Selection of the minimum data set and quantitative soil quality index for different Rhododendron simsii forest communities in southwestern China

Abstract

Background and Aims: Soil quality assessment is crucial for achieving sustainable soil management and maintaining ecosystem health. However, there is limited research on soil quality assessments in Rhododendron simsii forests. Methods In this study, we selected 17 soil physicochemical indicators as the total data set (TDS) and utilised principal component analysis (PCA) to construct the minimum data set (MDS). Linear/nonlinear scoring functions and additive/weighted additive methods were employed to calculate four soil quality indices (SQIs) to determine the SQIs of R. simsii forest communities (RD, Rhododendron delavayi; RI, Rhododendron irroratum; RM, Rhododendron delavayi × Rhododendron irroratum). Results The capillary porosity, total nitrogen, carbon-to-nitrogen ratio, and soil carbon density were identified as the MDS. The four SQIs showed consistent performance and exhibited significant positive correlations with each other (P < 0.001, n > 15). Nonlinear weighted additive integration (SQI_NL−W) yielded the highest discriminative effectiveness for the SQI among the R. simsii forest communities (R² = 0.848). The SQI of the Rhododendron delavayi forest was the highest, followed by that of the Rhododendron delavayi × Rhododendron irroratum forest of both species, and both community types exhibited significantly greater SQIs than did the Rhododendron irroratum forest. Conclusion Our findings indicated that prioritizing Rhododendron delavayi in the planting and cultivation of Rhododendron simsii or incorporating Rhododendron delavayi when planting Rhododendron irroratum can effectively enhance soil quality. Additionally, SQI_NL−W can provide a practical and relatively accurate quantitative tool for evaluating the SQ of rhododendron forests.