A remote sensing–based approach to the regionalization of socioeconomic indicators in an agricultural headwater catchment in Northern Benin

The agroecosystems of the rural continuum of the Sudano-Sahelian region of the Volta River Basin are undergoing severe degradation, resulting in serious declines in ecosystem service capacities and weakening community livelihoods, as agriculture remains the primary source of income in the region. This degradation is mainly driven by poor management of hydroclimatic risks, non-adapted agricultural practices characterized by intensive use of chemical fertilizers and pesticides, and insufficient consideration of spatial heterogeneity in decision-making processes. The objectives of this study are (i) to identify a Network of 30 m resolution Grid Cells (NGC) representative of the spatial heterogeneity of the studied agroecosystem – the Dassari headwater catchment (550 km 2 ), a tributary of the Volta River in northern Benin – and (ii) to regionalize plot-scale socioeconomic data to support improved decision-making. The NGC was derived by combining iterative principal component analysis (IPCA) with a Conditioned Latin Hypercube Sampling (CLHS) approach using 37 satellite-derived variables (e.g. Normalized Difference Vegetation Index, saturation index, coloration index), resulting in the selection of 150 grid cells. Field and laboratory investigations provided soil properties (e.g. texture, carbon, field capacity, nitrogen content) and socioeconomic data (e.g. harvest quantity, input costs, total production cost, and gross income), which were standardized and analyzed for major crops (millet, sorghum, maize, and cotton). Results show that natural spatial disparities among NGC cells translate into additional labor and input costs that may be unsustainable for farmers. Multiple linear regression models were developed to relate socioeconomic indicators to soil and remote sensing variables for each crop type, and the resulting regional models proved robust, with predicted and observed values closely aligned within the 95 % confidence interval, coefficients of determination exceeding 70 %, and p -values below 0.01.