Adoption of Organic Rice Farming in East Kolaka Regency, Indonesia: Factors and Stakeholder Collaboration
Bibliographic record
Abstract
Farmers' motivation towards a new technology is a key factor in the success of a new farming system adoption.The farmers' understanding of environmental functions and other factors determines the successful adoption of an organic lowland rice farming system.We, accompanied by the Bank of Indonesia, tried to help a group of paddy-rice farmers in East Kolaka Regency, Southeast Sulawesi Province, Indonesia, convert from a conventional to an organic rice farming system.Data were collected using surveys through interviews.The case study analysis used in-depth interviews, focus group discussions, and non-participatory observation.Using the Theory of Planned Behaviour, we found that farmers' interest in the organic farming system was highly positive.Farmers will decide to implement an organic farming system after seeing other farmers' success, but several factors, including limited policy support, must be resolved.However, adopting the organic rice farming system would be beneficial in increasing production and improving the local agricultural ecosystems.Still, collaborative roles of various stakeholders (e.g., government, universities, and extension workers with a participatory extension approach) were required.Strong collaborations among farmers as actors, extension workers as university-facilitated assistants, and the government as policymakers were essential in adopting technology at the farmer level.
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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 | 0.002 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".