Gérard Nounagnon Gouwakinnou

Assessing ecological sustainability in agricultural landscapes requires approaches that integrate land-cover change, its ecological effects, and their spatial determinants. This study analysed changes between 2023 and 2024 across six agricultural landscapes in northern Benin using the Landscape Ecological Sustainability Index (LESI), calculated for 1 km² landscape cells from Human Disturbance Coefficients (HDCs) assigned to satellite-derived land-cover classes. Interannual changes were assessed using ΔLESI, the Wilcoxon signed-rank test, Global Moran’s I, and the Local Indicators of Spatial Association (LISA). The contribution of land-cover transitions to HDC change was quantified and a Monte Carlo sensitivity analysis based on classification accuracy was additionally used to assess the robustness of the observed interannual changes to classification uncertainty, and complementary univariate and bivariate regression models examined the relationships between LESI variations and 11 biophysical and geographical variables, including their pairwise interactions. The interannual comparison between 2023 and 2024 showed a decrease in ecological sustainability in four sites, a slight increase in one, and relative stability in another. Significant spatial autocorrelation was detected in all sites (Moran’s I = 0.44–0.65; p < 0.001). Some spatially limited transitions exerted important effects on HDC change. Sensitivity analysis confirmed that the direction of ΔHDC was robust to classification uncertainty in five of the six landscapes. The low explanatory power of the univariate models (R² ≤ 0.081) indicates that no single variable independently explains the observed changes. The bivariate interaction analyses further showed that some associations were context-dependent, although their explanatory power remained limited, with the best-performing model accounting for only 10.9% of the variation in ΔLESI. This integrated framework provides a reproducible approach for assessing, mapping, and prioritising interannual change of ecological sustainability from remote sensing data to support evidence-based agricultural landscape planning and sustainable land management.

Assessing ecological sustainability in agricultural landscapes requires approaches that integrate land-cover change, its ecological effects, and their spatial determinants. This study analysed changes between 2023 and 2024 across 6 agricultural landscapes in northern Benin using the Landscape Ecological Sustainability Index (LESI), calculated for 1-km² landscape cells from Human Disturbance Coefficients (HDCs) assigned to satellite-derived land-cover classes. Spatio-temporal dynamics were assessed using ΔLESI, the Wilcoxon signed-rank test, Global Moran's I, and Local Indicators of Spatial Association (LISA). The contribution of land-cover transitions to HDC change was quantified, and univariate regression models examined the relationships between LESI variations and 11 biophysical and geographical variables. Ecological sustainability declined in 4 sites, improved slightly in one, and remained relatively stable in another. Significant spatial autocorrelation was detected in all sites (Moran's I = 0.44–0.65; p < 0.001). Some spatially limited transitions exerted important effects on HDC change. The low explanatory power of the univariate models (R² ≤ 0.081) indicates that no single variable independently explains the observed changes. This integrated framework provides a reproducible approach for assessing, mapping, and prioritising ecological sustainability trajectories from remote sensing data to support evidence-based agricultural landscape planning and sustainable land management.

Pseudocedrela kotschyi is a socio-economically important species for rural communities in sub-Saharan Africa. This study aims to identify the environmental drivers shaping the morphological traits of the species across different biogeographical zones for guiding effective conservation and domestication strategies. Measurements were taken from 3,086 fruits and 2,851 leaves that were collected in these zones. Principal Component Analysis (PCA) and hierarchical classification identified distinct morphological groups, while Multiple Correspondence Analysis (MCA) assessed the relationship between morphotypes and biogeographical zones. Morphological traits varied significantly between zones (p < 0.001). The Guineo-Congolian zone had the longest (10.27 ± 0.05cm) and heaviest fruits (51.59 ± 0.39g), while the Sudano-Guinean zone had the heaviest seeds (0.69 ± 0.01g). Three morphotypes were identified: morphotype 1 had small fruits with light seeds; morphotype 2 had long fruits with numerous seeds whereas morphotype 3 had heavy fruits with large seeds. The distortion features differ from one morphotype to another. Although certain traits were influenced by temperature and precipitation, relationships between morphology and climate remained weak. These findings highlight the importance of conservation strategies that are adapted to regional specificities and local environmental pressures. Tailoring conservation and domestication strategies to the distinct morphotypes and their associated ecological zones could enhance the sustainable use of, and resilience to climate change pressures experienced by Pseudocedrela kotschyi.

