Gender and endogenous knowledge inclusion for agroforestry systems improvement in Benin, West Africa
Bibliographic record
Abstract
Abstract In West Africa, and Benin particularly, local forest resources can potentially contribute to both increasing and stabilizing soil productivity. However, these resources continue to be neglected with efforts instead concentrated on promoting exotic species. This study aimed to prioritize local agroforestry species on agricultural landscape by investigating the gendered, socio-demographic and agro-pedological factors of local knowledge and use of agroforestry species amongst small-holder farmers in Benin. An agroforestry inventory combined with an ethno-agroforestry survey was conducted on 364 farms with 364 farmers. A cluster analysis based on farmers' socio-demographic and agroecological factors was used to cluster farmers into two homogeneous agroforestry systems. Median score, species diversity and ecological networks were established for these two systems. Results illustrate that gendered difference exist between the priority that farmers give to multi-purpose species and this prioritization depends on priority ecosystem services for farmers and gender. Therefore, it would be useful first to consider gender and specific needs of each category of farmer to optimize the choice of agroforestry species to be promoted in such systems. The successful introduction of identified species through extension programs requires more advanced research related to the real contribution of these species to farmland fertilization, as well as the nutrient transmission pathways to associated crops in an agroforestry system to address simultaneously specific ecological, economic and socio-cultural sustainability criteria, as well as improved crop production.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".