Effect of Striga hermonthica (Del.) Benth on Yield and Yield Components of Maize (Zea mays L.) Hybrids in Western Kenya
Bibliographic record
Abstract
Striga hermonthica (Striga) weed is widespread in Kenya, where it causes significant cereal crop losses, particularly when susceptible varieties are grown. The use of maize (Zea mays L.) genotypes that support reduced Striga hermonthica emergence can form an important basis for developing resistant cultivars. The objective of this study was to evaluate the response of diverse maize hybrids to Striga weed infestation and to identify high yielding and stable hybrids. Six experimental maize hybrids and three commercial hybrid checks (DK8031, PhB3253 and H513) were evaluated under Striga and Striga free conditions at Nyahera and Maseno in western Kenya in 2011 and 2012 growing seasons. The data collected included grain yields and other yield components; emerged Striga counts and Striga damage rating (on a scale of 1-5) at 8 and 10 weeks after planting (WAP). Significant differences (P < 0.05) were observed in grain yield and yield components; emerged Striga counts and Striga damage syndrome in both years. Negative correlation (r) was observed between emerged Striga and yield as well as between Striga damage ratings and yield. Maseno experimental hybrids produced much better grain yields and were more stable compared to commercial varieties under Striga infestation and in Striga free fields. Deployment of the tolerant/resistant high yielding well adapted hybrids coupled with other management options such as rotation with legumes and cultural practices could possibly help in depletion of Striga seed bank in the soils in western Kenya. EH12 and EH14 maize hybrids were formally released for commercialization in the Striga endemic areas of western Kenya.
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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 | 0.001 | 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".