Distinct contributions of drought avoidance and drought tolerance to yield improvement in dryland wheat cropping
Bibliographic record
Abstract
Abstract Crop avoidance and tolerance strategies are critical adaptive mechanisms of drought stress and play different roles in grain yield. However, little is known about the contribution of these two mechanisms to grain yield in old and modern wheat genotypes. Here, pot and field experiments were carried out to characterize and compare the mechanisms of drought avoidance and drought tolerance, and determine their differential contributions to the yield in six wheat genotypes. The pot experiment results demonstrated that the old genotypes acquired a better avoidance ability to adapt to drought stress. These avoidance abilities include larger root systems, lower leaf areas, low stomatal conductance, pale green leaf colour, higher degrees of leaf rolling and leaf waxiness. The modern genotypes displayed stronger drought tolerance advantages, such as high osmotic adjustment and antioxidant enzyme activity, and a smaller root system. Our field experiment further showed that under severe water‐deficit conditions, the old genotypes with stronger drought avoidance traits had higher yields and water use efficiency (WUEg), whereas the modern genotypes with strong drought tolerance characteristics produced higher yields and had higher WUEg under mild and intermediate water deficits. The results indicate that the relative contribution of drought tolerance and drought avoidance to grain yield depends to a large extent on the degree of drought stress and genotypes. Understanding the differential plant response depending on genotype and drought stress may help plant breeders develop drought‐resistant varieties suitable for drought‐prone environments under anticipated climate change scenarios.
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 | 0.000 |
| 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 | 0.000 | 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 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".