Influences on Farmer Behavior in Integrated Pest Management: IPM Knowledge, Local Wisdom, and Motivation in Palu City
Bibliographic record
Abstract
This study investigated the impact of Integrated Pest Management (IPM) knowledge, local wisdom, and farmer motivation on farmer behavior in IPM within Palu, Central Sulawesi.A causative multivariate analysis method was employed, incorporating path analysis for explanatory purposes, and an expost facto correlational research design was executed.Data were systematically and standardly collected via structured questionnaires and observational tests.Both descriptive and inferential analysis techniques were employed for the evaluation of the collected data.A sample of 115 horticultural farmers across six villages in Palu City was selected through a simple proportional sampling method.The study findings suggest a direct influence on farmer behavior in IPM from IPM knowledge (2.43%), local wisdom (2.13%), and farmer motivation (37.45%).Furthermore, evidence was found of significant increases in IPM behavior via motivation, from 2.43% to 4.24% with respect to IPM knowledge and from 2.13% to 6.10% concerning local wisdom.The study concludes that enhancements in IPM behavior could be achieved through bolstering IPM knowledge, local wisdom, and farmer motivation.These findings have implications for the improvement of integrated pest control practices among farmers.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".