Rice Farmers’ Attitudes toward Farm Management in Northeatern Thailand
Bibliographic record
Abstract
Rice production plays a key role for Thailand’s economy and for the food security and cash income of Thai small-scale farmers, especially in the Northeast region where the country’s largest area of rice cultivation is located. To increase rice production, the Thai government has introduced several strategies to support farmers such as new technologies, farm practices, and financial institutions. Achieving these strategies, the responsibility from the government and copperation from farmers are crucial. Specifically, these strategies will be more effective if they coincide with the attitudes of farmers. Accordingly, we aimed to estimate the technical efficiency of rice farms, including pure technical and scale efficiency, and to ultimately understand rice farmers’ attitudes toward farm management by comparing efficient and inefficient farms. Our findings suggested that there was significant requirement to increase technical and scale efficiencies of rice production in the study area. In addition, both efficient and inefficient rice farmers were favorable to farming, open to ideas, and strongly enjoy farm activities, such that they would cooperate with an extension officers when transferring information and/or training programs. Finally, policymaker should focus on both improving the quality of farm production and reducing production costs due to develop and establish new strategies and/or agricultural policies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".