Adoption of Agroforestry Practices in and around the Luki Biosphere Reserve in the Democratic Republic of the Congo
Bibliographic record
Abstract
Despite the technical, socio-economic and environmental challenges, indigenous subsistence agroforestry, generally referred to as slash-and-burn agriculture or bush-fallow farming, is a common practice for local populations in the Democratic Republic of Congo. This study analyzed the proportion of adopters and non-adopters, together with other factors that influence farmers’ choices of adopting agroforestry or that discourage its adoption in the Luki Biosphere Reserve (LBR) area. Data were collected through a survey of 390 households using a structured questionnaire. A logistic regression model, with SPSS Statistics software was fitted to the data against a binary response (1 = adopt; 0 = not adopt). The proportion of adopters of agroforestry practices in the LBR area far exceeds (more than three-fold) that of non-adopters. Six factors exert a positive and significant (p-value = 5%) effect on peasant decisions to adopt agroforestry practices in LBR, including age (51 to 60 years old), marital status, education level, main activity, land tenure and farmers’ membership in a local association. Gender, other age categories, household size, number of years of agroforestry experience, number of assets, distance between residence and fields, and access to credit did not positively influence the adoption of these practices. The results of this study would help engage the indigenous community with different sectors and disseminate agroforestry as a sustainable practice appropriate to the real needs of local populations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".