Pedological and Climatic Dataset for Agricultural Soil Suitability Assessment in the Communes of Diarère and Ngayokhème (Senegalese Groundnut Basin)

The dataset used in this study was compiled to assess the agricultural suitability of soils in the communes of Diarère and Ngayokhème, located in the southern part of Senegal’s Groundnut Basin. It integrates pedological data collected through field surveys and climatic data obtained from the NASA POWER (Prediction Of Worldwide Energy Resources) platform. The combination of these datasets allows the characterization of both soil properties and climatic conditions that influence agricultural production in rainfed systems. The pedological data were generated through field-based soil prospection carried out in cultivated areas of the study region. Soil samples were collected from the surface horizon (0–15 cm), corresponding to the main cultivation layer affected by agricultural practices and nutrient dynamics. Each sampling point was georeferenced using a Global Positioning System (GPS) device. The collected samples were analyzed in the laboratory following standard soil analysis procedures to determine key physicochemical properties relevant for land suitability assessment. The measured variables include soil pH (dimensionless), electrical conductivity (dS m⁻¹), soil organic matter (%), cation exchange capacity (cmol(+) kg⁻¹), and exchangeable base cations. The resulting dataset is organized as tabular data where each row corresponds to a sampling point and each column represents a soil parameter. Climatic data were obtained from the NASA POWER database, an open-access platform providing satellite-derived meteorological data. The climatic variables used in this study include precipitation (mm) and temperature (°C). These data correspond to the geographic coordinates of the study area and represent multi-year average climatic conditions. The spatial resolution corresponds to the grid resolution of the NASA POWER dataset, which is approximately 0.5° latitude/longitude. The dataset is stored in tabular format (Microsoft Excel), allowing straightforward access and reuse. Missing values were minimal due to controlled sampling and laboratory procedures; when present, they correspond to measurements that could not be determined during analysis. Potential uncertainties may arise from laboratory measurement precision and from the spatial interpolation inherent in satellite-derived climatic data. Despite these limitations, the dataset provides a consistent and reliable basis for evaluating soil suitability and agricultural potential in the study area.