"Land and Food Systems, Faculty of"@en . "DSpace"@en . "UBCV"@en . "Dorward, Caitlin Emma"@en . "2015-04-13T16:32:29Z"@en . "2015"@en . "Master of Science - MSc"@en . "University of British Columbia"@en . "There is growing awareness that climate change, economic instability, resource limitations and population growth are profoundly impacting the capacity of the contemporary global food system to meet human nutrition needs. Although there is widespread recognition that food systems must evolve in the face of these issues, a polarized debate has emerged around the merit of global-verses-local approaches to this evolution. Local food system advocates argue that increasing food self-reliance will concomitantly benefit human health, the environment, and local economies, while critics argue that only a globalized system will produce enough calories to efficiently and economically feed the world. This debate largely takes place in absence of knowledge of the current food self-reliance status of specific regions and capacity to increase it in the future. This study addressed this knowledge gap by developing methods to assess current (2011) status and model future (2050) capacity for land based food self-reliance in a diet satisfying nutritional recommendations and food preferences that accounts for seasonality of crop production, and comparing self-reliance in livestock raised with and without locally produced feedstocks. The methods were applied to the southwest British Columbia bio-region (SWBC). Results indicated that SWBC production of feed and food grain is a major constraint on self-reliance. Total dietary self-reliance of SWBC was 12% in 2011 if discounting livestock feed imports or 40% if including them. Self-reliance could be increased in 2050 in a Localized food system in which crops are allocated to agricultural lands in a manner that maximizes food self-reliance, but not in a Business as Usual (BAU) food system in which crop and livestock production follows 2011 patterns. The average of nine modeled scenarios for 2050 food self-reliance in the Localized food system was 26% if discounting livestock feed imports or 44% if including livestock raised with imported feed, and in the BAU food system was 8% and 23% respectively. Analysis revealed that both food systems are more sensitive to changes in farmland availability than climate change-induced changes in crop yield. Land use results indicate that horticultural crop production would dominate farmland use in a scenario of increased food self-reliance."@en . "https://circle.library.ubc.ca/rest/handle/2429/52681?expand=metadata"@en . " ASSESSMENT OF CURRENT STATUS AND MODELING OF FUTURE CAPACITY FOR LAND BASED FOOD SELF-RELIANCE IN SOUTHWEST BRITISH COLUMBIA by Caitlin Emma Dorward B.Sc., The University of British Columbia, 2010 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Integrated Studies in Land and Food Systems) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) April 2015 \u00C2\u00A9 Caitlin Emma Dorward, 2015 ii Abstract There is growing awareness that climate change, economic instability, resource limitations and population growth are profoundly impacting the capacity of the contemporary global food system to meet human nutrition needs. Although there is widespread recognition that food systems must evolve in the face of these issues, a polarized debate has emerged around the merit of global-verses-local approaches to this evolution. Local food system advocates argue that increasing food self-reliance will concomitantly benefit human health, the environment, and local economies, while critics argue that only a globalized system will produce enough calories to efficiently and economically feed the world. This debate largely takes place in absence of knowledge of the current food self-reliance status of specific regions and capacity to increase it in the future. This study addressed this knowledge gap by developing methods to assess current (2011) status and model future (2050) capacity for land based food self-reliance in a diet satisfying nutritional recommendations and food preferences that accounts for seasonality of crop production, and comparing self-reliance in livestock raised with and without locally produced feedstocks. The methods were applied to the southwest British Columbia bio-region (SWBC). Results indicated that SWBC production of feed and food grain is a major constraint on self-reliance. Total dietary self-reliance of SWBC was 12% in 2011 if discounting livestock feed imports or 40% if including them. Self-reliance could be increased in 2050 in a Localized food system in which crops are allocated to agricultural lands in a manner that maximizes food self-reliance, but not in a Business as Usual (BAU) food system in which crop and livestock production follows 2011 patterns. The average of nine modeled scenarios for 2050 food self-reliance in the Localized food system was 26% if discounting livestock feed imports or 44% if including livestock raised with imported feed, and in the BAU food system was 8% and 23% respectively. Analysis revealed that both food systems are more sensitive to changes in farmland availability than climate change-induced changes in crop yield. Land use results indicate that horticultural crop production would dominate farmland use in a scenario of increased food self-reliance. iii Preface The research reported in this thesis was part of the larger Southwest British Columbia Bio-regional Food System Design Project, led by Principal Investigator Dr. Kent Mullinix (Director- Institute for Sustainable Food Systems, Kwantlen Polytechnic University). Dr. Kent Mullinix (thesis co-supervisor) initiated the thesis research project. Dr. Sean Smukler, Assistant Professor, Applied Biology and Soil Science and Junior Chair, Agriculture and the Environment, University of British Columbia, (thesis co-supervisor) and Caitlin Dorward (thesis author) contributed to its conceptualization and design. Committee members Dr. Hannah Wittman (Associate Professor, Food Nutrition and Health, University of British Columbia) and Dr. Parthiphan Krishnan (Faculty Member, Faculty of Geography, Kwantlen Polytechnic University) provided guidance and technical advice throughout the study and a critical review of the final thesis. Anna Rallings (Kwantlen Polytechnic University, Sustainable Agriculture) provided technical advice on the development of farmland availability scenarios used in chapter three and conducted the associated GIS analysis. Katie Robinson (Registered Dietician) provided technical advice on the methodology used to estimate Food Need in chapter two and assisted with associated data analysis. Dr. Cornelia Sussmann (Institute for Sustainable Food Systems) assisted regularly with data collection and interpretation. Dr. Stephen Shechter (Associate Professor, Sauder School of Business, University of British Columbia) and Reza Skandari (PhD Candidate, Sauder School of Business, University of British Columbia) provided technical advice on design of the Localized food system optimization model and Rick White (Statistical Consulting and Research Laboratory, University of British Columbia) provided statistical advice. iv Table of contents Abstract ......................................................................................................................................................... ii Preface ......................................................................................................................................................... iii Table of contents .......................................................................................................................................... iv List of tables ................................................................................................................................................. vi List of figures ............................................................................................................................................... vii List of equations ........................................................................................................................................... ix Acknowledgements ....................................................................................................................................... x Dedication .................................................................................................................................................... xi Chapter 1: General introduction ................................................................................................................... 1 1.1 Background and rationale ................................................................................................................... 1 1.2 Case study region ................................................................................................................................ 2 1.3 Research scope ................................................................................................................................... 5 1.4 Research objectives ............................................................................................................................ 5 Chapter 2: Evaluation of current status of land-based food self-reliance in Southwest British Columbia ... 7 2.1 Introduction ........................................................................................................................................ 7 2.2 Methods .............................................................................................................................................. 9 2.2.1 Food need .................................................................................................................................... 9 2.2.2 Agricultural land use .................................................................................................................. 12 2.2.3 Quantity of crop food produced ................................................................................................ 13 2.2.4 Quantity of livestock products produced .................................................................................. 14 2.2.5 Diet and seasonality constraint on food self-reliance ............................................................... 16 2.2.6 Food self-reliance ....................................................................................................................... 16 2.3 Results and discussion ...................................................................................................................... 17 2.3.1 Food need .................................................................................................................................. 17 2.3.2 Agricultural land use .................................................................................................................. 19 2.3.3 Quantity of crop food produced ................................................................................................ 20 2.3.4 Quantity of livestock products produced .................................................................................. 20 2.3.5 Diet and seasonality constraint on food self-reliance ............................................................... 21 2.3.6 Food self-reliance ....................................................................................................................... 24 2.4 Conclusion ......................................................................................................................................... 26 Chapter 3: Modeling of future capacity for land-based food self-reliance in Southwest British Columbia 28 3.1 Introduction ...................................................................................................................................... 28 3.2 Methods ............................................................................................................................................ 32 v 3.2.1 Food self-reliance capacity ........................................................................................................ 32 3.2.2 Sensitivity of food self-reliance capacity to change in farmland availability and climate change-induced change in crop yield .............................................................................................................. 46 3.2.3 Land use ..................................................................................................................................... 47 3.3 Results and discussion ...................................................................................................................... 47 3.3.1 Food self-reliance capacity ........................................................................................................ 47 3.3.2 Sensitivity of food self-reliance capacity to change in farmland availability and climate change-induced change in crop yield .............................................................................................................. 50 3.3.3 Land use ..................................................................................................................................... 55 3.4 Conclusions ....................................................................................................................................... 56 Chapter 4: General summary and conclusions ............................................................................................ 58 4.1 Summary of findings ......................................................................................................................... 58 Assessing current food self-reliance status ........................................................................................ 58 Modeling future food self-reliance capacity ....................................................................................... 59 4.2 Strengths and contribution to the field of study .............................................................................. 59 4.3 Limitations and direction for future research ................................................................................... 60 4.4 Implications ....................................................................................................................................... 62 References ................................................................................................................................................... 64 Appendices .................................................................................................................................................. 75 Appendix I: List of foods included and excluded from the Preferred Diet ............................................. 75 Appendix II: Total British Columbia yield (tonnes) divided by the total British Columbia area (hectares) planted in commodity c (TYpc/Apc) .......................................................................................................... 77 Appendix III: Months of fresh availability in southwest British Columbia of select crops and livestock products1 ................................................................................................................................................. 79 vi List of tables Table 1: Southwest British Columbia Food Need (Nf) by Food Guide Group, 2011 ................................... 18 Table 2: Southwest British Columbia agricultural land use in 2011 ........................................................... 19 Table 3: Total yield (Ty) of crop food commodities in Southwest British Columbia in 2011, by Food Group .................................................................................................................................................................... 20 Table 4: Quantity (tonnes commodity weight) of livestock commodity produced by each livestock type (l/Hj) and results from Southwest British Columbia livestock production optimization model, including the maximum number of livestock that could be raised in 2011 if no feed was imported (Hjr), and corresponding maximum quantity of livestock production (TYrc) .............................................................. 21 Table 5: Southwest BC livestock production including livestock raised with imported feed (2011).......... 21 Table 6: Total Food Need (Nrf), the diet and seasonality constraint on food self-reliance (DSCf), and the hypothetical maximum portion of Total Food Need that can be satisfied by Southwest British Columbia food production (DCSrf/Nrf), by Food Group. .............................................................................................. 22 Table 7: Comparison of Southwest British Columbia (SWBC) food self-reliance, as measured in this study, to that of other regions ............................................................................................................................... 26 Table 8: Scenarios of 2050 land availability and climate change-induced changes in crop yield used in the Business as Usual and Localized Food System models ............................................................................... 33 Table 9: Projected 2050 population of southwest British Columbia by age and gender group ................. 35 Table 10: Land Capability Classification for Agriculture in British Columbia (33) ....................................... 36 Table 11: Southwest British Columbia farmland availability scenarios by regional district and Agricultural Capability Class ........................................................................................................................................... 39 Table 12: Scenarios of 2050 land availability and crop yield used for nominal range sensitivity analysis of the Business as Usual and Localized Food System models ......................................................................... 47 vii List of figures Figure 1: Province of British Columbia (left) and the Southwest British Columbia bio-region with Regional District boundaries indicated (right). Figure from Institute for Sustainable Food Systems, Kwantlen Polytechnic University; used with permission. ............................................................................................. 3 Figure 2: Generalized schematic of method used to assess food self-reliance in this study. White boxes represent input data, grey boxes represent calculated datasets, and black bubbles (which refer to equations explained in the methods section) represent operations performed on the data. .................. 10 Figure 3: Annual Servings per Food Group required to meet Canada\u00E2\u0080\u0099s Food Guide recommendations for the Southwest British Columbia (SWBC) population in 2011 (Sg) and Servings per Food Group in the SWBC Preferred Diet in 2011 (SPdg), including fish and seafood. Percentages indicate the percentage of Canada\u00E2\u0080\u0099s Food Guide recommendations that are met by the Preferred Diet. ........................................... 18 Figure 4: Comparison of Southwest British Columbia (SWBC) Total Yield (TYr) and amount of Total Yield that contributes to SWBC food self-reliance (TYr FSR) in 2011. For the Total Diet, and for those Food Groups containing livestock products (Meat & Alternatives, Milk & Alternatives, and Fats & Oils), results are presented from the analysis with and without feed imports available for livestock production. Percentages indicate the percent of Total Yield that contributes to SWBC food self-reliance and are calculated by dividing TYr by TYr FSR. .......................................................................................................... 23 Figure 5: Southwest British Columbia food self-reliance in 2011 by Food Group and the Total Diet (SRg). For the Total Diet, and for those Food Groups containing livestock products (Meat & Alternatives, Milk & Alternatives, and Fats & Oils) results are presented from the analysis with and without feed imports available for livestock production. The measurement of food self-reliance in Meat & Alternatives is only for the livestock product component of total Meat & Alternatives Food Need. ....................................... 24 Figure 6: Generalized schematic of the Localized food system optimization model. White boxes represent input data that is constant throughout all scenarios, white boxes with dashed outline represent input data that varies by scenario, grey boxes represent output data, and black bubbles (which refer to equations explained in this methods section) represent operations performed on the input data. ................................................................................................................................................... 41 Figure 7: Generalized schematic of the Business as Usual food system optimization model. White boxes represent input data that is constant throughout all scenarios, white boxes with dashed outline represent input data that varies by scenario, grey boxes represent output data, and black bubbles (which refer to equations explained in this methods section) represent operations performed on the data. ............................................................................................................................................................ 44 Figure 8: The Diet and Seasonality Constraint on Food Self-Reliance (DSC) and Mean 2050 Southwest British Columbia food self-reliance in the Business as Usual (BAU) and Localized food system models, for five Food Groups and the Total Diet. Mean refers to the mean of all crop yield and farmland availability scenarios. .................................................................................................................................................... 49 viii Figure 9: Sensitivity of food self-reliance in the Business as Usual (BAU) and Localized food systems, with and without feed imports, to modeled climate change-induced changes in crop yield, as indicated by the percent change of self-reliance in the decreased and increased crop yield scenarios with stable farmland availability from mean self-reliance (the average of self-reliance in all crop yield scenarios) with stable farmland availability. .................................................................................................................................. 51 Figure 10: Sensitivity of food self-reliance in the Business as Usual (BAU) and Localized food systems, with and without feed imports, to modeled changes in farmland availability, as indicated by the percent change of self-reliance in the increased non-farm use and agricultural expansion farmland availability scenarios with average crop yield from mean self-reliance (the average of self-reliance in all farmland availability scenarios) with average crop yield. .......................................................................................... 52 Figure 11: Sensitivity of total dietary food self-reliance in the Business as Usual and Localized food system models, with and without feed imports, to changes in farmland availability compared to changes in crop yield. ................................................................................................................................................ 54 Figure 12: Mean land use in the Business as Usual (BAU) and Localized food systems, with and without feed imports, in the stable farmland availability scenario. Mean refers to the mean across decreased, average, and increased crop yield. ............................................................................................................. 56 ix List of equations Equation 1 ................................................................................................................................................... 10 Equation 2 ................................................................................................................................................... 11 Equation 3 ................................................................................................................................................... 11 Equation 4 ................................................................................................................................................... 11 Equation 5 ................................................................................................................................................... 12 Equation 6 ................................................................................................................................................... 12 Equation 7 ................................................................................................................................................... 