Do farmers adopt advice on good pesticide practices? A case study of plant doctor recommended pesticide use in maize and tomato production
Bibliographic record
Abstract
Pesticides are now widely used to manage the recent outbreak of crop pests such as fall armyworm (Spodoptera frugiperda, FAW) and Phthorimaea absoluta (syn.Tuta absoluta).However, reports on farmers' pesticide use practices are often insufficient.Hence, this study aimed to assess how Kenyan maize and tomato farmers' use of pesticides aligns with plant doctor recommendations.We collected data from 600 randomly selected maize and tomato farmers (clinic users and non-clinic users) using a pre-tested structured questionnaire and key informant interviews with plant doctors and extension officers.Results suggest farmer practices matched plant doctor recommendations in over 80% of clinic users' cases.However, there were significant differences in recommended pesticide use and actual practices between Do farmers adopt advice on good pesticide practices?A case study of plant doctor recommended pesticide use in maize and tomato production plant clinic users and non-plant clinic users.Although plant clinic users were significantly more likely to wear Personal Protective Equipment (PPE) while working with pesticides, we observed inadequate PPE use among most farmers.This contributed to reported incidences of dizziness, headaches, and other acute pesticide health symptoms.Occasionally farmer practice does not match plant doctor recommendations due to the high cost of inputs, and lack of money to purchase the recommended inputs.Overall, this study shows that plant clinic participation resulted in more judicious use of pesticides and PPE wearing by farmers.The divergence in views means that there is a need to reconcile farmer actions and ideal situations through seminars, farmer field schools, barazas, and other information dissemination methods. Key highlights• Farmers follow plant doctor recommendations in over 80% of cases.Where they do not, it is mostly due to a lack of money to purchase recommended inputs.• While farmers apply suitable pesticides, they sometimes increase the strength or frequency of application, because they think this will lead to quicker pest control.• More than 85% of plant clinic users apply pesticides at the right time of the day, compared to only about 55% of non-clinic users.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".