Economic Aspects of Rice Combine Harvesting Service for Farmer in Northeast Thailand
Bibliographic record
Abstract
<p>Rice combine harvesting is popular among farmers due to a labor shortage and high wage labor. This condition impacts on the rapid expansion of business of rice combine harvester service. The objective of this research was to evaluate the service characteristics of rice combine harvester for farmer and factor affecting the use of combine harvester. Primary data was collected purposively 85 operators and randomly 729 farmers with statistic analysis. Results of the study indicated that the harvesting cost of 798.48 THB/rai for using a combine harvester in wet season is smaller than the cost of manual harvesting of 1,542.17 THB/rai. The important factors affecting the use of combine harvest were farmers’ education, farm size and family size. Net return from this service business is over 250 THB/rai or over 35 % of total profit that it is economic benefit for operators. But, the operators faced high cost of fuel and of repair and maintenance cost due to unskilled operation. Thus, the government should establish a network of harvester service operators as well as encourage more maintenance training for local operator in order to high utilization efficiency in rice combine harvester. Also, the government should support farmer to expand their farm sizes by the establishment of a group farmer to easy access the use of rice combine harvester and should give wider farmer awareness education for higher adoption of combine harvester use.</p>
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".