13 Equation 8 ................................................................................................................................................... 13 Equation 9 ................................................................................................................................................... 14 Equation 10 ................................................................................................................................................. 14 Equation 11 ................................................................................................................................................. 15 Equation 12 ................................................................................................................................................. 15 Equation 13 ................................................................................................................................................. 16 Equation 14 ................................................................................................................................................. 16 Equation 15 ................................................................................................................................................. 16 Equation 16 ................................................................................................................................................. 17 Equation 17 ................................................................................................................................................. 17 Equation 18 ................................................................................................................................................. 34 Equation 19 ................................................................................................................................................. 35 Equation 20 ................................................................................................................................................. 36 Equation 21 ................................................................................................................................................. 36 Equation 22 ................................................................................................................................................. 37 Equation 23 ................................................................................................................................................. 38 Equation 24 ................................................................................................................................................. 39 Equation 25 ................................................................................................................................................. 42 Equation 26 ................................................................................................................................................. 42 Equation 27 ................................................................................................................................................. 43 Equation 28 ................................................................................................................................................. 43 Equation 29 ................................................................................................................................................. 43 Equation 30 ................................................................................................................................................. 43 Equation 31 ................................................................................................................................................. 45 Equation 32 ................................................................................................................................................. 45 Equation 33 ................................................................................................................................................. 45 Equation 34 ................................................................................................................................................. 45 Equation 35 ................................................................................................................................................. 46 Equation 36 ................................................................................................................................................. 46 x Acknowledgements I would first like to acknowledge my co-supervisors, Dr. Sean Smukler and Dr. Kent Mullinix. I am so grateful for the support and guidance they provided me throughout the course of completing my MSc. I am particularly grateful that Kent encouraged and enabled my pursuit of graduate studies while I also worked under his direction as a Research Associate at the Institute for Sustainable Food Systems. Many thanks also to my committee members, Dr. Parthiphan Krishnan and Dr. Hannah Wittman. I have learned so much from the advice, perspectives and attention to detail that they contributed to this research. I am fortunate to work with a great interdisciplinary team of colleagues at the Institute for Sustainable Food Systems, and their support has also been instrumental to my success. Thanks in particular to Cornelia Sussmann (without whom I may never have cracked a \u00E2\u0080\u009Cpork joke\u00E2\u0080\u009D!), and to Caitriona Feeney. I am grateful for the support I received from my friends and fellow students at UBC. Thank you most of all to my lab-mates in the Sustainable Agricultural Landscapes Lab, who welcomed me into their world of field-based science and informed my food systems research interests immensely, and in particular to Bryanna Thiel (SAL swim team captain!) and Anna Rallings, both of who were there with me from day one through all the ups and downs. Finally I would like to acknowledge the financial support I received from the Institute for Sustainable Food Systems at Kwantlen Polytechnic University, the Faculty of Land and Food Systems\u00E2\u0080\u0099 Graduate Award and the Faculty of Land and Food Systems\u00E2\u0080\u0099 Leonard S. Klinck memorial fellowship. xi Dedication Thank you to my friends, family, and foremost to my partner Chris who supported me in so many ways during the course of my degree. I would like to dedicate this thesis to the many women who have inspired and nurtured my interest in food, food systems and farming: my Mom (first teacher in the kitchen and the garden); Kris and Micheline (partners and mentors in the most remote kitchens); Heather, Siena, Amy, Queenie, and Alisa (Sprouts pals and confidants); and Tara, Carole, and Barbara (who helped me take big next steps). 1 Chapter 1: General introduction 1.1 Background and rationale Concerns regarding the earth\u00E2\u0080\u0099s capacity to sustain a growing global population are not new. The relationship between agricultural productivity and population growth was first cited in Thomas Malthus\u00E2\u0080\u0099 infamous 1798 Essay on the Principle of Population, and the first formal international group that aimed to address problems of agricultural productivity was established in Europe a century later (1). Food security made its first notable appearance on the international political agenda with the League of Nations\u00E2\u0080\u0099 1935 publication of a report on the extent of global hunger and malnutrition (the Report on Nutrition and Public Health) and the 1945 formation of the Food and Agriculture Organization of the United Nations (FAO) (1). In 1946, FAO organized its first World Food Survey, an investigation into the sufficiency of global food production to meet food needs, which reported that the world was producing only enough calories to produce two thirds of its population sufficiently (1,2). More recently, a number of emerging concerns have heightened the world\u00E2\u0080\u0099s attention to the issue of global food security. As stated by the United Nations in their 2012 report Food and Agriculture: The Future of Sustainability , \u00E2\u0080\u009Cfor the first time at a global level, food production faces multiple limiting factors for key resources such as land, water, energy and inputs\u00E2\u0080\u009D (3). The global capacity to produce sufficient calories is threatened by climate change (4), an increasing global demand for animal protein (5), food price instability (6), a growing population (7), and a multitude of environmental crises driven by agriculture itself, including biodiversity loss, air and water pollution, and soil degradation (8). As such it is no surprise that there is widespread agreement that the world\u00E2\u0080\u0099s food systems must evolve if they are to maintain their ability to meet human nutrition needs into the future (4,9\u00E2\u0080\u009314). Despite agreement that food system change is necessary, however, polarized debates have emerged around the interlinked issues of preferred food system structure (\u00E2\u0080\u009Ccorporate\u00E2\u0080\u009D verses \u00E2\u0080\u009Ccommunity\u00E2\u0080\u009D), method (\u00E2\u0080\u009Cindustrial\u00E2\u0080\u009D verses \u00E2\u0080\u009Cecological\u00E2\u0080\u009D), and scale (\u00E2\u0080\u009Cglobal\u00E2\u0080\u009D verses \u00E2\u0080\u009Clocal\u00E2\u0080\u009D) (15). This study focuses in particular on the latter debate, to which the concept of food self-reliance, defined as the ability of an area to satisfy food needs with food grown locally, is central. While local food system advocates argue that increasing food self-reliance will also have a triple-bottom-line effect of benefiting human health/communities, the environment, and the economy (16\u00E2\u0080\u009319), some critics claim that only a globalized system can produce enough calories to feed the world in the future (20). A growing cohort challenges the assumptions of both global and local food systems advocates, arguing that there is nothing inherently \u00E2\u0080\u009Cgood\u00E2\u0080\u009D about either a global or a local system. Rather, they contend that the 2 outcomes of a given food system \u00E2\u0080\u009Cdepend on the actors and agendas that are empowered by [it]\u00E2\u0080\u009D (21). It follows that, although more localized food systems can result in positive sustainability outcomes, they likewise have the potential to \u00E2\u0080\u009Cstrengthen inequitable and unsustainable patterns of labor and the use of land and resources\u00E2\u0080\u009D (22). A major limitation to the debate regarding food system scale, however, is that it generally takes place in the absence of knowledge regarding the potential for specific regions to achieve greater food self-reliance based on their population size, dietary preferences, and the extent and bio-physical capacity of their land base. As such, these debates take place largely in the abstract. While some studies have measured current levels of food self-reliance at national, regional, or local scales (23\u00E2\u0080\u009325), few have examined capacity for food self-reliance in the future and/or what agricultural land use would look like in terms of crop mix and extent of production in a scenario of total or increased future food self-reliance. Rather than presenting evidence for or against the case to increase food self-reliance, this study aimed to address this knowledge gap. To do so, methods for the assessment of current status and modeling of future capacity for land based food self-reliance were developed and applied to a case study area. A review of pertinent literature, described in sections 2.1 and 3.1, revealed the importance of developing methods to measure food self-reliance for a diet satisfying nutritional recommendations and food preferences, that accounts for seasonality of crop production and year-round consumption of fresh fruits and vegetables, and allows for a comparison of outcomes when food self-reliance is defined to include livestock raised with local feed or defined to include livestock raised with imported feed. 1.2 Case study region The research reported on in this thesis was one component of a larger project entitled the Southwest BC Bio-Region Food System Design Project (Project), the broad objective of which is to explore the economic, environmental stewardship and food self-reliance potentials of a food system linked to the ecology of place (26). The Project\u00E2\u0080\u0099s specific scale and location of analysis is the \u00E2\u0080\u009Csouthwest British Columbia bio-region\u00E2\u0080\u009D (SWBC), and therefore this scale was used as the case study area for the research reported on in this thesis. Bio-regions are areas that share similar topography, plant and animal life, and human culture (27). Their boundaries are largely based on eco-regions but incorporate human settlement and activity patterns and can take political boundaries into consideration (27). Bio-regions have been suggested as a suitable scale for food system analysis and planning because resilience, 3 sustainability, food security, and aligning the human economy with environmental capacity and character of place are core themes shared between food system planning and bio-regionalism (27). It is acknowledged that \u00E2\u0080\u009Cno clear set of rules exists for delineating bio-regions\u00E2\u0080\u009D, and that the degree to which delineation methods emphasize ecological verses socio-political boundaries varies (27). To delineate SWBC, the Project used a method that incorporated population centres and regional district boundaries, terrestrial and marine eco-regions, and regional watershed boundaries (27). Delineated according to these considerations, SWBC is a 41,380km2 area in the southwest mainland corner of the province of British Columbia (BC) and is comprised of five Regional Districts: Metro Vancouver (also known as Greater Vancouver), Fraser Valley, Sunshine Coast, Powell River, and Squamish-Lillooet (Figure 1). This area is both an agricultural and urban centre within the province. Metro Vancouver alone is home to more than half of BC\u00E2\u0080\u0099s total population (almost 2.7 million in 2011), and is one of the fastest growing metropolitan areas in the country (28). The majority of agricultural land in SWBC is protected by the Agricultural Land Reserve (ALR), a provincially legislated zone in which agriculture is recognized as the priority use, farming is encouraged, and non-agricultural uses are controlled (29). In 2011, SWBC had almost 1,500 km2 of ALR land (30). SWBC is part of the Pacific Maritime Eco-zone, which has some of Canada\u00E2\u0080\u0099s warmest and wettest weather with relatively little variation in monthly temperatures (31). Its valleys receive as little as 290 mm of precipitation and up to 220 frost free days while the northern more mountainous area receives up to 3,000 mm of precipitation and as few as 100 frost free days (31). Agricultural soils are primarily Figure 1: Province of British Columbia (left) and the Southwest British Columbia bio-region with Regional District boundaries indicated (right). Figure from Institute for Sustainable Food Systems, Kwantlen Polytechnic University; used with permission. 4 gleysols, regosols and brunisols (32). Soil saturation is problematic in low-lying deltaic areas, but with proper drainage these soils are considered prime agricultural land (32,33). SWBC is a major centre for the production of dairy, egg, turkey, and broiler chicken, all of which are supply-managed commodities. The supply management system aims to ensure that the supply of these products meets Canadian demand, and that farmers receive prices that cover their costs of production. Production targets for the supply managed commodities are set by national marketing agencies (Canadian Dairy Commission, Egg Farmers of Canada, Turkey Farmers of Canada, and the Chicken Farmers of Canada respectively) and allocated to the provinces based on their share of total national demand. Within BC, the license to produce and market a supply-managed commodity is issued to farmers by provincial marketing boards (BC Dairy Association, BC Egg, BC Turkey Marketing Board, and BC Chicken Marketing Board respectively). SWBC is also a major producer of cranberry, blueberry raspberry and various other horticultural crops. The production and sale of greenhouse vegetables, processing vegetables, and storage crops, is regulated by the BC Vegetable Marketing Commission. An assessment of food self-reliance in SWBC is pertinent given public interest and local policy momentum around the issue. In 2014, for example, a series of stakeholder engagement events were held across SWBC to discuss the topic of a re-regionalizing the food system (34). When asked to rank a series of eight food system priorities which related to the economic, environmental, and social sustainability, participants at five out of six events identified increasing SWBC food self-reliance as the number one priority (34). Many SWBC municipalities have incorporated local food into their municipal plans and policies (35) and numerous regional social sector organizations advocate for and support food system re-localization (e.g., Farm Folk/City Folk, Society Promoting Environmental Conservation, Surrey/White Rock Food Action Coalition, the Whistler Centre for Sustainability, and others). It is important to recognize that calculations of regional food self-reliance are enormously dependant on the scale and other attributes of the \u00E2\u0080\u009Cregion\u00E2\u0080\u009D assessed. For example a region with a high food production to population density ratio is likely to have higher food self-reliance than one with a low ratio. Calculations that include population centers such as cities therefore generally benefit from a more expansive delineation of a \u00E2\u0080\u009Cregion\u00E2\u0080\u009D. In two previous assessments of self-reliance in British Columbia the region was defined as the province and included large expanses of land with low density populations far from the major cities in the province (23,36). Other studies have measured food self-reliance at various scales including municipal (24), regional (37,38), multi-state (25), and national (39). There seems to be no agreement as to what scale is preferable. Indeed, the scale most appropriate to the study of food 5 self-reliance might be considered as contentious as the concept of food self-reliance itself. As such, although a bio-region was selected as the scale at which to apply the methods developed in this study, this study does not purport that the bio-region is the optimal scale at which food self-reliance should be measured, and nor does it argue that a bio-region necessarily could or should map directly onto a population\u00E2\u0080\u0099s \u00E2\u0080\u009Cfoodshed\u00E2\u0080\u009D (defined as the geographic area from which a population derives the entirety of its food supply (40)). General conclusions about the appropriateness of a scale cannot be drawn from a single case study at a single scale such as this. By repeated application of these methods, however, to case study areas defined at different scales and according to different criteria, a comparison could be made to identify the scale the best meets food system objectives. 1.3 Research scope It is widely acknowledged that marine foods are important contributors to human nutrition (41). This is certainly true in SWBC, where fish and seafood, and in particular salmon, constitute not just an important part of diet but also the \u00E2\u0080\u009Ccultural, social, and economic fabric\u00E2\u0080\u009D of the community (42). Although current food self-reliance status of marine foods can be estimated relatively easily using publically available harvest data (for example, see (25)), modeling the future availability of marine foods is far more complicated as it must take into account fisheries management, trends in abundance, marine species\u00E2\u0080\u0099 biology, catch levels, habitat availability and quality, and other factors (for example see (43)) which are in turn effected by a multitude of external factors. This level of complex modeling (which would be further compounded by having to model the multiple marine species that contribute to the SWBC diet) was beyond the scope and objective of this study. Furthermore, there was a need for consistency between the assessment of current self-reliance status and modeled future self-reliance capacity as the intention of the study was to compare the two. As such, this study was limited to an assessment of self-reliance in foods that comprise the land-based portion of the diet (i.e., fruit, vegetables, dairy, meat, eggs, and legumes). As described in section 2.2.2, the contribution of marine foods to a nutritious diet was accounted for in order to not inflate dietary requirement for meat (an approach consistent with that used by the Food and Agriculture Organization of the United Nations in their 2012 report World Agriculture Towards 2030/2050 (5)), however marine food production in SWBC was not assessed and as such, a determination of self-reliance in marine foods is not included in this study. 1.4 Research objectives Chapter Two reviews the food self-reliance assessment literature and reports on the following research objectives: 6 1. To develop a method to evaluate land-based food self-reliance for a diet satisfying nutritional recommendations and food preferences that accounts for seasonality of crop production and allows for a comparison of outcomes when food self-reliance is defined as including livestock raised with local feed to that defined as including livestock raised with imported feed; and, 2. To apply this method to a case study region (SWBC) to assess its current food self-reliance status. Chapter Three reviews the food self-reliance modeling literature and reports on the following research objectives: 1. To develop models to assess the potential to increase future food self-reliance in a diet satisfying nutritional recommendations and food preferences, accounting for crop seasonality and the production of livestock feed and apply these models to the case study region (SWBC); 2. To assess the sensitivity of SWBC\u00E2\u0080\u0099s future food self-reliance capacity to changes in farmland availability and to climate change-induced changes in crop yield; and, 3. To determine the land-use outcomes of increasing SWBC food self-reliance. Chapter four summarizes the results from the study, describes its strengths and weaknesses, discusses its implications and contribution to the field of study, and suggests future research directions. 