A novel optimal data set approach for erosion‐impacted soil quality assessments—A case‐study of an agricultural catchment in the Chernozem region of Northeast China

Abstract Because soil erosion is a primary cause of land degradation globally, it has received increasing attention in food production regions, such as the Chernozem region of Northeast China. This study assessed soil quality under soil erosion degradation using a novel optimal data set (ODS) approach and a comparative minimum data set (MDS) approach to derive soil quality indices (SQIs) within an agricultural watershed in Heilongjiang Province, China. The selection of SQIs was contingent on 15 physical, chemical, and microbial properties measured at 52 grids. Soil erosion rates were determined using the Cs‐137 technique. Results showed that SQIs derived from the ODS and MDS approaches were strongly correlated ( r = 0.91, p < 0.01). However, soil quality inferred by SQIs varied significantly between erosion and deposition sites, namely, increasing from upstream to midstream to downstream areas. Changes in SQIs and erosion rates exhibited spatially opposing trends, reflecting the impact of soil erosion on soil quality, which was also confirmed by comparing representative soil properties at erosion and deposition sites. The ODS approach using the optimal fingerprinting factor screening procedure of the composite fingerprinting approach is a cause‐related method that uses a relatively strict indicator selection procedure, which mainly includes sample groupings, Kruskal–Wallis H test, discriminant function analysis, and conservative behaviour tests of factors. Thus, it can theoretically obtain more reliable results compared to MDS. Further studies are nevertheless needed to assess the feasibility of this novel approach in other cases.