# A global analysis of potential self-sufficiency and diversity displays diverse supply risks

Emmy Wassénius, Miina Porkka, Magnus Nyström, Peter Søgaard Jørgensen

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Emmy Wassénius, Miina Porkka, Magnus Nyström, Peter Søgaard Jørgensen. A global analysis of potential self-sufficiency and diversity displays diverse supply risks. Global Food Security, 2023, 37, pp.100673. 10.1016/j.gfs.2023.100673. hal-04682043

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Wassénius, Emmy; Porkka, Miina; Nyström, Magnus; Søgaard Jørgensen, Peter

A global analysis of potential self-sufficiency and diversity displays diverse supply risks

Published in:
GLOBAL FOOD SECURITY

DOI:
10.1016/j.gfs.2023.100673

Published: 01/06/2023

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CC BY

Please cite the original version:
Wassénius, E., Porkka, M., Nyström, M., & Søgaard Jørgensen, P. (2023). A global analysis of potential self-sufficiency and diversity displays diverse supply risks. *GLOBAL FOOD SECURITY*, 37, Article 100673.
https://doi.org/10.1016/j.gfs.2023.100673
# A global analysis of potential self-sufficiency and diversity displays diverse supply risks

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Emmy Wassénius<sup>a, b, *</sup>, Miina Porkka<sup>a, c</sup>, Magnus Nyström<sup>b</sup>, Peter Søgaard Jørgensen<sup>a, b</sup>

## ARTICLE INFO

### Keywords:

- Food production

- Self-sufficiency

- Diversity

- Risk

- Resilience

## ABSTRACT

International trade plays a fundamental role in today's globalized food system, however, trade-related disruptions to national food supply have become increasingly prevalent. Although national food self-sufficiency and the resilience of domestic food production are both increasingly discussed, they are rarely investigated in tandem. This hinders our understanding of the diversity of risks to national food supply. In this article we investigate the contribution of production to these risks, through the compilation of a comprehensive national production dataset and a multi-indicator assessment of self-sufficiency and diversity. Our results show that most of the world (127 countries and territories, 87% of the global population) achieves high levels of potential self-sufficiency (≥6 nutrients fulfilled), however only 33% of the world population (41 countries) are fully self-sufficient. Of countries with high levels of self-sufficiency, fruit and vegetable production (a proxy for many micronutrients) is the most common “missing” sufficiency. 66 countries (6% of population) have a low degree of self-sufficiency, highlighting potential vulnerability to trade-related disruptions. The relationship between sufficiency and diversity is not homogeneous, highlighting that some production systems are reliant on very few products.

## 1. Introduction

Stable food supply is an integral part of food security. International trade and domestic production of food are the two main contributors upon which this stability rests. Over the past decades international trade has undergone huge expansion (D'Odorico et al., 2014), and today, approximately 80% of the world's population is to some extent dependent on food trade to fulfil food demand (Porkka et al., 2013). Trade adds to stable food supply in several ways. It contributes to redistribute food between high and low producing nations (Fader et al., 2013) and as an insurance in case domestic production fails due to, for example, droughts or pest outbreaks (Hawkes and Murphy, 2009). However, as countries become progressively interlinked and dependent on global trade, disruptions in trade flow can be propagated or attenuated along food supply chains by various economic, political, and infrastructural factors (Davis et al., 2021). Examples of such disruptions include, clogging of maritime choke points, such as the 6-days blockage of the Suez Canal in 2021 due to the grounding of the containership Ever Given (Leggett, 2021; Wellesley et al., 2017), halted exports in response to the COVID-19 pandemic (Laborde et al., 2020), and export disruptions due

to violent conflict such as the current war in Ukraine (FAO, 2022). Disruptions can also emerge from protectionist trade strategies during crisis when producing countries prioritize food supply for their own populations, such as Indonesia's brief implementation of an export ban on palm oil (Jadhav, 2022).

Because of such trade-disruptive events, increasing attention has been directed towards countries' self-sufficiency. Self-sufficiency here refers to the capacity of a country to produce food domestically to cover their own needs (Clapp, 2017). However, self-sufficiency is more than just production of food (Puma et al., 2015) or calories alone (Beltran-Peña et al., 2020; Porkka et al., 2013; Schramski et al., 2019), it is also about the nutritional content and diversity of the food produced to meet the nutritional demands of the population. To lower the exposure to trade-related risks, domestic production needs to cover the full range of required nutrients. Another important aspect of nutritional stability is the diversity of food products that a country produces that provides the same nutritional components (e.g., grams of protein). Having a diverse portfolio of food products that contribute the same nutritional components provides response diversity, being the diversity of responses to shocks and disturbances (Elmqvist et al., 2003; Nyström,

<footnote><sup>a</sup> Global Economic Dynamics and the Biosphere, Royal Swedish Academy of Sciences, Stockholm, Sweden</footnote>

<footnote><sup>b</sup> Stockholm Resilience Centre, Stockholm University, Stockholm, Sweden</footnote>

<footnote><sup>c</sup> Water and Development Research Group, Aalto University, Espoo, Finland</footnote>

<footnote>* Corresponding author. Global Economic Dynamics and the Biosphere, Royal Swedish Academy of Sciences, Stockholm, Sweden.
E-mail address: emmy.wassenius@kva.se (E. Wassénius).</footnote>
2006).

