The cascade influence of grain trade shocks on countries in the context of the Russia-Ukraine conflict

The study examines how the Russia-Ukraine conflict disrupts global grain trade and propagates supply shocks through international networks. Focusing on wheat, barley and maize, which together provide a large portion of human calories and protein, the paper highlights the central roles of Russia and Ukraine in global exports, especially to lower-middle-income countries in West Asia, Southeast Asia and North Africa. The research questions are: (1) How many countries consider Russia and Ukraine major wheat import sources and to what extent does direct dependence arise? (2) How do cascade failures under shocks affect the dynamic evolution of trade network structure? (3) Which countries act as suppliers to fill gaps during propagation and what is the intensity of positive shocks? (4) Which countries face demand imbalances during cascade failure and how severe are the supply reductions? (5) To what extent do network characteristics predict exposure to supply shocks? The paper also accounts for COVID-19-era dynamics, price pressures, and heterogeneity by countries’ economic development levels, aiming to inform policy for resilient food trade systems.

The paper situates itself in complex systems and network science literature on contagion and cascading failures, drawing from random graph theory and studies of financial and input-output networks. Prior work shows that climate extremes, policy interventions (export restrictions), conflicts, and pandemics create remote coupling and systemic risk in food systems. Studies have quantified first-order shocks and risks (e.g., 2008–2011 food crisis, export bans, climate-driven yield variability) and assessed risk via trade network structures, but often: (i) analyze static snapshots or unweighted networks; (ii) focus on first-round impacts without modeling higher-order cascades; (iii) treat agents’ responses uniformly, overlooking dual roles (exporters vs. importers) and economic development differences; and (iv) use short time windows, missing longitudinal dynamics. This study addresses these gaps by modeling cascading failures in weighted, directed trade networks over 1995–2021, distinguishing positive and negative shocks, and incorporating GDP per capita into propagation to proxy price-related behavior and capacity to expand exports.

Data and network construction: The authors build annual weighted, directed global trade networks for wheat, barley, and maize using FAO trade matrices (1995–2021). Nodes are countries; edge weights are export quantities (metric tonnes). Production data come from FAOSTAT; country attributes (GDP, GDP per capita, poverty ratio, population) from the World Bank; trade agreements from WTO and Mario Larch’s RTA database. Coverage exceeds 90% of world GDP per capita and 85% of world trade volume. Shock propagation model: For each year, production is fixed and imports/exports can vary over discrete sub-annual time steps. Domestic supply IS = production + imports − exports. At t=1, an exogenous shock is applied: cessation or reduction of exports from Russia and Ukraine, creating import shortfalls for their direct partners. Affected importers reallocate unmet demand along existing trade links. The reallocated demand received by each alternative exporter is proportional to (a) the pre-shock bilateral trade share to the importer and (b) the exporter’s GDP per capita, reflecting higher willingness/capacity of richer exporters to expand exports under higher prices. Exporters’ domestic supply is reduced by the additional export load; if domestic supply space falls to zero, the exporter “fails,” removing it from the network and triggering further import shortfalls in its partners. Cascading continues until no further failures occur within the sub-annual time scale; results are reported at the annual level. National shock assessment: For each importer, negative shocks are measured as (i) total unmet imports (metric tonnes) due to partners’ failures and (ii) the ratio of unmet imports to domestic demand (domestic supply). The number of poor people affected is estimated as poverty ratio times population, scaled by the importer’s domestic shock ratio. Econometric analysis: Panel regressions relate countries’ exposure to shocks to network and country attributes. Dependent variables include inverse hyperbolic sine-transformed total shocks (metric tonnes) and shock share of domestic demand, plus a vulnerability ranking (log of rank). Key regressors: import diversification (In-degree to in-strength ratio; alternative closeness-strength ratios), export diversification (Out-ratio), betweenness, brokerage (structural holes proxy), PageRank, clustering, import concentration (C2 share of top two suppliers), RTAs count, GDP, population, and regional dummies. Estimation uses fixed effects and panel Tobit (to handle zeros), with robustness checks. Scenario analysis: Sensitivity tests vary Russia’s export reductions (−20%, −40%, −60%, −80%) while Ukraine’s exports are set to zero, comparing to full cessation by both. Cascades and exposures are recomputed across years to assess nonlinearities and timing when scenario differences diverge.

