Assessing Pesticide Sales Trends: An Agrovet Survey in Parasi, Rupandehi and Kapilvastu Districts of Lumbini Province
Bibliographic record
Abstract
Study assesses practices related to the sale of pesticides and safety measures, the provision of licenses and training for pesticide retailers, and the status of the most traded pesticides in the Parasi, Rupandehi, and Kapilvastu districts. 69 agrovet respondents were selected through a simple random sampling method in the regions. Insecticides were found to be the most demanded type of pesticide (79.7%), followed by fungicides (20.3%). Among the available insecticides, the combination of Chlorpyriphos 50% + Cypermethrin 5% EC was the most traded with index value 0.85. For fungicides, mancozeb was the top choice, followed by the herbicide ammonium salt glyphosate, while aluminum phosphide was the most favored rodenticide. During the study, lack of policies for the proper disposal of expired pesticides was observed. Additionally, there was a low percentage (38.2%) of personal protective equipment (PPE) sales, indicating farmers' minimal attention to pesticide exposure safety. The survey also revealed challenges faced by retailers, including issues such as open borders, the rising number of agrovets in local areas, difficulties in convincing farmers to adopt safety measures, and a lack of pesticide knowledge among farmers. This suggests that the government should ensure stricter monitoring and more rigorous enforcement of regulations regarding the sales of pesticides.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".