Soil Quality Evaluation Based on a Minimum Data Set (MDS)—A Case Study of Tieling County, Northeast China

Abstract

Soil quality is related to food security and human survival and development. Due to the acceleration of urbanization and the increase in abandoned land, the quality of topsoil has deteriorated, thus resulting in land degradation in recent years. In this study, a minimum data set (MDS) was constructed through principal component analysis (PCA) to determine the indicator data set for evaluating topsoil quality in Tieling County, northeast China. In addition, the soil quality index (SQI) was calculated to analyze the spatial distribution characteristics of the topsoil quality and the influencing factors. The results showed that the MDS included total potassium (TK), clay, zinc (Zn), soil organic matter (SOM), soil water content (SWC), cation exchange capacity (CEC), pH, and copper (Cu), which could replace all other indicators for assessing the topsoil quality in the research region. The overall soil quality of Tieling County showed a trend of being low in the east and high in the west, and it gradually increased from the hilly area to the plain area. The topsoil quality of Tieling County is divided into one to five levels, with grade-I being the best and grade-V being the worst. The proportion of Grade-II and grade-III is the largest, which is 28.5% and 26.3%, respectively, and grade-V is the smallest, which is 9.6%. The evaluation results are consistent with field research, which can provide a reference for other topsoil quality evaluations, and it also provides a basis for the formulation of soil quality improvement measures.

Evaluation of Soil Quality and Analysis of Barriers of Protection Forests along Tarim Desert Highway Based on a Minimum Data Set

“…The soil indicators with loadings not less than 0.5 on the same PC were then assigned to a group, and the soil indicators that may enter multiple groups were assigned to the group with lower correlation [24][25][26]. The norm value of each indicator within each group was calculated, and the indicators within each group whose norm value falls outside the range of the highest score of 10% were excluded, and then further analyzed to examine the correlation between the selected indicators within each group [15,27,28]. If there was a significant correlation between the selected indicators (p < 0.05) in the MDS, the indicator with the largest value of the norm was adopted; if there was no significant correlation between the indicators within the group, all the indicators within the group were retained in the MDS.…”