The impacts of polycrises on global grain availability and prices

The impacts of polycrises on global grain availability and prices | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article The impacts of polycrises on global grain availability and prices Jasper Verschuur, Anna Murgatroyd, Yiorgos Vittis, Aline Mosnier, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3969801/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Recent climatic events and conflict have heightened concern about the vulnerability of the global food system to systemic shocks. Yet it remains unclear what shocks are most pressing for a country’s food supply, and whether trade can mediate or amplify negative impacts. Here, using a newly developed global bilateral trade model for 177 countries and four major staple crops (maize, wheat, rice, soybean), we simulate the demand, price and trade impacts of the (i) Ukraine war, (ii) an energy price shock, (iii) imposed trade bans, and (iv) a compound (polycrisis) shock, on top of 54 years of crop production variability. The compound shock results in a 23 – 52% increase in consumer prices and, consequently, 7.3 – 16.5% loss to consumers. While the energy price shock is found to be the most important driver of the compound food shock across most regions and crops, the Ukraine war dominates impacts in Eastern Europe and Central Asia. Trade bans can affect certain regions disproportionately, particularly for Sub-Saharan Africa (rice) and Central Asia (rice, wheat). We find that, in many instances, trade adjustments can help cope with both supply and price shocks, although limits to the reliance on trade are found for tail risk events. In the compound shock event, the total negative consumer losses can be over USD 600 million for a single year, affecting virtually all countries simultaneously. Managing the risks of such shocks requires a reformed and better coordinated mix of national agricultural and fiscal policies as well as international trade regulations. Earth and environmental sciences/Environmental sciences/Environmental impact Scientific community and society/Agriculture Scientific community and society/Geography Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction The global food system is vulnerable to shocks that can impact production, trade and prices 1 – 4 . Disasters cause annual losses worth USD123 billion per year 5 , alongside weather variability that is estimated to account for around a third of yield variability 6 . Shocks can have a large effect on grain prices. For example, global grain supplies were dramatically impacted by droughts in 2010–2011, whose effects were exacerbated by a Russian export ban and low stocks-to-use ratios, leading to a doubling of global wheat prices 7 . Though droughts have become a recurrent phenomenon in recent years, food price volatility has been dominated by non-climatic factors, including the war in Ukraine and consequent rising fertilizer prices (driven by high energy prices), locust outbreaks, and trade restrictions imposed by various countries (e.g., export bans or quotas) 8 – 11 . These events, along with other factors such as the pandemic and an increasing number of civil and international conflicts, have led to a state of “polycrisis” which has resulted in a doubling of the number of people facing acute food insecurity in 2023 compared to 2019 12 . Countries have responded with a range of interventions including food or fertilizer subsidies and cash transfers or vouchers, often at considerable costs (in some cases over 0.75% of GDP) 9 . Several studies have sought to explore the vulnerability of countries to recent food system shocks, in particular the impacts of the Russian invasion of Ukraine through its effects on wheat and maize supply. One set of studies used trade data to reconstruct the global supply network of crop commodities used both for direct consumption and in the production of processed foods 13 – 15 . Another study combined trade data with information on stocks and food substitution behaviour to better characterise different countries’ vulnerabilities 16 . These models are typically static, preventing them from incorporating trade adjustments or quantifying the price effects of export reductions. Others have used a more complex dynamic supply-demand model, including stocks, to study the effect of the Ukraine war, fertilizer prices and export restrictions on global wheat prices 17 , 18 . While better able to represent short-term price spikes and supply/demand adjustments, these models do not explicitly represent bilateral trade flows between countries and so cannot incorporate potential import substitution. A third set of approaches have used global food system models to assess the impact on food prices of a supply disruption from Ukraine alone 19 or combined with fertilizer price increases 20 . These models assume a single global market and do not specify bilateral trade flows, so that trade substitution is unconstrained and rapid. General and partial equilibrium models of this type are designed to study long-run price and trade adjustments and their structure and parameterization means that the impacts of short-run shocks are underestimated. The aforementioned studies have provided important insights into the possible impacts of the Ukraine war and its associated food system shocks, but a number of key considerations remain unaddressed. First, no studies have explicitly included bilateral trade flows in a way that allows dynamic adjustments such as changing trading partners or increasing supply from existing exporters. Second, while attempts have been made to include multiple shocks within a single modelling framework, they are often implemented uniformly across countries (for example, a fertilizer price shock is assumed to affect all countries equally) or explore only a small subset of countries imposing export restrictions. Yet, we know that fertilizer price shocks affect countries unequally because of different fertilizer use and import dependency 21 . Similarly, many countries have been known to impose trade bans (import or export), though these were often targeted towards specific countries without affecting the rest of the world. Third, all studies have evaluated the effects of these shocks against a single baseline – for the Ukraine war, the situation in early 2022. This makes sense in trying to understand the historical impact of the war but assessment of risks to the global food system should consider the full range of possible production scenarios, ranging from bad years with major breadbasket failures 22 , 23 to good years with bumper harvests 24 , and their relative likelihood. In this study, we use a newly developed bilateral trade model of global grain supply for 177 countries to study the effects of compounding shocks to the world’s grain supply. The shocks we model are inspired by the events of 2022–2023: the Ukrainian war and its effects on grain supply, food prices and trade restrictions. We analyse their effects against a background of 54 plausible realisations of crop production variability across regions determined by historic weather variability 25 . This allows us to estimate the range of possible consequences for producer and consumer prices, and for patterns of bilateral trade. We demonstrate how countries and regions differ in their exposure to these shocks, and in their capacity to adjust by utilizing stocks, reducing exports, or changing trading partners. Our modelling framework both provide insights into different countries’ vulnerability as well as the spread of systemic risks through global grain supply networks. Our results will help efforts to stress-test global supply chains as well as in the design of effective strategies to enhance the resilience of food supply systems. Results Overview We developed a spatial equilibrium price model that simulates demand, supply, producer and consumer prices and trade flows across 177 countries (more fully described in Methods). It is calibrated to reproduce observed trade flows for the period 2017–2021, and relies on recently produced datasets of country-specific crop production costs 26 , 27 and detailed bilateral trade costs 28 . We consider four crops – maize, wheat, rice, soybeans – as they are strongly affected by the different shocks we model 20 and their importance for global food and feed consumption. Our baseline model ( Base ) incorporates climate variability information based on 54 years of historic climate data 25 allowing us to estimate the distribution of crop production given realistic patterns of weather-driven yield variability around the world. Against this background, four sets of exogenous shocks are modelled: (i) a supply shock caused by reduced production and restricted exports of Ukrainian grain ( Ukraine) , (ii) an energy price shock of the type associated with the Ukraine war increasing fertilizer, pesticide and diesel prices with knock-on effects on producer prices that are passed on to consumers ( Price ), (iii) imposed trade restrictions (import and export bans) motivated by those observed in 2022–2023 ( Trade ), and (iv) all shocks together, representing our “polycrisis” scenario ( All ). Consumer prices in the model do not include subsidies or marketing mark-ups, and hence should be interpreted as landed cost prices. Supply, demand and trade of food and feedstock quantities are treated in a homogenous commodity setting. Our model has been specifically designed to explore the short-run impacts of shock scenarios. We study impacts within a single year in which planting decisions have largely been made supporting an inelastic supply assumption. Countries can respond by utilizing grain stocks, adjusting imports from existing suppliers and, at a cost, diversify the scope of trading partners. Food production variability can drive consumer price fluctuations The set of weather-related yield variations based on 54 years of climate data (see Methods) were used to simulate production shortages and surpluses in one or more of the four crops, causing price fluctuations across domestic and foreign markets. Consumer price sensitivity to weather variability differs across crops and countries. The average consumer price variability (measured by the coefficient of variation) across countries is lowest for soybean (0.06), followed by rice and wheat (0.07) and highest for wheat (0.09). This is lower than the underlying yield variability (0.07 for rice and soybean, 0.10 for maize and 0.13 for wheat) suggesting an economic risk mitigation function of markets. The global food supply system thus partly buffers the impact of yield variability on consumer prices, mainly through international trade. In Fig. 1 we show price volatility and average consumer prices for the baseline scenario, across all four crops (Fig. 1 a) and for each crop separately (Fig. 1 b-e). Some countries face high price variability alongside low consumer prices, while other countries face both high average prices and high price variability. Most of Europe and North Africa have low prices and low volatility, due to the deep and diverse grain markets operating in Europe. Regions with high production serving predominantly domestic markets (Central Asia, Eastern Europe, Australia, South Africa) have low prices but relatively high volatility. In contrast, some large importers (China, Bolivia, Middle-East and Saharan countries) have higher average prices but low volatility. Countries in Sub-Saharan Africa face both high consumer prices and high volatility, caused by high trade costs 28 and risky suppliers. There are interesting variations across crops (Fig. 1 b-e). For maize, low consumer prices and price fluctuations are observed in Northern Africa, Eastern and Central Europe, due to their proximity to major maize suppliers in Europe. Contrarily, high transportation costs and reliance on variable production leads to high prices and high price variability in most Sub-Saharan African countries and landlocked South America. For wheat, high import dependency leads to high consumer prices in most of Africa, yet provide relatively stable supplies due to imports from a variety of countries (Eastern Europe, North America, Latin America and Western Europe). On the other hand, large wheat producers in South America, Eastern Europe and Central Asia are among the lowest cost producers, yet have but high price variability induced by high yield variability 6 . For rice, lower consumer prices are experienced in parts of Latin America, Central Asia, and South Asia and South-East Asia, given relatively low cost production for domestic markets. Low price variability is observed in Western Europe and North America, given stable imports from rice producing countries. Soybean patterns are dominated by high price variability and high prices in most of Africa, East and South-East Asia, given their reliance on soybean imports from Latin America, which are susceptible to weather-driven variability. Figure 1 The impacts of compound shocks The shocks to the global grain supply system that we model affect producer prices and trade, which impact consumer prices, consumption and consequently consumer surplus (see Supplementary Fig. 1). The consumer surplus is the difference between the price a consumer is willing to