Evaluation of Soil Quality of Pingliang City Based on Fuzzy Mathematics and Cluster Analysis

Author:

Zhao Zhenhua1,Yang Yifei2,Dong Bo134,Zhang Rui5,Chen Guangrong3,Pan Zhandong1,Du Dandan34

Affiliation:

1. College of Resources and Environmental Sclences, Gansu Agricultural University, Lanzhou 730070, China

2. College of Finance and Economics, Gansu Agricultural University, Lanzhou 730070, China

3. Dryland Agriculture Institute, Gansu Academy of Agricultural Sciences/Key Laboratory of Efficient Utilization of Water in Dry Farming of Gansu Province, Gansu Academy of Agricultural Sciences, Lanzhou 730070, China

4. Key Laboratory of Low-Carbon Green Agriculture in Northwestern China, Ministry of Agriculture and Rural Affairs, Lanzhou 730070, China

5. College of Water Conservancy and Hydropower Engineering, Gansu Agricultural University, Lanzhou 730070, China

Abstract

Pingliang City has a complex topography and diverse soil types. To realize the improvement of soil according to local conditions and the reasonable and sustainable use of soil resources, an evaluation of soil quality in Pingliang City was carried out, based on the soil distribution situation in Pingliang City, adopting a method combining fuzzy mathematics and cluster analysis of the main evaluation factors, such as soil organic matter, topsoil depth, soil erosion intensity, soil moisture regime, effective soil thickness, soil texture, soil profile structure, soil nutrient status and topographical parts, to carry out a comprehensive evaluation. A comprehensive evaluation of soil quality was conducted in seven counties under the jurisdiction of Pingliang City, and the evaluation results were compared and analyzed against the national standard, “Cultivated land quality grade”, to provide a basis for the selection of scientific soil improvement methods. The results of the arable land quality grades indicate that the quality of farmland in Pingliang City is divided into three to ten grades, and the average quality grade of farmland is 6.83, which is in the middle–lower level, and the overall grade distribution shows the characteristics of low in the middle and high in the east and west. The results of fuzzy mathematics combined with cluster analysis indicated the following trends in soil quality for the 12 soil genera: Chuan black gunny soil > yellow moist soil > sandy soil > silt soil > mulching helilu soil> loessal soil> loamy soil > slope loessal soil > arenosol > tillage leaching gray cinnamon soil > calcareous gray cinnamon soil > red clay soil. The results of the combination of fuzzy mathematics and clustering were significantly correlated with the results of the evaluation of the soil quality of arable land; the correlation coefficient was 0.884. This indicates that the method can accurately and objectively review the advantages and disadvantages of arable land soil and can be effectively applied to the evaluation of the soil quality of agricultural soils in other regions. It is a complement to the existing evaluation of the soil quality of arable land and at the same time provides a reference for the improvement of soil quality in agricultural regions.

Funder

NNational Key Research and Development Plan of China “Diagnosis of Main Obstacles to Capacity Enhancement and Response Strategies in Loess Hilly Areas”

Gansu Provincial Key Research and Development Program

Gansu Provincial Agricultural Science and Technology Special Project

Gansu Agricultural University’s Water Conservancy Engineering Program

Publisher

MDPI AG

Reference41 articles.

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