq.) * 171 Mean Standard deviation Minimum Maximum Median 5% Percentile 95% Percentile Scenario 1: no mulch on orchards 1.930 1.304 x 101 -5.511 x 10-3 1.879 x 102 3.704 x 10-3 2.217 x 10-3 7.008 x 10-3 Scenario 2: all orchards use mulch 4.593 2.703 x 101 8.065 x 10-6 3.013 x 102 3.497 x 10-3 1.826 x 10-3 9.230 x 10-3 Climate change (kg CO2 eq.) * Scenario 1: no mulch on orchards 5.531 x 10-1 9.287 x 10-1 -5.191 x 10-1 8.626 3.857 x 10-1 1.559 x 10-1 7.500 x 10-1 Scenario 2: all orchards use mulch 8.484 x 10-1 1.638 1.253 x 10-2 1.609 x 101 4.309 x 10-1 2.302 x 10-1 5.173 Eutrophication (kg PO4--- eq.) * Scenario 1: no mulch on orchards 2.739 x 10-3 3.935 x 10-3 -1.661 x 10-3 3.792 x 10-2 1.880 x 10-3 1.138 x 10-3 4.322 x 10-3 Scenario 2: all orchards use mulch 3.583 x 10-3 8.442 x 10-3 4.660 x 10-4 2.148 x 10-1 1.850 x 10-3 9.501 x 10-4 1.532 x 10-2 Freshwater aquatic ecotoxicity (kg 1,4-dichlorobenzene eq.) n.s. Scenario 1: no mulch on orchards 1.185 x 105 2.633 x 106 -7.536 x 103 8.059 x 107 2.900 x 102 1.439 x 101 4.560 x 104 Scenario 2: all orchards use mulch 1.435 x 104 1.031 x 105 -2.236 x 102 1.553 x 106 2.570 x 102 1.525 x 101 2.923 x 104 Human toxicity (kg 1,4-dichlorobenzene eq.) n.s. Scenario 1: no mulch on orchards 1.188 x 105 1.686 x 106 -2.576 x 105 4.058 x 107 1.246 x 103 9.879 x 101 9.387 x 104 Scenario 2: all orchards use mulch 8.782 x 104 9.146 x 105 7.056 2.106 x 107 1.174 x 103 8.015 x 101 9.328 x 104 Marine aquatic ecotoxicity (kg 1,4-dichlorobenzene eq.) n.s. Scenario 1: no mulch on orchards 1.238 x 108 1.940 x 109 -1.321 x 107 5.236 x 1010 6.089 x 105 2.383 x 104 1.205 x 108 Scenario 2: all orchards use mulch 2.198 x 108 4.146 x 109 3.847 x 101 1.242 x 1011 5.852 x 105 3.079 x 104 5.821 x 107 Ozone layer depletion (kg CFC-11 eq.) * Scenario 1: no mulch on orchards 1.612 x 10-2 9.072 x 10-2 -1.028 x 10-7 1.001 5.743 x 10-8 3.587 x 10-8 1.326 x 10-7 Scenario 2: all orchards use mulch 2.112 x 10-1 8.398 x 10-1 6.850 x 10-9 5.203 6.057 x 10-8 2.939 x 10-8 2.865 Photochemical oxidation (kg ethylene eq.) * Scenario 1: no mulch on orchards 4.455 x 10-4 8.911 x 10-4 -2.409 x 10-4 1.644 x 10-2 2.942 x 10-4 1.649 x 10-4 7.306 x 10-4 Scenario 2: all orchards use mulch 2.177 x 10-4 1.137 x 10-4 -7.265 x 10-5 1.052 x 10-3 1.908 x 10-4 1.006 x 10-4 4.443 x 10-4 Terrestrial ecotoxicity (kg 1,4-dichlorobenzene eq.) n.s. Scenario 1: no mulch on orchards 2.296 x 103 2.265 x 104 5.531 x 10-6 4.874 x 105 1.473 x 101 6.093 x 10-1 3.005 x 103 Scenario 2: all orchards use mulch 1.451 x 103 1.657 x 104 1.161 x 10-2 3.490 x 105 1.141 x 101 4.974 x 10-1 1.254 x 103 Table 37. Monte Carlo simulation results for the production of 1 kg apples, 0.653 kWh electricity + 0.653 MJ heat co-generation and 0.144 kg paper for all impact categories with parameter uncertainty from the orchard product systems only. Categories marked with * are significant at p < 0.05, n.s. are not significant. Mean Standard deviation Minimum Maximum Median 5% Percentile 95% Percentile Acidification potential (kg SO2 eq.) * Scenario 1: no mulch on orchards 3.754 x 10-3 2.957 x 10-3 1.075 x 10-5 3.100 x 10-2 3.214 x 10-3 2.567 x 10-3 4.656 x 10-3 Scenario 2: all orchards use mulch 2.895 x 10-3 3.520 x 10-3 -1.039 x 10-1 8.822 x 10-3 2.820 x 10-3 1.855 x 10-3 4.714 x 10-3 Climate change (kg CO2 eq.) n.s. Scenario 1: no mulch on orchards 3.738 x 10-1 7.898 x 10-2 1.443 x 10-4 8.309 x 10-1 3.647 x 10-1 2.769 x 10-1 5.151 x 10-1 Scenario 2: all orchards use mulch 3.830 x 10-1 5.035 x 10-1 -1.493 x 101 1.464 3.695 x 10-1 2.426 x 10-1 6.444 x 10-1 Depletion of abiotic resources – elements, ultimate reserves (kg antimony eq.) * 172 Mean Standard deviation Minimum Maximum Median 5% Percentile 95% Percentile Scenario 1: no mulch on orchards 1.069 x 10-3 8.125 x 10-3 -2.508 x 10-5 7.322 x 10-2 3.493 x 10-6 2.841 x 10-6 4.901 x 10-6 Scenario 2: all orchards use mulch 1.109 x 10-5 5.438 x 10-5 -1.706 x 10-3 5.722 x 10-5 1.259 x 10-5 1.171 x 10-5 1.440 x 10-5 Eutrophication (kg PO4--- eq.) * Scenario 1: no mulch on orchards 1.606 x 10-3 3.471 x 10-4 -9.729 x 10-7 4.813 x 10-3 1.542 x 10-3 1.208 x 10-3 2.217 x 10-3 Scenario 2: all orchards use mulch 1.387 x 10-3 1.562 x 10-3 -4.514 x 10-2 5.177 x 10-3 1.334 x 10-3 8.218 x 10-4 2.397 x 10-3 Freshwater aquatic ecotoxicity (kg 1,4-dichlorobenzene eq.) n.s. Scenario 1: no mulch on orchards 3.107 x 10-1 3.250 x 10-1 -2.532 x 10-1 4.762 2.463 x 10-1 1.754 x 10-1 4.605 x 10-1 Scenario 2: all orchards use mulch 2.841 x 10-1 8.887 x 10-1 -2.274 x 101 1.321 x 101 2.515 x 10-1 1.488 x 10-1 