Integrating bioenergy and food production on degraded landscapes in Indonesia for improved socioeconomic and environmental outcomes
Bibliographic record
Abstract
Abstract Growing bioenergy crops on degraded and underutilized land is a promising solution to meet the requirement for energy security, food security, and land restoration. This paper assesses the socioeconomic and environmental benefits of agroforestry systems based on nyamplung (tamanu) (Calophyllum inophyllum L.) in the Wonogiri district of Central Java, Indonesia. Data were collected through field observations and focus group discussions involving 20 farmers who intercrop nyamplung with maize, rice, and peanuts and utilize the species in honey production. Calculating each crop's net present value (NPV) demonstrates that when grown as monocultures, staple crops rice and peanuts lead to negative profitability, while maize generates only a marginal profit; yet honey production utilizing nyamplung produces a NPV nearly 300 times greater than maize. However, when utilizing nyamplung, honey is also the commodity most sensitive to decreases in production, followed by nyamplung–peanut and nyamplung–rice combinations. While decreases in production have little effect on the NPVs of rice, peanuts, and maize, these annual crops can only be cultivated for a maximum of 6 years within the nyamplung's 35‐year cycle, due to canopy closure after this time. Nyamplung‐based agroforestry systems can provide economic, social, and environmental gains on different scales. However, when considering the high profit potential of nyamplung combined with honey production, further research is needed to improve and develop bee husbandry practices so this becomes a viable option for local farmers.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".