The Inheritance of Yield Components and Beta Carotene Content in Sweet Potato
Bibliographic record
Abstract
Analysis of the gene action exhibited by an agronomic trait in crop plants is useful in the planning of an effective plant breeding program. This study sought to establish the inheritance variance of root yield components and beta carotene content present in sweet potato clones commonly grown in Kenya. Ten contrasting parents were selected based on their beta carotene content, dry matter content and yield potential. They were planted in pots in the green house at KEPHIS-Muguga, Kenya, where they were grafted into Ipomoea setosa to boost their ability to flower. Five parents were further selected based on their ability to flower under local environmental conditions and crossed in a 5 × 5 half diallel manner. Five parents and ten F1 clones arising from this hybridization were evaluated at KALRO-Kiboko, Kenya during two rainy seasons. The field trials were laid out in a randomized complete block design of three replications. The data were scored for root yield characteristics, beta carotene and root dry matter content. Griffing Method II, Model 1 diallel was used to estimate the general and specific combining ability. Genetic variability for beta carotene and root dry matter content indicated that the two traits were controlled mostly by both additive and dominant gene actions. Heterosis gene effects were found for beta carotene and root dry matter content which would be useful in the improvement of sweet potato productivity.
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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 | 0.000 |
| 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".