Evaluation of Drought Tolerance Ability in Wheat Genotypes Through Comprehensive Stress Indices
Bibliographic record
Abstract
The objective was to assess a range of stress indices to discern wheat genotypes resilient to drought stress, so forty-nine genotypes underwent scrutiny in both drought stress in rainfed conditions and non-stress settings (with supplementary irrigation), employing a 7 × 7 lattice layout with two replicates across years 2019 and 2020. The evaluation incorporated twenty stress indices anchored in yield under water stress (YS) and potential (YP) circumstances. Primary analysis indicated that eight indices (RDI, YSI, YI, K2STI, MRP, REI, RR and SSPI) did not give any new information, so they were eliminated in further analysis. Genotypes G33 (4234 kg ha-1) and G9 (2227 kg ha-1) were the best genotypes based on YP in 2019 and 2020, respectively. A positive association was observed between ATI and YP and between YS with DI and K1STI in the year 2019, while in the second year, such positive associations were not seen. We found some wheat genotypes G6, G9, G10 and G11 demonstrated high performance in both potential and rainfed conditions across two years, showing yield higher than 1,800 and 2,700 kg ha-1 for YS and YP, respectively, across both years. These genotypes were detected as the most tolerant genotypes by mean-based indices (TOL, HM, GMP, and MP) as well as SSI and ATI indices, so it can be concluded that these indices are more useful than other indices for identifying the most tolerant as well as the high yielding genotypes.
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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) | 0.000 | 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".