In this article we investigate the extent to which countries are nutritionally self-sufficient, as well as at which levels of production diversity this level of self-sufficiency is achieved. This analysis highlights the risks, or potential undesirable outcomes of the future (Wassénius and Crona, 2022), that exist to food supply in the context of trade-disruptive events. We do this through assembling a novel dataset on national food production covering all food sectors (both terrestrial and aquatic) and linking them to macro and micronutrient compositions. In light of the current war in Ukraine, we link the export of wheat from Ukraine and Russia to the risks we identify. This highlights how this paper, analysis, and the accompanying data and online interactive data tool can provide information on the added implications of trade disrupted by conflict.

## 2. Methods

Existing data on national food production either have high levels of detail (at the level of species and/or food items) but where terrestrial and aquatic production are separated into different datasets, or aquatic and terrestrial production data are present in the same dataset, but where species or food items have been aggregated. A novel dataset, that combines the high levels of details across both production sectors, was therefore needed to comprehensively analyse nutritional self-sufficiency and diversity. First, terrestrial and aquatic production data were integrated at the highest detail available (section 2.1), after which food products in this dataset were matched with nutritional data (section 2.2). This dataset was then used to calculate the level of nutritional self-sufficiency (section 2.3) and diversity (section 2.4) in each national production system. To compare the two different types of risk exposure, self-sufficiency and diversity where split into ordinal categories of high, medium and low (section 2.5). Considering the war in Ukraine and the timeliness of understanding these risks, data on wheat exports from Ukraine and Russia was analysed and compared to the levels of self-sufficiency and diversity in the importing countries (section 2.6).

### 2.1. Integration of terrestrial and aquatic production data

A full list of food products was assembled through combinations of terrestrial production data from FAOSTAT (Crops and Livestock Production module) (FAO, 2020a) and aquatic production data from FishStatJ (Fishery and Aquaculture Production Statistics) (FAO, 2020b). In this paper “food products” refer to the highest resolution category of food production possible, which in some cases are single species and in other cases are groups of species. Food products refers to products that could be consumed by humans, acknowledging that in some cases food products are used for other purposes. Items that are not used for human consumptions, such as fibres, rubbers and products solely used for animal feed were removed from the production data. A list of 2479 unique food products (2264 aquatic and 215 terrestrial) was created. Terrestrial animal food products were both grouped by species but also kept separated into the different animal parts (meat, fat, milk, eggs). The grouped variable was used to get an accurate representation of the taxonomic diversity that terrestrial animals add to the production mix. The higher resolution variable was kept due to the highly divergent nutritional composition of different parts of the animal (such as meat and fat).

A harmonised country list was created that encompasses not only nation states but also includes territories (areas of a country that are separate in geographical space from the main location of the nation state, such as island territories). A list of 276 these “countries” was assembled (from here on referred to as countries).

### 2.2. Integration of nutrient data

The production data was matched with the GENuS database (Smith et al., 2016) on nutritional composition of different food groups. The

GENuS database was selected because of the diversity of products, both terrestrial and aquatic, for which there was nutritional data. Matching was done through a semi-automatic method with both manual and string-matching techniques. For full details on combinations of the production datasets and the nutritional dataset see Supplementary Materials. Micronutrient selection was based on the most common sources of micronutrient deficiency (FAO and WHO, 1998). We acknowledge that deficiency in vitamin B12 is common, however the data in the GENuS database for this variable was not comprehensive, therefore this variable was excluded. Nutrients selected are composed of 3 micronutrients (carbohydrates, protein, and fat) and 5 micronutrients (Vitamin A, Folate, Iron, Zinc and Calcium). As a proxy for remaining essential micronutrients, we also added a metric for sufficient intake of fruits and vegetables (hereafter referred to as one of the nine nutrients assessed).

### 2.3. Calculating potential nutritional self-sufficiency

To assess the potential nutritional self-sufficiency, the contribution of domestic production to the nine selected nutrients was calculated. Our calculation of potential nutritional self-sufficiency (from here on referred to as nutritional self-sufficiency), is a hypothetical exercise that does not consider the export of food products, or that food products are sometimes used for other purposes, such as feed or biofuels either domestically or abroad. It is based on production alone and does not consider if the population receives sufficient nutrients. This distinction is important as differences in access to domestic production can create disparity between nutritionally sufficient production and nutritional sufficiency within the population. This situation is a simplification of the complexity of food trade and access; however, it highlights the residual risk that exists if exports and non-food uses were stopped in times of disruption, such as during conflict, disrupted trade routes or fears over local access, as with the Indonesian ban on palm oil exports (Jadhav, 2022; Laborde et al., 2020; Leggett, 2021; Wellesley et al., 2017).

The production of each macro and micronutrient was summed across all food products per country per year to give a single value (e.g. carbohydrates produced per nation per year). To account for losses and food waste (that will not contribute to nutritional sufficiency) all production was reduced by 13.8% which represents the global average (FAO, 2021). The total nutritional value was then divided by the national population of that country in that year (population data from FAOSTAT (FAO, 2020a)) and by 365 (days) to give the nutritional production per cap per day. To assess how production contributed to nutritional self-sufficiency, the levels of nutrients provided through domestic production was measured against known limits of nutritional sufficiency (see Table 1 for nutrients and sufficiency levels). A metric of nutritional self-sufficiency was then calculated as a range from 0 to 9 nutrients sufficiently filled from domestic production. If the national production of each of the nine nutrients exceeded the sufficiency limits (Table 1) the country would receive a nutritional sufficiency score of nine. If production exceeded the limits on eight of the nutrients, the score would similarly be eight. The yearly values where then averaged across the latest five years of data (2014–2018) to produce a representation of the “current” system and to account for interannual variability.

