The Largest Crop Production Shocks: Magnitude, Causes and Frequency (Supplemental Information)
Bibliographic record
Abstract
This pdf contains the supplemental information for "The Largest Crop Production Shocks: Magnitude, Causes and Frequency" Abstract of the main document: Food is the foundation of our society. We often take it for granted, but stocks are rarely available for longer than a year, and food production can be disrupted by catastrophic events, both locally and globally. To highlight such major risks to the food system, we analyzed FAO crop production data from 1961 to 2023 to find the largest crop production shock for every country and identify its causes. We show that large crop production shocks regularly happen in all countries. This is most often driven by climate (especially droughts), but disruptions by other causes like economic disruptions, environmental hazards (especially storms) and conflict also occur regularly. The global mean of largest country-level shocks averaged -29%, with African countries experiencing the most extreme collapses (-80% in Botswana), while Asian and Central European nations faced more moderate largest shocks (-5 to -15%). While global shocks above 5% are rare (occurring once in 63 years), continent-level shocks of this magnitude happen every 1.8 years on average. These results show that large disruptions to our food system frequently happen on a local to regional scale and can plausibly happen on a global scale as well. We therefore argue that more preparation and planning are needed to avoid such global disruptions to food production.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.329 | 0.056 |
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".