The Efficiency of Smallholder Rubber Plantations and the Factors That Influence It: A Case Study in Indonesia
Bibliographic record
Abstract
Rubber has an essential role in supporting the people's economy in Indonesia, but lately, rubber production has continued to decrease.One of the efforts that significantly affected rubber productivity was a more efficient allocation and use of resources.This effort had to be supported by solid empirical knowledge regarding the technical efficiency of production and resource allocation.The efficiency of smallholder rubber plantations helped increase per capita income in rural areas.This study aimed to evaluate the efficiency level of smallholder rubber plantations and suggest several priority areas to increase the efficiency of smallholder rubber production.This study used 318 families of rubber farmers (15% of rubber farmers).Data collection used a questionnaire.This study analyzes the efficiency of smallholder rubber plantations in Indonesia using the Data Envelopment Analysis (DEA) approach.The results show that most smallholder rubber plantations operate in relatively inefficient conditions.The average technical efficiency (TE) and allocative efficiency (AE) of smallholder rubber plantations were 0.791 and 0.473, respectively.This implies an opportunity to increase the TE and AE of smallholder rubber plantations.Increasing the efficiency of smallholder rubber plantations can be done by adopting the best technology available and by increasing the efficiency of resources owned by farmers, such as land, clonal planting materials, and fertilizers.Clonal planting materials, education, agricultural counseling and training, access to credit, market access, experience in rubber farming, and the gender of the plantation manager determine the efficiency of smallholder rubber plantations.Improving the ability of rubber farmers can be done through counseling and training.It is also necessary to develop credit programs that are more accessible to farmers.Future research can be directed to analyze the technology used and the contribution of women to smallholder rubber plantations which are efficient versus inefficient in increasing the productivity of smallholder rubber.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".