### 2.4. Calculating production diversity

Two metrics of production diversity were calculated: taxonomic diversity (the number of food items produced per nation per year, also referred to as taxonomic richness), and a production diversity metric calculated using the H-index (Seekell et al., 2017). Production diversity was calculated based on the number of food products that a country produces that provide the same number of kcal per capita per day. For example, a country with a production diversity score of 15 has 15 food products that each contribute to at least 15 kcal/cap/day. The production diversity metric (H-index) was selected as it includes an assessment of evenness (as opposed to taxonomic diversity). There are other
<caption>**Table 1**</caption>

Nutrients included in self-sufficiency analysis along with the limits set for achieving sufficiency. Note that the sufficiency limits for carbohydrates, protein and fat are each the lower bounds of the sufficiency range. Therefore, the sum of the caloric contribution of each of these three does not add up to the caloric sufficiency level.

<table><thead><tr><th>Nutrient</th><th>Sufficiency limit (per capita per day)</th><th>Methodology and source</th></tr></thead><tbody><tr><td>Carbohydrates</td><td>324.1g/day</td><td>Calculated as 55% of caloric intake (WHO and FAO, 2003) (estimated at 2357 kcal, following (FAO, 2020a)). 1 g of carbohydrates is approximately 4 kcal (Buchholz and Schoeller, 2004).</td></tr><tr><td>Protein</td><td>58.9 g/day</td><td>Calculated as 10% of caloric intake (estimated at 2357 kcal, following (FAO, 2020a)). 1 g of protein is approximately 4 kcal (Buchholz and Schoeller, 2004; WHO and FAO, 2003).</td></tr><tr><td>Fat</td><td>39.3 g/day</td><td>Calculated as 15% of caloric intake (estimated at 2357 kcal, following (FAO, 2020a)). 1 g of fat is approximately 9 kcal (Buchholz and Schoeller, 2004; WHO and FAO, 2003).</td></tr><tr><td>Fruit and vegetables</td><td>400 g/day</td><td>Aggregation of item groups "Citrus Fruit, Total", "Fruit Primary" and "Vegetables Primary". Limit as per recommendation in (WHO and FAO, 2003).</td></tr><tr><td>Vitamin A</td><td>434 µg RE/day</td><td>Daily requirement, mean across adult population (FAO and WHO, 1998).</td></tr><tr><td>Folate</td><td>350 µg/day</td><td>Daily requirement, mean across all adult age groups (FAO and WHO, 1998).</td></tr><tr><td>Iron</td><td>24.24 mg/day</td><td>Daily requirement, mean across all adult age groups and all levels of bioavailability (5,10,12,15%), (FAO and WHO, 1998).</td></tr><tr><td>Zinc</td><td>7.15 mg/day</td><td>Daily requirement, mean across all adult age groups and all levels of bioavailability (low, moderate, high) (FAO and WHO, 1998).</td></tr><tr><td>Calcium</td><td>1000 mg/day</td><td>Daily recommended intake for adults, Recommended intake instead of daily requirement due to natural loss (also called insensible losses) of calcium (FAO and WHO, 1998).</td></tr></tbody></table>

diversity indices that capture evenness, however we opted for this one as it is easily interpretable in relation to sufficiency. It is for example easy to compare a score of 20 (which means that 20 food products each provide at least 20 kcal/cap/day, totalling at least 400 kcal/cap/day) with the calorie sufficiency requirement of 2357 kcal/cap/day (Table 1).

Using two different metrics of diversity provides different perspectives on the risks and resilience of the food system. A high production diversity index (H-index) indicates that the national production system has many food products that provide a substantial contribution to fulfilling caloric and nutritional requirements. Having many food items as substantial contributors indicates that disruptions to a single food item will be less impactful than in highly specialised production systems with large skews towards few products. Taxonomic diversity, on the other hand, highlights the potential response diversity latent in the system. With a large variety of food products grown and harvested, the general resilience of the system is potentially higher. This has been explored in relation to both terrestrial (Seekell et al., 2017) and aquatic food systems (Troell et al., 2014). Diversity in crop production has also been seen to stabilize crop harvests (Renard and Tilman, 2019), especially where crops show diverse growing seasons which leads to asynchrony (Egli et al., 2020).

## 2.5. Categorical variables for comparison

To investigate the combination of self-sufficiency and diversity, and thus identify the specific supply risk landscape of each country, each

<caption>**Table 2**</caption>

Categorical division of variables.

