Wheat crop models underestimate drought stress in semi-arid and Mediterranean environments
Bibliographic record
Abstract
Under climate change and increasingly extreme weather, projections of water demand and drought stress from process-based crop models can inform risk management and adaptation strategies. Previous studies investigating maize crop models demonstrated considerable error in the simulation of water use, and no similar evaluation of wheat crop models exists. The aims of this study were to (1) evaluate wheat crop models’ performance in reproducing observed daily evapotranspiration (ET) for Mediterranean and semi-arid environments, and (2) identify factors and processes associated with model error and uncertainty. These were assessed with an ensemble of wheat crop models for two experiments, one conducted in Bushland, Texas, USA (three seasons, deficit and full irrigation) and another in Avignon, France (four rainfed seasons) with winter bread and durum wheat, respectively. Models were calibrated with all observed data for crop growth. The model ensemble median underestimated water use in all environments evaluated, suggesting a systematic bias. The relative error in underestimating daily ET was constant across levels of atmospheric evaporative demand; therefore, the absolute error was greater for days with larger evaporative demand. This implies errors in the soil water balance increase more rapidly under high evaporative demand conditions. Using a potential versus reference crop evapotranspiration approach did not explain relative model performance. However, the sensitivity analysis indicated that simulation of atmospheric evaporative demand terms explained much more uncertainty in seasonal water use than terms related to soil depth or root growth. Errors in simulated leaf area index were associated with errors in daily simulated ET, but the relationship varied with the growth stage. Collectively, the results suggest the need to improve simulation of atmospheric ET demand to avoid underestimating projected impacts of drought or required water resource availability for viable production systems.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".