Evaluating the effect of coal mining subsidence on the agricultural soil quality using principal component analysis

Quantifying the effects of coal mining subsidence on soil quality is critical for developing sustainable strategies in the local agriculture. The objective of this study was to assess the effects of slope position because of coal mining on soil quality using principal component analysis. Soil samples were collected from five positions in the subsided farmland: Top slope position (1), upper slope position (2), middle slope position (3), lower slope position (4), and bottom position (5). Samples from an adjacent non-subsided farmland (CK) were used as a reference. For each soil sample, 21 different physical, chemical and biological attributes were investigated. The principal components analysis (PCA) identified the bulk density (BD), salinity, organic matter (OM) content, urease enzymatic activity (UA), actinomycete quantities (AQ), polyphenol oxidase enzymatic activity (POA) and phosphatase enzymatic activity (PA) as the most sensitive indicators in a minimum data set (MDS) to assess the soil quality. The soil quality index (SQI) was highest for positions 5 (1.220), 4 (1.203), CK (1.101), 1 (1.093), 3 (1.080), and 2 (1.044). Positions 5 and 4 had higher SQI values but lower crop yields than CK, which suggests that higher SQI does not represent higher production, and other soil quality indicators, which were not investigated in this study, had stronger effects on the crop productivity. Overall, the slope position because of coal mining subsidence strongly affected the soil quality and crop yields.

Keywords
Coal mining subsidence, principal component analysis, slope position, soil quality index.