Correlation and Path Coefficient Analysis for Seed Yield and Agronomic Traits of Newly Introduced Pigeon Pea Genotypes in South Africa
Bibliographic record
Abstract
Pigeon pea is an important source of protein for smallholder farmers in South Africa. The average seed yield per hectare ranges from 0.5 to 1 tonne due to the use of un-improved varieties that succumb to both biotic and abiotic stresses. Understanding the relationship between seed yield and agronomic traits in a genetically diverse germplasm collection is a prerequisite for developing an efficient selection and breeding program. This study determined the relationship between seed yield and agronomic traits to identify key traits for selection. Two separate experiments involving seven short and six medium duration pigeon pea genotypes were planted in a randomized complete block design (RCBD), each with three replications. Agronomic traits, including yield and its components’ genotype mean data was subjected to correlation, path coefficient and stepwise regression analyses. The results showed a strong positive and significant correlation between number of pods per plant and seed yield in both medium and short duration genotypes. The number of pods per plant had a high direct effect on seed yield in both medium and short duration with values of (0.43) and (0.63) respectively. Number of branches and pod length exhibited high direct effect (0.30-0.99) for medium duration. However, seed yield per plant was an important predictor of seed yield in short duration while seed yield per plant, 100 seed weight and number of seeds per pod were key predictors of seed yield in medium duration genotypes. Therefore, these traits should be targeted for seed yield improvement in Pigeon pea.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".