Soil Quality Mediates the Corn Yield in a Thin-Layer Mollisol in Northeast China

Wei Fang, Xuemei Zhong, Xinhua Peng, Linyuan Li, Shaoliang Zhang and Lei Gao ()
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Wei Fang: State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
Xuemei Zhong: College of Earth Sciences, Guilin University of Technology, Guilin 541004, China
Xinhua Peng: State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
Linyuan Li: State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
Shaoliang Zhang: School of Resources and Environment, Northeast Agricultural University, Harbin 150030, China
Lei Gao: State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China

Land, 2023, vol. 12, issue 6, 1-15

Abstract: Soil quality (SQ) is critical to sustainable agricultural development. It is sensitive to the crop yield, especially in thin-layer black-soil regions, which have experienced severe degradation in recent years. However, how to evaluate the SQ and its influence on land productivity is not clear in regions with thin black-soil coverage. Therefore, an integrated soil quality index (SQI) was constructed using diverse datasets along a 30 km transect in a typical thin-layer black-soil region of China. The results showed that obvious soil degradation was observed in this area. Black-soil thickness (BST), soil organic matter (SOM), and the total nitrogen (TN) content were the most strongly correlated with corn yield among the 13 investigated indexes, with Pearson coefficients of 0.65, 0.39 and 0.34, respectively. The minimum-dataset-based SQI using six soil properties within 0–30 cm was the optimal solution for SQ evaluation in the study area. The good performance of the established SQI using the optimal method was supported by its strong correlation with the corn yield, with a Pearson coefficient and linear R 2 of 0.75 and 0.56, respectively. The BST identified by differences in colour across the soil profile provided powerful information for the SQI, the value of which would be underestimated by 8% if this index were ignored. The linear R 2 between the SQI and corn yield decreased from 0.56 to 0.49 when the BST index was removed. This study showed the significance of improving the SQ in thin-layer black-soil regions. The core of soil management is to prevent the losses of surface black soil and improve the SOM content in this region. These findings can help farmers and decision makers adopt proper measures to improve SQ and thereby crop yield.

Keywords: black-soil region; land productivity; soil quality index; soil degradation; black-soil thickness (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2023
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