Biophysical Sustainability of Food Systems in a Global and Interconnected World

Thesis submitted in partial fulfillment

of the requirements for the degree of

“DOCTOR OF PHILOSOPHY”

Dor Fridman

Submitted to the Senate of Ben-Gurion University

of the Negev

53/32/2/

Beer-Sheva

Biophysical Sustainability of Food Systems in a Global and Interconnected World

Thesis submitted in partial fulfillment

of the requirements for the degree of

“DOCTOR OF PHILOSOPHY”

Dor Fridman

Submitted to the Senate of Ben-Gurion University

of the Negev

Approved by the advisor Approved by the Dean of the Kreitman School of Advanced Graduate Studies

32/2/ 53/32/2/

Beer-Sheva

This work was carried out under the supervision of Prof. Meidad Kissinger

In the Department for Geography and Environmental Development

Faculty of Social Sciences

Research-Student’s Affidavit when Submitting the Doctoral Thesis for Judgment

I Dor Fridman, whose signature appears below, hereby declare that

(Please mark the appropriate statements):

V I have written this Thesis by myself, except for the help and guidance offered by my Thesis Advisors.

V The scientific materials included in this Thesis are products of my own research, culled from the period during which I was a research student.

___ This Thesis incorporates research materials produced in cooperation with others, excluding the technical help commonly received during experimental work. Therefore, I am attaching another affidavit stating the contributions made by myself and the other participants in this research, which has been approved by them and submitted with their approval.

Date: 18/2/20 Student’s name: Dor Fridman Signature:

Table of contents Table of contents v Acknowledgements vii List of figures viii List of tables ix List of equations x Abstract xi 1. Introduction 13 2. Background 16 2.1. Interactions between human and natural systems as the core of sustainability science 16 2.2. Sustainability in a global world 18 2.3. Approaches for sustainability assessment in a global and interconnected world 19 2.4. Food systems: challenges and pathways 22 2.5. Studying food systems sustainability 25 3. Methods 30 3.1. Aims and scope 30 3.2. The general course of the research 32 4. A multi-scale analysis of interregional sustainability: applied to Israel’s food supply 35 4.1. Disclaimer 35 4.2. Introduction 35 4.3. Methods 36 4.4. Results 42 4.5. Discussion 49 4.6. Conclusions 53 5. An integrated biophysical and ecosystem approach as a base for ecosystem services analysis across regions 54 5.1. Disclaimer 54 5.2. Introduction 54 5.3. Methods 55 5.4. Results 59 5.5. Discussion 65 5.6. Conclusions 70 6. Food security, sustainability and international trade – a global analysis using the functional regions typology 71 6.1. Disclaimer 71 6.2. Introduction 71 6.3. Methods 72 6.4. Results 78 6.5. Discussion 86 6.6. Conclusions 90 7. Synthesis 92 8. Conclusions 98 9. Bibliography 100

10. Annexes 113 10.1. Primary crop equivalents and conversion factors 113 10.2. Primary livestock equivalents and conversion factors 120 10.3. Livestock relative feed requirements keys 123 10.4. Crop groups 124 10.5. Country ISO3 codes 129 10.6. Flows to Israel – national scale 133 10.7. Flows to Israel – biome scale 831 10.8. Flows to Israel – ecoregion scale 139 10.9. Flows to Israel – river basin scale 182 10.10. Flows to Israel – first level administrative scale 183 10.11. Functional regions (ch. 5): description, footprints and potential 184 impacts 10.12. Querying the functional regions typology – an illustrative example 188 10.13. Utilization factor: Quantifying the level of agricultural use in different functional regions 189 10.14. Absolute values for selected indicators in different functional 191 regions 10.15. Local and imported national calorie supply from different groups of functional regions 192 א (תקציר) Hebrew abstract

Acknowledgments This dissertation is a product of challenging and interesting work conducted for over four years. I am happy to complete this work and submit it to the Kreitman School of Advanced Graduate Studies at the Ben-Gurion University of the Negev.

Over this period, I’ve enjoyed the support and encouragement of many, and I would like to use this section to express my deep appreciation for their work.

First, I would like to thank my dear family: my partner Hila and my sweet daughter Ofri for motivating and inspiring me daily, and for providing me with time and personal space to work. Many thanks for my Parents, Sister, Niece, Nephew, and grandparents; I thank you for being there when I’ve needed and for all mental, emotional, and financial support.

Second, I am grateful for my supervisor, Prof. Meidad Kissinger, for guiding my professional life while allowing me to navigate along the way, to make mistakes as well as to discover new insights and reveal new knowledge. I also thank Meidad for inspiring me intellectually, professionally, and personally; I am happy to work under his supervision and with him for over six years. Finally, I appreciate Meidad’s financial support through different scholarships and grants.

This Ph.D. dissertation was financed by the Israeli Science Fund (ISF) and by the Faculty of Humanities and Social Sciences and the Kreitman School for Advanced Graduate Studies at the Ben-Gurion University of the Negev. I sincerely appreciate all funding institutions; their support allowed me to dedicate most of my efforts to my research, resulting in new and exciting knowledge and high-quality publications.

I want to thank multiple researchers for their excellent advice and help. Specifically, I want to thank Prof. Thomas Köllener from Bayreuth University, Germany, for the time he dedicated to hosting me in Bayreuth and cooperating with him and his students. I also thank Dr. Thomas Kastner for the support in reproducing is own research and for the exciting discussion and cooperation. I thank Prof. Aletta Bon and the i-Div team for inviting me to the i-Div workshop on the interregional flow of ecosystem services. I also thank my dear colleagues for interesting discussions and advice.

Finally, I thank the department for Geography and environmental development for being a home for me for almost a decade and supporting my work and providing me with multiple professional opportunities. Special thanks to Prof. Tal Svoray, Prof. (Emeritus) Eli Stern, Mrs. Rachel Zimmerman, Mrs. Roni Blushtein-Livnon, Mr. Oron Moshe Guy, and Dr. Michael Dorman. Special thanks also to Mrs. Rachel Yonayov from the Kreitman school for being available and responsive to any question I had.

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List of figures Fig ‎3.1: A theoretical framework for an integrative approach to interregional sustainability assessment ...... 31 Fig ‎3.2: The general course of the research...... 32 Fig ‎4.1: Approaches to advance an interregional sustainability analysis of food supply...... 37 Fig ‎4.2: Food supply, cropland footprint, and calorie per capita by country of origin and crop group. The figure represents 99% of Israel’s food supply and 98% of is cropland footprint...... 43 Fig ‎4.3: Israel’s imported cropland footprint from different biomes, according to conservation status and conservation priority...... 44 Fig ‎4.4: Cropland footprint of Israel’s food supply in ecoregions in South America and Eurasia, and regional species loss impacts...... 46 Fig ‎4.5: Cropland footprint due to Israel’s food supply in major river systems in South America and Eurasia, at watershed and sub-basin scals...... 47 Fig ‎4.6: Cropland footprint due to Israel’s food supply in selected countries in South America and Eurasia, for cereal an oil crop yields at a scale of level one administrative unit. Yields are mapped for regions that contribute 1% or more to the total supply (in tons) of cereals and oil crops imported to Israel...... 48 Fig ‎5.1: A framework to integrate agricultural and environmental systems...... 56 Fig 5.2: A functional region typology relies on global available datasets to define unique classes within the agricultural and the environmental systems; an illustration of the concept using the global maize production system...... 60 Fig ‎5.3: Top supplying functional regions for 4 main staple crops. Each region provides at least 80% of that crop’s supply to Israel...... 62 Fig ‎5.4: Best suited functional regions by number of crops ...... 63 Fig ‎5.5: Water efficiency classes in water scarce areas. An illustration for wheat and for rice ...... 65 Fig ‎6.1: Codebooks of functional regions (Mean (empty circle), median (black circle), vertical line 25% -75% quintile). Standardized raw data are presented...... 75 Fig ‎6.2: Global map of the functional regions typology...... 80 Fig ‎6.3: Share of national calorie supply for selected top exporting and importing countries and for local consumers ...... 84 Fig ‎6.4: Association between international trade and the share of national calorie supply from environmental impact and most suitable functional regions...... 85 Fig ‎6.5: Countries’ staple food supply from different types of functional regions by environmental impact category and GDP per capita ...... 86 Fig ‎7.1: Adjusted DPSIR framework applied to an interregional sustainability analysis of Israel staples supply. Note: the figure is based upon chapter ‎6...... 94 Fig ‎10.1: Functional regions utilization relative to the homogeneous line ...... 190

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List of tables Table ‎2.1: Exemplar of literature on sustainability assessment of food systems with focus on biophysical methods and economic modeling...... 27 Table ‎4.1: Input SPAM data for disaggregating national crop flows ...... 39 Table ‎4.2: Environmental and social processes analyzed at various spatial and organizational scales ...... 42 Table ‎4.3: General description for selected sending systems ...... 44 Table ‎4.4: Selected environmental pressures and environmental and interregional implications of Israel’s cropland footprint ...... 49 Table ‎5.1: Data sources and data manipulations ...... 57 Table ‎5.2: Crop provision to Israel and its related environmental pressures and ecosystem dis-services for the top 8 functional regions...... 61 Table ‎5.3: Sub-optimal use of best-suited functional regions...... 64 Table ‎6.1: A short description of datasets used in this study ...... 73 Table ‎6.2: A hypothetical system for demonstrating the integration of the functional regions typology and the trade by origin dataset...... 76 Table ‎6.3: Results for country A from the hypothetical system...... 71 Table ‎6.4: Names and attributes of global functional regions...... 80 Table ‎6.5: Categories of functional regions with similar environmental impact and the most suitable functional regions...... 81 Table ‎6.6: Calorie supply by local consumption and by imports from different functional regions ...... 83 Table ‎10.1: Primary crop equivalents and conversion factors ...... 114 Table ‎10.2: Primary livestock equivalents and conversion factors ...... 121 Table ‎10.3: Livestock relative feed requirements keys ...... 124 Table ‎10.4: Crop’s groups: codes an names ...... 125 Table ‎10.5: Countries’ names an ISO3 code ...... 130 Table ‎10.6: Flows to Israel from other counties (national scale) ...... 134 Table ‎10.7: Cropland footprint of Israel in different biomes ...... 139 Table ‎10.8: Species lost and cropland footprint of Israel at an ecoregion scale ...... 140 Table ‎10.9: Flows to Israel from main river basins ...... 183 Table ‎10.10: Flows to Israel from first level administrative unit. Only regions that supply more than 1% of Isarel’s cereal and oil crops are included...... 184 Table ‎10.11: A description of the 24 functional regions and footprints and potential impacts related to Israel’s crops’ supply. Note that the sign ‘-’ stands for values smaller than 1...... 185 Table ‎10.12: Categories for utilization level ...... 190 Table ‎10.13: Absolute values for selected indicators in different functional regions ...... 192 Table ‎10.14: Local and imported national calorie supply from different groups of functional regions ...... 19

List of equations Eq. 4.1: Allocating local consumption to a grid scale ...... 39 Eq. ‎4.2: Estimating harvested land using yields and production quantity ...... 39 Eq. ‎4.3: Calculating yield grid from input data ...... 39 Eq. ‎4.4: Final equation for calculating local cropland embodied in the supply of a specific crop ...... 40 Eq. ‎4.5: National export production grid in a specific country ...... 40 Eq. ‎4.6: Estimating harvested land for exports using yields and production quantity ...... 40 Eq. ‎4.7: Calculating yield grid for exports from input data ...... 40 Eq. 4.8: Final equation for calculating exported cropland embodied in the supply of a specific crop ...... 40 Eq. 1.6 Calculating the domestic and imported consumption of country r from functional region i ...... 76 Eq. 1.6 Defintion of the sustainability metric ...... 77 Eq. ‎10.1: quantifying the utilization factor for different functional regions ...... 189

Abstract Food security in many countries is becoming more dependent on imports from other parts of the world. In many cases, environmental impacts associated with food consumption occur in remote cultivated systems. International trade that facilitates the flows of crops and embodied resources across regions is related to positive and negative environmental impacts. It follows that in a globalizing and interconnected world, the food system’s sustainability becomes interregional sustainability due to its high dependence on the state of remote ecosystems and the services they provide. While most studies analyze food systems related to environmental implications at the local/national scale, a growing number of them started researching environmental pressures in countries that produce food, driven by consumption occurring in other countries. However, only a few studies have combined both interregional flows of food crops and analysis of local environmental impacts from food production. This Ph.D. dissertation analyzes the interregional sustainability of the global food system; it links interregional environmental accounting with the local ecosystem approach. By covering the backbone of the human diet (wheat, soybean, maize, and rice), the analysis supports the notion that international trade also has benign environmental impacts. This conclusion holds for reduced soil loss, species loss, water stress, and the four selected staples. It further shows how imports from less stable production regions hamper the food security of different countries. Alternatively, it allows identifying hotspots of environmental impact associated with consuming countries. The knowledge generated by this research can support sustainable food system policies. The methods combined and developed in this research form a significant methodological contribution to the analysis of interregional sustainability. Including additional environmental pressures and impacts as well as social dimensions associated with agriculture would allow revealing other interactions and tradeoffs in the global food system. A time series analysis was also made possible recently and can demonstrate trends, and temporal dynamics otherwise remain unseen. Finally, complementing this

analysis with local, high-resolution case studies can be used to explore uncertainties in the global model, making it more useful for others.

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1. Introduction Human existence and well being depend on ecosystems that provide a variety of services to society. Yet, over the last two centuries, the capacity of ecosystems to provide services to human society has been significantly decreased. Whereas interactions between human and natural systems are associated with processes of environmental deterioration, these interactions are also a key component in the path towards sustainable living. Such interactions are no longer limited to the local environment; instead, they occur across organizational, temporal, and spatial scales. In a global world, sustainability becomes interregional, so the sustainability of any region depends on the bio-physical proper functioning of remote ecosystems. Interactions between remote human and natural systems (also known as telecouplings) are becoming more common due to an increase in the volume of international trade and the flow of information.

Exploring sustainability in a global world must take into account remote interactions between systems. In such a world, human activities in one region drive environmental pressures in other areas (e.g., land use). These pressures have an impact on the capacity of ecosystems to provide services, which highly depends on the local geographic context. It follows that there is a growing scientific acknowledgment of the need further to develop the telecoupling and interregional sustainability research approaches.

The global food system is an appropriate and essential case study for developing further the interregional sustainability approach. The main challenge of the global food system is to produce food while satisfying increasing demands while reducing negative environmental impacts associated with food production. Besides, food trade plays an important role in maintaining food security in several regions, allowing providing a sufficient amount of food in an area that cannot grow enough locally. The environmental impacts of food trade are arguable. In contrast, some suggest it prevent economic feedbacks from consumers leading to over-exploitation of ecosystems, and others show that it increases the global average efficiency of the system.

Most studies focused on interregional food systems quantified the flows of crops and embodied resources (e.g., cropland, water) between countries. While this scope of research contributed significantly, it overlooks local (i.e., sub-national) geographical context that facilitates the transition between environmental pressures and impacts. On the other hand, most studies exploring the effects of ecological change and pressures on the capacity of the ecosystem to provide services focus on a well-defined region (mostly at a local-to-landscape scale) and exclude flows of crops and resources away from or into this region. This dissertation aspires to conduct a food system interregional sustainability analysis by combining both approaches: environmental accounting that maps biophysical flows between countries and an ecosystem approach that explores changes to environmental state and the environmental impacts associated with remotely driven environmental pressures. It will identify, document, and analyze the linkages between food consumption in different regions of the world at a sub- national scale and the ecological sustainability of producing regions; It will further explore the extent to which local and global ecosystem services of different agricultural producing regions are influenced by growing export commodities.

Specifically, this Ph.D. addresses the following questions:

1. What are the global bio-physical flows related to the production of selected key traded agricultural commodities? 2. To what extent such flows can be related to impacts on domestic and global ecosystem services occurring in producing regions?

The rest of this dissertation is structured as follows. Chapter 2 provides a comprehensive literature review that laying the theoretical framework for this research. Chapter 3 describes the aims, scope, and rationale and outlines the structure, content and main methods used in this dissertation. Then chapters 4 - 6 lay down the tools and methods used to integrate the biophysical and ecosystem approach. Chapter 4 focuses on the rescaling of the country to country biophysical flows to a more detailed resolution (flows from a five arc-

minute grid to countries. This rescaling is a critical process to associate remote environmental pressures with local geographical context. Chapter 5 complete the link between remotely driven environmental pressures and local environmental impacts (in production regions). Chapter 6 apply the methods developed to a global case study of interregional flows of 4 major staple crops (wheat, maize, soybean, and rice). It provides essential and exciting insights on the interregional sustainability of different countries’ food supply and on the role played by international trade to promote sustainable food systems. Chapter 7 synthesizes the findings and conclusions from chapters 4 -6 and discusses them in light of the general background, global aims, and theoretical framework of this Ph.D. dissertation. Chapters 8 -10 include conclusions, bibliography, and appendices, respectively.

2. Background

2.1. Interactions between human and natural systems as the core of sustainability science Human society has depended on the environment since the first man has appeared on Earth. Ecological systems provide human beings with life- supporting goods (or provisioning services, e.g., food, materials, and fuels) and services (regulating, supporting and cultural) that: (a) benefit human beings directly, or (b) indirectly by regulating the functions of ecosystems at all scales (Costanza et al., 1997; Daily, 1997; Millenium Ecosystem Assessment, 2005a).

Over the last two centuries, human beings have altered natural ecosystems to the extent that threatens their capacity to facilitate current well being (Millenium Ecosystem Assessment, 2005; Rockström et al., 2009; Ellis et al., 2010). Humans exploit limited natural resources, such as: phosphorus or fresh water, drive land use change and biodiversity loss, alter the global nitrogen cycle, and put high pressures on fresh water and coastal ecosystems (Rockström et al., 2009). In addition, anthropogenic climate change had been determined to be a major direct driver for changes of various ecosystems (IPCC, 2007).

There multiple definitions for the concept of sustainability: it is described as the robustness of a system (Berry, Dernini, Burlingame, Meybeck, & Conforti, 2015) or as a process that “can be maintained without interruption, weakening or loss of valued qualities” (Daily & Ehrlich, 1992, p. 763). Others define it as living under the constraints of natural systems (Daily and Ehrlich, 1992), implying that there are measurable boundaries in which humanity can operate safely (Rockström et al., 2009). Haberl et al. (2004) perceive sustainability as an anthropocentric concept that stresses the need for a more equitable way to exploit natural resources, between regions and within nations today and in the future (WCED, 1987). This point of view suggests that sustainability science should focus on the interactions between societies and ecological systems (Haberl et al., 2004; Liu et al., 2013).

Different conceptual models have been used to describe the interaction between human and natural systems. Haines-Young and Potschin (2010) describe the links between biodiversity, ecosystem services, and social wellbeing. They use an analogy to a production chain, in which a cascade links ecosystem structure and processes with the benefits humans derive from ecosystems. They distinguish ecosystems function from service: the former is the capacity to do something, potentially useful, for humans, whereas the latter (ecosystem service) requires human beneficiaries that find that function to be helpful. Ecosystem functions depend on ecosystem structure and processes. While the links between biodiversity to human benefits from ecosystem services are often non-linear and indirect, it is agreed that ecosystems integrity is fundamental to human well being.

A different conceptual model, known as DPSIR (Drivers, Pressures, State, Impact, and Response), uses a suite of indicators linked under a causal framework that integrates aspects of environmental management and monitoring. Drivers are human actions aimed at fulfilling human needs and desires. Such actions pose pressures on the environment, which in turn change the biological, physical, and chemical state of ecological systems, and impact their functioning and capacity to provide life-supporting services. These impacts can reduce the level of human well being and may trigger a response to minimize these environmental pressures or impacts (Kristensen, 2004; Müller and Burkhard, 2012). When linked to the concept of ecosystem services, impact indicators relate to both the provisioning of and the benefits from a service, whereas state indicators mostly relate to ecosystem functions (Müller and Burkhard, 2012). Liu et al. (2007) emphasize the interdisciplinary nature of this field. They state that the complexity of coupled human and natural systems (CHANS) originates from non-linear, indirect, and reciprocal interactions occurring across space and over time. Instead, to reveal emergent properties of coupled systems, scientists should consider interactions across different scientific fields.

2.2. Sustainability in a global world The current global world shows increasing multidirectional flows from anywhere to everywhere (Ritzer and Dean, 2015). As a fundamental process of economic globalization, international trade has been through a continuous process of de-regulation, aiming to increase global income and economic growth (Bhagwati, 1994; Daly and Goodland, 1994).

International trade of food, for example, increases local and regional carrying capacity beyond their natural limits and enables the provision of food and other ecosystem services to regions with limited production capacity (Fader et al., 2013; Kummu et al., 2014). Some critics argue that the increase of unregulated international trade displaces resource use and adverse environmental and social impacts, mainly from developed to less-developed countries (Pascual et al., 2017). In addition, it restricts individual countries’ capability to set environmental and social regulations, worrying it will interfere with free trade (Daly and Goodland, 1994).

Over time, increasing trade volumes widen the spatial gap between regions of production to areas of consumption, and between remote human and natural systems (Kissinger and Rees, 2010a; Liu et al., 2013). As such, sustainability becomes “interregional sustainability,”: in which one place’s sustainability depends on the sustainability of many other places (Kissinger and Rees, 2010a; Kissinger et al., 2011). Interregional sustainability is not restricted only to traded goods, as it is also affected by passive flows or biophysical flows through species dispersal, as well as flows of information and co-production factors (Schröter et al., 2018). Another essential concept is the telecoupling of human and natural systems (Liu et al., 2007, 2013), emphasizing the complexity of interactions between and within human and natural systems, occurring across organizational, spatial, and temporal scales.

Effective policies in an interconnected world should account for the overall- interregional impacts of its implementation rather than just the local-national implications. There are various suitable mechanisms, but their

implementation requires information about interregional links (Kissinger et al., 2011). Transparency is also an essential requirement in collective decision making that involves multiple stakeholders characterized by asymmetric knowledge and power distribution (Munroe et al., 2019).

2.3. Approaches for sustainability assessment in a global and interconnected world In general, assessments of sustainability require some threshold defining the limit for which a specific flow or resource use is sustainable (Haberl et al., 2004). A few footprint indicators have pre-defined thresholds (e.g., ecological footprint), and for others, a sustainable limit can be assigned. For example, Dalin et al. (2017) set aquifers regeneration rates as a sustainability threshold for water extraction for irrigation. Though, in other cases (such as energy use), thresholds may vary across spatial scales (Haberl et al., 2004). At the planetary scale, seven boundaries (out of 9 themes considered) have been defined as the sustainability threshold for the proper functioning of fundamental Earth processes, operating at different spatial and temporal scales (Rockström et al., 2009). For some, a planetary boundary has already been defined (e.g., climate change), while others are slow processes occurring at the local and regional scale, for which a threshold had to be estimated (e.g., land-use change). The concept of planetary boundaries is complex due to uncertainties involved in defining thresholds, interactions among thresholds, and the complexity arising from the aggregative character of some processes. Yet, stating that three planetary boundaries have already been crossed, the concept successfully alerts the global community that it has exceeded the capacity of the plant to maintain its lifestyles and current level of well being (Rockström et al., 2009).

The path towards sustainability depends upon a deeper understanding of human and natural systems and their interactions (WCED, 1987), often occurring at a much finer scale than the global scale discussed above. Common methods for exploring the links between humans and the environment are the DPSIR framework and the ecosystem services approach,

which were also linked to each other (Haines-Young and Potschin, 2010; Müller and Burkhard, 2012). Here, the ecosystem services approach stands for the link between human and natural systems. Some suggest that the concept of ecosystem services is quite vague, but it continuously moves towards harmonization of terminology, classification, and processes (Seppelt et al., 2012; Schröter et al., 2014). The Millenium Ecosystem Assessment (2005) defines ecosystem services (ES) as benefits that humans derive from natural and human-made ecosystems. It classifies them into four categories: provision services (e.g. of food, fuel, and fiber), regulating, cultural, and supporting services. Others prefer to distinguish between the provision of ecosystem goods (i.e., MEA’s provision services) and ecosystem services (Costanza et al., 1997; Haines-Young & Potschin, 2010a) to differentiate between material and energy outputs of ecosystems to non-material outputs. Recently, some have adopted an economic distinction between ecosystem goods and services, to wit that services are generated by ecosystems and give rise to ecosystem goods which are valued by people (Haines-Young & Potschin, 2011). Quantification of ecosystems capacity to provide ecosystem services and of the flow of services (state and performance) is usually done with biophysical indicators, numerical models, and lookup tables (Remme et al., 2014; Schröter, Barton, et al., 2014; Seppelt, Dormann, Eppink, Lautenbach, & Schmidt, 2011; Tallis et al., 2012).

Interactions between human and natural systems occur at different and across spatial scales and over time (Liu et al., 2007, 2013). Distant interactions transform sustainability to interregional sustainability, emphasizing that any region is affected by and impacts a variety of other local and remote areas (Kissinger and Rees, 2010a; Kissinger et al., 2011). In fact, in an interregional world, local environmental policies can result in severe ecological damage affecting remote regions or the global community (Pascual et al., 2017). Similarly, areas providing ecosystem services often do not overlap with areas where such services are appreciated (Syrbe and Walz, 2012). Schröter et al. (2018) define four different types of ES flows across

regions: international trade, species migration, and dispersal, passive biophysical, and information flows. In these cases, the beneficiaries from each flow often depend on the actions and management occurring in remote regions. As such, sustainability assessment should include and measure both local and remote utilization of ecosystems (Koellner et al., 2019; Munroe et al., 2019). Although the growing need for an interregional assessment of ES, the local geographic context is still crucial for such assessments. The spatial configuration of landscapes (e.g., habitat fragmentation) largely affects environmental impact (Polasky et al., 2011). The effects of land use on the river and oceanic water quality depend on specific landscape structures and attributes. Desmit et al. (2018) evaluate this dependence using high- resolution data with low coverage, and Vörösmarty et al. (2010) use global datasets with moderate to low resolution. However, only a few global models of ecosystem services assessment are currently available (Costanza et al., 1997; Turner et al., 2007; Naidoo et al., 2008), while most work focus on below national scale.

Remote drivers and interregional environmental pressures are quantified as the environmental footprint of human activity (e.g., cropland required to provide a specific crop). The main approaches common in this field are biophysical accounting and environmental-economic accounting. They differ by the scope and scale and offer analytical tools for different sustainability problems (Kissinger and Rees, 2010a; Bruckner et al., 2015; Schaffartzik et al., 2015; Hubacek and Feng, 2016). The first relates to a family of accounting tools, all established upon data of physical material flows. These tools include material flow analysis (MFA) and life cycle assessment (LCA). The scope and coverage of LCA studies are usually limited, but provide a detailed, case- specific analysis of the selected product(s) or process(es) (Roy et al., 2009). Material flow analysis covers in- and out-flows of different materials, resources, biomass, and energy of a selected system. Systems at focus vary in scale from local to international (Haberl et al., 2004). The latter uses economic data with an environmental satellite account to model the

interactions between economic sectors and to estimate the requirements and the social and ecological outcomes of final demand and mostly consists of environmentally-extended input-output analysis (Miller and Blair, 2009). Regardless of the approach used, mapping biophysical flows through international trade primarily uses a national scale (i.e., country to country).

Exploring and quantifying the interactions between remote coupled human and natural systems is an important stepping stone towards a holistic approach to sustainability assessment in a global era, and a prerequisite for most sustainability policies advanced at various scales. This exploration requires tools that can mediate the spatial gap between consumers and producers. These tools will quantify interregional pressures and analyze the environmental impacts caused by remote consumers.

2.4. Food systems: challenges and pathways Food is a basic need for all living things, and the access to and utilization of food is a fundamental human right (UN General Assembly, 1948). Food security is a state in which “all people, at all times, have physical, social and economic access to sufficient, safe and nutritious food to meet their dietary needs and food preferences for an active and healthy life” (FAO, 2009, Article 2). Implied by definition, food security consists of 4 dimensions: food availability, physical and economic access to food, food utilization, and stability (FAO, IFAD & WFP 2013; FAO, 2009). Some perceive them as a pathway to food security linking food supply (availability) to households (accessibility) and the individual (utility). Stability describes the temporal dimension of food security, i.e., the ability of a food system to withstand shocks. Sustainability is perceived as a long-term fifth dimension of food security, describing the capability of the food system o function properly overtime (Berry et al., 2015).

A global analysis of food security focuses on the state of food availability in multiple countries and showing considerable improvements over the second half of the 20th century. Global increases in food production and the volume

of traded food are the most dominant drivers of this observed change. Yet, the effect of trade doesn’t equally apply to all countries. In contrast, least developed countries that are not able to fully participate in international trade remain in a state of food insecurity (Porkka et al., 2013). International trade appears to be necessary for achieving food security today and in the future, expressly, by increasing the global average yield (Kastner et al., 2014) and by providing food to regions with limited water and land resource (Fader et al., 2013; Kummu et al., 2014). Yet, additional changes are required to maintain or improve the state of food security into the future, including increasing productivity (agricultural intensification) and efficiency (reduce waste), as well as changing dietary requirements, such as: reduce animal products’ consumption. (Godfray et al., 2010; Foley et al., 2011).

Animal products, such as beef, poultry, or milk, are a common source of protein nowadays, and their increased consumption is associated with increasing levels of affluence (Godfray et al., 2010; Kastner et al., 2012). Yet, beef consumption is highly inefficient in terms of land, water, and energy. Shepon et al. (2016) calculated feed-to-livestock conversion efficiencies for the U.S. and found very low conversion ratios for beef (3%) and pork (9%), accounting for both caloric and protein fluxes. Eggs are the most efficient livestock product with 13% and 31% conversion ratios relating to caloric and protein fluxes. Beef consumption was also associated with high greenhouse gas emission levels, increased use of reactive nitrogen (Eshel et al., 2014), and biodiversity loss (Machovina et al., 2015). Beef exports between Brazil and Russia are associated with increased GHG emissions (Schierhorn et al., 2016). Brazilian exports of feed crops, such as soybeans, are associated with water scarcity, deforestation, and disruption of the global nitrogen cycle (Lassaletta et al., 2014; Flach et al., 2016; Godar et al., 2016).

There are several options to reduce the negative impacts of animal products’ consumption, such as reduction of livestock products’ intake, replacement of less efficient with more efficient livestock as a protein source (e.g., mono- gastric instead of ruminants), and reintegration of livestock into agricultural

systems to replicate patterns occurring in natural ecosystems (Lassaletta et al., 2014; Machovina et al., 2015). Eshel et al. (2016) point out that nutritional equivalent plant meat-replacements (e.g., soybeans) potentially reduce the environmental impacts of diets. The cropland saved due to prevented food loss (by feed-to-food conversion) can feed approximately 350 million more Americans (Shepon et al., 2018). Other authors estimate that low-beef diets demonstrate reduced greenhouse gas emissions, whereas a vegan diet showed the lowest emissions (Kim, Santo, et al., 2019). Dietary change alone is not sufficient for reducing agricultural, environmental impact; preferably, other supply-side measures should accompany it, such as sustainable intensification or reducing agricultural waste (Godfray et al., 2010; FAO, 2018; Willett et al., 2019). Food provision depends on various ecosystem functions and services, including soil conservation, structure and fertility, water provision, nutrient cycling, pollination, pest control, and genetic biodiversity (Zhang et al., 2007; Power, 2010; Wolff et al., 2017). Besides, environmental impacts from current food production systems include biodiversity threat and species loss, local and remote water scarcity, soil loss, and river and coastal water quality (Garnier et al., 2002; Lenzen et al., 2012; Chaudhary and Kastner, 2016; Flach et al., 2016; Borrelli et al., 2017; Dalin et al., 2017; Desmit et al., 2018). Often, impacts from food production reduce the capacity of the system to produce food in the future. Also, environmental results from crop production vary across crops, regions, and over time. For example, higher species loss due to crop production is associated with traded food crops, particularly those originating from tropical areas, like coffee, cocoa, palm oil, etc. (Chaudhary and Kastner, 2016). In addition, increased fertilizers use Risks River, coastal, or other land systems.

An integrated approach towards food system sustainability links the human diet, health, and environmental sustainability (Willett et al., 2019). This integrated approach is reflected in the global sustainable development goals (SDG), aspiring for achieving zero hunger, good health and wellbeing, and protecting water resources and biodiversity. While trade-off exists within and

between various goals (Pradhan et al., 2017), an integrated approach to food systems can accelerate the process of achieving multiple SDGs (FAO, 2018; Willett et al., 2019). Advancing healthy diets with restricted intake of animal- based protein is required at the demand side, whereas sustainable food production is a supply-side measure. The latter mostly relates to local: plot or landscape-scale sustainable intensification, which mainstream biodiversity and ecosystem protection and protect soils (FAO, 2018). Willett et al. (2019) set five global biophysical boundaries defining a safe operating space for food systems. According to this approach, a sustainable food system can be evaluated as a whole on a worldwide scale. Yet, cross-scale interactions are quite complex, and the location and how land is used is important to reduce trade-offs between different goals; for example: modifying agricultural production in other areas may increase land competition resulting in net increases of environmental impacts (Dalin and Outhwaite, 2019). Therefore, assessing the state of sustainability for different food systems should take a system-wide and multiscale approach acknowledging the implication of local impacts on a global and complex system (Willett et al., 2019).

2.5. Studying food systems sustainability Food systems consist of all elements and activities relating to food production, processing, packaging, distribution, preparation, and intake (Ericksen, 2008; Hammond and Dubé, 2012; Willett et al., 2019). Supply chain analysis is highly important to understand interactions between the food system’s components and to advance effective and sustainable governance of food and resource flows (Godar et al., 2016; Munroe et al., 2019). Yet numerous important insights arise from analyses that only focus on production and consumption (Kastner et al., 2014; Chaudhary and Kastner, 2016; Dalin et al., 2017; Ruiter et al., 2017; Sandström et al., 2017; Willett et al., 2019).

Although food systems are increasingly becoming global, their analysis is often restricted to a local or national scale, undermining the effects of international trade on the environment (Schröter et al., 2018; Dalin and

Outhwaite, 2019). Recent studies have estimated various footprints of local and remote food systems. Table ‎2.1 summarizes an exemplar of the literature on this issue. It covers the use of biophysical and environmental-economic accounting for food system analysis. Over 75% of the papers reviewed have used material flow to map interregional food systems. One report has constructed a physical multi-regional input-output table, which usually is compiled based on economic data (Bruckner et al., 2019). The papers have quantified the flows of crops and embodied resources across regions. Most commonly, nine out of twelve papers have quantified the land footprint. The following are virtual water (three papers) and nitrogen flows (two papers). Environmental-economic accounting was used to link agricultural products with other economic activities (e.g., food production, furniture manufacturing, etc.) or to allocate land use the human activity as a whole (Hubacek and Feng, 2016). Products’ coverage and temporal scope vary between studies, but in general, there is a tradeoff between the scope of the study, number and type of indicators used, and the temporal coverage. A few exceptions were identified (With a dashed border in Table ‎2.1). All three papers go beyond the national scale and refer to specific interregional environmental impacts, rather than just pressures.

