Adaptation, Path Coefficient and Correlation Study of Yield and Associated Traits in Common Bean (Phaseolus vulgaris L.) Genotypes
Bibliographic record
Abstract
The study evaluated new biofortified, high-yielding and disease-resistant bean varieties for their adaptability in Zimbabwe. The secondary objective was to assess the association between yield and other traits that contribute to grain yield and identify traits that have direct and indirect effects on grain yield. The study was conducted between December 2016 and June 2017 using an augmented design with 24 varieties replicated three times at Chinhoyi University of Technology Research Farm. Data was analysed using analysis of variance in the statistical analysis system (SAS) software. DAB482, DAB494 and DAB539 were identified as high-yielding varieties with desired disease tolerance to common bean blight (CBB), common leaf rust (CLR) and angular leaf spot). Therefore, these improved common bean varieties recommended for further multilocation evaluation with the potential to be released as commercial varieties. Correlation coefficient values and path analysis values (direct and indirect) showed that the weight of 100 randomly selected seeds per variety and the percentage number of plants that germinated per plot could be used for indirect selection for grain yield (kg/ha). A negative correlation indicates an inverse relationship between traits. This study observed significant negative correlations for CBB and CLR relative to grain yield (kg/ha). This suggests that the grain yield (kg/ha) decreases with increased disease incidence and severity, highlighting the importance of disease tolerance in achieving high grain yield. Hence, there is a need for the breeding program to select and advance varieties that are tolerant to diseases to achieve the desired improvement in grain yield performances.
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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".