Comparing Four Indexing Approaches to Define Soil Quality in an Intensively Cropped Region of Northern India - Lenka, Narendra Kumar; Meena, Bharat Prakash; Lal, Rattan; Khandagle, Abhishek; Lenka, Sangeeta; Shirale, Abhay Omprakash (Frontiers in Environmental Science, 2022)
The usefulness of the soil quality index (SQI) as a tool to evaluate management options has mostly been studied within the boundaries of a crop or experimental field, calling for the need to enhance its utility in regional-scale soil health assessment. Thus, four quantitative approaches for computing the SQI were evaluated with samples collected from 0 to 15 and 15 to 30 cm depths at 156 points from the Trans-Gangetic Plains of North India. Principal component analysis (PCA) and soil function (SF)-based approaches were used to select the minimum dataset from 18 soil parameters and assign weights to key indicators. In both approaches, two different data transformation methods were followed: 1) routine method with maximum or minimum values of indicator parameters and 2) percentile method with the 90th or 10th percentile value as the denominator or numerator for “more is better” and “less is better” scoring functions, respectively. The PCA output with factor loadings from the varimax rotation showed six principal components accounting for 75% of the total variance, with PC1 explaining the highest variance (26.8%) followed by PC2 (16%). The SF-based approach was better than PCA in terms of a higher correlation of SQI with rice and wheat yields. The percentile method showed a higher correlation in both PCA and SF methods. The SQI computed from 0 to 30 cm soil data did not show any superiority over that from 0 to 15 cm soil. Thus, the soil function–based approach with the percentile method of data transformation proved better to compute the SQI and establish a relationship with production function.