Heritability Studies of Yield and Yield Associated Traits in Wheat Genotypes
Bibliographic record
Abstract
This study was conducted to evaluate the performance of twenty three promising wheat genotypes and one indigenous cultivar over two years for yield and quality characteristics in order to assess the presence of variability for desired traits and a significant amount of variation for different parameters. Genetic parameters, correlations, and partial regressions were estimated for all the traits. Analysis of variance revealed significant differences among the genotypes for all the characters. The estimates of genotypic coefficient of variation (GCV) and phenotypic coefficient of variation (PCV) were high for grain yield per plant, plant height, biological yield and kernel weight and number of kernel per main spike. Broad sense heritability (H 2) estimates for various traits ranged from 50-100%. Grain yield per plant showed highly significant positive genetic and phenotypic correlation with kernel weight, number of kernels per main spike and number of spikelet’s per main spike. The total variability calculated through multiple correlation in the population for yield improvement accounted by fertile tiller number and kernel weight of main spike was 78.6% compared to 82.4% accounted by the all characters. It is concluded that more fertile tiller number and kernel weight of main spike are major yield contributing factors in selecting high yielding wheat 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 | 0.001 | 0.001 |
| 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".