Table 2‎ .1: Exemplar of literature on sustainability assessment of food systems with a focus on biophysical methods and economic modeling

Authors Interregional Use of Biophysical Products Geographical Timeframe Comments Flows Material Accounting Covered Focus Flow Resolution

Grote et al. NPK Flows Yes World 21 14 world 1997 NPK content per product (2005) (Macronutrients) average regions Würtenberger Land footprints Yes National 140 Switzerland 2001 The authors further estimate social and et al. (2006) average and four of its ecological utility scores for each trading producing region, to account for partners implications of different scenarios to sustainability Kissinger and Land footprint Yes National 31 Costa Rica 1970 - Rees (2010b) average and its export 2004 destinations Kastner et al. Land footprint Yes National Soy Beans Austria and 2005 Methodological contribution (2011) and Virtual water average four of its trading partners Kastner et al. Land footprint Yes National 49 Global at a 1961 - (2012) average sub- 2007 continental resolution Dalin et al. Virtual water Yes National 8 Global 1986 - The use of a spatially explicit model is (2012) average 2007 restricted by national yield data Weinzettel et Land footprint No Aggregated 8 + 49 non 94 countries 2004 Authors attribute lands use from al. (2013) (incl. water area national agricultural and 17 world primary agricultural production to for fisheries) average sectors regions other economic uses, based on GTAP-

MRIO Yu et al. (2013) Land footprint No Aggregated 8 + 49 non 129 countries 2007 The authors assess the land footprint national agricultural and regions of countries from 57 economic sectors, average sectors incl. Eight agricultural sectors, based on GTAP-MRIO Kastner et al. Land footprint Yes National 130 Global 1986 - (2014) average 2013 Lassaletta et al. Nitrogen flows Yes World 535 143 countries 1986 - Using Nitrogen content per product (2014) average aggregated to 2009 12 regions MacDonald et Land footprint, Yes National 16 - 149 Global 2000 - al. (2015) Virtual water, average 2009 and caloric and monetary flows Chaudhary and Species loss Yes Grid to 130 Global 2011 Detailed crop maps are for the year Kastner (2016) ecoregions 2000; species loss coefficients are scale based on an ecoregions scale Godar et al. Land footprint, Yes Municipal Few Few 2010 - Country and temporal coverage vary (2016) Deforestation level 2014 between crops. Resolutions are very high. Dalin et al. Groundwater Yes Grid to sub- >300 Global 2000; Water use is based on 26 crop (2017) depletion basin scale 2010 categories. Bruckner et al. Land footprint Yes* National 130 Global 1986 - (2019) average 2013

Godar et al. (2015) and Godar et al. (2016) developed a high-resolution biophysical trade model, mapping the flows from sub-national administrative regions to different countries globally. Their model was implemented for a few commodities produced in a few states, mostly in the tropics1. Other papers use the national biophysical trade model in conjunction with grid-scale production statistics to account for specific impacts modeled at different scales, e.g., ecoregions scale for species loss (Chaudhary and Kastner, 2016; Dalin et al., 2017). Notably, most studies mentioned above quantify the flows between countries. In doing so, they ignore spatial differentiation of production systems and environmental state occurring at a sub-national scale. Accounting for environmental pressures (such as resource use) is essential, but to quantifying social and ecological impacts, analyses should focus on a local landscape scale. Although other approaches (e.g., ecosystem services) tend to focus on much more local scales and quantify impacts, they tend to leave out interregional interactions, like consumption through imports. Promoting sustainable food systems is an integral part of the long term sustainability, as demonstrated by various SDGs. However, the global nature of current food systems complicates sustainability policies due to the interregional interaction between human and natural systems. Scientific research of food systems sustainability mostly focuses on the interregional resources’ flows between countries, or local environmental and social impact of production systems. Linking remote consumption with local environmental impacts is crucial for exploring and advancing sustainable, integrative food systems. It can help to promote sustainability policies in an interregional context, reducing interregional tradeoffs. It allows the flow of information on the impacts of production on food consumers, increasing their environmental awareness, and allowing for informed dietary choices. It can also contribute to national ecosystem services and food security assessments by accounting for the utilization and dependence on various remote ecosystems and production systems.

1 See: https://trase.earth/

3. Methods

3.1. Aims and scope Transforming to sustainable food systems in today’s global reality requires a holistic approach to exploring interactions between human and natural systems at multiple scales and across them. Food consumption puts pressure on both local and distant environments. In turn, it modifies their structure and function and changes their capacity to provide invaluable ecosystem services. As a result, it limits the benefits humans derive from ecosystems. These environmental and social vary across spatial scales and between distinct social groups.

Biophysical accounting methods quantify the displacement of resources embodied in the country to country food flows. By focusing on the national scale, these studies oversee sub-national geographic, ecological, and technological variations between production regions. Differently, studies using the ecosystem approach tend to focus on a landscape scale to relate human activities with environmental and social impact. This Ph.D. dissertation presents an integrative approach to the food system’s sustainability. Aimed to explore the interregional dimension of the global food system, this Ph.D. aspires to: (1) identify, document, and analyze the linkages between food consumption in different regions of the world and the ecological sustainability of producing regions. (2) To explore how growing export commodities influence local and global ecosystem services of different agricultural growing regions. (3) to generate a framework for food system sustainability accounting that will contribute to the evaluation of existing interregional connections.

Specifically, this work discusses the following questions: (1) what are the global biophysical flows related to the production of selected key traded agricultural commodities?; and (2) to what extent such flows can be connected to impacts on domestic and global ecosystem services occurring in producing regions?

A modified version of the DPSIR framework that accounts for interregional flows is the theoretical framework of this Ph.D. Applying this framework to food systems sustainability demonstrates how food consumption by specific countries (e.g., country A in Fig ‎3.1), as a driver of environmental change, is linked with environmental pressure

that occurs in local and remote (countries B –D) countries. Acknowledging local properties and the environmental state in specific local and remote agricultural systems (agricultural systems B –D) is a critical step in estimating local, regional, and remote impacts on food security and sustainability. Following the concept of interregional sustainability, environmental degradation in any region (e.g., agricultural system B) may impact any other location (e.g., regional and national C). For example, increased soil loss into the river upstream (Agricultural System B) may reduce soil fertility at the same location, affecting ‘regional and national B’. However, it can also reduce the downstream water quality with negative impacts on ‘regional and national C’. Finally, societies can respond to these changes in implementing policies at a local to interregional scale.

Fig 3‎ .1: A theoretical framework for an integrative approach to interregional sustainability assessment

The two main approaches for food systems’ sustainability fit along this causal chain, complementing each other and allowing bridging over the spatial gap exists between the driver-pressure (international) and state-pressure-impact (local) links. Biophysical accounting fits best to describe driver-pressure links, whereas an ecosystem approach

better describes state-impact links. This link mentioned above between spatial scales and research approaches will form a framework suitable for sustainability assessment of the global food system. It will provide tools to map flows of crops and embodied resources from specific production regions at a sub-national scale. Based on these new tools, this Ph.D. would be able to explore whether environmental footprints (at the national scale) are sufficient proxies for a sustainability assessment. Finally, this work is expected to assess the level of sustainability of the global food systems and the consumption of specific countries.

3.2. The general course of the research Integrating different approaches used for sustainability assessment is the core of this Ph.D. Fig ‎3.2 represents the logic on which this work’s workflow. It splits the dissertation into three working packages (WP), described below.

Fig 3‎ .2: General course of the research

a. WP 1: Global network of biophysical flows

Identifying interregional biophysical flows of crops and embodied resources correspond to the first working package (WP 1). It will focus on flows of 4 main staples (wheat, maize, soybeans, and rice) and embodied cropland and virtual water. These crops form the backbone of the global diet, whether consumed directly as plant material or indirectly due to their use as feedstock (Kastner et al., 2012; FAO, 2013). These crops are also associated with high pressures on land and water resources, and on global biogeochemical cycles, as well as on rainforest integrity (Lassaletta et al., 2014; Flach et al., 2016; Godar et al., 2016; Davis et al., 2018). WP 1 aims to link drivers with local and remote environmental pressures. Applied at a national (flows between countries) scale, it will cover a 27-years period. The adjusted DPSIR framework (See Fig 3‎ .1) demonstrates the need to associate crop production to a specific agricultural system (at a sub-national scale), to link environmental pressure and state. Therefore, the outcomes of WP 1 will be the flows of each crop and embodied resources from existing agricultural systems to each consuming country. Disaggregating the country-to-country flow data requires auxiliary spatial explicit global data on crop production, which is available for 2000, 2005, and 2010. WP 1 will focus only on the years 2005 and 2010. b. WP 2: Indicators of potential environmental impact The aim of WP 2 is to associate crop production to the potential impact on the ecosystem’s capacity to provide services. Applied to agricultural systems at a relatively high spatial resolution (5 arc minute with global coverage), it considers spatial geographic, ecological, and technological variations. As a complementary step to WP 1, this working package advances linkages between local and remote environmental pressures to an environmental state, implying potential ecological impacts. The environmental impacts of food production vary across areas. They may affect future food production capacity (e.g., soil degradation which reduces yield), or human well being at a local scale (e.g., reduced quantities of potable water), or impact on a planetary scale (Zhang et al., 2007; McLaughlin and

Kinzelbach, 2015; FAO, 2018). Dealing with food systems, this Ph.D. aspires to associate consumption with ecosystem services (or disservices) flowing to local and global beneficiaries, as well as to explore the food system’s sustainability of producing regions as a component in the food security of consuming countries. c. WP 3: Food system’s interregional sustainability The sustainability of food systems today depends on the actions and choices of multiple stakeholders spread worldwide. By considering only patterns of consumption and location of production, this working package builds on the achievement of WP 1 -2. It presents an integrated approach to the food system’s sustainability assessment. WP 3 explores the impacts of remote food consumption on different agricultural systems, referred to here as (reduced) ES provision. Quantifying ES provision has been done using indicators, processed based models, and simple lookup tables (Van Oudenhoven et al., 2012; Bagstad et al., 2013; Oudenhoven et al., 2018; Kim, Arnhold, et al., 2019). However, this Ph.D. has no intentions in quantifying the actual ES flows, but to use the potential environmental impact as a proxy value. It will differentiate between beneficiaries of different ES that flow locally, globally, and at an interregional scale.

4. A multi-scale analysis of interregional sustainability: applied to Israel’s food supply

4.1. Disclaimer This chapter is based upon a paper published recently in the journal “Science of the Total Environment”.

Fridman, D. and Kissinger, M. 2019. A multi-scale analysis of interregional sustainability: Applied to Israel’s food supply. Science of the Total Environment, 676(1): 524 -534. DOI: 10.1016/j.scitotenv.2019.04.054.

4.2. Introduction Food security in many countries is already dependent, at least in part, on food production in remote regions, and such dependency is expected to increase (Fader et al., 2013; Kummu et al., 2014). Interregional flows of food crops enhance the global interdependency and result in a state in which the food security of one county depends on the proper functioning of remote agricultural systems. International trade facilitates the virtual flow of resources (e.g., land and water) across regions, and links consumption with remote environmental impacts (Kissinger and Rees, 2010a; Schröter et al., 2018). In recent years a growing body of research has quantified interregional flows of primary food crops and flows of embodied resources, such as land, water, and fertilizers (Dalin et al., 2012; Kastner et al., 2014; Lassaletta et al., 2014). However, most studies to date are limited to the national resolution, i.e., the footprint of one nation on several others. Research at this scale mostly overlooks sub- and supra- national divisions of space that emphasize varied socio-ecological circumstances. Instead, integrating several spatial scales can advance a more comprehensive understanding of the interrelations between consumption and production systems (Liu et al., 2013; Pascual et al., 2017; Schröter et al., 2018). Although current studies advance our understanding of the interrelations and complexity of interregional food systems, only a few have advanced a detailed sub-national analysis of these systems,

exploring different relevant scales (Godar et al., 2015; Chaudhary et al., 2016; Dalin et al., 2017). Acknowledging the advantages and opportunities inherent in multiple scale analysis of the interactions between and within human and natural systems, and recognizing these systems’ interregional nature; the objective of this chapter is to propose a method to analyze interregional food system’s sustainability at multiple scales. It seeks to highlight the importance of analyses of that type by applying it to a case study of Israel’s food supply. Analyzing the flows of crops and croplands to Israel (receiving system) from multiple agricultural regions in different countries demonstrates research opportunities arising from the approach presented by this manuscript.

4.3. Methods The approach proposed in this chapter consists of three phases (see Fig ‎4.1 A-C). First, crop flows were allocated back to producing countries following Kastner et al. (2011) and Kastner et al. (2014). Second, national crop flows were rescaled to a five arc minute resolution (roughly 10 km at the equator) and converted to cropland footprint, using the spatial production allocation model (SPAM; You et al., 2014). In addition, flows of kilo-calories were calculated based on data from FAO (FAO, 2001). Finally, the cropland footprint of Israel was regionalized using different spatial- thematic units (scales), advancing a multi-scale analysis of the interregional sustainability of Israel’s food supply. The following paragraphs present the underlying datasets and the procedures used in order to advance this analysis.

Fig 4‎ .1: Approaches to advance an interregional sustainability analysis of food supply.

During the first phase (Fig ‎4.1A), a national account of biophysical flows to Israel was calculated following the method introduced by Kastner et al. (2011) and Kastner et al. (2014). It reallocates crop traders internationally, so crops consumed in specific countries are associated with the countries in which they were grown. This procedure presumes that a significant share of the environmental impact derived from food production occurs at the cultivation phase. In order to do so, the method applies input-output mathematics to a bi-lateral trade matrix of a crop’s primary equivalents, assuming that a country’s domestic supply and imported supply contribute proportional shares to its exports. In this chapter, the method was applied for 136 FAO crops (and 128 processed products; FAO 2015). Feedstock includes both trades of feeds and of feeds embodied in 6 livestock categories (and 64 processed livestock products) supplied to Israel. All processed products and livestock categories were converted to primary crop equivalents (see Table ‎10.1 and Table ‎10.2) following Kastner et al. (2014). Livestock

production was translated into total feed use based on relative feed requirement keys (see Table ‎10.3). Total feed use was then split into different crops based on the national feed composition data, as derived from FAO commodity balance (FAO, 2015). Similar to Kastner et al. (2011) and in order to prevent double-counting, any processed product was converted to a single primary crop equivalent based on its calorific value. Data were retrieved from FAOSTAT and from FAO commodity balances (FAO, 2015). Data on nutritional values of crops and derived products were retrieved from FAO (2001). As a second phase (Fig ‎4.1B), estimated crops’ flows to Israel from different countries are disaggregated using a spatial model of the global agricultural system given at a five arc minute resolution. The spatial production and allocation model (You et al., 2014), used for that purpose, is a set of plausible maps describing the spatial distribution of crops, through a process of disaggregation and compilation of relevant spatial explicit data, such as production statistics, land use data, biophysical “suitability”, accessibility, etc. (You et al., 2009). It includes indicators that describe production and system management (e.g., irrigated, high and low input rain-fed, and subsistence) of 42 crops and crop groups in 2000, 2005, and 2010. Data is provided in a tabular format in which each row can be allocated to a specific five arc minute (~10 km at the equator) grid cell using a standard global grid database (Harvest Choice, 2010).2 The procedure uses four grids SPAM as inputs per crop (or crops’ group) and an additional categorical grid of countries (see Table ‎4.1) in order to produce global maps of crop supply and cropland footprint at the above mentioned spatial resolution. It splits national flows of a certain crop to its specific sub-national production areas, assuming that exports to a specific country from the exported portion produced in each grid cell are proportional to the share of the same crop’s exports to this country at the national scale. The exported portion produced in a grid cell is defined as non- subsistence production since subsistence is, by definition aimed for local-personal

2 HCID was implemented as an R function and can be accessed via GitHub: https://gist.github.com/dof1985/cd48ab938780e4642c0b57e2741279fc

use (You et al., 2009). The following section will provide the equations used to estimate local consumption and imports for each country.

Table 4‎ .1: Input SPAM data for disaggregating national crop flows

Indicator units technology notation

Quantity produced Metric tones Total RT

Quantity produced Metric tones Subsistence RS

Harvested land Hectares Total HT

Harvested land Hectares Subsistence HS

Countries mask Categorical - C

Local consumption Let the flow of product i produced and consumed in a country s (C = s) and be

denoted as . In addition, let the national production of product i in country s be

denoted as . Using SPAM total production grid (denoted as RT, and given in tons), the local supply of product i to country s from each grid cell l in country s, can be expressed as in Eq. ‎4.1.

Eq. 4‎ .1: Allocating local consumption to a grid-scale

Assuming a total yield grid (YT, given in tons per hectare), harvested land embodied in the local consumption of crop i can be approximated using Eq. ‎4.2.

Eq. 4‎ .2: Estimating harvested land using yields and production quantity

Yield is defined as in Eq. ‎4.3.

Eq. 4‎ .3: Calculating yield grid from input data

Combining Eq. ‎4.2 and Eq. ‎4.3 results with Eq. ‎4.4, showing local land use embodied in the supply of crop i is:

Eq. 4‎ .4: Final equation for calculating local cropland embodied in the supply of a specific crop

Consumption from imports Let the flow of product i produced in county r (C = r) and imported to country s (C =

s) be denoted as . In addition, let the national exports production of product i in

country r be denoted as . For any country r ≠ s, the export production grid can be derived from Eq. ‎4.5.

Eq. 4‎ .5: National export production grid in a specific country

Similarly, equation Eq. ‎4.2 -Eq. ‎4.4 are re-defined in Eq. ‎4.6 -Eq. ‎4.8, respectively.

Eq. 4‎ .6: Estimating harvested land for exports using yields and production quantity

Eq. 4‎ .7: Calculating yield grid for exports from input data

Eq. 4‎ .8: Final equation for calculating exported cropland embodied in the supply of a specific crop

Global consumption grids of cropland flows and crop flow into Israel were constructed using this set of equations for 136 primary crops. In addition, calories’ flow was estimated by multiplying each crop flow with its calorific content (FAO, 2001). Crops were aggregated into five FAO crop groups: cereals, oil crops, fruit and vegetables, roots and tubers, pulses, and spices and stimulants (for details, see Table ‎10.4).

Finally, as demonstrated in Fig ‎4.1C, grids of Israel’s global cropland footprint were regionalized using different spatial units in order to advance an interregional sustainability analysis at multiple spatial scales. In order to construct and harmonize regionalization datasets, we have followed a SPAM’s procedure; we have produced a five arc-minute grid from each regionalization dataset. First, we converted each vector dataset to a 30 arc seconds (1 km grid) raster. Second, we aggregated each raster to a five arc-minute resolution applying the mode as a statistic. For nested datasets (e.g., HyroSHEDS, Olson ecoregions, etc.), we have applied this procedure to the unit at the lowest scale (highest resolution). The outcome of this process was a table holding information of each grid cell spatial id, as in the standard global grid database, and its regional id, derived from the regionalization dataset (e.g., ecoregions, biomes, etc.). Finally, we have grouped tables by a common division (.g. Biome), and aggregated crop flows, calorie flows, and cropland footprints for each regional unit. Following the regionalization process, major sending systems were identified, delineated, and then characterized based on existing literature, as partially shown in Table ‎4.2. The datasets used for regionalization describe both environmental and human phenomena and are briefly introduced next. Biomes and ecoregions are bio-geographic units on the continental-global and transnational/regional scales, respectively (Olson et al., 2001). Biomes are major types of ecological associations, describing global patterns of ecosystem form, process, and biodiversity. They are classified according to their predominant vegetation type, like rainforest or grassland (Campbell and Reece, 2005; Ellis and Ramankutty, 2008). Ecoregions are ecological units nested within biomes, characterized by unique and homogenous species assemblages. Unlike biomes, the delineation of ecoregions accounts for several local factors, including geological history, distribution of ecological communities, and species endemism (Olson et al., 2001). Ecoregions were used in conjunction with data from Chaudhary and Brooks (2017) to assess Israel’s regional species loss impact. They also served as a platform for setting global conservation priorities, such as Global 200, based upon their biodiversity intensity, the density of endemic species, and global habitat rarity (Olson and Dinerstein, 2002). Small-scale river catchments are nested within watersheds of major rivers and were used as another environmental scale that is highly relevant for analyzing water

quantity and quality (Garnier et al., 2002; Gabellone et al., 2005; Desmit et al., 2018). Small-scale river catchment and basins were represented by level 7 sub-basins from the HydroSHEDS dataset (Lehner and Grill, 2013). Finally, first-level administrative units (GADM, 2015) were used as sub-national social/policy-oriented spatial units.

Table 4‎ .2: Environmental and social processes analyzed at various spatial and organizational scales

Spatial/Organizational scale Selected issues analyzed at this scale Political/national (countries)  Role of trade in global phenomena (Lassaletta et al., 2014)  Role of trade in the efficiency of global/national food systems (Dalin et al., 2012; Kastner et al., 2014) Environmental  Coupled with global ecosystem (ES) assessments continental/global (biomes) (Costanza et al., 1997; Turner et al., 2007; Naidoo et al., 2008), it is possible to evaluate ES loss due to land use. Environmental  Species loss and biodiversity effect (Chaudhary and transnational/regional Brooks, 2017) (Olson’s ecoregions)  Impact on conservation efforts (Olson and Dinerstein, 2002) Environmental  Land-river-ocean continuum (Desmit et al., 2018) local/landscape to trans-  River nutrient loads and coastal habitat loss (Garnier et national (river catchment and al., 2002) sub-basins)  Drinking water quality (Costa et al., 2002) Political sub-national (first-  Environmental regulation and policy implementation level administrative units) (Mostert, 2003)  Socio-demographic and economic drivers for agricultural land-use change (Schierhorn et al., 2014; Meyfroidt et al., 2016)

4.4. Results Flows at a national scale – Fig ‎4.2 presents Israel’s food supply (by the quantity and per capita calories) and its cropland footprint by country and crop group (see Table ‎10.4 for details of the crop groups and Table ‎10.5 for country codes). Overall, Israel’s annual food supply is estimated at more than 8.7 million tons of crops, of which over 60% is imported. In terms of cropland footprint and calorie provision, Israel’s dependence on imports becomes more obvious. Israel’s annual cropland footprint totals close to 19,000 km2, providing a total of 6,635 kilo-calories per capita (Table ‎10.6). From the perspective of cropland and calorie supply, slightly more than 85% is embodied in imported crops, largely because most of the cropland-intensive and calorie-rich cereal and oil crops are imported.

Fig 4‎ .2: Food supply, cropland footprint, and calorie per capita by country of origin and crop group. The figure represents 99% of Israel’s food supply and 98% of is cropland footprint

Flows at the biome and ecoregions scales – Fig ‎4.3 presents a global analysis of Israel’s imported cropland footprint from different biomes, classified by conservation priority (i.e., inclusion in the Global 200 conservation scheme). Three biomes host almost 90% of Israel’s imported cropland footprint (and 85% of its total; Table ‎10.7). Temperate grasslands (6,656 km2) and temperate broadleaf forests (4,728 km2) account for almost 70% of the imported cropland footprint, followed by the tropical moist broadleaf forests (2,042 km2; 12%). Relative to other biomes from which Israel imports agricultural commodities, cropland footprint in tropical biomes puts more pressure on those areas prioritizing conservation.

Fig ‎4.3: Israel’s imported cropland footprint from different biomes, according to conservation status and conservation priority

Almost 75% of Israel’s imported cropland footprint originates from 3 different sending systems: the U.S., the Eurasian system, and the South American systems. Some examples in this analysis focus on the two latter systems in order to demonstrate the benefits of specific scales. Table ‎4.3 provides a short description of each system.

Table 4‎ .3: General description for selected sending systems

Sending Geographical Countries Biomes Main rivers system extent

Eurasian 40° – 55° N, Russia, Temperate grasslands Danube, Dnister, 27° – 37° E Ukraine and broadleaf forests Pivennyi Buh, Dnieper, Volga, Don South 15° – 40° S, Argentina, Tropical grasslands Amazon, Paraná, 45° – 65° W Brazil, and moist broadleaf Uruguay American Paraguay forests

Fig ‎4.4 presents species loss and cropland footprint at the scale of ecoregions, focusing on the Eurasian and South American sending systems (results for all other ecoregions are available in Table ‎10.8). These two major systems (as defined by their cropland footprint) account for over 17% of regional species loss (or 3.65 species lost) due to Israel crops’ imports. On average, regional species loss embodied in Israel’s cropland footprint is 0.034 ±0.145 species per ecoregion annually, and the average cropland footprint per ecoregion is 26.52 ±162.4 km2. The Pontic Steppe ecoregion, located in the Eurasian sending system, demonstrates relatively high species loss due to imports and the highest cropland footprint. Ecoregions in the South American system also have high species loss results (the highest being in the Alto Paraná ecoregion), despite relatively small cropland footprints. Some relevant ecoregions, including eastern Guinean forests (West Africa) and Ethiopian montane grasslands, are outside the selected sending systems. The cropland footprint in these ecoregions is relatively small (ranked in the 13th and 15th places, respectively), compared to their relatively high impact on species loss (ranked in the 3rd and 5th places, respectively). Imports from these regions provide food crops like Cocoa (99% of the imports from the eastern Guinean forests) and sesame (92% of the imports from the Ethiopian montane grasslands).

Fig 4.4‎ : Cropland footprint of Israel's food supply in ecoregions in South America and Eurasia, and regional species loss impacts.

Flows at a river catchment and a sub-basin scale – Fig ‎4.5 illustrates the cropland footprint due to imports on the river catchment scale in the two main sending systems (see Table ‎10.9). Watersheds in the Eurasian system account for 30% (5,027 km2) of Israel’s imported cropland footprint, and those in the South American account for 15% (2,453 km2). The Dnieper watershed is dominant in the Eurasian system, spreading across the Ukraine, Russia, and Belarus. The cropland footprint in this watershed is 1,617 km2 (10%), mostly in the downstream sub-basins located in Ukraine. The Don River is the second-largest footprint in this region (1,163 km2, 7%), which is split, at the sub-basin level, between the Ukraine and Russia. As in the case of the Dnieper, the observed footprint in the Don watershed is larger downstream. The Black Sea is the drainage basin for most of the watersheds in the Eurasian basin (the Volga River being an exception) with an aggregated footprint of 5,435 km2 (33%).

The South American system consists of three river systems: Paraná, Amazon, and Uruguay, from which oil crops and cereals are the main crops being imported by Israel. Other crops (e.g., pulses, roots, stimulants and sugar crops, etc.) constitute around 7%-8% of the cropland footprint in the Paraná and the Amazon watersheds, respectively. The Paraná River watershed located in northern Argentina, Paraguay, and southern Brazil, is the dominant watershed in the South American system, in terms of the imported cropland footprint, accounting for nearly 12% (2,072 km2).

Fig 4‎ .5: Cropland footprint due to Israel's food supply in major river systems in South America and Eurasia, at the watershed and sub-basin scales.

Flows on the scale of sub-national administrative units – Fig ‎4.6 illustrates the average yields of different sub-national administrative spatial units along with national scale cropland footprint (for cereal and oil crops only). The sub-national administrative units presented in the figure are those that contribute at least one percent to the total supply (in terms of weight; see also Table ‎10.10). Variations in

yields are observed in sub-national administrative units within the same country; for example, between Rostov (2.18 ton ha-1) region (oblast) and Stavropol (3.13 ton ha-1) territory (kraya) in Russia. In general, this region is characterized by higher yield gaps than the South American sending system (e.g., yields vary between 5.17 -5.71 ton ha-1 in Argentinean provinces).

Fig 4‎ .6: Cropland footprint due to Israel's food supply in selected countries in South America and Eurasia, for cereal and oil crop yields at a scale of level one administrative unit. Yields are mapped for regions that contribute 1% or more to the total supply (in tons) of cereals and oil crops imported to Israel.

Table ‎4.4 summarizes the key environmental pressures and implications, along with implications for interregional sustainability, for all five spatial scales analyzed scales in this study.

Table 4‎ .4: Selected environmental pressures and environmental and interregional implications of Israel’s cropland footprint

Spatial/ Environmental pressures Environmental Implications for organizational implications interregional scale sustainability Political Five sending regions Shifting imports towards - national (Ukraine, Russia, U.S., sending regions with higher (Countries) Argentina, and Brazil) yields (e.g., from Ukraine to provide over 60% of Israel’s the U.S.) or closing yield cropland footprint gaps might reduce Israel’s Approximately 71% of cropland footprint. Israel’s cereal, and oil crops supply can be traced back to these five countries. Environmental Israel’s crop imports are Reliance on grasslands can Dependence on tropical continental / heavily reliant on croplands be interpreted as both an regions, and in particular on global (Biomes) in grasslands worldwide. In impact on and an areas that are priorities for addition, a significant part of opportunity to increase conservation, may risk the imports originates in climate regulation services Israel’s future food tropical biomes that are (carbon sequestration). security, e.g. if priorities for conservation. conservation schemes cause a reduction of arable land or yields. Environmental Israel’s cropland footprint in These two major sending South American ecoregions transnational / the Eurasian and South systems (defined based on demonstrate high species regional American sending systems is their cropland footprint) loss per m2 of fields, which (Olson’s concentrated in five account for over 17% of implies that biodiversity ecoregions) ecoregions. regional species loss due to conservation there may Israel crops’ imports. have a lower impact on Israel’s food security than similar programs in the Eurasian system. Environmental Israel’s cropland footprint Agriculture downstream in A potential trade-off exists local / landscape occurs downstream some of the Black Sea basin may between the food-security to trans-national the major rivers in the Black cause coastal habitat interests of dependent (River Sea watershed, with degradation, reducing its countries, such as Israel catchment and potential effects on the capability to provide (e.g., intensification) and sub-basins) coastal environment. ecosystem services, those of local communities negatively affecting local (e.g., healthy, functioning communities’ quality of life. ecosystems). Political sub- Yields vary to some extent - Political, economic, and national (First between subnational social conditions at a local, level administrative units, which rather than national scale, administrative are major agricultural have to be considered when units) production areas. Higher attempting to promote yield gaps exist in European agricultural expansion or Russia and Ukraine than in intensification and may the South American sending have an effect on Israel’s system. food security.

4.5. Discussion To date, national scale analyses of food systems' sustainability usually rely on trade and production data. An analysis of this scale can be used to quantify the extent to which a country’s food supply depends on imports from other countries. It can also

identify opportunities to reduce a country’s cropland footprint, for example: by shifting imports from countries with low yields (e.g., Ukraine in the case of Israel) to countries with high yields. Alternatively, it may suggest closing yield gaps as a strategy to reduce environmental pressures and secure food sources. However, the national scale is still quite limited for answering questions, such as: to what extent can yield gaps be closed? Which policies should be promoted in order to do so? What are the potential environmental and social impacts of such policies? Understanding interregional sustainability and remote environmental or social impacts of food consumption require a more detailed analysis of food systems. The sub- and supra-national scales used in this study are appropriate for identifying multiple different impacts of consumption, such as potential impacts on ecosystem services (biomes), biodiversity (ecoregions), and water quality (watersheds), and are believed to be more suitable for describing and analyzing the interactions occurring between sending and receiving systems. The scale of analysis should be determined by its goals, yet there are some benefits to mapping crop flows at multiple scales, following the approach presented and illustrated in this paper. In this article, which focuses on the case of Israel, two major sending systems were identified based on the cropland footprint as estimated at a national scale. Delineating sending systems on the basis of this scale and indicator excludes some important ecoregions in which Israel’s food supply triggers high biodiversity impact, mainly in West Africa and Ethiopia. Hence, a multiple scale analysis may be used for identifying relevant sending systems. Second, it may help to refine some general questions, such as: what are the environmental impacts associated with a nation’s food supply? The results of this analysis suggest a few relevant research directions, including impact on biodiversity in tropical ecoregions (mainly in South America), and potential effects on river and coastal water quality in the Black Sea basin. Third, it allows mapping opportunities for promoting interregional policy for sustainable intensification. Such an opportunity exists in temperate grasslands, which are both a major source of cereals and oil crops, and potentially a major carbon sink (Schierhorn et al., 2013; Meyfroidt et al., 2016). The current approach could also be used to unveil potential risks for a country’s food security. As seen on the biome and ecoregion scales, actual or potential conservation

schemes in tropical sending systems may negatively affect Israel’s food security by reducing food production in sending systems (Olson and Dinerstein, 2002; Di Bitetti et al., 2003). Analyses like those suggested here, which are conducted on multiple scales, are essential for addressing a variety of questions. The scale of terrestrial biomes (and ecoregions) can provide some rough estimates for the environmental impact of Israel’s food supply. Israel’s reliance on grasslands, for example, indicates an impact on climate regulation or an opportunity to increase carbon sequestration through interregional landscape planning and management. Although somewhat useful, the biome scale is often too coarse. Instead, the ecoregion scale can account for local geographical attributes, allowing discussion of conservation priorities, quantification of biodiversity impact, and consideration of the potential impact on specific animal and plant species. The river-catchment scale can be used to identify rivers, lakes, and marine environments at risk due to the potential impact of agricultural intensification on both water quantity and quality. Like environmental characteristics, social characteristics also vary on different sub-national scales. Demographic, economic, and technological factors, as well as the perspectives of households and businesses, change across space, facilitating land use and management, and the way it interacts with natural systems. Trade-offs analysis is another advantage of multi-scale studies. For example, there are plausible trade-offs between interregional food supply and coastal and river habitat quality (Eurasian region) or local potable water quality (South American region). Intensification in the Eurasian region can increase wheat production significantly even under current rain-fed conditions (Schierhorn et al., 2014), and is considered a required step in order to make the region’s agricultural output competitive in global markets (BSEC, 2017). However, intensification in some croplands in the Eurasian region may increase the risk of eutrophication of rivers and the coastal environment of the Black Sea (Garnier et al., 2002). By being remotely dependent on this region for its food security, Israel is another stakeholder with regard to both environmental and agricultural policies. While irresponsible intensification may result in short-term benefits for both local farmers and remote consumers, it might also drive slow processes of environmental deterioration,

gradually increasing environmental, social, and economic costs. Importing countries are, to some extent, dependent on these regions and also partly responsible for the environmental and social impacts associated with agricultural activity. This interdependence between producers and consumers in different regions of the world supports the argument for international cooperation to promote sustainable and just agriculture. The case study of Israel's food system demonstrates multiple opportunities that lie within the multiscale analysis of interregional food systems. It further emphasizes the importance of the method presented in this study, which is relevant for the food system sustainability of any country today. The mapping approach presented in this article has two advantages over other published food systems' sustainability detailed analyses. It enables a global and relatively complete analysis of a country’s food supply, as opposed to other approaches which are currently limited to a few crops and a few production regions. In addition, by estimating and presenting the cropland footprint at different scales, it maintains complexities, such as trans-border environmental conflicts and cross-scale interactions, which could have been obscured otherwise. The analysis presented in this manuscript has a few limitations. First, the case study presents data for the year 2005 due to SPAM's limited temporal coverage. However, a recent publication of SPAM 2010 enables a time series analysis covering the years 2000, 2005, and 2010. Second, since data on the destination of crops produced in each region (or grid cell) is missing, a simplifying assumption had to be made, according to which the distribution of exports from each grid cell is proportional to the national scale distribution. Nevertheless, the approach presented here partially discriminates between the production of locally consumed crops to produce exports. In this analysis, the sending systems were first identified (e.g., based on pressure indicators), and only then environmental and social impacts were characterized and described (based on a literature review). While the shortcomings of this scoping method have been discussed previously, it was mainly a result of insufficient data. Global datasets or models that could be used as input data for such analyses (similar to the quantification of species loss impact) would allow to better link consumption to impacts, thereby providing valuable and reliable insights for policymakers. Due to

the interdisciplinary nature of this analysis, producing such datasets may require the work of several groups and researchers. Future research could focus on compiling global datasets for applying more advanced methods than a literature review.

4.6. Conclusions As the world’s food system becomes increasingly global, it is crucial to understand the implications of reliance and impact on remote regions. Though multi-scale analysis is widespread in a variety of disciplines, it is yet almost completely absent from studies of food systems. This chapter fills this gap by providing an analytical tool with wide geographical and crop coverage, suitable for advancing an interregional multi-scale analysis of national food systems. Multi-scale analyses of interregional food systems can provide an overview of a food system's environmental impacts and of its dependency, identify otherwise hidden hotspots of environmental impact, and recognize interregional shared interests and trade-offs. This manuscript proposes a method for food system multi-scale analysis. Its global scope and high crop resolution make it useful for national assessments of food security and of food-related environmental impacts. It helps to focus an assessment on geographical hotspots of dependence/impact and on specific and relevant environmental indicators. Furthermore, if data is available, this approach allows the quantification of some environmental impacts on a global scale. Although applied in this manuscript to the case study of Israel, the method can be used for national assessments of any other countries' food security and food system sustainability. In order to advance further the approach presented here, future studies will have to incorporate global consistent datasets and models, which will further improve such multi-scale analysis of interregional sustainability.

5. An integrated biophysical and ecosystem approach as a base for ecosystem services analysis across regions.

5.1. Disclaimer This chapter is based upon a paper published in the journal "Ecosystem Services".

Fridman, D. and Kissinger, M. 2018. An integrated biophysical and ecosystem approach as a base for ecosystem services analysis across regions. Ecosystem Services, 31B: 242 -254. DOI: 10.1016/j.ecoser.2018.01.005.

5.2. Introduction The world's agricultural systems mostly benefits humans through provisioning ecosystem services (i.e. food production), yet they may be negatively affected by flows of ecosystem dis-services (or environmental disruptions), and may produce such dis-services that affect other ecosystems, as well. For example: intensive cropping systems may increase land degradation and reduce soil fertility due to soil erosion (Zhang et al., 2007). International trade of agricultural commodities shifts dependence and environmental pressures from regions of consumption to remote agricultural systems.

