The Russia–Ukraine Conflict: A Global Impact Assessment in the Corn and Wheat Sectors
Bibliographic record
Abstract
According to data from the Food and Agricultural Organization of the United Nations (FAO), Ukraine was the fifth and ninth global producer and exporter of corn and wheat, respectively, before Russia’s invasion. The disruption of the supply chain in Ukraine in these food sectors due to the Russian–Ukrainian conflict can hamper global food security. Very little is known about how the war has impacted these important food sectors globally. This paper examines the global impact of the war on the corn and wheat sectors in the first year of the war and investigates the relationship between market vulnerability and trade ties with the conflicting regions. Analysis of FAO data shows that Ukraine suffered a 12.87% and 17.45% production decline in the corn and wheat sectors, respectively, compared to the 2012–2021 base years. Using the Acclimate economic network model, these shocks are applied to Ukraine’s corn and wheat network nodes to analyze their global impact. The production value and total losses are calculated and compared to the base year, both regionally and in some major global economic blocs. The results show that the corn sectors in Germany, Ukraine, Poland, and Belgium suffered the largest production value losses, while Ukraine, China, Venezuela, and Korea suffered the largest overall losses. In the wheat sector, Russia, Germany, Ukraine, and Canada suffered the largest production value losses, while Ukraine, Kazakhstan, Uzbekistan, and China suffered the largest overall losses. Overall, the corn sector was the hardest hit globally compared to the wheat sector, with the EU, the US, China, South America, and Africa being the hardest hit in the corn sector, while BRICS and Oceania were the hardest hit in the wheat sector. The study equally finds a strong correlation between increased regional food market vulnerability and Ukraine’s trade relations. These findings can contribute to better investment decisions, regional and sectoral emergency management planning, and the development of regulatory frameworks.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".