pay and actually pays (multiplied by the amount they consume), and represents how consumers are impacted by changing prices, both in terms of their expenditure and how much they adjust their consumption. We use consumer surplus as our metric of impact as it simultaneously captures price and demand effects, including country variations in crop-specific short-run demand elasticities. Other metrics, including those more relevant for food security (e.g., people facing hunger), could alternatively be considered in future work. Across all 54 yield instantiations, the compound shock ( All ) causes an increase in median global consumer price (weighted by demand) of 22.6% for soybean, 40.1% for maize, 47.8% for rice, and 51.5% for wheat. This results in a reduction of median global consumption of around 4.4% for soybean, 4.1% for maize, 4.5% for rice, and 8.9% for wheat. This highlights the higher demand elasticities for soybean and wheat compared to rice and maize. This reduces median global consumer surplus by 8.2% for soybean, 7.3% for maize, 7.9% for rice and 16.5% for wheat (Supplementary Fig. 1). The effect of the four shock scenarios on global and regional consumer surpluses are shown in Fig. 2 , again averaged over the 54 different yield patterns (equivalent plots for consumer prices and consumption are given in Supplementary Figs. 2–5 and 6–9 respectively). For maize, the energy price shock has the greatest effect in most regions, and is particularly severe for Central Asia, Oceania, and Sub-Saharan Africa where consumer prices rise by 50–100% (Supplementary Fig. 2). For wheat, the energy price shock is also dominant, except for in Eastern Europe (mainly Ukraine and Russia) where the Ukraine war is more important. Trade bans have a large impact on wheat prices in Central Asia (due to export bans imposed by Russia, Kyrgyzstan, and India) and Oceania (largely due to the Indian trade ban). The latter has a knock-on effect on increased demand for wheat from Australia, which raises consumer prices locally, as was observed in 2022 29 . The relative importance of the global energy price shock compared to the trade disruption from the Ukraine war was also found in other studies 17 , 20 . For rice, consumer surpluses in Sub-Saharan Africa, East Asia, Central Asia, Western Asia, and Eastern Europe were severely impacted by trade restrictions, particularly the rice export ban imposed by India. This ban led to a reduction in domestic prices and so positively impacted domestic Indian consumer surplus, a response observed after India restricted rice exports in 2023 30 . Consumer surpluses for rice in Central Asia are extremely sensitive to different shocks. This can be explained by a combination of background production volatility in the region, combined with dependencies on trade from South Asia, affected by trade restrictions, and Eastern Europe, affected by the Ukraine war. Soybean consumer surpluses are most influenced by the energy price shock which is felt relatively uniformly across all regions. Supplementary Fig. 10 provides more detail about how different countries are vulnerable to these shocks. Our country-level analysis shows that, especially for wheat and rice, countries within the same region can be affected in quite different ways by shocks, something that could not studied using a more aggregated model (e.g., regions instead of countries). Figure 2 Global systemic impact Within the interconnected global food supply network, even local production shocks can give rise to globally felt systemic impacts. This may happen when local shocks occur simultaneously (e.g., multiple breadbasket failures) or when a local shock is of such magnitude that it has global ramifications. We measure global systemic impact by the total negative consumer surplus across countries per weather year (i.e., the sum of negative country consumer surplus compared to the mean consumer surplus under the baseline scenario). We quantify the impact of weather-related variation in crop yields (from 54 years of yield data) and overlay the four shock scenarios ( Ukraine , Price , Trade , All ). Ranking the 54 weather years for each shock scenario allows us to empirically estimate the quantiles (quantile being the rank divided by the total number of weather-year, N = 54) of the negative consumer surplus, for a given scenario. Under the baseline scenario (Fig. 3 a), the worst consumer surplus loss is USD90.8 million for maize, USD31.2 million for wheat, USD64.4 million for rice, and USD72.3 million for soybean. When this is compounded with the All shock scenario the losses rise to USD246.4 million, USD145.6 million, USD135.6 million, and USD78.6 million for maize, wheat, rice, and soybean, respectively. This is 2.3 times greater (USD606.2 million versus USD258.7 million), with the largest relative increase occurring for wheat. The Ukraine war has a minor effect on the systemic risk for wheat, rice and soybean, but shifts the curve up for maize (to a worst case loss of USD125.1 million) (Fig. 3 a). Comparing All with the other three scenarios, the energy price shock can explain most of the response for maize while for the other crops the different shocks all contribute to the drop in consumer surplus in the All scenario. What is also apparent is the importance of the weather years for the systemic risk. For instance, the difference between the worst case scenario (tail risk event) and the median event (0.5 quantile) is in most cases larger than the additionality of the shock scenarios. In other words, the occurrence of a bumper harvest or breadbasket failure can be as great or greater than the additional effects of non-climatic shocks, emphasizing the importance of looking at those together. Under the baseline scenario, the proportion of countries that simultaneous experience negative surplus (Fig. 3 b) ranges from close to zero to 90% as yield conditions worsen. In most cases, there is a clear transition in the number of countries that experience simultaneous negative CS between fairly average years (quantile > 0.5) and very bad years (quantile < 0.5). In particular for wheat and soybean a clear transition is visible, with the food supply system being capable to deal with average years, but with many countries experiencing lower consumer surplus during some breadbasket failures. When this is compounded with the All scenario, virtually all countries simultaneously display drops in consumer surpluses. Comparison of All with the other scenarios indicates that the energy price shock makes the most significant contribution, while trade restrictions for wheat and rice also cause widespread negative consumer surplus across countries. Figure 3 Shifting trade patterns In our model the compound shocks in the All scenario led to multiple adjustments to global trade flows (compared to a baseline shown in Supplementary Fig. 11). These can be understood by looking how the different shock affect trade. For the Ukraine shock, we assumed reduced exports from Ukraine via sea with exports to some countries being stopped (though the scenario does not capture the Black Sea Grain Initiative). The shock led countries relying on Ukraine for imports (mainly wheat and maize) to source grains from elsewhere, while Ukrainian grain that would have been exported by sea was sold on European markets that could be accessed by land 31 . The reductions in exports in the Trade shock forced import-dependent countries to rely more on domestic markets (from production or stocks) or to source from elsewhere. The Price shock has a more systemic effect on trade. Rising input costs meant that producer prices accounted for a higher fraction of total consumer prices, although dependent on how the energy price shock increases producer prices. This effect makes trade costs less of a barrier to establishing new trade partners so that global markets became more competitive as countries competed to source the cheapest supplies. Figure 4 Changes in regional trade flows for the four grains after the All shock are shown in Fig. 4 . The results illustrated are averages over the observed 54 weather-driven yield patterns. A clear reduction in maize exports from Eastern Europe is visible, in particular to East Asia, North Africa, and Western Asia. North African countries, in turn, have sourced their shortfall from North America. Latin America and the Caribbean have increased interregional trade, at the expense of North American exports, due to improved relative cost competitiveness of local maize. Increases in producer prices in the All shock scenario (and similarly for the Price shock) reduce maize production by the large amount of 83.0 million tonnes while trade flows increase by 43.7 million tonnes as countries seek alternative suppliers and the relative cost of trade reduces. For wheat, international trade also increases in the All shock scenario (by 23.7 million tonnes) in the face of a global supply reduction of 85.8 million tonnes. Major shifts in trade flows are associated with the Ukraine war, with lower exports to North Africa, Sub-Saharan Africa and Western Asia. North Africa replaces its losses by increasing imports from NW-Europe and North America, while Sub-Saharan Africa mainly responds with increased imports from NW-Europe. This switch towards imports from NW-Europe was made possible by an abundance of low-cost Ukrainian wheat supply that could not be exported by sea on North-West, Eastern and Southern European markets 32 . Western Asia increased supplies from a variety of sources, in particular from Central Asia. The trade imbalance between South-East Asia and East Asia markedly increases with South-East Asia increasing exports to East Asia, while East Asia reduces exports in the opposite direction. Less extreme shifts are observed for rice, with reductions in both international trade (by 10.3 million tonnes) and production (by 15.6 million tonnes). The largest response is the reduction in intraregional rice trade in South Asia and the increased reliance on domestic supplies due to the export restriction of India. A similar response occurs in South-East Asia. Sub-Saharan African countries experience the largest import shock and as a result are forced to source more rice from within the continent. Soybean trade is relatively robust in the All shock scenario, except for decreases in Latin America and Caribbean exports to East Asia (which is partly substituted by soybean from North America), alongside a lower domestic supply. This is the result of diminishing global demand for soybean amid rising consumer prices. Trade buffers systemic shocks There is a long-standing debate about whether high levels of imports are beneficial for countries in allowing them to buffer shocks due to local yield variability and to access markets abroad 33 , 34 . We test this hypothesis by comparing a country’s import dependency (ID, the fraction of a country’s supply of a commodity made up of imports) with its average reduction in consumer surplus as yield patterns have increasingly severe effects on production. We ask whether a higher ID leads to a lower sensitivity to changes in consumer surplus, or vice versa, which we test by examining the Spearman rank correlation coefficient between ID and the ranked consumer surplus deviations. We are particular interested how this correlation changes for the average year (quantile 0.5 or above) versus the tail risk events (quantile 0.1 or below). For maize and rice, a high ID is correlated with lower reductions in consumer surplus under typical yield conditions, but the correlation is reversed in years when weather causes severe effects on agriculture (Fig. 5 ). Countries with extensive domestic production and low reliance on exports are better able to buffer themselves against more major weather-associated shocks affecting these grains, though in typical years there is an advantage to being able to access multiple international markets. The relationship for soybean is broadly similar but much weaker. The positive effects of high ID during good yield years disappears when the different shock scenarios are implemented. Relatively high levels of domestic production help protect consumers for bad years though do not completely mitigate the effect of the shock. For wheat, the shape of the curve differs between the shock scenarios. This indicates that a higher ID may be beneficial for some combinations of weather years and shocks, but not for others, emphasizing that imports or self-sufficiency does not provide a “one-size-fits-all” solution to build resilience to shocks to wheat supplies. Figure 5 Discussion & conclusions Fluctuations in food supplies and prices have been a growing cause of concern for governments and the international community since the 2007–2008 food crisis. While shocks to food supplies are a growing concern for countries with high levels of food insecurity, food price shocks leading to inflation of food prices is a concern for countries across the world. Food system modelling can be a helpful way of understanding fluctuations and suggesting how they can be mitigated. Previous analyses have tended to examine a relatively small number of shocks without taking into account year-to-year variation in weather affecting agricultural production. In addition, they have also poorly