Abstract
Abstract Soil fertility assessment is fundamental for sustainable land management and precision agriculture, particularly in semi-arid regions where soil properties exhibit considerable spatial variability. This study investigated the spatial distribution of major soil fertility indicators across Banaskantha district, Gujarat, India, using a systematic 15 × 15 km grid-based sampling approach integrated with Geographic Information System (GIS) analysis and multivariate statistics. A total of 46 geo-referenced composite soil samples were collected from agricultural fields representing all fourteen talukas and analysed for soil moisture, pH, electrical conductivity (EC), organic carbon (OC), available nitrogen (AvN), available phosphorus (AvP), and available potassium (AvK). GIS-based Inverse Distance Weighting (IDW) interpolation was employed to visualize the regional distribution of soil properties, while Pearson correlation analysis and Principal Component Analysis (PCA) were used to examine relationships among soil fertility indicators and identify the major factors influencing soil variability. The results revealed substantial spatial heterogeneity across the district, with alkaline and saline soils predominantly occurring in the western region, whereas comparatively higher soil moisture and organic carbon were observed in the eastern and northern areas. Available phosphorus was generally deficient throughout the district, while available nitrogen and potassium exhibited pronounced spatial variation associated with parent material, land use, and agricultural management practices. PCA identified salinity-related factors and organic matter–nutrient interactions as the principal contributors to soil fertility variability. The generated spatial distribution maps provide valuable baseline information for identifying nutrient-deficient zones and understanding regional patterns of soil fertility. Although the proposed nutrient management strategies require further validation through field-based agronomic studies, the integration of laboratory soil analysis, GIS, and multivariate statistical techniques provides an effective framework for regional soil fertility assessment and supports informed decision-making for sustainable land management in semi-arid environments.