Gendered trends and barriers in agroforestry adoption in Benin (West Africa)
Bibliographic record
Abstract
This study analyzed agroforestry technologies adoption bottlenecks and how to address them in Benin. We combined satellite imagery (PlanetScope images 2015 and 2023) processed from object-oriented classification with socio-demographic surveys conducted with 360 farmers selected through purposive sampling. Structural equation modeling (SEM) was used to analyze the factors influencing farmers' agroforestry practices, and gendered technology adoption was assessed via chi-square tests. The results illustrated that in addition to farmers' perception (λ = 0.654**), the structural equation modeling (SEM) indicated significant relations between level of formal education ((λ = − 0.431*), farming experience (λ = 0.654**), planting of cereals (λ = − 0.953***), legumes (λ = 0.647***) and tubers (λ = 0.385*), agro-ecological zones (β = 0.501***), land acquisition by inheritance (λ = 0.712***), limited access to land (β = − 0.012) and farmers' agroforestry practices. Analysis of the trend in the adoption of technologies shows a predominance of agroforestry parks (adopted by 91% of male farmers and 71% of female farmers) and a low diversity of agroforestry technologies adopted by farmers. The dominance of this type of agroforestry indicates the fragility of the forest environment in Benin, as the growth of agroforestry parks corresponds to the increase in the loss of forest area. Additionally, 29% of the male farmers were using at least one new agroforestry technology, while only 9% of the female farmers were using these technologies. Similarly, the constraints analysis revealed that the lack of manpower is one of the more significant specific challenges faced by women. Capacity building and support through gender-specific approaches are therefore needed to better promote this practice.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".