Efficiency of the Polycross and Controlled Hybridization Methods in Sweetpotato Breeding in Uganda
Bibliographic record
Abstract
Sweetpotato is an important crop in many parts of the world especially in developing countries. It is used for both human consumption as well as livestock feed. It is an important source of carbohydrates, vitamin C, fibre, iron, potassium, protein and β-carotene. Its production is, however, constrained by several biotic and abiotic factors, including pests and diseases, low soil fertility, drought, cold and salinity. Breeding is one of the ways to overcome some of these constraints and in sweetpotato the polycross or controlled cross methods can be used. To determine which of the two methods was more efficient, genotypes generated by both methods were evaluated over two seasons at Namulonge and Kachwekano. The type of cross (polycross or controlled) was significantly (P ≤ 0.05) different for storage root yield, response to sweetpotato virus disease, Alternaria blight, and harvest index (HI). The controlled cross families had a significantly higher mean HI of 43.2% than the polycross families with a mean HI of 31.8%. Therefore, controlled crosses could be deployed to systematically increase the HI in sweetpotato breeding populations. Significant (P ≤ 0.05) differences were observed among families for all traits. This stresses that the parents used in a cross are very important in generating genotypes with desired attributes. It was apparent that both the polycross and controlled crosses are good methods for generating new sweetpotato genotypes in a sweetpotato breeding program. Where aggregate performance was considered (selection index) the controlled crosses method produced more (75% of the top 20 desirable genotypes) than the polycross method across the two sites. However, the best three genotypes over the two sites were from the polycross family of Ejumula. Therefore, sweetpotato controlled crosses could be very useful for population improvement using recurrent selection while polycrosses could be suitable for variety development. Both hybridization methods require cautious selection of parents to match the breeding objectives.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".