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.

Understanding direct and indirect relationships of environmental, socio-economic and climate variables and the dynamics of epidemics is key to guiding targeted public health policy and interventions. This study investigates the direct and indirect effects of environmental and socio-economic factors on the COVID-19 dynamics in Africa (54 African countries from 2019 to 2021) using SEM approach. Specifically, the study aimed to: (i) assess the performance of two SEM estimation methods (Lisrel and PLS-SEM) in relationship to sample size (100, 200, 500, and 1000) and level of model complexity (No, two, and four indirect effects) and (ii) use the most performing SEM estimation method to examine direct and indirect effects of factors influencing the number of cases and deaths of COVID-19 in Africa. The results highlight a positive spatial correlation between factors such as temperature, humidity, age, the proportion of people aged over 65, and the COVID-19 incidence. Under the control of confounding factors, Lisrel turns out to be the most performing method, identifying climate, demographic and economic factors as the main determinants of COVID-19 dynamics. These factors have a direct and significant impact on the incidence of COVID-19. An indirect relationship was also observed between economic factors and the incidence of COVID-19 through air pollutants. The results highlight the importance of considering these factors in understanding the spread of the virus to avoid further disasters.

Birds use song for several goals as defending territory, alerting a partner to the presence of a predator, or wooing a female for breeding. This study aims to identify the morphometric parameters which determine the variability of the number of syllables in the “sexy” songs of yellow-fronted canary. For this purpose, sixteen morphometric parameters were measured among 170 yellow-fronted canaries captured, in four agroecological zones in North Benin. The results showed that the morphometry of yellow-fronted canary varies according to its habitat and the type of song. The categorization of the sample studied allowed to retain two groups which differ in the importance of body characters and the number of sexy syllables issued when singing. Yellow-fronted canaries from the Sudanese climate zone not only have superior morphometric characters compared to other zones, but also have a song composed of several different sexy syllables. A binary logistic regression model with selection of variables revealed that the index of beak shape, abdomen length, and tail length are the determining characteristics of the number of sexy syllables. These results are needed for future breeding and conservation studies of the species.

Understanding how countries’ socio-economic, environmental, health status, and climate factors have influenced the dynamics of COVID-19 is essential for public health, particularly in Africa. This study explored the relationships between African countries’ COVID-19 cases and deaths and their socio-economic, environmental, health, clinical, and climate variables. It compared the performance of Ordinary Least Square (OLS) regression, the spatial lag model (SLM), the spatial error model (SEM), and the conditional autoregressive model (CAR) using statistics such as the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Root Mean Square Error (RMSE), and coefficient of determination ($R^2$). Results showed that the SEM with the 10-nearest neighbours matrix weights performed better for the number of cases, while the SEM with the maximum distance matrix weights performed better for the number of deaths. For the cases, the number of tests followed by the adjusted savings, Gross Domestic Product (GDP) per capita, dependence ratio, and annual temperature were the strongest covariates. For deaths, the number of tests followed by malaria prevalence, prevalence of communicable diseases, adjusted savings, GDP, dependence ratio, Human Immunodeficiency Virus (HIV) prevalence, and moisture index of the moistest quarter play a critical role in explaining disparities across countries. This study illustrates the importance of accounting for spatial autocorrelation in modelling the dynamics of the disease while highlighting the role of countries’ specific factors in driving its dynamics.