<table><thead><tr><th>Variable</th><th>Category</th><th>Definition</th></tr></thead><tbody><tr><td>Sufficiency</td><td>Complete</td><td>9 nutrients fulfilled</td></tr><tr><td>Sufficiency</td><td>High</td><td>≥6 and &lt; 9 nutrients fulfilled</td></tr><tr><td>Sufficiency</td><td>Medium</td><td>≥3 and &lt; 6 nutrients fulfilled</td></tr><tr><td>Sufficiency</td><td>Low</td><td>≥0 and &lt; 3 nutrients fulfilled</td></tr><tr><td>Diversity</td><td>High</td><td>H-index &gt;20</td></tr><tr><td>Diversity</td><td>Medium</td><td>H-index 10-20</td></tr><tr><td>Diversity</td><td>Low</td><td>H-index &lt;10</td></tr></tbody></table>

variable was condensed into categories (Table 2). Self-sufficiency was split into four categories, highlighting low, medium, high and complete self-sufficiency. Self-sufficiency categories high, medium, and low were split evenly (thresholds at 3,6 and 9, see Table 2). Diversity was split evenly into three categories, low, medium, and high. For diversity, the categories are only set relative to the range of the data. Thus, high diversity does not indicate high resilience to production shocks, merely that it is high relative to other countries.

## 2.6. Reliance on imports, self-sufficiency, and diversity in times of conflict

In light of the Ukraine-Russia conflict and the discussions around the implications for food insecurity, and as one example of conflict-related disruptions, data was taken from FAOSTAT (FAO, 2020a) on the trade network and export volumes of wheat (whole, bran and flour) from Ukraine and Russia. The import volumes of the receiving countries were compared to the level of self-sufficiency in that country.

# 3. Results and discussion

## 3.1. Levels of self-sufficiency

We found that of all investigated countries only 41, representing approximately 33.5% of the global population, are completely nutritionally self-sufficient in production (Fig. 1B and C). Most of the global population instead live in countries that fulfil 7 or 8 nutrients through domestic production (approx. 50% of global population, Fig. 1B). Comparing our metric of nutritional self-sufficiency to caloric sufficiency, an often-used metric of self-sufficiency (Porkka et al., 2013), improves our understanding of risks to food stability (Fig. 1A). This comparison highlights that production in certain calorie-rich food products can portray a sense of self-sufficiency, although a nation might lack sufficient production in 1-9 key macro and micronutrients. This disaggregated view shows the reliance on import for specific functions in the food system. These findings add nuance to the discussion on food self-sufficiency and highlights the reliance, even in high-income countries (that do not usually struggle with food insecurity), on imports to fulfil adequate nutrient supply. To further investigate the nutritional self-sufficiency of individual countries, see the interactive data tool (http://emmywas.shinyapps.io/foodsupply_risks/) and Fig. S1 for maps of the geographical distribution of self-sufficiency for each nutrient. For countries that fulfil high levels of self-sufficiency in our analysis, it is important to remember that this represents food that can be made available to the population if exports are removed, food suitable for human consumption is no longer used for non-food uses (such as feed), and access is provided.

The approximately 15% of the global population that fulfil less than 7 nutrients are spread along the full remaining range (0-6, Fig. 1B). Countries that fulfil less than three nutrients have low self-sufficiency, and many of these additionally do not fulfil caloric requirements (Fig. 1). Countries with this low degree of self-sufficiency are, due to their lack of production, inherently dependent on imports. However, it is important to note that not all nations may have the capacity to import enough to fulfil food demand. In these cases, under- and malnutrition in parts of the population are the likely outcomes.
<figure><img src="image_6.png" /><figcaption>**Fig. 1.** Panel A: Comparison of nutritional sufficiency with caloric sufficiency. Red line highlights 100% sufficiency (represented as 2357 kcal/cap/day). Note that although nutritional sufficiency is measured as a discrete value annually, in this figure a five-year average is shown. The average removes the potentially strong influence of single high or low production years. Panel B: Distribution of global population (colours and text highlight percent of global population) across the different levels of nutritional self-sufficiency. Panel C: Distribution of countries across the different levels of nutritional self-sufficiency. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

There can be many reasons for why some countries rely so heavily on imports. For example, the biophysical settings may restrict production of certain foods (e.g. areas being too dry, cold, or nutrient-poor), or countries may have taken a strategic decision to allocate productive land for other uses than for food production (e.g. urban expansion, environmental protection, or tourism (Fader et al., 2013)). Saudi Arabia for example (fulfilling no nutrient sufficiency limits in our data), has bio-physical limitations in terms of water availability and is therefore highly import-dependent (Fader et al., 2013; Kajenthira Grindle et al., 2015). Due to the known risks of import-dependency, Saudi Arabia started targeted programs in the late 1970s to boost domestic food production and increase self-sufficiency. This was largely done through industrial food production based on imported inputs and unsustainable extractions of water (Kajenthira Grindle et al., 2015). Other countries may have had high levels of self-sufficiency but moved towards imports to free land for other activities and economic sectors, as has been seen in popular tourist destinations such as Bermuda (McKittrick, 2010).

It is also important to highlight that although this dataset provides a higher resolution breakdown that many other studies, it only contains information on primary food products. It does not include the flows of feed, fertilisers, and pesticides that much of the current production system depends on (Clapp, 2021; Jørgensen et al., 2018; Lassaletta et al., 2014; Potter et al., 2010). It is therefore possible that countries that in our analysis show high levels of production self-sufficiency can require production inputs that are not produced domestically – a masked import dependency.

