Analyzing Technical and Economic Performance for Developing Corn-Based Sheep Farming in Rural Indonesia
Bibliographic record
Abstract
In rural Indonesia, sheep are commonly raised using traditional methods with low technological innovation, leading to modest incomes for farmers.This study evaluates the technical and economic performance of sheep farming in the Anyer Subdistrict of Serang Regency, Banten Province.The research involved observing and interviewing 285 sheep farmers and employed Rapid Rural Appraisal (RRA) and Participatory Rural Appraisal (PRA) techniques to assess the potential agroecosystem for developing a corn-based sheep integration model.Descriptive analysis was utilized to evaluate the regional biophysical and technical performance of sheep farming, while Net Cash Benefit (NCB) and Cost-Return Analysis (CRA) were applied to determine the economic performance.Findings indicate that corn-based sheep farming has potential in the region, bolstered by the availability of corn by-products.However, the performance of sheep aged 2-3 years was found to be suboptimal, as evidenced by low average body weights (23 kg for 2-year-olds and 20.1 kg for 3-year-olds), small litter sizes (1.34 heads per litter), a high mortality rate (23.65%), and a low reproductive rate in ewes (1.55 heads/ewe/year).Despite these challenges, sheep farming proved to be profitable, with an average net benefit of IDR 2,420,400 per annum, not accounting for labor costs.The study suggests that the development of corn-based sheep farming could be supported by the introduction of feed processing technology and the use of superior sheep breeds to enhance performance and ensure business sustainability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".