Screening of Selected Sorghum Genotypes for Resistance to Covered Kernel Smut Disease in Western Kenya
Bibliographic record
Abstract
Sorghum is an important food security crop for arid and semi-arid tropics but its production is hampered by many biotic and abiotic factors including covered kernel smut disease (CKSD) caused by fungus Sporosorium sorghi in the Ustilaginaceae family. The disease attacks susceptible sorghum genotypes causing yield losses estimated at 43% in Western Kenya. This study determined the response of selected sorghum genotypes to CKSD under field and greenhouse conditions. A total of 15 elite sorghum genotypes were screened under field conditions in Migori and Homa Bay sites and under greenhouse at the University of Eldoret. Data on disease incidence and severity were collected per genotype and analyzed using R-Studio software and means were separated at 1% using Tukey’s test. Results showed significant differences among genotypes for disease incidence and severity under fields and greenhouse conditions. Disease incidence varied significantly (p < 0.001) among the genotypes ranging from zero (for T53, T30, IS3092, N4 and N68) to 64% (for Nyadundo2) under field conditions but ranged from 0-69% under greenhouse conditions. Similarly, severity followed the same trend with C26 having the worst attack with a score of 5 while T53 recorded the least (score of 1). This study has identified potential sources of resistance for covered kernel smut disease that can be utilized to manage the disease and significantly improve sorghum yields in the target regions.
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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".