Management of Snap Bean Insect Pests and Diseases by Use of Antagonistic Fungi and Plant Extracts
Bibliographic record
Abstract
Use of synthetic pesticides reduces the competitiveness of Kenyan snap bean pods due to stringent regulations by importers as a result of presence of chemical residues. This study was conducted to determine the effectiveness of local biopesticides in managing insect pests and diseases of snap beans. Field experiments were set up in farmer's field where Trichoderma spp. and Paecilomyce spp. and plant extracts from turmeric, garlic, ginger and lemon were applied weekly as foliar sprays. Plant extracts reduced the population of whiteflies and thrips by up to 58% and 41% while antagonistic fungi had a corresponding 30% and 18% reduction, respectively. Trichoderma spp. reduced severity of angular leaf spot (37.5%), rust (67%) and anthracnose (20.7%). Plant extracts and antagonistic fungi increased marketable pod yield by 25.6% and 17.3%, respectively. Results demonstrated that local environments are potential sources of biopesticides that can be exploited for integrated management of pests and diseases.
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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".