5.137 x 10-1 Human toxicity (kg 1,4-dichlorobenzene eq.) * Scenario 1: no mulch on orchards 5.453 x 10-1 9.510 x 10-1 -3.655 x 10-1 1.291 x 101 3.747 x 10-1 2.724 x 10-1 6.887 x 10-1 Scenario 2: all orchards use mulch 4.156 x 10-1 1.396 -3.599 x 101 2.039 x 101 3.732 x 10-1 2.273 x 10-1 7.409 x 10-1 Marine aquatic ecotoxicity (kg 1,4-dichlorobenzene eq.) n.s. Scenario 1: no mulch on orchards 8.809 x 102 7.399 x 102 -2.539 x 103 1.768 x 104 7.940 x 102 5.706 x 102 1.307 x 103 Scenario 2: all orchards use mulch 7.616 x 102 4.101 x 103 -1.237 x 105 2.707 x 104 7.467 x 102 4.354 x 102 1.441 x 103 Ozone layer depletion (kg CFC-11 eq.) * Scenario 1: no mulch on orchards 2.107 x 10-4 1.654 x 10-3 -1.457 x 10-9 1.443 x 10-2 5.425 x 10-8 4.304 x 10-8 8.331 x 10-8 Scenario 2: all orchards use mulch 5.809 x 10-8 9.175 x 10-8 -2.650 x 10-6 4.585 x 10-7 5.292 x 10-8 3.172 x 10-8 1.132 x 10-7 Photochemical oxidation (kg ethylene eq.) * Scenario 1: no mulch on orchards 2.073 x 10-4 5.052 x 10-4 -2.427 x 10-5 4.383 x 10-3 1.360 x 10-4 9.735 x 10-5 2.330 x 10-4 Scenario 2: all orchards use mulch 1.467 x 10-4 2.363 x 10-4 -7.040 x 10-3 9.605 x 10-4 1.400 x 10-4 8.731 x 10-5 2.603 x 10-4 Terrestrial ecotoxicity (kg 1,4-dichlorobenzene eq.) * Scenario 1: no mulch on orchards 1.027 x 10-2 2.084 x 10-3 3.226 x 10-4 2.865 x 10-2 9.822 x 10-3 8.696 x 10-3 1.254 x 10-2 Scenario 2: all orchards use mulch 5.440 x 10-3 7.828 x 10-3 -2.360 x 10-1 1.786 x 10-2 5.345 x 10-3 3.771 x 10-3 8.795 x 10-3 Land use - land competition (m2*yr) * Scenario 1: no mulch on orchards 3.138 6.291 x 10-2 3.096 3.980 3.121 3.102 3.228 Scenario 2: all orchards use mulch 4.561 3.472 3.820 x 10-1 2.687 x 101 3.516 1.176 1.146 x 101 Non-renewable resources – fossil (MJ) * Scenario 1: no mulch on orchards 1.384 x 101 5.486 x 101 -9.066 x 10-1 5.705 x 102 5.166 4.134 7.245 Scenario 2: all orchards use mulch 1.177 x 103 2.175 x 104 0.000 5.008 x 105 5.824 3.953 1.515 x 101 Non-renewable resources – nuclear (MJ) * Scenario 1: no mulch on orchards 1.203 x 101 7.470 x 101 -1.992 8.931 x 102 4.387 x 10-1 3.966 x 10-1 5.241 x 10-1 Scenario 2: all orchards use mulch 5.456 2.790 x 101 0.000 6.207 x 102 3.258 3.172 3.493 Non-renewable resources - primary forest (MJ) n.s. Scenario 1: no mulch on orchards 1.627 x 10-2 4.203 x 10-2 0.000 6.271 x 10-1 9.821 x 10-3 7.290 x 10-3 1.437 x 10-2 Scenario 2: all orchards use mulch 1.571 x 105 2.734 x 106 0.000 7.040 x 107 1.454 x 10-2 1.151 x 10-2 2.440 x 103 Renewable resources – biomass (MJ) * Scenario 1: no mulch on orchards 7.054 x 101 8.100 0.000 7.556 x 101 7.140 x 101 7.129 x 101 7.186 x 101 Scenario 2: all orchards use mulch 1.906 x 102 1.380 x 103 0.000 3.656 x 104 7.981 x 101 2.500 x 101 3.185 x 102 173 Mean Standard deviation Minimum Maximum Median 5% Percentile 95% Percentile Renewable resources – geothermal (MJ) n.s. Scenario 1: no mulch on orchards 1.337 x 10-1 7.845 x 10-1 0.000 9.982 1.296 x 10-2 1.252 x 10-2 1.380 x 10-2 Scenario 2: all orchards use mulch 4.931 x 101 8.728 x 102 0.000 2.101 x 104 1.104 x 10-2 6.956 x 10-3 1.494 x 10-1 Renewable resources – solar (MJ) n.s. Scenario 1: no mulch on orchards 4.557 x 10-3 2.546 x 10-3 0.000 2.622 x 10-2 4.174 x 10-3 4.136 x 10-3 4.252 x 10-3 Scenario 2: all orchards use mulch 8.952 x 10-4 2.917 x 10-3 0.000 3.690 x 10-2 3.247 x 10-4 2.643 x 10-4 7.310 x 10-4 Renewable resources – water (MJ) * Scenario 1: no mulch on orchards 1.821 2.102 x 10-1 0.000 2.020 1.841 1.805 1.893 Scenario 2: all orchards use mulch 3.294 x 104 4.556 x 105 0.000 9.089 x 106 4.141 3.463 9.007 x 102 Renewable resources – wind (MJ) * Scenario 1: no mulch on orchards 1.810 x 10-1 7.765 x 10-1 0.000 7.102 6.067 x 10-2 5.653 x 10-2 6.856 x 10-2 Scenario 2: all orchards use mulch 4.177 1.186 x 102 0.000 3.751 x 103 5.576 x 10-2 3.394 x 10-2 2.028 Table 38. Monte Carlo simulation results for the production of 1 kg apples, 0.653 kWh electricity + 0.653 MJ heat co-generation and 0.144 kg paper for all impact categories with parameter uncertainty from the paper product systems only. Categories marked with * are significant at p < 0.05, n.s. are not significant. Mean Standard deviation Minimum Maximum Median 5% Percentile 95% Percentile Acidification potential (kg SO2 eq.) * Scenario 1: no mulch on orchards 2.426 x 10-3 1.250 x 10-4 2.190 x 10-3 3.818 x 10-3 2.407 x 10-3 2.273 x 10-3 2.638 x 10-3 Scenario 2: all orchards use mulch 1.652 x 10-3 1.843 x 10-4 -3.643 x 10-4 5.716 x 10-3 1.634 x 10-3 1.479 x 10-3 1.858 x 10-3 Climate change (kg CO2 eq.) n.s. Scenario 1: no mulch on