captured short-term adjustments in food trade and demand which, in practice, effectively buffer shocks. In this study, we estimated variability of food production across 177 countries using 54 years of agricultural yield information subject to weather-driven shocks. We constructed a bilateral trade model to simulate adjustments in food import/export linkages based on detailed bilateral trade costs information, as well as information on stocks. We used a supply-constrained price equilibrium model to incorporate the effects of different supply, price, and trade shocks, and examine how consumer prices respond to supply-side shocks and trade adjustments. It is important to note that consumers in our model cannot switch between grains, hence potentially overestimating impacts on consumer surplus. The results reveal the sensitivity of the global food system to climate-related supply variability and as well as to further shocks imposed on this background. Across all crops and regions, the impact of rising energy prices (affecting the cost of diesel, pesticides and fertilizer) on crop production was found to have the largest effect on food prices, demand, and consumer surplus. In our model we assume that producers do not adjust their inputs in response to this shock, when in reality, producers may choose to reduce the use of fertilizer and pesticide (or alternatively utilise stocks) in response to rising input prices (as observed across many countries during 2022-2023 35 ), which in turn would reduce yields 21 . Were this to happen, producers would reduce supply and increase producer prices that would be passed on to consumers. We hypothesise that the overall effect would be roughly the same, but further analysis of this issue would be needed as we suspect there may be significant differences in the spatial pattern of the response caused by differential use of inputs. We find that the compound ( All ) shock could push global prices up by 22.6%, 40.1%, 47.8% and 51.5% for soybean, maize, rice and wheat respectively. These figures are in line but slightly lower than previous investigations of shocks motivated by the events of 2022–2023 food crisis, which found global market prices to increase by 60–100% 20 and up to 65% 17 . The FAO Global Cereals Price Index was around 55% higher in 2022 compared to the long-term average suggesting our model results are of a similar order to observed price shocks. Note that on shorter time spans of days or weeks global price fluctuations may be considerably higher due to aspects of grains markets that are not modelled here, such as speculation 36 , derivatives trading, and cross-sectoral interactions with other markets. The consumer prices modelled here are assumed to be landed costs, without any marketing mark-ups, and without governments interventions to respond to rising food prices. Governments in 2022 responded to rising food prices through a variety of interventions including food and fertilizer subsidies, tariff adjustments and price freezes 9 . These interventions determine the actual pass-through of shocks to local market prices, as well as acting counter-cyclically to stabilise demand during price spikes. At the price of increased complexity our modelling framework could be extended to explore these interventions and their first and second-order effects, The relatively minor impacts of the supply shock of the Ukraine war on global prices and consumption arise because countries are able to source maize and wheat from alternative trading partners, either those that they already traded with before the conflict or by establishing new trading relationships (at a cost). While in line with anecdotal evidence of trade shifts that did happen over the last two years, there is an ongoing debate about the short-run stickiness of trade flows after a shock 37 . Empirical studies of changing trade patterns during shocks, and what factors prevent adjustment, will be important to better calibrate our and similar shock-response trade models. Whilst our modelling illustrates the potential for trade adjustments to buffer shocks, our results highlight that trade cannot be relied upon to mitigate the most severe and pervasive compound shocks, which can cause systemic risks to the global food supply system. Our results show that trade can allow multiple shocks to combine and propagate to affect consumers throughout the world, especially for the worst, low probability scenarios. The worst-case shock under the baseline scenario (which just includes weather-driven yield variability) can cause a total negative consumer surplus of USD260 million which increases to USD606 million in the All shock scenario, with virtually all countries to some degree negatively affected. While having a high import dependency can help buffer against year-to-year weather variability, it is less able to buffer tail-risk events where domestic production capacity is important. Our results show the importance of considering the effects of non-climate shocks against the background of a realistic range of above or below-average yields determined by global weather patterns. Stress-testing the global food system and assessing strategies to build resilience requires a risk layering 38 approach to deal with the spectrum of shocks. Our results and further modelling may help inform policies around global risk funds, cash transfer programs, agricultural versus other sector investments, and trade. A risk layering approach to mitigating shocks will require a balanced set of agricultural system interventions, fiscal policies, and trade policies. The world requires a globalised food system so that food surplus countries can provide food for countries with a limited agricultural capacity to feed their population. Global trade in food is essential to ensure global food security, and the food system needs to be stress-tested to ensure it is resilient to plausible worse-case shocks. The consequence of failure – the inability to feed a large nation for example – could be catastrophic. The relative resilience shown by the global food system to recent shocks including the pandemic and Russia’s invasion of Ukraine are grounds for optimism, but not complacency. We believe that the modelling approach we have developed here, which can be extended to include further aspects of food system dynamics or different shocks (such as pest outbreaks, shipping disruption, or other conflicts), can be an useful tool to locate food system’s strengths and vulnerabilities and identify strategies to increase resilience. Declarations Acknowledgements This research has been conducted as part of the Oxford Martin School Programme on Systemic Resilience, for which JV, JWH and MO have received funding, and the FACT Alliance Jameel Index for Food and Trade Vulnerability, for which AMu and JWH have received funding. Author contributions: JV conceptualised the research, performed the analysis and led the writing of the manuscript. AMu. processed the yield anomaly data and contributed to the writing of the manuscript. YV, HCJG and AMo provided input to the analysis and contributed to the writing of the manuscript. MO and JWH conceptualised the research, provided input and contributed to the writing of the manuscript. Competing interests: Authors declare that they have no competing interests. Data availability: The data needed to reproduce the analysis is deposited in a Zenodo repository (upon acceptance). Code availability: The code needed to analyse the data and reproduce the figures it provided in Zenodo repository (upon acceptance). Methods Overview We provided a brief overview of the methodology, split into (i) the derivation of the 54 years of plausible yield variability, (ii) the spatial price equilibrium model, and (iii) the implementation of the various shocks into the model. Country yield variability For each country, we derive a set of representative yield anomalies, which are driven by large-scale climate variability (ignoring other factors affecting yield year-to-year). These yield anomalies should be interpreted as plausible deviations from the modern-day yield baseline, and not historical yields. A dataset of global modelled rainfed and irrigated crop yields 25 is used to calculate country yield anomalies for the four study crops (rice, wheat, maize, sorghum). This dataset was derived from a fused gridded crop model, which takes historical climate 39 and soil data, gridded maps of crop harvested area 40 and a daily process-based crop water model 41 to estimate 10x10km global pixel level crop yields for individual crop growing seasons 42 from 1961 to 2014. The model uses empirical yield-evapotranspiration relationships to estimate changes to crop yields (relative to the year 2010) under 54-years of historical climate conditions. A full description of the model and dataset is provided by ref 25 Pixel level crop yields from the fused gridded crop model are aggregated to country level and the annual timeseries for the period 1961-2014 and detrended using linear regression. Country level crop yield anomalies for each year in the 54-year period are calculated by dividing the detrended yield by the 54-year country level mean detrended yield. Global spatial price equilibrium model We model producer and consumer prices, supply, demand and trade flows using a newly developed global spatial price equilibrium model (SPEM). A SPEM is a multi-regional partial equilibrium model that links producers and consumers across regions 43 . Producers and consumers are linked together via domestic or international trade, for which a certain trade cost has to be paid. This includes all costs after leaving the farm, including storage, hinterland transportation, border and custom compliance, maritime transport, intermodal transfers, port fees, and imports tariffs. These have all been separately derived in previous work 28 . Compared to other global food systems models, the benefits of a SPEM is that it explicitly captures bilateral trade flows, which is not standard in many global food systems models. Second, it allows for trade diversion and the establishment of new trading partners. Third, it captures directional trade flows, meaning that countries can both import and export the same product. Fourth, a SPEM allows for embedding different types of shocks, including price shocks, supply shocks, and shocks to bilateral trade costs (e.g., trade bans). Although SPEM are usually set up for longer-term (partial) equilibrium simulations 44,45 , for instance given decreasing trade tariffs or cost, we adjust the standard SPEM formulation to make it suitable for shock simulations in the short-term (e.g., one-off shock). Moreover, we introduce stocks in the modelling framework, which are essential to understand supply shortages in the short-term but are usually omitted for future-orientated model applications. The SPEM model requires data on trade, transport and trade costs, prices, supply, demand, and information on the shape of the demand and supply curves (e.g., the demand and supply elasticities). At the baseline, the SPEM model assumes that the decision to supply from certain regions is purely based on cost differentials of the total landed cost of goods (that is the cost to produce crops and ship to the consumer). However, there are non-cost elements which determine where countries source from, and hence, the model need to be calibrated on existing trade data to capture cost and non-cost related factors that determine the supply network of specific countries 44,46 . In our SPEM model, we consider 177 countries for which we could collect all data required. An overview of the data sources is included in Supplementary Table 1. These countries have interconnected competitive markets that trade a homogenous crop, with trade flows modelled on a directional basis. When referring to trade flows here, we refer to both international trade flows and domestic supply. Producers in each country have a certain amount of supply to provide to the market (either domestic or foreign), which they sell at the highest possible price (following their supply curve). Consumers have a certain amount of demand for the good and buy goods at the lowest possible price (following their demand curve). Each country can trade with any other country, with a corresponding trade cost to source from domestic or foreign markets. In equilibrium, we can find the trade flows between countries that determine the producer and consumer prices. For each country, the total production and imports must match the total consumption and export in equilibrium. In the Supplementary Methods, we describe the trade cost formulation used, the calibration process, and the model set-up for incorporating different types of shocks. The reference period for the model is the 2017-2021 average trade network, to smooth out intra-year trade fluctuations. A separate SPEM is set-up for each crop considered, without considering any cross-grain substitution effects. Shock implementation We implement different types of shocks into the model, including production/yield variability, the Ukraine war, price shock, and trade bans. More details are provided in the Supplementary Methods. Base: For each country, we have 54 representative years of yield variability, resulting in years of higher or