Evaluation of Soil Quality Index Using Soil Mapping Units in Pashupathihal Sub-Watershed

The evaluation of soil quality parameters within the Pashupathihal sub-watershed (Dharwad district) unveiled considerable spatial and chemical variability among 46 soil mapping units (SMUs) across 12 distinct soil series. The Soil Quality Index (SQI) values fluctuated between 0.20 (YSJmA1) and 0.76 (MVDmA2g2Ca). The depth of the soil varied from 35 to 180 cm (with a mean of 163.07 cm), thus indicating heterogeneity. Additionally, the CaCO₃ levels ranged from 1.37% to 14.45% (mean 6.34%), which reflected moderate calcareousness. The pH values spanned from 7.01 to 8.96 (mean 8.70), alongside high base saturation levels (between 82.35% and 92.57%, mean 90.95%), suggesting the presence of alkaline and base-rich soils. Organic carbon content, measured at 2.54 to 6.77 g/kg (mean 3.75 g/kg), was notably low, thereby affecting fertility; however, the cation exchange capacity, which ranged from 21.29 to 56.49 cmol/kg (mean 49.72 cmol/kg), demonstrated strong nutrient retention. Additionally, electrical conductivity levels ranged from 0.16 to 0.43 dS/m (mean 0.27 dS/m), confirming non-saline conditions. The exchangeable sodium percentage varied between 3.17% and 10.17% (mean 8.32%), indicating non-sodic soil types. Importantly, Bartlett’s test (χ² = 293.807, p < 0.0001) and a KMO score of 0.597 suggested that the dataset was moderately suitable for factor analysis. Principal Component Analysis (PCA) revealed three principal components that accounted for 84.82% of the variance. The radar plot emphasizes variations in soil quality parameters across SMUs; it features pronounced radial extensions that indicate superior attributes, such as depth or cation exchange capacity (for instance, MGDmA2g2Ca). The linear regression graph, reveals strong positive correlations, like the relationship between depth and CaCO₃. Nevertheless, there are also negative trends, such as organic carbon decreasing with depth. This emphasizes the intricate interplay of soil properties, which is essential for understanding soil health and functionality. Cluster analysis categorized the soil mapping units (SMUs) into four unique clusters (C1–C4) according to dissimilarities in parameters such as pH, EC and organic carbon. This process facilitated the identification of management zones. C1 indicated soils with extreme characteristics that necessitate specialized interventions; however, C3 consisted of uniform, fertile soils that are appropriate for consistent management practices. Although the distinctions among the clusters were significant, all played a role in guiding effective soil management strategies. Soil Management Units (SMUs) like MVD (127 ha, SQI 0.76) and CPT (766 ha, SQI 0.70) demonstrated elevated Soil Quality Indices (SQI); however, YSJ (5 ha, SQI 0.20) showed poor quality, primarily because of erosion and nutrient depletion. The BTP series, encompassing 2464 ha with an SQI of 0.63, covered the most extensive area, which reflects general robust soil health. Nevertheless, targeted management is essential for the low-SQI zones to improve both productivity and sustainability.