Various studies have advanced biophysical accounts of resources and material embedded in international trade of food crops and related processed products. These indicators mostly stand for environmental pressure (Kastner et al., 2014; Mekonnen and Hoekstra, 2014; Lassaletta et al., 2016; Dalin et al., 2017). An ecosystem approach on the other hand focuses on local environmental properties, structures and functions, including for example analysis of habitat change and loss of biodiversity (Chaudhary et al., 2016) or soil degradation (Nachtergaele et al., 2010). Integrating biophysical accounting with an ecosystem approach can help relating the flow of provisioning services from one region to another to ecosystem dis-services generated in growing regions, and to the sustainability of both producing and consuming regions. To date only very limited numbers of studies have attempted to integrate both approaches (Würtenberger et al., 2006; Kissinger and Rees, 2009, 2010b; Chaudhary and Kastner, 2016; Dalin et al., 2017).

This chapter contributes to this effort by linking global provision to Israel of rice, maize, soybeans and wheat with ecosystem dis-services from agricultural producing regions at a resolution of a 5 arc-minute (~10 KM around the equator). It characterizes both agricultural and environmental systems in production regions, and integrates them to describe different classes of biophysical pressures and potential dis-services from agriculture. Each class stands as a 'functional region' in which either a trade-off or a synergy exists between agricultural efficiency and environmental impact.

5.3. Methods The primary objective of this chapter is to present an initial step of the integration of a global biophysical assessment of food crops (provisioning services) with its related local environmental impacts and ecosystem dis-services. To do so, it presents an analytical framework that integrates the agricultural and environmental systems into a coupled indicator system (Fig ‎5.1). The agricultural system measures environmental pressures posed by crop production, and plays a key role in linking these pressures to remote consumers. The environmental system uses environmental state indicators to indicate on how different pressures potentially affect the environment in different locations. For example, land used to provide a fixed quantity of wheat (measured by wheat yield) may be situated in an area with either high or low tendency for soil loss. This coupled indicator system balances human derived pressures against nature's capacity to function under pressures, and can be used to characterize production regions as unique areas with different functionalities, which are referred here as "functional regions".

Fig 5‎ .1: A framework to integrate agricultural and environmental systems.

This chapter presents the identification and demarcation of such functional regions, and the application of this concept to a case study of Israel's national supply of 4 main staples: wheat, maize, rice and soybeans. The process consists of two main stages: (a) Integrating a global agricultural dataset with a few ecological datasets to produce crop-specific functional regions map; (b) Conducting a sub-national assessment of biophysical flows to Israel and estimating flows from each functional region.

Producing crop-specific functional regions map. A functional region is a spatial-explicit production class defined by its relative agricultural performance and environmental state measured by different indicators. Here the agricultural system is described by two spatial explicit indicators for each crop: yield and water intensity. Two additional indicators are used to describe the environmental system and its capacity to withstand against human derived pressure. First, potential soil loss was based on a global USLE (universal soil loss equation) model (Nachtergaele et al., 2010) with some adjustments to the cover crop factor (c- factor). It was coupled with the indicator describing crop-specific yields. Second, the aridity index (Zomer et al., 2008) was used as an indicator for water availability and was coupled with the water intensity indicator. Table ‎5.1 presents all data sources and

comments on data processing. The spatial resolution of all datasets was at least 5 arc minute.

Table 5‎ .1: Data sources and data manipulations

System Indicator Dataset, year and source Comments

Agricultural Yield (ton / km2) Spatial production and allocation Dataset includes different crop- model (SPAM) for 2005 (You et specific production indicators, al., 2014). including yield, quantity, harvested and physical land. Water intensity (m3 Crop-specific blue water footprint Dataset also includes green and / ton) global grids averaged for 1996- grey water footprint. Currently, the 2005 (Mekonnen and Hoekstra, blue water footprint was used as it 2014) is also available as potable water. Note that water intensity was later converted to water efficiency. Environmental Potential soil loss Global USLE model was based on The model's c-factor was replaced (ton soil / ton crop) data from FAO GLADIS database. by crop specific coefficients taken Climate data was averaged for from Panagos, Borrelli, the years 1980-2000 Meusburger, et al. (2015). It was (Nachtergaele et al., 2010). possible since USLE was applied to growing regions of each crop separately. Aridity index Global aridity index from CGIAR- Both precipitation (P) and potential classes CSI was used. It uses annual evapo-transpiration (PET) datasets averages of climate data for the were resampled from a 30'' to a 5' years 1950-2000 (Zomer et al., spatial resolution. Then aridity 2008). index was calculated as the ratio P / PET. Finally, it was classified to 3 aridity classes: <= 0.2: Hyper arid & arid 0.2-0.5: Semi-arid >= 0.5: Sub-humid & humid

Each dataset, except from the aridity index, was split into two classes: "Low" and "High" relative to the mean value. Prior to this stage, all data points in the upper 0.05 % percentile were removed as outliers. A combination of these ordinal classes

resulted in two categorical representations of the agricultural and environmental systems; the agricultural system had 4 classes, for example: "HH" symbolizes a class in which both yields and water intensity are higher than average. The environmental systems had 6 classes, such as: "ArL" or "HuH" that indicate on arid region with potential soil loss lower than average, and a humid region with soil loss potential being higher than average. As mentioned before, the aridity index indicates on water availability which is lowest in arid areas and highest in humid regions (a description of every functional region is provided in Table ‎10.11).

Finally, the two systems were integrated resulting in 24 spatial explicit classes that represent different functional regions. Each region was named using the codes of its components from each system. Namely, the code "SeHLH" stand for a semi-arid functional region, with potential soil loss being higher than average, low yields and high water efficiency. Note that the last code of each functional region indicates the water efficiency, which is the inverse of water intensity. Namely, higher water efficiency means that a constant volume of irrigation water (blue water) result in higher crop provision.

Allocating national supply of crops to different functional regions In order to link consumption in any country to spatial explicit environmental indicators, a sub-national accounting of interregional biophysical flows was used. It is demonstrated on a case study of Israel's national supply of wheat, maize, soybeans and rice, but can be extended to include any crop out of the 136 FAO crops and can be equally applied to any country or world region.

A national account of biophysical flows to Israel was calculated following the method introduced by Kastner et al. (2011) and Kastner et al. (2014). This method traces countries apparent consumption of crops back to producing countries. It applies input- output mathematics to a bi-lateral trade matrix of a crop's primary equivalents, and assumes that a country's domestic supply and imported supply contribute proportional shares to its exports. Additional details on the reproduction of these methods are found in chapter ‎4.3. As a second stage, estimated crops flows to Israel from different nations are disaggregated using a spatial production model of the

global agricultural system given at a 5 arc minute resolution. The disaggregation process is described extensively in chapter ‎4.3. Flows of crops into Israel at a 5 arc minute spatial resolution were then converted into a boolean mask, in which grid cells that export an quantity to Israel are assigned as 1, and 0 otherwise. Finally, simple multiplication between the functional regions grid and the boolean mask filters out all grid cells that are not exporting to Israel. All GIS operations were performed in R (R Core Team, 2016) using the 'raster', 'sp', and 'rgeos' packages (Bivand et al., 2013; Hijmans, 2016; Bivand and Rundel, 2017). Maps and figures were produced using QGIS (QGIS Development Team, 2017).

5.4. Results The 4 major agricultural staples included in this research (wheat, maize, soybeans and rice) are grown all over the world and are a major source of calorific intake either directly as food products and indirectly as a feed for livestock. However, an integration of bio-physical and socio-economic (i.e. technology) circumstances suggests that different regions growing those crops are generating different levels of environmental pressure with different implications for ecosystem services. Fig ‎5.2 divides those agricultural staples growing lands into 4 agricultural regions based on the integration of yields and water intensity, and 6 environmental regions divided into three levels of aridity integrated with potential soil loss. The integration of all of the above categories generated 24 'functional regions' growing the key agricultural staples analyzed in this study. Such analysis reveals for example, that while in general the United States' mid- west (a key agricultural area) is characterized by high yields; its water intensity differs regionally. Although the mid-west's eastern region shows low water intensity, its western parts (mostly Kansas and Nebraska) present high water intensity.

Regions with the highest yields of maize includes: North America, South Brazil and North Argentina, Central Europe, and the Mediterranean (see Fig ‎5.2). These patterns however are crop dependent, and in the case of rice, Equatorial Africa and India also present high yield.

Fig 5‎ .2: A functional region typology relies on global available datasets to define unique classes within the agricultural and the environmental systems; an illustration of the concept using the global maize production system.

Focusing on the state of Israel as the case analyzed here reveals that the studied staples accounted for approximately 45 % of the state overall supply of agricultural crops (by weight). Similarly, they have been responsible for 61 % of the total daily intake of calories. More than 90 % of that supply originates from 8 types of functional regions (see Table ‎5.2), which are mostly dominated by wheat or by maize production. About 40 % of the total supply originates from a humid functional region, with high potential soil loss, but with high agricultural efficiency (HuHHH). Instances of this region are situated in the mid-west of the United States, the R‎‎ío de la Plata region of Argentina, and in Eastern Europe (see Fig ‎5.2). This region presents a trade-off between high yields and potential soil loss, as its 3,277 cultivated square KM potentially suffer staggering figure of 1,500 million tons of annual potential soil erosion. Unlike this region, the best suited one (HuLHH: humid; low potential soil loss; high biophysical efficiency) uses 3,110 square KM of agricultural land to grow crops imported to Israel, yet its potential soil loss is slightly less than 50 million tons a year. Instances of this region are distributed differently across crops. Maize best-suited

regions are distributed between different countries globally, while soybeans are more spatially restricted. However, both form a significant best-suited region in South Brazil. The latter also comprises best-suited region for soybeans. Wheat is best-suited in vast areas of Western Europe and the U.S. Finally, best-suited regions supplying rice to Israel are found mostly in the Ukraine but provide only a negligible portion of rice. Best-suited regions demonstrate a synergy between low potential impacts from agriculture to high crop provision. While it is situated in a relatively water rich region with a low potential soil loss, its agricultural practices induce high yields and relatively little blue water use. This best-suited region provides approximately a quarter of Israel's total supply. Other regions provide less than 10 % each and vary in both environmental and agricultural conditions.

Table ‎5.2: Crop provision to Israel and its related environmental pressures and ecosystem dis-services for the top 8 functional regions.

Fig ‎5.3 also shows the spatial and functional variation in contribution of different regions across different crops. For example, wheat's production is the most

widespread globally, followed by maize, soybeans and finally by rice. The latter two are mostly restricted to North and to South America, and to India and to South-East Asia respectively. Similar to its widespread production distribution, more than 80 % of Israel's wheat supply is split between 6 different functional regions. Most of the rice, maize and soybeans production are split between 5, 4 and 2 functional regions, respectively. In addition, some geographical locations may function differently for different crops. For example, the U.S state of Iowa is a marginal region regarding its suitability for wheat crops and it is classified as a "HuLLH" functional region (i.e. with low yields). Unlike it, if maize crops are considered, the same location split between two functional regions: "HuLHH" and "HuHHH". The first is a best suited region for maize, while the latter trades off high yields with high potential soil loss.

Fig 5‎ .3: Top supplying functional regions for 4 main staple crops. Each region provides at least 80% of that crop's supply to Israel.

While this typology allows mapping of production regions according to their multifunctionality, tradeoffs and synergies, it is far more flexible. Fig ‎5.4 displays Israel's supplying regions according to the number of crops for which each region is an

optimal region. Ukraine and the black sea region account as best suited for all four crops. Other major supplying regions, such as: North America, and the regions South from Brasilia in Brazil, is best suited for 3 different crops. A more detailed query can include the specific crops suited best in each region. For example: cropping systems in Brazil and in North America are best suited for wheat, soybeans, and maize. Other regions in Thailand, Malaysia and Indonesia are best suited for soybeans, maize and rice. Finally, while some regions in the West of Ukraine are best suited for wheat, rice and maize, in those areas situated in the East of Ukraine the wheat is replaced by soybeans.

Fig 5‎ .4: Best suited functional regions by number of crops

On average, 23% of best suited functional areas are misused. Namely, in some areas crops that are not best-suited are being grown. Almost all misused areas are situated in single-crop regions. E.g. 98 % of the best suited rice regions are not used to grow rice. Similarly, 77 % and 39 % of the regions that are best suited for maize and wheat, respectively (see Table ‎5.3).

Table 5‎ .3: Sub-optimal use of best-suited functional regions.

Best suited crops Optimal crops' area Sub-optimal crops' Sub-optimal crops' area (sqkm) area (sqkm) (percents) Rice (R) 2 109 98 % Maize (M) 64 219 77 % Wheat (W) 334 215 39 % Soybeans (S) 329 150 31 % R, M 8 41 84 % S, R 15 37 71 % W, R 92 13 13 % S, M 658 72 10 % W, S 210 20 9 % W, M 130 12 8 % W, S, R 146 31 17 % S, R, M 48 5 9 % W, R, M 206 1 1 % W, S, M 436 0 0 % W, S, R, M 432 0 0 % Total 3,111 924 23 %

Exploring the functional regions typology can be done using queries. Querying the typology may refer to only a subset of characteristics of these regions. For example, water efficiency classes may be mapped only for regions in which water availability is low (i.e. arid or semi-arid regions). Fig ‎5.5 provides an illustration of this concept for wheat and for rice. Some wheat supplying regions may enclose some unsustainable production patterns. These include vast areas in the Western U.S, India-Pakistan border, and some areas in West Asia. All of these are either arid or semi-arid regions with low water efficiency regarding wheat production. Rice crops show some unsustainable patterns as well, which are mainly situated in West India and in Spain. Although supply of wheat from Spain presents to some extent an un-sustainable production pattern, this pattern is far more obvious for Spanish rice supply.

Fig 5‎ .5: Water efficiency classes in water scarce areas. An illustration for wheat and for rice

5.5. Discussion The global nature of our food system and the increasing dependence of societies on remote production regions suggest that place based sustainability analysis and specifically ecosystem services and dis-services assessments should go beyond national boundaries and across spatial scales. However, only a few studies have

attempted to analyze rates of reliance or environmental impacts on specific supplying / exporting regions within remote countries. Fewer studies have attempted to integrate them in order to link remote environmental pressures with impact on ecosystem services. Coupling agricultural and environmental indicators to describe different classes of potential impacts, as illustrated in this manuscript, is an important step in the process of analyzing ecosystem services in such interconnected world.

The functional regions typology, presented in this study, combines different facets of cultivated systems; they relate to socio-economic and political factors as well as to ecological and historical ones. Global agricultural production areas were classified into 24 "functional regions" based on the interactions, trade-offs and synergies, between agricultural efficiency and dominant environmental conditions. Each region differs according to its potential environmental impact from agriculture. Finally, the "functional region" typology was applied to a case study linking provision of staple crops (measured by tons or by calories) to Israel with related potential soil loss and water scarcity.

The research revealed that most crops supplied to Israel are grown in 8 functional regions. This approach differentiates between the potential environmental impacts derived by similar biophysical pressures (e.g. cropland footprint) in different regions. Over half of the crops supply was produced in areas with high soil loss potential, and almost 15 % out of it originates from areas with high water scarcity (see Table ‎10.11 and chapter ‎10.12). Israel's dependence on these regions undermines its sustainability, and bear social and economic implications for the sustainability of remote societies. Finally, 23 % of crops supply is produced in sub-optimal classes. This implies that changes to Israel's supply sources have the potential to reduce consumption related environmental impacts.

Naidoo et al. (2008) noted that global data relevant for describing, quantifying and mapping ecosystem services are severely limited. Similarly, biophysical spatial explicit global datasets of the agricultural system are limited as well, covering only one or a few years (Monfreda et al., 2008; You et al., 2014). A few studies have used production patterns of the year 2000 to generate a time series of environmental

impacts from agriculture (Chaudhary and Kastner, 2016; Sandström et al., 2017). Yet, they have ignored agricultural expansion (or area stagnation) and intensification, that may have taken place since then. This study uses SPAM as an underlying global production model. It is the only global production grid available for more than one year3. The method used to disaggregate national to a grid-based flows assumes that exports from every grid cell are proportional to the national exports structure. Godar et al., (2015, 2016) managed to overcome that assumption by linking agricultural production data at a sub-national spatial resolution to countries' consumption. However, that study currently covers only few crops and major producing countries. Using model-based dataset seems un-inevitable for regions in which agricultural statistics are limited (You et al., 2009). Furthermore, the contribution of the disaggregation procedure used and described in this chapter stands out, considering that to date the vast majority of environmental accounting studies use the national scale as the most detailed production unit.

By mapping Israel's national food supply to a global production grid, this chapter acknowledges the notion that the correlation between environmental pressure and environmental impact is limited, and that the latter also depends on ecosystems structure and function. Therefore, it allows identifying potential environmental impacts that are distributed across large non-intensive areas. For example, about 2.5 % of Israel cropland footprint is in areas with high water scarcity and low yields (see Table ‎10.11 and chapter ‎10.12). These areas can be found in North and West India as well as in the Western parts of the U.S, and in Spain. Although it constitutes a small part of Israel's cropland footprint, this un-sustainable pattern of production and trade may undermine local communities' access to water and their capability to grow their own food. Production and trade patterns in these areas, and their consequences to local communities, may require a closer inspection.

This chapter differentiates producing regions according to their relative food provision services, and with regards to the relative potential dis-service from agriculture within them. One dis-service from agriculture included in this study is water scarcity. Irrigation of agricultural crops in some places exceeds the recharge rates of

3 This chapter is based upon SPAM 2005, since the version for 2010 was yet unpublished at that time.

underground water, and may reduce the quantity of potable water and of water available for future agricultural use. It seems that functional regions with high water scarcity levels (see Fig ‎5.5) demonstrate similar spatial pattern to areas that suffer from ground water depletion (Dalin et al., 2017). Accounting for interregional pressure on water resources is especially important for water-poor countries, such as Israel. Soil loss is another important dis-service from agricultural systems. It may alter hydrological flow regimes, damage non-terrestrial habitats, and has the potential to reduce soil fertility in fields (Zhang et al., 2007; Swallow et al., 2009). This study showed that Israel's staple supply relies heavily on areas with high risk for soil loss. Assessment of soil loss triggered by crop production is based on a global model of the universal soil loss equation (Nachtergaele et al., 2010). The USLE accounts for only a few water erosion mechanisms, and has some other limitations which suggest that its results should be carefully interpreted as the potential soil loss at a specific place (Yang et al., 2003; Panagos, Borrelli, Poesen, et al., 2015). Similarly, in this chapter soil loss in each functional region only indicates the potential of the region to "provide" such dis-service. Some regions with high potential soil loss are the Asian steppe, the North American prairie, and the South American pampas. While high soil loss generally poses a threat to future agriculture, it is less of a threat in these regions since they all are characterized by a deep topsoil rich with organic matter (Zhang et al., 2007). A refined indicator may benchmark the net rate of soil loss (or gain) against soil depth as a proxy for the "fertility time" left for any region under a specific cultivation system.

Either soil loss or water scarcity is caused by a set of environmental and social conditions. Crops' selection, the level of agricultural intensity, and cultivation practices, may differ between regions due to economic, cultural, political and technological drivers. Although global ecological datasets usually account for the influence of those drivers on ecosystem variables, global data is mostly lacking or highly limited in availability, quality and resolution. For example, cultivars that are adapted to dry climates may be preferred for water scarce regions over those that are currently being grown. In addition, promoting soil conservation tillage in different areas may reduce the potential soil loss impact of agriculture. Such datasets may be quite useful for a future analysis of the sustainability of food systems. While the

analysis presented in this chapter can provide some explanation to how human- related factors affect ecosystem dis-services from agriculture, future work should focus on fully understanding the data gaps and the technical limitations that detain a full implementation of this concept.

Another important research direction is the development and inclusion of additional pressure (e.g., nitrogen footprint) and state (e.g., biodiversity loss) indicators. These are required in order to have a more complete assessment of potential impacts embedded in countries food supply. However, using the current methodology, additional indicators in either agricultural or environmental system will trigger an exponential growth in the number of potential classes. For example, including additional two levels of nitrogen efficiency in the agricultural system will increase the number of functional regions from 24 to 48. While some studies that classify multi- functional regions used a similar approach (Gimona and Horst, 2007; Willemen et al., 2010), other classification methods, such as combining principal component analysis (PCA) and cluster analysis, may be used to generate a limited number of functional regions based on a multidimensional dataset (Raudsepp-Hearne et al., 2010; Queiroz et al., 2015).

A global and interregional analysis of ecosystem services and dis-services provides an important overview of the state of sustainability of socio-ecological systems. This chapter presented an illustrative case-study, applying the functional region typology to the case of Israel's staple crops. However, a more complete assessment should include more countries and additional products. Fortunately, such an extension is already feasible. For example, generating a global consumption grid of other countries is possible using the same data and methods. Furthermore, available spatially explicit datasets allow analyzing 136 FAO primary crops, using 28 additional crop grids, and 10 aggregates grids (e.g. tropical fruit, temperate fruit or vegetables). Although, functional regions as presented in this paper seem to be homogeneous regarding their agricultural practice and environmental attributes, this is not the case. Naidoo et al. (2008) showed that global analysis of ecosystem services assessment may yield different results from those that a local analysis would have produced. Local analysis based on data at higher spatial resolution may reveal spatial patterns that are

currently unexposed. For example, more detailed data on land cover, topography, and agricultural practices will provide better approximations of potential soil loss (Panagos, Borrelli, Poesen, et al., 2015). Nevertheless, a global assessment at a higher spatial resolution is restricted by data availability and to some extent by processing resources. By linking international flows of provision services with the functioning of local agricultural systems, the functional regions typology may be used as a global road map allowing the selection of landscape scale relevant case studies.

5.6. Conclusions In an interconnected world, the 'food system' sustainability of any given region and a major component of its 'food security' are increasingly dependent on ecosystem services originated from supplying regions in different parts of the world. Analyzing the state of ecosystem services at a local scale or by focusing only on producing regions is not enough. The functional region typology, which integrates agricultural and environmental components, links provision of ecosystem services with related ecosystem dis-services across scales, and is able to capture trade-offs between food provision and other functioning of cultivated systems. By linking human derived pressures to environmental impacts, this analytical framework comprises an initial step towards an integration of interregional biophysical accounting and ecosystem services assessment, which would support a robust and up-to-date sustainability assessment in a global age. Extending this analysis to include other countries and additional food products is feasible and can support global analysis of food system's sustainability. Nevertheless this framework should be extended and further developed to include other indicators that stand for additional pressures, and for ecosystem state.

6. Food security, sustainability and international trade – a global analysis using the functional regions typology

6.1. Disclaimer This chapter is based upon a manuscript aimed for publication and is currently in preparation.

6.2. Introduction During the second half of the 20th century the state of global and national food security has been significantly improved, due to an increase in food availability and food trade (Porkka et al., 2013). A few major staples: wheat, maize, and rice, constitute a large share of the global diet (Kastner et al., 2012). Other important crops, like soybeans, are widely used as a protein source for producing animal's feedstock. To date, cultivated systems already play a major role in global land use change and environmental degradation (Rockström et al., 2009; Ellis et al., 2010; Vörösmarty et al., 2010; Lassaletta et al., 2014; Chaudhary and Kastner, 2016; Godar et al., 2016; Desmit et al., 2018). Soybeans trade for example is associated with rainforest deforestation (Godar et al., 2016) and with water stress in Brazil (Flach et al., 2016), as well as disruption of the global nitrogen cycle (Lassaletta et al., 2014). Rice and wheat were also associated with chronic water stress in some parts of India (Davis et al., 2018). In addition, rice, maize and wheat utilize the highest share of the global croplands relative to all other crops. Increasing demand for food may increase the tension between food security and food system's sustainability (Godfray et al., 2010). Impacts from agricultural systems sometimes hamper their own capacity to maintain yields overtime (Zhang et al., 2007; Power, 2010), thus sustainable food systems are required also for maintaining and increasing food security in the future (Berry et al., 2015; FAO, 2018).

The volume of traded food crops has increased dramatically over the years, yet whether food trade is beneficial is still debated. On the one hand, food trade is associated with displacement of resource use and of environmental impacts (Lassaletta et al., 2014; Chaudhary and Kastner, 2016; Dalin et al., 2017; Fridman and Kissinger, 2018), and on the other hand it plays an important role in increasing global

average yields (Kastner et al., 2014) and maintaining food security (Fader et al., 2013; Porkka et al., 2013; Kummu et al., 2014). While trade is a significant component of the global food system, it must be integrated into studies that explore the links between food security and environmental sustainability. Specifically, such research should account for impacts and dependencies occurring between local and remote agricultural systems. Different publications have analyzed potential impacts from food production at a local scale with regional and global coverage (Ellis and Ramankutty, 2008; Václavík et al., 2013; Zanden et al., 2016; Levers et al., 2018). These include the conceptualization of the 'functional regions' typology that couples environmental state and agricultural performance indicators, delineating unique agricultural regions. They differ by a combination of environmental and agricultural indicators (Fridman and Kissinger, 2018). The latter work is presented in chapter ‎5, and uses this typology to explore interregional aspects of Israel's food supply.

This chapter aims to characterize the global food production system of 4 main staples (wheat, maize, rice, and soybeans), identifying regions associated with higher potential environmental impacts affecting local to global communities, as well as local food production. It further explores the environmental and food security consequences of national food sourcing from different functional regions. Finally it aims to explore the role of trade in achieving food security and advancing sustainable food systems.

6.3. Methods Achieving the objectives of this study is based on the following 3 step process:

a. Characterizing agricultural regions as social and natural systems. b. Linking national consumption of the selected crops to local and remote supplying regions. c. Analyzing links between food security, sustainability and trade.

In the first part the functional region typology is being created using agricultural performance and environmental state indicators. It is only applied to cultivated systems growing wheat, maize, soybeans and rice. The second part is the core of

biophysical analysis mapping production in each functional region to consumption in different countries. Finally, the third part integrates the results of the prior two parts with auxiliary information and discusses the links between food security, sustainability, and trade. A detailed description of the methods and data used in this analysis is described below.

Step 1: Characterizing agricultural regions as social and natural systems

The indicators used for this analysis are described in Table ‎6.1, and include 4 general environmental state indicators that measure water availability, soil loss and species loss, along with 4 crop specific (16 in total) agricultural performance indicators. The latter measure water (blue and total) and land requirements, and area harvested. Spatial explicit data were retrieved at or aggregated to 5 arcmin spatial resolution and rectified to fit the standard global harvest choice grid (Harvest Choice, 2010). The dataset was restricted only to grid cells with positive land harvested for at least one of the crops. Missing values were imputed based on a spatial moving window technique, co-linearity between input indicators was tested and ruled out, and a log transformation and z-score standardization were applied to each indicator. The final dataset included over 750,000 data points.

Table 6‎ .1: A short description of datasets used in this study

Indicator Description Source Temporal Original # of coverage spatial indicators resolution Soil loss Soil loss due to water erosion LS, R, K, and P factors are LS, R, P: Five 1 and associated with the taken from Nachtergaele 2000 arcmin cultivation of wheat, rice, et al. (2010), C factor4 K: 1990 soybean and maize. Panagos, Borrelli, C: 2010 Given in tons soil/ton crop. Meusburger, et al. (2015), and crop spatial production statistics from International Food Policy Research Institute (2019). Yield Yield is given in International Food Policy 2010 Five 4 kilogram/hectares for each Research Institute (2019). arcmin

4 LS: length-slope factor; R: rainfall intensity factor; K: soil erodibility factor; C: crop and management factor; P: support practices factor.

grid cell and each crop. Area harvested Area harvested given in International Food Policy 2010 Five 4 hectares for each grid cell Research Institute (2019). arcmin and each crop. Aridity index Annual average of the P/PET Trabucco and Zomer 1970 -2000 30 arcsec 1 ration (aridity index) can be (2019). > Five used to classify world regions arcmin according to the water availability class. Irrigation Modeled blue water footprint Mekonnen and Hoekstra 1996 - 2005 Five 4 requirements for each grid cell and ach (2011). arcmin, crop. Given in m3/ton crop. per crop Total water Modeled total water Mekonnen and Hoekstra 1996 – 2005 Five 4 requirements footprint (blue and green) for (2011). arcmin, each grid cell and each crop. per crop Given in m3/ton crop. Species loss Regional/global species loss Chaudhary and Brooks NA Five 2 due to crop production. (2017). arcmin, Calculated at an ecoregions for arable level and given in number of land species lost/ton crop. Global species loss account for global extinctions.

Clusters were derived using self organizing maps (SOM), an unsupervised artificial neural network (ANN) algorithm. Similar to K-means the algorithm iterates over the dataset and allocate different data point to nodes on the network (referred hereafter as clusters) based on the distance between a data point and a cluster's codebook. Unlike k-means, the SOM algorithm reduces the dimensionality of the data while preserving its topology (Kohonen, 2001). I have chose the number of nodes based on the quantization error and the Davies-Bouldin index calculated for all possible grids between 2x2 and 6x6. A 12 nodes (3x4) toroidal grid with hexagonal topology was chosen. The algorithm run over 2,400 iterations before it was terminated.

Then, interpreting our results, i.e. naming them as functional regions, was based upon their relative functioning on different observed dimensions. For that purpose, I have plotted the average z-score, as well as the first and third quarter and the median, for each indicator, comparing all clusters with each other (See Fig ‎6.1). This plot was used

for interpreting and naming the clusters following Levers et al. (2018) and Václavík et al. (2013). I also estimate the relative concentration of agricultural activity in any functional region using a metric is termed the utilization factor (see chapter ‎10.13).

Fig ‎6.1: Codebooks of functional regions (Mean (empty circle), median (black circle), vertical line 25% -75% quintile). Standardized raw data are presented.

Step 2: Linking national consumption of the selected crops to local and remote supplying regions This part integrates the functional region typology with data on crop supply by country of origin. Kastner et al. (2014) and Kastner et al. (2011) have developed an iterative procedure to reallocate bilateral trade flows, so only countries that grow specific crops can be accounted as the source crop's flow. I used data from Fridman and Kissinger (2019) for reproducing the mentioned methodology (see chapter 4). Table 6.2 and Table 6.3 present an illustrative example of the procedures used for integrating the trade dataset and functional regions typology. It describes a hypothetical system with three countries and three functional regions, and the results for one country.

Table 6.2: A hypothetical system for demonstrating the integration of the functional regions typology and the trade by origin dataset.

(A) Crop flows between countries by producing country (αr, s) Country A Country B Country C Total Country crop's Consumption consumption

coefficient (fr) Country A 5 2 0 7 0.87 Country B 8 6 0 14 0.96 Country C 1 2 0 3 0.78 Total Production 14 10 0 - -

(B) Crop production in functional regions by producing country (βs, i) FU 1 F U 2 FU 3 Total Production (from matrix A) Country A 3 (21%) 6 (43%) 5 (36%) 14 Country B 0 (0%) 5 (50%) 5 (50%) 10 Country C 0 (0%) 0 (0%) 0 (0%) 0

I divide each row element in the matrix B in Table 6.2 by the total production column

(originated in matrix A) to quantify the share of production (βs,i) in each functional region (i) out of the national output (of country r; given in brackets in Table 6.2). Equation 6.1 demonstrates the calculation of domestic and imported consumption from any functional region. The calculation use both the functional regions'

production shares and the trade by origin (elements αr,s in matrix A in Table 6.2). The

country's crop consumption coefficient (fr) represents the fraction of crop utilized as food, feed or being processed relative to the national supply of the crop (according to FAO food balance sheets).

Eq. 6‎ .1: Calculating the domestic and imported consumption of country r from functional region i

The domestic consumption in region r that originates from functional region i can be calculated using the formula where s = r, whereas for imported consumption s ≠ r. Table 6.3 demonstrates the implementation of this equation for country A. I have

quantified the relative contribution of each functional region to the national calorie intake, using crop-specific calorie content and total national calorie intake.

Table 6.3: Results for country A from the hypothetical system

Origin FU 1 F U 2 FU 3 Total Country A – 5 x 0.21 x 0.87 = 1.87 1.57 5 x 0.87 = 4.35 Domestic (r = s) 0.91 Country A – 0 (2 x 0.5 + 0 x 0) x 0.87 (2 + 0) x 0.87 = Imported (r ≠ s) 0.87 = 0.87 1.74 Total 0.91 2.74 2.44 7 x 0.87 = 6.09

Step 3: Analyzing links between food security, sustainability and trade Some functional regions may demonstrate tradeoffs between agricultural performance and potential environmental impacts (e.g., high yield but high soil-loss potential). In other cases, tradeoffs can exist within each domain, like low irrigation requirements with low yields. Some functional regions combine resource-efficient agricultural systems and with little potential for environmental impact. Measured relative to all other functional regions, these areas indicate the highest suitability for crop production. The first stage of this part was to identify functional regions with similar environmental impacts, such as soil loss, species loss, or water stress, as well as functional regions with the highest suitability (or 'most suitable') for crop production. The latter were determined separately for each crop (see Table 6.3). Taking on a comparative approach, I perceive a sustainable consumption pattern as one that relies more on the most-suitable functional regions relative to those with high environmental impacts (e.g., soil loss, water stress, or ecological vulnerability).

Therefore I define the sustainability metric (sr) as described in equation 6.2.

Eq. 6‎ .2: Definition of the sustainability metric

Whereas pr is the share of calories p supplied to country r from the most suitable functional regions or areas with high environmental impacts (i.e., imp include soil loss prone, water-stressed, and ecologically vulnerable functional regions).

I have plotted the share of national consumption from the most suitable functional

regions (pmost suitable) against the percentage of consumption from the functional

regions with high environmental impact (pimp), to visualize a sustainable and unsustainable spectrum of national food supply (see Fig 6.5). The line where the sustainability metric equals zero (45° line) serves as a sustainability threshold

separating between more sustainable (sr > 0) and less sustainable (sr < 0) national food systems. Per capita GDP (World Bank, 2017) indicates the capacity of countries to participate in international trade or to advance environmental policies within their borders.

Functional regions demonstrate trade-offs and synergies between agricultural performance and potential environmental impacts, and among each categories. I have identified and grouped together, functional regions with similar environmental impacts (e.g. exceptionally high water stress or species loss). I have also grouped together functional regions identified as the most suitable functional regions. I have quantified the share of national calorie intake from each group (most suitable vs. potential environmental impact), and discuss national consumption's level of sustainability on these two figures. I have also used GDP per capita GDP per capita (World Bank, 2017) as a proxy for the capability of countries to participate in international trade or to advance environmental policies within their own borders.

6.4. Results Overall the global cultivation of the four selected staples uses approximately 648 million hectares of cropland and 559 km3 of irrigation water. It leads to several environmental impacts, including potential soil loss (1.18 tera tons of soil), regional species loss (997 regional species lost on average), and global species loss (87 global species lost). However, such pressures and impacts are not spread equally over the world.

Table 6.2 presents the names and main properties of the twelve functional regions identified in this analysis. The utilization factor indicates regional specialization in arable lands, splitting functional regions into three utilization levels. The four major functional regions (FU 7 -10) produce 63% of the global staple production and provide most of the global soybeans (81%) and maize (80%) supply, but only 47% and 57% of the quantity of rice and wheat, respectively. Then, the five minor (FU 2, 4 -5 and 11 - 12) and three marginal (FU 1, 3, and 6) functional regions are responsible for 32% and 5% of the global production, respectively.

Table 6‎ .4: Names and attributes of global functional regions.

Footnote: regional crops' specialty was identified based upon the cropland indicator.

Two most-suitable functional regions are FU 7 for growing wheat, maize and soybeans and FU 12 for wheat only (see the map of the world's functional regions in Fig 6.2). These regions demonstrate high agricultural performance and low potential environmental impacts and are responsible for 31% of the total crop production. Except for the functional regions in which crop cultivation ends up with potentially

high environmental impacts (i.e., FU 1, 4 -6, and 8 -11) or those that are most suitable for agriculture, there are two more functional sub-optimal functional regions.

Fig 6‎ .2: Global map of the functional regions typology.

Table 6.3 aggregates individual functional regions based on their impact category. It appears that around 36% of the production of the studied staples occurs in water- stressed areas (FU 4, 8, 10 -11). In contrast, these semi-arid croplands use more than 70% of the global irrigation water (see Table 10.14). Similarly, the share of production in ecologically vulnerable (FU 1 and 6) functional regions is 3%, while it is responsible to 30% of the global species lost (i.e., global extinction) associated with staples cultivation. Soil-loss prone functional regions (FU 5 and 9) are responsible for 28% of the total potential soil loss and 23% of the crop production. FU 6 demonstrates both high levels of species loss (20%) and soil loss potential (5%) and is classified as ecological vulnerable to prevent double counting.