lower than average supply. We implement the yield variability in the model by changing the initial condition of the total supply and shift the supply curve. A positive supply shock movies the curve to the right hand side and a negative supply shock moves it to the left hand side. Ukraine war: For this scenario we make three adjustments in the model. First, we lower the supply for Ukraine to 60% of its baseline supply, in line with observed supply reduction in 2022/2023 47 . Second, we increase the trade costs to Russia (to cover a surge in insurance costs to trade with Russia). Third, we implement a version of the blockage of the Black Sea ports by increasing the trade costs to and from any country that is not part of the European Union and the United Kingdom, to capture the difficulty of sourcing Ukraine exports via sea. Price shock: A price shock is introduced to capture the increase in fertilizer, pesticide and diesel costs due to supply issues as well as the energy crisis over the last few years. We follow a similar methodology as in Verschuur et al 28 and estimate for every country the share of the fertilizer, pesticide and diesel costs to the total crop production cost. We then impose a price shock to these three input, increasing fertilizer and pesticide costs by 200% and diesel costs by 100% (see ref 28 for details). This yields a production price increment, which we assume is being passed through to the consumer Trade bans.We utilize a global database (https://www.globaltradealert.org/) on trade-related interventions taken by countries from 2022 onwards. This database covers which countries impose trade restrictions, which countries are affected by them, and for which commodities these restrictions apply. We extract all import and export trade bans implemented (hereafter trade bans) and encoded this in the model by imposing higher trade costs between these countries. Compound shock.In the compound shock, or polycrisis, scenario, we include all previously mentioned shocks at the same time to evaluate their compound impacts. References Nelson, G. C. et al. Climate change effects on agriculture: Economic responses to biophysical shocks. Proc. Natl. Acad. Sci. U. S. A. 111 , 3274–3279 (2014). Brown, M. E. et al. Do markets and trade help or hurt the global food system adapt to climate change? Food Policy 68 , 154–159 (2017). Lesk, C., Rowhani, P. & Ramankutty, N. Influence of extreme weather disasters on global crop production. Nature 529 , 84–87 (2016). Davis, K. F., Downs, S. & Gephart, J. A. Towards food supply chain resilience to environmental shocks. Nat. Food 2 , 54–65 (2021). FAO. The impact of disasters on agriculture and food security 2023 – Avoiding and reducing losses through investment in resilience . (FAO, 2023). doi:10.4060/cc7900en Ray, D. K., Gerber, J. S., Macdonald, G. K. & West, P. C. Climate variation explains a third of global crop yield variability. Nat. Commun. 6 , 1–9 (2015). Gaupp, F., Pflug, G., Hochrainer-Stigler, S., Hall, J. & Dadson, S. Dependency of Crop Production between Global Breadbaskets: A Copula Approach for the Assessment of Global and Regional Risk Pools. Risk Anal. 37 , 2212–2228 (2017). Behnassi, M. & El Haiba, M. Implications of the Russia–Ukraine war for global food security. Nat. Hum. Behav. 6 , 754–755 (2022). Amaglobeli, D., Gu, M., Hanedar, E., Hee Hong, G. & Thévenot, C. Policy Responses to High Energy and Food Prices,. IMF Work. Pap. 23 , 1–67 (2023). GRFC. 2023 Global Report on Food Crisis: Mid-year update . (2023). Roussi, A. Why gigantic locust swarms are challenging governments and researchers. Nature 579 , 330–330 (2020). WFP. A global food crisis. (2023). Available at: https://www.wfp.org/global-hunger-crisis. (Accessed: 1st November 2023) Zhang, Z., Abdullah, M. J., Xu, G., Matsubae, K. & Zeng, X. Countries’ vulnerability to food supply disruptions caused by the Russia-Ukraine war from a trade dependency perspective. Sci. Rep. 13 , 16591 (2023). Liu, L., Wang, W., Yan, X., Shen, M. & Chen, H. The cascade influence of grain trade shocks on countries in the context of the Russia-Ukraine conflict. Humanit. Soc. Sci. Commun. 10 , (2023). Laber, M., Klimek, P., Bruckner, M., Yang, L. & Thurner, S. Shock propagation from the Russia–Ukraine conflict on international multilayer food production network determines global food availability. Nat. Food 4 , 508–517 (2023). Bertassello, L., Winters, P. & Müller, M. F. Access to global wheat reserves determines country-level vulnerability to conflict-induced Ukrainian wheat supply disruption. Nat. Food 4 , (2023). Kuhla, K., Puma, M., Schewe, J., Kubiczek, P. & Otto, C. International cooperation could help avert a major food crisis due to the Russian invasion of Ukraine. Res. Sq. 1–9 (2022). Falkendal, T. et al. Grain export restrictions during COVID-19 risk food insecurity in many low- and middle-income countries. Nat. Food 2 , 11–14 (2021). Carriquiry, M., Dumortier, J. & Elobeid, A. Trade scenarios compensating for halted wheat and maize exports from Russia and Ukraine increase carbon emissions without easing food insecurity. Nat. Food 3 , 847–850 (2022). Alexander, P. et al. High energy and fertilizer prices are more damaging than food export curtailment from Ukraine and Russia for food prices, health and the environment. Nat. Food 4 , 84–95 (2023). Arndt, C., Diao, X., Dorosh, P., Pauw, K. & Thurlow, J. The Ukraine war and rising commodity prices: Implications for developing countries. Glob. Food Sec. 36 , 100680 (2023). Gaupp, F., Hall, J., Hochrainer-Stigler, S. & Dadson, S. Changing risks of simultaneous global breadbasket failure. Nat. Clim. Chang. 10 , 54–57 (2020). Kornhuber, K. et al. Risks of synchronized low yields are underestimated in climate and crop model projections. Nat. Commun. 14 , 1–10 (2023). Verschuur, J., Li, S., Wolski, P. & Otto, F. E. L. Climate change as a driver of food insecurity in the 2007 Lesotho-South Africa drought. Sci. Rep. 11 , 3852 (2021). Tuninetti, M. & Davis, K. F. Addressing global hotspots of climate-related crop production losses. Preprint (2022). Vittis, Y., Folberth, C., Bundle, S. C. & Obersteiner, M. Restoring Nature at Lower Food Production Costs. Front. Environ. Sci. 9 , 1–14 (2021). Vittis, Y., Mosnier, A. & Navarro, J. Agricultural costing model : technical documentation. 1–15 (2023). Available at: https://zenodo.org/records/7701784#.ZBMVXnaZOUk. Verschuur, J., Vittis, Y., Obersteiner, M. & Hall, J. W. Cost drivers of international grains and oilseeds trade. Res. Sq. (2023). doi:10.21203/rs.3.rs-3289367/v1 Nandy, S. & Thang, E. India’s wheat export ban pushes Australian FOB price to record high. S&P Global Commodity Insights: Agriculture (2022). Available at: https://www.spglobal.com/commodityinsights/en/market-insights/latest-news/agriculture/051722-indias-wheat-export-ban-pushes-australian-fob-price-to-record-high#:~:text=India%27s wheat export ban pushes Australian FOB price to record high,-Author Sampad Na. (Accessed: 8th December 2023) Mancombu, S. R. Domestic white rice prices drop by ₹4,000/tonne after export ban. The Hindu Business Line (2023). Available at: https://www.thehindubusinessline.com/economy/agri-business/domestic-white-rice-prices-drop-by-4000tonne-after-export-ban/article67120396.ece#:~:text=Domestic white rice prices (common,of the country’s shipments abroad. (Accessed: 8th December 2023) Brzezinkski, B., Cokelaere, H. & Gijs, C. EU has no easy options to help Ukraine shift its grain. Politico (2023). Available at: https://www.politico.eu/article/eu-ukraine-russia-black-sea-grain-deal-food-security-war/. (Accessed: 8th December 2023) Gijs, C. Poland, Hungary, Slovakia impose own Ukraine grain bans as EU measure expires. Politico (2023). Available at: https://www.politico.eu/article/poland-hungary-slovakia-extend-grain-bans-despite-blocs-lift/. (Accessed: 8th December 2023) Brown, P. T. & Caldeira, K. Greater future global warming inferred from Earth’s recent energy budget. Nature 552 , 45–50 (2017). Chen, B. & Villoria, N. B. Climate shocks, food price stability and international trade: Evidence from 76 maize markets in 27 net-importing countries. Environ. Res. Lett. 14 , (2019). European Commission. Ensuring availability and affordability of fertilisers. Agriculture and rural development (2023). Available at: https://agriculture.ec.europa.eu/common-agricultural-policy/agri-food-supply-chain/ensuring-availability-and-affordability-fertilisers_en. (Accessed: 8th December 2023) Lawson, J., Alam, R. & Etienne, X. Speculation and food-grain prices. Appl. Econ. 53 , 2305–2321 (2021). Reis, T. N. P. dos et al. Understanding the Stickiness of Commodity Supply Chains Is Key to Improving Their Sustainability. One Earth 3 , 100–115 (2020). Mechler, R. et al. Managing unnatural disaster risk from climate extremes. Nat. Clim. Chang. 4 , 235–237 (2014). Harris, I., Osborn, T. J., Jones, P. & Lister, D. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Sci. Data 7 , 109 (2020). Yu, Q. et al. A cultivated planet in 2010 – Part 2: The global gridded agricultural-production maps. Earth Syst. Sci. Data 12 , 3545–3572 (2020). Tuninetti, M., Tamea, S., D’Odorico, P., Laio, F. & Ridolfi, L. Global sensitivity of high‐resolution estimates of crop water footprint. Water Resour. Res. 51 , 8257–8272 (2015). Portmann, F. T., Siebert, S. & Döll, P. MIRCA2000—Global monthly irrigated and rainfed crop areas around the year 2000: A new high‐resolution data set for agricultural and hydrological modeling. Global Biogeochem. Cycles 24 , (2010). Takayama, T. & Judge, G. G. Spatial and Temporal Price and Allocation Models . (North-Holland Publishing Company, 1971). Janssens, C. et al. A sustainable future for Africa through continental free trade and agricultural development. Nat. Food 3 , 608–618 (2022). Mosnier, A. Tracking indirect effects of climate change mitigation and adaptation strategies in agriculture and land use change with a bottom-up global partial equilibrium model by. Univ. Nat. Resour. Life (2014). Jansson, T. & Heckelei, T. A new estimator for trade costs and its small sample properties. Econ. Model. 26 , 489–498 (2009). OECD. The impacts and policy implications of Russia’s aggression against Ukraine on agricultural markets. OECD Policy Responses on the Impacts of the War in Ukraine (2022). Available at: https://www.oecd.org/ukraine-hub/policy-responses/the-impacts-and-policy-implications-of-russia-s-aggression-against-ukraine-on-agricultural-markets-0030a4cd/. (Accessed: 8th December 2023) Additional Declarations There is NO Competing Interest. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3969801","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":276883259,"identity":"4c1f32ea-ba8f-49d2-bec4-303128237ec3","order_by":0,"name":"Jasper Verschuur","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0002-5277-4353","institution":"Environmental Change Institute, University of Oxford","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Jasper","middleName":"","lastName":"Verschuur","suffix":""},{"id":276883261,"identity":"7513728d-f132-4238-aec7-e14afb4659b4","order_by":1,"name":"Anna Murgatroyd","email":"","orcid":"https://orcid.org/0000-0002-7362-0273","institution":"University of Oxford","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Anna","middleName":"","lastName":"Murgatroyd","suffix":""},{"id":276883263,"identity":"a75ef99d-83f0-41d3-b695-e94ed7799977","order_by":2,"name":"Yiorgos Vittis","email":"","orcid":"","institution":"International Institute for Applied Systems Analysis","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yiorgos","middleName":"","lastName":"Vittis","suffix":""},{"id":276883264,"identity":"755ca551-b483-46f9-b98b-21bc1778c36f","order_by":3,"name":"Aline Mosnier","email":"","orcid":"","institution":"UN SDSN","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Aline","middleName":"","lastName":"Mosnier","suffix":""},{"id":276883266,"identity":"ba69bf88-b7e4-4798-884e-32f8d3e926a6","order_by":4,"name":"Michael Obersteiner","email":"","orcid":"https://orcid.org/0000-0001-6981-2769","institution":"University of Oxford","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Michael","middleName":"","lastName":"Obersteiner","suffix":""},{"id":276883268,"identity":"82625406-9fb5-448e-838d-a5bb3dc13539","order_by":5,"name":"Charles Godfray","email":"","orcid":"https://orcid.org/0000-0001-8859-7232","institution":"University of Oxford","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Charles","middleName":"","lastName":"Godfray","suffix":""},{"id":276883269,"identity":"b13f65b3-b150-4e3a-8724-fecdf7f980be","order_by":6,"name":"Jim Hall","email":"","orcid":"https://orcid.org/0000-0002-2024-9191","institution":"University of Oxford","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jim","middleName":"","lastName":"Hall","suffix":""}],"badges":[],"createdAt":"2024-02-19 11:12:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3969801/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3969801/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":52243085,"identity":"b105e28c-fead-4591-ae3f-ec6acab7e44c","added_by":"auto","created_at":"2024-03-08 08:06:24","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":331900,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Bivariate plot of the mean consumer price averaged across the 54 counterfactual years, and the coefficient of variation (COV) of consumer prices over the same period across all four crops (weighted average), all under the baseline scenario. (b-e) Show the same as (a) but for Maize (b), Wheat (c), Rice (d) and Soybean (e) specifically.