Tumpangsari-Agroforestry Practices and Its Socioeconomic Impact on Communities in the Gunung Arjuna-Lalijiwo Forest Reserve, East Java, Indonesia
Bibliographic record
Abstract
The issue of poverty remains a significant and persistent challenge in Indonesia, particularly in rural areas. Agroforestry has been practised in the Gunung Arjuna Reserve Forest, which has been managed by Perhutani since the 1970s. However, the absence of effective oversight and guidance has hindered substantial socioeconomic progress. In 2016, the management of 544.74 hectares of this forest was taken over by Universitas Brawijaya (UBF). This study focused on the Karangploso sub-district, specifically within the Tawangargo, Donowarih, and Ngenep villages under UBF's management. The study aimed to assess the impact of the transition from Perhutani's management to UBF's current administration of the Gunung Arjuna Reserve Forest. It did so by (i) analyzing factors influencing the total area of cultivated land in the tumpangsari-agroforestry system and (ii) evaluating the socioeconomic consequences of this management shift. A quantitative approach and convenience sampling technique were employed, selecting respondents based on accessibility and proximity to the researcher on-site. The results indicated that 34% of respondents cultivated crops on land areas ranging from 1.0 to 1.5 hectares. The regression analysis demonstrated a strong relationship (R2 = 0.847) between factors such as work duration, plant diversity, education level, income, and the number of family members engaged in farming and the total cultivated land area. The study also found that the transition from Perhutani to UBF management had a significantly positive impact on socioeconomic aspects, such as total income, social interactions, and infrastructure, as perceived by 67% of respondents. Furthermore, 70% of respondents acknowledged their reliance on the agroforestry system for income, with 88.86% strongly agreeing that it contributed to family income, and 84.57% strongly agreeing that it was a vital source of raw materials for sustenance. The implication of transferring the 544.74-hectare reserve forest to Universitas Brawijaya had a favourable outcome, enhancing the living standards of the communities in the vicinity. The tumpangsari-agroforestry system not only improved socioeconomic conditions but also fostered social interactions, improved infrastructure, and preserved the forest's ecology. It is recommended that the community continue cultivating a variety of crops with guidance from UB Forest management and other stakeholders. Creating national awareness about the benefits of agroforestry in rural poverty reduction is vital, emphasizing the need to explore and adopt diversified livelihood strategies.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".