Table 6‎ .5: Categories of functional regions with similar environmental impact and the most suitable functional regions.

Functional Functional Identified due to Production, Share of global regions category regions Million ton production Most suitable 7*, 12** Highest agricultural 741 31%

performance and lowest environmental impacts

Water stressed 4, 8, 10 -11 Semi-arid climate*** and 883 36% highest irrigation requirements Ecological 1, 6 Highest regional/global 64 3% vulnerable species loss Soil loss prone 5, 9 Highest soil loss 570 23%

* ** FU 7 is a most suitable functional region only for wheat, maize, and soybean. FU 12 is a most suitable functional region *** only for wheat. At least 50% of production occurs in arid or in semi-arid regions.

National calorie supply by different functional regions The four analyzed staples provide 71.4% of the global calorie intake from crops (either consumed directly or indirectly as animal feed; see Table 6.4). Soil loss prone functional regions provide close to 17% of the total global calorie intake, and water stress and ecological vulnerable functional regions supply about 25% and 3%, respectively. Scaled (by population) calorie intake emphasizes the role of both water stress and ecological vulnerable functional regions in the global food supply, showing increases of 3.5% and 0.75%, respectively. The 'Rainfed major agricultural regions' (FU 7) demonstrate the most significant difference between the non-scaled and the scaled global calorie intake (18.5% and 13.9%, respectively), yet this is still an essential source of calorie at a worldwide scale.

The most calorie intake of the consumed studied staples originated from domestic functional regions. As presented in Table 6.4, imports from a few specific remote areas account for over 15% of the global world's calorie intake. The most suitable 'Rainfed major agricultural regions' (FU 7) contributes slightly above 7.5%, and four additional regions (FU 2, 5, 8, and 12) are responsible for an extra 5.4%.

Table 6‎ .6: Calorie supply by local consumption and by imports from different functional regions

FU Name Calories, imports Calories, domestic Calories, Total Calories, total Tera calories (% of global) scaled by population; % of global 1 Maize in marginal 7.89 (0.1%) 109.54 (1.2%) 117.43 (1.3%) 1.6% vulnerable regions 2 Rainfed wheat in 136.64 (1.5%) 177.05 (1.9%) 313.69 (3.4%) 2.9% productive minor regions 3 Rainfed marginal 3.43 (<0.1%) 36.99 (0.4%) 40.42 (0.4%) 0.5% regions 4 Water stressed minor 48.55 (0.5%) 744.93 (8%) 793.48 (8.5%) 9.6% rice regions 5 Rainfed & soil-loss 123.05 (1.3%) 287.74 (3.1%) 410.79 (4.4%) 4.2% prone minor regions 6 Marginal vulnerable 7.01 (0.1%) 153.22 (1.6%) 160.23 (1.7%) 2.2% rice regions 7 Rainfed major 738.54 (7.9%) 993.82 (10.6%) 1,732.35 13.9% agricultural regions (18.5%) 8 Major water stressed 96.86 (1%) 569.76 (6.1%) 666.63 (7.1%) 6% agricultural regions 9 Irrigated & soil-loss 68.73 (0.7%) 1,078.35 (11.5%) 1,147.08 11.5% prone major regions (12.3%) 10 Rice & wheat in major 36.05 (0.4%) 659.75 (7.1%) 695.8 (7.4%) 11.3% water stressed regions 11 Wheat in minor water 46.27 (0.5%) 157.96 (1.7%) 204.23 (2.2%) 2% stressed regions 12 Rainfed wheat in 145.53 (1.6%) 247.29 (2.6%) 392.82 (4.2%) 3.4% minor regions - NA 1.67 (<0.1%) 7 (0.1%) 8.67 (0.1%) 0.1% - Total 1,460.22 (15.6%) 5,223.4 (55.8%) 6,683.62 69.1% (71.4%)

As presented in Fig 6.3, some top exporting countries, such as the US and Argentina, rely to a large extent on most suitable functional regions. Areas with higher environmental impacts (e.g., Brazil) and sub-optimal production systems (e.g., France and Australia) are more dominant in other large producing countries. Specifically, production in Brazil heavily relies on soil loss prone functional regions. The share of imported calories relative to the national intake quantifies the rate of dependence on a remote supply system. The most import-dependent areas include small islands in the Caribbean, Indian Ocean, and countries, such as Djibouti or Brunei Darussalam. The percentage that imports provide to the national calorie supply is over 70% (see Table

10.15). Importing countries in Fig 6.3 can import almost their entire calorie supply (e.g., Brunei Darussalam, Iceland, and Israel).

Alternatively, the calorie supply of some countries can originate from both domestic and remote functional regions (e.g., Japan and South Korea). Alongside food security, international trade seems to improve the environmental performance of the food supply to some countries. A significant portion from Japan and South Korea's imports originates from most suitable functional regions, whereas their domestic production share demonstrates high potential impacts for soil and water stress.

Fig 6.4 allows a broader inspection of the trade-related environmental benefits. It demonstrates how domestic calorie supply mostly originates from functional regions with high potential ecological impacts (blue bars in the top plot of Fig 6.4). Contrarily, the imported calorie supply primarily originates from the most suitable functional regions (orange bars in the bottom plot of Fig 6.4).

Countries that depend on food imports sometime rely on areas with high environmental impacts. For example, approximately 30% of Kuwait's calorie supply originates from functional regions with high environmental impacts (see Fig 6.3). Consumption in some countries drives local ecological impacts. For example, China's calorie supply originates from water-stressed and soil loss prone domestic functional regions. Uzbekistan relies almost exclusively on water-stressed cultivated areas. Similarly, North Korea's food supply depends practically entirely on soil loss prone functional regions.

Two countries: Guinea-Bissau and Zambia, have high shares of calorie supply from ecological vulnerable functional regions. However, the impact on species loss differs significantly between these two cases. Located in West Africa, Guinea-Bissau's domestic consumption originates mostly from the 'Marginal vulnerable rice regions' (FU 6) and drives global species loss. The food supply in Zambia drives regional species loss due to domestic consumption from the 'Maize in marginal vulnerable regions' (FU 1).

Fig 6‎ .3: Share of national calorie supply for selected top exporting and importing countries and for local consumers

Fig 6‎ .4: Association between international trade and the share of national calorie supply from environmental impact and most suitable functional regions.

Implications for food security and food systems' sustainability Fig 6.5 quantifies the level of sustainability of the national food supply. Some of the most unsustainable consumption patterns occur in Bangladesh, India, Pakistan, China, and Indonesia. Collectively they account for one-third of the global calories scaled by population. They rely on domestic functional regions with high levels of water stress (India, Pakistan, and Bangladesh), and of soil loss (China and Indonesia; see Table 10.15). Other countries with large populations, such as Brazil, Russia, and Nigeria, demonstrate unsustainable consumption patterns. Consumption originates in areas that are either water-stressed or prone to soil loss prone, is most common in countries with more sustainable consumption patterns (i.e., right-side of the sustainability threshold line). Only a few countries with sustainable food consumption patterns depend on ecological vulnerable functional regions (e.g., Croatia or Serbia). At a global glance, it seems that large shares of the calorie intake from both soil loss prone and water-stressed functional regions are imported (39% and 37%, respectively). Generally, there is a high correlation between food supply sustainability and per capita GDP. For countries with the lowest per capita GDP, the share of calorie intake from the most suitable functional regions does not exceed 20%. Instead, many of these

countries highly depend on ecologically vulnerable areas. Most states (66 out of 72;

see Table 10.15) with sustainable food supply (Sr > 0) also have sufficient food availability (above 2,500 daily calories per capita 92%). In contrast, only 60% of the countries with an unsustainable food supply have an adequate level of food availability (56 out of 94).

Fig 6‎ .5: Countries' staple food supply from different types of functional regions by environmental impact category and GDP per capita

6.5. Discussion Functional regions, food security, and food system's sustainability The analysis presented in this paper found that less than 20% of calories supply of the studied essential staples originates from the most suitable functional regions. A considerable share (48%) of the global calorie supply originates from areas with high potential environmental impacts. This paper focuses on soil loss, water stress, and species loss (as a proxy for biodiversity loss), which are core concepts discussed in sustainable food production (FAO, 2018). Recently it has been suggested that unsustainable food systems are those that do not exceed a certain threshold derived

from the concept of planetary boundaries (Willett et al., 2019). This analysis quantifies the level of sustainability of specific countries' food supply and can be used to identify food-related drivers for environmental impacts in domestic and remote functional regions. Countries that their food supplies depend on unsustainable production systems may suffer food insecurity in the long run (Berry et al., 2015). Dependence on water- stressed and soil loss prone functional regions can have adverse effects on food systems in the long term. By decreasing the share of irrigated agriculture or degrading soil organic matter, respectively, it eventually can lead to reduced yields. Functional regions with high species loss impacts emphasize the tension between human land use (e.g., for food production) and conservation (Fridman and Kissinger, 2019). Consequently, it also can lead to reduced food supply in countries that depend on the harvest of these areas.

Interregional sustainability and the role of international trade Recent years have brought attention to the interregional dimension of human- environmental systems (Kissinger and Rees, 2010; Liu et al., 2013). This chapter contributes to this emerging field of science by separately analyzing national food supply from the domestic and the imported functional regions, and by discussing the role of trade in the food supply sustainability. Some have identified international trade of food crops as a dominant influence on the increasing level of food availability of multiple countries worldwide. This increase is the result of both an increased volume of traded crops (Porkka et al., 2013) and a trade-driven rise in the global average yields (Kastner et al., 2014). The results from this chapter support these arguments by demonstrating how international trade of the selected staples contributes to the food supply sustainability and food security. First, it shows how national calorie intake of specific, trade-dependent countries, originated in remote functional regions. Second, it suggests that international trade allow imports from most-suitable functional regions, whereas domestic production is associated with high environmental impacts. Similar to findings from Porkka et al. (2013), this analysis shows that countries with lower per capita GDP tend to rely more on domestic functional regions and often drive higher levels of per calorie environmental impact. Although international trade seems

to have an overall positive effect on the interaction between staples production and the ecological systems, some countries still depend on imports from functional regions with high environmental impacts. For example, almost 30 and 70 countries import more than 5% of their national calorie intake from soil loss prone and water-stressed functional regions, respectively. Generally, species loss is mostly associated with domestic consumption. Due to the global importance of species loss, international and global mechanisms can be used to address this issue in ecologically vulnerable regions. For example, payment for ecosystem services (e.g., maintaining genetic resources) is suitable for addressing the global challenges associated with consumption from domestic production in countries such as Guinea-Bissau. Yet, any policy as this should secure adequate levels of food availability from imports and sufficient and just compensation to farmers that would lose their source of livelihood. While it seems that international trade has a significant role in increasing food security and reducing the environmental impact associated with the production of the four selected staples, but it is important also to acknowledge its limitations to mitigate these phenomena. For example, water-stressed functional regions provide 29% of the globally scaled calories. Explicitly, some of the most populated countries (Pakistan, India, China, and Bangladesh) depend on these regions. Any attempt to supply their national calorie intake from more sustainable sources will force other countries to consume food crops from water-stress areas. Instead, increasing food supply sustainability requires additional measures. For example, increasing the crop water efficiency can be achieved by promoting integrated agricultural systems and by choosing drought-tolerant crop species and cultivars. Yet, cultural preferences and economic barriers can impede these measures (Davis et al., 2018; FAO, 2018; Dalin and Outhwaite, 2019). Other actions that can advance a sustainable food supply and food security include modifying the production systems and dietary changes, in particular reducing the consumption of animal products (FAO, 2018; Shepon et al., 2018; Willett et al., 2019). This analysis indentifies regions with the potential to increase food production through agricultural intensification. Increasing yields in sub- optimal functional regions can result in higher food production and may form some new 'most-suitable' regions. For example, regions in the former Soviet Union are

currently growing wheat with relatively low yields (FU 2), but have the potential to increase them.

Methodological limitations One source of uncertainty is the use of proportional spatial weights to downscale national export data to a five arc minutes resolution (Fridman and Kissinger, 2019). An alternative is suggested by Godar et al. (2016) that present a spatially explicit analysis that identifies the actual locations of production for different consumers. However, the latter is limited to a few crops in some producing countries and is less suitable for a global analysis. Due to the dominance of the domestic consumption demonstrated in this analysis, this limitation has no discernible effects on the conclusions of this analysis. Yet relying on established methods, such as those presented by Godar et al. (2015) and applying them to additional crops (e.g., rice, wheat, and maize) at a global coverage can be used as input data of the functional regions typology enhancing the analysis of the food system sustainability. The focus of this analysis on four major staples encompasses a large share of the global calorie supply, both in terms of primary crop equivalents and consumed calories. Nevertheless, including other crops is required for a complete understanding of interregional and local links between consumers and producers. For example, intensively traded agricultural commodities from equatorial regions, such as coffee or cocoa, are often perceived as luxury products and are associated with much higher biodiversity impacts (Chaudhary and Kastner, 2016; Schröter et al., 2018; Kleemann et al., 2020). Including those may change the structure and interpretation of the typology. Also, including more pressure and state indicators can account for other interactions between human and natural systems. These include fertilizer use and nitrogen leaching (Liu et al., 2010; Mekonnen and Hoekstra, 2015), threats to river systems, coastal environments and endorheic lakes, and the permeability of the subsurface layer (Vörösmarty et al., 2010; Gleeson et al., 2014; Desmit et al., 2018; Wine and Laronne, 2020). This analysis focuses on the current state of the global food system and only implicitly suggests a set of policy actions to increase the system's sustainability. In particular, this typology cannot predict the change in sustainability in response to agricultural

expansion or intensification. This analysis does not deal with these questions; instead, a modeling approach is more appropriate to speculate future trends and the system responses. Besides, the capacity of this analysis to quantify the sustainability effects of dietary changes is limited since the input interregional trade data quantify the flows of primary crop equivalents. However, the recently published food and agriculture biomass input-output model (FABIO) can overcome this limitation and may be used in future studies to analyze the effects of dietary shifts (Bruckner et al., 2019). Finally, the functional regions' typology estimates the food system's sustainability on a relatively coarse scale. It is useful in identifying tradeoffs and hotspots of environmental impacts and guiding future high-resolution research towards them. Future research should focus on validating the results of this study against analyses performed at higher resolutions.

6.6. Conclusions This analysis covers approximately two-thirds of the global calorie intake from crops and their equivalents. By developing a functional region typology and linking it to global trade and consumption data, it quantifies the level of sustainability of national staple crop supply in more than 160 countries. Over one-third of the global calorie intake originates in areas with high environmental impacts (such as soil loss, species loss, and water stress), and approximately 20% originates in the regions that are the most suitable for crop production. The national variation in dependence on these types of functional regions demonstrates the links between food insecurity, unsustainable food supply, and poverty. The analysis finds an environmentally benign role of international trade, though it is limited in scope. Even more important, it leaves out multiple developing countries, further undermining their food security. Instead, transforming the global food system into a sustainable one requires other means than trade, including a shift towards low animal products diet and promoting sustainable agricultural practices to protect and enhance soil and water resources, as well as mainstreaming biodiversity into cultivated systems. The role of trade in such a system should be to regulate and allocate crop production based on the relative advantage of different regions. Extending this typology to include additional indicators for environmental pressure and state and to cover more crops is a meaningful research

lead, which will provide a better understanding of the links between food security, food system sustainability, and international trade.

7. Synthesis Exploring interregional interactions between human activities performed in any region and the state of ecosystems in other areas has become a crucial component of sustainability science over recent decades. Growing population and changes in preferences and demand are putting increasing pressures on the already deteriorated global environments. Yet, our well being and basis of existence depend on the proper functioning of the natural system. Food systems' sustainability was chosen to demonstrate these interactions. In a global world, the food security of multiple countries depends on food production occurring in various remote production regions.

Additionally, food production in any region alters the environment, changes its state, structure, and processes, and reduces its capacity to provide ecosystem services, including food provision. In a global and interconnected world, sustainability science should also focus on interregional sustainability. Recently, studies have started to acknowledge trade flows as an integral part of sustainability research, yet they mostly concentrate on interregional flows of resources between countries. Nevertheless, most studies exploring environmental impacts from human activities (e.g., agriculture) still focus on a local scale, to account for geographic, ecological, and technological differences occurring at a sub-national scale. This Ph.D. dissertation has combined these two approaches to identify, analyze, and document linkages between food consumption in different regions of the world and the ecological sustainability in producing regions.

A framework for analyzing food systems' interregional sustainability

The dissertation uses an adjusted version of the DPSIR (driver, pressures, state, impact, response) as its theoretical framework for an interregional analysis of the food systems sustainability (see Fig ‎7.1). This section uses the case study of Israel for illustrative purposes, but the framework was applied globally. In the first phase, the links between food consumption in Israel and food production regions are formed. Chapter four presents the methods used for linking consumption in any country to the producing area at a high spatial resolution. These Driver-Pressure links account for the

flow of crops and embodied resources across regions. Driven by Israel's food demand, almost half of its cropland footprint occurs in the USA, Ukraine, and Russia.

WP 2 aims at developing indicators of potential environmental impacts. Chapter four has already demonstrated the added value from overlaying additional information onto the interregional flow data. It points out some of the potentials in this interregional analysis. For example, it reveals hidden environmental impacts, tradeoffs, and interregional shared interests. Chapter five focuses on the State-Impact link, associating between environmental pressure and potential ecological impacts. It further processes indicators of potential environmental impact and present the functional region typology, which integrates both agricultural performance (i.e. Pressure) and environmental state. The disaggregation of interregional flow of crops to a global five arc minute grid allows linking food consumption in different countries to the state and impacts of food production in different functional regions which together indicate potential environmental impact from food consumption. Finally, chapter six applies a global scale food system's interregional sustainability analysis, by combining the functional regions typology with interregional flow data.

The example from Fig 7.1 emphasizes how diverse are the impacts due to consumption from one county. Almost 20% of Israel's cropland footprint5 occurs in the U.S.; this flow originates from three different unique production regions. Over 80% of this cropland footprint occurs in the most suitable regions for agriculture, whereas the rest split between water stressed and soil loss prone regions. Similarly, flows into specific functional regions originate from different countries. Indicators, such as: soil loss or species loss, imply for some potential impacts; other impacts, such as: water stress are characterized by a combination of a pressure indicator (e.g. irrigation volumes) with specific environmental context (e.g. aridity).

Through the ecosystem services approach, impacts are also associated with the benefits human populations derive from nature. By using the ecosystem services terminology, impacts stand for both ecosystem function and ecosystem service. Functions measure the capacity of a system to do something (e.g., grow food crops),

5 Due to supply of wheat, maize, rice and soybeans.

whereas a service is identified if such function is utilized, directly or indirectly, by human beings. While impacts are mostly local, their effects on the provisioning of ecosystem services differ across scales. For example, soil loss can reduce fertility in fields with an immediate impact on farmers. At a regional scale, regional species loss can reduce or eliminate valuable ecosystem functions and services (e.g., biological control, pollination). Finally, global extinctions (global species loss) reduce global genetic diversity. Indirectly, some of these impacts, such as water stress, soil loss, and regional species loss, have the potential to reduce the system's food production capacity, which affects the well being in importing regions as well. Additional work is required to quantify and map the benefits and impacts of food systems, and flowing across areas, through different mechanisms. This dissertation further contributes to these efforts. In addition, the potential effect of policy responses implemented at a local, regional, or interregional scale to increase food system sustainability (lacking in Fig ‎7.1) be identified using this framework. For its focus on international trade, this dissertation discusses the role of trade as such a solution.

Fig 7‎ .1: Adjusted DPSIR framework applied to an interregional sustainability analysis of Israel staples supply. Note: the figure is based upon chapter 6‎ .

International trade and food systems sustainability

This dissertation aims to estimate the effect of which interregional flows of selected food commodities on domestic and global ecosystem services. By focusing on the interactions between human and environmental systems, this approach allows exploring the sustainability of global and interregional food systems. In these systems, international trade plays a vital role in achieving and maintaining food security and reducing the overall impact of food systems on the environment. The findings presented in this Ph.D. are in line with results that were recently published. Specifically, it shows that staples' trade tends to reduce global average soil loss, species loss, and water scarcity derived by food trade. Including other crops will probably yield different results (Chaudhary and Kastner, 2016), but the analysis advanced in this dissertation covers a large share of the national calorie supply for most countries.

Although playing a crucial role in advancing interregional sustainability, not all countries equally participate in the international trade of food crops. Specifically, several countries with a lower per capita GDP mostly depend on domestic food supply, which in many cases had high environmental impacts. Therefore, several trade based interventions mechanisms (e.g., payment for ecosystem services or risk management) may reduce the net global ecological implications (e.g., global species loss). Food security is positively affected by international trade in two ways. First, it allows overcoming local resource limitations (e.g., land and water scarcity) of food production by importing additional carrying capacity. Second, since most imports of the selected staples originate from most suitable functional regions, trade reduces the environmental impact in food production regions, indirectly increases their stability (relative to local production), potentially securing food supply for the longer term.

The role of trade in promoting sustainable food systems is restricted. Although it can increase sustainability in some specific cases, trade can't by itself provide more sustainable food sources for all. Recalling that approximately a third of the global population depends on the least sustainable food sources, it becomes clear that other demand and supply-side measures should be taken. For example, reducing meat

consumption will reduce the overall pressures on land and water resources, which will allow shifting imports by other countries to the most suitable functional regions. The current version of the functional region typology includes many indicators that are relevant to sustainable food systems (FAO, 2018), and separately maps unsustainable use of soil, water, and biodiversity. It can be utilized to prioritize conservation in different regions or encouraging sustainable food production in others. In addition, it also map sub-optimal regions which contribute a significant share of calorie supplied globally. These regions are sub optimal due to lower yields, or less suitable environmental state. The potential of sustainable intensification, along with increased impacts associated with it, shall be further estimated.

Uncertainty, limitations and future research

The methods developed and used throughout this dissertation form a novel approach for food systems interregional sustainability analysis. These methods prove useful for analyzing national food consumption and revealing its hidden consequences. However, some uncertainties have to be mentioned.

Disaggregating national trade data using grid-scale production statistics are the backbone of this analysis. One source of uncertainty is the use of proportional spatial weights to downscale national export data to a five arc minutes resolution. For example, Godar et al. (2016) point to that the EU-28 leads deforestation in the Northern Cerrado ecoregions due to its Brazilian soy consumption. At the same time, China soy imports from Brazil originate from the Southern and South-Eastern Cerrado. This level of detail is an essential component when analyzing interregional sustainability and shall be pursued in future work. However, considering the dominance of the domestic consumption demonstrated in the global analysis, this limitation has no discernible effects on the broader conclusions of this dissertation. The study by Godar et al. (2016) is limited to a few crops in some producing countries and is less suitable for a global analysis. Future research can rely on established methods, such as those presented by Godar et al. (2015) and apply them to additional crops (e.g., rice, wheat, and maize) with global coverage.

Another critical notion of the approach presented in this dissertation is the integration of environmental and social datasets into the interregional analysis of the biophysical flows (of food commodities). In this manner, this dissertation uses a tiered approach, so the integration of such data is based upon a literature review (chapter 4) or on quantitative indicators (chapters 5 -6). Yet, the low data availability, particularly regarding the social and economic dimensions, adds some uncertainty to the results. Specifically, it restricts the interpretation of the environmental benefits from international trade to the included environmental aspects (e.g., soil loss, species loss, water stress). Including more indicators, such as nitrogen and phosphorous use and leaching, the risk to groundwater and river systems, etc. will probably reduce the share of most suitable functional regions and increase trade-offs between food production and environmental impacts or between impact categories. Therefore, a complete version of the functional region typology should be based on more relevant indicators. Still, future research should also focus on compiling a set of global-scale spatially explicit environmental, economic, and social indicators.

The approach used in this dissertation compares the relative advantages/dis- advantages across regions; therefore, it can't determine absolutely if a system is managed sustainably. By referring to various potential states on the spectra between unsustainable to sustainable uses, this approach can determine which cultivated systems are closer to a sustainable state. Defining thresholds for sustainable management or acceptable levels of environmental damage can promote an absolute definition of sustainable food systems.

Another important research direction is to include additional crops. For example, some "luxury" crops, such as cocoa or coffee, result with un-proportionally high environmental impacts (e.g., species loss) relative to their actual physical consumption. Including these crops is expected to reduce the food system sustainability of developed countries. Other crops, including other cereals, tubers, and starchy roots, are essential food items in some developed regions that are currently under-represented in this work. Besides, including additional food crops and the availability of new datasets of interregional biophysical flows (e.g., FABIO; Bruckner et

al., 2019) will allow analyzing the potential component of food system sustainability, such as effects of dietary shifts, including reduced meat consumption.

The novelty of this study makes uncertainty analysis and validation of a complicated task. This chapter indicates the primary sources of uncertainty; some of the uncertainty stems from the relatively coarse division of the global production grid to relatively few clusters. Future research should assess uncertainty due to the clustering approach by validating the typology against multiple local case studies.

8. Conclusions International trade plays an essential role in the globalized world in which we live. It has both positive and negative impacts on the environmental and social dimensions of sustainability. An account of the food system's interregional sustainability supports the notion that international trade has benign environmental impacts. It reduces soil loss, species loss, and water scarcity, associated with the lion share of the global diet (i.e., the supply of wheat, rice, maize, and soybeans).

Analyzing interregional flows of food crops regarding the local geographic context where crops are grown reveals patterns of dependence and impact. It shows how national reliance on food imports fromm less stable production regions can hamper food security. Alternatively, it allows identifying hotspots of environmental impact. These impacts and dependencies can have local, regional (e.g., water scarcity) and global implications (e.g., global extinctions). By identifying stakeholders associated with a specific food production region, this analysis can inform policymakers to promote sustainable food systems through effective policies.

This research also yields a considerable methodological contribution, adjusting the well-known DPSIR6 framework to analyze telecoupled human and natural systems. This conceptual model is the foundation for integrating biophysical environmental accounting with an ecosystem approach. Specifically, it provides a method to map crop flows from a very local (grid with five arc minute resolution) scale to remote

6 Driver-Pressure-State-Impact-Response

countries. By relying on a publicly available dataset, this procedure can be replicated for all states and all crops included in the FAO dataset.

Future research shall focus on extending and validating the functional region typology and on reducing uncertainties. First, including additional pressure and state indicators will provide a complete understanding of the interaction between human activities and the environment within cultivated systems. Extending the temporal coverage is another research opportunity. Currently, the method can be implemented for the years 2000, 2005, and 2010. A time series can reveal trends and processes occurring at local, global, and inter-regional scales. The functional region typology is used to prioritize regions according to their agricultural performance and potential environmental impact. Validating these capabilities using indicators estimated at a local resolution is encourages.

Second, some uncertainties exist in the biophysical analysis, stemming from various model parameters selected by the researcher. Running various plausible models will quantify uncertainty, providing the users with essential tools for evaluating their work. The flexibility of the approach to use different inputs allows it to improve over time. For example, other available datasets (e.g., FABIO) allow differentiating between pressures driven by crops and by livestock supply. Other datasets better follow actual supply chains; making these models available for multiple crops and countries is an important future task.

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10. Annexes

10.1. Primary crop equivalents and conversion factors Table 10‎ .1: Primary crop equivalents and conversion factors

Crop Crop FAO Name Primay Crop Primary Primary Crop FAO Equivalent Crop FAO FAO Name Code Code 44 Barley 1 44 Barley 45 Pot barley 1.048 44 Barley 46 Barley pearled 1.042 44 Barley 48 Barley flour and 1.033 44 Barley grits 49 Malt of barley 1.108 44 Barley 50 Malt extracts 1.105 44 Barley 89 Buckwheat 1 89 Buckwheat 90 Flour of 1.042 89 Buckwheat buckwheat 101 Canary seed 1 101 Canary seed 108 Cereals nes 1 108 Cereals nes 111 Flour of cereals 1.071 108 Cereals nes 112 Brans 0.897 108 Cereals nes 94 Fonio 1 94 Fonio 95 Flour of fonio 1.05 94 Fonio 103 Mixed grain 1 103 Mixed grain 56 Maize 1 56 Maize 57 Germ of maize 1.048 56 Maize 58 Flour of maize 1.02 56 Maize 59 Bran of maize 1.067 56 Maize 60 Oil of maize 2.483 56 Maize 61 Cake of maize 1.048 56 Maize 68 Pop corn 1 56 Maize 79 Millet 1 79 Millet 80 Flour of millet 1 79 Millet 75 Oats 1 75 Oats 76 Oats rolled 0.997 75 Oats 92 Quinoa 1 92 Quinoa 27 Rice paddy 1 27 Rice paddy 30 Rice milled 1.286 27 Rice paddy 35 Bran of rice 0.986 27 Rice paddy 36 Oil of rice bran 3.157 27 Rice paddy 37 Bran cake of rice 0.895 27 Rice paddy 38 Rice flour 1.307 27 Rice paddy 39 Rice fermented 0.475 27 Rice paddy beverage 71 Rye 1 71 Rye 72 Flour of rye 1.069 71 Rye 83 Sorghum 1 83 Sorghum 84 Flour of sorghm 1 83 Sorghum 85 Bran sorghum 0.897 83 Sorghum 86 Beer sorghum 0.117 83 Sorghum

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97 Triticale 1 97 Triticale 15 Wheat 1 15 Wheat 16 Flour of wheat 1.09 15 Wheat 17 Bran of wheat 0.638 15 Wheat 18 Macaroni 1.099 15 Wheat 19 Germ of wheat 1.144 15 Wheat 20 Bread 0.746 15 Wheat 21 Bulgur 1.033 15 Wheat 22 Pastry 1.105 15 Wheat 110 Wafers 1.314 15 Wheat 203 Bambara beans 1 203 Bambara beans 176 Beans, dry 1 176 Beans, dry 181 Broad beans dry 1 181 Broad beans dry 191 Chick-peas 1 191 Chick-peas 195 Cow peas dry 1 195 Cow peas dry 201 Lentils 1 201 Lentils 210 Lupins 1 210 Lupins 187 Peas, dry 1 187 Peas, dry 197 Pigeon peas 1 197 Pigeon peas 211 Pulses nes 1 211 Pulses nes 205 Vetches 1 205 Vetches 125 Cassava 1 125 Cassava 126 Flour of cassava 3.101 125 Cassava 127 Cassava tapioca 3.321 125 Cassava 128 Cassava dried 2.339 125 Cassava 129 Cassava starch 3.321 125 Cassava 116 Potatoes 1 116 Potatoes 117 Flour of potatoes 5.209 116 Potatoes 118 Potatoes frozen 1.09 116 Potatoes 121 Potato tapioca 5.403 116 Potatoes 149 Roots, tubers nes 1 149 Roots, tubers nes 122 Sweet potatoes 1 122 Sweet potatoes 136 Taro (cocoyam) 1 136 Taro (cocoyam) 137 Yams 1 137 Yams 135 Yautia (cocoyam) 1 135 Yautia (cocoyam) 221 Almonds 1 221 Almonds 231 Almonds shelled 2.496 221 Almonds 216 Brazil nuts 1 216 Brazil nuts 229 Brazilnut shelled 2.083 216 Brazil nuts 217 Cashew nuts 1 217 Cashew nuts 230 Cashew nuts 2.278 217 Cashew nuts shelled 220 Chestnuts 1 220 Chestnuts 225 Hazelnuts 1 225 Hazelnuts 233 Hazelnuts shelled 2.172 225 Hazelnuts 234 Nuts nes 1 234 Nuts nes 223 Pistachios 1 223 Pistachios 222 Walnuts 1 222 Walnuts 232 Walnuts shelled 2.221 222 Walnuts 515 Apples 1 515 Apples

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518 Apples juice 0.979 515 Apples 519 Apples juice 3.458 515 Apples concentrated 526 Apricots 1 526 Apricots 527 Apricots dried 5.289 526 Apricots 572 Avocados 1 572 Avocados 486 Bananas 1 486 Bananas 558 Berries nes 1 558 Berries nes 552 Blueberries 1 552 Blueberries 461 Carobs 1 461 Carobs 591 Cashewapple 1 591 Cashewapple 531 Cherries 1 531 Cherries 530 Sour cherry 1 530 Sour cherry 554 Cranberries 1 554 Cranberries 550 Currants 1 550 Currants 577 Dates 1 577 Dates 569 Figs 1 569 Figs 570 Figs dried 3.466 569 Figs 620 Fruit nes dried 3.658 569 Figs 512 Citrus fruit nes 1 512 Citrus fruit nes 619 Fruit nes fresh 1 619 Fruit nes fresh 541 Stone fruit nes 1 541 Stone fruit nes 603 Fruit tropical nes 1 603 Fruit tropical nes 549 Gooseberries 1 549 Gooseberries 507 Grapefruit and 1 507 Grapefruit and pomelo pomelo 509 Grapefruit juice 2.438 507 Grapefruit and pomelo 510 Grapefruit juice 9.125 507 Grapefruit and concentrated pomelo 560 Grapes 1 560 Grapes 561 Raisins 5.642 560 Grapes 562 Grape juice 1.151 560 Grapes 563 Must of grapes 1.151 560 Grapes 564 Wine 1.283 560 Grapes 565 Vermouths and 2.585 560 Grapes similar 592 Kiwi 1 592 Kiwi 497 Lemons and 1 497 Lemons and limes limes 498 Lemon juice 1.467 497 Lemons and limes 499 Lemon juice 7.733 497 Lemons and concentrated limes 571 Mangoes 1 571 Mangoes 583 Mango juice 1.378 571 Mangoes 584 Mango pulp 1.444 571 Mangoes 568 Melons 1 568 Melons 490 Oranges 1 490 Oranges 491 Orange juice 1.235 490 Oranges 492 Orange juice 4.676 490 Oranges

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concentrated 600 Papayas 1 600 Papayas 534 Peaches and 1 534 Peaches and nectarines nectarines 521 Pears 1 521 Pears 587 Persimmons 1 587 Persimmons 574 Pineapples 1 574 Pineapples 575 Pineapples 3 574 Pineapples canned 576 Pineapples juice 2.154 574 Pineapples 580 Pineapples juice 6.885 574 Pineapples concentrated 489 Plantains 1 489 Plantains 536 Plums 1 536 Plums 537 Plums dried 4 536 Plums 538 Plum juice 1.365 536 Plums 539 Plum juice 4.135 536 Plums concentrated 523 Quinces 1 523 Quinces 547 Raspberries 1 547 Raspberries 544 Strawberries 1 544 Strawberries 495 Tangerines, 1 495 Tangerines, mandarines, mandarines, clementines clementines 496 Tangerines juice 1.344 495 Tangerines, mandarines, clementines 567 Watermelons 1 567 Watermelons 366 Artichokes 1 366 Artichokes 367 Asparagus 1 367 Asparagus 414 Beans, green 1 414 Beans, green 358 Cabbages 1 358 Cabbages 426 Carrots 1 426 Carrots 393 Cauliflower 1 393 Cauliflower 401 Chillies, 1 401 Chillies, peppers,green peppers,green 397 Cucumbers, 1 397 Cucumbers, gherinks gherinks 399 Eggplants 1 399 Eggplants 406 Garlic 1 406 Garlic 407 Leeks 1 407 Leeks 372 Lettuce 1 372 Lettuce 446 Green maize 1 446 Green maize 447 Sweet corn 0.152 446 Green maize frozen 448 Sweet corn 0.216 446 Green maize prepared 449 Mushrooms 1 449 Mushrooms 450 Mushrooms dried 12.333 449 Mushrooms 451 Mushrooms 1 449 Mushrooms canned 430 Okra 1 430 Okra

