Climate change increases the interannual variance of summer crop yields globally through changes in temperature and water supply
Bibliographic record
Abstract
Year-to-year variance of agricultural productivity is an important determinant of food security. Previous global analyses described increased yield volatility from warming, but it has become increasingly clear that changes in water availability are also a key determinant of yields. Here, we provide the first global quantification of climate change impacts on maize, soybean, and sorghum yield variance due to changes in temperature and soil moisture. Pairing an empirical crop model with CMIP6 simulations indicates that changes in temperature and soil moisture increase interannual weather-induced yield variance by [Formula: see text] across crops. This increase is driven roughly equally by previously quantified increased temperature stress variance, as well as by increased covariance between temperature and soil moisture stresses. Results using a simple land surface model are consistent with those using CMIP6 simulations in indicating that this increased covariance is driven by warmer air more quickly drying soils and by dry soils promoting greater warming.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".