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.

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.

Assessing land use and land cover (LULC) changes is crucial in sub-Saharan Africa due to population growth and land degradation, emphasizing the significance of effective land use planning and management. In this study, the authors used the modules for land use change simulations plugin within the quantum geographic information system software to analyze and predict LULC change in the Ouessè municipality. The authors used Landsat images and a cellular automata-artificial neural network model to predict future LULC scenarios. Major LULC categories included woodlands, agricultural land, plantations, and rock domes. Changes in these categories were observed between 1986 and 2019, with predictions showing further shifts until 2049. This approach allowed for a detailed and accurate assessment of changes over time, providing valuable insights into land use dynamics in the study area. Smart agriculture and livestock technologies, along with effective business models, are needed to achieve a sustainable balance between human activities and natural resources.