Agroforestry contributions to smallholder farmer food security in Indonesia
Bibliographic record
Abstract
Abstract Agroforestry has potential for strengthening the climate change resilience of smallholder farmers in Southeast Asia. In Indonesia, the food security challenges faced by smallholders will likely worsen due to climate change impacts. Agroforestry provides and option for strengthening climate change resilience, while contributing to food access, income, health, and environmental stability. To evaluate the evidence for such benefits, this systematic review identifies 22 peer-reviewed articles published between 2000 and 2019 which assess agroforestry’s contributions to food security in Indonesia, mostly in Java or Sumatra. Analysis of the studies indicate that traditional and commercial agroforestry contribute to food security in diverse ways: for example, traditional homegardens offer 20% more dietary diversity than commercial counterparts, while commercial homegardens may contribute up to five times more income. Agri-silviculture contributions fall along a timber versus non-timber forest product continuum that displays a similar tradeoff between diversity and income. Those systems with a commercial focus may receive 54% of their income from a single commodity crop such as coffee, while traditional systems allow greater access to plants with medicinal benefits. Nearly all agroforestry systems offered indirect benefits for food security, such as allowing more off-farm work than traditional agriculture and contributing to environmental stability: users of agroforestry were found by one study to collect 83% less fuelwood from natural forests. One study highlighted that agroforestry options have up to 98% greater net present value (for periods over 30 years) compared to slash and burn style agriculture. However, very few studies of Indonesian agroforestry focused explicitly on financial analysis and food security, indicating the need for further research. Given the similar situations faced by many Southeast Asia countries, our findings contribute to emerging trends throughout the region regarding the relationship between agroforestry and food security.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 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".