Genetic Variation for Striga hermonthica Resistance and Yield Among Sorghum Accessions in Nigeria
Bibliographic record
Abstract
Striga hermonthica (Delile) Benth., commonly referred to as witch weed, is a major constraint to sorghum (Sorghum bicolor (L.) Moench) production in the Northern region of Nigeria because of high yield losses due to infestation. To identify parental lines useful in breeding for S. hermonthica resistant sorghum genotypes adapted to Nigeria, twenty-five sorghum accessions were evaluated in Nigeria across three test environments. Both phenotypic and genetic components influenced the variation observed in the sorghum accessions. The estimates for the genetic coefficient of variation, heritability and genetic advance for the area under Striga number progress curve (ASUNPC), Striga emergence counts, yield and other agronomic traits, obtained in this study revealed that genetic gain for resistance to S. hermonthica could be realized through selection. Based on the performance of the 25 sorghum accessions SRN39, Danyana, Sepon82, and SAMSORG40 were the top four accessions found to be most resistant to S. hermonthica. Assessment of resistance was based on the low Striga emergence counts and the ASUNPC values. These accessions can be used as donor sources of S. hermonthica resistant genes for introgression into cultivars adapted to Nigeria, followed by recombination breeding for pyramiding the different resistance mechanisms.
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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 | 0.000 | 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) | 0.000 | 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".