Integrating Metarhizium anisopliae entomopathogenic fungi with border cropping reduces black bean aphids (Aphis fabae) damage and enhances yield and quality of French bean
Bibliographic record
Abstract
French bean growers, rely mainly on pesticides for pest management. the acceptable tolerance for pesticides residue in French beans is a major concern and has led to several tonnes of the crop continuously rejected and listed as unsafe for human consumption. There is growing demand for alternative approaches and products that are effective at managing pests without the side-effects associated with reliance on pesticides. A field study to determine the combined effects of Metarhizium anisopliae, (Metarril WP E9 and Biomagic) biopesticides and border crops (Sunflower and wheat) on aphid population, damage severity, growth, yield and quality of French bean. A two-factor experiment was conducted at the Egerton University, Kenya First factor included two border crops (sunflower and wheat) and no border crop (control). Second factor included spraying Metarril WPE9 (2×10 8 cfu/g), Biomagic (2×10 8 cfu/ml) biopesticides, alpha-cypermethrin (synthetic insecticide) and water. Data on growth, yield and quality parameter were collected and analyzed using the SAS version 9.4M8. Results showed that M. anisopliae and border crop significantly (p < 0.0005) enhanced growth, yield and quality of French bean in both seasons. French bean grown with wheat or sunflower borders showed a significant reduction in aphid population (p < 0.0001) and damage severity (p < 0.0001) when sprayed with various treatments compared to the control. Plots with wheat border caused an increase in collar diameter of French bean. The plots (Metarril and wheat border) caused a 4% and 5% increase in marketable yield, a 2% and 12% reduction in non-marketable yield. To exploit the benefits of biopesticides, the study recommends their integration with and border crops. Thus, French bean growers could benefit more from fungal-based biopesticides in aphid-IPM approach, as it reduces pre-harvest intervals and residues compared to synthetic insecticides.
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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 | 0.001 |
| 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".