orchards 2.477 x 10-1 1.057 x 10-1 -2.482 x 10-1 9.527 x 10-1 2.462 x 10-1 8.658 x 10-2 4.173 x 10-1 Scenario 2: all orchards use mulch 2.472 x 10-1 1.923 x 10-2 -1.052 x 10-2 6.838 x 10-1 2.460 x 10-1 2.319 x 10-1 2.666 x 10-1 Depletion of abiotic resources – elements, ultimate reserves (kg antimony eq.) * Scenario 1: no mulch on orchards 3.075 x 10-6 8.941 x 10-7 2.121 x 10-6 2.125 x 10-5 2.949 x 10-6 2.418 x 10-6 3.926 x 10-6 Scenario 2: all orchards use mulch 1.221 x 10-5 2.966 x 10-6 -1.408 x 10-5 5.033 x 10-5 1.190 x 10-5 8.541 x 10-6 1.677 x 10-5 Eutrophication (kg PO4--- eq.) * Scenario 1: no mulch on orchards 1.312 x 10-3 1.332 x 10-4 1.097 x 10-3 2.462 x 10-3 1.287 x 10-3 1.156 x 10-3 1.554 x 10-3 Scenario 2: all orchards use mulch 1.151 x 10-3 1.053 x 10-3 -1.840 x 10-4 1.679 x 10-2 9.133 x 10-4 7.888 x 10-4 2.199 x 10-3 Freshwater aquatic ecotoxicity (kg 1,4-dichlorobenzene eq.) * Scenario 1: no mulch on orchards 2.219 x 10-1 5.798 x 10-1 1.305 x 10-1 1.823 x 101 1.869 x 10-1 1.504 x 10-1 2.939 x 10-1 Scenario 2: all orchards use mulch 1.559 x 10-1 1.481 x 10-1 -2.593 x 10-1 4.518 1.428 x 10-1 1.211 x 10-1 1.993 x 10-1 Human toxicity (kg 1,4-dichlorobenzene eq.) * Scenario 1: no mulch on orchards 2.971 x 10-1 3.335 x 10-1 2.033 x 10-1 7.130 2.604 x 10-1 2.249 x 10-1 3.677 x 10-1 Scenario 2: all orchards use mulch 2.343 x 10-1 1.160 x 10-1 -2.892 x 10-1 2.278 2.188 x 10-1 1.846 x 10-1 3.088 x 10-1 Marine aquatic ecotoxicity (kg 1,4-dichlorobenzene eq.) * Scenario 1: no mulch on orchards 5.896 x 102 6.549 x 102 3.560 x 102 1.774 x 104 5.199 x 102 4.168 x 102 8.482 x 102 Scenario 2: all orchards use mulch 3.611 x 102 2.277 x 102 -1.885 x 103 4.502 x 103 3.356 x 102 2.865 x 102 4.550 x 102 174 Mean Standard deviation Minimum Maximum Median 5% Percentile 95% Percentile Ozone layer depletion (kg CFC-11 eq.) * Scenario 1: no mulch on orchards 4.078 x 10-8 5.451 x 10-9 3.015 x 10-8 7.609 x 10-8 3.991 x 10-8 3.364 x 10-8 5.066 x 10-8 Scenario 2: all orchards use mulch 3.491 x 10-8 5.258 x 10-9 -1.242 x 10-8 1.229 x 10-7 3.400 x 10-8 3.000 x 10-8 4.300 x 10-8 Photochemical oxidation (kg ethylene eq.) * Scenario 1: no mulch on orchards 8.666 x 10-5 8.039 x 10-6 7.238 x 10-5 2.089 x 10-4 8.554 x 10-5 7.765 x 10-5 9.868 x 10-5 Scenario 2: all orchards use mulch 7.640 x 10-5 9.920 x 10-6 -7.837 x 10-5 3.152 x 10-4 7.571 x 10-5 7.051 x 10-5 8.366 x 10-5 Terrestrial ecotoxicity (kg 1,4-dichlorobenzene eq.) * Scenario 1: no mulch on orchards 9.389 x 10-3 1.850 x 10-3 6.735 x 10-3 2.563 x 10-2 8.977 x 10-3 7.463 x 10-3 1.261 x 10-2 Scenario 2: all orchards use mulch 3.952 x 10-3 6.048 x 10-4 2.851 x 10-4 1.024 x 10-2 3.876 x 10-3 3.254 x 10-3 4.941 x 10-3 Land use - land competition (m2*yr) * Scenario 1: no mulch on orchards 2.760 2.459 x 10-2 2.635 2.881 2.755 2.730 2.806 Scenario 2: all orchards use mulch 3.220 6.105 x 10-3 3.205 3.276 3.219 3.212 3.232 Non-renewable resources – fossil (MJ) * Scenario 1: no mulch on orchards 3.639 2.617 x 10-1 3.044 4.695 3.613 3.266 4.118 Scenario 2: all orchards use mulch 3.964 1.656 x 10-1 3.467 4.791 3.952 3.724 4.242 Non-renewable resources – nuclear (MJ) * Scenario 1: no mulch on orchards 3.933 x 10-1 9.675 x 10-2 2.058 x 10-1 1.352 3.760 x 10-1 2.789 x 10-1 5.644 x 10-1 Scenario 2: all orchards use mulch 3.256 5.774 x 10-1 1.759 5.824 3.204 2.421 4.269 Non-renewable resources - primary forest (MJ) * Scenario 1: no mulch on orchards 9.796 x 10-3 1.733 x 10-3 7.086 x 10-3 2.917 x 10-2 9.462 x 10-3 7.804 x 10-3 1.293 x 10-2 Scenario 2: all orchards use mulch 1.490 x 10-2 3.744 x 10-3 8.616 x 10-3 3.703 x 10-2 1.401 x 10-2 1.046 x 10-2 2.185 x 10-2 Renewable resources – biomass (MJ) * Scenario 1: no mulch on orchards 6.324 x 101 3.769 x 10-1 6.257 x 101 6.525 x 101 6.317 x 101 6.278 x 101 6.395 x 101 Scenario 2: all orchards use mulch 7.420 x 101 1.179 x 10-1 7.401 x 101 7.534 x 101 7.417 x 101 7.407 x 101 7.442 x 101 Renewable resources – geothermal (MJ) * Scenario 1: no mulch on orchards 1.087 x 10-2 2.639 x 10-3 5.993 x 10-3 2.623 x 10-2 1.037 x 10-2 7.698 x 10-3 1.581 x 10-2 Scenario 2: all orchards use mulch 9.625 x 10-3 5.807 x 10-4 8.102 x 10-3 1.228 x 10-2 9.568 x 10-3 8.774 x 10-3 1.067 x 10-2 Renewable resources – solar (MJ) * Scenario 1: no mulch on orchards 4.494 x 10-3 2.097 x 10-3 9.838 x 10-4 2.729 x 10-2 4.096 x 10-3 2.028 x 10-3 8.294 x 10-3 Scenario 2: all orchards use mulch 2.877 x 10-4 2.987 x 10-5 2.101 x 10-4 4.118 x 10-4 2.866 x 10-4 2.419 x 10-4 3.412 x 10-4 Renewable resources – water (MJ) * Scenario 1: no mulch on orchards 