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403 Onions, dry 1 403 Onions, dry 402 Onions,shallots, 1 402 Onions,shallots, green green 417 Peas, green 1 417 Peas, green 394 Pumpkins, 1 394 Pumpkins, squash, gourds squash, gourds 373 Spinach 1 373 Spinach 423 String beans 1 423 String beans 388 Tomatoes 1 388 Tomatoes 389 Tomato juice 4.471 388 Tomatoes concentrated 390 Tomato juice 1 388 Tomatoes 391 Tomato paste 4.941 388 Tomatoes 392 Tomatoes peeled 1.118 388 Tomatoes 463 Vegetables nes 1 463 Vegetables nes fresh fresh 266 Oil of castor 1 265 Oil of castor beans beans 249 Coconuts 1 249 Coconuts 250 Coconuts 3.587 249 Coconuts desiccated 251 Copra 3.457 249 Coconuts 252 Oil of coconuts 4.804 249 Coconuts 253 Cake, copra 0.895 249 Coconuts 329 Cottonseed 1 329 Cottonseed 242 Groundnuts in 1 242 Groundnuts in shell shell 243 Groundnuts 1.37 242 Groundnuts in shelled shell 244 Oil of groundnuts 2.135 242 Groundnuts in shell 245 Cake of 0.877 242 Groundnuts in groundnuts shell 246 Groundnuts 1.401 242 Groundnuts in prepared shell 247 Peanut butter 1.423 242 Groundnuts in shell 263 Karite nuts 1 263 Karite nuts 264 Butter of karite 1.228 263 Karite nuts nuts 333 Linseed 1 333 Linseed 334 Oil of linseed 1.775 333 Linseed 335 Cake linseed 0.895 333 Linseed 299 Melonseed 1 299 Melonseed 292 Mustard seed 1 292 Mustard seed 293 Oil of mustard 1.885 292 Mustard seed seed 295 Flour of mustard 1 292 Mustard seed seed 254 Oil palm fruit 1 254 Oil palm fruit 257 Palm oil 1 257 Palm oil 339 Oilseeds nes 1 339 Oilseeds nes

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260 Olives 1 260 Olives 261 Olive oil 5.051 260 Olives 262 Olives,preserved 0.623 260 Olives 274 Oil of olive 5.051 260 Olives residues 256 Palm kernels 1 256 Palm kernels 259 Palmkernel cake 0.275 256 Palm kernels 258 Oil of palm 1.72 256 Palm kernels kernels 296 Poppy seed 1 296 Poppy seed 297 Oil of poppy seed 1.659 296 Poppy seed 270 Rapeseed 1 270 Rapeseed 271 Oil of rapeseed 1.789 270 Rapeseed 272 Cake, rapseed 0.895 270 Rapeseed 280 Safflower 1 280 Safflower 281 Oil of safflower 2.815 280 Safflower 289 Sesame seed 1 289 Sesame seed 290 Oil of sesame 1.543 289 Sesame seed seed 291 Cake of sesame 0.656 289 Sesame seed seed 236 Soybeans 1 236 Soybeans 237 Oil of soyabeans 2.639 236 Soybeans 238 Cake of soya 0.779 236 Soybeans beans 239 Soya sauce 0.167 236 Soybeans 240 Soya paste 0.34 236 Soybeans 241 Soya curd 0.173 236 Soybeans 267 Sunflower seed 1 267 Sunflower seed 268 Oil of sunflower 2.87 267 Sunflower seed seed 269 Cake, sunflower 0.895 267 Sunflower seed 276 Tung oil 1 275 Tung oil 711 Anise 1 711 Anise 226 Arecanuts 1 226 Arecanuts 689 Pimento 1 689 Pimento 693 Cinnamon 1 693 Cinnamon 698 Cloves 1 698 Cloves 661 Cocoa beans 1 661 Cocoa beans 662 Cocoa paste 1.14 661 Cocoa beans 664 Cocoa butter 1.717 661 Cocoa beans 665 Cocoa powder 0.63 661 Cocoa beans 666 Chocolate 0.949 661 Cocoa beans products nes 656 Coffee green 1 656 Coffee green 657 Coffee roasted 1.191 656 Coffee green 659 Coffee extracts 2.745 656 Coffee green 720 Ginger 1 720 Ginger 224 Kolanuts 1 224 Kolanuts 671 Mate 1 671 Mate 702 Nutmeg 1 702 Nutmeg

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687 Pepper 1 687 Pepper white/long/black white/long/black 723 Spices nes 1 723 Spices nes 161 Sugar crops nes 1 161 Sugar crops nes 160 Sugar and syrup 0.795 161 Sugar crops nes nes 667 Tea 1 667 Tea 672 Extract tea 0.45 667 Tea 164 Sugar refined 1 164 Sugar refined 156 Sugar cane 0.078 164 Sugar refined 157 Sugar beets 0.181 164 Sugar refined 162 Sugar, centrifugal 0.964 164 Sugar refined raw 163 Sugar, 0.907 164 Sugar refined noncentrifugal 165 Molasses 0.599 164 Sugar refined 169 Beet pulp 0.181 164 Sugar refined

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10.2. Primary livestock equivalents and conversion factors Table 10‎ .2: Primary livestock equivalents and conversion factors

Livestock Livestock FAO Primay Primary Primary FAO Name Livestock Livestock Livestock FAO Code Equivalent FAO Code Name 1035 Pigmeat 1 1035 Meat, pig 1038 Pork 0.675 1035 Meat, pig 1039 Bacon - ham of 1.11 1035 Meat, pig pigs 1041 Pig meat sausages 1.279 1035 Meat, pig 1042 Pig meat 0.733 1035 Meat, pig preparations 1043 Lard 2.767 1035 Meat, pig 1036 Offals of pigs 0.347 1035 Meat, pig 1040 Pig butcher fat 2.184 1035 Meat, pig 882 Cow milk, whole 1 1780 Milk, total fresh 892 Yoghurt 1.344 1780 Milk, total concentrated 888 Skim milk of cows 0.574 1780 Milk, total 893 Buttermilk curdled 1.23 1780 Milk, total 886 Butter of cow milk 11.754 1780 Milk, total 885 Cream, fresh 3.197 1780 Milk, total 894 Whole cow milk 2.197 1780 Milk, total evaporated 889 Whole cow milk 5.262 1780 Milk, total condensed 897 Whole cow milk dry 8.131 1780 Milk, total 901 Cheese whole cow 6.344 1780 Milk, total milk 898 Skim milk dry 5.934 1780 Milk, total 907 Processed cheese 1.689 1780 Milk, total 173 Lactose 6.344 1780 Milk, total 890 Whey condensed 0.426 1780 Milk, total 900 Whey dry 5.672 1780 Milk, total 883 Standardized milk 0.787 1780 Milk, total 887 Ghee from cow 14.311 1780 Milk, total milk 891 Yoghurt 1 1780 Milk, total 895 Skim milk 1.279 1780 Milk, total evaporated 896 Skim milk 4.443 1780 Milk, total condensed 899 Buttermilk dry 6.344 1780 Milk, total 903 Whey fresh 0.426 1780 Milk, total 904 Cheese skim cow 4.049 1780 Milk, total milk 905 Whey cheese 1.18 1780 Milk, total 908 Reconstituted milk 1 1780 Milk, total 917 Casein 7 1780 Milk, total 951 Buffalo milk 1 1780 Milk, total

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952 Butter of buffalo 7.392 1780 Milk, total milk 953 Ghee from buffalo 9 1780 Milk, total milk 954 Skim milk of buffalo 0.423 1780 Milk, total 955 Cheese buffalo 2.773 1780 Milk, total milk 1130 Camel milk 1 1780 Milk, total 1020 Goat milk 1 1780 Milk, total 1021 Cheese goat milk 4.058 1780 Milk, total 1022 Butter of goat milk 10.391 1780 Milk, total 1023 Skim milk of goat 0.507 1780 Milk, total 982 Sheep milk 1 1780 Milk, total 983 Butter of sheep 7.617 1780 Milk, total milk 984 Cheese sheep milk 3.298 1780 Milk, total 985 Skim milk of sheep 0.511 1780 Milk, total 1062 Hen eggs 1 1783 Eggs, primary 1063 Eggs liquid hen 1.137 1783 Eggs, primary 1064 Eggs dry hen 4.273 1783 Eggs, primary 916 Egg albumine 0.353 1783 Eggs, primary 1091 Eggs excluding hen 1 1783 Eggs, primary eggs 867 Meat of cattle 1 1806 Beef and Buffalo, meat 870 Beef boneless 1.948 1806 Beef and Buffalo, meat 874 Beef sausages 4.065 1806 Beef and Buffalo, meat 875 Beef preparations 3.026 1806 Beef and Buffalo, meat 869 Fat of cattle 11 1806 Beef and Buffalo, meat 871 Cattle butcher fat 11 1806 Beef and Buffalo, meat 872 Beef dried salted 2.636 1806 Beef and smoked Buffalo, meat 873 Meat extracts 3.091 1806 Beef and Buffalo, meat 947 Buffalo meat 1 1806 Beef and Buffalo, meat 948 Offals of buffalo 1.364 1806 Beef and Buffalo, meat 949 Fat of buffalo 11 1806 Beef and Buffalo, meat 977 Mutton and lamb 1 1807 Sheep and goat, meat 978 Offals of sheep 0.445 1807 Sheep and goat, meat 979 Fat of sheep 3.43 1807 Sheep and goat, meat 1017 Goat meat 1 1807 Sheep and goat, meat

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1018 Offals of goats 0.951 1807 Sheep and goat, meat 1019 Fat of goats 6.886 1807 Sheep and goat, meat 1058 Chicken meat 1 1808 Meat, poultry 1059 Offal of chickens 1.025 1808 Meat, poultry 1061 Chicken meat 1.352 1808 Meat, poultry canned 1080 Turkey meat 1 1808 Meat, poultry 1081 Offals liver turkeys 1.087 1808 Meat, poultry 1069 Duck meat 1 1808 Meat, poultry 1075 Offals liver ducks 0.467 1808 Meat, poultry 1073 Goose meat 1 1808 Meat, poultry 1074 Offals liver geese 0.442 1808 Meat, poultry 1060 Fat liver 1.535 1808 Meat, poultry preparations 1089 Pigeons other birds 1 1808 Meat, poultry

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10.3. Livestock relative feed requirements keys Table 10‎ .3: Livestock relative feed requirements keys

Animal Category Relative feed requirement 1035: Meat, pig 3 1780: Milk, total 0.5 1783: Eggs, primary 1.5 1806: Beef and buffalo, 4 meat 1807: Sheep and goat, 2 meat 1808: Meat, poultry 3

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10.4. Crop groups Table 10‎ .4: Crop's groups: codes an names

Group Group Name Item Item Name Code Code 1714 Crops Primary 800 Agave fibres nes 1714 Crops Primary 711 Anise, badian, fennel, coriander 1714 Crops Primary 226 Areca nuts 1714 Crops Primary 782 Bastfibres, other 1714 Crops Primary 459 Chicory roots 1714 Crops Primary 689 Chillies and peppers, dry 1714 Crops Primary 693 Cinnamon (canella) 1714 Crops Primary 698 Cloves 1714 Crops Primary 661 Cocoa, beans 1714 Crops Primary 656 Coffee, green 1714 Crops Primary 821 Fibre crops nes 1714 Crops Primary 773 Flax fibre and tow 1714 Crops Primary 720 Ginger 1714 Crops Primary 839 Gums, natural 1714 Crops Primary 777 Hemp tow waste 1714 Crops Primary 677 Hops 1714 Crops Primary 780 Jute 1714 Crops Primary 224 Kola nuts 1714 Crops Primary 809 Manila fibre (abaca) 1714 Crops Primary 671 Maté 1714 Crops Primary 702 Nutmeg, mace and cardamoms 1714 Crops Primary 687 Pepper (piper spp.) 1714 Crops Primary 748 Peppermint 1714 Crops Primary 754 Pyrethrum, dried 1714 Crops Primary 788 Ramie 1714 Crops Primary 836 Rubber, natural 1714 Crops Primary 789 Sisal 1714 Crops Primary 723 Spices, nes 1714 Crops Primary 157 Sugar beet 1714 Crops Primary 156 Sugar cane 1714 Crops Primary 161 Sugar crops, nes 1714 Crops Primary 667 Tea 1714 Crops Primary 826 Tobacco, unmanufactured 1714 Crops Primary 692 Vanilla 1717 Cereals,Total 44 Barley 1717 Cereals,Total 89 Buckwheat 1717 Cereals,Total 101 Canary seed 1717 Cereals,Total 108 Cereals, nes 1717 Cereals,Total 94 Fonio 1717 Cereals,Total 103 Grain, mixed 1717 Cereals,Total 56 Maize 1717 Cereals,Total 79 Millet 1717 Cereals,Total 75 Oats 1717 Cereals,Total 92 Quinoa 1717 Cereals,Total 27 Rice, paddy

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1717 Cereals,Total 71 Rye 1717 Cereals,Total 83 Sorghum 1717 Cereals,Total 97 Triticale 1717 Cereals,Total 15 Wheat 1720 Roots and 125 Cassava Tubers,Total 1720 Roots and 116 Potatoes Tubers,Total 1720 Roots and 149 Roots and tubers, nes Tubers,Total 1720 Roots and 122 Sweet potatoes Tubers,Total 1720 Roots and 136 Taro (cocoyam) Tubers,Total 1720 Roots and 137 Yams Tubers,Total 1720 Roots and 135 Yautia (cocoyam) Tubers,Total 1726 Pulses,Total 203 Bambara beans 1726 Pulses,Total 176 Beans, dry 1726 Pulses,Total 181 Broad beans, horse beans, dry 1726 Pulses,Total 191 Chick peas 1726 Pulses,Total 195 Cow peas, dry 1726 Pulses,Total 201 Lentils 1726 Pulses,Total 210 Lupins 1726 Pulses,Total 187 Peas, dry 1726 Pulses,Total 197 Pigeon peas 1726 Pulses,Total 211 Pulses, nes 1726 Pulses,Total 205 Vetches 1729 Treenuts,Total 221 Almonds, with shell 1729 Treenuts,Total 216 Brazil nuts, with shell 1729 Treenuts,Total 217 Cashew nuts, with shell 1729 Treenuts,Total 220 Chestnut 1729 Treenuts,Total 225 Hazelnuts, with shell 1729 Treenuts,Total 234 Nuts, nes 1729 Treenuts,Total 223 Pistachios 1729 Treenuts,Total 222 Walnuts, with shell 1732 Oilcrops, Oil 265 Castor oil seed Equivalent 1732 Oilcrops, Oil 249 Coconuts Equivalent 1732 Oilcrops, Oil 329 Cottonseed Equivalent 1732 Oilcrops, Oil 242 Groundnuts, with shell Equivalent 1732 Oilcrops, Oil 336 Hempseed Equivalent 1732 Oilcrops, Oil 277 Jojoba seed Equivalent 1732 Oilcrops, Oil 310 Kapok fruit Equivalent 1732 Oilcrops, Oil 263 Karite nuts (sheanuts)

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Equivalent 1732 Oilcrops, Oil 333 Linseed Equivalent 1732 Oilcrops, Oil 299 Melonseed Equivalent 1732 Oilcrops, Oil 292 Mustard seed Equivalent 1732 Oilcrops, Oil 254 Oil palm fruit Equivalent 1732 Oilcrops, Oil 257 Oil, palm Equivalent 1732 Oilcrops, Oil 339 Oilseeds nes Equivalent 1732 Oilcrops, Oil 260 Olives Equivalent 1732 Oilcrops, Oil 256 Palm kernels Equivalent 1732 Oilcrops, Oil 296 Poppy seed Equivalent 1732 Oilcrops, Oil 270 Rapeseed Equivalent 1732 Oilcrops, Oil 280 Safflower seed Equivalent 1732 Oilcrops, Oil 328 Seed cotton Equivalent 1732 Oilcrops, Oil 289 Sesame seed Equivalent 1732 Oilcrops, Oil 236 Soybeans Equivalent 1732 Oilcrops, Oil 267 Sunflower seed Equivalent 1732 Oilcrops, Oil 305 Tallowtree seed Equivalent 1732 Oilcrops, Oil 275 Tung nuts Equivalent 1735 Vegetables 366 Artichokes Primary 1735 Vegetables 367 Asparagus Primary 1735 Vegetables 414 Beans, green Primary 1735 Vegetables 358 Cabbages and other brassicas Primary 1735 Vegetables 426 Carrots and turnips Primary 1735 Vegetables 378 Cassava leaves Primary 1735 Vegetables 393 Cauliflowers and broccoli Primary 1735 Vegetables 401 Chillies and peppers, green Primary 1735 Vegetables 397 Cucumbers and gherkins Primary 1735 Vegetables 399 Eggplants (aubergines)

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Primary 1735 Vegetables 406 Garlic Primary 1735 Vegetables 407 Leeks, other alliaceous vegetables Primary 1735 Vegetables 372 Lettuce and chicory Primary 1735 Vegetables 446 Maize, green Primary 1735 Vegetables 449 Mushrooms and truffles Primary 1735 Vegetables 430 Okra Primary 1735 Vegetables 403 Onions, dry Primary 1735 Vegetables 402 Onions, shallots, green Primary 1735 Vegetables 417 Peas, green Primary 1735 Vegetables 394 Pumpkins, squash and gourds Primary 1735 Vegetables 373 Spinach Primary 1735 Vegetables 423 String beans Primary 1735 Vegetables 388 Tomatoes Primary 1735 Vegetables 463 Vegetables, fresh nes Primary 1735 Vegetables 420 Vegetables, leguminous nes Primary 1738 Fruit Primary 515 Apples 1738 Fruit Primary 526 Apricots 1738 Fruit Primary 572 Avocados 1738 Fruit Primary 486 Bananas 1738 Fruit Primary 558 Berries nes 1738 Fruit Primary 552 Blueberries 1738 Fruit Primary 461 Carobs 1738 Fruit Primary 591 Cashewapple 1738 Fruit Primary 531 Cherries 1738 Fruit Primary 530 Cherries, sour 1738 Fruit Primary 554 Cranberries 1738 Fruit Primary 550 Currants 1738 Fruit Primary 577 Dates 1738 Fruit Primary 569 Figs 1738 Fruit Primary 512 Fruit, citrus nes 1738 Fruit Primary 619 Fruit, fresh nes 1738 Fruit Primary 542 Fruit, pome nes 1738 Fruit Primary 541 Fruit, stone nes 1738 Fruit Primary 603 Fruit, tropical fresh nes 1738 Fruit Primary 549 Gooseberries 1738 Fruit Primary 507 Grapefruit (inc. pomelos)

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1738 Fruit Primary 560 Grapes 1738 Fruit Primary 592 Kiwi fruit 1738 Fruit Primary 497 Lemons and limes 1738 Fruit Primary 571 Mangoes, mangosteens, guavas 1738 Fruit Primary 568 Melons, other (inc.cantaloupes) 1738 Fruit Primary 490 Oranges 1738 Fruit Primary 600 Papayas 1738 Fruit Primary 534 Peaches and nectarines 1738 Fruit Primary 521 Pears 1738 Fruit Primary 587 Persimmons 1738 Fruit Primary 574 Pineapples 1738 Fruit Primary 489 Plantains and others 1738 Fruit Primary 536 Plums and sloes 1738 Fruit Primary 523 Quinces 1738 Fruit Primary 547 Raspberries 1738 Fruit Primary 544 Strawberries 1738 Fruit Primary 495 Tangerines, mandarins, clementines, satsumas 1738 Fruit Primary 567 Watermelons

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10.5. Country ISO3 codes Table 10‎ .5: Countries' names an ISO3 code

ISO3 Country short name Code AFG Afghanistan ALB Albania DZA Algeria AGO Angola ATG Antigua and Barbuda ARG Argentina ARM Armenia AUS Australia AUT Austria AZE Azerbaijan BHS Bahamas BHR Bahrain BGD Bangladesh BRB Barbados BLR Belarus BEL Belgium BLZ Belize BEN Benin BTN Bhutan BOL Bolivia (Plurinational State of) BIH Bosnia and Herzegovina BWA Botswana BRA Brazil BRN Brunei Darussalam BGR Bulgaria BFA Burkina Faso BDI Burundi CPV Cabo Verde KHM Cambodia CMR Cameroon CAN Canada CAF Central African Republic TCD Chad CHL Chile CHN China, mainland COL Colombia COM Comoros COG Congo COK Cook Islands CRI Costa Rica CIV Cóte d'Ivoire HRV Croatia CUB Cuba CYP Cyprus CZE Czechia

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PRK Democratic People's Republic of Korea COD Democratic Republic of the Congo DNK Denmark DJI Djibouti DMA Dominica DOM Dominican Republic ECU Ecuador EGY Egypt SLV El Salvador GNQ Equatorial Guinea ERI Eritrea EST Estonia SWZ Eswatini ETH Ethiopia ETH Ethiopia PDR FJI Fiji FIN Finland FRA France GAB Gabon GMB Gambia GEO Georgia DEU Germany GHA Ghana GRC Greece GRD Grenada GTM Guatemala GIN Guinea GNB Guinea-Bissau GUY Guyana HTI Haiti HND Honduras HUN Hungary ISL Iceland IND India IDN Indonesia IRN Iran (Islamic Republic of) IRQ Iraq IRL Ireland ISR Israel ITA Italy JAM Jamaica JPN Japan JOR Jordan KAZ Kazakhstan KEN Kenya KIR Kiribati KWT Kuwait KGZ Kyrgyzstan LAO Lao People's Democratic Republic LVA Latvia

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LBN Lebanon LSO Lesotho LBR Liberia LBY Libya LTU Lithuania LUX Luxembourg MDG Madagascar MWI Malawi MYS Malaysia MLI Mali MLT Malta MTQ Martinique MRT Mauritania MUS Mauritius MEX Mexico FSM Micronesia (Federated States of) MNG Mongolia MNE Montenegro MAR Morocco MOZ Mozambique MMR Myanmar NAM Namibia NPL Nepal NLD Netherlands NZL New Zealand NIC Nicaragua NER Niger NGA Nigeria NIU Niue NOR Norway PSE Occupied Palestinian Territory OMN Oman PAK Pakistan PAN Panama PNG Papua New Guinea PRY Paraguay PER Peru PHL Philippines POL Poland PRT Portugal PRI Puerto Rico QAT Qatar KOR Republic of Korea MDA Republic of Moldova ROU Romania RUS Russian Federation RWA Rwanda KNA Saint Kitts and Nevis LCA Saint Lucia VCT Saint Vincent and the Grenadines

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WSM Samoa STP Sao Tome and Principe SAU Saudi Arabia SEN Senegal SRB Serbia SLE Sierra Leone SGP Singapore SVK Slovakia SVN Slovenia SLB Solomon Islands SOM Somalia ZAF South Africa SSD South Sudan ESP Spain LKA Sri Lanka SDN Sudan SDN Sudan (former) SUR Suriname SWE Sweden CHE Switzerland SYR Syrian Arab Republic TJK Tajikistan THA Thailand MKD The former Yugoslav Republic of Macedonia TLS Timor-Leste TGO Togo TON Tonga TTO Trinidad and Tobago TUN Tunisia TUR Turkey TKM Turkmenistan UGA Uganda UKR Ukraine ARE United Arab Emirates GBR United Kingdom TZA United Republic of Tanzania USA United States of America URY Uruguay UZB Uzbekistan VUT Vanuatu VEN Venezuela (Bolivarian Republic of) VNM Viet Nam ESH Western Sahara YEM Yemen ZMB Zambia ZWE Zimbabwe

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10.6. Flows to Israel – national scale Table 10‎ .6: Flows to Israel from other counties (national scale)

ISO3 Code Country Name Kilo-calories Cropland Crop Flow per (km2) (1000's tons) Capita/Day UKR Ukraine 1109.790996 3654.402144 920.4078843 ARG Argentina 1053.81764 1667.112169 886.2156998 USA United States of 1003.126156 2165.188615 860.2494196 America ISR Israel 955.0718551 2654.479003 3348.16102 RUS Russian Federation 581.3907301 2693.260026 487.1861517 BRA Brazil 448.1295198 1621.093077 425.9645814 DEU Germany 202.6582512 274.0614819 234.4493447 BGR Bulgaria 168.9093374 360.5462371 139.6943554 TUR Turkey 140.8310945 431.7614741 127.1288844 PRY Paraguay 127.2401828 457.10039 165.1979764 FRA France 98.77925198 111.6480585 156.2030238 ROU Romania 61.38214244 207.3125455 47.20581122 GBR United Kingdom 55.52644531 49.57816289 179.3377207 ETH Ethiopia 54.39103191 358.4860288 27.1075691 THA Thailand 53.82552253 170.6705456 56.69441527 ITA Italy 48.5828397 79.61290521 43.40744311 HUN Hungary 46.00205649 110.8774596 38.55669883 CAN Canada 43.06770011 155.6482391 34.35337411 CHE Switzerland 39.32329707 58.07078302 34.32322918 IND India 32.3582762 352.5386441 28.70095987 URY Uruguay 23.03122597 87.79771893 20.50525099 POL Poland 21.8311102 64.99874116 20.86701072 BEL Belgium 20.35979518 13.07456149 72.59978001 AUT Austria 19.28928647 28.48858573 17.81892426 CIV Cóte d'Ivoire 15.62181747 168.2378727 10.70594193 NLD Netherlands 14.209959 11.10677251 50.31503563 ESP Spain 13.85667233 40.02109086 18.28607215 MYS Malaysia 13.79807024 11.07850212 6.300297126 EGY Egypt 13.09190171 14.87596659 14.10075627 VNM Viet Nam 12.63589005 92.79181032 21.39823656 DNK Denmark 11.60953655 16.58076103 9.77625559 CHN China, mainland 10.3451763 34.53183521 17.61822124 IDN Indonesia 9.920345995 88.14810105 9.445601605 GHA Ghana 9.704216947 167.3670188 6.547372082 KAZ Kazakhstan 9.440630961 81.45245824 9.583409877 AUS Australia 9.368211705 48.10098029 7.964260181 CZE Czechia 8.534043431 16.98558773 7.279938085 BLR Belarus 6.278114155 17.67309917 5.254222385 IRL Ireland 5.684690582 5.461222701 10.38361935 FIN Finland 5.406901245 10.39291452 10.75841257 PHL Philippines 4.099933117 12.53078343 6.197985389 MDA Republic of 4.064963901 21.09855611 3.587544809 Moldova PAN Panama 3.644937166 14.89142712 3.786752169

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LTU Lithuania 3.459904726 11.10682075 2.790745872 SWE Sweden 3.396734307 6.662468817 2.85765473 MEX Mexico 3.064661589 20.32038489 8.415206454 SVK Slovakia 2.963956272 7.480662069 2.524364778 CMR Cameroon 2.917120604 60.81341887 2.10603408 GRC Greece 2.768163868 7.195530442 5.224328812 CYP Cyprus 2.634236022 14.49764697 3.525907048 JOR Jordan 2.407310729 16.02351906 10.60820247 ERI Eritrea 2.067028498 23.28425045 1.615174345 GTM Guatemala 2.036350176 18.89192412 1.834450474 ZAF South Africa 1.296370908 9.125538822 19.82621494 MUS Mauritius 0.997370896 1.278851233 9.250589709 PAK Pakistan 0.937213873 2.513890168 5.15099738 LBN Lebanon 0.923899783 3.553945289 3.706194364 LVA Latvia 0.855544533 2.660735658 0.747892258 TZA United Republic of 0.844013944 49.41770395 2.109554994 Tanzania LKA Sri Lanka 0.736887245 14.43333146 2.487741534 ECU Ecuador 0.655047622 24.82323723 0.762618439 CHL Chile 0.624935871 1.454363042 1.874610345 NZL New Zealand 0.527181414 0.645548347 0.518365676 HRV Croatia 0.510224646 1.310753359 0.464495644 BOL Bolivia 0.508041791 2.765492172 0.453705804 (Plurinational State of) FJI Fiji 0.450280387 1.274001145 3.171653497 PER Peru 0.449555222 2.236748935 0.441952225 EST Estonia 0.420882676 1.469619692 0.334065329 UZB Uzbekistan 0.361364974 1.647856719 0.383452112 TGO Togo 0.342277155 3.41310472 0.230218729 GUY Guyana 0.342020347 0.5361873 2.460463405 UGA Uganda 0.323875266 18.56791493 0.957243546 TUN Tunisia 0.298598488 7.818991046 0.49041593 CUB Cuba 0.286454791 3.984407613 4.183884829 SDN Sudan 0.266296175 4.018451291 0.569088768 SLB Solomon Islands 0.252952027 3.959454868 0.173222689 BLZ Belize 0.225148347 0.876536804 1.604807951 SEN Senegal 0.224356079 1.880974436 0.137760685 ZWE Zimbabwe 0.21865952 0.392256127 1.99985544 DOM Dominican Republic 0.20055948 4.450925304 0.324380673 JAM Jamaica 0.199408163 0.326051084 1.845066925 PRT Portugal 0.175312719 0.632852953 0.203426976 NIC Nicaragua 0.158579364 0.797218193 0.123828116 GEO Georgia 0.154556104 0.827285014 0.225400838 GNB Guinea-Bissau 0.154232509 3.643082378 0.170305383 KEN Kenya 0.144972736 1.93156582 0.739461085 MMR Myanmar 0.134926779 1.149346341 0.105514009 MAR Morocco 0.131991504 1.22206839 0.349671725 VEN Venezuela 0.127300213 1.389391458 0.069423292 (Bolivarian Republic of)

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LUX Luxembourg 0.12373519 0.17911213 0.10382359 SVN Slovenia 0.121947612 0.155543013 0.110019474 COL Colombia 0.118712334 1.30652058 0.148957494 MOZ Mozambique 0.118318158 1.031318886 0.526298146 IRN Iran (Islamic 0.114925829 0.62771418 0.395122694 Republic of) NOR Norway 0.096841237 0.205627424 0.080136442 NGA Nigeria 0.085696406 0.875039185 0.06077186 HND Honduras 0.084019594 0.630393742 1.117545135 PNG Papua New Guinea 0.075168271 1.150339165 0.051439515 MDG Madagascar 0.073977277 1.646306227 0.088954161 SOM Somalia 0.072895731 0.572368104 0.035472368 SWZ Eswatini 0.071193857 0.870295111 1.128606422 BEN Benin 0.067493106 2.262657531 0.071681072 TTO Trinidad and 0.060632843 0.268253654 0.54105033 Tobago ZMB Zambia 0.04733841 0.050024209 0.435115639 BRB Barbados 0.042080009 0.070452861 0.390302097 AZE Azerbaijan 0.038023938 0.17184197 0.033454033 MKD The former 0.037603886 0.109441604 0.036543713 Yugoslav Republic of Macedonia JPN Japan 0.032201092 0.061973192 0.032209346 ARM Armenia 0.032169021 0.127490661 0.031938783 LBR Liberia 0.02771152 1.08498407 0.018634558 PRK Democratic 0.026613463 0.205205729 0.028446024 People's Republic of Korea BGD Bangladesh 0.025817966 0.158218981 0.023456251 IRQ Iraq 0.022469178 0.096034228 0.019797638 KGZ Kyrgyzstan 0.015191707 0.072366644 0.034351413 LAO Lao People's 0.01481846 0.063395836 0.027695119 Democratic Republic SLE Sierra Leone 0.012849748 0.232264614 0.008638734 GNQ Equatorial Guinea 0.012816437 0.861028447 0.008614299 HTI Haiti 0.012241646 0.18288611 0.008739335 GRD Grenada 0.010605648 0.105284617 0.005638527 CRI Costa Rica 0.008300345 0.038389591 0.040736973 BIH Bosnia and 0.007485008 0.020328428 0.006707266 Herzegovina KOR Republic of Korea 0.006925013 0.046863496 0.015559349 YEM Yemen 0.006419027 0.044511846 0.005022648 AFG Afghanistan 0.006065994 0.04794132 0.0057012 SYR Syrian Arab 0.005246443 0.046322878 0.064438889 Republic SLV El Salvador 0.004936497 0.051950067 0.012200478 BRN Brunei Darussalam 0.004871335 0.005606735 0.002345904 MLI Mali 0.003635303 0.053270861 0.002755862 COG Congo 0.003295047 0.018398963 0.026488314 MNG Mongolia 0.002074192 0.009300906 0.002159422

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RWA Rwanda 0.001935499 0.103390766 0.013088123 NER Niger 0.001866059 0.008095356 0.001663092 COD Democratic 0.001326106 0.063798 0.003106252 Republic of the Congo DMA Dominica 0.001242373 0.042852396 0.021217801 NPL Nepal 0.001205831 0.00738645 0.001932868 TJK Tajikistan 0.001196484 0.009463443 0.017048614 GMB Gambia 0.001045475 0.009947559 0.000508906 ALB Albania 0.000962066 0.004405536 0.000834156 OMN Oman 0.00065401 0.001286713 0.000552231 DZA Algeria 0.000537096 0.008014823 0.00084702 MLT Malta 0.000482215 0.000906821 0.001922165 SGP Singapore 0.000389521 0.000583507 0.000598361 BFA Burkina Faso 0.000338185 0.005199623 0.000377069 GIN Guinea 0.000314035 0.00378214 0.000226066 LSO Lesotho 0.000156455 0.00125774 0.002357347 BTN Bhutan 0.000156337 0.000817847 0.000738141 TKM Turkmenistan 0.00013853 0.001015318 0.000314334 ARE United Arab 0.000101539 0.000549754 0.000515575 Emirates STP Sao Tome and 9.19E-05 0.004941771 6.18002E-05 Principe BDI Burundi 7.60E-05 0.001267635 0.000528665 KHM Cambodia 6.95E-05 0.000808061 0.001208135 MWI Malawi 6.59E-05 0.000800513 5.59288E-05 SUR Suriname 6.33E-05 0.000176983 6.38878E-05 VCT Saint Vincent and 4.99E-05 0.000198451 2.74384E-05 the Grenadines NAM Namibia 2.83E-05 0.000123208 2.3685E-05 MRT Mauritania 1.81E-05 3.88192E-05 0.000125986 GAB Gabon 6.54E-06 0.00018143 1.6839E-05 CAF Central African 5.01E-06 0.000248382 2.07608E-05 Republic LCA Saint Lucia 3.53E-06 4.66309E-05 3.58644E-06 CPV Cabo Verde 2.86E-06 4.96092E-05 1.48494E-05 TCD Chad 2.72E-06 3.65858E-05 2.25348E-06 QAT Qatar 1.77E-06 2.4299E-06 2.9335E-06 AGO Angola 1.35E-06 8.76166E-06 2.84803E-06 SAU Saudi Arabia 1.14E-06 6.88934E-06 1.54591E-06 BWA Botswana 2.60E-07 2.88357E-06 1.92052E-07 VUT Vanuatu 1.61E-07 2.36422E-06 1.25934E-07 BHR Bahrain 1.36E-07 1.10942E-06 1.13572E-06 ATG Antigua and 2.63E-08 1.92E-07 8.83E-08 Barbuda KWT Kuwait 1.51E-09 5.02E-09 1.46E-09 DJI Djibouti 1.36E-09 6.36E-09 1.06E-09 LBY Libya 1.04E-11 9.56E-11 8.64E-12 BHS Bahamas 0 0 0 COK Cook Islands 0 0 0 COM Comoros 0 0 0

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ESH Western Sahara 0 0 0 FSM Micronesia 0 0 0 (Federated States of) ISL Iceland 0 0 0 KIR Kiribati 0 0 0 KNA Saint Kitts and 0 0 0 Nevis MNE Montenegro 0 0 0 MTQ Martinique 0 0 0 NIU Niue 0 0 0 PRI Puerto Rico 0 0 0 PSE Occupied 0 0 0 Palestinian Territory SRB Serbia 0 0 0 SSD South Sudan 0 0 0 TLS Timor-Leste 0 0 0 TON Tonga 0 0 0 WSM Samoa 0 0 0

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10.7. Flows to Israel – biome scale Table 10‎ .7: Cropland footprint of Israel in different biomes

Biome Name Cropland in Cropland in Total Cropland Global200 Areas Other Areas (km2) (km2) (km2) Temperate Grasslands, 301.26 6354.94 6656.20

Savannas & Shrublands Temperate Broadleaf 267.09 4461.26 4728.36

& Mixed Forests Tropical & Subtropical 1663.56 378.81 2042.38

Moist Broadleaf Forests Tropical & Subtropical 762.34 663.90 1426.24 Grasslands,

Savannas & Shrublands Deserts & Xeric 17.96 352.29 370.25 Shrublands Tropical & Subtropical 171.53 187.74 359.27

Dry Broadleaf Forests Mediterranean Forests, 295.62 0.04 295.65

Woodlands & Scrub Montane Grasslands 272.14 3.34 275.48

& Shrublands Temperate Conifer 168.88 55.11 223.99 Forests Boreal Forests/Taiga 19.70 78.00 97.70 Flooded Grasslands 1.94 33.39 35.33

& Savannas Mangroves 2.03 15.48 17.51 Tropical & Subtropical 7.79 0.84 8.63

Coniferous Forests Tundra 0.05 0.00 0.05

138

10.8. Flows to Israel – ecoregion scale Table 10‎ .8: Species lost and cropland footprint of Israel at an ecoregion scale

