Genetic inheritance of pulp colour and selected traits of cassava (<scp><i>Manihot esculenta</i></scp> Crantz<i>)</i> at early generation selection
Bibliographic record
Abstract
BACKGROUND: The early generation selection of cassava quantitative and qualitative traits saves breeding resources as it can shorten breeding schemes. Inheritance analysis provides important breeding information for developing new improved varieties. This study aimed at developing an F1 segregating cassava population and determining mode of gene action of pulp colour and selected traits at early generation selection (F1 seedling and clones). RESULTS: The 15 families exhibited significant (P < 0.05) phenotypic variation between offspring. The general combining ability (GCA) was significant for all traits except cassava brown streak disease on leaves, whereas specific combining ability (SCA) was significant for all evaluated traits. The Garukansubire and Gitamisi genotypes were the best general combiners for improving fresh storage root yield, while G1 and G2 were the best general combiners for improved carotenoid (yellow/orange pulp colour) and delayed physiological postharvest deterioration. The pulp colour had the highest GCA/SCA ratio and percent sum of squares due to GCA. CONCLUSION: The 15 F1 families exhibited essential genetic diversity for cassava improvement. The expression of most cassava traits was controlled by both additive and non-additive gene action. The study elucidated the role of dominance effects over the additive effects for the evaluated traits. However, the pulp colour was predominantly controlled by additive gene action. This implies the possibility of improving cassava through conventional breeding using recurrent selection for most traits. © 2017 Society of Chemical Industry.
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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".