- Direct dependence and exposure: In 2021, Russia and Ukraine were the world’s first- and fifth-largest wheat exporters (13.70% and 9.75% shares, respectively). Several countries were extremely dependent on their exports: for wheat (e.g., Eritrea, Kazakhstan, Mongolia, Armenia >99% from RU/UA); for barley (e.g., Egypt, Kazakhstan, Tajikistan, Armenia >99%); for maize (e.g., Georgia, Mongolia, Armenia, Azerbaijan >95%). The initial shock causes more than 50% reductions in direct imports for 30 countries, including Eritrea, Seychelles, Kazakhstan, and Mongolia. - Network-level impacts: Cascades undermine connectivity and integrity of trade networks. Post-shock networks show fewer edges, lower global efficiency, increased average path length, and declines in clustering and the size of the giant component (sharpest declines after 2009). Due to COVID-19-era dynamics, barley and maize networks showed somewhat reduced connectivity declines, whereas the wheat network’s connectivity decline increased but with indications of greater resilience (slower propagation due to lower aggregation and longer paths). All three networks are scale-free and thus vulnerable to targeted disruptions of core exporters. - Positive shocks (export load): During cascades, major exporters bear increased import demand—consistently the United States, Canada, France, Argentina, and Brazil; Australia’s export burden rises markedly after COVID-19. For wheat, overall transferred demand rose 24.19% after COVID-19; Oceania’s rose 95.97%, while North America’s fell 19.66%. For barley, overall transferred demand decreased 7.46%; Western Europe and South America saw declines (−52.41% and −62.94%), while Southern Europe and Oceania saw sharp increases (+97.70% and +96.24%). For maize, overall transferred demand decreased 21.37%, but North America’s burden surged (+98.36%), increasing pressure on the United States. - Negative shocks (unmet imports): Wheat shortages concentrate in West Asia, North Africa, and Southeast/East Asia (e.g., Egypt, Turkey, Sudan, Lebanon, Indonesia). After COVID-19, shortages in East and Southeast Asia expanded by 86.30% and 79.80%. Africa and Asia bear 40.75% and 53.15% of wheat supply shocks; West Asia and North Africa have 43.56% and 33.10% of demand gaps uncompensated. Barley shortages concentrate in West Asia and North Africa (e.g., Saudi Arabia, Turkey, Libya, Tunisia, Israel); Central Europe also sees increases post-COVID. Africa and Asia bear 33.48% and 66.14% of barley shocks; West Asia, North Africa, and Southern Europe face 66.14%, 33.48%, and 13.82% uncompensated gaps, respectively. Maize shortages concentrate in Europe and East Asia (e.g., Italy, Netherlands, Lithuania, Denmark, China, Korea), with Europe bearing 93.79% of maize shocks; Southern and Eastern Europe have 46.08% and 25.18% uncompensated gaps. - Country-level impacts: In volume, Egypt, Turkey, Brazil, and Nigeria tend to bear the largest wheat import reductions; Lebanon, Libya, Israel, and Latvia experience particularly high shock shares relative to domestic demand (>45% for wheat). For barley, Saudi Arabia, Turkey, Libya, and Lebanon feature prominently (several >30% of domestic supply). For maize, top shocks cluster in European countries (e.g., Italy, Netherlands, Latvia), with smaller shares of domestic demand than for wheat/barley. Accounting for poverty burdens, populous LM countries (e.g., Indonesia, Egypt, Nigeria, Sudan, Lebanon) see the largest numbers of poor affected. - Intermediary roles and contagion: Eastern and Southern European exporters (e.g., Moldova, Romania, Bulgaria) often act as intermediaries that transmit shocks; their domestic supply is compressed by redirected demand, leading to their failure and further contagion. - Scenario analysis: As Russia’s export cuts deepen (−20% to −80%) with Ukraine at zero, average annual redistributed import demand increases from 24.89 to 41.58 million tonnes (wheat), 0.46 to 6.60 million (barley), and 0.54 to 1.26 million (maize). Average annual global supply shortfalls rise from 3.87 to 8.26 million tonnes (wheat), 0.54 to 1.26 million (barley), and 0.90 to 1.11 million (maize). Worst-case scenarios generate pronounced cascades after 2009 (wheat), 2002 (barley), and 2016 (maize). Regional patterns persist: Americas/Europe expand exports; Africa/Asia face shortages. - Determinants of vulnerability (regressions): Greater import diversification (higher In-ratio or closeness-strength variants) significantly reduces both shock volume and shock share (wheat, barley). Export diversification (Out-ratio) is not significant. Brokerage (structural holes) is positively associated with vulnerability; betweenness is generally insignificant. Higher PageRank (centrality) increases exposure. Higher import concentration (C2) is associated with lower shocks (selecting a few main suppliers plus buffers can reduce exposure). Population increases wheat shocks; GDP is generally insignificant. More RTAs increase maize shock exposure and, in ranking regressions, higher RTA counts correlate with higher vulnerability. Regional fixed effects: Asia and Africa raise vulnerability in wheat and barley; in maize, Europe is most exposed, followed by Africa.

