Agroforestry systems and their impact on livelihood improvement of tribal farmers in a tropical moist deciduous forest in Bangladesh
Bibliographic record
Abstract
This study investigates the composition of and preferences by farmers related to trees and crops planted in agroforestry systems, and their role on the livelihood of tribal farmers in a tropical moist deciduous forest in Tangail, Bangladesh. Data was collected from 150 tribal farmers practicing different types of agroforestry systems in Madhupur Sal forest, using a mixed-method strategy that included a survey, focus group discussion, key informant interviews, and direct observation. According to the results, tribal farmers used a total of 22 trees and 33 crop species in their existing agroforestry systems, indicating a rich composition and high diversity. Acacia auriculiformis was the most common tree species (with 82% of farmers possessing this species), followed by Mangifera indica (75%), Acacia sp. (73%), and Gmelina arborea (54%). Interviews revealed that agroforestry systems have provided numerous benefits and greatly enhanced farmers’ livelihoods through better access to food, timber, fodder, and fuelwood and greater access to livelihood capitals (except social capital). Though agroforestry practices increase species diversity, provide economic returns, and help farmers maintain their livelihoods, tribal farmers face several constraints including bureaucracy and a lack of alternative market facilities. Our study can be of interest for future policy interventions focusing on sustainable reforestation practices, how to solve the problems faced by the farmers, and livelihood improvement in Bangladesh.
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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 | 0.001 |
| 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) | 0.002 | 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".