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3969801/v1/f52bb9c69915a19d0f131260.png"},{"id":52243086,"identity":"4365cf0b-0de7-40c5-80a1-4fc9e93eb4d9","added_by":"auto","created_at":"2024-03-08 08:06:24","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":300779,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Median deviation (across the 54 events) of the normalized total consumer surplus (CS) under the various shock scenarios compared to the average regional consumer surplus for the baseline scenario (across the 54 events). (b-e) Show the same results but for the crop-specific CS.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3969801/v1/002b85b4ceaff082e9014c0a.png"},{"id":52243179,"identity":"7715a48e-162e-4072-8166-9535dda96259","added_by":"auto","created_at":"2024-03-08 08:14:25","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":226129,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Total amount of a negative consumer surplus (CS) during a single event (sum of all negative CS across countries). (b) The fraction of countries (out of the 177 considered) that experience a negative consumer surplus. The quantiles indicates the rank of the weather year compared to the total number of weather years in our sample (N = 54), ordered by severity (rank 1 is worst case).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3969801/v1/9949972865969576439f494f.png"},{"id":52243177,"identity":"cb1a0da1-1b09-42ae-afb2-089a88226fa3","added_by":"auto","created_at":"2024-03-08 08:14:25","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":556632,"visible":true,"origin":"","legend":"\u003cp\u003eAbsolute change in average interregional trade in the ‘All shocks’ scenario (average over 54 events) compared to the ‘Baseline’ scenario. The matrix indicates regional international trade, while the last column indicates change in domestic supply for countries within the region. The diagonal indicate the changes of the within region international trade flows.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-3969801/v1/f831f8bd50992f066862a07f.png"},{"id":52243088,"identity":"e788eaf6-b87e-4ccb-a42b-50b1082b0b9b","added_by":"auto","created_at":"2024-03-08 08:06:25","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":239512,"visible":true,"origin":"","legend":"\u003cp\u003e(a) The Spearman rank correlation coefficient between country consumer surplus anomaly and the country import dependency (ID) of a country for maize. This correlation is run per ranked consumer surplus anomaly across countries. (b-d) Show the same figures but for Wheat (b), Rice (c) and Soybean (d). A positive relationship indicates that a lower ID can help buffer CS anomalies while a negative relationship indicates that a higher ID can help buffer the CS anomalies. The quantiles indicates the rank of the weather year compared to the total number of weather years in our sample (N = 54), ordered by severity (rank 1 is worst case).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-3969801/v1/fdeeb296ae079adc29b60f4c.png"},{"id":55178177,"identity":"61004920-3742-4904-8338-ba95e54caa37","added_by":"auto","created_at":"2024-04-23 16:38:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1790530,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3969801/v1/26ed851b-5def-4fd5-97c5-91c985cd73d9.pdf"},{"id":52244399,"identity":"50262d50-e42a-4809-908a-cc3746f2bb7a","added_by":"auto","created_at":"2024-03-08 08:22:25","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2310108,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"Supplementaryinformationpolycrisis.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3969801/v1/c586af649fc0f0fa07e9282d.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"The impacts of polycrises on global grain availability and prices","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe global food system is vulnerable to shocks that can impact production, trade and prices\u003csup\u003e\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Disasters cause annual losses worth USD123\u0026nbsp;billion per year\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e, alongside weather variability that is estimated to account for around a third of yield variability\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Shocks can have a large effect on grain prices. For example, global grain supplies were dramatically impacted by droughts in 2010\u0026ndash;2011, whose effects were exacerbated by a Russian export ban and low stocks-to-use ratios, leading to a doubling of global wheat prices\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Though droughts have become a recurrent phenomenon in recent years, food price volatility has been dominated by non-climatic factors, including the war in Ukraine and consequent rising fertilizer prices (driven by high energy prices), locust outbreaks, and trade restrictions imposed by various countries (e.g., export bans or quotas)\u003csup\u003e\u003cspan additionalcitationids=\"CR9 CR10\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. These events, along with other factors such as the pandemic and an increasing number of civil and international conflicts, have led to a state of \u0026ldquo;polycrisis\u0026rdquo; which has resulted in a doubling of the number of people facing acute food insecurity in 2023 compared to 2019\u003csup\u003e12\u003c/sup\u003e. Countries have responded with a range of interventions including food or fertilizer subsidies and cash transfers or vouchers, often at considerable costs (in some cases over 0.75% of GDP)\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSeveral studies have sought to explore the vulnerability of countries to recent food system shocks, in particular the impacts of the Russian invasion of Ukraine through its effects on wheat and maize supply. One set of studies used trade data to reconstruct the global supply network of crop commodities used both for direct consumption and in the production of processed foods\u003csup\u003e\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Another study combined trade data with information on stocks and food substitution behaviour to better characterise different countries\u0026rsquo; vulnerabilities\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. These models are typically static, preventing them from incorporating trade adjustments or quantifying the price effects of export reductions. Others have used a more complex dynamic supply-demand model, including stocks, to study the effect of the Ukraine war, fertilizer prices and export restrictions on global wheat prices\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. While better able to represent short-term price spikes and supply/demand adjustments, these models do not explicitly represent bilateral trade flows between countries and so cannot incorporate potential import substitution. A third set of approaches have used global food system models to assess the impact on food prices of a supply disruption from Ukraine alone\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e or combined with fertilizer price increases\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. These models assume a single global market and do not specify bilateral trade flows, so that trade substitution is unconstrained and rapid. General and partial equilibrium models of this type are designed to study long-run price and trade adjustments and their structure and parameterization means that the impacts of short-run shocks are underestimated.\u003c/p\u003e \u003cp\u003eThe aforementioned studies have provided important insights into the possible impacts of the Ukraine war and its associated food system shocks, but a number of key considerations remain unaddressed. First, no studies have explicitly included bilateral trade flows in a way that allows dynamic adjustments such as changing trading partners or increasing supply from existing exporters. Second, while attempts have been made to include multiple shocks within a single modelling framework, they are often implemented uniformly across countries (for example, a fertilizer price shock is assumed to affect all countries equally) or explore only a small subset of countries imposing export restrictions. Yet, we know that fertilizer price shocks affect countries unequally because of different fertilizer use and import dependency\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Similarly, many countries have been known to impose trade bans (import or export), though these were often targeted towards specific countries without affecting the rest of the world. Third, all studies have evaluated the effects of these shocks against a single baseline \u0026ndash; for the Ukraine war, the situation in early 2022. This makes sense in trying to understand the historical impact of the war but assessment of risks to the global food system should consider the full range of possible production scenarios, ranging from bad years with major breadbasket failures\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e to good years with bumper harvests\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, and their relative likelihood.\u003c/p\u003e \u003cp\u003eIn this study, we use a newly developed bilateral trade model of global grain supply for 177 countries to study the effects of compounding shocks to the world\u0026rsquo;s grain supply. The shocks we model are inspired by the events of 2022\u0026ndash;2023: the Ukrainian war and its effects on grain supply, food prices and trade restrictions. We analyse their effects against a background of 54 plausible realisations of crop production variability across regions determined by historic weather variability\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. This allows us to estimate the range of possible consequences for producer and consumer prices, and for patterns of bilateral trade. We demonstrate how countries and regions differ in their exposure to these shocks, and in their capacity to adjust by utilizing stocks, reducing exports, or changing trading partners. Our modelling framework both provide insights into different countries\u0026rsquo; vulnerability as well as the spread of systemic risks through global grain supply networks. Our results will help efforts to stress-test global supply chains as well as in the design of effective strategies to enhance the resilience of food supply systems.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eOverview\u003c/h2\u003e \u003cp\u003eWe developed a spatial equilibrium price model that simulates demand, supply, producer and consumer prices and trade flows across 177 countries (more fully described in Methods). It is calibrated to reproduce observed trade flows for the period 2017\u0026ndash;2021, and relies on recently produced datasets of country-specific crop production costs\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e and detailed bilateral trade costs\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. We consider four crops \u0026ndash; maize, wheat, rice, soybeans \u0026ndash; as they are strongly affected by the different shocks we model\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e and their importance for global food and feed consumption.\u003c/p\u003e \u003cp\u003eOur baseline model (\u003cem\u003eBase\u003c/em\u003e) incorporates climate variability information based on 54 years of historic climate data\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e allowing us to estimate the distribution of crop production given realistic patterns of weather-driven yield variability around the world. Against this background, four sets of exogenous shocks are modelled: (i) a supply shock caused by reduced production and restricted exports of Ukrainian grain (\u003cem\u003eUkraine)\u003c/em\u003e, (ii) an energy price shock of the type associated with the Ukraine war increasing fertilizer, pesticide and diesel prices with knock-on effects on producer prices that are passed on to consumers (\u003cem\u003ePrice\u003c/em\u003e), (iii) imposed trade restrictions (import and export bans) motivated by those observed in 2022\u0026ndash;2023 (\u003cem\u003eTrade\u003c/em\u003e), and (iv) all shocks together, representing our \u0026ldquo;polycrisis\u0026rdquo; scenario (\u003cem\u003eAll\u003c/em\u003e). Consumer prices in the model do not include subsidies or marketing mark-ups, and hence should be interpreted as landed cost prices. Supply, demand and trade of food and feedstock quantities are treated in a homogenous commodity setting.\u003c/p\u003e \u003cp\u003eOur model has been specifically designed to explore the short-run impacts of shock scenarios. We study impacts within a single year in which planting decisions have largely been made supporting an inelastic supply assumption. Countries can respond by utilizing grain stocks, adjusting imports from existing suppliers and, at a cost, diversify the scope of trading partners.