The study answers its research questions by showing that (i) direct dependence on Russia/Ukraine is high for many LM countries, especially in West Asia, North Africa, and parts of Asia; (ii) trade shocks initiate cascading failures that degrade network connectivity and reconfigure flows; (iii) major exporters in the Americas, Europe, and Australia absorb most positive shocks, while intermediary Eastern/Southern European countries often fail and propagate shocks; (iv) shortages concentrate in LM regions with limited ability to exploit alternative channels, exacerbated during COVID-19; and (v) network position and country attributes significantly predict vulnerability—diversified import portfolios and lower brokerage reduce risk, whereas high centrality and sparse local structures elevate exposure. These insights highlight the systemic nature of food security risks, the importance of network resilience, and the role of trade structure and regional characteristics in buffering or amplifying shocks. The findings underscore policy levers—diversification, clustering, strategic reserves, and targeted aid—to mitigate cascading risks and protect vulnerable populations.

The paper provides the first longitudinal, network-based quantification of cascading failures in global grain trade due to the Russia-Ukraine conflict (1995–2021), distinguishing positive (export) and negative (import) shocks and incorporating GDP per capita to proxy price-related responses. It shows that wheat trade is most vulnerable yet displays resilience through reduced aggregation and longer paths; LM countries in Africa, West Asia, and Southeast Asia bear disproportionate shortages; and major exporters (US, Canada, France, Argentina, Brazil, and post-COVID Australia) shoulder increased export loads while intermediary European countries propagate shocks. Policy recommendations include: (1) strengthen regional trade agreements paired with emergency grain reserves; (2) build dense, closed-triad trade clusters to create multiple diffusion channels; (3) diversify import sources and reduce dependence on export hubs; (4) rationalize supplier portfolios—designate a few main suppliers with additional buffer partners; (5) expand domestic production capacity among high-centrality exporters; (6) invest in alternative crops, processing flexibility, and real-time monitoring of prices and risks. These measures can enhance resilience, reduce vulnerability, and stabilize global food security during prolonged geopolitical disruptions.

The modeling uses annual trade and production data with sub-annual cascade dynamics but excludes several real-world mechanisms: (i) price formation and endogenous supply/demand responses beyond a GDP-per-capita-weighted allocation; (ii) cross-commodity substitution (e.g., replacing one grain with another); (iii) more granular within-country factors and inventories; and (iv) forward-looking behavior or network-generating processes. Results reflect physical mass-balance flows on observed networks. Future work could couple the cascade model with economic equilibrium models (e.g., CGE), incorporate price dynamics and substitution elasticities, and explore endogenous network evolution under sustained shocks.