Identification of Resistance Sources to Wheat Stem Rust from Introduced Genotypes in Kenya
Bibliographic record
Abstract
Stem rust Puccinia graminis Pers. f. sp. tritici of wheat is the most important disease in Kenya. Emergence of race Ug99 and other variants virulent to host resistance genes including Sr31 has rendered 95% of Kenyan cultivars susceptible. This study aimed to identify new sources of resistance to stem rust in a collection of exotic genotypes. Three hundred and sixteen wheat genotypes were screened at the Kenya Agricultural and Livestock Research Organization (KALRO) in Njoro for two seasons in 2015. The host reaction to disease was evaluated based on the modified Cobb scale. The relative Final Rust Severity (rFRS), Average Coefficient of Infection (ACI) and relative Area Under Disease Progress Curve (rAUDPC) were used to characterize the genotypes for stem rust resistance. Agronomic traits were also recorded. Six genotypes namely ALBW-100, ALBW- 204, EPCBW-261, EPCBW-295, PCHP-309 and PCHPBW-310 with significantly low ACI, rAUDPC and rFRS were identified. Thirty five genotypes showed Pseudo-Black Chaff (PBC) phenotype associated with resistant gene Sr2, a source of partial resistance in wheat. The genotypes also showed low disease severity (20-25%) and Moderately Susceptible (MS) – Susceptible (S) infection types in both seasons. Genotypes had significant differences (p ≤ 0.05) on plant height, 1000-kernel weight and number of tillers indicating genetic variation which could be exploited in breeding for resistance to stem rust. The negative relationship between agronomic variables involving plant height, spikelet length and 1000-kernel weight showed harmful effects of stem rust on plant characteristics including yield. The stem rust resistant genotypes with good agronomic traits could be introgressed into adapted Kenyan backgrounds while the genotypes showing presence of PBC could be utilized to develop durable stem rust resistant wheat. Inheritance studies to elucidate the exact genes conferring resistance to stem rust could be conducted for breeders to exploit their genetic variability.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".