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eFood production variability can drive consumer price fluctuations\u003c/h2\u003e \u003cp\u003eThe set of weather-related yield variations based on 54 years of climate data (see Methods) were used to simulate production shortages and surpluses in one or more of the four crops, causing price fluctuations across domestic and foreign markets. Consumer price sensitivity to weather variability differs across crops and countries. The average consumer price variability (measured by the coefficient of variation) across countries is lowest for soybean (0.06), followed by rice and wheat (0.07) and highest for wheat (0.09). This is lower than the underlying yield variability (0.07 for rice and soybean, 0.10 for maize and 0.13 for wheat) suggesting an economic risk mitigation function of markets. The global food supply system thus partly buffers the impact of yield variability on consumer prices, mainly through international trade.\u003c/p\u003e \u003cp\u003eIn Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e we show price volatility and average consumer prices for the baseline scenario, across all four crops (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea) and for each crop separately (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb-e). Some countries face high price variability alongside low consumer prices, while other countries face both high average prices and high price variability. Most of Europe and North Africa have low prices and low volatility, due to the deep and diverse grain markets operating in Europe. Regions with high production serving predominantly domestic markets (Central Asia, Eastern Europe, Australia, South Africa) have low prices but relatively high volatility. In contrast, some large importers (China, Bolivia, Middle-East and Saharan countries) have higher average prices but low volatility. Countries in Sub-Saharan Africa face both high consumer prices and high volatility, caused by high trade costs\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e and risky suppliers.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThere are interesting variations across crops (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb-e). For maize, low consumer prices and price fluctuations are observed in Northern Africa, Eastern and Central Europe, due to their proximity to major maize suppliers in Europe. Contrarily, high transportation costs and reliance on variable production leads to high prices and high price variability in most Sub-Saharan African countries and landlocked South America. For wheat, high import dependency leads to high consumer prices in most of Africa, yet provide relatively stable supplies due to imports from a variety of countries (Eastern Europe, North America, Latin America and Western Europe). On the other hand, large wheat producers in South America, Eastern Europe and Central Asia are among the lowest cost producers, yet have but high price variability induced by high yield variability\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. For rice, lower consumer prices are experienced in parts of Latin America, Central Asia, and South Asia and South-East Asia, given relatively low cost production for domestic markets. Low price variability is observed in Western Europe and North America, given stable imports from rice producing countries. Soybean patterns are dominated by high price variability and high prices in most of Africa, East and South-East Asia, given their reliance on soybean imports from Latin America, which are susceptible to weather-driven variability.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eThe impacts of compound shocks\u003c/h2\u003e \u003cp\u003eThe shocks to the global grain supply system that we model affect producer prices and trade, which impact consumer prices, consumption and consequently consumer surplus (see Supplementary Fig.\u0026nbsp;1). The consumer surplus is the difference between the price a consumer is willing to pay and actually pays (multiplied by the amount they consume), and represents how consumers are impacted by changing prices, both in terms of their expenditure and how much they adjust their consumption. We use consumer surplus as our metric of impact as it simultaneously captures price and demand effects, including country variations in crop-specific short-run demand elasticities. Other metrics, including those more relevant for food security (e.g., people facing hunger), could alternatively be considered in future work.\u003c/p\u003e \u003cp\u003eAcross all 54 yield instantiations, the compound shock (\u003cem\u003eAll\u003c/em\u003e) causes an increase in median global consumer price (weighted by demand) of 22.6% for soybean, 40.1% for maize, 47.8% for rice, and 51.5% for wheat. This results in a reduction of median global consumption of around 4.4% for soybean, 4.1% for maize, 4.5% for rice, and 8.9% for wheat. This highlights the higher demand elasticities for soybean and wheat compared to rice and maize. This reduces median global consumer surplus by 8.2% for soybean, 7.3% for maize, 7.9% for rice and 16.5% for wheat (Supplementary Fig.\u0026nbsp;1).\u003c/p\u003e \u003cp\u003eThe effect of the four shock scenarios on global and regional consumer surpluses are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, again averaged over the 54 different yield patterns (equivalent plots for consumer prices and consumption are given in Supplementary Figs.\u0026nbsp;2\u0026ndash;5 and 6\u0026ndash;9 respectively). For maize, the energy price shock has the greatest effect in most regions, and is particularly severe for Central Asia, Oceania, and Sub-Saharan Africa where consumer prices rise by 50\u0026ndash;100% (Supplementary Fig.\u0026nbsp;2). For wheat, the energy price shock is also dominant, except for in Eastern Europe (mainly Ukraine and Russia) where the Ukraine war is more important. Trade bans have a large impact on wheat prices in Central Asia (due to export bans imposed by Russia, Kyrgyzstan, and India) and Oceania (largely due to the Indian trade ban). The latter has a knock-on effect on increased demand for wheat from Australia, which raises consumer prices locally, as was observed in 2022\u003csup\u003e29\u003c/sup\u003e. The relative importance of the global energy price shock compared to the trade disruption from the Ukraine war was also found in other studies\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor rice, consumer surpluses in Sub-Saharan Africa, East Asia, Central Asia, Western Asia, and Eastern Europe were severely impacted by trade restrictions, particularly the rice export ban imposed by India. This ban led to a reduction in domestic prices and so positively impacted domestic Indian consumer surplus, a response observed after India restricted rice exports in 2023\u003csup\u003e30\u003c/sup\u003e. Consumer surpluses for rice in Central Asia are extremely sensitive to different shocks. This can be explained by a combination of background production volatility in the region, combined with dependencies on trade from South Asia, affected by trade restrictions, and Eastern Europe, affected by the Ukraine war. Soybean consumer surpluses are most influenced by the energy price shock which is felt relatively uniformly across all regions.\u003c/p\u003e \u003cp\u003eSupplementary Fig.\u0026nbsp;10 provides more detail about how different countries are vulnerable to these shocks. Our country-level analysis shows that, especially for wheat and rice, countries within the same region can be affected in quite different ways by shocks, something that could not studied using a more aggregated model (e.g., regions instead of countries).\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eGlobal systemic impact\u003c/h2\u003e \u003cp\u003eWithin the interconnected global food supply network, even local production shocks can give rise to globally felt systemic impacts. This may happen when local shocks occur simultaneously (e.g., multiple breadbasket failures) or when a local shock is of such magnitude that it has global ramifications. We measure global systemic impact by the total negative consumer surplus across countries per weather year (i.e., the sum of negative country consumer surplus compared to the mean consumer surplus under the baseline scenario). We quantify the impact of weather-related variation in crop yields (from 54 years of yield data) and overlay the four shock scenarios (\u003cem\u003eUkraine\u003c/em\u003e, \u003cem\u003ePrice\u003c/em\u003e, \u003cem\u003eTrade\u003c/em\u003e, \u003cem\u003eAll\u003c/em\u003e). Ranking the 54 weather years for each shock scenario allows us to empirically estimate the quantiles (quantile being the rank divided by the total number of weather-year, N\u0026thinsp;=\u0026thinsp;54) of the negative consumer surplus, for a given scenario.\u003c/p\u003e \u003cp\u003eUnder the baseline scenario (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea), the worst consumer surplus loss is USD90.8\u0026nbsp;million for maize, USD31.2\u0026nbsp;million for wheat, USD64.4\u0026nbsp;million for rice, and USD72.3\u0026nbsp;million for soybean. When this is compounded with the \u003cem\u003eAll\u003c/em\u003e shock scenario the losses rise to USD246.4\u0026nbsp;million, USD145.6\u0026nbsp;million, USD135.6\u0026nbsp;million, and USD78.6\u0026nbsp;million for maize, wheat, rice, and soybean, respectively. This is 2.3 times greater (USD606.2\u0026nbsp;million versus USD258.7\u0026nbsp;million), with the largest relative increase occurring for wheat. The Ukraine war has a minor effect on the systemic risk for wheat, rice and soybean, but shifts the curve up for maize (to a worst case loss of USD125.1\u0026nbsp;million) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). Comparing \u003cem\u003eAll\u003c/em\u003e with the other three scenarios, the energy price shock can explain most of the response for maize while for the other crops the different shocks all contribute to the drop in consumer surplus in the \u003cem\u003eAll\u003c/em\u003e scenario. What is also apparent is the importance of the weather years for the systemic risk. For instance, the difference between the worst case scenario (tail risk event) and the median event (0.5 quantile) is in most cases larger than the additionality of the shock scenarios. In other words, the occurrence of a bumper harvest or breadbasket failure can be as great or greater than the additional effects of non-climatic shocks, emphasizing the importance of looking at those together.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eUnder the baseline scenario, the proportion of countries that simultaneous experience negative surplus (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb) ranges from close to zero to 90% as yield conditions worsen. In most cases, there is a clear transition in the number of countries that experience simultaneous negative CS between fairly average years (quantile\u0026thinsp;\u0026gt;\u0026thinsp;0.5) and very bad years (quantile\u0026thinsp;\u0026lt;\u0026thinsp;0.5). In particular for wheat and soybean a clear transition is visible, with the food supply system being capable to deal with average years, but with many countries experiencing lower consumer surplus during some breadbasket failures. When this is compounded with the \u003cem\u003eAll\u003c/em\u003e scenario, virtually all countries simultaneously display drops in consumer surpluses. Comparison of \u003cem\u003eAll\u003c/em\u003e with the other scenarios indicates that the energy price shock makes the most significant contribution, while trade restrictions for wheat and rice also cause widespread negative consumer surplus across countries.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eShifting trade patterns\u003c/h2\u003e \u003cp\u003eIn our model the compound shocks in the \u003cem\u003eAll\u003c/em\u003e scenario led to multiple adjustments to global trade flows (compared to a baseline shown in Supplementary Fig.\u0026nbsp;11). These can be understood by looking how the different shock affect trade. For the \u003cem\u003eUkraine\u003c/em\u003e shock, we assumed reduced exports from Ukraine via sea with exports to some countries being stopped (though the scenario does not capture the Black Sea Grain Initiative). The shock led countries relying on Ukraine for imports (mainly wheat and maize) to source grains from elsewhere, while Ukrainian grain that would have been exported by sea was sold on European markets that could be accessed by land\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. The reductions in exports in the \u003cem\u003eTrade\u003c/em\u003e shock forced import-dependent countries to rely more on domestic markets (from production or stocks) or to source from elsewhere. The \u003cem\u003ePrice\u003c/em\u003e shock has a more systemic effect on trade. Rising input costs meant that producer prices accounted for a higher fraction of total consumer prices, although dependent on how the energy price shock increases producer prices. This effect makes trade costs less of a barrier to establishing new trade partners so that global markets became more competitive as countries competed to source the cheapest supplies.