Crop diversity buffers the impact of droughts and high temperatures on food production
Bibliographic record
Abstract
Abstract Weather extremes like droughts and heat waves are becoming increasingly frequent worldwide, with severe consequences for agricultural production and food security. Although the effects of such events on the production of major crops is well-documented, the response of a larger pool of crops is unknown and the potential of crop diversity to buffer agricultural outputs against weather extremes remains untested. Here, we evaluate whether increasing the diversity of crop portfolios at the country level confers greater resistance to a country’s overall yield and revenues against losses to droughts and high temperatures. To do this, we use 58 years of annual data on weather, crop yields and agricultural revenues for 109 crops in 127 countries. We use the spatial distribution of each crop and their cropping cycle to determine their exposure to weather events. We find that growing greater crop diversity within countries reduces the negative impacts of droughts and high temperatures on agricultural outputs. For drought, our results suggest that the effect is explained not only by crop diversity itself, but also by the sensitivity of the most abundant crops (in terms of harvested areas) to this extreme. Countries dedicating more land to minor, drought-tolerant crops reduce the average sensitivity of country-scale crop portfolios and show greater resistance of yield and revenues to drought. Our study highlights the unexploited potential for putting crop biodiversity to work for greater resilience to weather, specifically in poorer developing countries that are likely to suffer disproportionately from climate change impacts.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".