Assessment of soil quality in an arid and barren mountainous of Shandong province, China

Abstract

Forest soils are important components of forest ecosystems, and soil quality assessment as a decision-making tool to understand forest soil quality and maintain soil productivity is essential. Various methods of soil quality assessment have been developed, which have occasionally generated inconsistent assessment results between soil types. We assessed the soil quality of five communities (herb, shrub, Quercus acutissima, Pinus thunbergii, and Q. acutissima–P. thunbergii mixed plantation) using two common methods of dry and barren mountains in the Yimeng Mountain area, China. Sixteen soil physical, chemical and biological properties were analysed. The soil quality index was determined using the established minimum data set based on the selection results of principal component analysis and Pearson analysis. Silt, soil total phosphorus (P), soil total nitrogen (N), L-leucine aminopeptidase, acid phosphatase and vector length were identified as the most representative indicators for the minimum data set. Linear regression analysis showed that the minimum data set can adequately represent the total data set to quantify the impact of different communities on soil quality (P < 0.001). The results of linear and non-linear methods of soil quality assessment showed that the higher soil quality index was Pinus forest (0.59 and 0.54), and the soil quality index of mixed plantation (0.41 and 0.45) was lower, which was similar to the herb community (0.37 and 0.44). Soil quality was mostly affected by soil chemical properties and extracellular enzyme activities of different communities, and the different reasons for the low soil quality of mixed plantations were affected by soil organic carbon (C) and total C. Overall, we demonstrate that the soil quality index based on the minimum data set method could be a useful tool to indicate the soil quality of forest systems. Mixed plantations can improve soil quality by increasing soil C, which is crucial in ecosystem balance.