Our analysis highlights both the high vulnerability of nations with low self-sufficiency, but also the perhaps overlooked vulnerability to trade-related supply risks in countries that reach a high, but not complete, level of nutritional self-sufficiency.

### 3.1.1. Implication of self-sufficiency for understanding cascading risks

The war in Ukraine has triggered discussions of the potential cascading risks. Ukraine, and Russia, are both large exporters of several agricultural commodities, a key one being wheat (FAO, 2020a). The lack of a harvest season in Ukraine and potential export restrictions or sanctions from Russia (FAO, 2022) have created the potential for this geopolitical conflict to create cascading risks to food security. However, in the communication of these risks it is often only the share of imports

of key food products that is addressed (FAO, 2022). With the added analysis and accompanying dataset in this article, this discussion can be further nuanced. Ukraine and Russia export wheat to 160 countries, however the top 30 countries receive 90% of the exports (by quantity). The level of production self-sufficiency within these countries varies from low to complete and will greatly influence the severity of a potential disruption or loss of imports. From this added analysis, we see that six countries in the top 30 countries importing wheat from Ukraine or Russia have low self-sufficiency (Yemen, Lebanon, Libya, United Arab Emirates, Georgia and Jordan) and an additional two have medium self-sufficiency (Bangladesh and Kenya). In these countries, the risk of food insecurity from this disruption is high, if this loss of imports cannot be compensated for by other trading partners. Even with compensation from other trading partners there is also a risk due to likely increases in price from changing supply and demand, and the increasing prices of fuel and fertilisers (FAO, 2022).

## 3.2. Levels of diversity

National production systems vary substantially in production diversity, with the majority of the global population living in countries with a high production diversity (58% of population). However, 41% of the population live in countries of medium production diversity and 1% in countries with low diversity (Fig. 2). There is a positive relationship between nutritional self-sufficiency and diversity, where countries that fulfil more nutrients through domestic production tend to have more diverse production systems (Fig. 2). Countries with low self-sufficiency have a production diversity index of 1–18 (low-medium diversity). Medium self-sufficiency is achieved at 5–19 (low-medium diversity) and high at 9–25 (medium-high diversity). Complete self-sufficiency is achieved in domestic production systems with a diversity of 14–27 (medium-high diversity). These ranges highlight the considerable overlap between categories, indicating that production systems that produce the same levels of nutritional sufficiency, do so at very different diversity levels. For example, Bolivia is completely self-sufficient at an average production diversity of 27. Comparatively, Turkmenistan is also completely self-sufficient, but at an average production diversity of 15. This difference highlights the different levels of reliance on few products to maintain food self-sufficiency and thus the higher risks from
<figure><img src="image_7.png" /><figcaption>**Fig. 2.** Distribution of production diversity index (H-index) and taxonomic richness. Panel A highlights the distribution of the global population (percentage, rounded to two decimals) across average production diversity. Panel B, highlights where each country (data point) falls in this continuum. Colour and shape of points highlights the level of self-sufficiency fulfilled (see legend in top right corner). Panel C highlights the distribution of the global population (percentage, rounded to two decimals) across taxonomic richness.</figcaption></figure>

production shocks due to lower response diversity (Folke, 2010; Kummu et al., 2020; Nyström et al., 2019). For further examples and a detailed breakdown of the contribution of each food item to fulfilling nutritional sufficiency, see the interactive database tool (https://emmywas.shinyapps.io/foodsupplyrisks/).

Additionally, these levels of production diversity are coupled with a range of different taxonomic diversity. Countries with a low production diversity (0–10) range from 2 to 83 in taxonomic diversity, medium production diversity from 18 to 268 and high from 44 to 390. High levels of taxonomic diversity can be an indication of and a proxy for response diversity as it increases the likelihood for a broader representation of important (response) traits, which are suited for certain environmental conditions (Elmqvist et al., 2003; Nyström, 2006).

Although production and taxonomic diversity are both important for increasing the resilience of the system they are inherently in opposition. By maximising taxonomic diversity through a large number of different food products, fewer will make up a substantial contribution and production diversity will hence be lower. The large range of diversity levels of self-sufficient nations potentially indicate some of these trade-offs. A more focused food portfolio (with low production diversity) trades-off the prioritization of known risks of food insecurity (achieving sufficiency, but with few products) with the potential for higher taxonomic diversity to respond to unknown risks in the future. However, if a large percentage of the nutritional sufficiency is dependent on a single food item or very few items, i.e., low average production diversity score, there is high risk that shocks and stresses to this item may jeopardize food sufficiency.

### 3.3. Combined analysis of self-sufficiency and diversity displays diverse supply risks

Our analysis of national self-sufficiency and production diversity highlights that there is a wide variety in the make-up of national production systems (Fig. 3). The widely varying composition of national production systems in terms of self-sufficiency and diversity translates into a risk landscape, where some countries are more at risk from trade-

related disruptions (due to high import dependence) and others face higher risks from specialisation or homogenisation of the domestic production. Looking at the geographic distribution of self-sufficiency (Fig. 3) also highlights that availability of land or country size may be a contributing factor to the differences seen between countries. Among countries that are completely self-sufficient, there are countries such as Croatia, Costa Rica and Vietnam that have relatively higher exposure to production disruptions due to their lower diversity (few items that provide nutritional sufficiency). In medium and high sufficiency countries, the risk to food stability is made up of both production and trade-related risks. In low sufficiency and low diversity countries, food stability may not be possible to achieve from domestic food production alone and focus instead may need to lie in strengthening trade networks.

