Assessing Sustainability of Smallholder Beef Cattle Farming in Indonesia: A Case Study Using the FAO SAFA Framework
Bibliographic record
Abstract
This article aims to assess the sustainability of smallholder beef cattle farms in Indonesia, where there is a national goal to improve the country’s beef self-sufficiency, and to explore and discuss potential improvement limitations and solutions. This article presents a sustainability assessment based on the FAO SAFA (Sustainability Assessment of Food and Agriculture Systems) of six selected family farms representing three types of family farming systems (with only family labour; with hired labour; and with hired labour and a 'middleman in marketing system'). Individual structured interviews based on the SAFA guidelines were conducted and the results analysed with the SAFA Tool software. The results showed that the SAFA sustainability performance generally scored better in the farming system with relatively more resources and hired labour, and the household head also working as middleman, as compared to the other two farming systems with some or no hired labour. These results indicate that the larger room for sustainability improvement relies in the farming systems with only family labour. Lack of information, training and economical resources showed to be two main drivers that explain part of these differences. These results suggest that the government’s role in increasing awareness, providing information and training and facilitating sustainable development practices is critical.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".