Ecoregion Ecoregion name Regional Israel's Rank Rank Main crop Share of total code species cropland (species (cropland supplied by ecoregion lost footprint loss) footprint) ecoregion supply (km2) NT0150 Parana-Paraiba 1.538 744.6 1 5 Soybeans 50% interior forests AT0111 Eastern Guinean 1.025 275.2 2 11 Cocoa, beans 98% forests NT0101 Araucaria moist 0.621 212.5 3 19 Soybeans 64% forests AT1007 Ethiopian montane 0.583 252.3 4 13 Sesame seed 92% grasslands and woodlands PA0814 Pontic steppe 0.569 2862.9 5 1 Wheat 58% NT0803 Humid Pampas 0.527 911.5 6 4 Maize 75% NT0224 Panamanian dry 0.452 3.7 7 176 Maize 64% forests PA0419 East European forest 0.424 1453.4 8 2 Wheat 48% steppe IM0155 Sri Lanka montane 0.378 4.3 9 163 Tea 100% rain forests PA0404 Balkan mixed forests 0.359 385.5 10 8 Wheat 58% NT0801 Argentine Espinal 0.347 421.6 11 7 Maize 73% PA1206 Cyprus Mediterranean 0.346 36.5 12 57 Barley 71% forests PA0412 Central European 0.346 1266.5 13 3 Wheat 55% mixed forests IM0107 Chao Phraya 0.337 36.7 14 55 Rice, paddy 95% freshwater swamp forests NA0803 Central and Southern 0.336 325.7 15 10 Wheat 61%

139

mixed grasslands PA0416 Crimean 0.324 60.7 16 40 Wheat 80% Submediterranean forest complex AT1008 Ethiopian montane 0.281 12.1 17 97 Sesame seed 97% moorlands NA0409 Mississippi lowland 0.261 87.6 18 32 Rice, paddy 43% forests NT0103 Bahia coastal forests 0.252 42.1 19 51 Coffee, green 39% IM1402 Indochina mangroves 0.251 11.9 20 98 Rice, paddy 72% NT0704 Cerrado 0.233 704.6 21 6 Soybeans 74% NA0805 Central tall grasslands 0.232 244.2 22 15 Maize 62% AT0112 Ethiopian montane 0.232 84.0 23 34 Sesame seed 84% forests NT0710 Uruguayan savanna 0.228 207.6 24 20 Soybeans 73% NT0130 Isthmian-Pacific moist 0.226 8.4 25 111 Maize 59% forests IM0154 Sri Lanka lowland rain 0.207 7.9 26 118 Tea 84% forests NT0162 Sierra Madre de 0.200 4.8 27 153 Coffee, green 50% Chiapas moist forest NA0414 Southern Great Lakes 0.188 157.0 28 25 Maize 41% forests NA0810 Northern mixed 0.185 173.4 29 23 Wheat 53% grasslands IM0202 Central Indochina dry 0.179 107.1 30 28 Rice, paddy 86% forests NA0804 Central 0.177 214.7 31 18 Maize 52% forest/grasslands transition zone NA0812 Northern tall 0.167 72.1 32 38 Wheat 60% grasslands NT0178 Western Ecuador 0.167 8.3 33 114 Cocoa, beans 71% moist forests

140

NA0813 Palouse grasslands 0.147 46.3 34 47 Wheat 96% IM0141 Northern Vietnam 0.142 7.4 35 125 Coffee, green 56% lowland rain forests PA0410 Central Anatolian 0.138 77.4 36 35 Wheat 73% deciduous forests AT1013 Ruwenzori-Virunga 0.131 0.5 37 329 Coffee, green 92% montane moorlands PA0432 Po Basin mixed 0.129 28.4 38 66 Maize 71% forests PA0809 Kazakh forest steppe 0.129 351.1 39 9 Wheat 79% NA0815 Western short 0.126 200.7 40 22 Wheat 64% grasslands AT0130 Western Guinean 0.124 42.1 41 50 Cocoa, beans 97% lowland forests NT0214 Ecuadorian dry 0.122 5.7 42 142 Cocoa, beans 65% forests PA0431 Pannonian mixed 0.119 200.8 43 21 Wheat 47% forests NT0104 Bahia interior forests 0.117 33.0 44 63 Coffee, green 40% PA0504 Carpathian montane 0.115 94.8 45 30 Wheat 73% conifer forests NA0801 California Central 0.113 36.5 46 56 Rice, paddy 23% Valley grasslands NT0210 Chaco 0.109 246.0 47 14 Maize 55% NA0807 Flint Hills tall 0.107 13.9 48 90 Wheat 57% grasslands AA0123 Sulawesi lowland rain 0.106 33.7 49 61 Cocoa, beans 68% forests NT0708 Humid Chaco 0.105 108.5 50 27 Cassava 42% AT0721 Victoria Basin forest- 0.104 21.6 51 75 Coffee, green 79% savanna mosaic NA0811 Northern short 0.103 266.5 52 12 Wheat 89% grasslands NT0140 Mato Grosso tropical 0.101 67.3 53 39 Soybeans 90% dry forests

141

IM0147 Red River freshwater 0.100 2.9 54 188 Rice, paddy 82% swamp forests PA0803 Central Anatolian 0.095 18.8 55 80 Wheat 68% steppe IM0211 Southern Vietnam 0.094 8.6 56 109 Coffee, green 55% lowland dry forests NA0415 Upper Midwest 0.093 57.4 57 41 Maize 60% Forest/Savanna transition zone PA0402 Atlantic mixed forests 0.092 172.1 58 24 Sugar beet 68% NA0517 Middle Atlantic coastal 0.089 37.5 59 53 Wheat 42% forests PA0445 Western European 0.086 218.7 60 17 Rye 40% broadleaf forests NT0165 Southern Andean 0.083 11.5 61 100 Grapefruit (inc. 25% Yungas pomelos) IM0108 Chao Phraya lowland 0.080 3.9 62 172 Rice, paddy 73% moist deciduous forests NA0404 Central U.S. 0.079 73.2 63 37 Maize 39% hardwood forests PA1202 Anatolian conifer and 0.079 54.1 64 43 Wheat 42% deciduous mixed forests IM0165 Tonle Sap-Mekong 0.078 7.7 65 123 Rice, paddy 69% peat swamp forests IM0164 Tonle Sap freshwater 0.078 6.4 66 136 Rice, paddy 73% swamp forests NT0703 Campos Rupestres 0.078 4.4 67 160 Maize 45% montane savanna NT1407 Bocas del Toro-San 0.077 0.9 68 286 Nutmeg, mace 40% Bastimentos Island- and San Blas mangroves cardamoms IM0210 Southeastern 0.077 22.5 69 74 Coffee, green 67% Indochina dry

142

evergreen forests PA0405 Baltic mixed forests 0.075 53.7 70 44 Rye 36% IM0166 Upper Gangetic 0.074 48.2 71 46 Sesame seed 38% Plains moist deciduous forests NT0905 Guayaquil flooded 0.074 1.0 72 278 Cocoa, beans 78% grasslands IM0206 Khathiar-Gir dry 0.073 73.3 73 36 Soybeans 64% deciduous forests IM0138 Northern Khorat 0.072 3.4 74 183 Rice, paddy 87% Plateau moist deciduous forests PA0515 Northern Anatolian 0.071 43.6 75 48 Wheat 56% conifer and deciduous forests PA0444 West Siberian 0.069 85.4 76 33 Wheat 80% broadleaf and mixed forests PA1220 Southern Anatolian 0.069 39.8 77 52 Wheat 33% montane conifer and deciduous forests PA0435 Rodope montane 0.069 13.1 78 93 Wheat 55% mixed forests NT0209 Central American dry 0.068 5.4 79 145 Grapefruit (inc. 31% forests pomelos) PA0420 Eastern Anatolian 0.067 33.9 80 60 Wheat 70% deciduous forests IM0209 South Deccan Plateau 0.064 18.1 81 81 Coffee, green 67% dry deciduous forests NT1406 Belizean Reef 0.061 0.7 82 306 Cassava 29% mangroves IM0207 Narmada Valley dry 0.061 37.2 83 54 Soybeans 67% deciduous forests PA0422 Euxine-Colchic 0.061 24.1 84 70 Maize 67% deciduous forests

143

NT0145 Northwestern Andean 0.061 3.9 85 171 Coffee, green 51% montane forests NA0814 Texas blackland 0.059 9.7 86 106 Wheat 38% prairies PA0817 South Siberian forest 0.059 89.4 87 31 Wheat 77% steppe NA0701 Western Gulf coastal 0.059 14.3 88 88 Rice, paddy 47% grasslands AT1015 Southern Rift 0.057 3.7 89 174 Coffee, green 58% montane forest- grassland mosaic AT0108 East African montane 0.055 4.0 90 170 Tea 49% forests PA0421 English Lowlands 0.055 19.6 91 79 Sugar beet 93% beech forests NT0106 Caatinga Enclaves 0.054 0.8 92 291 Cassava 41% moist forests NT0112 Central American 0.053 1.1 93 271 Nutmeg, mace 38% montane forests and cardamoms NT0160 Serra do Mar coastal 0.052 6.5 94 132 Maize 41% forests NT0121 Eastern Cordillera real 0.049 4.0 95 167 Coffee, green 62% montane forests PA0810 Kazakh steppe 0.048 230.8 96 16 Wheat 83% NT1405 Belizean Coast 0.047 0.9 97 288 Maize 51% mangroves AT0121 Mount Cameroon and 0.046 0.1 98 450 Cocoa, beans 84% Bioko montane forests AT0126 Northwestern 0.045 33.1 99 62 Cocoa, beans 92% Congolian lowland forests IM0124 Malabar Coast moist 0.045 4.7 100 154 Coffee, green 51% forests IM0152 Southern Annamites 0.045 5.0 101 150 Coffee, green 92%

144

montane rain forests NT0908 Parana flooded 0.044 7.8 102 122 Maize 54% savanna AT0101 Albertine Rift montane 0.044 4.4 103 157 Coffee, green 91% forests AT0109 Eastern Arc forests 0.044 1.3 104 254 Cashew nuts, 31% with shell IM0112 Eastern Java-Bali 0.043 2.6 105 194 Coffee, green 54% montane rain forests IM0163 Tenasserim-South 0.042 7.8 106 121 Rice, paddy 95% Thailand semi- evergreen rain forests AT0114 Guinean montane 0.042 2.1 107 212 Cocoa, beans 98% forests IM0150 South Western Ghats 0.041 3.0 108 186 Coffee, green 56% moist deciduous forests PA1222 Tyrrhenian-Adriatic 0.041 17.7 109 83 Wheat 47% Sclerophyllous and mixed forests PA1211 Italian sclerophyllous 0.041 29.8 110 65 Wheat 49% and semi-deciduous forests AT0102 Atlantic Equatorial 0.040 13.3 111 92 Cocoa, beans 93% coastal forests NA0802 Canadian Aspen 0.038 94.9 112 29 Wheat 58% forests and parklands IM0134 North Western Ghats 0.038 5.2 113 149 Coffee, green 55% moist deciduous forests IM0139 Northern Thailand- 0.037 4.0 114 166 Rice, paddy 97% Laos moist deciduous forests NA0417 Willamette Valley 0.037 2.2 115 206 Wheat 54% forests

145

NT0152 Pernambuco interior 0.036 1.9 116 223 Oranges 62% forests NT0303 Central American 0.036 5.3 117 147 Grapefruit (inc. 45% pine-oak forests pomelos) AT1403 Guinean mangroves 0.035 1.3 118 260 Cashew nuts, 96% with shell NA1309 Snake/Columbia 0.034 56.3 119 42 Wheat 81% shrub steppe IM0113 Eastern Java-Bali rain 0.033 5.9 120 140 Coconuts 60% forests AA0124 Sulawesi montane 0.033 12.1 121 96 Cocoa, beans 81% rain forests IM0146 Peninsular Malaysian 0.032 8.4 122 112 Oil, palm 43% rain forests IM0137 Northern Indochina 0.032 20.6 123 76 Coffee, green 69% subtropical forests IM0201 Central Deccan 0.031 28.1 124 67 Soybeans 52% Plateau dry deciduous forests PA0904 Nile Delta flooded 0.031 10.2 125 104 Rice, paddy 83% savanna IM0151 South Western Ghats 0.031 2.1 126 216 Coffee, green 62% montane rain forests AT0116 KwaZulu-Cape 0.030 1.1 127 272 Grapefruit (inc. 88% coastal forest mosaic pomelos) PA1201 Aegean & Western 0.030 27.0 128 69 Wheat 24% Turkey sclerophyllous and mixed forests IM0120 Lower Gangetic 0.030 14.1 129 89 Sesame seed 41% Plains moist deciduous forests NT0909 Southern Cone 0.029 3.4 130 184 Grapefruit (inc. 38% Mesopotamian pomelos) savanna NT0212 Chiquitano dry forests 0.029 9.6 131 108 Soybeans 86%

146

NA0808 Montana Valley and 0.028 13.7 132 91 Wheat 95% Foothill grasslands AT0707 Guinean forest- 0.025 23.8 133 71 Cocoa, beans 81% savanna mosaic IM0105 Brahmaputra Valley 0.025 2.6 134 195 Tea 37% semi-evergreen forests IM0158 Sumatran lowland rain 0.025 15.1 135 86 Coconuts 41% forests PA0409 Celtic broadleaf 0.025 35.4 136 58 Sugar beet 89% forests PA0436 Sarmatic mixed 0.024 109.9 137 26 Barley 49% forests NT0139 Maranhao Babacu 0.024 7.9 138 119 Cassava 51% forests NT0129 Isthmian-Atlantic 0.023 1.7 139 234 Maize 43% moist forests NT0102 Atlantic Coast 0.023 0.2 140 398 Cassava 58% restingas IM0203 Chhota-Nagpur dry 0.023 10.7 141 103 Sesame seed 53% deciduous forests IM1301 Deccan thorn scrub 0.022 34.1 142 59 Coffee, green 38% forests NA0411 Northeastern coastal 0.022 6.4 143 134 Maize 43% forests NA0413 Southeastern mixed 0.021 19.9 144 78 Wheat 45% forests AT0103 Cameroonian 0.021 1.1 145 273 Cocoa, beans 71% Highlands forests AT0107 Cross-Sanaga-Bioko 0.021 1.7 146 232 Cocoa, beans 78% coastal forests IM0168 Western Java rain 0.021 2.6 147 196 Coconuts 80% forests IM0135 North Western Ghats 0.020 1.9 148 221 Coffee, green 68% montane rain forests

147

NT0127 Hispaniolan moist 0.020 3.6 149 178 Grapefruit (inc. 58% forests pomelos) IM0129 Mindanao-Eastern 0.020 8.1 150 116 Coconuts 100% Visayas rain forests NT0202 Atlantic dry forests 0.019 5.0 151 151 Cassava 30% NT0170 Tocantins-Araguaia- 0.019 7.1 152 128 Cassava 80% Maranhao moist forests AT0716 Southern Acacia- 0.019 7.8 153 120 Coffee, green 27% Commiphora bushlands and thickets IM1303 Northwestern thorn 0.019 30.0 154 64 Sugar cane 63% scrub forests PA0501 Alps conifer and 0.019 15.4 155 85 Rye 50% mixed forests NT1304 Caatinga 0.019 43.0 156 49 Cassava 41% NT0233 Veracruz dry forests 0.018 0.3 157 359 Grapefruit (inc. 67% pomelos) NT0154 Peten-Veracruz moist 0.018 4.1 158 165 Sugar cane 51% forests NT0144 Northeastern Brazil 0.018 0.6 159 323 Cassava 88% restingas IM0162 Sundarbans 0.018 1.3 160 246 Sesame seed 55% freshwater swamp forests AT1405 Southern Africa 0.017 0.0 161 520 Grapefruit (inc. 94% mangroves pomelos) AT0705 East Sudanian 0.017 23.0 162 73 Sesame seed 76% savanna PA0805 Eastern Anatolian 0.017 17.1 163 84 Wheat 78% montane steppe AA0119 Solomon Islands rain 0.017 4.0 164 169 Cocoa, beans 97% forests IM0157 Sumatran freshwater 0.017 0.9 165 287 Coconuts 53%

148

swamp forests IM0111 Eastern highlands 0.017 20.3 166 77 Rice, paddy 32% moist deciduous forests IM0303 Northeast India- 0.016 0.4 167 349 Sesame seed 44% Myanmar pine forests NA0809 Nebraska Sand Hills 0.016 4.8 168 152 Maize 63% mixed grasslands NT0135 Madeira-Tapajos 0.016 15.0 169 87 Cassava 33% moist forests NT0171 Trinidad and Tobago 0.015 0.3 170 371 Sugar cane 99% moist forests PA0408 Caucasus mixed 0.015 11.8 171 99 Wheat 49% forests NA0403 Appalachian/Blue 0.015 7.3 172 126 Maize 49% Ridge forests NA0407 Eastern Great Lakes 0.015 6.6 173 131 Maize 43% lowland forests IM0208 Northern dry 0.015 3.7 174 177 Rice, paddy 50% deciduous forests IM0136 Northern Annamites 0.015 1.7 175 230 Coffee, green 69% rain forests NT0176 Veracruz moist forests 0.014 1.8 176 226 Grapefruit (inc. 69% pomelos) NT0105 Bolivian Yungas 0.014 1.4 177 245 Brazil nuts, 92% with shell AT1012 Maputaland- 0.014 0.7 178 307 Grapefruit (inc. 93% Pondoland bushland pomelos) and thickets IM0167 Western Java 0.014 1.2 179 264 Coconuts 48% montane rain forests NT0302 Belizian pine forests 0.014 0.1 180 455 Grapefruit (inc. 67% pomelos) AT0120 Mascarene forests 0.013 1.3 181 247 Sugar cane 100% PA1209 Iberian sclerophyllous 0.013 27.9 182 68 Rice, paddy 43%

149

and semi-deciduous forests NA0529 Southeastern conifer 0.013 9.6 183 107 Grapefruit (inc. 71% forests pomelos) NA0512 Eastern Cascades 0.013 3.1 184 185 Wheat 36% forests NT0204 Baj?o dry forests 0.013 1.3 185 259 Walnuts, with 29% shell IM0159 Sumatran montane 0.012 2.4 186 200 Cocoa, beans 58% rain forests IM0156 Sulu Archipelago rain 0.012 0.2 187 388 Coconuts 100% forests NA0405 East Central Texas 0.012 1.9 188 222 Sorghum 26% forests PA1215 Northeastern Spain & 0.012 7.6 189 124 Rice, paddy 28% Southern France Mediterranean forests NT0213 Cuban dry forests 0.011 3.5 190 180 Grapefruit (inc. 83% pomelos) PA1221 Southwest Iberian 0.011 6.7 191 130 Rice, paddy 46% Mediterranean sclerophyllous and mixed forests AT0712 Northern Congolian 0.011 12.9 192 94 Cocoa, beans 88% forest-savanna mosaic NT0167 Talamancan montane 0.011 0.3 193 373 Sorghum 70% forests IM0212 Sri Lanka dry-zone 0.011 2.5 194 199 Coconuts 70% dry evergreen forests IM0149 South China-Vietnam 0.011 4.0 195 168 Rice, paddy 30% subtropical evergreen forests IM0145 Peninsular Malaysian 0.011 0.1 196 447 Oil, palm 65% peat swamp forests

150

NT0903 Enriquillo wetlands 0.011 0.0 197 545 Cocoa, beans 49% IM0119 Kayah-Karen 0.011 2.7 198 191 Rice, paddy 96% montane rain forests NA0412 Ozark Mountain 0.010 2.3 199 202 Wheat 47% forests NA1202 California interior 0.010 4.3 200 161 Grapefruit (inc. 30% chaparral and pomelos) woodlands NT0230 Southern Pacific dry 0.010 0.7 201 303 Walnuts, with 39% forests shell OC0702 Hawaii tropical low 0.010 0.2 202 390 Walnuts, with 35% shrublands shell IM0126 Meghalaya 0.010 0.9 203 279 Rice, paddy 21% subtropical forests AA0203 Sumba deciduous 0.010 1.0 204 277 Cocoa, beans 78% forests NT0151 Pernambuco coastal 0.010 0.4 205 342 Cassava 48% forests PA1219 Southeastern Iberian 0.010 0.2 206 394 Grapefruit (inc. 39% shrubs and pomelos) woodlands OC0202 Hawaii tropical dry 0.010 0.8 207 289 Grapefruit (inc. 40% forests pomelos) PA0811 Kazakh upland 0.010 5.6 208 144 Wheat 99% NT1401 Alvarado mangroves 0.010 0.4 209 347 Cassava 79% OC0201 Fiji tropical dry forests 0.010 1.2 210 262 Sugar cane 94% NT1301 Araya and Paria xeric 0.010 0.2 211 392 Cocoa, beans 99% scrub NA0523 Piney Woods forests 0.009 4.4 212 159 Rice, paddy 31% OC0106 Hawaii tropical moist 0.009 0.8 213 293 Grapefruit (inc. 45% forests pomelos) AA0201 Lesser Sundas 0.009 2.0 214 217 Coconuts 52% deciduous forests NT0211 Chiapas Depression 0.009 0.2 215 384 Sugar cane 40% dry forests

151

IM0142 Orissa semi- 0.009 0.8 216 294 Rice, paddy 54% evergreen forests NT0215 Hispaniolan dry 0.009 0.8 217 290 Cocoa, beans 57% forests PA0907 Ussuri-Wusuli 0.009 2.5 218 198 Barley 46% meadow and forest meadow AT0115 Knysna-Amatole 0.009 0.1 219 473 Grapefruit (inc. 92% montane forests pomelos) NA1312 Tamaulipan mezquital 0.008 4.3 220 162 Sorghum 58% NT0232 Tumbes-Piura dry 0.008 0.7 221 302 Cocoa, beans 32% forests AA0409 Southeast Australia 0.008 7.0 222 129 Wheat 61% temperate forests IM0106 Cardamom Mountains 0.008 1.2 223 263 Rice, paddy 66% rain forests PA1218 South Appenine 0.008 1.3 224 251 Wheat 51% mixed montane forests NA0401 Allegheny Highlands 0.008 2.7 225 190 Maize 50% forests IM0701 Terai-Duar savanna 0.008 0.6 226 317 Rice, paddy 34% and grasslands IM0114 Greater Negros- 0.008 1.3 227 255 Coconuts 100% Panay rain forests NT0218 Jamaican dry forests 0.008 0.1 228 442 Sugar cane 100% NT0164 South Florida 0.008 0.1 229 466 Grapefruit (inc. 91% rocklands pomelos) IM0160 Sumatran peat 0.008 2.1 230 213 Coconuts 62% swamp forests IM0123 Luzon rain forests 0.008 2.8 231 189 Coconuts 100% NA1201 California coastal 0.008 2.1 232 215 Grapefruit (inc. 84% sage and chaparral pomelos) AA1206 Mount Lofty 0.007 1.3 233 250 Wheat 48% woodlands

152

AA1203 Eyre and York mallee 0.007 3.7 234 173 Wheat 81% NT0180 Xingu-Tocantins- 0.007 3.5 235 181 Cassava 64% Araguaia moist forests NT0310 Trans-Mexican 0.007 1.0 236 275 Grapefruit (inc. 68% Volcanic Belt pine-oak pomelos) forests NT0168 Tapajos-Xingu moist 0.007 4.4 237 158 Cassava 46% forests AT0706 Eastern Miombo 0.007 6.4 238 135 Sugar cane 60% woodlands NA0504 Atlantic coastal pine 0.007 0.2 239 385 Wheat 33% barrens NT0161 Sierra de los Tuxtlas 0.007 0.1 240 479 Grapefruit (inc. 88% pomelos) NA1302 Central Mexican 0.007 1.6 241 237 Grapefruit (inc. 62% matorral pomelos) NT0225 Patia Valley dry 0.007 0.0 242 522 Sugar cane 53% forests NT1403 Bahamian mangroves 0.007 0.2 243 395 Sugar cane 70% NT0217 Jalisco dry forests 0.007 0.4 244 341 Grapefruit (inc. 66% pomelos) NA0402 Appalachian mixed 0.007 4.2 245 164 Maize 52% mesophytic forests PA0901 Amur meadow steppe 0.007 8.1 246 117 Wheat 54% NT0115 Choco-Darien moist 0.007 0.6 247 326 Maize 67% forests IM0127 Mentawai Islands rain 0.007 0.3 248 374 Coconuts 38% forests NT0179 Windward Islands 0.007 0.1 249 451 Grapefruit (inc. 77% moist forests pomelos) NA0505 Blue Mountains 0.006 2.2 250 210 Wheat 84% forests AT0704 Central Zambezian 0.006 11.0 251 101 Coffee, green 52% Miombo woodlands NT0111 Central American 0.006 0.9 252 281 Grapefruit (inc. 69%

153

Atlantic moist forests pomelos) IM1404 Myanamar Coast 0.006 0.3 253 378 Rice, paddy 85% mangroves NA0516 Klamath-Siskiyou 0.006 1.3 254 258 Grapefruit (inc. 25% forests pomelos) PA0446 Zagros Mountains 0.006 10.0 255 105 Wheat 71% forest steppe IM0301 Himalayan subtropical 0.006 0.7 256 300 Mushrooms 24% pine forests and truffles NT0228 Sinaloan dry forests 0.006 1.1 257 269 Grapefruit (inc. 34% pomelos) IM1401 Godavari-Krishna 0.006 0.2 258 418 Rice, paddy 52% mangroves NA0201 Sonoran-Sinaloan 0.006 0.9 259 280 Wheat 44% transition subtropical dry forest AT1004 Drakensberg montane 0.006 2.3 260 203 Grapefruit (inc. 92% grasslands, pomelos) woodlands and forests PA1205 Crete Mediterranean 0.006 0.7 261 311 Olives 40% forests IM0102 Borneo lowland rain 0.006 5.9 262 139 Oil, palm 58% forests IM1304 Thar desert 0.006 10.9 263 102 Sesame seed 58% PA0437 Sichuan Basin 0.006 1.5 264 242 Tomatoes 28% evergreen broadleaf forests NT0221 Magdalena Valley dry 0.006 0.2 265 417 Cocoa, beans 93% forests NA0806 Edwards Plateau 0.006 1.7 266 236 Wheat 38% savanna AT1202 Lowland fynbos and 0.006 0.4 267 336 Grapefruit (inc. 56% renosterveld pomelos) AA1209 Southwest Australia 0.006 8.3 268 113 Wheat 83%

154

savanna NT0148 Pantanos de Centla 0.006 0.2 269 387 Grapefruit (inc. 44% pomelos) IM0161 Sundaland heath 0.005 1.1 270 267 Coconuts 38% forests IM1405 Sunda Shelf 0.005 0.5 271 330 Coconuts 44% mangroves NA1310 Sonoran desert 0.005 6.1 272 137 Wheat 30% IM0403 Western Himalayan 0.005 0.6 273 320 Rice, paddy 23% broadleaf forests NT1006 Northern Andean 0.005 0.2 274 408 Cocoa, beans 77% paramo PA0424 Huang He Plain mixed 0.005 6.5 275 133 Tomatoes 29% forests IM0131 Mizoram-Manipur- 0.005 1.2 276 261 Sesame seed 22% Kachin rain forests PA0415 Changjiang Plain 0.005 5.8 277 141 Tomatoes 30% evergreen forests NA1307 Meseta Central 0.005 2.3 278 204 Walnuts, with 34% matorral shell NT0904 Everglades 0.005 0.9 279 284 Grapefruit (inc. 84% pomelos) NT0141 Monte Alegre varzea 0.005 0.4 280 335 Cassava 73% IM0204 East Deccan dry- 0.005 0.6 281 322 Rice, paddy 61% evergreen forests PA1214 Mediterranean 0.005 8.2 282 115 Olives 74% woodlands and forests IM0104 Borneo peat swamp 0.005 0.9 283 282 Oil, palm 48% forests AT0125 Northern Zanzibar- 0.005 0.7 284 310 Cashew nuts, 26% Inhambane coastal with shell forest mosaic NT0201 Apure-Villavicencio 0.005 0.4 285 348 Sesame seed 75% dry forests

155

NT0205 Balsas dry forests 0.004 0.6 286 325 Grapefruit (inc. 69% pomelos) IM1001 Kinabalu montane 0.004 0.1 287 491 Oil, palm 72% alpine meadows NT0128 Iquitos varzea 0.004 0.7 288 308 Cassava 77% IM0121 Luang Prabang 0.004 0.8 289 292 Rice, paddy 68% montane rain forests NT1305 Cayman Islands xeric 0.004 0.1 290 469 Sugar cane 100% scrub NT0175 Venezuelan Andes 0.004 0.2 291 412 Sesame seed 96% montane forests PA0608 Scandinavian and 0.004 51.1 292 45 Sugar beet 47% Russian taiga NT0131 Jamaican moist 0.004 0.2 293 389 Sugar cane 100% forests IM0144 Peninsular Malaysian 0.004 0.2 294 416 Coconuts 56% montane rain forests NT0136 Magdalena Valley 0.004 0.4 295 340 Cocoa, beans 91% montane forests AA0803 Southeast Australia 0.004 8.5 296 110 Wheat 55% temperate savanna AA1207 Murray-Darling 0.004 5.6 297 143 Wheat 74% woodlands and mallee NT0120 Cuban moist forests 0.004 0.4 298 337 Grapefruit (inc. 83% pomelos) NT0142 Napo moist forests 0.004 1.2 299 265 Coffee, green 48% NA0416 Western Great Lakes 0.004 4.4 300 156 Wheat 48% forests AA1210 Southwest Australia 0.004 1.5 301 243 Wheat 66% woodlands AA0702 Brigalow tropical 0.004 6.1 302 138 Sorghum 64% savanna PA0802 Altai steppe and semi- 0.004 2.0 303 218 Tomatoes 43% desert AT0119 Maputaland coastal 0.004 0.2 304 382 Grapefruit (inc. 56%

156

forest mosaic pomelos) NT0108 Catatumbo moist 0.004 0.1 305 430 Cocoa, beans 94% forests AT0117 Madagascar lowland 0.004 1.2 306 266 Cloves 56% forests PA0513 Mediterranean conifer 0.004 0.4 307 338 Olives 95% and mixed forests NT0122 Eastern Panamanian 0.003 0.0 308 550 Maize 34% montane forests AA1202 Esperance mallee 0.003 3.7 309 175 Wheat 88% NT0304 Cuban pine forests 0.003 0.1 310 445 Grapefruit (inc. 83% pomelos) IM0130 Mindoro rain forests 0.003 0.2 311 404 Coconuts 100% NA0528 South Central Rockies 0.003 2.5 312 197 Wheat 92% forests NT1404 Bahia mangroves 0.003 0.0 313 497 Grapefruit (inc. 60% pomelos) NT0113 Chiapas montane 0.003 0.0 314 513 Grapefruit (inc. 79% forests pomelos) PA0812 Middle East steppe 0.003 3.4 315 182 Vegetables, 42% fresh nes AA0102 Banda Sea Islands 0.003 0.2 316 410 Coconuts 79% moist deciduous forests AT0717 Southern Africa 0.003 2.2 317 208 Grapefruit (inc. 97% bushveld pomelos) IM0304 Sumatran tropical 0.003 0.0 318 532 Cocoa, beans 66% pine forests NT0222 Maracaibo dry forests 0.003 0.2 319 420 Cocoa, beans 97% AT1009 Highveld grasslands 0.003 1.3 320 253 Grapefruit (inc. 97% pomelos) PA0801 Alai-Western Tian 0.003 2.6 321 193 Tomatoes 39% Shan steppe PA1311 Central Asian riparian 0.003 1.9 322 220 Tomatoes 49% woodlands

157

PA0102 Yunnan Plateau 0.003 1.3 323 248 Beans, dry 34% subtropical evergreen forests NT0207 Cauca Valley dry 0.003 0.0 324 516 Sugar cane 92% forests IM0143 Palawan rain forests 0.003 0.3 325 376 Coconuts 100% NT1316 Tehuacan Valley 0.003 0.1 326 449 Grapefruit (inc. 51% matorral pomelos) PA0433 Pyrenees conifer and 0.003 0.5 327 327 Wheat 47% mixed forests AT0128 Southern Zanzibar- 0.003 0.8 328 297 Sugar cane 68% Inhambane coastal forest mosaic PA1208 Iberian conifer forests 0.003 1.0 329 276 Rice, paddy 32% PA1308 Caspian lowland 0.003 5.3 330 148 Wheat 67% desert NT1312 Motagua Valley 0.003 0.0 331 535 Coffee, green 52% thornscrub NA1203 California montane 0.003 0.4 332 334 Grapefruit (inc. 47% chaparral and pomelos) woodlands IM0116 Irrawaddy freshwater 0.003 0.2 333 424 Beans, dry 97% swamp forests NT0126 Gurupa varzea 0.003 0.0 334 498 Cassava 57% AA1205 Kwongan heathlands 0.003 0.4 335 353 Wheat 70% (Swan Coastal Plain Scrub and Woodlands) AT1305 Ethiopian xeric 0.003 1.7 336 229 Sesame seed 100% grasslands and shrublands NA1305 Great Basin shrub 0.003 7.2 337 127 Wheat 82% steppe PA0417 Daba Mountains 0.003 1.3 338 257 Tomatoes 29% evergreen forests

158

NT0308 Sierra Madre de 0.003 0.1 339 486 Walnuts, with 43% Oaxaca pine-oak shell forests NT0707 Guyanan savanna 0.003 0.7 340 304 Cassava 42% AA0104 Buru rain forests 0.003 0.2 341 397 Coconuts 69% NA0513 Florida sand pine 0.003 0.0 342 504 Grapefruit (inc. 89% scrub pomelos) NT0177 Veracruz montane 0.003 0.0 343 531 Grapefruit (inc. 89% forests pomelos) NT0173 Uatuma-Trombetas 0.002 1.8 344 227 Cassava 73% moist forests NA0522 Okanogan dry forests 0.002 0.8 345 296 Wheat 87% IM0118 Jian Nan subtropical 0.002 3.6 346 179 Tomatoes 31% evergreen forests PA0611 West Siberian taiga 0.002 23.1 347 72 Wheat 55% NT0119 Costa Rican seasonal 0.002 0.0 348 512 Oranges 31% moist forests PA0101 Gizhou Plateau 0.002 1.6 349 239 Tomatoes 30% broadleaf and mixed forests NT0174 Ucayali moist forests 0.002 0.3 350 357 Cocoa, beans 71% NA0524 Puget lowland forests 0.002 0.2 351 399 Potatoes 50% NT0309 Sierra Madre del Sur 0.002 0.3 352 375 Grapefruit (inc. 49% pine-oak forests pomelos) NT0146 Oaxacan montane 0.002 0.0 353 528 Grapefruit (inc. 68% forests pomelos) PA1216 Northwest Iberian 0.002 1.3 354 252 Barley 20% montane forests PA1217 Pindus Mountains 0.002 0.7 355 301 Sugar beet 57% mixed forests NA0519 Northern California 0.002 0.1 356 454 Grapefruit (inc. 52% coastal forests pomelos) NT0117 Cordillera La Costa 0.002 0.1 357 490 Cocoa, beans 61% montane forests

159

AT0713 Sahelian Acacia 0.002 12.7 358 95 Sugar cane 42% savanna PA0818 Tian Shan foothill arid 0.002 1.9 359 224 Tomatoes 53% steppe AA0402 Eastern Australian 0.002 1.1 360 268 Sorghum 50% temperate forests PA0401 Appenine deciduous 0.002 0.3 361 368 Wheat 43% montane forests NT0223 Maranon dry forests 0.002 0.0 362 502 Beans, dry 44% NA1303 Chihuahuan desert 0.002 4.5 363 155 Grapefruit (inc. 34% pomelos) PA0815 Sayan Intermontane 0.002 0.6 364 315 Wheat 75% steppe PA0411 Central China loess 0.002 2.1 365 211 Tomatoes 37% plateau mixed forests AT1203 Montane fynbos and 0.002 0.2 366 409 Grapefruit (inc. 98% renosterveld pomelos) AT0722 West Sudanian 0.002 5.4 367 146 Cashew nuts, 34% savanna with shell PA0430 Northeast China Plain 0.002 1.7 368 233 Tomatoes 24% deciduous forests NT0166 Southwest Amazon 0.002 1.3 369 256 Cassava 76% moist forests NA1311 Tamaulipan matorral 0.002 0.2 370 425 Grapefruit (inc. 89% pomelos) AA0115 Northern New Guinea 0.002 0.7 371 299 Cocoa, beans 87% lowland rain and freshwater swamp forests PA0609 Trans-Baikal conifer 0.002 2.0 372 219 Wheat 79% forests NA0518 North Central Rockies 0.002 2.3 373 205 Wheat 97% forests IM0132 Myanmar coastal rain 0.002 0.2 374 386 Beans, dry 98% forests

