Smallholder Perceptions Toward Oil Palm Agroforestry in Tropical Peatlands, Indonesia: Do Farmers Reject Sustainable Alternatives?
Bibliographic record
Abstract
Abstract The use of tropical peatlands as the last frontier for oil palm expansion raises environmental and socio-economic concerns. In response, alternative cropping systems, such as oil palm agroforestry, have emerged as a more diversified approach that integrates various crops within oil palm plots. This system has the potential to mitigate both the environmental and economic risks associated with monoculture oil palm cultivation on peatlands. This study uses a case study approach to explore the factors influencing smallholder decisions to diversify oil palm cultivation in privately owned peatlands in West Kalimantan, Indonesia. Using a mixed-method approach combining questionnaires and semi-structured interviews, we identified key positive and negative factors influencing smallholder decision to diversify oil palm plots located in peatland area on private land. Our results indicate that 41.67% of smallholders surveyed are currently practicing oil palm agroforestry. Meanwhile 51.67% express interest in practicing agroforestry or continue with their agroforestry plots. We categorized the influencing factors into four main groups: agronomic, institutional, socio-economic, and biophysical. We also found that smallholders practicing oil palm agroforestry tend to have lower total incomes but benefit from greater flexibility due to enhanced food security provided by a greater diversity of crops cultivated for self-consumption and market revenues. These results exemplify that factors associated with diversification are not a singular, uniform process but a dynamic interplay between global and local socio-ecological contexts.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".