This breadth of risks indicates that there is no single solution to make food systems more resilient, instead tailored solutions are necessary for the conditions of each nation. In an import-dependent country, risk mitigation could include increasing (as high reliance on only few trade partners could lead to significant reduction in food availability if the partner countries experience a food system shock) or decreasing (as overly connected countries are exposed to rapid spread of disturbances in various parts of the trade network) trade links (Kummu et al., 2020), having adequate reserves (Marchand et al., 2016) or focusing on boosting domestic production, either through formal or informal/household production. Countries with a lower production diversity index may be vulnerable to domestic production shocks to important food items. In these cases, increasing the evenness of production by producing a wider range of food items that fulfil the same nutritional demands would increase redundancy and buffer against disturbances. In cases where there is very low production for a particular nutritional category (low nutritional redundancy), shifting production, if biophysical conditions allow, to provide other nutritional demands can be a solution. If taxonomic diversity is low, diversifying production to maintain the potential and competencies to produce rare food items can buffer and build capacity for dealing with unknown and unforeseen shocks.

While diverse solutions are pursued at the national level, including adaptations to the biophysical conditions, it is important to connect the
<figure><img src="image_8.png" /><figcaption>**Fig. 3.** Combination of national self-sufficiency and diversity. Panels show the different categories of self-sufficiency (A: Complete, B: High, C: Medium and D: Low), only countries that belong to that category are highlighted in the map. The colours in all facets represent the categorical level of diversity (High, Medium, and Low).</figcaption></figure>

local/national production system to the globally connected system. Shocks from changing climate can affect large breadbasket regions and can co-occur across multiple locations (Gaupp et al., 2020). Shocks can also co-occur in terrestrial and marine food production (Cottrell et al., 2019). Trade related shocks could cascade through the global system, as trade acts as a connection between geographically disparate places (Heslin et al., 2020), and shocks can also cascade through complex supply chains (Davis et al., 2021; Nyström et al., 2019). These are examples of systemic risks (Centeno et al., 2015; Helbing, 2013) that connect risks to each other. When many countries face the same risks simultaneously, they may also mitigate them in the same way, creating amplified risks. This has been prevalent with the COVID-19 pandemic where the risks of food insecurity resulted in many countries setting food export restrictions (Laborde et al., 2020), further exacerbating trade-related food risks.

## 4. Conclusion

Through the assembly of a novel comprehensive dataset covering both terrestrial and aquatic food production, down to species/food product level along with nutritional composition, we investigated how heterogenous the global food system is in relation to risks from food production on supply. Our results show that most of the world (127 countries and territories, 87% of the global population) achieves high levels of potential self-sufficiency (≥6 nutrients fulfilled), however only 33% of the world population (41 countries) are fully self-sufficient. 66 countries (6% of population) have a low degree of self-sufficiency, highlighting potential vulnerability to trade-related disruptions. The relationship between sufficiency and diversity is not homogeneous, highlighting that some production systems are reliant on very few products, and thus at risk from production shocks – another risk in the face of trade-related disruptions. With this diverse set of risks, there is no single solution that will help us prevent and manage these risks. With the

dataset and the accompanying interactive database tool, this article provides a first step towards identifying and addressing these risks.

## Data repository

The novel dataset created for this article can be found at: https://doi.org/10.7910/DVN/VQW92D.

## Contributions

Conceptualisation: EW, MP, MN, PSJ; Methodology: EW, MP, Software: EW; Data Curation: EW; Formal analysis: EW; Visualization: EW; Writing: EW, MN, MP, PSJ.

## Funding

EW, MP and PSJ are funded by the Erling-Persson Family Foundation through the Global Economic Dynamics and the Biosphere programme (GEDB) at the Royal Swedish Academy of Sciences. MP is funded by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No. 819202). MN and PSJ are funded by The Swedish Research Council, grant no. 2020-04586. PSJ is funded by the European Union (ERC, INFLUX, 101039376), Marianne and Marcus Wallenberg Foundation (MMW 2017.0137), FORMAS grant no. 2020-00371 and the IKEA Foundation. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council. Neither the European Union nor the granting authority can be held responsible for them.

## Declaration of competing interest

The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influence
the work reported in this paper.

## Data availability

The links to the data and code repository are embedded in the
manuscript.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.gfs.2023.100673.

## References

Beltran-Peña, A., Rosa, L., D'Odorico, P., 2020. Global food self-sufficiency in the 21st century under sustainable intensification of agriculture. Environ. Res. Lett. 15, 095004 https://doi.org/10.1088/1748-9326/ab9388.

Buchholz, A.C., Schoeller, D.A., 2004. Is a calorie a calorie? Am. J. Clin. Nutr. 79, S899-S906. https://doi.org/10.1093/ajcn/79.5.899S.

Centeno, M.A., Nag, M., Patterson, T.S., Shaver, A., Windawi, A.J., 2015. The emergence of global systemic risk. Annu. Rev. Sociol. 41, 65-85. https://doi.org/10.1146/annurev-soc-073014-112317.

Clapp, J., 2021. Explaining growing glyphosate use: the political economy of herbicide-dependent agriculture. Global Environ. Change 67, 102239. https://doi.org/10.1016/j.gloenvcha.2021.102239.