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003c/p\u003e \u003cp\u003eChanges in regional trade flows for the four grains after the \u003cem\u003eAll\u003c/em\u003e shock are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The results illustrated are averages over the observed 54 weather-driven yield patterns. A clear reduction in maize exports from Eastern Europe is visible, in particular to East Asia, North Africa, and Western Asia. North African countries, in turn, have sourced their shortfall from North America. Latin America and the Caribbean have increased interregional trade, at the expense of North American exports, due to improved relative cost competitiveness of local maize. Increases in producer prices in the \u003cem\u003eAll\u003c/em\u003e shock scenario (and similarly for the \u003cem\u003ePrice\u003c/em\u003e shock) reduce maize production by the large amount of 83.0\u0026nbsp;million tonnes while trade flows increase by 43.7\u0026nbsp;million tonnes as countries seek alternative suppliers and the relative cost of trade reduces.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor wheat, international trade also increases in the \u003cem\u003eAll\u003c/em\u003e shock scenario (by 23.7\u0026nbsp;million tonnes) in the face of a global supply reduction of 85.8\u0026nbsp;million tonnes. Major shifts in trade flows are associated with the Ukraine war, with lower exports to North Africa, Sub-Saharan Africa and Western Asia. North Africa replaces its losses by increasing imports from NW-Europe and North America, while Sub-Saharan Africa mainly responds with increased imports from NW-Europe. This switch towards imports from NW-Europe was made possible by an abundance of low-cost Ukrainian wheat supply that could not be exported by sea on North-West, Eastern and Southern European markets\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Western Asia increased supplies from a variety of sources, in particular from Central Asia. The trade imbalance between South-East Asia and East Asia markedly increases with South-East Asia increasing exports to East Asia, while East Asia reduces exports in the opposite direction.\u003c/p\u003e \u003cp\u003eLess extreme shifts are observed for rice, with reductions in both international trade (by 10.3\u0026nbsp;million tonnes) and production (by 15.6\u0026nbsp;million tonnes). The largest response is the reduction in intraregional rice trade in South Asia and the increased reliance on domestic supplies due to the export restriction of India. A similar response occurs in South-East Asia. Sub-Saharan African countries experience the largest import shock and as a result are forced to source more rice from within the continent. Soybean trade is relatively robust in the \u003cem\u003eAll\u003c/em\u003e shock scenario, except for decreases in Latin America and Caribbean exports to East Asia (which is partly substituted by soybean from North America), alongside a lower domestic supply. This is the result of diminishing global demand for soybean amid rising consumer prices.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eTrade buffers systemic shocks\u003c/h2\u003e \u003cp\u003eThere is a long-standing debate about whether high levels of imports are beneficial for countries in allowing them to buffer shocks due to local yield variability and to access markets abroad\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. We test this hypothesis by comparing a country\u0026rsquo;s import dependency (ID, the fraction of a country\u0026rsquo;s supply of a commodity made up of imports) with its average reduction in consumer surplus as yield patterns have increasingly severe effects on production. We ask whether a higher ID leads to a lower sensitivity to changes in consumer surplus, or vice versa, which we test by examining the Spearman rank correlation coefficient between ID and the ranked consumer surplus deviations. We are particular interested how this correlation changes for the average year (quantile 0.5 or above) versus the tail risk events (quantile 0.1 or below).\u003c/p\u003e \u003cp\u003eFor maize and rice, a high ID is correlated with lower reductions in consumer surplus under typical yield conditions, but the correlation is reversed in years when weather causes severe effects on agriculture (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Countries with extensive domestic production and low reliance on exports are better able to buffer themselves against more major weather-associated shocks affecting these grains, though in typical years there is an advantage to being able to access multiple international markets. The relationship for soybean is broadly similar but much weaker. The positive effects of high ID during good yield years disappears when the different shock scenarios are implemented. Relatively high levels of domestic production help protect consumers for bad years though do not completely mitigate the effect of the shock. For wheat, the shape of the curve differs between the shock scenarios. This indicates that a higher ID may be beneficial for some combinations of weather years and shocks, but not for others, emphasizing that imports or self-sufficiency does not provide a \u0026ldquo;one-size-fits-all\u0026rdquo; solution to build resilience to shocks to wheat supplies.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion \u0026 conclusions","content":"\u003cp\u003eFluctuations in food supplies and prices have been a growing cause of concern for governments and the international community since the 2007\u0026ndash;2008 food crisis. While shocks to food supplies are a growing concern for countries with high levels of food insecurity, food price shocks leading to inflation of food prices is a concern for countries across the world.\u003c/p\u003e \u003cp\u003eFood system modelling can be a helpful way of understanding fluctuations and suggesting how they can be mitigated. Previous analyses have tended to examine a relatively small number of shocks without taking into account year-to-year variation in weather affecting agricultural production. In addition, they have also poorly captured short-term adjustments in food trade and demand which, in practice, effectively buffer shocks. In this study, we estimated variability of food production across 177 countries using 54 years of agricultural yield information subject to weather-driven shocks. We constructed a bilateral trade model to simulate adjustments in food import/export linkages based on detailed bilateral trade costs information, as well as information on stocks. We used a supply-constrained price equilibrium model to incorporate the effects of different supply, price, and trade shocks, and examine how consumer prices respond to supply-side shocks and trade adjustments. It is important to note that consumers in our model cannot switch between grains, hence potentially overestimating impacts on consumer surplus.\u003c/p\u003e \u003cp\u003eThe results reveal the sensitivity of the global food system to climate-related supply variability and as well as to further shocks imposed on this background. Across all crops and regions, the impact of rising energy prices (affecting the cost of diesel, pesticides and fertilizer) on crop production was found to have the largest effect on food prices, demand, and consumer surplus. In our model we assume that producers do not adjust their inputs in response to this shock, when in reality, producers may choose to reduce the use of fertilizer and pesticide (or alternatively utilise stocks) in response to rising input prices (as observed across many countries during 2022-2023\u003csup\u003e35\u003c/sup\u003e), which in turn would reduce yields\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Were this to happen, producers would reduce supply and increase producer prices that would be passed on to consumers. We hypothesise that the overall effect would be roughly the same, but further analysis of this issue would be needed as we suspect there may be significant differences in the spatial pattern of the response caused by differential use of inputs.\u003c/p\u003e \u003cp\u003eWe find that the compound (\u003cem\u003eAll\u003c/em\u003e) shock could push global prices up by 22.6%, 40.1%, 47.8% and 51.5% for soybean, maize, rice and wheat respectively. These figures are in line but slightly lower than previous investigations of shocks motivated by the events of 2022\u0026ndash;2023 food crisis, which found global market prices to increase by 60\u0026ndash;100%\u003csup\u003e20\u003c/sup\u003e and up to 65%\u003csup\u003e17\u003c/sup\u003e. The FAO Global Cereals Price Index was around 55% higher in 2022 compared to the long-term average suggesting our model results are of a similar order to observed price shocks. Note that on shorter time spans of days or weeks global price fluctuations may be considerably higher due to aspects of grains markets that are not modelled here, such as speculation\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e, derivatives trading, and cross-sectoral interactions with other markets.\u003c/p\u003e \u003cp\u003eThe consumer prices modelled here are assumed to be landed costs, without any marketing mark-ups, and without governments interventions to respond to rising food prices. Governments in 2022 responded to rising food prices through a variety of interventions including food and fertilizer subsidies, tariff adjustments and price freezes\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. These interventions determine the actual pass-through of shocks to local market prices, as well as acting counter-cyclically to stabilise demand during price spikes. At the price of increased complexity our modelling framework could be extended to explore these interventions and their first and second-order effects,\u003c/p\u003e \u003cp\u003eThe relatively minor impacts of the supply shock of the Ukraine war on global prices and consumption arise because countries are able to source maize and wheat from alternative trading partners, either those that they already traded with before the conflict or by establishing new trading relationships (at a cost). While in line with anecdotal evidence of trade shifts that did happen over the last two years, there is an ongoing debate about the short-run stickiness of trade flows after a shock\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Empirical studies of changing trade patterns during shocks, and what factors prevent adjustment, will be important to better calibrate our and similar shock-response trade models.\u003c/p\u003e \u003cp\u003eWhilst our modelling illustrates the potential for trade adjustments to buffer shocks, our results highlight that trade cannot be relied upon to mitigate the most severe and pervasive compound shocks, which can cause systemic risks to the global food supply system. Our results show that trade can allow multiple shocks to combine and propagate to affect consumers throughout the world, especially for the worst, low probability scenarios. The worst-case shock under the baseline scenario (which just includes weather-driven yield variability) can cause a total negative consumer surplus of USD260\u0026nbsp;million which increases to USD606\u0026nbsp;million in the \u003cem\u003eAll\u003c/em\u003e shock scenario, with virtually all countries to some degree negatively affected. While having a high import dependency can help buffer against year-to-year weather variability, it is less able to buffer tail-risk events where domestic production capacity is important.\u003c/p\u003e \u003cp\u003eOur results show the importance of considering the effects of non-climate shocks against the background of a realistic range of above or below-average yields determined by global weather patterns. Stress-testing the global food system and assessing strategies to build resilience requires a risk layering\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e approach to deal with the spectrum of shocks. Our results and further modelling may help inform policies around global risk funds, cash transfer programs, agricultural versus other sector investments, and trade. A risk layering approach to mitigating shocks will require a balanced set of agricultural system interventions, fiscal policies, and trade policies.\u003c/p\u003e \u003cp\u003eThe world requires a globalised food system so that food surplus countries can provide food for countries with a limited agricultural capacity to feed their population. Global trade in food is essential to ensure global food security, and the food system needs to be stress-tested to ensure it is resilient to plausible worse-case shocks. The consequence of failure \u0026ndash; the inability to feed a large nation for example \u0026ndash; could be catastrophic. The relative resilience shown by the global food system to recent shocks including the pandemic and Russia\u0026rsquo;s invasion of Ukraine are grounds for optimism, but not complacency. We believe that the modelling approach we have developed here, which can be extended to include further aspects of food system dynamics or different shocks (such as pest outbreaks, shipping disruption, or other conflicts), can be an useful tool to locate food system\u0026rsquo;s strengths and vulnerabilities and identify strategies to increase resilience.