Canopy Temperature and Chlorophyll Content are Effective Measures of Drought Stress Tolerance in Durum Wheat
Bibliographic record
Abstract
Durum wheat (Triticum durum L.) is used for the preparation of multiple food products, including pasta and bread. Its production is restricted due to diverse environmental stresses i.e. drought and heat stress. Here, comparative analysis of durum wheat varieties was done by studying canopy temperature depression (CTD) and chlorophyll content (CHL), yield and yield contributing traits to evaluate their performance under stress and low stress conditions. Twelve durum wheat genotypes were studied under stressful and low-stress conditions in Gachsaran region of Iran. CTD and CHL were measured at two stages, from the emergence of fifty percent of inflorescence (ZGS 54) to watery ripe stage (ZGS 71). According to stress tolerance index (STI), mean productivity (MP) and geometric mean productivity (GMP) indices, genotype G10 exhibited the most, while genotype G6, the least relative tolerance, respectively. Based on MP and GMP, genotype G10 was found to be drought tolerant, while genotype G2 displayed the lowest amount of MP and GMP. Therefore these genotypes are recommended to be used as genitors in artificial hybridization for improvement of drought tolerance in other cultivars. All indices had high correlation with grain yield under stress and non-stress condition, indicating more suitability of these indices for selection of resistant genotype. Results of the present study showed that among drought tolerance indices, harmonic mean (HM), GMP, CTD and modified STI index (K2STI) can be used as the most suitable indicators for screening drought tolerant cultivars.
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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 | 0.000 | 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".