7 Chapter 2: Evaluation of current status of land-based food self-reliance in Southwest British Columbia 2.1 Introduction There is growing awareness that issues such as climate change, food and energy price instability, population growth, and changing dietary preferences will have a profound impact on the capacity of the global food system to meet human nutrition needs in the future. Although there is widespread recognition that food systems must evolve in the face of these critical sustainability challenges (4,9\u00E2\u0080\u009314), a polarized, global-verses-local debate over how to do so has emerged amongst scientists, policy-makers, activists, and the private sector. Local food systems are characterized by increased food self-reliance, defined generally as the ability to satisfy food needs with food grown locally. Numerous benefits are claimed by local food systems proponents, who describe their potential to strengthen economies (16,44\u00E2\u0080\u009346), confer social benefits (17,47,48), reduce negative environmental impacts associated with bringing food from farm to plate (49,50) and improve community health, nutrition, and food safety (18,19,51,52). Numerous grassroots and non-governmental organizations advocate for the emergence of local food systems (53,54). Others, however, argue that the local food system movement has not yet succeeded in addressing structural and economic food system injustices, particularly those related to race and class (55). Some point to evidence that reducing \u00E2\u0080\u009Cfood miles\u00E2\u0080\u009D by sourcing food locally does not always reduce greenhouse gas emissions or contribute positively to climate change solutions (56\u00E2\u0080\u009359). They also discredit the argument that locally produced foods have universally greater nutritional value or are otherwise superior in quality (60,61). Some contend that a globalized food system, based on the trade of foods produced in areas with competitive advantages in terms of capital, energy, and/or labour, will be better able to facilitate a sustainable food supply in the future (20). Local food advocacy is present in British Columbia, Canada (BC), where a small but diverse agriculture sector produced over 200 primary agricultural products and generated over $2.6 billion in farm cash receipts on less than 3% of the province\u00E2\u0080\u0099s land base in 2011 (62). BC\u00E2\u0080\u0099s top agricultural commodities in terms of sales include dairy, chicken, floriculture products, beef, nursery products, greenhouse tomato, blueberry, greenhouse pepper, egg and mushroom. BC is one of Canada\u00E2\u0080\u0099s leading fruit and berry producers (62). Many BC residents, motivated by purported ecological, economic, and social benefits, seek to source their food closer to home (34,63\u00E2\u0080\u009366) and provincial food security experts have identified increasing food self-reliance as a key climate change adaptation strategy (67,68). In their paper on the 8 impact of climate change on BC food security, Ostry et al. (10) describe the availability of the province\u00E2\u0080\u0099s fruit and vegetables as dependant on \u00E2\u0080\u009Ca single, likely-to-be heavily climate change affected region\u00E2\u0080\u009D (California, USA) and argue that increasing local fruit and vegetable production capacity \u00E2\u0080\u009Cmakes sense in a future where produce from California may not be as available as it is at present nor at prices as low as they are at present\u00E2\u0080\u009D (p.19). This sentiment is echoed in a 2014 advocacy report issuing a \u00E2\u0080\u009Cwake-up call that BC needs to become more self-reliant to secure access to healthy food for [its] future\u00E2\u0080\u009D (69). Furthermore, an increasing number of municipal governments in the province are introducing policies supportive of food system localization (35). However, in contrast to this public interest and local policy momentum, the provincial Ministry of Agriculture\u00E2\u0080\u0099s policy and programming largely supports a commoditized, export-oriented model of agriculture (70). In 2011, the value of agricultural exports from BC\u00E2\u0080\u0099s $10.9 billion agriculture, seafood, and agri-food industries totalled $1.5 billion (62). Underlying this context, however, is a lack of understanding of how much capacity British Columbia has to meet the food needs of its residents. Two previous studies measured BC\u00E2\u0080\u0099s food self-reliance at the provincial scale (23,36), but neither utilized newer methodologies for calculating food self-reliance such as discounting the production of any single crop that exceeds consumption of that crop and accounting for the effect of seasonality of production on the capacity to meet food need (25,37,71,72). Furthermore, these studies used different assumptions and methodologies and therefore their results differ substantially from one another. In particular, one study (36) assessed food self-reliance for a diet that satisfies standard nutrition recommendations while another study (23) assessed food self-reliance for a diet that only satisfies the food preferences of the population. One study (23) defined food self-reliance in livestock products as including only those livestock that could have been raised with local feed while another (36) used a definition that allowed for livestock feed imports from outside the region. The inconsistency defining what constitutes \u00E2\u0080\u009Clocal\u00E2\u0080\u009D livestock production is evident throughout the food self-reliance literature (25,38). Given that livestock production requires extensive land resource to grow feed (73), this methodological difference could result in substantially different estimations of food self-reliance status and thus merits comparison. Given public interest, academic focus, and local policy momentum regarding the issue of food self-reliance in British Columbia, further assessment of the food self-reliance status in this region is warranted. This motivated the research objectives reported on in this chapter, which were to: 1. Develop a method to evaluate land-based food self-reliance for a diet satisfying nutritional recommendations and food preferences that accounts for seasonality of crop production and 9 allows for a comparison of outcomes when food self-reliance is defined as including livestock raised with local feed to that defined as including livestock raised with imported feed; and to, 2. Apply this method to a case study region, the southwest British Columbia bio-region (SWBC) to assess its current food self-reliance status. Section 2.2 reports on the methodology developed to assess current food self-reliance status with and without a requirement for local production of livestock feed, and section 2.3 presents and discusses the results of the application of this method to the assessment of SWBC\u00E2\u0080\u0099s food self-reliance status in 2011. The chapter concludes, in section 2.4, with a discussion of the implications for the future of SWBC\u00E2\u0080\u0099s trade relationships with proximal regions and ongoing reliance on a globalized food system. 2.2 Methods Food self-reliance is determined by food need, agricultural land use, crop food and livestock production, and a diet and seasonality constraint (Figure 2). These elements are described below. All data was from 2011 unless otherwise specified; 2011 was chosen as it is the year of the most recent Census of Agriculture, which was a major data source. For a description of the case study region, see section 1.2. 2.2.1 Food need \u00E2\u0080\u009CFood Need\u00E2\u0080\u009D was defined as the quantity of food required to meet dietary recommendations in a manner aligned with the \u00E2\u0080\u009CPreferred Diet\u00E2\u0080\u009D (what the population is actually eating). The Preferred Diet was estimated using the Canadian food availability dataset. This dataset is developed by subtracting exports, manufacturing, waste, and year-end stocks from the total national food supply, and distinguishes between quantities of food consumed in fresh form (e.g., apples) and in processed form (e.g., apple sauce) (74,75). It has been used as a proxy for the average diet of British Columbians (23). Studies pertaining to other countries have used similar national datasets in comparable ways (76,77). For this study, the dataset was assumed to be a reasonable representation of the Preferred Diet of Canadians, and thus that of SWBC residents. Dietary recommendations were ascertained from Canada\u00E2\u0080\u0099s Food Guide (CFG), which specifies the number of \u00E2\u0080\u009CServings\u00E2\u0080\u009D from five \u00E2\u0080\u009CFood Groups\u00E2\u0080\u009D (Fruits & Vegetables, Grains, Milk & Alternatives, Meat & Alternatives, and Oils & Fat) that should be consumed daily by each of nine demographic groups delineated by age and gender (78). A serving is a unit specific to CFG, the specific mass or volume of which varies by food type. 10 The method developed by Kantor (79) and Buzby et al. (80) was used to estimate Food Need based on these two datasets. First, the per capita quantity of food in the Preferred Diet was scaled to SWBC using: Equation 1 \u00F0\u009D\u0091\u0083\u00F0\u009D\u0091\u0091\u00F0\u009D\u0091\u0093 = \u00F0\u009D\u0091\u0083 \u00C3\u0097 \u00F0\u009D\u0090\u00B6\u00F0\u009D\u0091\u008E\u00F0\u009D\u0091\u0093 Where f denotes an individual food, f= 1,\u00E2\u0080\u00A6143; Pdf denotes the quantity (kilograms) of food f in the Preferred Diet; P denotes the SWBC population; and Caf denotes the quantity (kilograms) of food f available per capita as identified in the Canadian availability dataset (74). All foods in the dataset, including fish and seafood, were included in this study with the exception of those not in Canada\u00E2\u0080\u0099s Food Guide, those reported as aggregate categories not comparable to agricultural production data, and those for which 2011 agricultural production data were not available (Appendix I). Figure 2: Generalized schematic of method used to assess food self-reliance in this study. White boxes represent input data, grey boxes represent calculated datasets, and black bubbles (which refer to equations explained in the methods section) represent operations performed on the data. 11 Secondly, the servings required per Food Group to satisfy annual CFG recommendations for SWBC\u00E2\u0080\u0099s population and the Servings per Food Group in the Preferred Diet was calculated using Equation 2 and Equation 3: Equation 2 \u00F0\u009D\u0091\u0086\u00F0\u009D\u0091\u0094 = \u00E2\u0088\u0091 \u00F0\u009D\u0091\u0083\u00F0\u009D\u0091\u008E \u00C3\u0097 \u00F0\u009D\u0091\u0086\u00F0\u009D\u0091\u008E\u00F0\u009D\u0091\u0094 \u00C3\u0097 365\u00F0\u009D\u0091\u008E\u00F0\u009D\u0091\u0099\u00F0\u009D\u0091\u0099 \u00F0\u009D\u0091\u008E Where g denotes a CFG Food Group, g=1, \u00E2\u0080\u00A6 5; a denotes a CFG demographic group, a=1\u00E2\u0080\u00A69; Sg denotes the annual quantity (Servings) from Food Group g required to meet CFG recommendations for the SWBC population; Pa denotes the SWBC population of age/gender group a (28); and Sag denotes the quantity (Servings) from Food Group g required daily to meet CFG recommendations for an individual in age/gender group a (78). Those aged 0-2 were not included as there are no CFG recommendations for this demographic, which is assumed by the CFG to be primarily breast-fed. Equation 3 \u00F0\u009D\u0091\u0086\u00F0\u009D\u0091\u0083\u00F0\u009D\u0091\u0091\u00F0\u009D\u0091\u0094 = \u00E2\u0088\u0091 (\u00F0\u009D\u0091\u0083\u00F0\u009D\u0091\u0091\u00F0\u009D\u0091\u0093 \u00C3\u0097 (\u00F0\u009D\u0091\u0084\u00F0\u009D\u0091\u0086)\u00F0\u009D\u0091\u0093\u00E2\u0088\u00921)\u00F0\u009D\u0091\u0093\u00E2\u0088\u0088\u00F0\u009D\u0091\u0094 Where SPdg denotes the quantity (Servings) of Food Group g in the Preferred Diet and (Q/S)f denotes the quantity (grams or millilitres) per Serving of food f (81). The CFG does not specify the quantity of egg (a function of egg size) in a Serving, so an average (Q/S)f of all egg sizes was used. For those Food Groups where Servings in the Preferred Diet were less than the number required to meet CFGs recommendations, Preferred Diet food quantities were increased proportionally such that they cumulatively satisfied CFG recommendations and converted to tonnes using: Equation 4 \u00F0\u009D\u0090\u00B4\u00F0\u009D\u0091\u0091\u00F0\u009D\u0091\u0093 =( (\u00F0\u009D\u0091\u0083\u00F0\u009D\u0091\u0091\u00F0\u009D\u0091\u0093 \u00C3\u0097 (\u00F0\u009D\u0091\u0084\u00F0\u009D\u0091\u0086)\u00F0\u009D\u0091\u0093\u00E2\u0088\u00921)\u00F0\u009D\u0091\u0086\u00F0\u009D\u0091\u0083\u00F0\u009D\u0091\u0091\u00F0\u009D\u0091\u0094\u00C3\u0097 \u00F0\u009D\u0091\u0086\u00F0\u009D\u0091\u0085\u00F0\u009D\u0091\u0094) \u00C3\u0097 ((\u00F0\u009D\u0091\u0084\u00F0\u009D\u0091\u0086)\u00F0\u009D\u0091\u00931,000,000) Where Adf denotes the quantity (tonnes) of food f in the Preferred Diet, adjusted to meet CFG recommendations. For those Food Groups where Servings in the Preferred Diet were greater than or equal to the number required to meet CFGs recommendations, no adjustment was necessary. 12 Finally, to derive total Food Need, food waste at the institutional, retail, and household levels was adjusted for and waste-adjusted food quantities were converted to their equivalent fresh or commodity weight (e.g., apple sauce to fresh apples) using: Equation 5 \u00F0\u009D\u0091\u0081\u00F0\u009D\u0091\u0093 = \u00F0\u009D\u0090\u00B4\u00F0\u009D\u0091\u0091\u00F0\u009D\u0091\u0093 \u00C3\u0097\u00F0\u009D\u0091\u008A\u00F0\u009D\u0091\u0093 \u00C3\u0097 \u00F0\u009D\u0090\u00B6\u00F0\u009D\u0091\u0093 Where Nf denotes the need (tonnes commodity weight) for food f in the bioregion; Wf denotes the waste factor for food f (74), and Cf denotes the commodity conversion factor for food f (81,82). For commodities consumed fresh, the commodity conversion factor is 1. It was assumed that shortening, margarine, and salad oils were made with oil from Canola (Brassica napus) as it is a commonly used type of oil from a species that can be grown in SWBC. 2.2.2 Agricultural land use 2011 agricultural land use data were retrieved from the 2011 Census of Agriculture, which reports the quantity of farmland used for food production and other purposes (83). For this study the following Census of Agriculture land use categories were assumed to comprise the total land used for the production of food crops and livestock products: hay, field crops, vegetables, fruits, berries, nuts, greenhouse vegetables, mushrooms, tame or seeded pasture, natural land for pasture, summer fallow, and barnyards. With the exception of \u00E2\u0080\u009Cbarnyards\u00E2\u0080\u009D, these data were retrieved at the Census Consolidated Subdivision (CCS) scale, which is defined as an area of at least 25km2 and/or having a population of at least 100,000 (84). CCSs are rarely subject to boundary changes and are therefore useful for longitudinal data analysis (84). As the area allocated to \u00E2\u0080\u009Cbarnyards\u00E2\u0080\u009D is included in an aggregated Census of Agriculture category (\u00E2\u0080\u009Call other land\u00E2\u0080\u009D), it was estimated it using additional data sources using: Equation 6 \u00F0\u009D\u0090\u00B5\u00F0\u009D\u0091\u008E = \u00E2\u0088\u0091\u00E2\u0088\u0091\u00F0\u009D\u0090\u00B5\u00F0\u009D\u0091\u008E\u00F0\u009D\u0091\u0097 \u00C3\u0097 \u00F0\u009D\u0090\u00BB\u00F0\u009D\u0091\u0097\u00F0\u009D\u0091\u00A0\u00F0\u009D\u0091\u008E\u00F0\u009D\u0091\u0099\u00F0\u009D\u0091\u0099 \u00F0\u009D\u0091\u0097\u00F0\u009D\u0091\u008E\u00F0\u009D\u0091\u0099\u00F0\u009D\u0091\u0099 \u00F0\u009D\u0091\u00A0 Where j denotes a specific livestock type (j = beef cattle, dairy cattle, lambs, hogs, broilers, turkeys, or layers); s denotes a CCS in SWBC; Hjs denotes the number of livestock j in CCS s; Baj denotes barn area (hectares) required for housing livestock j (85\u00E2\u0080\u009388); and Ba denotes the total barn area (hectares) in SWBC. 13 2.2.3 Quantity of crop food produced It was assumed that food produced in SWBC first satisfied fresh Food Need (e.g., apples), then processed Food Need (e.g., apple sauce). The quantity (tonnes) of crop foods produced in SWBC in 2011 was estimated using regionally specific data whenever possible, and Canadian data secondarily (Appendix II). For vegetable, fruit, and agronomic crops, the following equation was used: Equation 7 \u00F0\u009D\u0091\u0087\u00F0\u009D\u0091\u008C\u00F0\u009D\u0091\u009F\u00F0\u009D\u0091\u0090 =\u00F0\u009D\u0091\u0087\u00F0\u009D\u0091\u008C\u00F0\u009D\u0091\u009D\u00F0\u009D\u0091\u0090\u00F0\u009D\u0090\u00B4\u00F0\u009D\u0091\u009D\u00F0\u009D\u0091\u0090\u00C3\u0097 \u00F0\u009D\u0090\u00B4\u00F0\u009D\u0091\u009F\u00F0\u009D\u0091\u0090 \u00F0\u009D\u0091\u0093\u00F0\u009D\u0091\u009C\u00F0\u009D\u0091\u009F \u00F0\u009D\u0091\u0090 \u00E2\u0088\u0088 \u00F0\u009D\u0091\u00A3 Where c denotes an individual crop food commodity; v denotes the subset of those commodities that are vegetable, fruit, or agronomic crops; TYrc denotes the total SWBC yield (tonnes) of commodity c; TYpc denotes the total provincial yield (tonnes) of commodity c (89); Apc denotes the provincial area (hectares) planted in commodity c (89); and Arc denotes the SWBC area (hectares) planted to commodity c (83); 2011 data for mushrooms were not available so a five year (2002 \u00E2\u0080\u0093 2007) average was used. The preponderance of British Columbia\u00E2\u0080\u0099s tree fruit is grown in the semi-arid south-central portion of the province, where temperatures and insolation levels are particularly favorable to fruit production and greater than in the SWBC bio-region. However, as SWBC-specific yield data is not available, a 25% reduction in TYpc/Apc for tree fruit was applied based on consultation with a regional pomologist (Mullinix, Kent. Conversation with: Dorward, Caitlin. 2014 Jan) to account for likely regional reduction in production potential. Based on BC Ministry of Agriculture factsheets it was assumed that 100% of the barley and oats grown in the region to be consumed by livestock (90). As data regarding end-use of corn grain or wheat were not available, it was assumed that 50% was used for livestock feed and 50% went to human consumption. For greenhouse-grown crops the following equation was used: Equation 8 \u00F0\u009D\u0091\u0087\u00F0\u009D\u0091\u008C\u00F0\u009D\u0091\u009F\u00F0\u009D\u0091\u0090 =\u00F0\u009D\u0090\u00B4\u00F0\u009D\u0091\u009F\u00F0\u009D\u0091\u0090\u00F0\u009D\u0090\u00B4\u00F0\u009D\u0091\u009D\u00F0\u009D\u0091\u0090\u00C3\u0097 \u00F0\u009D\u0091\u0087\u00F0\u009D\u0091\u008C\u00F0\u009D\u0091\u009D\u00F0\u009D\u0091\u0090 \u00F0\u009D\u0091\u0093\u00F0\u009D\u0091\u009C\u00F0\u009D\u0091\u009F \u00F0\u009D\u0091\u0090 \u00E2\u0088\u0088 \u00F0\u009D\u0091\u0094 Where g denotes the subset of commodities that are greenhouse-grown (i.e. tomatoes, cucumbers, and peppers). The estimated quantity of greenhouse-grown tomatoes, cucumbers, and peppers produced in SWBC were added (respectively) to the estimated quantity of field-grown tomatoes, cucumbers, and peppers produced in SWBC (described above) to arrive at an estimated total production of each vegetable type. 14 For canola seed the following equation was used: Equation 9 \u00F0\u009D\u0091\u0087\u00F0\u009D\u0091\u008C\u00F0\u009D\u0091\u009F\u00F0\u009D\u0091\u0090 =\u00F0\u009D\u0091\u0087\u00F0\u009D\u0091\u008C\u00F0\u009D\u0091\u009B\u00F0\u009D\u0091\u0090\u00F0\u009D\u0090\u00B4\u00F0\u009D\u0091\u009B\u00F0\u009D\u0091\u0090\u00C3\u0097 \u00F0\u009D\u0090\u00B4\u00F0\u009D\u0091\u009F\u00F0\u009D\u0091\u0090 \u00C3\u0097\u00F0\u009D\u0091\u0082\u00F0\u009D\u0091\u0086 \u00F0\u009D\u0091\u0093\u00F0\u009D\u0091\u009C\u00F0\u009D\u0091\u009F \u00F0\u009D\u0091\u0090 = \u00F0\u009D\u0091\u009C Where o denotes the specific commodity canola seed; TYnc denotes the total national yield (tonnes) of commodity c (89); An denotes the national area (hectares) planted in commodity c; Arc denotes the SWBC area (hectares) planted in commodity c (83)(41); and O/S denotes the national canola oil yield (tonnes oil produced/tonne seed crushed) (89). 2.2.4 Quantity of livestock products produced For comparative purposes, livestock production was assessed according to two definitions of food self-reliance. In the first, food self-reliance was defined as including livestock raised with local feed only. In the second, food self-reliance was defined as including livestock raised with imported feed. To assess food self-reliance defined as including livestock raised with imported feed, livestock products from all livestock present in SWBC in 2011 were considered to contribute to SWBC\u00E2\u0080\u0099s food self-reliance. For each livestock product, this quantity was estimated by multiplying the number of milking (in the case of dairy cow), laying (in the case of layer hen) or slaughtered (in the case of beef cow, broiler, turkey, and pig) animals by the quantity of product produced per animal (i.e., fluid milk, eggs, beef, chicken, turkey, or pork) (Equation 10): Equation 10 \u00F0\u009D\u0091\u0087\u00F0\u009D\u0091\u008C\u00F0\u009D\u0091\u009F\u00F0\u009D\u0091\u0090 = \u00F0\u009D\u0090\u00BB\u00F0\u009D\u0091\u0097\u00F0\u009D\u0091\u009F \u00C3\u0097\u00F0\u009D\u0091\u0099\u00F0\u009D\u0090\u00BB\u00F0\u009D\u0091\u0097 \u00F0\u009D\u0091\u0093\u00F0\u009D\u0091\u009C\u00F0\u009D\u0091\u009F \u00F0\u009D\u0091\u0090 = \u00F0\u009D\u0091\u0099 To assess food self-reliance defined as including livestock raised with local feed only, the livestock products that were considered to contribute to the region\u00E2\u0080\u0099s food self-reliance were only those that could have been produced from animals pastured in and/or fed feed grown in SWBC. Using this method, rather than quantifying production based on the number of animals that were actually present in SWBC in 2011, an optimization model was developed to estimate the hypothetical maximum number of livestock that could have been raised if no livestock feed was imported. Model inputs were the hectares of pasture and livestock feed grown in SWBC and the feed requirements and production by livestock type. Model outputs were the optimal number of each livestock type that should be raised in SWBC in order to maximize self-reliance in livestock products and the resulting feed and pasture use and quantity of livestock products produced. 15 The optimization model was created in Microsoft Excel (91) and solved using OpenSolver (92,93). The model\u00E2\u0080\u0099s objective function was to maximize total production of livestock products using only regionally produced livestock feed and pasture (Equation 11) and decision variables were the number of head of beef cattle, dairy cattle, lambs, hogs, broilers, turkeys, and layers that should be raised in the study area in order to do so (Hjr). Equation 11 \u00F0\u009D\u0091\u0080\u00F0\u009D\u0091\u008E\u00F0\u009D\u0091\u00A5\u00F0\u009D\u0091\u0096\u00F0\u009D\u0091\u009A\u00F0\u009D\u0091\u0096\u00F0\u009D\u0091\u00A7\u00F0\u009D\u0091\u0092 \u00E2\u0088\u0091\u00F0\u009D\u0091\u0087\u00F0\u009D\u0091\u008C\u00F0\u009D\u0091\u009F\u00F0\u009D\u0091\u0090\u00F0\u009D\u0091\u0090\u00E2\u0088\u0088\u00F0\u009D\u0091\u0099= \u00E2\u0088\u0091(\u00F0\u009D\u0090\u00BB\u00F0\u009D\u0091\u0097\u00F0\u009D\u0091\u009F \u00C3\u0097\u00F0\u009D\u0091\u0099\u00F0\u009D\u0090\u00BB\u00E2\u0081\u0084 \u00F0\u009D\u0091\u0097\u00F0\u009D\u0091\u008A\u00F0\u009D\u0091\u0090)\u00F0\u009D\u0091\u008E\u00F0\u009D\u0091\u0099\u00F0\u009D\u0091\u0099 \u00F0\u009D\u0091\u0099 Where l denotes the subset of commodities that are livestock products (l = beef, dairy products, lamb, pork, chicken, turkey, or egg). Where j denotes a specific livestock type (j = beef cattle, dairy cattle, lamb, hog, broiler, turkey, or layer), Hjr denotes the head of livestock j that are raised in SWBC and pastured locally and/or fed locally grown feed only, and l/Hj denotes the quantity (tonnes) of commodity c produced by livestock type j (89). Given the objective to determine the maximum quantity of livestock products that could be produced using only bio-regionally available pasture and feed, two model constraints were defined. The first was that total livestock feed and pasture allocated to livestock in SWBC cannot exceed the quantity that was available in 2011 (Equation 12). The second was that total production of any one livestock product cannot exceed the SWBC population\u00E2\u0080\u0099s Total Need for that product in 2011 (Equation 13). Equation 12 \u00E2\u0088\u0091(\u00F0\u009D\u0090\u00BB\u00F0\u009D\u0091\u0097\u00F0\u009D\u0091\u009F \u00C3\u0097 \u00F0\u009D\u0091\u0085\u00F0\u009D\u0091\u0097\u00F0\u009D\u0091\u0091)\u00F0\u009D\u0091\u008E\u00F0\u009D\u0091\u0099\u00F0\u009D\u0091\u0099 \u00F0\u009D\u0091\u0097\u00E2\u0089\u00A4\u00F0\u009D\u0091\u0087\u00F0\u009D\u0091\u008C\u00F0\u009D\u0091\u009D\u00F0\u009D\u0091\u0091\u00F0\u009D\u0090\u00B4\u00F0\u009D\u0091\u009D\u00F0\u009D\u0091\u0091\u00C3\u0097 \u00F0\u009D\u0090\u00B4\u00F0\u009D\u0091\u009F\u00F0\u009D\u0091\u0091 Where d denotes a livestock feed crop (d = grain, meal & silage; hay; pasture); Rjd denotes the annual quantity (tonnes/head/year) of feed crop d required by livestock type j, including accounting for the feed requirements of the breeding stock and/or replacement offspring (estimated using the method developed by Cowell and Parkinson (94); TYpd denotes the total provincial yield (tonnes) of livestock feed d (95) (Schmidt, Orlando. Conversation with: Caitlin Dorward. 2014 June 20. And Hatfield, Jill. Conversation with: Caitlin Dorward. 2014 June 24); Apd denotes the provincial area (hectares) planted in livestock feed d (83); and Ard denotes the SWBC area (hectares) planted in livestock feed d (83). 16 Equation 13 \u00F0\u009D\u0091\u0087\u00F0\u009D\u0091\u008C\u00F0\u009D\u0091\u009F\u00F0\u009D\u0091\u0090 \u00E2\u0089\u00A4 \u00F0\u009D\u0091\u0081\u00F0\u009D\u0091\u0093 \u00F0\u009D\u0091\u0093\u00F0\u009D\u0091\u009C\u00F0\u009D\u0091\u009F \u00F0\u009D\u0091\u0090 \u00E2\u0088\u0088 \u00F0\u009D\u0091\u0099 2.2.5 Diet and seasonality constraint on food self-reliance Given the objective to assess food self-reliance in a diet that meets dietary recommendations in a manner aligned with food preferences, it was assumed that no substitution between foods occurs (e.g., Food Need for tropical and citrus fruit cannot be satisfied by locally producible fruits) and that fresh fruits and vegetables are consumed year round (e.g., fresh strawberries are consumed in winter). Given these assumptions, there is an upper ceiling on the level of food self-reliance possible in SWBC because Food Need for foods that cannot be grown in SWBC and for foods consumed fresh out-of-season can only be satisfied by imports. This upper ceiling was termed the \u00E2\u0080\u009Cdiet and seasonality constraint on food self-reliance\u00E2\u0080\u009D and it was calculated using: Equation 14 \u00F0\u009D\u0090\u00B7\u00F0\u009D\u0091\u0086\u00F0\u009D\u0090\u00B6\u00F0\u009D\u0091\u0093 =\u00F0\u009D\u0091\u0081\u00F0\u009D\u0091\u009312\u00C3\u0097\u00F0\u009D\u0091\u0080\u00F0\u009D\u0091\u009C\u00F0\u009D\u0091\u0093 Where DSCf denotes the diet and seasonality constraint on food self-reliance for food f (tonnes); Nf/12 denotes the monthly need (tonnes) of food f in fresh form (Appendix I); and Mof denotes the number of months that food f is available fresh within SWBC (96). This constraint represents a ceiling on the portion of total Food Need that could ever be satisfied by SWBC production, regardless of how much food is actually produced in SWBC in a given year. In keeping with the definition of Food Need as the quantity of food required to meet dietary recommendations in a manner aligned with the Preferred Diet, any SWBC production in excess of this ceiling is not considered to contribute to total food self-reliance as that amount was assumed to not be preferred by the population. 