160

AA1208 Naracoorte 0.002 0.3 375 354 Wheat 54% woodlands AA0204 Timor and Wetar 0.002 0.4 376 344 Coconuts 87% deciduous forests AA0107 Huon Peninsula 0.002 0.1 377 452 Cocoa, beans 97% montane rain forests IM0153 Southwest Borneo 0.002 0.2 378 406 Coconuts 64% freshwater swamp forests AA0128 Vogelkop-Aru lowland 0.002 0.4 379 332 Cocoa, beans 61% rain forests AT1402 East African 0.002 0.0 380 503 Sugar cane 82% mangroves AT1006 Eastern Zimbabwe 0.002 0.0 381 525 Sugar cane 83% montane forest- grassland mosaic NA0527 Sierra Nevada forests 0.002 0.4 382 350 Grapefruit (inc. 41% pomelos) PA0406 Cantabrian mixed 0.002 0.8 383 298 Maize 20% forests IM0117 Irrawaddy moist 0.002 0.5 384 331 Beans, dry 79% deciduous forests IM0115 Himalayan subtropical 0.002 0.1 385 441 Rice, paddy 45% broadleaf forests NT0907 Pantanal 0.002 0.9 386 285 Soybeans 75% PA0434 Qin Ling Mountains 0.002 0.6 387 318 Tomatoes 28% deciduous forests NA0303 Sierra Madre Oriental 0.002 0.2 388 401 Grapefruit (inc. 52% pine-oak forests pomelos) PA0610 Urals montane tundra 0.002 1.7 389 228 Wheat 47% and taiga PA0816 Selenge-Orkhon 0.002 2.1 390 214 Wheat 78% forest steppe PA0808 Gissaro-Alai open 0.002 1.7 391 231 Wheat 54% woodlands

161

PA0429 North Atlantic moist 0.001 0.4 392 333 Sugar beet 46% mixed forests NT0137 Magdalena-Uraba 0.001 0.2 393 419 Cocoa, beans 75% moist forests AA0120 Southeastern Papuan 0.001 0.3 394 358 Cocoa, beans 97% rain forests AT0725 Zambezian and 0.001 1.5 395 244 Grapefruit (inc. 69% Mopane woodlands pomelos) NT0153 Peruvian Yungas 0.001 0.2 396 402 Cocoa, beans 42% IM1406 Sundarbans 0.001 0.1 397 437 Rice, paddy 32% mangroves PA0519 Sayan montane 0.001 2.2 398 207 Wheat 77% conifer forests NT0305 Hispaniolan pine 0.001 0.1 399 461 Grapefruit (inc. 88% forests pomelos) AT1010 Jos Plateau forest- 0.001 0.1 400 484 Sesame seed 99% grassland mosaic PA0908 Yellow Sea saline 0.001 0.1 401 476 Tomatoes 40% meadow NA1304 Colorado Plateau 0.001 2.7 402 192 Wheat 61% shrublands IM0401 Eastern Himalayan 0.001 0.2 403 426 Rice, paddy 27% broadleaf forests AA0116 Northern New Guinea 0.001 0.1 404 448 Cocoa, beans 82% montane rain forests AT0715 Somali Acacia- 0.001 2.9 405 187 Sesame seed 60% Commiphora bushlands and thickets PA0804 Daurian forest steppe 0.001 1.8 406 225 Wheat 82% AA0118 Seram rain forests 0.001 0.2 407 413 Coconuts 71% NT0138 Marajo Varzea forests 0.001 0.2 408 423 Cassava 89% IM0125 Maldives- 0.001 0.0 409 614 Coconuts 100% Lakshadweep-Chagos Archipelago tropical

162

moist forests AA0127 Vogelkop montane 0.001 0.1 410 457 Cocoa, beans 82% rain forests NT0206 Bolivian montane dry 0.001 0.1 411 456 Brazil nuts, 54% forests with shell PA0903 Nenjiang River 0.001 0.2 412 380 Beans, dry 31% grassland NT1402 Amapa mangroves 0.001 0.1 413 487 Grapefruit (inc. 53% pomelos) IM0169 Hainan Island 0.001 0.1 414 475 Tomatoes 29% monsoon rain forests NA0511 Colorado Rockies 0.001 0.7 415 314 Wheat 66% forests AT0711 Northern Acacia- 0.001 0.7 416 312 Beans, green 62% Commiphora bushlands and thickets AT1401 Central African 0.001 0.0 417 501 Cocoa, beans 71% mangroves NT0220 Leeward Islands dry 0.001 0.0 418 615 Sugar cane 99% forests IM0103 Borneo montane rain 0.001 0.3 419 361 Oil, palm 65% forests IM0172 Taiwan subtropical 0.001 0.2 420 427 Tomatoes 19% evergreen forests PA0601 East Siberian taiga 0.001 18.0 421 82 Wheat 70% AT1201 Albany thickets 0.001 0.0 422 511 Grapefruit (inc. 94% pomelos) NT0157 Purus-Madeira moist 0.001 0.3 423 365 Cassava 70% forests AT0123 Nigerian lowland 0.001 0.1 424 438 Cashew nuts, 92% forests with shell NT0229 Sinu Valley dry forests 0.001 0.0 425 506 Cocoa, beans 72% NT0235 Yucatan dry forests 0.001 0.2 426 422 Grapefruit (inc. 90% pomelos)

163

PA0502 Altai montane forest 0.001 0.5 427 328 Wheat 69% and forest steppe NA0408 Gulf of St. Lawrence 0.001 0.2 428 403 Potatoes 66% lowland forests NT0902 Cuban wetlands 0.001 0.0 429 524 Grapefruit (inc. 83% pomelos) NT1004 Cordillera Central 0.001 0.0 430 534 Olives 64% paramo PA0443 Ussuri broadleaf and 0.001 0.9 431 283 Barley 52% mixed forests PA0503 Caledon conifer 0.001 0.2 432 407 Barley 56% forests NT0118 Cordillera Oriental 0.001 0.1 433 493 Cocoa, beans 64% montane forests NA0530 Wasatch and Uinta 0.001 0.2 434 415 Wheat 90% montane forests PA0518 Qionglai-Minshan 0.001 0.1 435 429 Tomatoes 34% conifer forests NT0156 Purus varzea 0.001 0.2 436 405 Cassava 93% IM0205 Irrawaddy dry forests 0.001 0.1 437 478 Beans, dry 87% PA0426 Manchurian mixed 0.001 1.6 438 240 Tomatoes 22% forests AA0122 Southern New Guinea 0.001 0.3 439 364 Cocoa, beans 74% lowland rain forests NA0608 Mid-Continental 0.001 1.7 440 235 Wheat 54% Canadian forests PA1210 Illyrian deciduous 0.001 0.2 441 411 Maize 61% forests NT0109 Cauca Valley 0.001 0.0 442 539 Sugar cane 96% montane forests NT0125 Guianan moist forests 0.001 0.6 443 324 Sugar cane 96% PA0521 Tian Shan montane 0.001 0.1 444 462 Wheat 39% conifer forests AA0121 Southern New Guinea 0.001 0.2 445 393 Cocoa, beans 75% freshwater swamp

164

forests AA1401 New Guinea 0.001 0.1 446 485 Cocoa, beans 68% mangroves IM1302 Indus Valley desert 0.001 0.1 447 453 Sugar cane 78% AA0801 Cantebury-Otago 0.001 0.4 448 343 Wheat 47% tussock grasslands NT1201 Chilean matorral 0.001 1.1 449 270 Plums and 44% sloes PA1213 Mediterranean dry 0.001 1.0 450 274 Rice, paddy 69% woodlands and steppe PA1305 Azerbaijan shrub 0.001 0.3 451 369 Grapes 26% desert and steppe IM0502 Western Himalayan 0.001 0.1 452 496 Sugar cane 76% subalpine conifer forests PA1212 Mediterranean acacia- 0.001 0.4 453 346 Olives 47% argania dry woodlands and succulent thickets OC0105 Fiji tropical moist 0.001 0.1 454 436 Sugar cane 95% forests AA0125 Trobriand Islands rain 0.001 0.0 455 542 Cocoa, beans 98% forests PA0516 Nujiang Langcang 0.001 0.1 456 463 Beans, dry 42% Gorge alpine conifer and mixed forests PA0514 Northeastern 0.001 0.1 457 483 Grapefruit (inc. 25% Himalayan subalpine pomelos) conifer forests NA0302 Sierra Madre 0.001 0.3 458 360 Grapefruit (inc. 33% Occidental pine-oak pomelos) forests NA0503 Arizona Mountains 0.001 0.3 459 377 Grapefruit (inc. 53% forests pomelos)

165

NA1313 Wyoming Basin shrub 0.001 0.7 460 313 Wheat 73% steppe PA0813 Mongolian- 0.001 2.4 461 201 Tomatoes 27% Manchurian grassland AA0117 Queensland tropical 0.001 0.1 462 481 Sorghum 38% rain forests NT1315 Sechura desert 0.001 0.6 463 316 Brazil nuts, 36% with shell NT1309 La Costa xeric 0.001 0.1 464 439 Sesame seed 36% shrublands NA1308 Mojave desert 0.001 0.6 465 321 Grapefruit (inc. 47% pomelos) AA0105 Central Range 0.001 0.2 466 391 Cocoa, beans 90% montane rain forests PA1013 Ordos Plateau steppe 0.001 0.7 467 305 Tomatoes 27% IM0128 Mindanao montane 0.001 0.0 468 500 Coconuts 97% rain forests NA0507 Cascade Mountains 0.001 0.1 469 434 Wheat 37% leeward forests IM1403 Indus River Delta- 0.000 0.0 470 572 Sugar cane 40% Arabian Sea mangroves AT0202 Madagascar dry 0.000 0.3 471 372 Cocoa, beans 44% deciduous forests AT0906 Zambezian coastal 0.000 0.0 472 529 Sugar cane 92% flooded savanna NA0502 Alberta/British 0.000 0.4 473 352 Wheat 74% Columbia foothills forests PA0902 Bohai Sea saline 0.000 0.1 474 480 Tomatoes 41% meadow AA0126 Vanuatu rain forests 0.000 0.1 475 460 Cocoa, beans 96% PA1001 Altai alpine meadow 0.000 0.3 476 370 Wheat 75% and tundra PA1203 Canary Islands dry 0.000 0.0 477 508 Grapefruit (inc. 55%

166

woodlands and pomelos) forests AT0719 Southern Miombo 0.000 0.4 478 339 Sugar cane 97% woodlands NT0709 Llanos 0.000 0.3 479 355 Sesame seed 66% AT0118 Madagascar 0.000 0.2 480 400 Cocoa, beans 45% subhumid forests IM0133 Nicobar Islands rain 0.000 0.0 481 568 Cashew nuts, 23% forests with shell AA0108 Japen rain forests 0.000 0.0 482 580 Cocoa, beans 80% AA0708 Trans Fly savanna 0.000 0.1 483 495 Cocoa, beans 72% and grasslands NT0181 Yucatan moist forests 0.000 0.1 484 464 Sugar cane 56% NT0163 Solimoes-Japura 0.000 0.1 485 440 Cocoa, beans 62% moist forest AT1307 Hobyo grasslands and 0.000 0.1 486 489 Sesame seed 100% shrublands IM0171 South Taiwan 0.000 0.0 487 596 Tomatoes 34% monsoon rain forests PA1310 Central Asian 0.000 1.6 488 241 Tomatoes 79% northern desert AT0127 Sao Tome and 0.000 0.0 489 597 Cocoa, beans 100% Principe moist lowland forests AT0708 Itigi-Sumbu thicket 0.000 0.0 490 585 Chick peas 34% PA0509 Hengduan Mountains 0.000 0.1 491 470 Tomatoes 28% subalpine conifer forests NT0404 Valdivian temperate 0.000 0.4 492 351 Plums and 34% forests sloes NT1308 Guajira-Barranquilla 0.000 0.0 493 521 Cocoa, beans 75% xeric scrub NT1010 High Monte 0.000 0.3 494 366 Grapefruit (inc. 29% pomelos) PA1317 Junggar Basin semi- 0.000 0.7 495 309 Tomatoes 39%

167

desert AA0106 Halmahera rain 0.000 0.1 496 488 Coconuts 73% forests NA0410 New England/Acadian 0.000 0.3 497 362 Potatoes 73% forests AT0907 Zambezian flooded 0.000 0.1 498 471 Sugar cane 96% grasslands PA0806 Emin Valley steppe 0.000 0.1 499 435 Tomatoes 48% NT1306 Cuban cactus scrub 0.000 0.0 500 594 Grapefruit (inc. 83% pomelos) AT1011 Madagascar ericoid 0.000 0.0 501 632 Cloves 74% thickets PA1325 Red Sea Nubo- 0.000 1.3 502 249 Vegetables, 30% Sindian tropical desert fresh nes and semi-desert PA1204 Corsican montane 0.000 0.0 503 557 Plums and 54% broadleaf and mixed sloes forests PA0413 Central Korean 0.000 0.1 504 432 Nuts, nes 49% deciduous forests NT0802 Argentine Monte 0.000 0.8 505 295 Apricots 23% AA0103 Biak-Numfoor rain 0.000 0.0 506 569 Cocoa, beans 70% forests NT0132 Japura-Solimoes- 0.000 0.1 507 443 Cassava 76% Negro moist forests PA0905 Saharan halophytics 0.000 0.1 508 459 Rice, paddy 77% PA1307 Baluchistan xeric 0.000 0.2 509 381 Sugar cane 87% woodlands AT1322 Succulent Karoo 0.000 0.1 510 446 Grapefruit (inc. 99% pomelos) NT1002 Central Andean puna 0.000 0.1 511 431 Olives 76% AT0905 Saharan flooded 0.000 0.1 512 468 Sorghum 99% grasslands NA1306 Gulf of California xeric 0.000 0.0 513 527 Chick peas 46% scrub

168

AT1303 East Saharan 0.000 0.0 514 507 Sesame seed 99% montane xeric woodlands PA1321 North Saharan steppe 0.000 2.2 515 209 Rice, paddy 84% and woodlands OC0701 Hawaii tropical high 0.000 0.0 516 587 Walnuts, with 40% shrublands shell NA0510 Central Pacific coastal 0.000 0.1 517 492 Walnuts, with 24% forests shell AT0122 Niger Delta swamp 0.000 0.0 518 582 Cashew nuts, 98% forests with shell AA0704 Carpentaria tropical 0.000 0.4 519 345 Sorghum 91% savanna PA1017 Southeast Tibet 0.000 0.2 520 383 Beans, dry 32% shrublands and meadow AT0106 Cross-Niger transition 0.000 0.0 521 579 Cashew nuts, 98% forests with shell AA0411 Tasmanian Central 0.000 0.0 522 544 Wheat 71% Highland forests IM0101 Andaman Islands rain 0.000 0.0 523 563 Rice, paddy 42% forests NA0406 Eastern forest/boreal 0.000 0.3 524 367 Maize 42% transition AT1314 Nama Karoo 0.000 0.2 525 396 Grapefruit (inc. 98% pomelos) PA0520 Scandinavian coastal 0.000 0.0 526 543 Barley 57% conifer forests NT0133 Jurua-Purus moist 0.000 0.1 527 472 Cassava 85% forests AA0405 Northland temperate 0.000 0.2 528 428 Maize 44% forests AA1204 Jarrah-Karri forest 0.000 0.0 529 553 Wheat 57% and shrublands PA1318 Kazakh semi-desert 0.000 0.6 530 319 Tomatoes 53%

169

NT0306 Miskito pine forests 0.000 0.0 531 589 Grapefruit (inc. 83% pomelos) PA0418 Dinaric Mountains 0.000 0.0 532 499 Rye 50% mixed forests NA1301 Baja California desert 0.000 0.1 533 458 Grapefruit (inc. 42% pomelos) AT0904 Lake Chad flooded 0.000 0.0 534 608 Sesame seed 98% savanna PA1019 Tian Shan montane 0.000 0.3 535 379 Tomatoes 33% steppe and meadow PA1306 Badkhiz-Karabil semi- 0.000 0.1 536 444 Chick peas 47% desert PA0517 Qilian Mountains 0.000 0.0 537 583 Beans, dry 48% conifer forests AA0408 Richmond temperate 0.000 0.0 538 548 Kiwi fruit 62% forests PA0507 Elburz Range forest 0.000 0.0 539 519 Figs 59% steppe IM0109 Chin Hills-Arakan 0.000 0.0 540 575 Beans, dry 40% Yoma montane forests AA0406 Northland temperate 0.000 0.0 541 518 Maize 80% kauri forests AT1404 Madagascar 0.000 0.0 542 606 Sugar cane 68% mangroves PA0407 Caspian Hyrcanian 0.000 0.0 543 533 Grapes 30% mixed forests IM0122 Luzon montane rain 0.000 0.0 544 599 Coconuts 97% forests NT0182 Guianan piedmont 0.000 0.0 545 514 Soybeans 47% and lowland moist forests PA1022 Yarlung Zambo arid 0.000 0.0 546 509 Grapefruit (inc. 49% steppe pomelos) IM0501 Eastern Himalayan 0.000 0.0 547 603 Grapefruit (inc. 47%

170

subalpine conifer pomelos) forests NA0508 Central and Southern 0.000 0.0 548 555 Walnuts, with 19% Cascades forests shell PA0414 Changbai Mountains 0.000 0.0 549 510 Tomatoes 24% mixed forests AA0412 Tasmanian temperate 0.000 0.0 550 567 Grapes 76% forests PA0508 Helanshan montane 0.000 0.0 551 556 Sunflower 45% conifer forests seed AT0714 Serengeti volcanic 0.000 0.0 552 605 Beans, dry 81% grasslands PA1312 Central Asian 0.000 0.3 553 356 Tomatoes 45% southern desert AT1309 Kalahari xeric 0.000 0.2 554 421 Grapefruit (inc. 96% savanna pomelos) NT0227 Sierra de la Laguna 0.000 0.0 555 631 Grapefruit (inc. 74% dry forests pomelos) NT1307 Galapagos Islands 0.000 0.0 556 577 Cauliflowers 93% xeric scrub and broccoli AT0901 East African 0.000 0.0 557 656 Sugar cane 97% halophytics AA0410 Southland temperate 0.000 0.0 558 578 Wheat 43% forests NT0906 Orinoco wetlands 0.000 0.0 559 624 Cocoa, beans 94% AA0110 Louisiade Archipelago 0.000 0.0 560 644 Cocoa, beans 98% rain forests AA0111 New Britain-New 0.000 0.0 561 560 Cocoa, beans 98% Ireland lowland rain forests NT0158 Rio Negro 0.000 0.0 562 571 Cassava 64% campinarana PA1327 Sahara desert 0.000 1.6 563 238 Rice, paddy 86% PA0423 Hokkaido deciduous 0.000 0.0 564 573 Wheat 72% forests

171

PA1302 Alashan Plateau 0.000 0.3 565 363 Sunflower 60% semi-desert seed AT0903 Inner Niger Delta 0.000 0.0 566 590 Rice, paddy 37% flooded savanna PA0506 East Afghan montane 0.000 0.0 567 600 Rice, paddy 80% conifer forests PA1313 Central Persian desert 0.000 0.2 568 414 Figs 59% basins NT0114 Chimalapas montane 0.000 0.0 569 662 Grapefruit (inc. 48% forests pomelos) NT1314 San Lucan xeric scrub 0.000 0.0 570 623 Grapefruit (inc. 60% pomelos) PA1018 Sulaiman Range 0.000 0.0 571 588 Rice, paddy 90% alpine meadows NT0124 Guayanan Highlands 0.000 0.0 572 574 Rice, paddy 71% moist forests NT1008 Southern Andean 0.000 0.1 573 482 Grapefruit (inc. 22% steppe pomelos) PA1003 Eastern Himalayan 0.000 0.0 574 559 Grapefruit (inc. 61% alpine shrub and pomelos) meadows PA0440 Taiheiyo evergreen 0.000 0.0 575 530 Rice, paddy 68% forests NA0514 Fraser Plateau and 0.000 0.0 576 517 Wheat 55% Basin complex AT1319 Somali montane xeric 0.000 0.0 577 554 Sesame seed 100% woodlands PA0606 Okhotsk-Manchurian 0.000 0.1 578 467 Wheat 52% taiga PA1020 Tibetan Plateau alpine 0.000 0.1 579 477 Tomatoes 24% shrublands and meadows PA0439 Southern Korea 0.000 0.0 580 611 Pears 56% evergreen forests PA1002 Central Tibetan 0.000 0.1 581 433 Beans, dry 93%

172

Plateau alpine steppe AA1003 Southland montane 0.000 0.0 582 536 Wheat 52% grasslands PA0906 Tigris-Euphrates 0.000 0.0 583 561 Sugar cane 67% alluvial salt marsh PA0427 Nihonkai evergreen 0.000 0.0 584 601 Rice, paddy 92% forests NT1003 Central Andean wet 0.000 0.0 585 584 Mangoes, 55% puna mangosteens, guavas AT0709 Kalahari Acacia- 0.000 0.0 586 515 Grapefruit (inc. 99% Baikiaea woodlands pomelos) PA1008 Kopet Dag woodlands 0.000 0.0 587 562 Figs 92% and forest steppe PA1328 South Iran Nubo- 0.000 0.1 588 494 Sugar cane 65% Sindian desert and semi-desert PA1105 Kamchatka Mountain 0.000 0.0 589 523 Barley 97% tundra and forest tundra NT1005 Cordillera de Merida 0.000 0.0 590 661 Wheat 54% paramo PA1009 Kuhrud-Kohbanan 0.000 0.0 591 540 Figs 70% Mountains forest steppe AT1321 Southwestern Arabian 0.000 0.0 592 546 Maize 98% montane woodlands PA1021 Western Himalayan 0.000 0.0 593 581 Rice, paddy 46% alpine shrub and Meadows NT0805 Patagonian steppe 0.000 0.1 594 465 Apricots 26% NT0159 Santa Marta montane 0.000 0.0 595 665 Cocoa, beans 46% forests PA0428 Nihonkai montane 0.000 0.0 596 570 Rice, paddy 80% deciduous forests

173

AT1005 East African montane 0.000 0.0 597 677 Sesame seed 58% moorlands NT0107 Caqueta moist forests 0.000 0.0 598 586 Cassava 82% AA1201 Coolgardie woodlands 0.000 0.0 599 526 Wheat 92% AT1003 Drakensberg alti- 0.000 0.0 600 648 Grapes 38% montane grasslands and woodlands PA0510 Hokkaido montane 0.000 0.0 601 591 Wheat 88% conifer forests PA0441 Taiheiyo montane 0.000 0.0 602 604 Rice, paddy 67% deciduous forests IM0302 Luzon tropical pine 0.000 0.0 603 650 Cocoa, beans 79% forests AT0710 Mandara Plateau 0.000 0.0 604 655 Cocoa, beans 63% mosaic PA1309 Central Afghan 0.000 0.0 605 558 Anise, badian, 81% Mountains xeric fennel, woodlands coriander NT0147 Orinoco Delta swamp 0.000 0.0 606 641 Rice, paddy 98% forests NA0605 Eastern Canadian 0.000 0.0 607 505 Soybeans 72% forests AT0723 Western Congolian 0.000 0.0 608 564 Sugar cane 99% forest-savanna mosaic PA0505 Da Hinggan-Dzhagdy 0.000 0.0 609 547 Tomatoes 27% Mountains conifer forests AA0413 Tasmanian temperate 0.000 0.0 610 612 Wheat 42% rain forests NT0702 Beni savanna 0.000 0.0 611 610 Figs 62% AA0705 Einasleigh upland 0.000 0.0 612 576 Sorghum 43% savanna AT0908 Zambezian 0.000 0.0 613 629 Sugar cane 68% halophytics

174

PA0511 Honshu alpine conifer 0.000 0.0 614 642 Rice, paddy 50% forests NT0169 Tepuis 0.000 0.0 615 640 Rice, paddy 94% NT0143 Negro-Branco moist 0.000 0.0 616 607 Cassava 94% forests NT0402 Magellanic subpolar 0.000 0.0 617 565 Grapefruit (inc. 74% forests pomelos) PA1010 Mediterranean High 0.000 0.0 618 647 Sugar beet 93% Atlas juniper steppe PA1012 Northwestern 0.000 0.0 619 616 Sugar beet 41% Himalayan alpine shrub and meadows PA1326 Registan-North 0.000 0.0 620 538 Rice, paddy 36% Pakistan sandy desert IM0901 Rann of Kutch 0.000 0.0 621 619 Mushrooms 33% seasonal salt marsh and truffles PA1110 Scandinavian 0.000 0.0 622 552 Barley 85% Montane Birch forest and grasslands PA1330 Taklimakan desert 0.000 0.1 623 474 Wheat 30% AT1320 Southwestern Arabian 0.000 0.0 624 541 Maize 94% foothills savanna PA1319 Kopet Dag semi- 0.000 0.0 625 621 Sugar cane 60% desert PA1322 Paropamisus xeric 0.000 0.0 626 595 Anise, badian, 78% woodlands fennel, coriander AT1312 Madagascar 0.000 0.0 627 602 Sugar cane 86% succulent woodlands PA1314 Eastern Gobi desert 0.000 0.0 628 537 Sunflower 75% steppe seed IM0140 Northern Triangle 0.000 0.0 629 638 Chick peas 46% subtropical forests NA0615 South Avalon-Burin 0.000 0.0 630 666 Potatoes 85% oceanic barrens

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AA1309 Tirari-Sturt stony 0.000 0.0 631 551 Wheat 43% desert AT0113 Granitic Seychelles 0.000 0.0 632 690 Cinnamon 99% forests (canella) NA0602 Central Canadian 0.000 0.0 633 549 Wheat 37% Shield forests NA0526 Sierra Juarez & San 0.000 0.0 634 670 Grapefruit (inc. 92% Pedro Martir pine-oak pomelos) forests NT0149 Paramaribo swamp 0.000 0.0 635 676 Rice, paddy 99% forests AT1311 Madagascar spiny 0.000 0.0 636 634 Sugar cane 88% thickets AA0404 Nelson Coast 0.000 0.0 637 633 Kiwi fruit 61% temperate forests PA1006 Karakoram-West 0.000 0.0 638 593 Sugar beet 29% Tibetan Plateau alpine steppe NA0506 British Columbia 0.000 0.0 639 609 Potatoes 48% mainland coastal forests IM0170 Nansei Islands 0.000 0.0 640 669 Tea 66% subtropical evergreen forests PA1016 Sayan Alpine meadow 0.000 0.0 641 613 Wheat 39% and tundra AT1014 South Malawi 0.000 0.0 642 675 Peas, dry 42% montane forest- grassland mosaic AA0112 New Britain-New 0.000 0.0 643 657 Cocoa, beans 98% Ireland montane rain forests AT0129 Western Congolian 0.000 0.0 644 635 Sugar cane 99% swamp forests NT0219 Lara-Falcon dry 0.000 0.0 645 671 Maize 78%

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forests NT1303 Atacama desert 0.000 0.0 646 592 Grapefruit (inc. 36% pomelos) AA0101 Admiralty Islands 0.000 0.0 647 674 Cocoa, beans 98% lowland rain forests AT0124 Northeastern 0.000 0.0 648 617 Coffee, green 84% Congolian lowland forests NT1001 Central Andean dry 0.000 0.0 649 598 Grapefruit (inc. 34% puna pomelos) PA0442 Tarim Basin 0.000 0.0 650 620 Sunflower 59% deciduous forests and seed steppe AT0726 Zambezian Baikiaea 0.000 0.0 651 630 Sugar cane 99% woodlands AT1302 Arabian Peninsula 0.000 0.0 652 628 Maize 58% coastal fog desert AA1002 Central Range sub- 0.000 0.0 653 678 Mushrooms 46% alpine grasslands and truffles PA1329 South Saharan steppe 0.000 0.0 654 566 Sorghum 99% and woodlands PA1301 Afghan Mountains 0.000 0.0 655 667 Anise, badian, 91% semi-desert fennel, coriander NA0501 Alberta Mountain 0.000 0.0 656 654 Peas, dry 82% forests PA1004 Ghorat-Hazarajat 0.000 0.0 657 643 Anise, badian, 74% alpine meadow fennel, coriander AT0718 Southern Congolian 0.000 0.0 658 625 Coffee, green 94% forest-savanna mosaic AT1313 Masai xeric 0.000 0.0 659 652 Beans, green 44% grasslands and shrublands

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PA1015 Qilian Mountains 0.000 0.0 660 636 Wheat 32% subalpine meadow NA0603 Cook Inlet taiga 0.000 0.0 661 659 Potatoes 98% AT0110 Eastern Congolian 0.000 0.0 662 660 Coffee, green 55% swamp forests PA1324 Qaidam Basin semi- 0.000 0.0 663 618 Rapeseed 51% desert NA0515 Great Basin montane 0.000 0.0 664 692 Potatoes 51% forests PA1333 Red Sea coastal 0.000 0.0 665 653 Rice, paddy 84% desert AA0706 Kimberley tropical 0.000 0.0 666 622 Grapes 44% savanna AA0802 Eastern Australia 0.000 0.0 667 627 Sorghum 52% mulga shrublands AA0701 Arnhem Land tropical 0.000 0.0 668 639 Oranges 37% savanna AT0104 Central Congolian 0.000 0.0 669 645 Coffee, green 94% lowland forests IM0402 Northern Triangle 0.000 0.0 670 699 Tea 38% temperate forests PA1014 Pamir alpine desert 0.000 0.0 671 646 Wheat 64% and tundra AT0203 Zambezian 0.000 0.0 672 685 Maize 33% Cryptosepalum dry forests PA1316 Great Lakes Basin 0.000 0.0 673 649 Nuts, nes 94% desert steppe AT0201 Cape Verde Islands 0.000 0.0 674 684 Sugar cane 91% dry forests AT1306 Gulf of Oman desert 0.000 0.0 675 651 Sorghum 50% and semi-desert PA1005 Hindu Kush alpine 0.000 0.0 676 673 Anise, badian, 91% meadow fennel, coriander

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PA0607 Sakhalin Island taiga 0.000 0.0 677 663 Potatoes 61% PA1011 North Tibetan 0.000 0.0 678 626 Beans, dry 31% Plateau-Kunlun Mountains alpine desert AT0724 Western Zambezian 0.000 0.0 679 687 Maize 35% grasslands NA0601 Alaska Peninsula 0.000 0.0 680 668 Barley 100% montane taiga PA0438 South Sakhalin-Kurile 0.000 0.0 681 693 Potatoes 79% mixed forests AA1301 Carnarvon xeric 0.000 0.0 682 664 Wheat 38% shrublands NA0607 Interior Alaska/Yukon 0.000 0.0 683 637 Potatoes 99% lowland taiga AT0801 Al Hajar Al Gharbi 0.000 0.0 684 672 Sorghum 47% montane woodlands NA0521 Northern transitional 0.000 0.0 685 683 Wheat 65% alpine forests AT1318 Socotra Island xeric 0.000 0.0 686 701 Maize 99% shrublands NT1313 Paraguana xeric 0.000 0.0 687 702 Maize 67% scrub AA0403 Fiordland temperate 0.000 0.0 688 695 Wheat 76% forests NA0509 Central British 0.000 0.0 689 680 Wheat 93% Columbia Mountain forests AA0707 Mitchell grass downs 0.000 0.0 690 658 Sorghum 50% NA1102 Aleutian Islands 0.000 0.0 691 696 Barley 100% tundra NA0611 Newfoundland 0.000 0.0 692 698 Potatoes 94% Highland forests NA1117 Pacific Coastal 0.000 0.0 693 682 Barley 100% Mountain icefields and

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tundra AT0702 Angolan Mopane 0.000 0.0 694 686 Maize 95% woodlands PA0604 Kamchatka-Kurile 0.000 0.0 695 700 Potatoes 47% taiga NA1106 Beringia lowland 0.000 0.0 696 681 Barley 100% tundra AT0701 Angolan Miombo 0.000 0.0 697 679 Coffee, green 76% woodlands AA1001 Australian Alps 0.000 0.0 698 707 Wheat 75% montane grasslands PA1106 Kola Peninsula tundra 0.000 0.0 699 694 Potatoes 67% PA0603 Kamchatka-Kurile 0.000 0.0 700 688 Potatoes 53% meadows and sparse forests NA0520 Northern Pacific 0.000 0.0 701 703 Barley 100% coastal forests AA1302 Central Ranges xeric 0.000 0.0 702 689 Oranges 86% scrub PA1323 Persian Gulf desert 0.000 0.0 703 706 Figs 28% and semi-desert NA1101 Alaska/St. Elias 0.000 0.0 704 704 Barley 100% Range tundra AT1001 Angolan montane 0.000 0.0 705 716 Cassava 57% forest-grassland mosaic AA0703 Cape York tropical 0.000 0.0 706 711 Sorghum 43% savanna NA1107 Beringia upland 0.000 0.0 707 709 Barley 100% tundra PA1315 Gobi Lakes Valley 0.000 0.0 708 705 Vegetables, 54% desert steppe fresh nes AT1316 Namibian savanna 0.000 0.0 709 712 Maize 95% woodlands PA0605 Northeast Siberian 0.000 0.0 710 691 Potatoes 67%

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taiga AT1002 Angolan scarp 0.000 0.0 711 715 Cassava 92% savanna and woodlands AA0709 Victoria Plains tropical 0.000 0.0 712 710 Oranges 89% savanna PA1332 West Saharan 0.000 0.0 713 708 Beans, green 48% montane xeric woodlands PA1103 Cherskii-Kolyma 0.000 0.0 714 714 Potatoes 71% mountain tundra PA1112 Trans-Baikal Bald 0.000 0.0 715 718 Vegetables, 53% Mountain tundra fresh nes PA1108 Northwest Russian- 0.000 0.0 716 717 Vegetables, 49% Novaya Zemlya fresh nes tundra AA0414 Westland temperate 0.000 0.0 717 721 Oranges 84% forests PA1331 Tibesti-Jebel Uweinat 0.000 0.0 718 719 Wheat 100% montane xeric woodlands AT1310 Kaokoveld desert 0.000 0.0 719 720 Maize 74% NA1111 Interior Yukon/Alaska 0.000 0.0 720 696 Barley 100% alpine tundra NA1118 Torngat Mountain 0.000 0.0 720 713 Potatoes 85% tundra

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10.9. Flows to Israel – river basin scale Table 10‎ .9: Flows to Israel from main river basins

Main basin Main river Sending Cropland footprint (km2) Share of ID system imported cropland footprint Cereals Oilcrops Other crops Total 6070016970 Paraná South 646.24 1277.07 149.14 2072.45 13% American 2070007930 Dnieper Eurasian 1246.33 369.37 1.47 1617.17 10% 2070006590 Don Eurasian 843.00 312.95 6.82 1162.77 7% 2070068680 Volga Eurasian 627.23 76.96 11.02 715.20 4% 2070008490 Danube Eurasian 491.41 207.22 4.95 703.58 4% 2070008000 Pivdennyi Eurasian 309.60 106.67 0.21 416.47 3% Buh 2070008350 Dnister Eurasian 315.45 95.91 0.22 411.58 2% 6070007000 Amazon South 21.41 216.48 21.42 259.30 2% American 6070016760 Uruguay South 48.20 154.81 7.92 210.93 1% American Grand total 4548.86 2817.41 203.17 7569.45 46%

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10.10. Flows to Israel – first level administrative scale Table 10‎ .10: Flows to Israel from first level administrative unit. Only regions that supply more than 1% of Isarel's cereal and oil crops are included.

Country Level 1 administrative Cropland Yield Share of total unit footprint (tons supply of cereals (km2) ha-1) and oil crops

Argentina Buenos Aires 444.29 5.71 5.33% Argentina Córdoba 449.54 5.61 5.30% Argentina Entre Ríos 128.61 5.17 1.27% Argentina Santa Fe 272.38 5.25 3.00% Brazil Mato Grosso 311.50 2.71 1.78% Brazil Paraná 284.31 2.66 1.59% Russia Krasnodar 139.86 3.71 1.09% Russia Rostov 229.79 2.19 1.05% Ukraine Crimea 210.33 2.88 1.27% Ukraine Donets'k 195.36 2.46 1.01% Ukraine Kharkiv 207.40 2.40 1.04% Ukraine Khmel'nyts'kyy 183.19 2.63 1.01% Ukraine Luhans'k 243.53 2.24 1.14% Ukraine Odessa 283.72 2.44 1.46% Ukraine Vinnytsya 209.23 2.67 1.17%

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10.11. Functional regions (ch. 5): description, footprints and potential impacts. Table 10‎ .11: A description of the 24 functional regions and footprints and potential impacts related to Israel's crops' supply. Note that the sign '-' stands for values smaller than 1.

