Evaluation of Resistance of Cassava Half-Sib Progenies to Cassava Mosaic Disease and Their Agronomic Performances in Western Kenya
Bibliographic record
Abstract
Cassava mosaic disease (CMD) caused by Bemisia tabaci is among the major contributors to low cassava yield in Africa and therefore requires instituting control measures. Due to genetic diversity in cassava, only clones with superior agronomic traits, disease resistance and high yield are selected and released to farmers or deployed in breeding program. This study was conducted to evaluate the resistance of cassava half-sib families to CMD. Field trials were conducted at Kenya Agricultural and Livestock Research Organization (KALRO), at Kakamega and Alupe research stations in western Kenya from June 2016 to June 2017. Sixty progenies were compared to that of their five parents by planting cuttings in 4 × 2 meters plots. Data were collected on plant height, number of roots per plant, harvest index, root yield, dry matter content, cassava mosaic disease and whiteflies infestation. Twenty three genotypes had a mean score of 1.0 to CMD, implying that they are resistant. Cassava grown at Alupe was observed to have high number of susceptible genotypes compared to cassava grown at Kakamega, indicating the effect of the environment on the genotypes. Parental genotypes, Kaleso and MM96/4271 presented high number of progenies showing CMD resistance. Genotypes, P4G1 and P2G3 with mean root yield of 31.6 t ha-1 and 30.0 t ha-1 were the highest yielding in term of root yield. A number of half-sib families generated from MM96/4271, Kaleso and MM96/0686 performed well with respect to yield recorded on their respective parents. Evaluation of new cassava varieties under local disease conditions would most likely improve the productivity of cassava through selection of resistant clones.
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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".