Land

The agrarian sector, as the key source of livelihood in Sub-Saharan Africa (SSA), has become highly vulnerable to changes in extension service deliveries. Farmers mainly lack access to technical advice, financial credits, farming inputs and mechanization tools while environmental challenges reinforce the adaptation of sustainable management practices. Therefore, an understanding how multi-functional actor relationships determine agricultural knowledge and information (AKI) sharing is required. This study contributes to filling this gap by characterizing horizontal and vertical interactions. By applying a social network analysis, we mapped actor relations along public–private-community co-operations to provide insights into structural dependencies at different administrative levels. Related to three sites distributed over Burkina Faso and Ghana, local perceptions were collected in stakeholder workshops to generate social network narratives. These narratives were analyzed by various metrics to identify patterns of partnerships and key actors. Study results reveal for Burkina Faso a slight shared network topology, while both sites in Ghana reflect a top-down flow of AKI. The statistical findings indicate that agricultural extension services are primarily delivered to farmers through a few key actors such as NGOs and farm-based organizations/cooperatives. Especially at the community level, the results show many reciprocal links between farmers, business actors and NGOs. This highlights a shift toward a pluralistic agricultural extension service system and underpins the demand for policies to support the long-term viability of these actors, in particular for regions where public extension agents are under-represented.

Vegetation drought is a critical manifestation of ecosystem vulnerability in high-altitude, water-limited regions under climate change. The Yellow River Water Conservation Area (YRWC), as the core water source of the Yellow River Basin, is highly sensitive to variations in hydrothermal conditions. In this study, a Temperature–Vegetation–Precipitation Drought Index (TVPDI) was constructed to characterize the spatio-temporal dynamics of vegetation drought in the YRWC for 2003, 2012, and 2019. The XGBoost–SHAP framework was further employed to quantitatively analyze the nonlinear response characteristics and relative contributions of key factors within the TVPDI framework. Scenario-based spatial simulations of vegetation drought for 2035 are then conducted based on the GeoSOS-FLUS model. The results indicate that vegetation drought in the YRWC exhibits a relatively stable spatial pattern, with drought severity gradually intensifying from southeast to northwest and moderate drought as the dominant type. Precipitation is the key variable of TVPDI, followed by land surface temperature, while NDVI mainly plays a nonlinear regulatory role. Among external factors, atmospheric moisture conditions show relatively higher explanatory relevance, whereas topographic and human activity factors exert comparatively weaker influences. Scenario-based simulation results suggest that vegetation drought may be alleviated under low-emission pathways, whereas high-emission scenarios substantially exacerbate drought severity and associated risks. This study presents an interpretable, index-based analytical framework combined with scenario-based spatial simulation for characterizing vegetation drought in the YRWC, thereby providing scientific support for ecological management and climate adaptation strategies in the Yellow River Basin.

In Japan, unlike in many other countries, urbanization has progressed while original rural road structures have been retained, leading to distinctive urban sprawl areas with intermingling residential lots and farmland. Currently, much of Japan’s urban areas consist of urban sprawl areas, posing considerable challenges for infrastructure development. However, for such urban sprawl areas in Japan, it is difficult to say that methods have been established to identify their spatial distribution based on quantitative evaluation. Therefore, for this study, we used machine learning to investigate a system that extracts sprawling urban areas from aerial photographs divided into meshes. In the system’s design, we prioritized precision to ensure the reliable detection of urban sprawl areas. Consequently, the accuracy of identifying sprawl areas achieved precision of 0.81, recall of 0.63, and an F-score of 0.71. Examination of the classification results of sprawl areas revealed that most misclassifications occurred near class boundaries. By contrast, areas with particularly high levels of urban sprawl showed few misclassifications.