2.2.6 Food self-reliance Self-reliance for each food (SRf) was calculated by counting the minimum of the two values: total SWBC production (TYrc) or the diet and seasonality constraint on food self-reliance (DSCf), using: Equation 15 \u00F0\u009D\u0091\u0086\u00F0\u009D\u0091\u0085\u00F0\u009D\u0091\u0093 = \u00F0\u009D\u0091\u009A\u00F0\u009D\u0091\u0096\u00F0\u009D\u0091\u009B (\u00F0\u009D\u0090\u00B7\u00F0\u009D\u0091\u0086\u00F0\u009D\u0090\u00B6\u00F0\u009D\u0091\u0093, \u00F0\u009D\u0091\u0087\u00F0\u009D\u0091\u008C\u00F0\u009D\u0091\u009F\u00F0\u009D\u0091\u0090)\u00F0\u009D\u0091\u0081\u00F0\u009D\u0091\u0093\u00C3\u0097 100% To calculate food self-reliance by Food Group (SRg) all foods belonging to one Food Group were considered simultaneously, using: 17 Equation 16 \u00F0\u009D\u0091\u0086\u00F0\u009D\u0091\u0085\u00F0\u009D\u0091\u0094 = \u00E2\u0088\u0091 min(\u00F0\u009D\u0090\u00B7\u00F0\u009D\u0091\u0086\u00F0\u009D\u0090\u00B6\u00F0\u009D\u0091\u0093, \u00F0\u009D\u0091\u0087\u00F0\u009D\u0091\u008C\u00F0\u009D\u0091\u009F\u00F0\u009D\u0091\u0090)\u00F0\u009D\u0091\u008E\u00F0\u009D\u0091\u0099\u00F0\u009D\u0091\u0099 \u00F0\u009D\u0091\u0093\u00E2\u0088\u0088\u00F0\u009D\u0091\u0094\u00E2\u0088\u0091 \u00F0\u009D\u0091\u0081\u00F0\u009D\u0091\u0093\u00F0\u009D\u0091\u008E\u00F0\u009D\u0091\u0099\u00F0\u009D\u0091\u0099 \u00F0\u009D\u0091\u0093\u00E2\u0088\u0088\u00F0\u009D\u0091\u0094\u00C3\u0097 100% To calculate food self-reliance for the total diet (SR) all foods in the diet were considered simultaneously, using: Equation 17 \u00F0\u009D\u0091\u0086\u00F0\u009D\u0091\u0085 = \u00E2\u0088\u0091 min(\u00F0\u009D\u0090\u00B7\u00F0\u009D\u0091\u0086\u00F0\u009D\u0090\u00B6\u00F0\u009D\u0091\u0093 , \u00F0\u009D\u0091\u0087\u00F0\u009D\u0091\u008C\u00F0\u009D\u0091\u009F\u00F0\u009D\u0091\u0090)\u00F0\u009D\u0091\u008E\u00F0\u009D\u0091\u0099\u00F0\u009D\u0091\u0099 \u00F0\u009D\u0091\u0093\u00E2\u0088\u0091 \u00F0\u009D\u0091\u0081\u00F0\u009D\u0091\u0093\u00F0\u009D\u0091\u008E\u00F0\u009D\u0091\u0099\u00F0\u009D\u0091\u0099 \u00F0\u009D\u0091\u0093\u00C3\u0097 100% 2.3 Results and discussion 2.3.1 Food need With a total population of approximately 2.69 million, SWBC is one of the nation\u00E2\u0080\u0099s most populous regions (28). For this population, servings of food in the Preferred Diet were found to be less than the number required to meet CFG recommendations in all food groups except for Fats and Oils (Figure 3). The discrepancy between the Preferred Diet and CFG recommendations was greatest in the Fruits & Vegetables and Milk & Alternatives Food Groups, for which Servings in the Preferred Diet were 57% and 49% of those required to satisfy CFG recommendations respectively. For the Grains Group, the Servings of food in the Preferred Diet were only slightly lower than the CFG recommendation. Servings of Fats & Oils in the Preferred Diet exceed the number recommended by Canada\u00E2\u0080\u0099s Food Guide. These findings are fairly consistent with a 2004 study of adult British Columbian\u00E2\u0080\u0099s food consumption patterns, which found that the majority do not meet minimum recommendations for consumption of Fruits & Vegetables or Milk & Alternatives, while they do meet those for Grains (97). For Meat & Alternatives, the same study found that the majority of women did not meet recommended consumption levels while the majority of men do (97) while this study suggests that the total population under-consumes by 26%. This study\u00E2\u0080\u0099s lower overall estimation of Meat & Alternatives consumption in the Preferred Diet could result from the fact that some meat alternatives such as lentils, nuts, and tofu, are either not tracked in the food availability dataset used to estimate the Preferred Diet, or could not be included in this analysis for reasons explained in the methods section. 18 By adjusting the Preferred Diet to meet CFG recommendations, total Food Need for SWBC was estimated in tonnes commodity weight (Table 1). Note that, although this study focused on food self-reliance in the land-based components of the diet, the contribution of fish and seafood to total Food Need was accounted for in order to not inflate dietary requirement for meat. Table 1: Southwest British Columbia Food Need (Nf) by Food Guide Group, 2011 Food Group Nf (tonnes food weight) Fruits & Vegetables Fruit 275,665 Vegetables 334,879 Sub-Total 610,544 Grain Sub-Total 129,870 Milk & Alternatives Sub-Total 366,787 Meat & Alternatives Eggs 33,169 Legumes 12,617 Meat 125,472 Fish and Seafood 32,744 Sub-Total 204,002 Fats & Oils Sub-Total 49,290 Total Diet 1,360,494 Figure 3: Annual Servings per Food Group required to meet Canada\u00E2\u0080\u0099s Food Guide recommendations for the Southwest British Columbia (SWBC) population in 2011 (Sg) and Servings per Food Group in the SWBC Preferred Diet in 2011 (SPdg), including fish and seafood. Percentages indicate the percentage of Canada\u00E2\u0080\u0099s Food Guide recommendations that are met by the Preferred Diet. 19 2.3.2 Agricultural land use Actual agricultural land use in SWBC in 2011 was dominated by livestock feedstuff, with hay, pasture and corn silage together comprising 74% of total land use (78,466 hectares). Not reported in the available data is the type(s) of livestock this land supports, however it is likely that some is used for livestock operations not associated with food production (e.g., horses). The second most common use of agricultural land is for fruits and vegetables, which together make up 19% of total agricultural land use (19,893 hectares). The remaining 7% of land is used for production of legumes, fats & oils, non-food crops and for housing livestock (Table 2). Table 2: Southwest British Columbia agricultural land use in 2011 Canada Food Guide Food Group Arc (hectares) Fruits & Vegetables Fruit 12,930 Vegetables 6,963 Sub-Total 19,893 Food Grain Sub-Total 462 Meat & Alternatives Legumes 37 Livestock Barn Area 1,185 Sub-Total 1,222 Fats & Oils Sub-Total1 385 Livestock Fodder Hay 28,661 Pasture 40,318 Corn Silage 9,102 Feed Grain 1,385 Canola Meal1 385 Sub-Total 79,466 Non-Food Crop Use Sub-Total 5,321 Total 106,749 1 Canola seed produces both a food product (oil) and feed product (canola meal); therefore its acreage is included under both Fats & Oils and Livestock Fodder but only counted once in the grand total. 20 2.3.3 Quantity of crop food produced SWBC food crop production is dominated by vegetables and fruit, which together comprise 87% of total food crop production by weight (Table 3). Berries (blueberry [Vaccinium corymbosum], cranberry [Vaccinium macrocarpon], raspberry [Rubus idaeus], and strawberry [Fragaria \u00C3\u0097 ananassa]) comprised 96% of total fruit production in 2011 and greenhouse vegetables (cucumber [Cucumis sativus], bell pepper [Capsicum annum], and tomato [Lycopersicum esculentum]) 42% of total vegetable production. This is consistent with SWBC\u00E2\u0080\u0099s reputation as a provincial centre for horticultural crop production characterized by substantial greenhouse and berry industries (62). Table 3: Total yield (Ty) of crop food commodities in Southwest British Columbia in 2011, by Food Group Food Group Ty (tonnes commodity weight) Fruits & Vegetables Fruit 85,119 Vegetables 252,702 Sub-Total 337,821 Grain Sub-Total 3,252 Meat & Alternatives (Legumes) Sub-Total 75 Fats & Oils Sub-Total 266 Total 387,805 2.3.4 Quantity of livestock products produced Results from the livestock production optimization model indicate that, if feed imports were not available, a maximum of 4,748 milking cows, in addition to their breeding stock and replacement herd, could be supported by regionally produced livestock feed. Together, these cows could produce 44,500 tonnes of fluid milk (Table 4). At this level of production, all available grain feed would be utilized but over 40,000 hectares of hay and 28,000 hectares of pasture would remain unused, indicating that feed grain is the most limited component of the livestock diet and therefore the limiting factor to livestock production in SWBC that is not dependent on feed imports. Although SWBC-produced feedstuffs could be fed to livestock types other than dairy cattle, doing so would result in lower total production of livestock product in a scenario in which feed imports were not available. As such, the livestock production optimization model results indicate that zero production of other livestock products (beef, lamb, pork, eggs, chicken, and turkey) is optimal from a food self-reliance perspective when food self-reliance is defined as including only livestock products produced with imported feed (Table 4). 21 Table 4: Quantity (tonnes commodity weight) of livestock commodity produced by each livestock type (l/Hj) and results from Southwest British Columbia livestock production optimization model, including the maximum number of livestock that could be raised in 2011 if no feed was imported (Hjr), and corresponding maximum quantity of livestock production (TYrc) Livestock Type Livestock Product \u00F0\u009D\u0092\u0084\u00F0\u009D\u0091\u00AF\u00E2\u0081\u0084 \u00F0\u009D\u0092\u008B Hjr TYrc (tonnes commodity weight) Beef Cows Beef 0.419 0 0 Dairy Cows Fluid Milk 9.372 4,748 44,500 Lambs Lamb 0.024 0 0 Pigs Pork 0.094 0 0 Layers Eggs 0.015 0 0 Broilers Chicken 0.002 0 0 Turkeys Turkey 0.008 0 0 SWBC is recognized as being the heart of the provincial dairy, poultry, and pork industries and produced approximately 117,000 head of cattle including 51,419 milking dairy cows, 9,400 sheep and lambs, 76,700 hogs, and 118 million birds (broilers, layers, and turkeys) in 2011 (83). Total production of livestock products includes production from livestock raised with imported feed is outlined in Table 5. Table 5: Southwest BC livestock production including livestock raised with imported feed (2011) Livestock Product TYrc (tonnes commodity weight) Beef 752 Fluid Milk 454,529 Lamb 281 Pork 14,516 Eggs 33,286 Chicken 176,149 Turkey 24,132 Comparing livestock production with and without imported feed reveals the degree to which SWBC\u00E2\u0080\u0099s livestock operations are currently dependent on feed imports from other regions. This trend is not unique to SWBC but, rather, mirrors a global trend towards the decoupling of livestock production from a local land base or integrated farming system that supports it (98,99). By the year 2000, for example, 72% of global poultry production and 55% of global pork production was sustained by feed imported from other regions (98). 2.3.5 Diet and seasonality constraint on food self-reliance The diet and seasonality constraint on food self-reliance was calculated per individual food type but for brevity is reported here by Food Group (Table 6). Given this constraint, it was calculated that the upper 22 ceiling for food self-reliance for the total diet in SWBC is 77%, regardless of how much food is actually produced in SWBC. This is primarily due to the need to import preferred foods not able to be grown in the region (Table 6). Table 6: Total Food Need (Nrf), the diet and seasonality constraint on food self-reliance (DSCf), and the hypothetical maximum portion of Total Food Need that can be satisfied by Southwest British Columbia food production (DCSrf/Nrf), by Food Group. Food Group Nrf (tonnes food weight) DSCf (tonnes food weight) DCSrf/Nrf Fruit & Vegetables Fruit 275,665 90,631 33% Vegetables 334,879 243,223 73% Sub-Total 610,544 333,855 55% Grain Sub-Total 129,870 116,498 90% Meat & Alternatives Eggs 33,169 33,169 100% Legumes 12,617 3,876 31% Meat 125,472 125,472 100% Sub-Total 162,518 Milk & Alternatives Sub-Total 366,787 366,787 100% Fats & Oils Sub-Total 49,290 49,290 100% Total 1,327,750 1,028,948 77% To determine how much of the food produced in SWBC contributes to SWBC\u00E2\u0080\u0099s food self-reliance, total SWBC production was compared to the diet and seasonality constraint on food self-reliance for each crop (Figure 4). For the Total Diet, and for those Food Groups containing livestock products (Meat & Alternatives, Milk & Alternatives, and Fats & Oils) we include results based on the number of livestock that were reported present in SWBC in 2011 and those based on the number of livestock that could have been raised on locally available feed and pasture only. Considering the livestock production possible using SWBC-produced grain and pasture, only 62% of total regional production of crop and livestock products could contribute to food self-reliance. The remaining 38% of food produced (comprised entirely of crops in the Fruit & Vegetable Food Group) are in excess of the diet and seasonality constraint on food self-reliance. In the Fruits & Vegetables food group, blueberries, cranberries, raspberries, mushrooms, Brussels sprouts, greenhouse cucumbers, greenhouse peppers, pumpkins and squash, and greenhouse tomatoes comprise the entirety of production in excess of the diet and seasonality constraint. The portion of SWBC-produced food that contributes to food self-23 reliance would increase to 88% if the definition of self-reliant food production were expanded to include livestock products that were produced using imported feed. The values in Figure 4 are specific to the foods included in this study and the Preferred Diet of this region, and would change if these parameters were altered. For example, decreased consumption of tropical fruits would increase the diet and seasonality constraint for Fruits & Vegetables and corresponding level of potential food self-reliance. Without increasing the total area farmed in SWBC, self-reliance itself could be increased in SWBC if the discrepancy between Total Yield and the amount of Total Yield that contributes to SWBC food self-reliance was reduced. This could be achieved if the population were to substitute consumption of processed foods grown in SWBC for those consumed fresh out of season, or to substitute consumption of fruits produced in SWBC for currently consumed tropical and citrus fruit. The Canadian Food Availability dataset used to estimate the Preferred Diet in this study indicates that per capita consumption of tropical and citrus fruit has increased by 18% over Figure 4: Comparison of Southwest British Columbia (SWBC) Total Yield (TYr) and amount of Total Yield that contributes to SWBC food self-reliance (TYr FSR) in 2011. For the Total Diet, and for those Food Groups containing livestock products (Meat & Alternatives, Milk & Alternatives, and Fats & Oils), results are presented from the analysis with and without feed imports available for livestock production. Percentages indicate the percent of Total Yield that contributes to SWBC food self-reliance and are calculated by dividing TYr by TYr FSR. 24 1986 levels and the per capita consumption of fruits that can be grown in SWBC has increased by 25% over 1986 levels (74). A diversification of SWBC agricultural production away from crops produced in excess of the diet and seasonality constraint on food self-reliance could potentially facilitate an increase in food self-reliance without increasing total farmed area, but would be counter to current provincial trends in agricultural land use. From 1986 \u00E2\u0080\u0093 2006, for example, field vegetable production in BC declined by 40%, greenhouse vegetable production increased by 437%, and blueberry production increased 245% (36). 2.3.6 Food self-reliance Total food self-reliance of SWBC in 2011 was calculated by Food Group and for the total diet. To illustrate the impact of the two alternative definitions of food self-reliance used in this study, results based on the number of livestock that were actually present in SWBC in 2011 are presented and compared with results based on the number of livestock that could have been raised on locally available feed and pasture only (Figure 5). For the latter, total dietary food self-reliance is 12% with the Food Group self-reliance being highest in fruit and vegetables (21%), followed by dairy (10%), and fruit (4%). If the definition of food self-reliance is expanded to include livestock products that were produced using imported feed, total dietary food self-reliance would be 40% due to increased self-reliance in the Meat & Alternatives, Milk & Alternatives, and Fats & Oils Food Groups. Figure 5: Southwest British Columbia food self-reliance in 2011 by Food Group and the Total Diet (SRg). For the Total Diet, and for those Food Groups containing livestock products (Meat & Alternatives, Milk & Alternatives, and Fats & Oils) results are presented from the analysis with and without feed imports available for livestock production. The measurement of food self-reliance in Meat & Alternatives is only for the livestock product component of total Meat & Alternatives Food Need. 25 Increasing SWBC\u00E2\u0080\u0099s food self-reliance could potentially be achieved through shifts in production or in the preferred diet mentioned early, or by increasing total farmed area. Underutilization of agricultural land is an issue in SWBC (45). In 2010/2011, for example, almost 18,000 hectares of agricultural land in Metro Vancouver (a regional district in SWBC), was classified as having potential for farming but not farmed (100). This comprised 25% of Metro Vancouver\u00E2\u0080\u0099s total area of land protected for farming by the provincial ALR (100). Increased utilization of available agricultural land with the potential for farming could increase food self-reliance if production on it specifically targeted crops whose current production levels are lower than Food Need. Results from two previous studies of food self-reliance in British Columbia are summarized in Table 7. As described in the introduction, they used disparate and now outdated methodologies and measured food self-reliance at a larger scale than in this study. A straightforward comparison of results is therefore not possible, but reasons behind some general trend differences can be speculated. Fruit & Vegetable self-reliance results from this study are lower than previous studies likely because the delineated SWBC bio-region does not include BC\u00E2\u0080\u0099s major tree fruit producing region (the Okanagan) and because the previous studies do not discount production in excess of the diet and seasonality constraint on food self-reliance. Likewise, self-reliance in food grain and livestock products is higher in the 2001 study than in this study because SWBC does not include the province\u00E2\u0080\u0099s major feed and food grain producing regions (the Peace River, Bulkley Nechako, and Fraser Fort George Regional Districts). The 2006 study did not account for the production of feed to support provincial livestock; therefore their estimate of food self-reliance in livestock products and grains is high as all livestock present in the province in 2006 were counted and all grains produced in 2006 are considered to be for human consumption. Comparison to results from studies of other regions must also be done with caution due to discrepancies in scale of analysis and the methods used. Unlike this study\u00E2\u0080\u0099s results, for example, analyses of the food self-reliance status of 13 states in the northeast United States and of western-Oregon\u00E2\u0080\u0099s Willamette Valley found food self-reliance to be higher in livestock products than in plant-based foods (25,38) (Table 7). Neither study considered the capacity of the study region to produce livestock without feed imports and nor did they discount production according to a diet and seasonality constraint. 26 Table 7: Comparison of Southwest British Columbia (SWBC) food self-reliance, as measured in this study, to that of other regions Study Region (Year) (citation) Food Category British Columbia (2001)(23) British Columbia (2006) (36) Northeast US (2001-2009 average) (25) Willamette Valley, US (2008) (38) SWBC (2011) (this study) Fruit 36% 49% 18% 24% 21% Vegetables 43% 35% 26% ~8% Grain 14% 54% 8% 67% 1% Fats & Oils (not measured) 10% (not measured) 0% Meat & Eggs 64%1 101% 36% 58% 0.04%1 Milk 57%1 251% 59% 10%1 Total Diet 48% (not measured) 12%1 1 Self-reliance based on the number of livestock that could have been raised on locally available feed and pasture only. 2.4 Conclusion Like other studies (23\u00E2\u0080\u009325,38,94) in this chapter the current food self-reliance status of a sub-national region was measured according to current population, land use, and a diet that satisfies nutritional recommendations. Where the methodology used in this chapter differs from other studies was in its strict approach to the definition of food self-reliance that considers the diet and seasonality constraint on food self-reliance and its assessment and comparison of food self-reliance status with and without the availability of livestock feed imports. This study revealed that the Preferred Diet of southwest British Columbians falls substantially short of meeting nutritional recommendations for the consumption of foods in the Fruits & Vegetables and Milk & Alternatives Food Groups but nearly meets recommendations for the Grains and Meat & Alternatives Food Groups. Agricultural land use is dominated by livestock fodder production, followed by fruit and vegetable production, which together comprise the majority (87%) of crop food production in SWBC. SWBC production of feed grain was found to be a major constraint on self-reliance in livestock products; self-reliance in Meat & Alternatives is less than 1% and Milk & Alternatives is 10% if considering the availability of SWBC grown feed and pasture compared to 49% and 86% respectively if including livestock raised with imported feed. Total dietary self-reliance of SWBC is 12% if discounting livestock feed imports or 40% if including livestock raised with imported feed. While establishing a food self-reliance baseline is important, it is only a first step in informing the discourse around what an alternate food system future might look like. While many studies have measured current levels of food self-reliance at national, regional, or municipal scales, few have taken the next step of calculating the capacity to increase food self-reliance in the future, or what agricultural 27 land use would look like in terms of crop mix and extent of production in a scenario of increased food self-reliance. In order to truly bring the local verses global debate out of the abstract, an understanding of current food self-reliance status must be complemented by an understanding of the capacity to increase future food self-reliance given population growth projections, dietary trends, and bio-physical resources. This is the focus of the next chapter. 