Minimum dataset with integrated scoring and indexing methods for soil quality assessment

Soil quality (SQ) is a key determinant of agricultural productivity and environmental sustainability, yet its assessment is challenged by the diverse functions of soil and the absence of universally accepted indicators. This study aimed to develop a crop yield-correlated minimum dataset (MDSCorr) for SQ assessment and evaluate its performance across multiple U.S. regions. Over a five-year period, data (n = 576) from geo-referenced composite soils at 0–30 cm depth were collected from gypsum amended cover crop integrated corn-soybean rotation experimental sites at Shorter (Alabama), Farmland (Indiana), and Hoytville and Piketon (Ohio). Using the available soil and crop yield data, six scoring functions (four linear and two nonlinear) and three indexing approaches (additive, weighted additive, and Nemoro) were evaluated to calculate the SQ index (SQI). The MDSCorr identified a reduced set of key soil properties most strongly associated with corn productivity, including total organic carbon, microbial biomass carbon, active carbon, total nitrogen, and aggregate-related physical indicators explaining SQ. Using different scoring and indexing approaches, the calculated SQI values at the Indiana site, used as a reference ranged from 0.31 to 0.6. Among the approaches, linear scoring with threshold limits and additive indexing produced the most consistent SQI values, reducing variability to within ±1% compared to the total dataset (TDS). The MDSCorr-based SQI showed strong positive correlations with the TDS-derived SQI (R² = 0.53 to 0.93) and outperformed the principal component analysis-based MDS (MDSPCA) in terms of reliability and consistency. Based on MDSCorr-derived SQI values, the relative SQ rankings for the four study sites were: Hoytville > Indiana > Alabama > Piketon. While calibration and validation are recommended across geographic regions and cropping systems, the MDSCorr approach, when combined with linear scoring and additive indexing, has the potential to provide a simplified and transferable framework for SQ assessment.