Functional Functional region Wheat Soybeans Rice Maize Total Cropland Potential Total Calories region description supply supply supply supply crop footprint soil loss water use per capita code (Tons) (Tons) (Tons) (Tons) supply (KM2) (Million (Million per day (Tons) Tons a m3) Year) Hu H H H Humid / Sub-humid High potential soil loss 438,354 316,670 623 715,656 1,471,304 3,278 1,493 6 1,989 High yields High water efficiency

Hu L H H Humid / Sub-humid Low potential soil loss 472,603 269,994 2,482 252,292 997,371 3,111 47 3 1,328 High yields High water efficiency

Hu H H L Humid / Sub-humid High potential soil loss 52,557 43,381 34,471 134,824 265,233 526 319 54 352 High yields Low water efficiency

Hu L L H Humid / Sub-humid Low potential soil loss 162,763 14,229 7,280 46,520 230,793 1,390 16 1 305 Low yields High water efficiency

Hu L H L Humid / Sub-humid Low potential soil loss 36,186 10,919 43,299 91,602 182,007 397 14 41 237 High yields Low water efficiency

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Se H H H Semi-Arid High potential soil loss 32,155 14,519 5,029 84,311 136,014 333 141 0 184 High yields High water efficiency

Se H H L Semi-Arid High potential soil loss 55,208 3,016 21,975 48,525 128,724 242 304 69 168 High yields Low water efficiency

Se L L H Semi-Arid Low potential soil loss 110,783 1,169 34 6,522 118,508 783 8 2 156 Low yields High water efficiency

Se L H H Semi-Arid Low potential soil loss 83,176 1,267 451 8,098 92,991 341 3 1 122 High yields High water efficiency

Se L H L Semi-Arid Low potential soil loss 44,788 749 4,493 32,245 82,275 162 6 32 110 High yields Low water efficiency

Se L L L Semi-Arid Low potential soil loss 39,767 554 230 685 41,235 207 4 12 54 Low yields Low water efficiency

Hu H L H Humid High potential soil loss 31,164 561 21 3,882 35,629 177 14 0 47 Low yields High water efficiency

Hu L L L Humid Low potential soil loss 12,720 1,222 8,826 8,271 31,039 151 2 5 39 Low yields Low water efficiency

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Ar H H L Arid / hyper-arid High potential soil loss 9,749 0 7,246 6,404 23,399 46 231 14 30 High yields Low water efficiency

Ar L L L Arid / hyper-arid Low potential soil loss 9,940 0 59 1 10,000 50 1 1 13 Low yields Low water efficiency

Se H L H Semi-Arid High potential soil loss 7,323 193 77 1,024 8,617 45 3 0 11 Low yields High water efficiency

Ar L H L Arid / hyper-arid Low potential soil loss 6,816 0 22 407 7,245 28 1 1 10 High yields Low water efficiency

Se H L L Semi-Arid High potential soil loss 4,976 41 80 475 5,571 27 2 1 7 Low yields Low water efficiency

Ar L H H Arid / hyper-arid Low potential soil loss 4,158 0 6 14 4,178 18 0 0 5 High yields High water efficiency

Hu H L L Humid High potential soil loss 1,263 47 60 1,160 2,530 11 1 0 3 Low yields Low water efficiency

Ar H H H Arid / hyper-arid High potential soil loss 176 0 1,766 14 1,956 2 52 0 2 High yields High water efficiency

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Ar H L L Arid / hyper-arid High potential soil loss 1 0 196 0 198 1 0 0 - Low yields Low water efficiency

Ar L L H Arid / hyper-arid Low potential soil loss 104 0 0 5 110 1 0 0 - Low yields High water efficiency

Ar H L H Arid / hyper-arid High potential soil loss 50 0 1 0 51 0 0 0 - Low yields High water efficiency

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10.12. Querying the functional regions typology – an illustrative example An important benefit of the functional region typology is the ability to formulate queries to aggregate results in a diversity of ways. A demonstration of this concept follows next. Consider the information on all functional regions from Table ‎10.11 as an inherent part of this annex (hereafter: the table). a. Simple queries. The easiest way to formulate a query is to target one indicator. For example, how much of the crops supplied to Israel originated from areas with high soil loss potential? This simple query requires as subset of all regions with high soil loss potential. Since this indicator has only two levels (high and low), half of the functional regions are to be included in the subset. These are the indicators that have the symbol "H" in the second location, describing soil loss potential, e.g: HuHLH, ArHHL, etc. Aggregating the supply from originates from those regions, using Table ‎10.11, results in a total of 2 million tons (accounts for 54 % of all crops supply). b. Complex queries Querying the functional regions isn't restricted to simple queries only. A more complex query could target questions like how much food originates from areas that experience both potential high soil loss and high water scarcity. Indicators that fit this query are a subset of the indicators included in the example of the simple query above, and for which the aridity index category is labeled by either Ar or Se and the water efficiency category is labeled as low, e.g. ArHLL. Aggregation shows that these regions send 304,530 tons of crops to Israel, which stands as 7.5 % relative to the crops that originate from areas with high soil loss potential. Similarly, one can address a question relating to other variables, i.e. the cropland footprint. For example: how much cropland footprint area is used in regions with high water scarcity and low yields. Defining areas with high water scarcity as the intersection between low water availability and inefficient use of water; these

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regions include functional region codes like: ArHLL, SeLLL, etc. Such regions cover an area of only 284.5 square KM (approximately, 2.5 %) of the total cropland footprint.

10.13. Utilization factor: Quantifying the level of agricultural use in different functional regions Utilization factor is an indicator calculated by dividing the share of cropland by the share of grid cell area (hereafter: gca) for each cluster. This indicator quantifies the extent to which an activity (cropland) occurs in a pre-determined region (cluster), relative to a hypothetic homogenous distribution of the activity across space. The hypothetical homogeneous line represents a case in the area share of any cluster equals exactly the cropland share that is within its borders. The value of an indicator at this line would be equal to 1, and it is represented by the 45° line (see Fig ‎10.1). The indicator itself is calculated using the Eq. ‎10.1.

Eq. 10‎ .1: quantifying the utilization factor for different functional regions

While interoperating the extremes a most and least utilized (for very high or very low values, respectively) is quite straight forward; the values between them and around the homogeneity line form a spectrum of utilization and breaking it into categories can be debatable. Based on the specific results in this study we use the following terminology (see Table ‎10.12).

Table 10‎ .12: Categories for utilization level

Utility factor Utilization level Other interpretations < 0.5 Least utilized Considered as marginal regions with high potential environmental impact due to agriculture 0.5 – 2 Minor croplands Considered as secondary food production regions > 2 Major croplands Considered as major food production regions with global importance

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Fig 10‎ .1: Functional regions utilization relative to the homogeneous line

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10.14. Absolute values for selected indicators in different functional regions

Table 10‎ .13: Absolute values for selected indicators in different functional regions

FU Blue water Green water Global Regional Soil loss Cropland Crop Wheat Soybean Rice Maize (km3) (km3) species lost species lost (Million ton) (Million ha) production production production production production (Million ton) (Million (Million (Million (Million ton) ton) ton) ton) 1 0.61 81.23 8.82 1,099.41 18,316.63 21.9 37.67 1.55 0.39 0.49 35.24 2 5.95 165.81 1.33 467.26 14,026.28 48.71 110.27 109.07 0.59 0.54 0.07 3 <0.01 0.25 2.31 176.99 8,957.98 6.87 14.68 0.34 0.82 7.34 6.18 4 95.6 409.54 28.09 3,756.81 180,099.38 72.7 320.36 0.92 0.84 289.24 29.36 5 1.52 231.31 8.34 992.62 67,794.46 44.35 141.85 1.94 47.74 37.59 54.58 6 0.09 125.82 17.76 1,615.27 60,295.06 31.67 60.74 0.61 0.12 35.53 24.48 7 6.19 759.67 5.01 1,179.2 312,377.43 138.7 643.69 94.7 171.56 0.36 377.07 8 85.51 204.59 0.89 479.93 95,197.07 47.64 238.47 93.83 10.11 0.95 133.58 9 130.7 427.14 8.05 1,939.61 262,112.83 88.73 427.92 63.69 32.78 216.33 115.12 10 199.83 260.28 2.93 1,566.51 120,713.18 76.67 248.01 116.25 0.22 108.74 22.8 11 26.18 138.97 0.88 230.52 11,293.27 32.18 75.81 58.31 0.42 0.39 16.69 12 5.9 145.46 1.73 363.81 25,315.9 35.81 134.72 97.64 0.19 1.64 35.25 Unclassified 0.06 3.41 1.26 82.07 2,042.33 1.13 3.24 0.79 0.05 1.8 0.6 Non food 0.96 3.69 0.05 12.65 945.7 0.91 2.86 2.27 0.02 0.3 0.27 use Total / 559.1 2,957.17 87.45 997.33* 1,179,487.5 647.97 2,460.29 641.91 265.85 701.42 851.29 Average*

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10.15. Local and imported national calorie supply from different groups of functional regions

Table 10‎ .14: Local and imported national calorie supply from different groups of functional regions

GDP per Food Sustainability Grand ISO3 Country Name Popualtion Most suitable Sub optimal Soil loss Ecological vulnerability Water stress Total capita availability status total

Millions, 2010 Millions,

International International

Domestic Domestic Domestic Domestic Domestic Domestic

Import Import Import Import Import Import

TTO Trinidad and Tobago 1.33 28911 Sufficient Sustainable 52 0 7 0 5 0 1 1 20 0 85 1 86 BHS Bahamas 0.36 37040 Sufficient Sustainable 56 0 4 0 9 0 1 0 14 0 84 0 84 MDV Maldives 0.33 16304 Sufficient Unsustinable 19 0 16 0 3 0 2 0 40 0 80 0 80 ATG Antigua and Barbuda 0.09 18206 Insufficient Unsustinable 26 0 18 0 8 0 1 0 27 0 80 0 80 GRD Grenada 0.11 13424 Insufficient Sustainable 46 0 8 1 7 0 2 0 18 0 81 1 82 JPN Japan 127.35 37490 Sufficient Sustainable 39 0 5 1 6 2 1 1 10 13 61 17 78 VCT Saint Vincent and the 0.11 11448 Sufficient Sustainable 45 0 8 1 4 0 0 0 19 0 76 1 77 Grenadines LCA Saint Lucia 0.18 12909 Sufficient Sustainable 46 0 8 0 4 0 1 0 17 0 76 0 76 BRN Brunei Darussalam 0.4 67751 Sufficient Unsustinable 14 0 12 0 9 0 2 0 38 1 75 1 76 DJI Djibouti 0.83 - Insufficient Sustainable 37 0 9 0 3 0 1 0 24 0 74 0 74 JAM Jamaica 2.74 9435 Sufficient Sustainable 42 0 15 0 3 0 1 0 19 0 80 0 80 BRB Barbados 0.28 15761 Sufficient Sustainable 59 0 5 0 5 0 0 0 16 0 85 0 85 KWT Kuwait 2.99 58810 Sufficient Unsustinable 24 0 15 1 9 0 3 0 16 0 67 1 68 KOR Republic of Korea 48.45 32575 Sufficient Sustainable 43 0 8 0 6 11 0 0 10 7 67 18 85 ISL Iceland 0.32 46619 Sufficient Sustainable 34 0 29 0 6 0 0 0 5 0 70 0 70 YEM Yemen 22.76 - Insufficient Sustainable 30 0 19 2 3 0 0 0 16 4 68 6 74 CPV Cabo Verde 0.49 6200 Sufficient Unsustinable 29 0 7 7 11 0 1 0 20 0 68 7 75 ISR Israel 7.42 34928 Sufficient Sustainable 41 1 3 1 5 0 0 0 13 2 62 4 66 TUN Tunisia 10.63 10115 Sufficient Sustainable 40 0 6 13 4 0 0 0 8 0 58 13 71 GEO Georgia 4.39 9746 Sufficient Unsustinable 28 1 4 1 2 0 0 6 26 7 60 15 75 LBN Lebanon 4.34 19891 Sufficient Sustainable 38 3 5 2 5 0 1 0 14 0 63 5 68 JOR Jordan 6.46 11131 Sufficient Sustainable 27 0 7 2 6 0 1 0 16 0 57 2 59

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PRT Portugal 10.59 31838 Sufficient Unsustinable 26 0 11 1 10 0 1 0 9 9 57 10 67 MYS Malaysia 28.28 20540 Sufficient Unsustinable 30 0 7 0 8 0 2 2 15 14 62 16 78 ARE United Arab Emirates 8.44 54804 Sufficient Unsustinable 25 0 8 0 7 0 2 0 16 0 58 0 58 MLT Malta 0.43 31877 Sufficient Sustainable 24 0 16 6 6 0 0 0 10 0 56 6 62 CRI Costa Rica 4.67 15905 Sufficient Sustainable 45 0 4 0 5 0 0 7 13 7 67 14 81 PAN Panama 3.68 21423 Sufficient Sustainable 42 0 3 0 7 0 0 19 12 2 64 21 85 VEN Venezuela (Bolivarian 29.04 - Sufficient Unsustinable 31 0 7 0 11 4 2 8 7 10 58 22 80 Republic of) OMN Oman 2.8 34900 Sufficient Unsustinable 17 0 12 0 7 0 1 0 19 0 56 0 56 DZA Algeria 37.06 10796 Sufficient Sustainable 36 1 9 16 2 0 0 0 7 0 54 17 71 KNA Saint Kitts and Nevis 0.05 23708 Insufficient Unsustinable 16 0 13 0 9 0 0 0 16 0 54 0 54 MRT Mauritania 3.61 3454 Sufficient Unsustinable 25 0 9 1 2 0 0 1 14 11 50 13 63 CYP Cyprus 1.1 34038 Sufficient Sustainable 25 0 11 2 3 0 0 0 12 0 51 2 53 GAB Gabon 1.56 14414 Sufficient Sustainable 26 0 7 6 5 0 1 3 14 0 53 9 62 CUB Cuba 11.28 - Sufficient Sustainable 39 0 6 0 9 0 1 16 8 0 63 16 79 DOM Dominican Republic 10.02 12782 Sufficient Unsustinable 35 0 4 0 4 0 1 1 12 19 56 20 76 SEN Senegal 12.95 2769 Insufficient Unsustinable 16 0 4 1 8 0 1 6 20 15 49 22 71 IRQ Iraq 30.96 9064 Sufficient Unsustinable 15 5 8 1 4 0 1 0 17 23 45 29 74 KIR Kiribati 0.1 1927 Sufficient Sustainable 9 0 28 0 0 0 0 0 5 0 42 0 42 MUS Mauritius 1.23 16798 Sufficient Sustainable 50 0 18 0 2 0 1 0 16 0 87 0 87 COG Congo 4.11 3473 Insufficient Unsustinable 19 0 4 1 4 0 1 0 15 0 43 1 44 IRL Ireland 4.47 53041 Sufficient Sustainable 20 0 13 11 5 0 0 0 4 0 42 11 53 COL Colombia 46.45 11893 Sufficient Sustainable 35 0 3 1 6 3 1 7 4 10 49 21 70 ARM Armenia 2.96 9286 Sufficient Sustainable 15 11 4 9 2 0 1 0 20 0 42 20 62 SUR Suriname 0.53 16188 Sufficient Unsustinable 29 0 4 0 3 0 1 2 7 37 44 39 83 FJI Fiji 0.86 10518 Sufficient Unsustinable 9 0 31 0 2 0 1 3 21 0 64 3 67 NLD Netherlands 16.62 52278 Sufficient Sustainable 23 3 12 2 6 0 0 0 5 0 46 5 51 EGY Egypt 78.08 10340 Sufficient Unsustinable 28 1 4 1 2 19 0 0 10 17 44 38 82 SAU Saudi Arabia 27.26 44037 Sufficient Sustainable 19 4 5 2 6 0 1 0 8 2 39 8 47 BEL Belgium 10.94 48011 Sufficient Sustainable 24 2 11 12 4 0 0 1 5 0 44 15 59 MAR Morocco 31.64 6456 Sufficient Sustainable 27 4 6 7 4 0 1 2 4 16 42 29 71 MKD The former Yugoslav 2.1 13440 Sufficient Sustainable 29 4 6 3 2 2 1 0 1 16 39 25 64 Republic of Macedonia SLV El Salvador 6.22 7312 Sufficient Unsustinable 28 0 2 0 4 18 1 9 9 8 44 35 79 PER Peru 29.26 10066 Sufficient Unsustinable 28 0 5 3 3 3 0 6 5 21 41 33 74 STP Sao Tome and 0.18 3329 Insufficient Sustainable 17 0 9 4 5 0 0 0 7 0 38 4 42 Principe LUX Luxembourg 0.51 108197 Sufficient Sustainable 18 6 10 7 4 0 0 0 6 0 38 13 51

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BIH Bosnia and 3.85 11114 Sufficient Sustainable 29 26 3 11 3 0 1 0 1 0 37 37 74 Herzegovina ITA Italy 60.51 42859 Sufficient Unsustinable 22 7 7 1 3 21 0 0 4 4 36 33 69 ESP Spain 46.18 37393 Sufficient Unsustinable 18 3 7 2 6 1 0 0 4 14 35 20 55 NZL New Zealand 4.37 36593 Sufficient Sustainable 14 4 11 12 2 0 0 0 5 7 32 23 55 HND Honduras 7.62 4867 Sufficient Unsustinable 25 0 2 0 4 13 1 2 9 10 41 25 66 GMB Gambia 1.68 2347 Sufficient Unsustinable 9 0 3 0 7 0 1 23 12 1 32 24 56 GTM Guatemala 14.34 7555 Insufficient Unsustinable 30 2 3 1 3 23 0 12 9 0 45 38 83 SVK Slovakia 5.43 26072 Sufficient Sustainable 17 17 4 7 9 0 0 0 1 13 31 37 68 ALB Albania 3.15 11019 Sufficient Unsustinable 16 1 4 1 2 0 0 0 8 40 30 42 72 HTI Haiti 9.9 1611 Insufficient Unsustinable 7 0 1 2 14 0 0 11 9 11 31 24 55 CIV Cóte d'Ivoire 18.98 2679 Sufficient Unsustinable 5 0 2 1 3 7 1 15 18 4 29 27 56 DMA Dominica 0.07 12004 Sufficient Unsustinable 11 0 8 0 1 0 0 0 10 0 30 0 30 NIC Nicaragua 5.82 4612 Sufficient Unsustinable 21 0 2 1 6 9 0 27 7 12 36 49 85 CHL Chile 17.15 20602 Sufficient Unsustinable 20 3 3 11 4 0 0 0 4 25 31 39 70 SVN Slovenia 2.05 33498 Sufficient Sustainable 19 27 5 2 3 0 0 0 2 0 29 29 58 MEX Mexico 117.89 17817 Sufficient Unsustinable 22 1 1 5 2 1 0 15 5 17 30 39 69 CHE Switzerland 7.83 64479 Sufficient Sustainable 16 14 8 8 5 0 0 0 4 0 33 22 55 ECU Ecuador 15 10341 Insufficient Unsustinable 21 0 6 0 3 15 0 3 3 13 33 31 64 DNK Denmark 5.55 50947 Sufficient Sustainable 19 20 3 3 5 0 0 0 1 0 28 23 51 LBR Liberia 3.96 1420 Insufficient Unsustinable 4 0 2 11 8 0 1 15 13 0 28 26 54 NOR Norway 4.89 60290 Sufficient Sustainable 11 0 7 10 4 0 0 0 2 0 24 10 34 LVA Latvia 2.09 20953 Sufficient Sustainable 19 0 2 14 3 0 0 0 2 0 26 14 40 MNG Mongolia 2.71 7480 Insufficient Unsustinable 7 0 2 51 2 0 0 0 14 0 25 51 76 MNE Montenegro 0.62 16783 Sufficient Sustainable 15 1 5 3 2 0 0 2 2 0 24 6 30 GRC Greece 11.11 33966 Sufficient Unsustinable 15 5 4 6 3 1 0 0 3 22 25 34 59 GBR United Kingdom 62.31 42154 Sufficient Sustainable 12 0 4 32 4 0 0 0 3 0 23 32 55 IRN Iran (Islamic Republic 74.46 13806 Sufficient Unsustinable 18 3 1 7 4 9 1 0 4 26 28 45 73 of) LTU Lithuania 3.07 23992 Sufficient Sustainable 13 21 3 2 4 0 0 0 3 0 23 23 46 GNB Guinea-Bissau 1.59 1747 Insufficient Unsustinable 2 0 1 2 3 0 0 43 13 1 19 46 65 ZWE Zimbabwe 13.08 2353 Insufficient Unsustinable 11 2 3 6 2 11 3 27 4 7 23 53 76 PHL Philippines 93.44 5710 Sufficient Unsustinable 9 0 3 1 2 1 0 11 9 42 23 55 78 ZAF South Africa 51.45 12452 Sufficient Sustainable 10 39 3 6 2 7 0 1 4 14 19 67 86 AGO Angola 19.55 7692 Insufficient Unsustinable 11 0 2 2 3 0 0 11 2 0 18 13 31 AUT Austria 8.4 52129 Sufficient Sustainable 13 24 4 11 3 0 0 0 2 0 22 35 57 GUY Guyana 0.79 7075 Sufficient Unsustinable 16 0 7 10 1 0 0 4 3 46 27 60 87 KEN Kenya 40.91 3319 Insufficient Unsustinable 9 3 2 11 2 1 1 36 8 2 22 53 75

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ROU Romania 21.86 20320 Sufficient Sustainable 12 24 3 9 2 5 0 0 1 24 18 62 80 VNM Viet Nam 89.05 5089 Sufficient Unsustinable 10 0 3 0 4 11 0 3 3 53 20 67 87 LKA Sri Lanka 20.76 9203 Insufficient Unsustinable 5 0 8 0 1 1 0 1 4 55 18 57 75 DEU Germany 83.02 46927 Sufficient Sustainable 11 15 3 13 4 0 0 0 2 1 20 29 49 SLB Solomon Islands 0.53 2059 Insufficient Sustainable 3 0 10 1 0 0 0 1 2 0 15 2 17 NAM Namibia 2.18 9205 Insufficient Sustainable 8 4 4 14 1 0 2 6 3 0 18 24 42 BLZ Belize 0.31 7222 Sufficient Unsustinable 15 0 4 2 2 23 0 16 7 8 28 49 77 CZE Czechia 10.55 33505 Sufficient Sustainable 8 26 2 18 3 0 0 0 1 0 14 44 58 VUT Vanuatu 0.24 3245 Sufficient Unsustinable 2 0 7 0 1 0 0 0 3 0 13 0 13 EST Estonia 1.3 26182 Sufficient Sustainable 8 0 4 17 1 0 0 0 1 0 14 17 31 NGA Nigeria 159.71 4932 Sufficient Unsustinable 4 0 2 0 1 9 0 13 6 1 13 23 36 CMR Cameroon 20.62 3086 Sufficient Unsustinable 4 0 2 3 1 2 0 22 5 0 12 27 39 SWE Sweden 9.38 48514 Sufficient Sustainable 8 0 4 27 3 0 0 0 2 0 17 27 44 TGO Togo 6.31 1225 Insufficient Unsustinable 3 0 2 0 2 0 0 29 6 4 13 33 46 AZE Azerbaijan 9.1 14375 Sufficient Unsustinable 6 0 1 0 1 0 0 0 6 47 14 47 61 GHA Ghana 24.26 3732 Sufficient Unsustinable 3 0 3 1 1 0 0 21 4 3 11 25 36 TUR Turkey 72.14 20042 Sufficient Sustainable 7 21 1 6 2 1 0 0 3 17 13 45 58 CAN Canada 34.13 45044 Sufficient Sustainable 9 42 0 12 1 0 0 0 2 0 12 54 66 THA Thailand 66.4 14397 Sufficient Unsustinable 7 0 2 0 4 16 0 3 2 47 15 66 81 IDN Indonesia 240.68 8285 Sufficient Unsustinable 7 0 4 1 1 38 0 5 2 23 14 67 81 MOZ Mozambique 23.97 1027 Insufficient Unsustinable 4 0 3 6 1 0 0 25 4 1 12 32 44 FRA France 63.23 42322 Sufficient Sustainable 7 22 1 12 4 0 0 0 1 11 13 45 58 HUN Hungary 10.02 24493 Sufficient Sustainable 8 46 1 7 2 3 0 0 1 3 12 59 71 CHN China, mainland 1359.82 8811 Sufficient Unsustinable 8 5 0 1 2 35 0 0 1 24 11 65 76 BEN Benin 9.51 2705 Sufficient Unsustinable 3 0 1 0 1 11 0 13 6 1 11 25 36 POL Poland 38.2 23969 Sufficient Sustainable 8 21 1 8 1 0 0 0 1 0 11 29 40 SLE Sierra Leone 5.75 1414 Insufficient Unsustinable 2 0 1 8 1 0 0 31 5 5 9 44 53 FIN Finland 5.37 45916 Sufficient Sustainable 5 0 3 15 2 0 0 0 1 0 11 15 26 RUS Russian Federation 143.62 24272 Sufficient Unsustinable 5 15 1 4 2 1 0 0 1 29 9 49 58 BFA Burkina Faso 15.54 1503 Sufficient Unsustinable 2 0 1 1 1 19 0 6 3 3 7 29 36 GIN Guinea 10.88 1871 Sufficient Unsustinable 3 0 1 4 0 0 0 37 5 10 9 51 60 HRV Croatia 4.34 24219 Sufficient Sustainable 4 67 1 2 3 0 0 1 1 0 9 70 79 AFG Afghanistan 28.4 2094 Insufficient Unsustinable 2 7 1 5 0 0 0 0 6 65 9 77 86 BOL Bolivia (Plurinational 10.16 6613 Insufficient Unsustinable 8 4 0 2 0 41 0 8 1 2 9 57 66 State of) BGR Bulgaria 7.39 17399 Sufficient Sustainable 4 40 1 11 1 0 0 0 1 6 7 57 64 BGD Bangladesh 151.13 2883 Insufficient Unsustinable 4 0 2 3 1 4 0 3 2 70 9 80 89

195

BWA Botswana 1.97 14126 Insufficient Unsustinable 2 0 0 0 1 0 4 1 1 0 8 1 9 AUS Australia 22.4 45354 Sufficient Sustainable 4 6 0 22 1 0 0 0 2 4 7 32 39 CAF Central African 4.35 1201 Insufficient Unsustinable 4 0 1 16 0 0 0 13 1 0 6 29 35 Republic PRK Democratic People's 24.5 - Insufficient Unsustinable 2 0 0 0 3 65 0 0 3 2 8 67 75 Republic of Korea TZA United Republic of 44.97 2228 Insufficient Unsustinable 2 0 1 5 0 3 0 30 1 9 4 47 51 Tanzania KGZ Kyrgyzstan 5.33 4149 Sufficient Unsustinable 3 0 0 1 1 0 0 0 2 61 6 62 68 BLR Belarus 9.49 17288 Sufficient Sustainable 4 13 0 6 0 0 0 0 0 4 4 23 27 MLI Mali 13.99 2088 Sufficient Unsustinable 2 0 1 5 0 1 0 22 1 15 4 43 47 BRA Brazil 195.21 14868 Sufficient Unsustinable 4 26 0 1 1 38 0 5 1 4 6 74 80 URY Uruguay 3.37 17713 Sufficient Sustainable 4 39 0 1 1 27 0 0 0 8 5 75 80 TKM Turkmenistan 5.04 8617 Sufficient Unsustinable 2 2 0 9 1 0 0 0 2 66 5 77 82 TLS Timor-Leste 1.08 2930 Insufficient Unsustinable 2 0 2 2 1 32 0 12 1 30 6 76 82 NER Niger 15.89 747 Sufficient Unsustinable 1 0 1 0 1 0 1 1 1 0 5 1 6 TCD Chad 11.72 1733 Insufficient Unsustinable 1 0 1 4 0 0 0 6 0 1 2 11 13 TJK Tajikistan 7.63 2260 Insufficient Unsustinable 2 1 0 0 1 1 0 0 1 65 4 67 71 MDG Madagascar 21.08 1553 Insufficient Unsustinable 1 0 1 8 0 0 0 7 2 56 4 71 75 MDA Republic of Moldova 3.57 8550 Sufficient Sustainable 4 64 0 4 0 0 0 0 1 11 5 79 84 RWA Rwanda 10.84 1459 Insufficient Unsustinable 0 1 0 0 0 7 1 6 0 2 1 16 17 UGA Uganda 33.99 1572 Insufficient Unsustinable 1 0 0 3 0 11 0 13 1 1 2 28 30 ETH Ethiopia 87.1 1260 Insufficient Unsustinable 2 6 0 13 0 4 0 8 1 5 3 36 39 KAZ Kazakhstan 15.92 20751 Sufficient Unsustinable 2 12 0 34 0 0 0 0 0 13 2 59 61 MWI Malawi 15.01 969 Insufficient Unsustinable 1 3 1 14 0 31 0 12 0 1 2 61 63 IND India 1205.63 4225 Insufficient Unsustinable 1 1 0 1 0 9 0 3 0 56 1 70 71 LAO Lao People's 6.4 4850 Insufficient Unsustinable 0 0 0 0 1 10 0 5 1 71 2 86 88 Democratic Republic KHM Cambodia 14.37 2712 Insufficient Unsustinable 1 0 1 3 0 29 0 9 0 47 2 88 90 SWZ Eswatini 1.19 7515 Insufficient Unsustinable 1 0 0 0 0 0 1 56 0 11 2 67 69 PAK Pakistan 173.15 3907 Insufficient Unsustinable 0 0 0 1 0 0 0 1 0 68 0 70 70 UKR Ukraine 46.05 7824 Sufficient Sustainable 1 64 0 1 0 3 0 0 0 2 1 70 71 PRY Paraguay 6.46 10405 Sufficient Sustainable 0 55 0 0 0 16 0 1 0 1 0 73 73 ARG Argentina 40.37 23521 Sufficient Sustainable 0 67 0 0 0 3 0 0 0 3 0 73 73 LSO Lesotho 2.01 2546 Sufficient Unsustinable 0 0 0 9 0 0 0 61 0 1 0 71 71 NPL Nepal 26.85 2356 Sufficient Unsustinable 0 0 0 0 1 29 0 1 1 45 2 75 77 MMR Myanmar 51.93 3038 Sufficient Unsustinable 0 0 0 3 0 41 0 3 0 32 0 79 79 ZMB Zambia 13.22 3129 Insufficient Unsustinable 0 12 0 7 0 7 0 57 0 1 0 84 84 SRB Serbia 9.65 14512 Sufficient Sustainable 1 60 0 20 0 0 0 4 0 0 1 84 85

196

USA United States of 312.25 54437 Sufficient Sustainable 1 62 1 2 0 4 0 0 0 15 2 83 85 America UZB Uzbekistan 27.77 4652 Sufficient Unsustinable 0 2 0 0 0 0 0 0 0 78 0 80 80

197

תקציר ביטחון מזון של מדינות רבות בעולם הופך תלוי יותר ויותר במסחר בין לאומי. השפעות סביבתיות הקשורות בצריכת מזון מתרחשות במקרים רבים במערכות חקלאיות מרוחקות. מסחר בין לאומי הוא אמצעי למעבר של מזון ובאופן וירטואלי גם של המשאבים הגלומים בהפקתו בין אזורים שונים בעולם. יש אשר רואים בו כגורם שתורם לשמירת הסביבה ויש כאלו שמדגישים את הנזק הסביבתי הכרוך בו. בעולם גלובלי ומקושר, הקיימות של מערכות מזון הינה קיימות בין אזורית, זאת לאור התלות הגבוהה של מדינות שונות באזורי ייצור מרוחקים. הספרות המחקרית העוסקת בניתוח הלחצים הסביבתיים במדינות אשר מייצרות מזון, אשר נגרמים כתוצאה מצריכת מזון במדינות מרוחקות הולכת ומתרחבת בשנים האחרונות. מחקרים אחרים מתמקדים באזורים מסוימים ובקנה מידה מקומי במטרה לנתח את השפעות הסביבתיות הקשורות בייצור מזון. עם זאת, מספר מצומצם מאוד של מחקרים חיברו יחדיו את ההיבט הבין אזורי של אספקת מזון ואת ההקשר הגיאוגרפי המקומי ואת ההשפעה הקשורה בייצור מזון. עבודת הדוקטורט הזו מנתחת את הקיימות הבין אזורית של מערכת המזון הגלובלית, תוך יצירת חיבור בין אספקת מזון מקומית ומיובאת במדינות שונות, עם ההקשר הגיאוגרפי של ייצור המזון באזורים שונים. מחקר זה מתמקד ב4- גידולי מזון עיקריים: חיטה, סויה, תירס ואורז, אשר מהווים את המרכיב העיקרי בתזונה הגלובלית. ממצאי המחקר תומכים ברעיון שלפיו מסחר בין לאומי במזון הינו בעל השפעות סביבתיות חיוביות. מבחינת הגידולים החקלאיים שנבחנו במחקר זה נמצא כי המסחר תורם להפחתה בסחף הקרקע, בהכחדת מינים ולתלות פחותה באזורים בהם ישנה עקת מים קשה. בנוסף, הממצאים מציגים כיצד ביטחון מזון של מדינה תלוי ביציבות המערכת הטבעית באזורים אשר מספקים לה את המזון. לצד זאת, מאפשר המחקר לזהות אזורים בהם הנזקים הסביבתיים כתוצאה מצריכת מזון במדינות שונות הם גבוהים. הממצאים שעולים מעבודה זו יכולים לתמוך גם בקידומן של מערכות מזון מקיימות. נדבך נוסף וחשוב הוא התרומה המתודולוגית שעולה מתוך מחקר זה. השיטות אשר פותחו ומוזגו בעת המחקר מהוות כלי עבודה חשוב לחקר קיימות בין אזורית. מחקר עתידי יוכל לכלול מחווני לחץ סביבתי והשפעה סביבתית נוספים, על מנת לזהות מורכבויות נוספות במערכת מזון העולמית. ניתוח לאורך זמן של הקיימות הבין אזורית של מערכת מזון התאפשר אף הוא לאחרונה ובכוחו להציג מגמות חשובות לאורך זמן ובקני מידה שונים. לבסוף, ניתוח של מקרי בוחן מקומיים ברזולוציה גבוהה ישלים את הניתוח הנוכחי ויאפשר לבחון את מידת אי-הוודאות הכרוכה בניתוח ברמה הגלובלית.

הצהרת תלמיד המחקר עם הגשת עבודת הדוקטור לשיפוט

אני החתום מטה מצהיר/ה בזאת: )אנא סמן(:

V חיברתי את חיבורי בעצמי, להוציא עזרת ההדרכה שקיבלתי מאת מנחה/ים.

V החומר המדעי הנכלל בעבודה זו הינו פרי מחקרי מתקופת היותי תלמיד/ת מחקר.

___ בעבודה נכלל חומר מחקרי שהוא פרי שיתוף עם אחרים, למעט עזרה טכנית

הנהוגה בעבודה ניסיונית. לפיכך מצורפת בזאת הצהרה על תרומתי ותרומת שותפי למחקר, שאושרה על ידם ומוגשת בהסכמתם.

תאריך /81/2/2/2 שם התלמיד/ה דור פרידמן חתימה

העבודה נעשתה בהדרכת: פרופ' מידד קיסינגר

במחלקה לגיאוגרפיה ופיתוח סביבתי

בפקולטה למדעי הרוח והחברה

הקיימות הביו-פיסית של מערכות מזון בעולם גלובלי ומקושר

מחקר לשם מילוי חלקי של הדרישות לקבלת תואר "דוקטור לפילוסופיה " "

מאת מא ת

דור פרידמן פרידמן

הוגש לסינאט אוניברסיטת בן גוריון בנגב

אישור המנחה ______

אישור דיקן בית הספר ללימודי מחקר מתקדמים ע "ש קרייטמן ______

ט התש' פ"אדר /53/32/2 /32/2

באר שבע באר שבע

הקיימות הביו-פיסית של מערכות מזון בעולם גלובלי ומקושר

מחקר לשם מילוי חלקי של הדרישות לקבלת תואר "דוקטור לפילוסופיה"

מאת

דור פרידמן

הוגש לסינאט אוניברסיטת בן גוריון בנגב

ט' אדר התש"פ /53/32/2

באר שבע