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research has been conducted as part of the Oxford Martin School Programme on Systemic Resilience, for which JV, JWH and MO have received funding, and the FACT Alliance Jameel Index for Food and Trade Vulnerability, for which AMu and JWH have received funding.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJV conceptualised the research, performed the analysis and led the writing of the manuscript. AMu. processed the yield anomaly data and contributed to the writing of the manuscript. YV, HCJG and AMo provided input to the analysis and contributed to the writing of the manuscript. MO and JWH conceptualised the research, provided input and contributed to the writing of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAuthors declare that they have no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data needed to reproduce the analysis is deposited in a Zenodo repository (upon acceptance).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe code needed to analyse the data and reproduce the figures it provided in Zenodo repository (upon acceptance).\u0026nbsp;\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eOverview\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe provided a brief overview of the methodology, split into (i) the derivation of the 54 years of plausible yield variability, (ii) the spatial price equilibrium model, and (iii) the implementation of the various shocks into the model.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCountry yield variability\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor each country, we derive a set of representative yield anomalies, which are driven by large-scale climate variability (ignoring other factors affecting yield year-to-year). These yield anomalies should be interpreted as plausible deviations from the modern-day yield baseline, and not historical yields.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA dataset of global modelled rainfed and irrigated crop yields\u003csup\u003e25\u003c/sup\u003e is used to calculate country yield anomalies for the four study crops (rice, wheat, maize, sorghum). This dataset was derived from a fused gridded crop model, which takes historical climate\u003csup\u003e39\u003c/sup\u003e and soil data, gridded maps of crop harvested area\u003csup\u003e40\u003c/sup\u003e and a daily process-based crop water model\u003csup\u003e41\u003c/sup\u003e to estimate 10x10km global pixel level crop yields for individual crop growing seasons\u003csup\u003e42\u003c/sup\u003e from 1961 to 2014. The model uses empirical yield-evapotranspiration relationships to estimate changes to crop yields (relative to the year 2010) under 54-years of historical climate conditions. A full description of the model and dataset is provided by ref\u003csup\u003e25\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePixel level crop yields from the fused gridded crop model are aggregated to country level and the annual timeseries for the period 1961-2014 and detrended using linear regression. Country level crop yield anomalies for each year in the 54-year period are calculated by dividing the detrended yield by the 54-year country level mean detrended yield.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eGlobal spatial price equilibrium model\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe model producer and consumer prices, supply, demand and trade flows using a newly developed global spatial price equilibrium model (SPEM). A SPEM is a multi-regional partial equilibrium model that links producers and consumers across regions\u003csup\u003e43\u003c/sup\u003e. Producers and consumers are linked together via domestic or international trade, for which a certain trade cost has to be paid. This includes all costs after leaving the farm, including storage, hinterland transportation, border and custom compliance, maritime transport, intermodal transfers, port fees, and imports tariffs. These have all been separately derived in previous work\u003csup\u003e28\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCompared to other global food systems models, the benefits of a SPEM is that it explicitly captures bilateral trade flows, which is not standard in many global food systems models. Second, it allows for trade diversion and the establishment of new trading partners. Third, it captures directional trade flows, meaning that countries can both import and export the same product. Fourth, a SPEM allows for embedding different types of shocks, including price shocks, supply shocks, and shocks to bilateral trade costs (e.g., trade bans). Although SPEM are usually set up for longer-term (partial) equilibrium simulations\u003csup\u003e44,45\u003c/sup\u003e, for instance given decreasing trade tariffs or cost, we adjust the standard SPEM formulation to make it suitable for shock simulations in the short-term (e.g., one-off shock). Moreover, we introduce stocks in the modelling framework, which are essential to understand supply shortages in the short-term but are usually omitted for future-orientated model applications.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe SPEM model requires data on trade, transport and trade costs, prices, supply, demand, and information on the shape of the demand and supply curves (e.g., the demand and supply elasticities). At the baseline, the SPEM model assumes that the decision to supply from certain regions is purely based on cost differentials of the total landed cost of goods (that is the cost to produce crops and ship to the consumer). However, there are non-cost elements which determine where countries source from, and hence, the model need to be calibrated on existing trade data to capture cost and non-cost related factors that determine the supply network of specific countries\u003csup\u003e44,46\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn our SPEM model, we consider 177 countries for which we could collect all data required. An overview of the data sources is included in Supplementary Table 1. These countries have interconnected competitive markets that trade a homogenous crop, with trade flows modelled on a directional basis. When referring to trade flows here, we refer to both international trade flows and domestic supply. Producers in each country have a certain amount of supply to provide to the market (either domestic or foreign), which they sell at the highest possible price (following their supply curve). Consumers have a certain amount of demand for the good and buy goods at the lowest possible price (following their demand curve). Each country can trade with any other country, with a corresponding trade cost to source from domestic or foreign markets. In equilibrium, we can find the trade flows between countries that determine the producer and consumer prices. For each country, the total production and imports must match the total consumption and export in equilibrium.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the Supplementary Methods, we describe the trade cost formulation used, the calibration process, and the model set-up for incorporating different types of shocks. The reference period for the model is the 2017-2021 average trade network, to smooth out intra-year trade fluctuations. A separate SPEM is set-up for each crop considered, without considering any cross-grain substitution effects.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eShock implementation\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe implement different types of shocks into the model, including production/yield variability, the Ukraine war, price shock, and trade bans. More details are provided in the Supplementary Methods. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBase: For each country, we have 54 representative years of yield variability, resulting in years of higher or lower than average supply. We implement the yield variability in the model by changing the initial condition of the total supply and shift the supply curve. A positive supply shock movies the curve to the right hand side and a negative supply shock moves it to the left hand side.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUkraine war: For this scenario we make three adjustments in the model. First, we lower the supply for Ukraine to 60% of its baseline supply, in line with observed supply reduction in 2022/2023\u003csup\u003e47\u003c/sup\u003e. Second, we increase the trade costs to Russia (to cover a surge in insurance costs to trade with Russia). Third, we implement a version of the blockage of the Black Sea ports by increasing the trade costs to and from any country that is not part of the European Union and the United Kingdom, to capture the difficulty of sourcing Ukraine exports via sea.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePrice shock: A price shock is introduced to capture the increase in fertilizer, pesticide and diesel costs due to supply issues as well as the energy crisis over the last few years. We follow a similar methodology as in Verschuur et al\u003csup\u003e28\u003c/sup\u003e and estimate for every country the share of the fertilizer, pesticide and diesel costs to the total crop production cost. We then impose a price shock to these three input, increasing fertilizer and pesticide costs by 200% and diesel costs by 100% (see ref\u003csup\u003e28\u003c/sup\u003e for details). This yields a production price increment, which we assume is being passed through to the consumer\u003c/p\u003e\n\u003cp\u003eTrade bans.We utilize a global database (https://www.globaltradealert.org/) on trade-related interventions taken by countries from 2022 onwards. This database covers which countries impose trade restrictions, which countries are affected by them, and for which commodities these restrictions apply. We extract all import and export trade bans implemented (hereafter trade bans) and encoded this in the model by imposing higher trade costs between these countries.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCompound shock.In the compound shock, or polycrisis, scenario, we include all previously mentioned shocks at the same time to evaluate their compound impacts.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eNelson, G. C. \u003cem\u003eet al.\u003c/em\u003e Climate change effects on agriculture: Economic responses to biophysical shocks. \u003cem\u003eProc. Natl. Acad. Sci. U. S. A.\u003c/em\u003e \u003cstrong\u003e111\u003c/strong\u003e, 3274\u0026ndash;3279 (2014).\u003c/li\u003e\n \u003cli\u003eBrown, M. 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(Accessed: 8th December 2023)\u003cstrong\u003e\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-3969801/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3969801/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRecent climatic events and conflict have heightened concern about the vulnerability of the global food system to systemic shocks. Yet it remains unclear what shocks are most pressing for a country’s food supply, and whether trade can mediate or amplify negative impacts. Here, using a newly developed global bilateral trade model for 177 countries and four major staple crops (maize, wheat, rice, soybean), we simulate the demand, price and trade impacts of the (i) Ukraine war, (ii) an energy price shock, (iii) imposed trade bans, and (iv) a compound (polycrisis) shock, on top of 54 years of crop production variability. The compound shock results in a 23 – 52% increase in consumer prices and, consequently, 7.3 – 16.5% loss to consumers. While the energy price shock is found to be the most important driver of the compound food shock across most regions and crops, the Ukraine war dominates impacts in Eastern Europe and Central Asia. Trade bans can affect certain regions disproportionately, particularly for Sub-Saharan Africa (rice) and Central Asia (rice, wheat). We find that, in many instances, trade adjustments can help cope with both supply and price shocks, although limits to the reliance on trade are found for tail risk events. In the compound shock event, the total negative consumer losses can be over USD 600 million for a single year, affecting virtually all countries simultaneously. Managing the risks of such shocks requires a reformed and better coordinated mix of national agricultural and fiscal policies as well as international trade regulations.\u003c/p\u003e","manuscriptTitle":"The impacts of polycrises on global grain availability and prices","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-08 08:06:20","doi":"10.21203/rs.3.rs-3969801/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"55305737-5960-43a9-b07f-b26eb5f59927","owner":[],"postedDate":"March 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":29183348,"name":"Earth and environmental sciences/Environmental sciences/Environmental impact"},{"id":29183349,"name":"Scientific community and society/Agriculture"},{"id":29183350,"name":"Scientific community and society/Geography"}],"tags":[],"updatedAt":"2024-04-24T17:15:50+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-08 08:06:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3969801","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3969801","identity":"rs-3969801","version":["v1"]},"buildId":"-HB7Z8yhvgn0wM9Nzuekk","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}