Determining Drivers of Chili Farming Participation: Insights from Upper Citarum Watershed, Indonesia
Bibliographic record
Abstract
Considering its role on household consumption as well as production, in Indonesia chili is one of the most important vegetable commodities but also problematic in term of high risk and volatile price.Previous studies lack of detailed analyses on the factors influencing farmers' decisions to participate in chili farming and to expand their cultivation areas.Understanding farmer behavior is crucial for designing interventions that can effectively address the risks and challenges of chili farming.This study aimed to determine the behavior of farmers in chili farming.The research was conducted in one of the vegetable production centers in the Upper Citarum Watershed, West Java Province.Using the double-hurdle approach, the results show that the positive factors for the probability of participation are farmers' exposure to price volatility, the role of farming in the household economy, and positive attitudes towards vegetable farming as a way to increase income.For farmers who decided to participate, factors conducive to scaling up were self-financing, availability of family labor, and experience in vegetable farming.Results of this research imply that improvement of chili farming performance requires policy for increasing farmer access to market price, promoting contract farming, strenghtening coordination among farmers in utilising market price information for planning planting time and cropping pattern.The findings also contribute to the development of policies and practices that can improve the sustainability and profitability of chili farming.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".