1.510 3.831 x 10-1 8.193 x 10-1 3.746 1.440 1.045 2.231 Scenario 2: all orchards use mulch 3.957 8.405 x 10-2 3.739 4.339 3.949 3.831 4.110 Renewable resources – wind (MJ) * Scenario 1: no mulch on orchards 4.954 x 10-2 9.918 x 10-3 3.060 x 10-2 1.077 x 10-1 4.781 x 10-2 3.755 x 10-2 6.810 x 10-2 Scenario 2: all orchards use mulch 4.687 x 10-2 2.195 x 10-3 4.115 x 10-2 5.665 x 10-2 4.665 x 10-2 4.358 x 10-2 5.096 x 10-2 175 Table 39. Monte Carlo simulation results for the production of 1 kg apples, 0.653 kWh electricity + 0.653 MJ heat co-generation and 0.144 kg paper for all impact categories with parameter uncertainty from the co-generation product systems only. Categories marked with * are significant at p < 0.05, n.s. are not significant. Mean Standard deviation Minimum Maximum Median 5% Percentile 95% Percentile Acidification potential (kg SO2 eq.) * Scenario 1: no mulch on orchards 2.505 x 10-3 3.203 x 10-5 2.450 x 10-3 3.059 x 10-3 2.499 x 10-3 2.472 x 10-3 2.555 x 10-3 Scenario 2: all orchards use mulch 1.567 x 10-3 2.231 x 10-5 1.528 x 10-3 1.722 x 10-3 1.563 x 10-3 1.541 x 10-3 1.610 x 10-3 Climate change (kg CO2 eq.) * Scenario 1: no mulch on orchards 2.780 x 10-1 5.109 x 10-3 2.655 x 10-1 3.160 x 10-1 2.773 x 10-1 2.710 x 10-1 2.872 x 10-1 Scenario 2: all orchards use mulch 2.397 x 10-1 8.513 x 10-4 2.379 x 10-1 2.438 x 10-1 2.396 x 10-1 2.384 x 10-1 2.412 x 10-1 Depletion of abiotic resources – elements, ultimate reserves (kg antimony eq.) * Scenario 1: no mulch on orchards 2.869 x 10-6 1.347 x 10-7 2.823 x 10-6 6.371 x 10-6 2.857 x 10-6 2.837 x 10-6 2.899 x 10-6 Scenario 2: all orchards use mulch 1.169 x 10-5 6.505 x 10-9 1.168 x 10-5 1.181 x 10-5 1.169 x 10-5 1.169 x 10-5 1.170 x 10-5 Eutrophication (kg PO4--- eq.) * Scenario 1: no mulch on orchards 1.317 x 10-3 9.481 x 10-5 1.197 x 10-3 2.490 x 10-3 1.297 x 10-3 1.228 x 10-3 1.475 x 10-3 Scenario 2: all orchards use mulch 8.325 x 10-4 5.138 x 10-6 8.250 x 10-4 8.713 x 10-4 8.313 x 10-4 8.270 x 10-4 8.410 x 10-4 Freshwater aquatic ecotoxicity (kg 1,4-dichlorobenzene eq.) * Scenario 1: no mulch on orchards 1.716 x 10-1 2.051 x 10-2 1.546 x 10-1 4.165 x 10-1 1.672 x 10-1 1.587 x 10-1 1.951 x 10-1 Scenario 2: all orchards use mulch 1.164 x 10-1 2.064 x 10-2 1.124 x 10-1 7.036 x 10-1 1.149 x 10-1 1.132 x 10-1 1.194 x 10-1 Human toxicity (kg 1,4-dichlorobenzene eq.) * Scenario 1: no mulch on orchards 2.453 x 10-1 3.269 x 10-2 2.230 x 10-1 9.429 x 10-1 2.400 x 10-1 2.288 x 10-1 2.705 x 10-1 Scenario 2: all orchards use mulch 1.818 x 10-1 9.417 x 10-3 1.737 x 10-1 3.858 x 10-1 1.803 x 10-1 1.761 x 10-1 1.903 x 10-1 Marine aquatic ecotoxicity (kg 1,4-dichlorobenzene eq.) * Scenario 1: no mulch on orchards 4.757 x 102 4.898 x 101 4.338 x 102 1.401 x 103 4.669 x 102 4.452 x 102 5.239 x 102 Scenario 2: all orchards use mulch 2.841 x 102 2.069 x 101 2.774 x 102 8.461 x 102 2.822 x 102 2.790 x 102 2.901 x 102 Ozone layer depletion (kg CFC-11 eq.) * Scenario 1: no mulch on orchards 4.331 x 10-8 4.007 x 10-9 3.608 x 10-8 7.098 x 10-8 4.245 x 10-8 3.825 x 10-8 5.097 x 10-8 Scenario 2: all orchards use mulch 3.290 x 10-8 3.273 x 10-9 2.884 x 10-8 5.269 x 10-8 3.190 x 10-8 2.958 x 10-8 3.978 x 10-8 Photochemical oxidation (kg ethylene eq.) * Scenario 1: no mulch on orchards 8.745 x 10-5 1.727 x 10-6 8.397 x 10-5 1.107 x 10-4 8.715 x 10-5 8.553 x 10-5 9.036 x 10-5 Scenario 2: all orchards use mulch 7.321 x 10-5 2.671 x 10-6 6.865 x 10-5 9.089 x 10-5 7.279 x 10-5 6.984 x 10-5 7.823 x 10-5 Terrestrial ecotoxicity (kg 1,4-dichlorobenzene eq.) * Scenario 1: no mulch on orchards 1.044 x 10-2 5.306 x 10-3 5.873 x 10-3 8.164 x 10-2 8.937 x 10-3 6.573 x 10-3 1.902 x 10-2 Scenario 2: all orchards use mulch 3.609 x 10-3 7.197 x 10-5 3.496 x 10-3 4.106 x 10-3 3.594 x 10-3 3.523 x 10-3 3.745 x 10-3 Land use - land competition (m2*yr) * Scenario 1: no mulch on orchards 3.476 1.669 9.900 x 10-1 1.346 x 101 3.090 1.614 6.630 Scenario 2: all orchards use mulch 3.209 6.470 x 10-4 3.203 3.212 3.209 3.208 3.210 Non-renewable resources – fossil (MJ) * Scenario 1: no mulch on orchards 4.002 9.020 x 10-2 3.732 5.055 3.992 3.888 4.139 Scenario 2: all orchards use mulch 3.859 1.265 x 10-1 0.000 3.995 3.860 3.814 3.920 176 Mean Standard deviation Minimum Maximum Median 5% Percentile 95% Percentile Non-renewable resources – nuclear (MJ) * Scenario 1: no mulch on orchards 3.921 x 10-1 1.990 x 10-2 3.185 x 10-1 8.354 x 10-1 3.907 x 10-1 3.837 x 10-1 4.002 x 10-1 Scenario 2: all orchards use mulch 3.176 1.005 x 10-1 0.000 3.181 3.179 3.179 3.180 Non-renewable