Is Water Stress the Root Cause of the Observed Nonlinear Relationship Between Yield Losses and Temperature?
Bibliographic record
Abstract
Abstract Observational analyses consistently find that yields of major rainfed crops increase with temperature up to a threshold of approximately 32C, above which they reduce sharply. Two damage pathways have been suggested to explain this relationship: that high temperatures directly stress crops and drive yield loss, or that high temperatures induce water stress in crops, which in turn drives yield loss. Here we explore a third pathway: that soil water stress limits both agricultural productivity and evaporative cooling, giving rise to the nonlinear relationship between temperature and yield. Determining which of these pathways underpins the yield‐temperature relationship is important for predicting future crop productivity because climate change is expected to alter the co‐variability between temperature and water availability. To examine this third pathway, we use cumulative growing‐season transpiration from an idealized land surface model as a proxy for yield. This approach reproduces the observed yield‐temperature relationship, even though the model includes no mechanisms that limit productivity at high temperatures. In experiments where the influence of temperature on soil moisture is suppressed, yields still decline during hot, dry periods in a manner consistent with the observations. We conclude that water stress, and its influence on evaporative cooling, temperature, and agricultural productivity, drives the yield‐temperature relationship found in crops that experience episodic water stress. This framework explains the muted sensitivity of irrigated yields to high atmospheric temperatures, and suggests that future yield outcomes depend more critically on changes in rainfall than suggested by estimates that attribute yield losses primarily to temperature variations.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".