Evaluation of Sweetpotato (Ipomoea batatas (L.) Lam.) Genotypes for Resistance to Alternaria Leaf Petiole and Stem Blight (Alternaria spp.) in Uganda
Bibliographic record
Abstract
Alternaria leaf petiole and stem blight (Alternaria spp.) is an important sweetpotato (Ipomoea batatas (L.) Lam.) disease in Uganda. Severity of the disease varies with environment, with higher disease levels recorded under high moisture and humidity conditions. To breed for resistance to this disease, germplasm that is resistant must be identified through multi-locational trials. This study was conducted to evaluate selected sweetpotato genotypes for stable resistance to Alternaria blight across sites and seasons. Thirty sweetpotato genotypes from different agro-ecological zones of Uganda and the National Sweetpotato Program were evaluated for resistance to Alternaria blight using fungicide treatment and Alternaria blight pathogen inoculation at Namulonge and Kachwekano over three seasons. There were highly significant differences among the genotypes for Alternaria blight severity with higher disease levels at Kachwekano than Namulonge. Genotypes Shock, Silk Luwero and the resistant check Tanzania had the lowest Alternaria severity and were therefore the most resistant while NASPOT 1 and NASPOT 7 had the highest severity values and were the most susceptible. Improved cultivars were more susceptible than the landraces. Genotypes Tanzania and Namusoga and environment Namulonge 2015B were the most stable for Alternaria blight. Treatment with fungicide resulted in variable reductions in Alternaria blight severity among genotypes across seasons and sites with NASPOT 1 having the lowest percentage reduction of 40.8% between the Alternaria inoculated and fungicide treated plots. Kigaire recorded the highest percentage disease reduction of 63.6%. Those genotypes with acceptable performance for Alternaria blight may be used as parents in breeding new genotypes with improved performance.
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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 | 0.000 | 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".