Diallel Analysis for Area, Chlorophyll and Temperature of Flag Leaf in Bread Wheat under Well Watered and Drought Stress Conditions
Bibliographic record
Abstract
To study genetic analysis of some physiological traits of drought stress in wheat using diallel techniques, an experiment was performed on ten bread wheat genotypes as parents and their 45 F1 hybrids in a randomized complete block design with three replicates under well-watered and drought stress conditions at the Research Farm of Faculty of Agriculture, Sohag University, Egypt during season of 2018/19. The results showed significant differences between the genotypes (G), Parents (P), F1 crosses, P vs. F1, GCA and SCA under well-watered and drought stress in the flag leaf area (FLA), flag leaf chlorophyll content (FLCC) and flag leaf temperature (FLT), except FLCC for F1 crosses exhibited insignificant differences. The significant differences were found in the interaction of SCA × Env., in all studied traits and GCA × Env., for FLT., indicating the involvement of both additive and dominance gene action in their inheritance. The most desirable heterotic effects were considered as the largest positive heterosis estimates for FLA and FLCC, and the lowest negative for FLT. The parent numbers P9, P8 and P3 were the best general combiner for FLA under normal irrigation and drought stress. While the parents P2, P7 and P9 were the best general combiner for FLCC, under normal irrigation and (P1, P2 and P3) under drought stress. Therefor the parents P1, P4 and P5 were the best general combiner for FLT under normal irrigation, also the P3, P4 and P5 were the best general combiner for FLT under drought stress conditions. Under normal irrigation and drought stress conditions as well as the combined data, the additive genetic components of variation (VA) in F1 ’s was much greater than dominance component (VD), as expressed by the (VA/VD) ratio which was more than unity for the FLA, and FLT under normal irrigation and their combined. This indicates that the additive gene effects in F1 crosses are more important than dominance and plays the major role in the inheritance of these studied traits.
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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 | 0.001 |
| 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 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".