Developing a Sustainable Beef Cattle Business Model for Smallholder Farms in South Kalimantan's Drylands
Bibliographic record
Abstract
South Kalimantan has great resource potential for the beef cattle business as an effort to reduce dependence on imported beef in Indonesia.This study aims to analyze environmental, economic, social and technological resources to build and recommend a sustainable beef cattle business model on dry land in South Kalimantan.The research used Partial Least Squares Structural Equation Modelling (PLS-SEM) and Cross-Impact Matrix Multiplication Applied to Classification (MICMAC) analysis on a survey of 110 respondents, which includes interviews and focus group discussions.PLS-SEM assesses the impact of environmental, economic, social, and technological factors, finding they contribute 40.1% to business sustainability and 48.4% to income.MICMAC identifies critical variables for sustainability, highlighting housing technology, disease and feed, and communication with extension agents as pivotal.The study suggests policies addressing these factors, emphasizing their importance in enhancing farmers' abilities and business sustainability.Capital, waste utilization, reproductive technology, and communication with research institutions are identified as regulatory variables crucial for sustaining the beef cattle business.This is important because housing technology, disease and influence on livestock productivity, and communication with extension workers are important to improve farmers' ability to carry out their business so that it is sustainable.These findings provide a foundation for informed policy formulation to develop a robust and sustainable beef cattle industry in South Kalimantan, reducing dependence on imported beef.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".