Genetic Variation, Heritability, Phenotypic and Genotypic Correlation Studies for Yield and Yield Components in Promising Barley Genotypes
Bibliographic record
Abstract
Eighty six promising new barley genotypes and three checks including one indigenous cultivar (Hordeum vulgare L. var Rum) were grown in two successive seasons of 2005 and 2006 to assess the presence of variability for desired traits and 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 entries 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, biological yield and number of kernels per main spike. Broad sense heritability estimates for various traits ranged from 68 to 99.7%. Grain yield per plant showed high significant positive genetic and phenotypic correlation with only number of kernels per main spike. Multiple correlations of characters (0.36), via. fertile tiller number and number of kernels per main spike which were significant with grain yield were far from the multiple correlation of all characters (0.96). The total variability calculated through multiple correlation in the population for yield improvement accounted by fertile tiller number and number of kernels per main spike was 36 % compared to 96 % accounted by all other characters. It was concluded that more fertile tiller number and number of kernels per main spike are major yield contributing factors in selecting high yielding barley 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 | 0.001 | 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) | 0.001 | 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".