Smallholder Cattle Development in Indonesia: Learning from the Past for an Outcome-Oriented Development Model
Bibliographic record
Abstract
Despite numerous programs implemented for beef self-sufficiency in Indonesia, beef demand has steadily increased, while national beef production supplies only half of the national market demand.Smallholder farmers plays a pivotal role in beef sector since more than 90% of cattle production in Indonesia is developed by smallholder farmers.The paper aims to review and recommend a model for smallholder cattle development in Indonesia.The paper collected data from literature review and assess the trajectory of cattle development in Indonesia, focusing on recent national programs to increase the cattle population and how it evolved.The vast majority of cattle production is operated by smallholder farmers characterized by traditional practices and, heavily relying on nature as a feed source, have limited cattle production/productivity. Delivered cattle development programs have had little impact on increasing the cattle population and narrowing the domestic beef market demand gap.Efforts to increase small-scale livestock farming will narrow the supply-demand gap in the beef market and improve farmers' livelihoods.The paper highlighted that despite the implementation of national programs, the heterogeneous agroecological, socio-economic, and cultural conditions across regions should be considered in cattle development programs to achieve sustainable outcomes.Based on previous research for development initiatives, this recommendation is formulated into different models according to the cattle farming systems.Implication of these varying model is that development programs need to consider local conditions and no one-size-fits-all approach.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".