Identification of Factors Affecting Decisions to Adopt Pesticides at Lowland Rice Farms in Indonesia
Bibliographic record
Abstract
Pesticides have been widely adopted in the farming industry to control weeds, pests, and diseases in order to minimize yield losses and maintain the quality of lowland rice products; however, farmers often over-apply pesticides. This study analyzed key factors that affected the decision of lowland rice farmers in adopting pesticides and the frequency of pesticide application. A double-hurdle model was used to estimate the factors that affected the decisions of farmers to adopt pesticides and determine the frequency of pesticide application. These results demonstrate that the adoption of pesticides was high (86%) at lowland rice farms in the study area. Lowland rice farmers were found to apply pesticides an average of eight times. Gender, age, education level, access to extension, farming experience, and access to credit significantly affected the decisions of farmers to adopt pesticides in controlling weeds, pests, and diseases at lowland rice farms. The independent variable also significantly affected the frequency of pesticide application. Towards the goal, government and non-government organizations had to increase human resources through education, agricultural extension services to young farmers had to be improved. Specifically, extension material was provided on environmentally-friendly methods of controlling weeds, pests, and diseases and other alternatives to reduce the use of pesticides at lowland rice farms.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".