Analysis of Suitable Farm Size for Fattening Pon Yang Kham Cattle
Bibliographic record
Abstract
Pon Yang Kham Livestock Cooperative Limited (PYK Coop) is a local business organisation located in Sakon Nakhon province, Thailand. It has an essential economic role in assisting farmers and the community by creating a fair income distribution. However, PYK Coop has been facing problems from an oversupply of fattening cattle and the numbers involved in cattle production. This research investigated the costs and returns of production and the suitable farm size for beef cattle production to supply PYK Coop. The primary data were collected from 409 farmers in the cooperative using a questionnaire. The costs and returns of production were analysed and classified by the size of the farm to determine the most appropriate farm size for investment. The results revealed that the average number of cattle for individual farmers was 10.10 consisting of 3.50 bulls and cows, 2.64 calves and growing cattle, and 3.50 feedlot cattle. The cost of cattle production was divided into variable and fixed costs, which ranged annually between THB 17,279.13 and THB 300,185.88. The total annual income was THB 274,836.43. The annual net return of production (total revenue minus cost) was THB 25,984.81, while the annual net return of production per head of cattle was THB 25,984.81. The optimal farm size for beef cattle for the cooperative was a medium-sized farm. Overall, the results suggested that PYK Coop should encourage farmers to raise fattening cattle as a part-time occupation and aim to have no more than 10 head of cattle per farm. In doing so, PYK Coop should adjust the slaughter quota in accord with the number of beef cattle supplied by the farmers and should increase distribution channels to accommodate future production potential.
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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 | 0.000 | 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) | 0.003 | 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".