Soil quality assessment under different land uses in an alpine grassland

Soil quality index (SQI) is widely practiced form plot to national scales to assess the status and use potential of soils. However, how to objectively choose relevant indicators and score these indicators to generate comprehensive SQI is still a major challenge because of the complexity and site-specificity of soils. The objective of this study is to develop SQI using different indicators selecting methods (total data set, minimum data set and revised minimum data set) and scoring methods (linear and non-linear) to evaluate the influences of three land uses (CL, cropland; GR, grazing grassland; FE, grassland enclosure) on soil quality in an alpine grassland. Fourteen soil indicators representing soil physical, chemical and biological properties were measured at 0-20 cm depth. One-way analysis of variance and principal component analysis were used with the fourteen indicators to select the total data set, minimum data set and revised minimum data set. Eleven soil indicators exhibited treatment differences were identified as the total data set. However, only two (AN and MBC) and four (MWD, SOC, AN and MBC) soil indicators were retained in the minimum data set and revised minimum data set, respectively. The six SQIs developed in this study quantified the effects of different land uses on soil quality equally well regarding both sensitivity and accuracy. However, the differentiating ability of SQI calculated using the non-linear scoring-revised minimum data set method (SQI-NLRM) was better than other SQIs based on minimum data set and revised minimum data set because of the highest F value and greatest correlation coefficient with SQI based on total data set. Under GR and CL treatment, SQI-NLRM values were 15.15% and 69.70% lower than that under FE treatment. These results indicated that land use conversions can significantly change the soil quality in the alpine grassland, and the SQI-NLRM developed in this study provides a sensitive and effective approach for quantitative evaluation of soil quality.