Soil Quality Evaluation and Its Relationship to Soil Productivity In a Major Grain-Producing Region of the North China Plain.

The minimum dataset (MDS) indicator method and the standard score function (SSF) normalization method are often used to evaluate soil quality. Selecting the MDS indicator methods and determining the cutoff values for the SSF, however, may affect on accuracy of soil quality evaluation. Additionally, to what extent the soil quality affect soil productivity under the condition of modern agricultural management has arisen a public concern. It is therefore necessary to further refine methods to accurately calculate soil quality indexes (SQIs) and to elucidate the relationship between soil quality and crop yields. In this paper, three MDSs were created with principal component analysis (PCA) using topsoil indicators (n = 99) from Yucheng County, a major grain-producing region of the North China Plain, and the cutoff values were acquired by regressing soil indicators and crop yields. The SQIs were then evaluated and compared with yield data of maize (Zea mays L.) and wheat (Triticum aestivum L.). Among all MDS indicator methods, which selected indicators using norm values considering the loadings in multi-PCs, preformed better than the other methods. The SQI based on this MDS had a higher correlation not only with that of the total dataset (TDS) but also with crop yield. Soil quality was calculated more accurately in terms of the cutoff values based on crop yields than the commonly approved values for a larger area. All evaluation results revealed that the Yucheng County has moderate soil quality and soil texture, soil electric conductivity, and total N were the major limiting factors affecting crop yields. Under the condition of modern agriculture, the variation of crop yields can only be explained about 28% by soil quality. Thus, management practices and environmental factors should be comprehensively considered in modern agricultural production.