The Impact of Rice Field Functional Shifts on Sustainability and Greenhouse Gas Emissions in Tabanan Regency, Bali, Indonesia
Bibliographic record
Abstract
This study aimed to investigate the driving factors behind functional shifts in rice fields and to assess the contribution of rice field activities to greenhouse gas emissions.Data were collected from 58 respondents, including heads of subak systems, agriculture officers, representatives from the Tabanan Department of Agriculture and Horticulture, and academicians.Leverage analysis, Interpretive Structural Modeling (ISM), and emission calculations using the 2019 IPCC approach were employed, along with time series data from 2016 to 2020.The findings revealed that the primary determinants of functional shifts included pest attacks and diseases, rice field selling prices, availability of production facilities, housing needs and tourism growth in the study area, family participation in rice field management, marketing institutions, enforcement of awig-awig subak (penalties for rice field conversion), and the conditions of irrigation canals and roads to the farming area.Emission analysis, focusing on methane, demonstrated that the reduction in rice field area due to functional shifts between 2016 and 2020 led to a 21.95 percent decrease in greenhouse gas emissions.
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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 | 0.000 |
| 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 | 0.000 | 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".