Comparative Studies on Soil Quality Index Estimation of a Hilly-Zone Sub-Watershed in Karnataka

M. Bhargava Narasimha Yadav (), P. L. Patil and M. Hebbara
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M. Bhargava Narasimha Yadav: Department of Soil Science and Agricultural Chemistry, University of Agricultural Sciences, Dharwad 580 005, India
P. L. Patil: Yettinagudda Campus, University of Agricultural Sciences, Dharwad 580 005, India
M. Hebbara: Department of Soil Science and Agricultural Chemistry, University of Agricultural Sciences, Dharwad 580 005, India

Sustainability, 2023, vol. 15, issue 24, 1-26

Abstract: The assessment of soil quality aims to evaluate the utility and health of soils. In agricultural studies, soil productivity can be likened to soil quality. Evaluating the Soil Quality Index (SQI) solely based on surface properties offers an incomplete picture because productivity is influenced by both surface and subsurface characteristics, with the latter associated with pedogenic processes. Additionally, relying on weighted averages of soil properties from a soil profile for the SQI may offer an overall summary, but it can occasionally obscure variations that manifest across different soil horizons. Therefore, the present study was conducted to assess the SQI in the Ganjigatti sub-watershed using data from 27 soil profiles and three different methods: (1) assessment of horizon-wise SQI by subjecting the soil properties of every horizon to principal component analysis (PCA), followed by the calculation of the weighted averages of the SQI for each soil profile (SQI-1); (2) calculation of the weighted averages of the soil properties for each soil profile, subjected to PCA, and followed by an SQI assessment (SQI-2); and (3) SQI assessment considering the properties of the Ap horizon for each soil profile (SQI-3). Additionally, to validate SQI methodologies, correlation studies were conducted against major crop yields in the sub-watershed. The results showed that cation exchange capacity (CEC) has the most significant weight and contribution to the SQI determined using MDS, followed by porosity, exchangeable sodium percentage (ESP), organic carbon (OC), CN ratio, and total N. SQI-1 was most strongly correlated with crop yield; the correlation coefficient ranged from 0.69 to 0.74. Among all the three methodologies, SQI-1 and -2 were better methods for assessment of SQI compared to SQI-3. In the SQI-1 method, the soil quality of pedons ranged from 0.26 (pedon-26) to 0.74 (pedon-11). The majority of the area in the sub-watershed (72.40%) fell within the medium category of SQI (0.35–0.55), followed by the high category of SQI (>0.55), which comprised 12.92%, and the low SQI (<0.35), which comprised 6.45% of the sub-watershed.

Keywords: soil quality index; productivity; principal component analysis; horizon wise; correlation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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