28 Chapter 3: Modeling of future capacity for land-based food self-reliance in Southwest British Columbia 3.1 Introduction The research community has responded to concern regarding the capacity of the global food system to meet human nutrition needs into the future with the development of tools to assess current and future food system capacity, and identify problems and potential solutions. These tools include scenario analysis and mathematical and spatial modeling. Much of this research effort has focused on the global or multi-region scale. McCalla and Revoredo (101), for example, identified at least 30 major independent, model-based analyses of global food supply and demand that were conducted between 1950 and 2000. Reilly and Willenbockel (102) reviewed seven additional, more recent studies that explore the future of the global food system to the year 2050 by applying scenario analysis and modeling methodologies to global or multi-region scales. Studies such as these have advanced methodologies for the study of food systems and fostered widespread recognition that food systems must evolve in the face of challenges to their capacity and efficacy. They have not, however, fostered consensus as to exactly what this evolution should look like. Rather, a polarized debate has emerged regarding what scale is appropriate for a future food system. Some advocate for localized food systems, characterized by increased regional food self-reliance (the ability to satisfy food needs with food grown locally), as the preferred strategy to increase food system resilience. Others contend that only globalized food systems (which favour food items produced in locations which have competitive advantages in terms of capital, production capacity, energy, and/or labour), will facilitate a sustainable food supply in the future (chapter 2). Unless increasing a region\u00E2\u0080\u0099s food self-reliance is possible, however, there is little merit in advocating for doing so. Furthermore, even if increasing food self-reliance is theoretically possible, analysis of the merits and demerits of food system localization, which may transcend food self-reliance exclusively, is impossible in the absence of better understanding of how and the extent to which localization could be achieved. Global scale studies have compared food systems with differing production methods (e.g., organic verses conventional (103)) diets (e.g.: subsistence verses affluent (104)), and other features. Although this has resulted in the generation of important information about the future of food systems in general, their aims and scale of analysis preclude them from informing the global verses local debate specifically. As such, the debate occurs largely in the abstract, in the absence of an understanding of the capacity for, and outcomes of, food system localization given a particular region\u00E2\u0080\u0099s population growth projections, dietary trends, and bio-physical resources. 29 To address this knowledge gap and bring rigour to the local versus global debate, some studies have used food system modeling techniques to assess food self-reliance capacity at national, regional, or municipal scales. Van Bers and Robinson (39) used a spreadsheet model to calculate the potential for Canada to feed itself in a \u00E2\u0080\u009Csustainable 2031\u00E2\u0080\u009D scenario of reduced meat consumption, minimal population growth and minimal loss of agricultural land, and the widespread adoption of sustainable agricultural production practices. Given these assumptions, their findings suggested that Canada could achieve future self-sufficiency in grains, oilseeds, pulses, and potatoes but not in fruits and vegetables (39). Although the authors suggested that strategically re-allocating land to the production of vegetables for local consumption rather than for export could improve Canada\u00E2\u0080\u0099s potential for vegetable self-reliance, they did not conduct this analysis. Another national-scale study assessed the potential to localize the UK\u00E2\u0080\u0099s food system and found that the additional land needed for food production to achieve UK food self-reliance was equal to at least 16% of the land farmed at the time of the study (94). The study did not, however, report what food self-reliance levels would be possible without increasing farmed area (94). Similar methodologies were employed by Colasanti and Hamm (24) to assess food self-reliance capacity of Detroit, Michigan. Their study compared the land required for bio-intensive production of fruits and vegetables with the quantity of land available for urban food production and found that Detroit could theoretically satisfy up to 31% and 17% of its population\u00E2\u0080\u0099s demand for vegetables and fruit respectively (24). Other studies have used optimization models to assess the capacity to achieve food self-reliance given differing diets and land-use priorities. Optimization models seek to maximize or minimize some criteria(ion) by choosing among an available set of alternatives, subject to a set of constraints (105,106). An optimization model is comprised of an objective function (the value of which the model is designed to maximize or minimize), two or more decision variables (the variables adjusted by the model in order to maximize or minimize the objective function), and one or more constraints (conditions or rules that constrain the selection of decision variable values) (105). Optimization models that allocated crops to agricultural land in a manner that minimized distance between points of production and consumption have been used to assess food self-reliance capacity for New York State (NYS) and southeastern Minnesota (37,107). Results indicated that NYS could meet 34% of food need while transporting food an average of 49km, and that southeastern Minnesota could meet 100% of its food needs within a 1.5 million hectare \u00E2\u0080\u009Cfoodshed\u00E2\u0080\u009D (37,107). A localized NYS food system has also been modeled in an optimization model that prioritized which foods to grow in-state based on the 30 goal to maximize economic return to land; using this approach it was found that NYS could achieve 69% food self-reliance (108). These studies modeled contemporary food self-reliance in a diet that met accepted national guidelines only with food that could be produced in the study region (i.e.: no tropical fruit or other imports, and a winter diet of processed or stored foods), and did not consider factors such as future population increase, changes in land availability, or yield variability. In British Columbia, Canada (BC), where a growing segment of the population is seeking to source their food closer to home and provincial food security experts have identified increasing provincial food self-reliance as a key climate change adaptation strategy (10,69), the capacity to increase BC\u00E2\u0080\u0099s food self-reliance has been the focus of previous studies. One, completed in 1978, used an optimization model that minimized land use to determine how much land would be required to meet current and future food requirements under a variety of scenarios, including 100% contemporary (1976) self-sufficiency with exports maintained, and maximum future (1996) self-sufficiency with and without beef production (109). Results indicated that self-reliance could be achieved in 1976 even while maintaining exports but that, unless beef production ceased, insufficient land would be available to meet projected 1996 levels of food need (109). Although this study\u00E2\u0080\u0099s approach to projecting future food self-reliance capacity was novel at the time of publication, it did not assess scenarios of reduced land availability or changes to crop yield. Furthermore, the data used and results themselves are now long out of date. A second, more recent study, conducted at the provincial scale, estimated that 2.8 million hectares of land would be needed to produce a healthy diet for British Columbians in the year 2025. This was determined by calculating the per capita area required to produce a complete diet multiplied by the projected 2025 population (23). Although this study is more current, it has a number of limitations. The study did not report land requirements in a food self-reliant future per crop or per crop group, or the total increase in farmed area that would be required over the area farmed in the year of publication. The total area required for future food self-reliance was not compared to the quantity of farmable land in the province, and no assessment was done of how food self-reliant BC could be in 2025 without increasing farmed area. A third study, conducted at the sub-provincial scale, assessed the land area requirement for food self-reliance of BC\u00E2\u0080\u0099s Greater Vancouver Regional District, and determined that 2.1 million hectares of land would be required to support the region\u00E2\u0080\u0099s 2050 population (110). Although this study considered how land requirements would change if the population adhered to different diets, like others mentioned 31 here, it did not assess how sensitive the capacity for food self-reliance is to factors that may impinge upon self-reliance in the future, most notably climate change and farmland availability. Climate change is expected to \u00E2\u0080\u009Craise the stakes\u00E2\u0080\u009D of agriculture in BC by presenting several fundamentally new challenges; temperature, unseasonal precipitation, and the frequency of extreme weather events are all expected to increase by the year 2020, and sea level rise will likely affect coastal agricultural areas by the year 2100 (111). Just as concerns surrounding the capacity of the global food system to facilitate a sustainable food supply in the future are not unique to BC, neither are anticipated climate change impacts on agriculture and farmland availability. The Intergovernmental Panel on Climate Change (IPCC) points to changing inter-annual yield variability as likely having global impact. They report that the likely climate change impact on crop yield according to the majority of reviewed climate change modeling projects is a 10-25% decrease in crop yield by the year 2050 (112). According to BC-specific climate change modeling, however, it is expected that BC\u00E2\u0080\u0099s growing degree days will increase over levels in the 1971-2000 baseline period (113), which could have a positive impact on some crops and negative impact on others. Given the current uncertainty of climate science and the fact that many unpredictable socio-economic and geo-political elements factor into the climate-impact equation, there is a high level of uncertainty as to whether or to what degree climate change will be detrimental or beneficial to BC\u00E2\u0080\u0099s agriculture sector. Perhaps a more certain and imminent threat to food self-reliance capacity is the loss of farmland. Although the majority of farmland in BC is currently protected by the Agricultural Land Reserve (ALR), a provincial zone in which agriculture is recognized as the priority use (29), palpable threats of urban, industrial, or other non-farm use or development of this land persist. Private or publically owned ALR parcels, for example, can be removed from the ALR on a case-by-case basis through an exclusion application process. Between the ALR\u00E2\u0080\u0099s 1973 inception and 2011, southwest British Columbia lost 9% of its ALR as a result of such applications (30). Furthermore, 2014 saw the passing of controversial legislation which split the ALR into two zones; legislation around non-farm use of ALR land has been relaxed in Zone Two, which comprises 90% of total provincial ALR (114). Critics of the legislation cite concerns that it represents a serious erosion of the ALR\u00E2\u0080\u0099s original mandate, and will ultimately reduce provincial food security (115,116). Finally, the underutilization of agricultural land has been identified as a common land use pattern in some parts of the province (45,100,117\u00E2\u0080\u0093120). In BC\u00E2\u0080\u0099s Greater Vancouver Regional District, almost 18,000 hectares of agricultural land (or 25% of total ALR in that Regional District) was classified as having potential for farming but not being farmed in 2014 (100). Although this 32 farmland is not necessarily at risk of urban development, its underutilized status renders it effectively lost from agriculture in the short term and non-farm use makes it more vulnerable to permanent long term loss (45). Given the public interest in increasing food self-reliance in BC and the limitations of previous studies, further assessment of the future food self-reliance capacity in this region is warranted. The 2011 food self-reliance status of a sub-region of BC, namely the south-west BC bio-region (SWBC) was assessed in chapter two, but no assessment has been made of SWBC\u00E2\u0080\u0099s future food self-reliance capacity and how it may be impacted by climate change or land availability. Results from the study provide a suitable baseline against which to compare the capacity of this region to increase food self-reliance through a localized food system, and the degree to which climate change induced crop yield variability and uncertainty of farmland availability may impinge upon this capacity. As such, the study objectives reported on in this chapter are: 1. To develop models to assess the potential to increase future food self-reliance in a diet satisfying nutritional recommendations and food preferences, accounting for crop seasonality and the production of livestock feed and apply these models to the SWBC case study area; 2. To assess the sensitivity of the SWBC\u00E2\u0080\u0099s future food self-reliance capacity to changes in farmland availability and to climate change-induced changes in crop yield; and, 3. To determine the land-use outcomes of increasing food self-reliance in SWBC. To achieve these objectives, models of a Business as Usual food system (defined as a food system in which future crop and livestock production follows 2011 patterns) and a Localized food system (defined as a future food system in which crops are allocated to agricultural land in a manner that maximizes food self-reliance) were developed and applied to the study area of the southwest BC bio-region (SWBC) for the year 2050. 3.2 Methods 3.2.1 Food self-reliance capacity SWBC\u00E2\u0080\u0099s food self-reliance capacity in a 2050 Business as Usual (BAU) food system was estimated using a spreadsheet model in combination with a previously developed livestock production optimization sub-model. Together, they modeled food self-reliance outcomes if crop and livestock production in 2050 follows 2011 patterns. SWBC\u00E2\u0080\u0099s food self-reliance capacity in a 2050 Localized food system was estimated using an optimization model that modeled food self-reliance outcomes if crops are allocated to agricultural land in 2050 in a manner that maximizes food self-reliance. 33 Model input data for this study was retrieved at the regional district scale (see Equation 19 - Equation 24) and the Localized food system optimization model was purposefully designed with regional district scale decision variables and a multi-objective function that optimized for food self-reliance at the regional district and SWBC scales (see Equation 25 - Equation 30). This design enables the output of regional district-scale data that will be relevant for municipal and regional district government stakeholders who have an interest in understanding how their jurisdictions fit into a bio-regional food system (i.e. contribute to in terms of food production and benefit from in terms of self-reliance and economic development). In addition it will allow ecological indicators that must be modeled at a finer spatial scale than the bio-region (e.g. nutrient balance) to be incorporated into the model in the future. Prior assessment of SWBC\u00E2\u0080\u0099s 2011 food self-reliance with local feed only (see Chapter 2) revealed the degree to which livestock production in SWBC is currently dependent on feed imports from other regions. As such, future food self-reliance capacity in this chapter was modeled both with and without the availability of imported feed. In addition to modeling with and without the availability of imported feed, the models were run under nine scenarios of future farmland availability and climate change-induced change in crop yield and comparisons drawn between SWBC\u00E2\u0080\u0099s food self-reliance capacity in these two systems on average (the average of all nine scenarios), in a worst case future scenario (Increased non-farm use, decreased yield), and a best case future scenario (agricultural expansion, increased yield) (Table 8). As described in a forthcoming section, these scenarios were also used to perform an analysis of the sensitivity of food self-reliance capacity to changes in farmland availability and climate change-induced changes in crop yield. Table 8: Scenarios of 2050 land availability and climate change-induced changes in crop yield used in the Business as Usual and Localized Food System models Scenario Farmland Availability Crop Yield 1 Increased non-farm use Increased 2 Increased non-farm use Average Worst Case (3) Increased non-farm use Decreased 4 Stable Increased 5 Stable Average 6 Stable Decreased Best Case (7) Agricultural Expansion Increased 8 Agricultural Expansion Average 9 Agricultural Expansion Decreased 34 Model input data To enable comparison, the Business as Usual and Localized food system models shared input datasets including food need and the diet and seasonality constraint on food self-reliance, farmland availability and capability for agriculture, and crop yield. Food need and the diet and seasonality constraint on food self-reliance In chapter two, food need by age and gender group (the quantity of food needed to meet dietary recommendations within preferred diet parameters) for 131 foods from 67 agricultural crops and livestock products was estimated by determining the quantity of food available in the \u00E2\u0080\u009CPreferred Diet\u00E2\u0080\u009D of SWBC\u00E2\u0080\u0099s population and adjusting this to meet dietary recommendations from Canada\u00E2\u0080\u0099s Food Guide. Preferred Diet refers to what the available data indicates that people are actually buying to eat. This diet includes both crops that can be grown locally (e.g., apples, carrots) and crops that must be imported (e.g., bananas, mangoes). To derive 2050 SWBC food need per food type for use in the 2050 food system models, food need per age and gender group determined in chapter two was multiplied by the projected 2050 population of each age and gender group (Equation 18). Equation 18 \u00F0\u009D\u0091\u0081\u00F0\u009D\u0091\u0093 = \u00E2\u0088\u0091 \u00E2\u0088\u0091 \u00F0\u009D\u0091\u0081\u00F0\u009D\u0091\u0093\u00F0\u009D\u0091\u008E \u00C3\u0097 \u00F0\u009D\u0091\u0083\u00F0\u009D\u0091\u008E\u00F0\u009D\u0091\u0094\u00F0\u009D\u0091\u009F\u00F0\u009D\u0091\u008E\u00F0\u009D\u0091\u0099\u00F0\u009D\u0091\u0099 \u00F0\u009D\u0091\u0094\u00F0\u009D\u0091\u008E\u00F0\u009D\u0091\u0099\u00F0\u009D\u0091\u0099 \u00F0\u009D\u0091\u009F Where f denotes an individual food (f= 1,\u00E2\u0080\u00A6131); r denotes a regional district in SWBC (r = Fraser Valley, Greater Vancouver, Squamish Lillooet, Sunshine Coast, or Powell River); g denotes a Canada Food Guide age and gender group; Nf denotes the projected total annual SWBC food need (tonnes) for food f in the year 2050; Nfa denotes the total annual need (tonnes) for food f for age and gender group a; and Ppar denotes the projected 2050 population of age and gender group a in regional district r. At the time this study was conducted, SWBC population projections from BC Stats, the provincial statistical agency, were only available to the year 2036. It was assumed that the population growth rate projected by BC Stats for the 15 year period from 2020 \u00E2\u0080\u0093 2035 would continue for the following 15 years (i.e., from 2035 to 2050). The geometric extrapolation method (GOE) (121) was used to develop a population projection for the year 2050 (Table 9). As described by Smith et al.(121), the GOE method \u00E2\u0080\u009Cassumes that the population will change at the same annual rate over the projection horizon as during the base period\u00E2\u0080\u009D. 35 Table 9: Projected 2050 population of southwest British Columbia by age and gender group Age/Gender Group Projected 2050 Population Female Age 15-19 112,312 Age 20-50 869,905 Age 50+ 1,101,291 Male Age 15-19 115,708 Age 20-49 885,129 Age 50+ 994,360 Male & Female Age 10--14 227,142 Age 1--4 145,918 Age 5--9 212,760 SWBC Total 4,664,524 As in chapter two, the diet and seasonality constraint was calculated per food type to account for the assumptions that no substitution between foods occurs (e.g., Food Need for tropical and citrus fruit cannot be satisfied by locally producible fruits) and that fresh fruits and vegetables are consumed year round (e.g., fresh strawberries are consumed in winter) (Equation 14). Farmland availability and agricultural capability Spatial and non-spatial datasets were used to develop three farmland availability scenarios: stable, agricultural expansion, and increased non-farm use. Spatial analysis was performed in ArcGIS 10.2 (122). In the stable land availability scenario, 2050 farmland availability was equal to the amount of farmland in food production in 2011, as reported in the 2011 Census of Agriculture. The following Census of Agriculture land use categories were considered comprising farmland in food production: hay, field crops, vegetables, fruits, berries, nuts, greenhouse vegetables, mushrooms, tame or seeded pasture, natural land for pasture, summer fallow, and barn area. Barn area is reported in the aggregated Census of Agriculture category as \u00E2\u0080\u009Call other land\u00E2\u0080\u009D. As such, it was estimated it using (Equation 19): Equation 19 \u00F0\u009D\u0090\u00B5\u00F0\u009D\u0091\u008E\u00F0\u009D\u0091\u009F = \u00E2\u0088\u0091 \u00F0\u009D\u0090\u00B5\u00F0\u009D\u0091\u008E\u00F0\u009D\u0091\u0097 \u00C3\u0097 \u00F0\u009D\u0090\u00BB\u00F0\u009D\u0091\u0097\u00F0\u009D\u0091\u009F\u00F0\u009D\u0091\u008E\u00F0\u009D\u0091\u0099\u00F0\u009D\u0091\u0099 \u00F0\u009D\u0091\u0097 Where j denotes a specific livestock type (j = beef cattle, dairy cattle, lambs, hogs, broilers, turkeys, or layers); Hjs denotes the number of livestock j in regional district r; Baj denotes barn area (hectares) 36 required for housing livestock j (85\u00E2\u0080\u009388); and Bar denotes the total barn area (hectares) in regional district r. Total area of farmland used for food production per regional district was estimated using (Equation 20): Equation 20 \u00F0\u009D\u0090\u00B9\u00F0\u009D\u0091\u0093\u00F0\u009D\u0091\u009F = \u00F0\u009D\u0090\u00B5\u00F0\u009D\u0091\u008E\u00F0\u009D\u0091\u009F + \u00E2\u0088\u0091 \u00F0\u009D\u0090\u00B9\u00F0\u009D\u0091\u0093\u00F0\u009D\u0091\u0090\u00F0\u009D\u0091\u009F\u00F0\u009D\u0091\u008E\u00F0\u009D\u0091\u0099\u00F0\u009D\u0091\u0099 \u00F0\u009D\u0091\u0090 Where Ffcr denotes the area of farmland in food production for crop c in regional district r in 2011; and Ffr denotes the total area of farmland in food production in regional district r in 2011. It was assumed that the majority of farmland in food production was located on \u00E2\u0080\u009Cfarmable\u00E2\u0080\u009D ALR land, defined as any ALR not occupied by 20 metre wide road corridors (123), water bodies (124), protected areas (125\u00E2\u0080\u0093127), woody vegetation (128,129), and not classified as Class 7 according to the Land Capability Classification for Agriculture in British Columbia (130,131). The latter classification system groups mineral and organic soils into seven classes according to their potentials and limitations for agriculture (33). In general, from Class 1 to Class 6 the range of suited crops decreases, and the management and inputs required to produce those crops increases (Table 10) (33). Table 10: Land Capability Classification for Agriculture in British Columbia (33) Class Capability for Agriculture Class 1 \u00E2\u0080\u0093 4 Capable of sustained production of common cultivated field crops. Class 5 Capable only of producing perennial forage crops or specially adapted crops. Class 6 Capable only of providing sustained natural grazing for domestic livestock. Class 7 Incapable of use for either arable culture or grazing. The extent and agricultural capability of farmable ALR was estimated using spatial Land Capability Classification for Agriculture in British Columbia datasets (130,131). In the Greater Vancouver, Powell River, and Sunshine Coast regional districts, the total area of farmland reported to be in food production in 2011 was smaller than farmable ALR. This is consistent with reports that the underutilization of the ALR was evident in SWBC at the time of the 2011 Census (45,100,117\u00E2\u0080\u0093120). For these regional districts, land capability for agriculture was therefore estimated by assuming that the area of farmland in food production in each agricultural capability class was proportional to the area of total ALR land in each agricultural capability class (Equation 21): Equation 21 \u00F0\u009D\u0090\u00BF\u00F0\u009D\u0090\u00B4\u00F0\u009D\u0091\u0086\u00F0\u009D\u0091\u009F\u00F0\u009D\u0091\u009E =\u00F0\u009D\u0090\u00B4\u00F0\u009D\u0090\u00BF\u00F0\u009D\u0091\u0085\u00F0\u009D\u0091\u009F\u00F0\u009D\u0091\u009E\u00F0\u009D\u0090\u00B4\u00F0\u009D\u0090\u00BF\u00F0\u009D\u0091\u0085\u00F0\u009D\u0091\u009F\u00C3\u0097 \u00F0\u009D\u0090\u00B9\u00F0\u009D\u0091\u0093\u00F0\u009D\u0091\u009F 37 Where r denotes a regional district; q denotes an agricultural capability class; LASrq denotes the quantity (hectares) of class q farmland available in the stable land availability scenario in regional district r; ALRrq denotes the quantity (hectares) of class q farmable ALR land in regional district r; ALRr denotes the quantity (hectares) of total farmable ALR land in regional district r; and Ffrc denotes the quantity (hectares) of