Minimum Data Set for Assessing Soil Quality in Farmland of Northeast China

Soil quality assessment provides a tool for agriculture managers and policy makers to gain a better understanding of how various agricultural systems affect soil resources. Soil quality of Hailun County, a typical soybean (Glycine max L. Merill) growing area located in Northeast China, was evaluated using soil quality index (SQI) methods. Each SQI was computed using a minimum data set (MDS) selected using principal components analysis (PCA) as a data reduction technique. Eight MDS indicators were selected from 20 physical and chemical soil measurements. The MDS accounted for 74.9% of the total variance in the total data set (TDS). The SQI values for 88 soil samples were evaluated with linear scoring techniques and various weight methods. The results showed that SQI values correlated well with soybean yield (r = 0.658**) when indicators in MDS were weighted by the regression coefficient computed for each yield and index. Stepwise regression between yield and principal components (PCs) indicated that available boron (AvB), available phosphorus (AvP), available potassium (AvK), available iron (AvFe) and texture were the main factors limiting soybean yield. The method used to select an MDS could not only appropriately assess soil quality but also be used as a powerful tool for soil nutrient diagnosis at the regional level.