Baobab (Adansonia digitata L.) is a multiple-use species of high socio-economic importance in tropical Africa. It faces anthropogenic, climatic and parasitic pressures that are likely to affect its sustainability. This study provides new elements for more effective baobab management in the context of climate and global change in Togo. Specifically, it aims to: (i) determine the impact of climate change on the spatial distribution of A. digitata and A. trifasciata, and (ii) identify priority areas for the conservation and sustainable use of A. digitata co-products in the face of climate change and parasitism in Togo. The algorithm based on the maximum entropy of the baobab was developed in order to model potential habitats. Modelling was based on 17 environmental variables and occurrences of A. digitata and its parasite, Analeptes trifasciata in West Africa. The algorithm of maximum entropy (MaxEnt) was used. Forecasts were performed for SSP 126 and SSP 585 scenarios. Habitats were prioritised for conservation by combining current and future models using Zonation software. Currently, A. digitata and to A. trifasciata are predicted to occupy about 80.1 % and 91 % of the Togolese territory, respectively. By 2055, about 50 % of the highly favourable habitat for A. digitata will have been lost due to unfavourable habitat. This decline is greater in the SSP 585 scenario than SSP 126 scenario. It is recommended to implement an integrated pest management strategy and to introduce A. digitata in the most favourable areas, considering the spatial distribution of the parasite. Conservation of A. digitata must take into account areas suitable for its growth, development and reproduction. In situ conservation in agroforestry systems and home gardens should be promoted for the sustainable use of A. digitata by-products in Togo.

The intensification of anthropogenic pressures, particularly those related to agriculture driven by increasing demands for food and cash crops, generates negative environmental externalities. Assessing these externalities is essential to better identify and implement measures that promote the environmental sustainability of rural landscapes. This study aims to develop a multi-criteria assessment method of the negative environmental externalities of rural landscapes in the northern Benin agricultural basin, based on satellite-derived data. Starting from a 12-class land cover map produced through satellite image classification, the evaluation was conducted in three steps. First, the 12 land cover classes were reclassified into Human Disturbance Coefficients (HDCs) via a weighted sum model multi-criteria analysis based on nine criteria related to the negative environmental externalities of anthropogenic activities. Second, the HDC classes were spatially aggregated using a regular grid of 1 km² landscape cells to produce the Landscape Environmental Sustainability Index (LESI). Finally, various discretization methods were applied to the LESI for cartographic representation, enhancing spatial interpretation. Results indicate that most areas exhibit moderate environmental externalities (HDC and LESI values between 2.5 and 3.5), covering 63–75% (HDC) and 83–94% (LESI) of the respective sites. Areas of low environmental externalities (values between 1.5 and 2.5) account for 20–24% (HDC) and 5–13% (LESI). The LESI, derived from accessible and cost-effective satellite data, offers a scalable, reproducible, and spatially explicit tool for monitoring landscape sustainability. It holds potential for guiding territorial governance and supporting transitions towards more sustainable land management practices. Future improvements may include, among others, refining the evaluation criteria and introducing variable criteria weighting schemes depending on land cover or region.

The increasing anthropogenic pressures on rural landscapes, particularly those arising from agricultural intensification, necessitate robust methods to assess environmental sustainability. This study proposes a multi-criteria approach for evaluating the environmental sustainability of rural landscape in northern Benin, using satellite-derived land cover data. The methodology was applied to 3 sites representative of rural landscapes in northern Benin. A 12-class land cover map, produced via the Moringa processing chain, was reclassified into Human Disturbance Coefficients (HDC) based on nine weighted environmental impact criteria. These were then spatially aggregated into 1 km² grid cells to produce the Landscape Environmental Sustainability Index (LESI). Results indicate that most areas exhibit moderate anthropogenic impact (HDC and LESI values between 2.5 and 3.5), covering 63–75% (HDC) and 83–94% (LESI) of the respective sites. Areas of low impact (values between 1.5 and 2.5) account for 20–24% (HDC) and 5–13% (LESI). The LESI, derived from accessible and cost-effective satellite data, offers a scalable, reproducible, and spatially explicit tool for monitoring landscape sustainability. It holds potential for guiding territorial governance and supporting transitions towards more sustainable land management practices. Future improvements may include refining evaluation criteria and introducing variable weighting schemes by land cover or region.

Tropical agroforestry systems make an important contribution to mitigating the effects of climate change and conserving biodiversity. Despite the scientific gaps regarding the ecosystem services of these particular ecosystems, understanding how tropical agroforestry systems work is essential if we are to take full advantage of the ecosystem services they provide. Although there has been a boom in the study of the ecosystem services provided by agroforestry systems, few syntheses are structuring this knowledge in the context of the challenges posed by climate change. This study aims to analyse global trends in agroforestry-related ecosystem services and their implications for climate change mitigation based on a bibliometric analysis of 1965 articles published between 1992 and 2022 and extracted from the Scopus database. These extracted data were analysed using the Biobliometrix R tool to examine sources, authors and documents. The results reveal a notable increase in interest in agroforestry’s ecosystem services, coupled with a rise in international collaborations, especially among African countries, Germany, and France. These partnerships reflect a shared commitment to understanding agroforestry systems and developing sustainable solutions. Furthermore, our analysis identifies key research themes, guiding future studies. Ecosystem services, climate dynamics, and biodiversity emerge as central aspects of agroforestry research. The findings highlight agroforestry’s multiple benefits, particularly its role in adapting to and mitigating climate change while significantly contributing to biodiversity conservation. A thorough evaluation of agroforestry systems is, therefore, crucial to developing innovative, integrated strategies that address current environmental and climate challenges.