Assessment of G × E interaction and heritability for simplification of selection in spring wheat genotypes
Bibliographic record
Abstract
While evaluating genotypes for yield in multi-environment tests, the variation can only be observed in the relative yield performance of genotypes across environments. Eighteen (18) wheat genotypes, along with two standard farmer check varieties, were tested under normal and late sowing conditions for yield comparison, heritability, and selection response to understand the causes of G × E interaction and identification of specific desirable traits and genotypes. Analysis of variance showed highly significant differences (P < 0.01) for spikes m −2 , seed yield, and harvest index, while significant differences (P < 0.05) were observed for spikelets spike −1 and grains spike −1 . The environmental component revealed highly significant differences (P < 0.01) for all traits except for grain weight spike −1 , which exhibited significant differences (P < 0.05). However, the G × E interaction showed highly significant differences (P < 0.01) only for harvest index. The better accessions may further be tested for performance under late sowing conditions. The accessions also have potential for utilization in breeding programs for accumulating the genes of interest in genotypes which otherwise failed to perform better in late sowing environments. The selected accessions can be extremely useful for breeding cultivars to fill the gap between cultivars under conditions of very early or very late sowing.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".