Identification of Drought-Responsive QTLs in Triticeae under Field Conditions
Bibliographic record
Abstract
Drought stress represents a significant constraint on Triticeae crop productivity, particularly affecting wheat, barley, and rye in semi-arid regions. In this review, we systematically examine field-validated quantitative trait loci (QTLs) that are associated with drought-responsive traits in Triticeae, emphasizing their relevance to breeding programs. We begin by addressing the agricultural impact of drought stress and the limitations of controlled-environment studies compared to field-based evaluations. We then explore the methodologies used for QTL mapping under field conditions, including phenotyping strategies, statistical models, and the challenges posed by environmental heterogeneity. The review identifies key drought-responsive QTLs linked to traits such as root architecture, water-use efficiency, stay-green, canopy temperature, and grain yield components. A detailed case study on wheat highlights successful QTL discovery, validation across genetic backgrounds, and integration into elite lines via marker-assisted selection. Furthermore, we discuss how genomic resources such as high-density SNP arrays, GWAS, and transcriptomic tools are enhancing the precision of QTL identification. Looking ahead, we outline the promise of genomic selection, gene editing, and participatory breeding in accelerating the development of drought-resilient culTriticeae tivars. This study underscores the importance of multidisciplinary approaches and real-world validation in translating QTL research into sustainable agricultural outcomes under climate variability.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".