Assessment of Rice Farming Sustainability: Evidence from Indonesia Provincial Data
Bibliographic record
Abstract
Indonesia is rated the highest rice consumer and the third-largest producer in the world, consequently, farming is one of the most strategic production systems in the country. Therefore, this study aims to assess the sustainability of rice farming at the provincial level in Indonesia. Furthermore, 32 sustainability indicators, which are categorized into five dimensions, namely economic, ecological, social, technological, and institutional were used. The rapid appraisal approach (Rapsusagri), consisting of multi-dimensional scaling (MDS) analysis was adopted to assess the sustainability of rice farming. Monte Carlo simulation was used to define the validity and sensitivity analysis to assess the dominant attributes which affect sustainability. The result showed that the economic and social dimensions are at a better level, meanwhile the ecological, technological, and institutional still have various weaknesses and needs improvement. Furthermore, irrigated paddy areas, agricultural infrastructure, rice productivity, use of chemical and organic fertilizers, cropping index, land suitability, village accessibility, officers, and agricultural extension institution were pointed out as the leveraging indicators for sustaining the rice farming system. Also, provinces in Java Island were found to have higher sustainability levels than others. However, it is predicted that this condition will last for a short period due to rapid land conversion, therefore Indonesia needs to consider the development of rice production areas outside Java islands.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".