Soil quality is defined as the capacity of soil to function effectively within an ecosystem to sustain plant productivity, maintain or enhance water and air quality and support human health and habitation. This study assessed soil quality and its spatial heterogeneity in the Holihosur sub-watershed, Belagavi district, Karnataka, using 490 surface soil samples. These samples were analyzed through principal component analysis (PCA) and geospatial methods. PCA identified 5 components with eigenvalues greater than one, collectively explaining 60.76 % of the total variance in soil properties. From 12 evaluated parameters, a minimum data set revealed key soil quality indicators: pH, available potassium, organic carbon, available nitrogen, iron and boron. Geostatistical analysis selected the circular semi-variogram model as optimal based on minimal root mean square error, enabling ordinary kriging interpolation. The spatial autocorrelation range of 608.96 m, with a 320 m grid sampling interval, provided sufficient resolution for capturing spatial dependence patterns. This facilitated the creation of detailed thematic maps to support targeted soil management. The moderate nugget-to-sill ratio (0.65) indicated that spatial heterogeneity arose from both systematic pedogenic processes and random environmental influences (climate and management of crops). Spatial distribution results showed that approximately 16 % of the watershed had moderate soil quality (SQI: 0.35-0.55), while the majority (76.35 %) exhibited higher soil quality (SQI: 0.55-0.75), indicating generally favorable conditions for agriculture.
Maunika et al. (Wed,) studied this question.