Evaluation of wheat genotypes for drought toleranceusing PEG-6000 at different phenological stages
Bibliographic record
Abstract
Drought stress significantly affects the growth and yield of wheat crop. However, tremendous variability exists amongst genotypes concerning their reaction to drought stress. Therefore, figuring out the effects of drought stress on yield and growth of crop plants might assist to select the superior genotypes. In the current study, forty wheat genotypes were assessed for drought tolerance using PEG-6000 at seedling, tillering, heading and maturity stage at PMAS-Arid Agriculture University, Rawalpindi, Punjab, Pakistan during 2022-23. ANOVA revealed significant variation (P≤0.05) among genotypes for all traits in all the growth stages in both conditions (drought and normal). Mean comparison analysis showed the genotypes LLR-25 (12.73g), Lasani-08 (12.69g) and WC-26 (12.57g) were high-yielding and also performed well in terms of root-shoot-related traits under drought condition. Correlation depicted the highly significant positive association of yield per plant with 1000 grain weight (0.85**) as well as positive association with traits i.e., root length at seedling (0.08), tillering (0.27) and heading (0.14) stage, no. of crown roots at tillering (0.13), no. of seminal roots at tillering (0.04) and heading (0.17) under drought condition. Moreover, spike length showed a positive highly significant association with root length (0.48**) at seedling and no. of seminal roots (0.4**) at heading stage under drought condition. PCA bi-plot analysis emphasized genotypes related to specific traits showing their importance under drought stress condition. Moreover, PCA bi-plot revealed the distinctness and strong association with key drought-resilient traits of genotype Chakwal-50. Hence, this study identified the genotypes viz. Chakwal-50, LLR-25, Lasani-08 and WC-26 have desirable root parameters as well as found best regarding yield and related traits under water deficit condition. The selected genotypes can be sown directly under water-deficit condition and may be further utilized for drought-tolerance breeding programs for varietal improvements in the current scenario of the changing climate.
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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 | 0.000 | 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) | 0.001 | 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".