Clapp, J., 2017. Food self-sufficiency: making sense of it, and when it makes sense. Food Pol. 66, 88-96. https://doi.org/10.1016/j.foodpol.2016.12.001.

Cottrell, R.S., Nash, K.L., Halpern, B.S., Remenyi, T.A., Corney, S.P., Fleming, A., Fulton, E.A., Hornborg, S., Johne, A., Watson, R.A., Blanchard, J.L., 2019. Food production shocks across land and sea. Nat. Sustain. 2, 130-137. https://doi.org/10.1038/s41893-018-0210-1.

Davis, K.F., Downs, S., Gephart, J.A., 2021. Towards food supply chain resilience to environmental shocks. Nat Food 2, 54-65. https://doi.org/10.1038/s43016-020-00196-3.

D'Odorico, P., Carr, J.A., Laio, F., Ridolfi, L., Vandoni, S., 2014. Feeding humanity through global food trade. Earth's Future 2, 458-469. https://doi.org/10.1002/2014ef000250.

Egli, L., Schröter, M., Scherber, C., Tscharntke, T., Seppelt, R., 2020. Crop asynchrony stabilizes food production. Nature 588, E7-E12. https://doi.org/10.1038/s41586-020-2965-6.

Elmqvist, T., Folke, C., Nynström, M., Peterson, G., Bengtsson, J., Walker, B., Norberg, J., 2003. Response diversity, ecosystem change, and resilience. Front. Ecol. Environ. 1, 488-494. https://doi.org/10.1890/1540-9295(2003)001[0488:RDECAR]2.0.CO;2.

Fader, M., Gerten, D., Krause, M., Lucht, W., Cramer, W., 2013. Spatial decoupling of agricultural production and consumption: quantifying dependences of countries on food imports due to domestic land and water constraints. Environ. Res. Lett. 8, 014046 https://doi.org/10.1088/1748-9326/8/1/014046.

FAO, 2022. Information Note: the Importance of Ukraine and the Russian Federation for Global Agricultural Markets and the Risks Associated with the Current Conflict, 25 March 2022 Update.

FAO, 2021. Sustainable development goals : 12.3.1 global food losses and waste [WWW Document]. URL http://www.fao.org/sustainable-development-goals/indicators/1231/en/, 9.12.21.

FAO, 2020a. FAOSTAT. Food and Agriculture Organization of the United Nations, Rome.

FAO, 2020b. FishStatJ. Food and Agriculture Organization of the United Nations, Rome.

FAO and WHO, 1998. Vitamin and Mineral Requirements in Human Nutrition. Second edition, Bangkok, Thailand, 92.4.154612.3.

Folke, C., 2010. Resilience thinking: integrating resilience, adaptability and transformability. Ecol. Soc. 15.

Gaupp, F., Hall, J., Hochrainer-Stigler, S., Dadson, S., 2020. Changing risks of simultaneous global breadbasket failure. Nat. Clim. Change 10, 54-57. https://doi.org/10.1038/s41558-019-0600-z.

Hawkes, C., Murphy, S., 2009. An overview of global food trade. In: Trade, Food, Diet and Health: Perspective and Policy Options. John Wiley & Sons, Inc. pp. 16-32.

Helbing, D., 2013. Globally networked risks and how to respond. Nature 497, 51-59. https://doi.org/10.1038/nature12047.

Heslin, A., Puma, M.J., Marchand, P., Carr, J.A., Dell'Angelo, J., D'Odorico, P., Gephart, J.A., Kummu, M., Porkka, M., Rulli, M.C., Seekell, D.A., Suweis, S., Tavoni, A., 2020. Simulating the cascading effects of an extreme agricultural production shock: global implications of a contemporary US dust bowl event. Front. Sustain. Food Syst. 4, 1-12. https://doi.org/10.3389/ufsus.2020.00026.

Jadhav, R., 2022. Indonesia's palm oil export ban leaves global buyers with no plan B | Reuters [WWW Document]. Reuters. URL https://www.reuters.com/business/indo-nesias-palm-oil-export-ban-leaves-global-buyers-with-no-plan-b-2022-04-25/, 5.22.22.

Jørgensen, P.S., Aktipis, A., Brown, Z., Carrière, Y., Downes, S., Dunn, R.R., Epstein, G., Frisvold, G.B., Hawthorne, D., Gröhn, Y.T., Gujar, G.T., Jasovský, D., Klein, E.Y., Klein, F., Lhermie, G., Mota-Sanchez, D., Omoto, C., Schlüter, M., Scott, H.M., Wernli, D., Carroll, S.P., 2018. Antibiotic and pesticide susceptibility and the Anthropocene operating space. Nat. Sustain. 1, 632-641. https://doi.org/10.1038/s41893-018-0164-3.

Kajenthira Grindle, A., Siddiqi, A., Anadon, L.D., 2015. Food security amid water scarcity: insights on sustainable food production from Saudi Arabia. Sustain. Prod. Consum. 2, 67-78. https://doi.org/10.1016/j.spc.2015.06.002.