resources - primary forest (MJ) * Scenario 1: no mulch on orchards 9.495 x 10-3 7.824 x 10-5 9.220 x 10-3 1.130 x 10-2 9.490 x 10-3 9.485 x 10-3 9.498 x 10-3 Scenario 2: all orchards use mulch 1.396 x 10-2 4.418 x 10-4 0.000 1.398 x 10-2 1.397 x 10-2 1.397 x 10-2 1.397 x 10-2 Renewable resources – biomass (MJ) * Scenario 1: no mulch on orchards 7.974 x 101 3.580 x 101 2.488 x 101 3.368 x 102 7.195 x 101 3.721 x 101 1.468 x 102 Scenario 2: all orchards use mulch 7.392 x 101 2.340 0.000 7.399 x 101 7.399 x 101 7.399 x 101 7.399 x 101 Renewable resources – geothermal (MJ) * Scenario 1: no mulch on orchards 1.249 x 10-2 5.685 x 10-4 1.096 x 10-2 1.445 x 10-2 1.243 x 10-2 1.163 x 10-2 1.352 x 10-2 Scenario 2: all orchards use mulch 9.546 x 10-3 3.023 x 10-4 0.000 9.596 x 10-3 9.554 x 10-3 9.542 x 10-3 9.572 x 10-3 Renewable resources – solar (MJ) * Scenario 1: no mulch on orchards 4.148 x 10-3 4.178 x 10-6 4.137 x 10-3 4.208 x 10-3 4.147 x 10-3 4.143 x 10-3 4.153 x 10-3 Scenario 2: all orchards use mulch 2.766 x 10-4 8.770 x 10-6 0.000 2.791 x 10-4 2.768 x 10-4 2.761 x 10-4 2.778 x 10-4 Renewable resources – water (MJ) * Scenario 1: no mulch on orchards 1.805 8.358 x 10-2 1.572 2.123 1.798 1.680 1.951 Scenario 2: all orchards use mulch 3.944 1.248 x 10-1 0.000 3.953 3.948 3.946 3.950 Renewable resources – wind (MJ) * Scenario 1: no mulch on orchards 5.761 x 10-2 2.196 x 10-3 5.136 x 10-2 6.615 x 10-2 5.741 x 10-2 5.429 x 10-2 6.135 x 10-2 Scenario 2: all orchards use mulch 4.645 x 10-2 1.471 x 10-3 0.000 4.665 x 10-2 4.649 x 10-2 4.643 x 10-2 4.657 x 10-2 177 Appendix D – Uncertainty Literature Review Supplementary Tables Table 40. Types of LCA studies and reporting of uncertainty in the International Journal of Life Cycle Assessment from January 2014-August 2018. In the “Uncertainty analysis performed/discussed” column, “performed” refers to uncertainty analysis performed in an LCA case study, and “discussed” refers to non-LCA studies which discussed the use of a particular type of uncertainty analysis in general. Number of studies reviewed Uncertainty analysis performed/discussed Uncertainty analysis reported as not performed Uncertainty mentioned but not quantified No mention of uncertainty International Journal of Life Cycle Assessment ALCA CLCA ALCA+CLCA Not an LCA 397 16 8 310 119 (30%) 3 (19%) 3 (38%) 44 (14%) 15 (4%) 144 (36%) 119 (30%) 0 (0%) 9 (56%) 4 (25%) 0 (0%) 4 (1%) 2 (25%) 149 (48%) 3 (38%) 113 (36%) Table 41. Types of LCA studies and reporting of uncertainty in Journal of Cleaner Production from January 2014-August 2018. Number of studies reviewed Uncertainty analysis performed Uncertainty analysis reported as not performed Uncertainty mentioned but not quantified No mention of uncertainty Journal of Cleaner Production ALCA CLCA ALCA+CLCA 1349 60 17 231 (17%) 8 (13%) 3 (18%) 20 (1%) 430 (32%) 667 (50%) 0 (0%) 29 (48%) 23 (38%) 0 (0%) 1 (59%) 4 (24%) Table 42. Types of LCA studies and reporting of uncertainty in Sustainability from January 2014-August 2018. Number of studies reviewed Uncertainty analysis performed Uncertainty analysis reported as not performed Uncertainty mentioned but not quantified No mention of uncertainty Sustainability ALCA CLCA ALCA+CLCA 140 6 1 9(6%) 0 (0%) 0 (0%) 1 (1%) 56 (40%) 74 (53%) 0 (0%) 5 (83%) 1 (17%) 0 (0%) 1 (100%) 0 (0%) Table 43. Types of LCA studies and reporting of uncertainty in Applied Energy from January 2014-August 2018. Number of studies reviewed Uncertainty analysis performed Uncertainty analysis reported as not performed Uncertainty mentioned but not quantified No mention of uncertainty Applied Energy ALCA CLCA ALCA+CLCA 214 17 0 40 (19%) 4 (24%) 0 (0%) 5 (2%) 72 (34%) 97 (45%) 0 (0%) 9 (53%) 4 (24%) 0 (0%) 0 (0%) 0 (0%) 178 Table 44. Types of LCA studies and reporting of uncertainty in Science of the Total Environment from January 2014-August 2018. Number of studies reviewed Uncertainty analysis performed Uncertainty analysis reported as not performed Uncertainty mentioned but not quantified No mention of uncertainty Science of the Total Environment ALCA CLCA ALCA+CLCA 162 9 3 20 (12%) 1 (11%) 0 (0%) 3 (2%) 74 (46%) 65 (40%) 0 (0%) 6 (67%) 2 (22%) 0 (0%) 2 (67%) 1 (33%) Table 45. Types of LCA studies and reporting of uncertainty in Resources, Conservation and Recycling from January 2014-August 2018. Number of studies reviewed Uncertainty analysis performed Uncertainty analysis reported as not performed Uncertainty mentioned but not quantified No mention of uncertainty Resources, Conservation and Recycling ALCA CLCA ALCA+CLCA 