The potential consequences of grain-trade disruption on food security in the Middle East and North Africa region
Bibliographic record
Abstract
The Middle East and North Africa (MENA) region has seen remarkable population growth over the last century, outpacing other global regions and resulting in an over-reliance on food imports. In consequence, it has become heavily dependent on grain imports, making it vulnerable to trade disruptions (e.g., due to the Russia-Ukraine War). Here, we quantify the importance of imported grains for dietary protein and energy, and determine the level of import reductions at which countries are threatened with severe hunger. Utilizing statistics provided by the Food and Agriculture Organization (FAO), we employed a stepwise calculation process to quantify the allocation of both locally produced and imported grains between the food and feed sectors. These calculations also enabled us to establish a connection between feed demand and production levels. Our analysis reveals that, across the MENA region, 40% of total dietary energy (1,261 kcal/capita/day) and 63% of protein (55 g/capita/day) is derived from imported grains, and could thus be jeopardized by trade disruptions. This includes 164 kcal/capita/day of energy and 11 g/capita/day of protein imported from Russia and Ukraine. If imports from these countries ceased completely, the region would thus face a severe challenge to adequately feed its population. This study emphasizes the need for proactive measures to mitigate risks and ensure a stable food and feed supply in the MENA region.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".