Genetic Variability and Correlation Studies of Grain Yield and Related Agronomic Traits in Maize
Bibliographic record
Abstract
A total of fourty two maize hybrids produced through a full diallel mating design and seven parental inbred lines were evaluated in three sites located at different agro-ecological zones in Kenya to determine the genetic parameters governing the inheritance of grain yield and related agronomical traits. The genetic parameters studied among the traits included the mean performance, genotypic variances, phenotypic variances, genotype by environment variances, broad sense heritability and phenotypic and genotypic correlation coefficients. Significant differences were recorded for all traits studied thereby revealing the diversity of the maize genotypes. The grain yield and days to maturity which showed high heritability had low genotypic variances suggesting the involvement of non-additive gene action which could be utilized through heterosis breeding. Ear height and plant height showed the highest heritability and high genotypic variances suggesting the preponderance of additive gene action. Grain yield was positively and strongly correlated with ear height and plant height. The tall plants with high ear placement gave better yields and this could be attributed to the high dry matter accumulation function carried out by the high number of leaves possessed. The positive relationships observed in this study imply that the desirable traits in these hybrids could be exploited in further breeding activities for the development of composites and synthetics for the resource constrained maize farmers who cannot access hybrid seeds every year.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".