total farmland in food production in 2011 in regional district r. In the Squamish Lillooet and Fraser Valley Regional Districts, the total area of farmland reported to be in food production in 2011 was found to be greater than farmable ALR. The area of farmland in food production in excess of farmable ALR was assumed to be Crown (publically owned) range land managed by the BC government, of which there are over 300,000 hectares in SWBC (29,132). Given its designation as range land, all of this area was assumed to be classified as Class 6 (capable only of providing sustained natural grazing for domestic livestock). In the agricultural expansion land availability scenario, 2050 farmland availability expands to also include all class 1 \u00E2\u0080\u0093 6 ALR that was covered with woody vegetation in 2011. The assumption is that these areas could be cleared and prepared for farming. The extent of woody vegetated ALR was estimated using land use cover data (128,129) and their capability for agriculture using Land Capability Classification for Agriculture in British Columbia datasets referenced above, and added this to the total amount of land available in the stable scenario to arrive at the total area available in the agricultural expansion scenarios using (Equation 22): Equation 22 \u00F0\u009D\u0090\u00BF\u00F0\u009D\u0090\u00B4\u00F0\u009D\u0090\u00B8\u00F0\u009D\u0091\u009F\u00F0\u009D\u0091\u009E = \u00F0\u009D\u0090\u00BF\u00F0\u009D\u0090\u00B4\u00F0\u009D\u0091\u0086\u00F0\u009D\u0091\u009F\u00F0\u009D\u0091\u009E +\u00F0\u009D\u0091\u008A\u00F0\u009D\u0091\u009F\u00F0\u009D\u0091\u009E Where LAErq denotes the quantity (hectares) of class q land available for farming in the agricultural expansion scenario in regional district r; and Wrq denotes the quantity (hectares) of ALR class q land occupied by woody vegetation in 2011. As described in section 3.1, despite its protection under the ALR, threats to the integrity of SWBC\u00E2\u0080\u0099s farmland persist in the form of exclusion applications (which can result in removal of land from the ALR for non-farm development and primarily occur at the urban-ALR interface (45)), underutilization of ALR land (which occurs sporadically throughout the landscape and renders parcels effectively lost from agriculture in the short term (45,100)), and potential future changes to or weakening of the ALR legislation. However, because SWBC municipalities and regional districts are not planning for farmland loss (35), modeling a \u00E2\u0080\u009Crealistic\u00E2\u0080\u009D farmland loss scenario was not appropriate. As such, the objective of the 38 increased non-farm use scenario was to model food self-reliance outcomes in a hypothetical scenario in which some or all of these factors may impinge upon farmland availability, collectively resulting in decreased land available for farming. To best reflect the unpredictable parcel-scale impact of the above described factors and to avoid being overly prescriptive as to which specific parcels might be affected, a randomization procedure, described below, was incorporated into the method used to identify parcels impacted by hypothetical increased non-farm use. Scenarios in which an increase and decrease of farmland availability occurred at the same magnitude were compared. As such, in the increased non-farm use scenario farmland availability was decreased at the regional district scale by the same amount it increased in the agricultural expansion scenario. In the Powell River and Sunshine Coast Regional Districts, this decrease was greater than the total quantity of farmland available in the stable scenario. Farmland availability in these regional districts was therefore assumed to be zero and their farmland loss in excess of stable farmland availability was re-allocated to the Greater Vancouver, Fraser Valley, and Squamish Lillooet Regional Districts proportional to their share of total farmland in the stable scenario. Within the Greater Vancouver, Fraser Valley, and Squamish Lillooet Regional Districts the size and location of individual parcels in the \u00E2\u0080\u009Cfarmable ALR\u00E2\u0080\u009D dataset developed for the stable scenario were identified using a cadastre layer (133) and assigned a random number using a random number generator in Microsoft Excel (91). Parcels were then sorted by ascending random number and selected sequentially until the total area of the selected parcels reached total area of decrease in that regional district. Finally, the Land Capability for Agriculture dataset was used to identify the agricultural capability class of the lost parcels. Total area lost per capability class, per regional district was subtracted from the total available in the stable farmland availability scenario to derive farmland availability in the increased non-farm use scenario (Equation 23). Equation 23 \u00F0\u009D\u0090\u00BF\u00F0\u009D\u0090\u00B4\u00F0\u009D\u0091\u0088\u00F0\u009D\u0091\u009F\u00F0\u009D\u0091\u009E = \u00F0\u009D\u0090\u00BF\u00F0\u009D\u0090\u00B4\u00F0\u009D\u0091\u0086\u00F0\u009D\u0091\u009F\u00F0\u009D\u0091\u009E \u00E2\u0088\u0092 \u00F0\u009D\u0091\u0083\u00F0\u009D\u0090\u00BF\u00F0\u009D\u0091\u009F\u00F0\u009D\u0091\u009E for r = Greater Vancouver, Fraser Valley, and Squamish Lillooet Where LAUrq denotes the quantity (hectares) of class q land available for farming in the increased non-farm use scenario in regional district r; and PLrq denotes the area (hectares) of parcels lost to increased non-farm use as calculated using the method described above. Total area of farmland availability in all three scenarios is presented in Table 11. 39 Table 11: Southwest British Columbia farmland availability scenarios by regional district and Agricultural Capability Class Regional district Capability for agriculture Increased non-farm use Stable Agricultural expansion Fraser Valley Class 1-5 18,753 41,706 59,430 Class 6 14,985 14,985 14,985 Greater Vancouver Class 1-5 9,574 32,486 52,406 Class 6 2 5 8 Powell River Class 1-5 0 568 8,149 Class 6 0 0 33 Sunshine Coast Class 1-5 0 314 3,342 Class 6 0 0 0 Squamish Lillooet Class 1-5 518 10,368 18,236 Class 6 5,016 6,316 8,055 SWBC TOTAL 48,848 106,749 164,643 Crop yield Given the uncertainty of 2050 crop yields, food self-reliance potential was modeled under three crop yield scenarios: average, decreased, and increased. Average crop yield (tonnes/hectare) was derived from a ten year (2002 \u00E2\u0080\u0093 2011) average of BC crop yield wherever possible and Canadian crop yield secondarily (Equation 24). For mushroom, ten year data was not available so a five year (2002 \u00E2\u0080\u0093 2007) average was used. As in chapter two, tree fruit yield was reduced by 25% as a conservative estimate of likely reduction in regional production potential. Equation 24 \u00F0\u009D\u0090\u00B4\u00F0\u009D\u0091\u008C\u00F0\u009D\u0091\u0090 =\u00E2\u0088\u0091 (\u00F0\u009D\u0091\u0087\u00F0\u009D\u0091\u008C\u00F0\u009D\u0091\u009D\u00F0\u009D\u0091\u0090\u00F0\u009D\u0091\u00A6\u00F0\u009D\u0090\u00B4\u00F0\u009D\u0091\u009D\u00F0\u009D\u0091\u0090\u00F0\u009D\u0091\u00A6)\u00F0\u009D\u0091\u008E\u00F0\u009D\u0091\u0099\u00F0\u009D\u0091\u0099 \u00F0\u009D\u0091\u00A610 Where c denotes an individual commodity; y denotes a year in the ten year period from 2002 \u00E2\u0080\u0093 2011; AYc denotes the SWBC yield (tonnes/hectare) of commodity c in the average yield scenario; TYpcy denotes the total BC yield (tonnes) of commodity c in year y (89); and Apcy denotes the area (hectares) in BC planted to commodity c in year y (83,89). In all scenarios, per the Land Capability Classification for Agriculture in British Columbia, it was assumed that Class 5-6 land can only be used for structure-based agriculture (greenhouse vegetables and mushrooms), and for pasture and barn (structure) components of livestock land use requirements. It was assumed that fruit, vegetables, food grain, and feed grain could only be grown on Class 1-4 land. 40 The IPCC points to changing inter-annual yield variability as being a likely global impact of climate change (112) and by the year 2039 it is expected that BC\u00E2\u0080\u0099s growing degree days will increase over levels in the 1971-2000 baseline period (113). While this could have positive yield impacts for some crops in SWBC, the impact on future crop yield of other climate change outcomes such as increased pests and diseases, and the success of climate change adaptation initiatives such as the breeding of climate-adapted crop cultivars, improvements to agricultural technology, is difficult to predict. Due to the uncertainty surrounding the impact that climate change may have on crop yields, this study aimed to account for potential extremes. The IPCC reports that the majority of reviewed climate change modeling initiatives suggest the potential for 10-25% decreases in crop yield by the year 2050 as a result of climate change (112). It was therefore assumed in the decreased crop yield scenario that yield would decrease by 25% over average crop yield. The increased crop yield scenario considered the inverse effect, namely a 25% increase over average crop yield. Localized food system optimization model As described in section 3.1, previous studies have used deterministic optimization models to assess the capacity to achieve food self-reliance given differing diets and land-use priorities (108,134,135). In this study, this method was deemed appropriate for modeling the Localized food system because optimization models can be used to determine how to \u00E2\u0080\u009Cbest\u00E2\u0080\u009D allocate scarce resources to activities given a pre-determined evaluation criteria (105). They are especially useful in instances when the numbers of resources and/or activities involved are inordinate. In the case of the Localized food system model, the scarce resource of interest is the SWBC land base, the activities are crop production, and the evaluation criterion is to maximize food self-reliance. Stochastic optimization, an optimization method which takes uncertainty of input parameters into account when seeking an optimum solution, was investigated as an alternative to deterministic optimization but ultimately deemed too complex to offer utility for this study. This is discussed further in section 4.3. The Localized food system optimization model was created in Microsoft Excel (91) and solved using OpenSolver, an open source Excel VBA add-in that extends Excel\u00E2\u0080\u0099s built-in Solver with a more powerful Linear Programming solver (92,93). In choosing the optimization modeling tool, it was deemed important to choose software that would enable replication of this method in future studies. OpenSolver was ultimately selected because it is open source and therefore has no cost associated with its use, and does not require knowledge of a programming language making it very accessible for use by a range of users with varying levels of modeling expertise (105). To develop an optimization model using 41 OpenSolver, the model\u00E2\u0080\u0099s Objective Function, Constraints, and Decision Variables are defined as mathematical formulae within the spreadsheet and identified using the OpenSolver dialogue box. Given input data (food need, diet and seasonality constraint on food self-reliance, land availability and agricultural capability, and crop and livestock product yield), the model seeks a unique optimal solution that specifies the amount of land that should be devoted to each crop to maximize food self-reliance, the amount of each food crop that would be produced in SWBC in order to achieve that level of food self-reliance, and the maximum level of food self-reliance achieved (Figure 6). The model\u00E2\u0080\u0099s decision variables were how much Class 1-4 and how much Class 5-6 land within each regional district should be allocated to the production of each food for that regional district and how much should be allocated to the production of each food for other regional district in SWBC in order to maximize the objective function. As described in section 2.2.1, the localized food system was developed with an objective function designed to optimize for food self-reliance at the regional district and SWBC scale. The objective function is therefore defined as the sum of total yield that contributes to food self-reliance (TY FSR) in each regional district and in SWBC overall (Equation 25). Figure 6: Generalized schematic of the Localized food system optimization model. White boxes represent input data that is constant throughout all scenarios, white boxes with dashed outline represent input data that varies by scenario, grey boxes represent output data, and black bubbles (which refer to equations explained in this methods section) represent operations performed on the input data. 42 Equation 25 \u00F0\u009D\u0091\u0080\u00F0\u009D\u0091\u008E\u00F0\u009D\u0091\u00A5\u00F0\u009D\u0091\u0096\u00F0\u009D\u0091\u009A\u00F0\u009D\u0091\u0096\u00F0\u009D\u0091\u00A7\u00F0\u009D\u0091\u0092 \u00F0\u009D\u0091\u0087\u00F0\u009D\u0091\u008C \u00F0\u009D\u0090\u00B9\u00F0\u009D\u0091\u0086\u00F0\u009D\u0091\u0085 = \u00E2\u0088\u0091 [\u00E2\u0088\u0091 \u00E2\u0088\u0091(\u00F0\u009D\u0090\u00B4\u00F0\u009D\u0091\u0085\u00F0\u009D\u0091\u0091\u00F0\u009D\u0091\u0090\u00F0\u009D\u0091\u009E\u00F0\u009D\u0091\u009F \u00C3\u0097 \u00F0\u009D\u0091\u008C\u00F0\u009D\u0091\u0090)\u00F0\u009D\u0091\u008E\u00F0\u009D\u0091\u0099\u00F0\u009D\u0091\u0099 \u00F0\u009D\u0091\u009E\u00F0\u009D\u0091\u008E\u00F0\u009D\u0091\u0099\u00F0\u009D\u0091\u0099 \u00F0\u009D\u0091\u0090+\u00E2\u0088\u0091\u00E2\u0088\u0091(\u00F0\u009D\u0090\u00B4\u00F0\u009D\u0090\u00B5\u00F0\u009D\u0091\u009F\u00F0\u009D\u0091\u0090\u00F0\u009D\u0091\u009E\u00F0\u009D\u0091\u009F \u00C3\u0097 \u00F0\u009D\u0091\u008C\u00F0\u009D\u0091\u0090)\u00F0\u009D\u0091\u008E\u00F0\u009D\u0091\u0099\u00F0\u009D\u0091\u0099 \u00F0\u009D\u0091\u009E\u00F0\u009D\u0091\u008E\u00F0\u009D\u0091\u0099\u00F0\u009D\u0091\u0099\u00F0\u009D\u0091\u0090]\u00F0\u009D\u0091\u008E\u00F0\u009D\u0091\u0099\u00F0\u009D\u0091\u0099 \u00F0\u009D\u0091\u009F Where q denotes a Class of agricultural land (q = Class 1-4 or Class 5-6). ARdcqr denotes the area (hectares) used to grow commodity c on Class q land in regional district r for that regional district; Yc denotes the yield (tonnes commodity weight/hectare) of crop c (Yc is either the average, decreased, or increased yield depending on the scenario); and ABrcqr denotes the area (hectares) used to grow crop c on Class q land in regional district r for other regional districts in SWBC. As previously reported (Chapter 2), livestock product yield (tonnes meat, milk, or eggs produced per hectare) were calculated using the method developed by Cowell and Parkinson (94), which accounts for the total area requirement to produce feed for the animal from birth to slaughter and the feed requirement for maintaining the breeding stock and/or replacement herd. Regionally-specific data were used wherever possible and Canadian data secondarily. When modeling food self-reliance without feed imports, livestock product yield accounted for barn, feed grain, hay, and pasture land requirements. When modeling food self-reliance with feed imports, livestock product yield accounted for barn, hay and pasture land requirements only. Given the objective to model food self-reliance under nine scenarios of 2050 farmland availability and crop yield, both with and without the availability of imported livestock feed, four model constraints were defined. First, Class 1-4 land utilization in each regional district could not exceed Class 1-4 land availability in each regional district (Equation 26). Equation 26 \u00E2\u0088\u0091(\u00F0\u009D\u0090\u00B4\u00F0\u009D\u0091\u0085\u00F0\u009D\u0091\u0091\u00F0\u009D\u0091\u0090\u00F0\u009D\u0091\u009E\u00F0\u009D\u0091\u009F + \u00F0\u009D\u0090\u00B4\u00F0\u009D\u0090\u00B5\u00F0\u009D\u0091\u009F\u00F0\u009D\u0091\u0090\u00F0\u009D\u0091\u009E\u00F0\u009D\u0091\u009F)\u00F0\u009D\u0091\u008E\u00F0\u009D\u0091\u0099\u00F0\u009D\u0091\u0099 \u00F0\u009D\u0091\u0090\u00E2\u0089\u00A4 \u00F0\u009D\u0090\u00BF\u00F0\u009D\u0090\u00B4\u00F0\u009D\u0091\u009F\u00F0\u009D\u0091\u009E \u00F0\u009D\u0091\u0093\u00F0\u009D\u0091\u009C\u00F0\u009D\u0091\u009F \u00F0\u009D\u0091\u009E = \u00F0\u009D\u0090\u00B6\u00F0\u009D\u0091\u0099\u00F0\u009D\u0091\u008E\u00F0\u009D\u0091\u00A0\u00F0\u009D\u0091\u00A0 1 \u00E2\u0088\u0092 4; \u00F0\u009D\u0091\u0093\u00F0\u009D\u0091\u009C\u00F0\u009D\u0091\u009F \u00F0\u009D\u0091\u008E\u00F0\u009D\u0091\u0099\u00F0\u009D\u0091\u0099 \u00F0\u009D\u0091\u009F Where LArq denotes the farmland available of class q in regional district r in the land availability scenario being run (stable, agricultural expansion, or increased non-farm use). Second, total (Class 1 \u00E2\u0080\u0093 6) land utilization in each regional district could not exceed total (Class 1-6) land availability in each Regional District (Equation 27). 43 Equation 27 \u00E2\u0088\u0091 \u00E2\u0088\u0091(\u00F0\u009D\u0090\u00B4\u00F0\u009D\u0091\u0085\u00F0\u009D\u0091\u0091\u00F0\u009D\u0091\u0090\u00F0\u009D\u0091\u009E\u00F0\u009D\u0091\u009F + \u00F0\u009D\u0090\u00B4\u00F0\u009D\u0090\u00B5\u00F0\u009D\u0091\u009F\u00F0\u009D\u0091\u0090\u00F0\u009D\u0091\u009E\u00F0\u009D\u0091\u009F)\u00F0\u009D\u0091\u008E\u00F0\u009D\u0091\u0099\u00F0\u009D\u0091\u0099 \u00F0\u009D\u0091\u0093\u00F0\u009D\u0091\u008E\u00F0\u009D\u0091\u0099\u00F0\u009D\u0091\u0099 \u00F0\u009D\u0091\u009E\u00E2\u0089\u00A4 \u00E2\u0088\u0091 \u00F0\u009D\u0090\u00BF\u00F0\u009D\u0090\u00B4\u00F0\u009D\u0091\u009F\u00F0\u009D\u0091\u009E\u00F0\u009D\u0091\u008E\u00F0\u009D\u0091\u0099\u00F0\u009D\u0091\u0099 \u00F0\u009D\u0091\u009E \u00F0\u009D\u0091\u0093\u00F0\u009D\u0091\u009C\u00F0\u009D\u0091\u009F \u00F0\u009D\u0091\u008E\u00F0\u009D\u0091\u0099\u00F0\u009D\u0091\u0099 \u00F0\u009D\u0091\u009F Third, production of each commodity for each regional district could not exceed the diet and seasonality constraint on food self-reliance for each food in each regional district (Equation 28). Equation 28 \u00E2\u0088\u0091(\u00F0\u009D\u0090\u00B4\u00F0\u009D\u0091\u0085\u00F0\u009D\u0091\u0091\u00F0\u009D\u0091\u0090\u00F0\u009D\u0091\u009E\u00F0\u009D\u0091\u009F \u00C3\u0097 \u00F0\u009D\u0091\u008C\u00F0\u009D\u0091\u0090)\u00F0\u009D\u0091\u008E\u00F0\u009D\u0091\u0099\u00F0\u009D\u0091\u0099 \u00F0\u009D\u0091\u009E\u00E2\u0089\u00A4 \u00F0\u009D\u0090\u00B7\u00F0\u009D\u0091\u0086\u00F0\u009D\u0090\u00B6\u00F0\u009D\u0091\u0093\u00F0\u009D\u0091\u009F \u00F0\u009D\u0091\u0093\u00F0\u009D\u0091\u009C\u00F0\u009D\u0091\u009F \u00F0\u009D\u0091\u008E\u00F0\u009D\u0091\u0099\u00F0\u009D\u0091\u0099 \u00F0\u009D\u0091\u009F, \u00F0\u009D\u0091\u0093\u00F0\u009D\u0091\u009C\u00F0\u009D\u0091\u009F \u00F0\u009D\u0091\u008E\u00F0\u009D\u0091\u0099\u00F0\u009D\u0091\u0099 \u00F0\u009D\u0091\u0093 Fourth, total production of each food in SWBC could not exceed the diet and seasonality constraint on food self-reliance for that food in SWBC (Equation 29). Equation 29 \u00E2\u0088\u0091 \u00E2\u0088\u0091(\u00F0\u009D\u0090\u00B4\u00F0\u009D\u0090\u00B5\u00F0\u009D\u0091\u009F\u00F0\u009D\u0091\u0090\u00F0\u009D\u0091\u009E\u00F0\u009D\u0091\u009F \u00C3\u0097 \u00F0\u009D\u0091\u008C\u00F0\u009D\u0091\u0090)\u00F0\u009D\u0091\u008E\u00F0\u009D\u0091\u0099\u00F0\u009D\u0091\u0099 \u00F0\u009D\u0091\u009E\u00F0\u009D\u0091\u008E\u00F0\u009D\u0091\u0099\u00F0\u009D\u0091\u0099 \u00F0\u009D\u0091\u009F\u00E2\u0089\u00A4 \u00E2\u0088\u0091 \u00F0\u009D\u0090\u00B7\u00F0\u009D\u0091\u0086\u00F0\u009D\u0090\u00B6\u00F0\u009D\u0091\u0093\u00F0\u009D\u0091\u009F\u00F0\u009D\u0091\u008E\u00F0\u009D\u0091\u0099\u00F0\u009D\u0091\u0099 \u00F0\u009D\u0091\u009F \u00F0\u009D\u0091\u0093\u00F0\u009D\u0091\u009C\u00F0\u009D\u0091\u009F \u00F0\u009D\u0091\u008E\u00F0\u009D\u0091\u0099\u00F0\u009D\u0091\u0099 \u00F0\u009D\u0091\u0093 Food self-reliance capacity of SWBC in the localized food system (denoted by SRL) was calculated using (Equation 30): Equation 30 \u00F0\u009D\u0091\u0086\u00F0\u009D\u0091\u0085\u00F0\u009D\u0090\u00BF =\u00E2\u0088\u0091 \u00E2\u0088\u0091 \u00E2\u0088\u0091 (\u00F0\u009D\u0090\u00B4\u00F0\u009D\u0090\u00B5\u00F0\u009D\u0091\u009F\u00F0\u009D\u0091\u0090\u00F0\u009D\u0091\u009E\u00F0\u009D\u0091\u009F \u00C3\u0097 \u00F0\u009D\u0091\u008C\u00F0\u009D\u0091\u0090)\u00F0\u009D\u0091\u008E\u00F0\u009D\u0091\u0099\u00F0\u009D\u0091\u0099 \u00F0\u009D\u0091\u009E\u00F0\u009D\u0091\u008E\u00F0\u009D\u0091\u0099\u00F0\u009D\u0091\u0099 \u00F0\u009D\u0091\u009F\u00F0\u009D\u0091\u008E\u00F0\u009D\u0091\u0099\u00F0\u009D\u0091\u0099 \u00F0\u009D\u0091\u0090\u00E2\u0088\u0091 \u00F0\u009D\u0091\u0081\u00F0\u009D\u0091\u0093\u00F0\u009D\u0091\u008E\u00F0\u009D\u0091\u0099\u00F0\u009D\u0091\u0099 \u00F0\u009D\u0091\u0093 Where Nf denotes the projected total annual SWBC food need (tonnes) for food f in the year 2050. Business as Usual food system model The Business as Usual (BAU) food system model was a spreadsheet model created in Microsoft Excel (91). When modeling food self-reliance capacity without feed imports, a previously developed livestock production optimization model was incorporated as a sub-model (see chapter 2). Given input data (food need, the diet and seasonality constraint on food self-reliance, 2011 SWBC land use, land availability by land capability for agriculture class, crop yields, livestock yields, and livestock barn, pasture, and grain feed requirements), the BAU model determines 2050 land use (the amount of land that would be devoted to each crop in the Business as Usual food system), the amount of each food crop and livestock product that would be produced, and the level of food self-reliance achieved (Figure 7). 