136 10 2 33 (24%) 3 (30%) 0 (0%) 6 (4%) 28 (21%) 69 (51%) 0 (0%) 5 (50%) 2 (20%) 0 (0%) 0 (0%) 2 (100%) Table 46. Types of LCA studies and reporting of uncertainty in Journal of Industrial Ecology from January 2014-August 2018. Number of studies reviewed Uncertainty analysis performed Uncertainty analysis reported as not performed Uncertainty mentioned but not quantified No mention of uncertainty Journal of Industrial Ecology ALCA CLCA ALCA+CLCA 121 18 1 23 (19%) 1 (6%) 1 (100%) 4 (3%) 66 (55%) 28 (23%) 2 (11%) 13 (72%) 2 (11%) 0 (0%) 0 (0%) 0 (0%) Table 47. Reported uncertainty analysis in LCA case studies in the International Journal of Life Cycle Assessment from 2014-2018. Note that 2018 only includes 8 months. Number of studies reviewed Studies with uncertainty analysis % of studies with uncertainty analysis International Journal of Life Cycle Assessment 2014 111 31 28% 2015 78 26 33% 2016 64 17 27% 2017 103 35 34% 2018 (Jan-Aug) 70 19 27% 179 Table 48. Reported uncertainty analysis in LCA case studies in Journal of Cleaner Production from 2014-2018. Note that 2018 only includes 8 months. Number of studies reviewed Studies with uncertainty analysis % of studies with uncertainty analysis Journal of Cleaner Production 2014 176 35 20% 2015 229 40 17% 2016 339 63 19% 2017 403 50 12% 2018 (Jan-Aug) 279 57 20% Table 49. Reported uncertainty analysis in LCA case studies in Sustainability from 2014-2018. Note that 2018 only includes 8 months. Number of studies reviewed Studies with uncertainty analysis % of studies with uncertainty analysis Sustainability 2014 8 0 0% 2015 21 0 0% 2016 36 4 11% 2017 40 4 10% 2018 (Jan-Aug) 42 1 2% Table 50. Reported uncertainty analysis in LCA case studies in Applied Energy from 2014-2018. Note that 2018 only includes 8 months. Number of studies reviewed Studies with uncertainty analysis % of studies with uncertainty analysis Applied Energy 2014 47 6 13% 2015 55 9 16% 2016 57 14 25% 2017 47 9 19% 2018 (Jan-Aug) 25 6 24% Table 51. Reported uncertainty analysis in LCA case studies in Science of the Total Environment from 2014-2018. Note that 2018 only includes 8 months. Number of studies reviewed Studies with uncertainty analysis % of studies with uncertainty analysis Science of the Total Environment 2014 31 3 10% 2015 16 2 13% 2016 35 5 14% 2017 38 6 16% 2018 (Jan-Aug) 54 5 9% 180 Table 52. Reported uncertainty analysis in LCA case studies in Resources, Conservation and Recycling from 2014-2018. Note that 2018 only includes 8 months. Number of studies reviewed Studies with uncertainty analysis % of studies with uncertainty analysis Resources, Conservation and Recycling 2014 20 7 35% 2015 37 4 11% 2016 20 7 35% 2017 38 8 21% 2018 (Jan-Aug) 33 10 30% Table 53. Reported uncertainty analysis in LCA case studies in Journal of Industrial Ecology from 2014-2018. Note that 2018 only includes 8 months. Number of studies reviewed Studies with uncertainty analysis % of studies with uncertainty analysis Journal of Industrial Ecology 2014 14 2 14% 2015 18 3 17% 2016 36 2 6% 2017 35 6 17% 2018 (Jan-Aug) 37 12 32% Table 54. Types of uncertainty analysis performed in ALCA and CLCA studies in the International Journal of Life Cycle Assessment from 2014-2018. Studies that employed a combination of uncertainty analysis methods are counted under each method. ALCA CLCA ALCA+CLCA International Journal of Life Cycle Assessment Monte Carlo-like approaches Probabilistic (other than Monte Carlo) 65 4 1 0 1 0 Taylor Series 2 0 0 Scenario Analysis 11 1 1 Qualitative/pedigree 2 0 0 Sensitivity Analysis Fuzzy Logic Bayesian 24 0 0 1 0 0 0 0 0 Regression Analysis 0 0 0 Not Indicated/Other 15 0 1 181 Table 55. Types of uncertainty analysis performed in ALCA and CLCA studies in Journal of Cleaner Production from 2014-2018. Studies that employed a combination of uncertainty analysis methods are counted under each method. ALCA CLCA ALCA+CLCA Journal of Cleaner Production Monte Carlo-like approaches Probabilistic (other than Monte Carlo) 144 4 6 1 1 0 Taylor Series 2 0 0 Scenario Analysis 9 0 0 Qualitative/pedigree 7 0 1 Sensitivity Analysis Fuzzy Logic Bayesian 53 3 0 1 0 0 1 0 0 Regression Analysis 1 0 0 Not Indicated/Other 11 0 0 Table 56. Types of uncertainty analysis performed in ALCA and CLCA studies in Sustainability from 2014-2018. Studies that employed a combination of uncertainty analysis methods are counted under each method. ALCA CLCA ALCA+CLCA Sustainability Monte Carlo-like approaches Probabilistic (other than Monte Carlo) 5 1 0 0 0 0 Taylor Series 0 0 0 Scenario Analysis 0 