Joint effects of temperature and humidity on cotton yield vulnerability to verticillium wilt disease under climate change
Bibliographic record
Abstract
BACKGROUND: Verticillium dahliae (Kleb.), a soil-borne fungus that can persist in soil for more than a decade, poses a persistent threat to global cotton production. However, its potential impact on cotton yield under changing climatic conditions remains poorly understood. This study aimed to quantify the influence of climate on cotton vulnerability to verticillium wilt disease in Xinjiang, China, and to project future risks under different climate scenarios. RESULTS: Data from 2011-2021 revealed an additional 8.7% cotton yield loss per decade due to verticillium wilt in the absence of control measures. A climate-driven model identified the optimum temperature range for maximum disease severity as 25-28.5 °C, while relative humidity exceeding 82% markedly increased vulnerability. The model further indicated that cotton yield declines by 18.8% for every 0.1 increase in the disease vulnerability index. Projections show that climate change will amplify disease risk, leading to additional yield losses of up to 8.5% under the SSP1-2.6 scenario and 18.4% under the SSP5-8.5 scenario by the late 21st century. CONCLUSION: Climate change is expected to intensify verticillium wilt-induced yield losses in major cotton-growing regions. These results underscore the urgency of integrating disease-control strategies with climate adaptation planning. By coupling field-based disease management with climate projections, this study advances understanding of crop-pathogen-climate interactions and provides a framework for developing resilient cotton production systems under future climate conditions. © 2025 Society of Chemical Industry.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".