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

Area of Science:

• Agricultural Science
• Soil Science
• Environmental Science

Background:

• Soil quality (SQ) is crucial for agricultural productivity and environmental health.
• Assessing SQ is complex due to diverse soil functions and lack of universal indicators.

Purpose of the Study:

• Develop a crop yield-correlated minimum dataset (MDSCorr) for SQ assessment.
• Evaluate the MDSCorr's performance across different U.S. regions.

Main Methods:

• Collected soil and crop yield data from experimental sites over five years.
• Evaluated six scoring functions and three indexing approaches to calculate the Soil Quality Index (SQI).
• Identified key soil properties (e.g., total organic carbon, microbial biomass carbon) strongly linked to corn productivity.

Main Results:

• The MDSCorr identified key soil properties influencing corn yield.
• Linear scoring with threshold limits and additive indexing yielded consistent SQI values.
• MDSCorr-based SQI strongly correlated with total dataset-derived SQI (R² = 0.53–0.93) and outperformed MDSPCA.

Conclusions:

• The MDSCorr approach, using linear scoring and additive indexing, offers a simplified and transferable framework for SQ assessment.
• Relative soil quality rankings were established for the study sites.
• Further calibration and validation across diverse regions and cropping systems are recommended.