44 Figure 7: Generalized schematic of the Business as Usual food system optimization model. White boxes represent input data that is constant throughout all scenarios, white boxes with dashed outline represent input data that varies by scenario, grey boxes represent output data, and black bubbles (which refer to equations explained in this methods section) represent operations performed on the data. 45 BAU land use for food crops, barn, pasture, and feed grain in the stable land availability scenarios was assumed to reflect 2011 land use (Equation 31). Equation 31 \u00F0\u009D\u0090\u00BF\u00F0\u009D\u0091\u0088\u00F0\u009D\u0091\u0086\u00F0\u009D\u0091\u0090\u00F0\u009D\u0091\u009F = \u00F0\u009D\u0090\u00B9\u00F0\u009D\u0091\u0093\u00F0\u009D\u0091\u0090\u00F0\u009D\u0091\u009F Where LUScr denotes land use for crop c in regional district r in the stable scenario. In the Increased Non-Farm Use and Agricultural Expansion scenarios, agricultural land use per crop was assumed to change at the regional district scale proportional to the total regional district change in farmland availability (Equation 32, Equation 33). Equation 32 \u00F0\u009D\u0090\u00BF\u00F0\u009D\u0091\u0088\u00F0\u009D\u0091\u0088\u00F0\u009D\u0091\u0090\u00F0\u009D\u0091\u009F =\u00E2\u0088\u0091 \u00E2\u0088\u0091 \u00F0\u009D\u0090\u00BF\u00F0\u009D\u0090\u00B4\u00F0\u009D\u0091\u0088\u00F0\u009D\u0091\u009F\u00F0\u009D\u0091\u0090\u00F0\u009D\u0091\u008E\u00F0\u009D\u0091\u0099\u00F0\u009D\u0091\u0099 \u00F0\u009D\u0091\u0090\u00F0\u009D\u0091\u008E\u00F0\u009D\u0091\u0099\u00F0\u009D\u0091\u0099 \u00F0\u009D\u0091\u009E\u00F0\u009D\u0090\u00B9\u00F0\u009D\u0091\u0093\u00F0\u009D\u0091\u009F\u00C3\u0097 \u00F0\u009D\u0090\u00B9\u00F0\u009D\u0091\u0093\u00F0\u009D\u0091\u009F\u00F0\u009D\u0091\u0090 Where LUUcr denotes land use for crop c in Regional District r in the increased non-farm use scenario. Equation 33 \u00F0\u009D\u0090\u00BF\u00F0\u009D\u0091\u0088\u00F0\u009D\u0090\u00B8\u00F0\u009D\u0091\u0090\u00F0\u009D\u0091\u009F =\u00E2\u0088\u0091 \u00E2\u0088\u0091 \u00F0\u009D\u0090\u00BF\u00F0\u009D\u0090\u00B4\u00F0\u009D\u0091\u0088\u00F0\u009D\u0091\u009F\u00F0\u009D\u0091\u0090\u00F0\u009D\u0091\u008E\u00F0\u009D\u0091\u0099\u00F0\u009D\u0091\u0099 \u00F0\u009D\u0091\u0090\u00F0\u009D\u0091\u008E\u00F0\u009D\u0091\u0099\u00F0\u009D\u0091\u0099 \u00F0\u009D\u0091\u009E\u00F0\u009D\u0090\u00B9\u00F0\u009D\u0091\u0093\u00F0\u009D\u0091\u009F\u00C3\u0097 \u00F0\u009D\u0090\u00B9\u00F0\u009D\u0091\u0093\u00F0\u009D\u0091\u009F\u00F0\u009D\u0091\u0090 Where LUEcr denotes land use for crop c in regional district r in the agricultural expansion scenario. The quantity of each food produced in each regional district was calculated using: Equation 34 \u00F0\u009D\u0091\u0087\u00F0\u009D\u0091\u008C\u00F0\u009D\u0091\u009F\u00F0\u009D\u0091\u0090 = \u00F0\u009D\u0091\u008C\u00F0\u009D\u0091\u0090 \u00C3\u0097 \u00E2\u0088\u0091 \u00F0\u009D\u0090\u00BF\u00F0\u009D\u0091\u0088\u00F0\u009D\u0091\u009F\u00F0\u009D\u0091\u0090\u00F0\u009D\u0091\u008E\u00F0\u009D\u0091\u0099\u00F0\u009D\u0091\u0099 \u00F0\u009D\u0091\u009E Where TYrc denotes the total yield of commodity c in regional district r; Yc denotes the yield (tonnes/hectare) of commodity c in the crop yield scenario being run (average, decreased, or increased); and LUrc denotes the land used for commodity c in regional district r in the farmland availability scenario being run (stable, agricultural expansion, or increased non-farm use). In scenarios without feed imports, the livestock products that were considered contributing to the region\u00E2\u0080\u0099s food self-reliance were only those that could have been produced from animals pastured and/or fed feed grown in SWBC. Using this method, rather than quantifying production based on the number of animals that were actually raised in SWBC in 2011, a previously developed optimization model (see ch.2) was used to estimate the hypothetical maximum number of livestock that could have 46 been raised if no livestock feed was imported. Model inputs were hectares of pasture and livestock feed grown in SWBC, and feed requirement and production by livestock type. Model outputs were the optimal number of each livestock type that should be raised in SWBC in order to maximize self-reliance in livestock products, and concomitant feed and pasture use and quantity of livestock products produced. In scenarios with feed imports available, livestock production was assumed to change at the regional district scale proportional to the total regional district change in barn area (Equation 35). Equation 35 \u00F0\u009D\u0091\u0087\u00F0\u009D\u0091\u008C\u00F0\u009D\u0091\u009F\u00F0\u009D\u0091\u0097 =\u00F0\u009D\u0090\u00B5\u00F0\u009D\u0091\u0087\u00F0\u009D\u0091\u008C\u00F0\u009D\u0091\u009F\u00F0\u009D\u0091\u0097\u00F0\u009D\u0090\u00B5\u00F0\u009D\u0091\u008E\u00F0\u009D\u0091\u009F\u00F0\u009D\u0091\u0097\u00C3\u0097 \u00F0\u009D\u0090\u00BF\u00F0\u009D\u0091\u0088\u00F0\u009D\u0091\u009F\u00F0\u009D\u0091\u0097 Where TYrj denotes the total yield from livestock j in regional district r in 2050; BTYrj denotes the total yield from livestock j in regional district r in the baseline year of 2011; BArj denotes the barn area allocated to livestock j in regional district r 2011; and LUrj denotes the land use for livestock j in regional district r in 2050. BTYrj was estimated by multiplying the average Canadian yield per livestock type j by the number of slaughtered, laying or milking animals of livestock type j in regional district r in 2011 (83,89). Food self-reliance capacity of SWBC in the Business as Usual food system (denoted by SRBAU) was calculated using (Equation 36): Equation 36 \u00F0\u009D\u0091\u0086\u00F0\u009D\u0091\u0085\u00F0\u009D\u0090\u00B5\u00F0\u009D\u0090\u00B4\u00F0\u009D\u0091\u0088 =\u00E2\u0088\u0091 \u00E2\u0088\u0091 [\u00F0\u009D\u0091\u009A\u00F0\u009D\u0091\u0096\u00F0\u009D\u0091\u009B(\u00F0\u009D\u0091\u0087\u00F0\u009D\u0091\u008C\u00F0\u009D\u0091\u009F\u00F0\u009D\u0091\u0090 , \u00F0\u009D\u0090\u00B7\u00F0\u009D\u0091\u0086\u00F0\u009D\u0090\u00B6\u00F0\u009D\u0091\u009F\u00F0\u009D\u0091\u0090)]\u00F0\u009D\u0091\u008E\u00F0\u009D\u0091\u0099\u00F0\u009D\u0091\u0099 \u00F0\u009D\u0091\u009F\u00F0\u009D\u0091\u008E\u00F0\u009D\u0091\u0099\u00F0\u009D\u0091\u0099 \u00F0\u009D\u0091\u0090\u00E2\u0088\u0091 \u00F0\u009D\u0091\u0081\u00F0\u009D\u0091\u0093\u00F0\u009D\u0091\u008E\u00F0\u009D\u0091\u0099\u00F0\u009D\u0091\u0099 \u00F0\u009D\u0091\u0093 3.2.2 Sensitivity of food self-reliance capacity to change in farmland availability and climate change-induced change in crop yield Nominal range sensitivity analysis was performed to assess the sensitivity of model output (food self-reliance) to variation in uncertain model input parameters, namely 2050 farmland availability and climate change-induced change in crop yield. This method, which is appropriate for deterministic models such as those used in this study, involves individually varying one model input while holding all others fixed and assessing the effect on model output (136,137). Sensitivity analysis on these models therefore involved modeling under three levels of future crop yield (increased, average, and decreased) and three levels of future farmland availability (increased non-farm use, stable, and agricultural expansion) for a total of nine scenarios (Table 12). To assess sensitivity to farmland availability, food self-47 reliance capacity with average crop yield was compared in the increased non-farm use, stable, and agricultural expansion farmland scenarios (scenarios 2, 5, and 8). Although these scenarios are not comprehensive of all possible future permutations of land availability and crop yield, they represent within what were assumed to be plausible extreme values based on the available data. To assess sensitivity to crop yield, food self-reliance capacity with stable farmland availability was compared in the increased, average, and decreased crop yield scenarios (scenarios 4, 5, and 6). Table 12: Scenarios of 2050 land availability and crop yield used for nominal range sensitivity analysis of the Business as Usual and Localized Food System models Scenario Farmland Availability Crop Yield 2 Increased non-farm use Average 4 Stable Increased 5 Stable Average 6 Stable Decreased 8 Agricultural Expansion Average 3.2.3 Land use Land use per crop type was a modeled output in both the BAU and Localized food system models. To compare land use patterns between the two food system models, land use is reported by Food Group, land used for the production of feed or food crops that do not contribute to food self-reliance, and land that is unallocated to food production for consumption within SWBC. 3.3 Results and discussion 3.3.1 Food self-reliance capacity Model outcomes illustrated substantial differences between the capacity for SWBC food self-reliance in the Localized and Business as Usual (BAU) food system (Figure 8). For some Food Groups, the Localized system in the worst case 2050 scenario (decreased crop yield and increased non-farm use) provided an equivalent level of food self-reliance to the BAU system in a best case 2050 scenario (increased crop yield and agricultural expansion). Results differed greatly depending on whether the modeled scenario included feed imports or not. Without feed imports, total dietary self-reliance in the Localized food system was only 10% higher than the BAU food system in the worst case scenario, but 17% higher on average and 24% higher in the best case scenario. Without feed imports, self-reliance in Fruits & Vegetables and Milk & Alternatives was higher in the Localized food system in all scenarios while self-reliance in Grains was equal in both systems in the worst case scenario but higher by 12% in the Localized system in the best case scenario. 48 Self-reliance without feed imports in Fats & Oils was less than 1% in all scenarios, for both food system models. Not surprisingly, the food self-reliance capacity of both systems increased when feed imports were included, particularly for Food Groups comprising livestock production (Meat & Alternatives, Milk & Alternatives, and Fats & Oils). Modeled self-reliance in Meat & Alternatives in the BAU and Localized systems climbed from 0% without feed imports to an average of 36% and 67% with feed imports, respectively. Similarly, Milk & Alternatives self-reliance climbed from 19% without feed imports to 71% with feed imports in the Localized system and from 6% to 71% in the BAU system. Interestingly, Localized food system self-reliance in Fruit & Vegetables was 4% lower with, than without, feed imports, and Grain self-reliance was lower by 1%. This did not, however, reduce total dietary food system self-reliance of the Localized food system with feed imports. Rather, the difference between total dietary self-reliance capacities of the two systems increased with feed imports over the difference without feed imports. Total dietary food self-reliance with feed imports was 14% higher in the Localized than the BAU system in the worst case scenario, 25% higher on average and 28% higher in the best case scenario. With feed imports, self-reliance in Fruits & Vegetables, Meat & Alternatives, and Milk & Alternatives was higher in the Localized than the BAU system in all scenarios but this pattern did not hold true for Grains or Fats & Oils. While Grain self-reliance was higher in the Localized than the BAU system in the mean and best case scenarios, it was equal in both systems in the worst case scenario. Fats & Oils self-reliance was higher in the Localized than the BAU system in the mean and best case scenarios, but lower in the Localized system by 1% in the worst case scenario. In chapter two it was reported that SWBC\u00E2\u0080\u0099s current (2011) total dietary food self-reliance status is 12% without feed imports or 40% with feed imports. This provides a useful baseline against which to compare SWBC\u00E2\u0080\u0099s capacity for food self-reliance in the future. Results from this study suggest that, despite a higher population in 2050, food self-reliance could theoretically increase over 2011 levels in a Localized food system. Mean 2050 total dietary food self-reliance of the Localized system was 14% higher than 2011 food self-reliance without feed imports and 8% higher than 2011 with feed imports. 2050 self-reliance in the BAU system, however, decreased from 2011 levels by 4% without feed imports and 17% with feed imports. Even in the Localized food system with feed imports, average food self-reliance potential was below the maximum possible given the diet and seasonality constraint on food self-reliance. The Localized system fell short by 30% and the BAU by 55%. Without feed imports, the respective shortfalls climb to 52% and 70%. 49 Consistent with outcomes of the previous study of SWBC\u00E2\u0080\u0099s 2011 food self-reliance status (chapter two), model outcomes from this study suggest that, given the limited potential for grain production within SWBC, bio-regional livestock production will continue to be extremely dependent on feed imports into the future. Although an in-depth analysis of SWBC\u00E2\u0080\u0099s policy and regulatory frameworks as they relate to food self-reliance capacity was beyond the scope of this study, it warrants mentioning that, as described in Ch.2, the production and import of several livestock products in SWBC (eggs, dairy products, chicken, and turkey) is regulated by the provincial and federal governments through a system of supply management. As described in chapter two, SWBC is a major centre for production of dairy, eggs, and broiler chickens. So long as the supply management system persists it is unlikely that even in a completely localized food system future the SWBC production of supply managed livestock products, or the importation of livestock feed required to support such production, will curtail. Results from the Localized food system model illustrate that, regardless of farmland availability and crop yield levels, grain and oilseeds production is unlikely to ever make substantial contributions to SWBC food self-reliance. Interestingly, this outcome is consistent with some factors that play an important role in food system design but were not modeled in this study, including crop suitability for shipping, agronomic suitability, and farm profitability. Regardless of the food self-reliance outcomes of doing so, for example, it is more logical to import grains and oilseeds than horticultural crops because they are Figure 8: The Diet and Seasonality Constraint on Food Self-Reliance (DSC) and Mean 2050 Southwest British Columbia food self-reliance in the Business as Usual (BAU) and Localized food system models, for five Food Groups and the Total Diet. Mean refers to the mean of all crop yield and farmland availability scenarios. 50 easily stored and transported due to their low moisture content and long storage life whereas horticultural crops are easily damaged and highly perishable. Furthermore, although grain production is agronomically possible in SWBC, its climate is more suitable to horticultural crops. A focus on horticultural crop production rather than grains and oilseeds production in SWBC also makes sense from an economic perspective. When the price of farmland is high, as it is in SWBC, horticultural crop production is more economically feasible than grain and oilseeds production as horticultural crops command a higher return per acre. This trend is also evident in outcomes from the BAU model outcomes, which are based on current farmland use that is largely influenced by economic factors. It was also modeled in Peters et al.\u00E2\u0080\u0099s 2011 study of the capacity to meet New York State food needs with food grown locally while maximizing land use value, which found that, given the low economic return of grain production, land should only be devoted to it if demand for higher-value crops such as fruits and vegetables have already been satisfied and suitable land is still available (108). Overall, the self-reliance results from this study suggest that a localized food system can make substantial contributions to regional food need but not confer 100% food self-reliance. This is generally consistent with studies of other regions, regardless of whether \u00E2\u0080\u009Cregion\u00E2\u0080\u009D was defined as a county, a province/state, or a municipality (23,24,39,71,94,107,108). Of those studies reviewed in the development of this research only two, the 1978 assessment of BC (109) and the 2014 study of southeast Minnesota (37) that 100% total dietary food self-reliance could be achieved on the region\u00E2\u0080\u0099s land base. Both of these studies, however, considered regions with very high farmland availability to population ratios compared to this study. Rates of farmland availability in the studies of southeast Minnesota and 1978 BC, both of which modeled a complete diet accounting for the land requirements to produce livestock feed locally, were 5.4 hectares per capita and 1 hectare per capita respectively, whereas the SWBC, even in the scenario of agricultural expansion, had only 0.04 hectares of farmland per capita. With more land available per person, it is unsurprising that these studies reported higher capacity for food self-reliance than was found possible for SWBC. 3.3.2 Sensitivity of food self-reliance capacity to change in farmland availability and climate change-induced change in crop yield With and without feed imports, sensitivity of self-reliance in Fruits & Vegetables, Grains, Milk & Alternatives, and the total diet to climate change-induced change in crop yield was higher in the Localized food system than the BAU food system (Figure 9). With and without feed imports, sensitivity 51 of Meat & Alternatives and Fats & Oils to changes in crop yield was marginal in both food systems, although sensitivity of Fats & Oils was slightly higher in the Localized food system with feed imports. In the BAU food system, sensitivity of self-reliance in Fruit & Vegetables and Grain to changes in crop yield was equal with and without feed imports whereas the sensitivity of self-reliance in Milk & Alternatives and the Total Diet was lower with than without feed imports by 3% and 1% respectively. In the Localized food system with feed imports, sensitivity of self-reliance in Fruit & Vegetables, Milk & Alternatives and the Total diet to changes in crop yield increased slightly over sensitivity without feed imports, while sensitivity of self-reliance in Grains decreased. Modeled outcomes of increased crop yield suggest that climate change-induced change to average crop yield could be beneficial to SWBC food self-reliance. Seizing this potential benefit will depend largely on the success with which SWBC agriculture adapts to climate change challenges. Climate change adaptation in agriculture means not just adaptation of farming practices and physical resources but also agri-food policy and agriculture\u00E2\u0080\u0099s financial, knowledge, and social and human resources (138). Developing new crop cultivars, investing in climate-smart water infrastructure such as drainage infrastructure and efficient irrigation, stabilizing farm income, and developing farmer capacity through Figure 9: Sensitivity of food self-reliance in the Business as Usual (BAU) and Localized food systems, with and without feed imports, to modeled climate change-induced changes in crop yield, as indicated by the percent change of self-reliance in the decreased and increased crop yield scenarios with stable farmland availability from mean self-reliance (the average of self-reliance in all crop yield scenarios) with stable farmland availability. 52 training and support for new entrants, are a few examples of climate change adaptation strategies that have been recommended for BC specifically (111). With and without feed imports, sensitivity of self-reliance in Fruits & Vegetables, Grains, Milk & Alternatives, and the total diet to changes in farmland availability was higher in the Localized food system than the BAU food system (Figure 10). With and without feed imports, sensitivity of Fats & Oils to changes in farmland availability was marginal in both food systems, although it increased slightly in the Localized food system with feed imports. In the BAU food system, sensitivity of self-reliance in Fruit & Vegetables and Grain to changes in farmland availability was equal with and without feed imports whereas the sensitivity of self-reliance in Meat & Alternatives, Milk & Alternatives and the Total Diet to changes in farmland availability was higher with than without feed imports. In the Localized food system Figure 10: Sensitivity of food self-reliance in the Business as Usual (BAU) and Localized food systems, with and without feed imports, to modeled changes in farmland availability, as indicated by the percent change of self-reliance in the increased non-farm use and agricultural expansion farmland availability scenarios with average crop yield from mean self-reliance (the average of self-reliance in all farmland availability scenarios) with average crop yield. 53 with feed imports, sensitivity of self-reliance in all Food Groups and the total diet to changes in farmland availability increased with feed imports. Overall, the Localized and Business as Usual food system models both exhibited greater sensitivity to change in farmland availability than to changes in crop yield (Figure 11). Furthermore, total dietary food self-reliance of the Localized food system was more sensitive to climate change-induced decreases in crop yield and farmland availability than increases (Figure 11 - bottom) while climate change-induced increases and decreases in crop yield and farmland availability affected the Business as Usual model more uniformly (Figure 11 - top). Although the magnitude of modeled change in farmland availability was greater than that of climate change-induced change in crop yield, a comparison of food self-reliance sensitivity to the two factors is relevant because the magnitude of change for both is within what were assumed to be plausible extreme values based on available data. A limitation of this analysis, however, is that crop yield is affected by many factors other than climate change that were not modeled in this study such as the availability of agricultural inputs and irrigation, site-specific farm characteristics, farmer skill levels, and availability of farm labour. The limited availability of SWBC-specific crop yield data and absence of crop yield modeling specific to this region hampered a thorough analysis of crop yield\u00E2\u0080\u0099s impact on food self-reliance capacity and should be an area of future study. 54 Figure 11: Sensitivity of total dietary food self-reliance in the Business as Usual and Localized food system models, with and without feed imports, to changes in farmland availability compared to changes in crop yield. 55 3.3.3 Land use In all food system models, both with and without feed imports, Fruit & Vegetables together with Milk & Alternatives comprise the first or second largest use of farmland (Figure 12). In the BAU food system, a very substantial portion of farmland in all scenarios was allocated to the production of feed or food crops that do not contribute to food self-reliance (65% without feed imports and 34% with feed imports). This includes land producing food crops in excess of the diet and seasonality constraint on food self-reliance, of which blueberry production is exemplar. Given average blueberry yield, 100% of SWBC need for fresh and processed blueberry can be satisfied on 1,446 hectares of land. In the BAU system with stable land availability, however, almost six times as much land (8,205 hectares) is allocated to blueberry production. Although this excess land holds economic value in an export-oriented system, it is superfluous if food self-reliance is the primary criteria for the food system\u00E2\u0080\u0099s evaluation, as it instead could have been used to produce crops which increase total dietary food self-reliance. Conversely, in the Localized system, land use for crop production is matched with food need in such a way that there is no use of farmland that does not contribute to food self-reliance. Additional farmland that does not contribute to food self-reliance in the BAU food system is land that is allocated to hay production or pasture that cannot support livestock production because of the insufficient availability of SWBC-grown feed grain to make up a balanced livestock diet. In the BAU system with stable land availability and average crop yield, for example, almost 40,000 hectares of pasture and over 28,000 hectares of hay remain unused after the approximately 11,000 hectares of feed grain are utilized for dairy production. This represents more than half (65%) of total farmland that is available in the stable land availability scenario. A similar land-use pattern is seen in the increased non-farm use and agricultural expansion scenarios in the BAU food system. In the Localized food system, although the impact of feed grain shortfall is not as acute as in the BAU model, model outputs do show that without feed imports, some farmland is left unallocated to local food production in all farmland availability scenarios (Figure 12). Unallocated land is entirely class 5 and 6 land that is deemed only suitable for the pasture component of dairy cattle, beef cattle, and sheep and lamb feed requirements. Because these livestock also require hay and feed grain, however, the availability of class 1-4 land limits the ability to allocate all class 5-6 land to pasture production. Interestingly, unallocated farmland was also an outcome of the capacity to meet New York State food needs with food grown locally while maximizing land use value; it found that large areas of land theoretically available for agriculture would not necessarily be farmed in a food system designed to 56 maximize land use value (108). Although their model focused on economic value rather than food self-reliance as the primary driver of food system design, it corroborates the somewhat counter-intuitive outcome of this study that a localized food system may not require all of the land technically available for food production (108). This should not be construed, however, to mean that there is no use for unallocated class 5 & 6 land in a localized food system. It is possible, for example, that such land could be more fully utilized if pasture-based livestock production systems were used. Furthermore, unfarmed class 5 & 6 land could hold immense value from an ecosystem services perspective if it were configured as hedgerows or woodland set-asides. Hedgerows have been demonstrated to mitigate erosion and crop damage from wind (139), provide overwintering habitat for insects and pollinators that are beneficial for farming (140), reduce nutrient leaching (141), and provide other environmental benefits. 3.4 Conclusions Like other studies (23,24,37,39,94,108,109,134), this study assessed the capacity to increase a region\u00E2\u0080\u0099s future food self-reliance. Uniquely, this study applied a strict approach to food self-reliance that considered seasonality of production (the availability of locally produced food at the same time that consumers demand it) and modeled food self-reliance capacity both with and without a restriction on livestock feed imports. Furthermore, the study evaluated the sensitivity of future food self-reliance Figure 12: Mean land use in the Business as Usual (BAU) and Localized food systems, with and without feed imports, in the stable farmland availability scenario. Mean refers to the mean across decreased, average, and increased crop yield. 