Strategies to Improve Wheat's Drought and Heat Resistance
Bibliographic record
Abstract
Wheat ( Triticum aestivum L.) is a globally important staple crop whose productivity is increasingly threatened by climate stressors, particularly drought and heat. This study comprehensively reviews the physiological and molecular responses of wheat to drought and high temperature conditions, elaborates on the effects on plant growth and yield components, explores genetic strategies aimed at enhancing wheat stress resistance, including conventional breeding, molecular marker-assisted selection and gene editing technology, evaluates the role of agronomic and management measures such as optimized irrigation, nutrient management and crop adjustment in alleviating the effects of stress, and also focuses on the application of biotechnology and omics approaches (including transcriptomics, proteomics and microbiome engineering) in improving wheat adaptability. The effectiveness of the integrated strategy is evaluated through case studies in the Indo-Gangetic Plain, the Australian Wheat Belt and the Mediterranean region. This study highlights the importance of integrating multidisciplinary innovations for developing climate-resilient wheat systems, points out current knowledge gaps, and proposes directions for future research and development.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".