0 0 Qualitative/pedigree 2 0 0 Sensitivity Analysis Fuzzy Logic Bayesian 1 0 0 0 0 0 0 0 0 Regression Analysis 0 0 0 Not Indicated/Other 0 0 0 Table 57. Types of uncertainty analysis performed in ALCA and CLCA studies in Applied Energy from 2014-2018. Studies that employed a combination of uncertainty analysis methods are counted under each method. ALCA CLCA ALCA+CLCA Applied Energy Monte Carlo-like approaches Probabilistic (other than Monte Carlo) 23 5 3 0 0 0 Taylor Series 0 0 0 Scenario Analysis 3 0 0 Qualitative/pedigree 0 0 0 Sensitivity Analysis Fuzzy Logic Bayesian 6 0 0 1 0 0 0 0 0 Regression Analysis 0 0 0 Not Indicated/Other 3 0 0 182 Table 58. Types of uncertainty analysis performed in ALCA and CLCA studies in Science of the Total Environment from 2014-2018. Studies that employed a combination of uncertainty analysis methods are counted under each method. ALCA CLCA ALCA+CLCA Science of the Total Environment Monte Carlo-like approaches Probabilistic (other than Monte Carlo) 17 0 0 1 0 0 Taylor Series 0 0 0 Scenario Analysis 1 0 0 Qualitative/pedigree 0 0 0 Sensitivity Analysis Fuzzy Logic Bayesian 0 0 0 0 0 0 0 0 0 Regression Analysis 0 0 0 Not Indicated/Other 2 0 0 Table 59. Types of uncertainty analysis performed in ALCA and CLCA studies in Resources, Conservation and Recycling from 2014-2018. Studies that employed a combination of uncertainty analysis methods are counted under each method. ALCA CLCA ALCA+CLCA Resources, Conservation and Recycling Monte Carlo-like approaches Probabilistic (other than Monte Carlo) 17 1 1 0 0 0 Taylor Series 0 0 0 Scenario Analysis 5 1 0 Qualitative/pedigree 0 0 0 Sensitivity Analysis Fuzzy Logic Bayesian 6 1 0 1 0 0 0 0 0 Regression Analysis 0 0 0 Not Indicated/Other 3 0 0 Table 60. Types of uncertainty analysis performed in ALCA and CLCA studies in Journal of Industrial Ecology from 2014-2018. Studies that employed a combination of uncertainty analysis methods are counted under each method. ALCA CLCA ALCA+CLCA Journal of Industrial Ecology Monte Carlo-like approaches Probabilistic (other than Monte Carlo) 14 1 1 0 0 0 Taylor Series 0 0 0 Scenario Analysis 1 0 1 Qualitative/pedigree 2 0 0 Sensitivity Analysis Fuzzy Logic Bayesian 3 0 0 0 0 0 0 0 0 Regression Analysis 0 0 0 Not Indicated/Other 2 0 0 183 Table 61. Number of studies in the International Journal of Life Cycle Assessment that either did or did not perform uncertainty analysis, categorized by the major industrial sectors with defined base uncertainty values (Frischknecht et al 2005). Percentages refer to how much of the total of each sector either performed or did not perform uncertainty analysis. Within each sector, papers are further subdivided by LCA type; CLCA in this case refers to all LCA studies that employed consequential modeling, including combination ALCA/CLCA studies. Studies with uncertainty analysis Studies with no uncertainty analysis Total number of studies International Journal of Life Cycle Assessment Thermal energy, electricity, semi-finished products, working material, waste treatment services ALCA 29% 71% 289 CLCA 29% 71% 14 Transport ALCA 30% 70% 27 CLCA 0% 0% 0 Infrastructure ALCA 46% 54% 39 CLCA 50% 50% 2 Primary energy carriers, metals, salts ALCA 29% 71% 49 CLCA 14% 86% 7 184 Table 62. Number of studies in Journal of Cleaner Production that either did or did not perform uncertainty analysis, categorized by the major industrial sectors with defined base uncertainty values (Frischknecht et al 2005). Percentages refer to how much of the total of each sector either performed or did not perform uncertainty analysis. Within each sector, papers are further subdivided by LCA type; CLCA in this case refers to all LCA studies that employed consequential modeling, including combination ALCA/CLCA studies. Studies with uncertainty analysis Studies with no uncertainty analysis Total number of studies Journal of Cleaner Production Thermal energy, electricity, semi-finished products, working material, waste treatment services ALCA 16% 84% 1014 CLCA 16% 84% 43 Transport ALCA 17% 83% 75 CLCA 17% 83% 6 Infrastructure ALCA 19% 81% 154 CLCA 8% 92% 13 Primary energy carriers, metals, salts ALCA 30% 70% 115 CLCA 7% 93% 15 185 Table 63. Number of studies in Sustainability that either did or did not perform uncertainty analysis, categorized by the major industrial sectors with defined base uncertainty values (Frischknecht et al 2005). Percentages refer to how much of the total of each sector either performed or did not perform uncertainty analysis. Within each sector, papers