Using geostatistics and GIS approaches to characterize and map soil spatial variability in the Nubian Nasr Area of Aswan Governorate, Egypt

Precision agriculture heavily relies on detailed spatial information about soil characteristics to promote long-term soil and plant health. The present research sought to evaluate, predict, map, and analyze the spatial variability of physicochemical properties in the Nubian Nasr Area of Aswan Governorate. Soil properties were measured, including electrical conductivity, texture, organic matter, calcium carbonate, pH, cation exchange capacity, exchangeable sodium percentage, available nutrients, and sodium absorption ratio. The mean values of the studied soil properties ranged from 2.85 to 449.58, with high values observed for available potassium, sand, available nitrogen, and CEC, and low values for other properties. The geographical distribution of these attributes was mapped and characterized using classical and geostatistical approaches. Spatial variability was quantified using semi-variogram models, and maps of projected values were created using ordinary kriging. Results indicated significant spatial variability in soil properties, with strong correlations between certain parameters. The semi-variogram models that were determined to be best appropriate for the qualities under study were the exponential, Gaussian, K-Bessel, and J-Bessel models. The maps produced offer vital data for precision farming, allowing customized management plans to enhance soil health. Geostatistical techniques effectively characterized, predicted, and mapped spatial soil variability.