Assessing the Spatial Variability of Soil Quality Index of Ganjigatti Sub-Watershed Using GIS-Based Geostatistical Modeling Yadav M. Bhargava Narasimha*, Patil P.L.1, Rundan V.2, Vyas R. Veda, Nthebere Knight3 Department of Soil Science and Agricultural Chemistry, UAS, Dharwad-580 005, India 1Vice-Chancellor, UAS, Dharwad-580 005, India 2Department of Agronomy, UAS, Dharwad-580 005, India 3Department of Soil Science and Agricultural Chemistry, PJTSAU, Hyderabad-500 030, India *E-mail: bhargavnarasimha444@gmail.com
Online Published on 20 February, 2024. Abstract The present study was conducted to appraise the soil quality and its spatial variability from 393 surface soil samples of the Ganjigatti sub-watershed of Karnataka by using geospatial techniques. Principal component analysis was applied to identify the MDS from a set of fourteen soil quality indicators. The major factors that influence soil quality include pH, OC, available N, Zn, B, P and Mn. Six leading PCs were significant based on an eigenvalue of '>1' and explained 74.71% of the variance in soil parameters. SQI (Soil Quality Index) and RSQI (Relative Soil Quality Index) values ranged from 0.41 to 0.81 and 0.51 to 1.00 respectively. The geo-database was subjected to ordinary kriging through the best-fit experimental semivariogram based on the lowest root mean square error. The study concluded that the measured SQI (range 720.82 m) in regular gird sampling at a given scale was enough to capture spatial dependence using the ordinary kriging technique and to derive thematic maps for efficient soil management strategies at the sub-watershed level. The higher nugget: sill ratio (0.81) indicates that the spatial variability or dependency is primarily caused by stochastic factors. The SQI map of the Ganjigatti sub-watershed showed that about 9.69% of the sub-watershed had medium SQI (0.35-0.55), whereas 80.87% of the area had higher SQI (0.55-0.75). Top Keywords Soil quality index, Principal component analysis, Spatial variability, Ordinary kriging. Top |