57 capacity in two food system models to farmland availability and climate change-induced changes to crop yield, both key factors affecting the capacity of global food systems to meet human nutrition needs and compared the land use outcomes of food system localization to those of a Business as Usual food system. The study\u00E2\u0080\u0099s results indicate that SWBC\u00E2\u0080\u0099s future food self-reliance status could be increased over current (2011) levels in a Localized food system in which crops are allocated to agricultural land in a manner that maximizes food self-reliance, but not in a Business as Usual food system in which future crop and livestock production follows 2011 patterns. On average, modeled 2050 food self-reliance in a Localized food system was 10% higher than that in a Business as Usual food system without feed imports, and 17% higher with feed imports. The sensitivity analysis revealed that both food systems are more sensitive to changes in farmland availability than to climate change-induced changes in crop yield, however it was acknowledged that other sources of crop yield variability that were not modeled could have a greater impact on food self-reliance sensitivity. The land use impacts of food system localization in SWBC demonstrate that horticultural crop production would dominate farmland use in a scenario of increased food self-reliance, and overall the study reaffirmed previous evidence (chapter two) that the capacity for the production of food grain or feed grain in SWBC is extremely limited. As such, the continued importation of livestock feed and food grain is likely unavoidable even in the localized food system future delineated by SWBC. Modeling of the Localized and BAU food systems was based solely on variations of farmland availability and expected crop yield. Modeling of the economic outcomes or environmental impacts associated with either food system was beyond the scope of this study, as was crop yield and land use change modeling that incorporated climate models in a dynamic manner. Doing so would add further richness to the discussion of the merits and detriments of a local food system. This is described further in the following chapter. 58 Chapter 4: General summary and conclusions 4.1 Summary of findings Assessing current food self-reliance status The second chapter of this thesis reported on the two objectives to develop a methodology to evaluate land-based food self-reliance for a diet satisfying nutritional recommendations and food preferences that accounts for seasonality and allows for a comparison of outcomes when food self-reliance is defined as including livestock raised with local feed to that defined as including livestock raised with imported feed; and to apply this novel to a case study area for the year 2011. The methodology developed used Canada\u00E2\u0080\u0099s Food Guide recommendations to adjust food quantities in the Preferred Diet to quantities that met nutritional guidelines. A diet and seasonality constraint on food self-reliance was calculated to reflect the assumption that SWBC residents will consume imported fresh fruits and vegetables outside of their season of SWBC availability, and that their diets will include foods, such as rice and tropical fruits, that cannot be grown in SWBC. Data from the Census of Agriculture, and other sources, was used to estimate the quantity of food crops produced in SWBC and an optimization model was developed to estimate the maximum quantity of livestock products that could have been produced using only bio-regionally available feed. Results of assessment of SWBC\u00E2\u0080\u0099s 2011 food self-reliance status revealed that the Preferred Diet of southwest British Columbians falls substantially short of meeting nutritional recommendations for the consumption of Fruits & Vegetables and Milk & Alternatives but nearly meets recommendations for Grains and Meat & Alternatives. The study confirmed that livestock fodder, fruit, and vegetable production dominated SWBC agricultural land use in 2011, and that the production of grains and oilseeds accounted for less than 1% of total land use. Given the diet and seasonality constraint on food self-reliance, only 78% of the food needed for the SWBC population, by weight, can be grown in SWBC. 14% consists of grains, legumes, fruit and vegetables that are either not agronomically possible to grow in SWBC, and the remaining 8% are consumed fresh out of the season of SWBC fresh availability. The limited production of feed grain was found to be a major constraint on self-reliance for livestock products. When counting only the livestock products that could have been produced with bio-regionally available feed, self-reliance in Meat & Alternatives was less than 1% and Milk & Alternatives was only 10%. Alternatively, counting livestock that could have been raised on imported feed, the self-reliance in these groups was substantially higher at 49% and 86% respectively. Total dietary self-reliance of SWBC was 12% if discounting livestock feed imports or 40% if counting livestock raised on imported feed. 59 Modeling future food self-reliance capacity In the third chapter of this thesis I modeled food self-reliance for SWBC given a projected population for 2050 and explored how this might change given variation in farmland availability and climate change, two factors having potential to substantially impact SWBC\u00E2\u0080\u0099s food self-reliance capacity in the future. I then reported on three objectives related the future food self-reliance capacity. The first objective was to assess the potential to increase SWBC\u00E2\u0080\u0099s food self-reliance given a diet that satisfies nutritional recommendations and food preferences, accounts for crop seasonality and the production of livestock feed. The second objective was to assess the sensitivity of future food self-reliance capacity to changes in crop yield and farmland availability, and the third and final objective was to determine the land-use outcomes of increasing food self-reliance. The study\u00E2\u0080\u0099s results indicated that SWBC\u00E2\u0080\u0099s future food self-reliance status could be increased over current (2011) levels in a Localized food system in which crops are allocated to agricultural land in a manner that maximizes food self-reliance, but not in a Business as Usual food system in which future crop and livestock production patterns follow that evident in 2011. On average, modeled 2050 food self-reliance in a Localized food system was 10% higher than that in a Business as Usual food system without feed imports, and 17% higher with feed imports. In both modeled food systems, self-reliance capacity was found to be more sensitive to potential changes in farmland availability than to climate change-induced deviations in crop yield. Although the magnitude of modeled change in farmland availability was greater than that of climate change-induced deviation in crop yield, a comparison of food self-reliance sensitivity to the two factors was relevant because the magnitude of change for both was within their plausible upper and lower bounds. Other factors that affect crop yield however, such as farmer skill level, availability of agricultural inputs, or others, may have a greater effect on yield than climate change would alone. The analysis of land use outcomes in the Localized system revealed that a focus on horticultural crop production is optimal for food self-reliance and that the capacity for the production of food grain or feed grain in SWBC is extremely limited. 4.2 Strengths and contribution to the field of study In many regions in which the global verses local debate is playing out, there is a lack of sufficient information regarding current regional food self-reliance status, the capacity to increase it, and what the outcomes of doing so might be (142). Having identified this as a significant impediment to constructive discourse around the future of food systems it was the objective of this study to develop methods for measuring current food self-reliance status and modeling future food self-reliance capacity. Strengths of the method devised for measuring current food self-reliance status were that it drew upon, integrated 60 and built on methods used in the food self-reliance assessment literature, including adjusting the diet to meet nutritional recommendations, accounting for the seasonality of food production and consumption, discounting production of food in excess of food need, and calculating food self-reliance both with and without the availability of livestock feed imports (25,72,79,94). In addition to these strengths, its contributions to the field of study include the development of novel elements (the diet and seasonality constraint on food self-reliance and the optimization-based method for determining the ability to feed livestock with regionally-produced fodder), and offered a comparison of food self-reliance assessed according to two definitions of \u00E2\u0080\u009Clocal\u00E2\u0080\u009D livestock. To assess future food self-reliance capacity, I integrated these accepted methodologies and novel elements into two food system models that modeled future food self-reliance capacity, land use outcomes, and sensitivity to farmland availability and crop yield change under two alternative food system futures (Localized and Business as Usual). Strengths of the models included the ability to report output data by food type, the ability to include or exclude the constraint that livestock are fed locally grown feedstuffs, and the ability to use a scenario-based sensitivity analysis to assess how changes to model input variables (in particular changes to farmland availability and crop yield) effect output values. As contribution to the field of study, these models offer a unique way to assess food self-reliance capacity based on a goal to maximize regional production to meet food need, rather than to minimize distance food is transported, or to maximize the number of individuals supplied a whole diet. 4.3 Limitations and direction for future research Limitations of this study are largely related to the limitations of the input data used, the scope of the research, and the nature of a single case study. Input data used to calculate crop yield was limited in that it was not specific to the SWBC study area, but rather representative of provincial (or in a few cases, national) averages. The Land Capability Classification for Agriculture in British Columbia dataset used to delineate land suitable for the production of fruit, vegetables, food and feed grain, and hay, from that suitable only for structure based agriculture, barns, and pasture, also has limitations. The land surveys conducted to develop this dataset were completed in the 1970s and do not account for any changes which may have occurred on the landscape since that time. Furthermore, the methodology that the classification scheme is based on assumes a model of mechanized, larger scale agriculture and does not reflect alternative production practices which might be used on smaller scale, more human intensive farms (143). This lack of region-61 specific yield and soil capability data in British Columbia is a limitation for many agricultural researchers and farmers and offers an area of future research focus. As stated by Porter et al. (112) \u00E2\u0080\u009Cthe impacts of climate change on food systems are expected to be widespread, complex, geographically and temporally variable, and profoundly influenced by socio-economic conditions\u00E2\u0080\u009D. However, in this study analysis of food self-reliance sensitivity to climate change was static in nature and relied on a simplistic representation of potential climate change impacts to agriculture. Furthermore, additional factors that affect crop yield were not accounted for. This analysis would benefit from a more robust method of accounting for uncertainty such as probabilistic modeling. This could be implemented in a Monte-Carlo simulation optimization model (144). This method was investigated for use in this study but ultimately deemed to be beyond its resource capacity and basic objectives. Integrating Monte-Carlo simulation into a future iteration of the Localized food system model would allow for modeling of the optimal allocation of crops to agricultural land under conditions of crop yield uncertainty rather than simply crop yield increase or decrease. As described in section 1.3, the scope of this study was an assessment of self-reliance in foods that comprise the land-based portion of the diet (i.e., fruit, vegetables, dairy, meat, eggs, and legumes). A fruitful area of future research would be to incorporate fish and seafood contribution to self-reliance. An assessment of the potential for closed containment or land-based aquaculture to contribute to satisfaction of fish and seafood demand would be particularly timely given mounting pressures on marine and freshwater fish stocks (41) and evidence that open pen fish farming has the potential to negatively impact native fish species (145). Assessment of how dietary changes impact food self-reliance capacity could also be fruitful. Possibilities include substituting locally producible fruit for tropical and citrus, reducing meat consumption, and substituting processed foods for those consumed fresh out of season. Other studies have demonstrated that dietary changes are important food system drivers (146,147). Given the findings from this study that only 77% of the SWBC diet can be grown in the bio-region due to the diet and seasonality constraint on food self-reliance, and that the potential for SWBC production of livestock feed is so limited, an assessment of how SWBC\u00E2\u0080\u0099s food self-reliance capacity could change under the adoption of alternative diets is warranted. Finally, this study could be used as the basis for further assessment of Localized and Business as Usual food system futures by expanding the models to include environmental impact and economic outcome 62 indicators. Doing so could bring additional, important information to the debate regarding a preferred food system future. 4.4 Implications Model results suggested that SWBC could increase its future food self-reliance status from that theoretically achieved in 2011 through food system localization but that it cannot achieve 100% self-sufficiency in a nutritious diet given current dietary preferences of the population. Dietary changes such as substituting locally producible foods for tropical fruits and vegetables could increase capacity for self-reliance. Grain and livestock feed production were found to be limiting factors to food self-reliance as measured at the scale of the SWBC bio-region. These results illustrate the potential to increase food self-reliance by changing patterns of land use but also that, even in a more localized food system, trade relationships with other regions is likely to remain an important part of a resilient SWBC food system. Interestingly, a recent report on province-wide dialogues held on the future of local and sustainable food systems in BC found that sustainable regional food systems are viewed by food system stakeholders as being \u00E2\u0080\u009Cin relationship with, but not wholly dependent upon, global food trading systems\u00E2\u0080\u009D (148), a perspective aligned with the results of this study. As anticipated, the outcome of the food self-reliance assessment was greatly impacted by whether \u00E2\u0080\u009Clocal\u00E2\u0080\u009D livestock production was defined as including or excluding livestock raised with imported feed. These findings support those of Galloway et al. (98) that international trade of livestock feed allows feed importers to \u00E2\u0080\u009Cescape what would otherwise be binding resource constraints\u00E2\u0080\u009D on local production. For SWBC, defining \u00E2\u0080\u009Clocal\u00E2\u0080\u009D livestock products as those produced only with locally grown feed paints a picture of a local food system that contrasts starkly with how the region is typically conceived (Table 4 and Table 5) and with how the livestock industries often characterize their sector (the BC Pork Producers\u00E2\u0080\u0099 Association, for example, brands BC pork under the slogan \u00E2\u0080\u009Cproudly produced close to home\u00E2\u0080\u009D (149)). Determining which definition of \u00E2\u0080\u009Clocal\u00E2\u0080\u009D livestock production is best suited to SWBC, or any case study area, may ultimately depend on whether economic or environmental objectives are the priority of efforts to increase food self-reliance. The de-coupling of livestock production from the land base that supports it has drastically shifted global patterns of land and water use and the discharge of effluents such as nitrogen away from their balance in a non-trading system (98). SWBC exemplifies this; the concentration of livestock operations in SWBC\u00E2\u0080\u0099s Fraser Valley regional district, which is facilitated by the ability to import feed, is a source of ongoing environmental concern as it has been linked to nitrogen 63 contamination of groundwater (150). As described in chapter three, however, the continued importation of food and feed grains for livestock makes sense for SWBC from an economic perspective given its high population density, expensive and limited farmland, and climate more suitable to horticultural crop production. Comparing self-reliance measured according to these two definitions of \u00E2\u0080\u009Clocal\u00E2\u0080\u009D livestock enables the evaluation of these environmental and economic trade-offs and represents a positive step towards reconciling those trade-offs in the design of a sustainable future food system. Farmers and other food system actors make land-use decisions based on many factors that were not modeled in this study, such as their capacity, financial situation, personal preferences and the policy environment in which they work. As such, this research was not able or intended to predict what the SWBC food system will look like in the year 2050. Instead, it provides valuable information for the discussion about food system localization in SWBC; specifically, that SWBC has the capacity to increase its future food self-reliance status from that theoretically achieved in 2011 through food system localization. These results will likely excite local food advocates in the region, however this study does not suggest localization should necessarily be pursued, or pursued at this scale. Such general conclusions cannot be drawn from a single case study. Ultimately, increasing food self-reliance should only be pursued if doing so facilitates the achievement of important food system and societal goals. Such goals may vary from region to region, but in most cases likely relate to the ability to satisfy human nutrition in a socially, ecologically, and economically sustainable manner. The results from this research could be used to study the effect that increasing food self-reliance would have on the pursuit of these goals, but in and of themselves do not provide an answer to the question of whether or not the region \u00E2\u0080\u009Cshould\u00E2\u0080\u009D localize. 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Can Water Resour J. 1993;18(March 2015):217\u00E2\u0080\u009327. 75 Appendices Appendix I: List of foods included and excluded from the Preferred Diet Foods from CANSIM Database Included in the Preferred Diet Fruit & Vegetables Apple, dried Orange, fresh Carrot, fresh Apple, juice Papaya, fresh Carrot, frozen Apple, pie filling Peach, canned Cauliflower, fresh Apple, sauce Peach, fresh Celery, fresh Apple, fresh Pear, canned Corn, canned Apple, frozen Pear, fresh Corn, fresh Apple, canned Pear, canned Corn, frozen Apricot, canned Pear, fresh Cucumber, fresh Apricot, fresh Pineapple, canned Lettuce, fresh Avocado, fresh Pineapple, fresh Manioc, fresh Banana, fresh Pineapple, juice Mushroom, canned Blueberry, fresh Plum, fresh Mushroom, fresh Blueberry, frozen Raspberry, frozen Onions and shallot, fresh Blueberry, canned Strawberry, canned Pea, canned Cherry, fresh Strawberry, fresh Pea, fresh Cherry, frozen Strawberry, frozen Pea, frozen Coconut, fresh Asparagus, canned Pepper, fresh Cranberry, fresh Asparagus, fresh Potato, frozen Date, fresh Beans green and wax, canned Potato, sweet, fresh Fig, fresh Beans green and wax, fresh Potato, white, fresh Grape, juice Beans green and wax, frozen Pumpkin and squash, fresh Grapefruit, juice Beet, canned Radish, fresh Grapefruit, fresh Beet, fresh Rutabaga and turnip, fresh Grape, fresh Broccoli & Cauliflower, frozen Spinach, fresh Guava and Mango, fresh Broccoli, fresh Spinach, frozen Lemon, juice Brussels sprout fresh Tomato, juice Lemon, fresh Brussels sprout, frozen Tomato, canned Lime, fresh Cabbage, fresh Tomato, fresh Orange, juice Carrot, canned Tomato, pulp, paste and puree Meat & Alternatives Baked and canned bean Egg Peanut Beef and veal Lima bean Pork Chicken and Stewing hen Mutton and lamb Turkey 76 Foods from CANSIM Database Included in the Preferred Diet Milk & Alternatives Buttermilk Cottage cheese Powder buttermilk Cheddar cheese Partly skimmed milk 1% Powder skim milk Chocolate drink Partly skimmed milk 2% Standard milk 3.25% Concentrated skim milk Processed cheese Variety cheese Concentrated whole milk Skim milk Fats & Oils Grains Butter Shortening, shortening oil Pot and pearl barley Margarine Grain Rice Salad oils Corn flour and meal Rye flour Oatmeal and rolled oat Wheat flour Foods from CANSIM Database Not Included in Preferred Diet Beverages and alcoholic beverages Other processed potatoes Nectarine Powder whey Vegetables not specified Artichoke Sweetened concentrated skim milk Other edible roots Kohlrabi Milkshake Fruits not specified Garlic Ice cream Other citrus Leek Sherbet Other berries Okra Ice milk Offal Parsley Cream Tree nuts Parsnip Other whole milk products Kiwis Rappini Olives Watermelon Maple sugar Potato chips Other melons Refined sugar 77 Appendix II: Total British Columbia yield (tonnes) divided by the total British Columbia area (hectares) planted in commodity c (TYpc/Apc) Commodity TYpc/Apc Data Source Barley, grain 2.42 1 Beans, other dry 2.03 1 Corn, grain 8.79 1 Oat, grain 2.45 1 Wheat, grain 3.65 1 Apple 20.25 2 Blueberry 5.28 2 Cherry, sweet 5.14 2 Cranberry 10.4 2 Grape 5.62 2 Peach 7.13 2 Pear 16.84 2 Plum and prune 5.71 2 Raspberry 6.39 2 Strawberry 5.8 2 Asparagus 1.79 3 Beans, green and wax 5.68 3 Beet 29.51 3 Broccoli 5.4 3 Brussels sprout 12.64 3 Cabbage 16.17 3 Carrot 32.71 3 Cauliflower 11.33 3 Celery 6.97 3 Corn, sweet 5.77 3 Cucumber, field 9.27 3 Cucumber, greenhouse 466.97 6 Dry onion 32.37 3 Lettuce 26.12 3 Mushroom 1,169.15 4 Pea, green 3.71 3 Pepper, field 16.41 3 Pepper, greenhouse 224.77 6 Potato 29.96 5 Pumpkin 26.61 3 Radish 14.82 3 Rutabaga and turnip 20.63 3 78 Commodity TYpc/Apc Data Source Shallot and green onion 13.84 3 Spinach 9.53 3 Squash and zucchini 10.33 3 Tomato, field 21.13 3 Tomato, greenhouse 593.02 6 (1) CANSIM Table 001-0010 (\"production\"/\"seeded area\") (Statistics Canada, 2014) (2) CANSIM Table 001-0009 (\"marketed production\"/\"cultivated area\"), (Statistics Canada, 2014) (3) CANSIM Table 001-0013 (\"marketed production\"/\"seeded area\"), (Statistics Canada, 2014) (4) CANSIM Table 001-0012 (\"production, fresh and processed\"/\"area beds total\"), (Statistics Canada, 2014) (5) CANSIM Table 001-0014 (\"marketed production\"/\"seeded area\"), (Statistics Canada, 2014) (6) CANSIM Table 001-0006, (Statistics Canada, 2014) 79 Appendix III: Months of fresh availability in southwest British Columbia of select crops and livestock products1 Crop/Livestock Product Months Fresh Availability Crop/Livestock Product Months Fresh Availability Apple 9 Beans, green and wax 3 Apricot 2 Beet 12 Avocado 0 Broccoli 6 Banana 0 Brussels sprout 4 Blueberry 2 Cabbage 8 Cherry 2 Carrot 9 Coconut 0 Cauliflower 6 Cranberry 2 Celery 5 Date 0 Corn 4 Fig 0 Cucumber 9 Grapefruit 0 Lettuce 6 Grape 1 Manioc 0 Guava and Mango 0 Mushroom 12 Lemon 0 Onion and shallot 12 Lime 0 Pea 2 Orange 0 Pepper 8 Papaya 0 Potato 12 Peach 1 Pumpkin and squash 9 Pear 5 Radish 7 Pineapple 0 Rutabaga and turnip 11 Plum 2 Spinach 7 Raspberry 2 Sweet potato 0 Strawberry 4 Tomato 9 Milk Products 12 Beans and legumes 12 Barley 12 Beef 12 Corn for Grain 12 Chicken 12 Oat 12 Egg 12 Rice 0 Lamb 12 Rye 0 Peanut 0 Wheat 12 Pork 12 Honey 12 Turkey 12 Asparagus 2 Canola Oil 12 1Adapted from Get Local in Southwest British Columbia (Farm Folk City Folk, 2012) "@en . "Thesis/Dissertation"@en . "2015-05"@en . "10.14288/1.0166128"@en . "eng"@en . "Integrated Studies in Land and Food Systems"@en . "Vancouver : University of British Columbia Library"@en . "University of British Columbia"@en . "Attribution-NonCommercial-NoDerivs 2.5 Canada"@en . "http://creativecommons.org/licenses/by-nc-nd/2.5/ca/"@en . "Graduate"@en . "Assessment of current status and modeling of future capacity for land based food self-reliance in southwest British Columbia"@en . "Text"@en . "http://hdl.handle.net/2429/52681"@en .