Assessment of Soil Quality of Croplands in the Corn Belt of Northeast China

Xiaoyan Li, Huiying Li, Limin Yang and Yongxing Ren
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Xiaoyan Li: College of Earth Sciences, Jilin University, Changchun 130012, China
Huiying Li: College of Earth Sciences, Jilin University, Changchun 130012, China
Limin Yang: College of Earth Sciences, Jilin University, Changchun 130012, China
Yongxing Ren: College of Earth Sciences, Jilin University, Changchun 130012, China

Sustainability, 2018, vol. 10, issue 1, 1-16

Abstract: The increasing global demands for land resource with increasing population have resulted in occurrence of soil degradation in many regions of the world. Assessment of soil quality has become the basic work for agricultural sustainable development and selecting regional indicators effectively has become very important since there are no standard evaluation methods and universal indicators. In this study, taking the Corn Belt of Northeast China as the study area, seven indicators—obstacle horizon thickness, cation exchange capacity, pH, soil organic matter, total nitrogen, total potassium, and available Fe—were selected to constitute the minimum data set from sixteen indictors of the total data set to assess the soil quality. The soil quality of the study area was dominated by moderate grade, increasing from west to east. The soil quality of Yushu, Changchun and Shuangyang had higher values, and that of Nongan was the lowest. We found that the distribution of cation exchange capacity has a good consistency with the assessment result of the soil quality. Black soils were distributed in the middle part of the study region from north to south and accounted for a higher quality, exactly where the areas of rapid urbanization are located. An ANOVA analysis showed that soil quality in the Corn Belt of Northeast China was greatly affected by topographic factors and agricultural management and climate was not the principal factor affecting soil quality. Though the minimum data set slightly reduced the evaluation accuracy, a large sampling density in our study was able to improve the precision loss that resulted from reducing the number of indicators to a certain extent.

Keywords: soil quality index; minimum data set; integrated quality index; environmental factors (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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