are further subdivided by LCA type; CLCA in this case refers to all LCA studies that employed consequential modeling, including combination ALCA/CLCA studies. Studies with uncertainty analysis Studies with no uncertainty analysis Total number of studies Sustainability Thermal energy, electricity, semi-finished products, working material, waste treatment services ALCA 8% 92% 100 CLCA 0% 100% 4 Transport ALCA 0% 100% 8 CLCA 0% 0% 0 Infrastructure ALCA 0% 100% 27 CLCA 0% 100% 3 Primary energy carriers, metals, salts ALCA 11% 89% 9 CLCA 0% 100% 6 186 Table 64. Number of studies in Applied Energy that either did or did not perform uncertainty analysis, categorized by the major industrial sectors with defined base uncertainty values (Frischknecht et al 2005). Percentages refer to how much of the total of each sector either performed or did not perform uncertainty analysis. Within each sector, papers are further subdivided by LCA type; CLCA in this case refers to all LCA studies that employed consequential modeling, including combination ALCA/CLCA studies. Studies with uncertainty analysis Studies with no uncertainty analysis Total number of studies Applied Energy Thermal energy, electricity, semi-finished products, working material, waste treatment services ALCA 21% 79% 107 CLCA 17% 83% 6 Transport ALCA 19% 81% 26 CLCA 50% 50% 2 Infrastructure ALCA 10% 90% 10 CLCA 0% 0% 0 Primary energy carriers, metals, salts ALCA 20% 80% 82 CLCA 11% 89% 9 187 Table 65. Number of studies in Science of the Total Environment that either did or did not perform uncertainty analysis, categorized by the major industrial sectors with defined base uncertainty values (Frischknecht et al 2005). Percentages refer to how much of the total of each sector either performed or did not perform uncertainty analysis. Within each sector, papers are further subdivided by LCA type; CLCA in this case refers to all LCA studies that employed consequential modeling, including combination ALCA/CLCA studies. Studies with uncertainty analysis Studies with no uncertainty analysis Total number of studies Science of the Total Environment Thermal energy, electricity, semi-finished products, working material, waste treatment services ALCA 11% 89% 138 CLCA 0% 100% 7 Transport ALCA 0% 100% 1 CLCA 0% 0% 0 Infrastructure ALCA 0% 100% 7 CLCA 0% 0% 0 Primary energy carriers, metals, salts ALCA 28% 72% 82 CLCA 17% 83% 6 188 Table 66. Number of studies in Resources, Conservation and Recycling that either did or did not perform uncertainty analysis, categorized by the major industrial sectors with defined base uncertainty values (Frischknecht et al 2005). Percentages refer to how much of the total of each sector either performed or did not perform uncertainty analysis. Within each sector, papers are further subdivided by LCA type; CLCA in this case refers to all LCA studies that employed consequential modeling, including combination ALCA/CLCA studies. Studies with uncertainty analysis Studies with no uncertainty analysis Total number of studies Resources, Conservation and Recycling Thermal energy, electricity, semi-finished products, working material, waste treatment services ALCA 21% 79% 104 CLCA 30% 70% 10 Transport ALCA 67% 33% 6 CLCA 0% 0% 0 Infrastructure ALCA 62% 38% 8 CLCA 0% 0% 0 Primary energy carriers, metals, salts ALCA 31% 69% 13 CLCA 0% 100% 2 189 Table 67. Number of studies in Journal of Industrial Ecology that either did or did not perform uncertainty analysis, categorized by the major industrial sectors with defined base uncertainty values (Frischknecht et al 2005). Percentages refer to how much of the total of each sector either performed or did not perform uncertainty analysis. Within each sector, papers are further subdivided by LCA type; CLCA in this case refers to all LCA studies that employed consequential modeling, including combination ALCA/CLCA studies. Studies with uncertainty analysis Studies with no uncertainty analysis Total number of studies Journal of Industrial Ecology Thermal energy, electricity, semi-finished products, working material, waste treatment services ALCA 20% 80% 89 CLCA 12% 88% 17 Transport ALCA 21% 79% 14 CLCA 50% 50% 2 Infrastructure ALCA 18% 82% 11 CLCA 0% 0% 0 Primary energy carriers, metals, salts ALCA 33% 67% 6 CLCA 0% 100% 3
Thesis/Dissertation
eng
Biology
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Attributional and consequential life cycle assessment of wood chip and bark mulch use on Okanagan apple orchards, including uncertainty assessment and best practices recommendations
Text