The effect of agricultural technology on improving farming business performance and the welfare: Evidence from the welfare of rice farmers in Tabanan regency
Bibliographic record
Abstract
This research focuses on the welfare of rice farmers in Tabanan Regency as a form of support for the sustainability of the agricultural sector to support the food security and sovereignty of Bali Province, which is still primarily supported by Tabanan Regency. This research was conducted in Tabanan Regency, Bali Province, known as the "rice barn" of Bali Province, with the largest area of rice fields and the most significant number of farmers in Bali Province. The approach used in this research is a quantitative approach using a questionnaire, and the analysis technique used in this research is SEM-PLS (Structural Equation Modeling Partial Least Square) analysis, with 167 rice farmers as respondents to this research. The findings from this research show that farming business performance can mediate the influence of agricultural technology adoption on the welfare of rice farmers in Tabanan Regency. The findings from this research are strengthened by interviews showing that rice farmers in Tabanan Regency have confidence in themselves in farming. This is demonstrated by farmers' efforts to use various developments in agricultural technology, such as using superior seeds and modern agricultural equipment, which can shorten the working time, among others, to improve the welfare of rice farmers in Tabanan Regency.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".