EFISIENSI PENGGUNAAN INPUT PAKAN DAN KEUNTUNGAN PADA USAHA TERNAK BABI DI KECAMATAN TARERAN KABUPATEN MINAHASA SELATAN
Bibliographic record
Abstract
ABSTRACTFEED EFFICIENCY AND PROFITABILITY OF PIG FARM AT TARERAN DISTRICT OF SOUTH MINAHASA REGENCY. Tareran district is located in the South Minahasa regency consisted of 12 (twelve) villages and has the area of 7602.45 hectares or 76024.5 Km2. Generally, Tareran community work as farmers. The most pig populations at Tareran district were found at the three villages including villages of Lansot, Rumoong Atas, and Rumoong Atas Dua. The problems of this study are that; first, is the use of production cost in the pig business at the Tareran district efficient? Second, does the production cost provide benefits? Research objectives are to evaluate the type and total cost of production in the pig business at the Tareran district, to analyze the efficiency of input usedin pig farming, and to determine the optimum use of inputs in achieving the business benefits pigs. The research was conducted in the Tareran district of South Minahasa Regency involving the number of 30 breeders as respondents. Data collections were conducted in the period time of 2 months. The data in this study were obtained from two sources of primary and secondary data. Samples were taken using purposive sampling method. The use of this technique was always based on certain characteristics obtained through the population. The results of these studies showed the production cost of pig farm was Rp18.557.038 per period per year with a gain of about Rp 13.611.309 per period per year. Inefficient use of food inputs indicating to the farmers need to reduce feeding cost because it caused cost redundancy. It was known that animal body weight achieved was different for each type of pig. In addition, the use of ration inputs to achieve maximum body weight was also different for each animal.Keywords: Efficiency, Ration Input, Profit, Pig Farm, Tareran District.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".