Kummu, M., Kinnunen, P., Lehikoinen, E., Porkka, M., Queiroz, C., Röös, E., Troell, M., Weil, C., 2020. Interplay of trade and food system resilience: gains on supply diversity over time at the cost of trade independency. Global Food Secur. 24, 100360 https://doi.org/10.1016/j.gfs.2020.100360.

Laborde, D., Mamun, A., Parent, M., 2020 [WWW Document]. COVID-19 Food Trade Policy Tracker. International Food Policy Research Institute. URL https://www.ifp.ri.org/project/covid-19-food-trade-policy-tracker.

Lassaletta, L., Billen, G., Grizzetti, B., Garnier, J., Leach, A.M., Galloway, J.N., 2014. Food and feed trade as a driver in the global nitrogen cycle: 50-year trends. Biogeochemistry 118, 225-241. https://doi.org/10.1007/s10533-013-9923-4.

Leggett, T., 2021. Egypt's Suez Canal blocked by huge container ship - BBC News [WWW Document]. BBC News. URL https://www.bbc.com/news/world-middle-east-56505413, 8.24.21.

Marchand, P., Carr, J.A., Dell'Angelo, J., Fader, M., Gephart, J.A., Kummu, M., Magliocca, N.R., Porkka, M., Puma, M.J., Ratajczak, Z., Rulli, M.C., Seekell, D.A., Suweis, S., Tavoni, A., D'Odorico, P., 2016. Reserves and trade jointly determine exposure to food supply shocks. Environ. Res. Lett. 11, 095009 https://doi.org/10.1088/1748-9326/11/9/095009.

McKittrick, T.A., 2010. The Role of Agriculture in Bermuda's Future.

Nynström, M., 2006. Redundancy and response diversity of functional groups: implications for the resilience of coral reefs. AMBIO A J Hum. Environ. 35, 30-35. https://doi.org/10.1579/0044-7447-35.1.30.

Nynström, M., Jouffray, J.-B., Norström, A.v., Crona, B., Søgaard Jørgensen, P., Carpenter, S.R., Bodin, Ö., Galaz, V., Folke, C., 2019. Anatomy and resilience of the global production ecosystem. Nature 575, 98-108. https://doi.org/10.1038/s41586-019-1712-3.

Porkka, M., Kummu, M., Siebert, S., Varis, O., 2013. From food insufficiency towards trade dependency: a historical analysis of global food availability. PLoS One 8, e82714. https://doi.org/10.1371/journal.pone.0082714.

Potter, P., Ramankutty, N., Bennett, E.M., Donner, S.D., 2010. Characterizing the spatial patterns of global fertilizer application and manure production. Earth Interact. 14, 1-22. https://doi.org/10.1175/2009EI288.1.

Puma, M.J., Bose, S., Chon, S.Y., Cook, B.I., 2015. Assessing the evolving fragility of the global food system. Environ. Res. Lett. 10, 024007 https://doi.org/10.1088/1748-9326/10/2/024007.

Renard, D., Tilman, D., 2019. National food production stabilized by crop diversity. Nature 571, 257-260. https://doi.org/10.1038/s41586-019-1316-y.

Schramski, J.R., Woodson, C.B., Steck, G., Munn, D., Brown, J.H., 2019. Declining country-level food self-sufficiency suggests future food insecurities. BioPhys. Econ. Resour. Qual. 4, 12. https://doi.org/10.1007/s41247-019-0060-0.

Seekell, D., Carr, J., Dell'Angelo, J., D'Odorico, P., Fader, M., Gephart, J., Kummu, M., Magliocca, N., Porkka, M., Puma, M., Ratajczak, Z., Rulli, M.C., Suweis, S., Tavoni, A., 2017. Resilience in the global food system. Environ. Res. Lett. 12, 025010 https://doi.org/10.1088/1748-9326/aa5730.

Smith, M.R., Micha, R., Golden, C.D., Mozaffarian, D., Myers, S.S., 2016. Global expanded nutrient supply (GENuS) model: a new method for estimating the global dietary supply of nutrients. PLoS One 11, e0146976. https://doi.org/10.1371/journal.pone.0146976.

Troell, M., Naylor, R.L., Metian, M., Beveridge, M., Tyedmers, P.H., Folke, C., Arrow, K. J., Barrett, S., Crépin, A.-S., Ehrlich, P.R., Gren, A., Kautsky, N., Levin, S.A., Nyborg, K., Österblom, H., Polasky, S., Scheffer, M., Walker, B.H., Xepapadeas, T., de Zeeuw, A., 2014. Does aquaculture add resilience to the global food system? Proc. Natl. Acad. Sci. U. S. A. 111, 13257-13263. https://doi.org/10.1073/pnas.1404067111.

Wassénius, E., Crona, B.I., 2022. Adapting risk assessments for a complex future. One Earth 5, 35-43. https://doi.org/10.1016/j.oneear.2021.12.004.

Wellesley, L., Preston, F., Lehne, J., Bailey, R., 2017. Chokepoints in global food trade: assessing the risk. Res. Transp. Bus. Manag. 25, 15-28. https://doi.org/10.1016/j.rtbm.2017.07.007.

WHO and FAO, 2003. Diet, nutrition, and the prevention of chronic diseases: report of a joint WHO/FAO Expert Consultation (WHO technical report No. 916). Presented at the Expert Consultation on Diet, Nutrition, and the Prevention of Chronic Diseases. World Health Organization, Geneva.