Matches and mismatches between the global distribution of major food crops and climate suitability
Bibliographic record
Abstract
Over the course of history, humans have moved crops from their regions of origin to new locations across the world. The social, cultural and economic drivers of these movements have generated differences not only between current distributions of crops and their climatic origins, but also between crop distributions and climate suitability for their production. Although these mismatches are particularly important to inform agricultural strategies on climate change adaptation, they have, to date, not been quantified consistently at the global level. Here, we show that the relationships between the distributions of 12 major food crops and climate suitability for their yields display strong variation globally. After investigating the role of biophysical, socio-economic and historical factors, we report that high-income world regions display a better match between crop distribution and climate suitability. In addition, although crops are farmed predominantly in the same climatic range as their wild progenitors, climate suitability is not necessarily higher there, a pattern that reflects the legacy of domestication history on current crop distribution. Our results reveal how far the global distribution of major crops diverges from their climatic optima and call for greater consideration of the multiple dimensions of the crop socio-ecological niche in climate change adaptive strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".