Selection of the minimum data set and quantitative soil quality indices for different azalea forest communities in southwestern China

Soil quality assessment is crucial for achieving sustainable soil management and maintaining ecosystem health. However, there is limited research on soil quality assessments in azalea forests.

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 azalea forest communities (RD, Rhododendron delavayi; RI, Rhododendron irroratum; RM, Rhododendron delavayi × Rhododendron irroratum).

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 azalea 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 forest community types exhibited significantly greater SQIs than did the Rhododendron irroratum forest.

Our results demonstrate that the Rhododendron delavayi has higher soil quality. In addition, the SQI based on the MDS method could be a useful tool to indicate the soil quality of azalea forest communities, and SQI_NL-W can provide a better practical, quantitative tool for SQI.