Why don't smallholder farmers in Kenya use more biopesticides?
Bibliographic record
Abstract
BACKGROUND: Although Kenya has a relatively high number of registered biopesticide products, little is known about biopesticide use by smallholders. This paper documents farmers' current use and perception of chemical pesticides and biopesticides, their willingness to pay for biopesticides, and the key challenges to biopesticide uptake. RESULTS: A survey found that chemical pesticides are used widely by smallholders despite awareness of the risks to human health and the environment. Almost half of respondents showed awareness of biopesticides, but current use in the survey localities was low (10%). Key reasons for the low use of biopesticides by smallholders in this study are: perceptions of effectiveness, primarily speed of action and spectrum of activity, availability and affordability. Smallholders who used biopesticides cited effectiveness, recommendation by advisory services and perception of safety as key reasons for their choice. Although farmers viewed both pesticides and biopesticides as costly, they invested in the former due to their perceived effectiveness. Average willingness to pay, above current chemical pesticide expenditures per cropping season was 9.6% (US$5.7). Willingness to pay differed significantly between counties, and was higher among farmers with more education or greater awareness of the health risks associated with pesticide use. CONCLUSION: This study confirms the low use of biopesticide products in the survey areas, alongside high use of conventional chemical pesticides. In order to promote greater uptake of biopesticides, addressing farmers' awareness and their perceptions of effectiveness is important, as well as increasing the knowledge of those providing advice and ensuring registered products are available locally at competitive prices. © 2020 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".