Scientia Agricultura Sinica ›› 2021, Vol. 54 ›› Issue (14): 3043-3056.doi: 10.3864/j.issn.0578-1752.2021.14.010


Advance in Indicator Screening and Methodologies of Soil Quality Evaluation

LI Xin1(),ZHANG WenJu1(),WU Lei1,REN Yi2,ZHANG JunDa2,XU MingGang1   

  1. 1Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences/National Engineering Laboratory for Improving Quality of Arable Land, Beijing 100081
    2Cultivated Land Quality Monitoring and Protection Center, Ministry of Agriculture and Rural Affairs, Beijing 100125
  • Received:2020-09-04 Accepted:2020-11-10 Online:2021-07-16 Published:2021-07-26
  • Contact: WenJu ZHANG;


【Objective】 The objectives of the present study were to synthesize the current information on soil quality assessment method and indicator system, and to present the hot topics and frontiers related to soil quality, so as to, provide references for Chinese scholars and experts in the field of soil quality evaluation research and application. 【Method】 The published articles regarding the selection of soil quality indicators, construction of minimum data set, and selection of soil quality evaluation methods were collected based on Web of Science and CNKI databases using bibliometrics method, and a total of 415 articles and 155 minimum data sets related to soil quality evaluation were screened. Development trend, frontier fields and current problems of global soil quality assessment during the past 30 years were analyzed according to selection frequency of indicators, assessment method and construction of minimum data set. 【Result】 The soil quality evaluation indicator system mainly included 25 physical, 36 chemical, 35 biological and 19 environmental indicators. Soil organic matter, as the core indicator of soil quality, was selected with the highest frequency of 96.6%, followed by pH, total nitrogen, available phosphorus, available potassium, and bulk density, with a frequency more than 50%. The selection frequency of biological indicators such as microbial biomass and soil enzyme activity was less than 25%, while increasing over time. Principal component analysis, minimizing indicator redundancy and reflecting most of the information of original variables, was the most widely used for minimum data set construction method. Soil organic matter, available phosphorus, bulk density, and pH were selected into the minimum data set with a frequency of 67.7%, 43.2%, 34.8%, and 34.2%, respectively, being widely used to characterize soil quality. Nowadays, the most studies on soil quality evaluation focus on the utilization of principal component analysis to select soil quality indicators and establish soil quality index for comprehensive soil quality evaluation, which was suitable for sustainable soil management.【Conclusion】 Soil organic matter, available phosphorus, soil pH, bulk density and soil water content were the main parameters selected for soil quality evaluation. Construction a comprehensive and objective soil quality indicator system and the integration with the information technology would be the focus in future research. The application of evaluation indicators in large-scale soil quality assessment was the trend of future development.

Key words: bibliometrics, Web of science database, CNKI database, soil quality, minimum data set, assessment method

Fig. 1

Development of soil quality connotation"

Fig. 2

Frequency distribution of soil quality indicators based on Web of Science and CNKI databases"

Fig. 3

Change of soil quality indicators selection frequency based on Web of science and CNKI databases during 1990-2019 Error bar represents standard error, and the frequency is the average of the indicator selection frequency every 10 years"

Table 1

Principles, advantages and disadvantages of the MDS construction method"

Construction method
Selecting according to loadings, also can be used in conjunction with the correlationship and Norm value
可降维以减少变量,体现原始变量 信息
Reducing dimensionality to reduce variables and reflecting original information
Unclear signs of factor loads, unclear meaning of comprehensive evaluation function
Classifying indicators through R-type cluster
Intuitive and concise conclusion
Hard to get result
with numberous indicators
Regression analysis with indicators screened by PCA, selecting by significance test
Retaining indicators with the most significant impact, high prediction accuracy
Hard to get stable result when independent variables have a small influence on the dependent variable
Combining correspondence analysis and multiple regression analysis, regressioning with environmental factors in each step of calculation
Displaying the sorting results of plots, objects and environmental factors on the same sorting chart
Mostly used in the influence of soil indicators on the of plant communities, a small application range
Selecting independent variables through canonical correlation analysis and extracting partial least squares factors
providing a more reasonable regression model to directly reflect the original variable information
Not applicable with few indicators
Expert experience
Selecting based on experience and actual situation of the research area
Selecting indicators with comprehensive reflectivity
主观随意性大,评价结果存在差 异性
Different evaluation results caused by large subjectivity

Fig. 4

Selection frequency of soil quality indicators in the MDS based on Web of Science and CNKI databases"

Table 2

Advantages and disadvantages of the soil quality assessment method"

Assessment method
Multiple-variable indicator kring method
Using multiple-variable transformation to integrate soil quality combined with GIS to complete the evaluation
Automatic evaluation, dynamic monitoring, expanding the scope of evaluation
Complicated calculation process, lack of basis for model selection
[8], [34]
Dynamics method
Using system dynamic method to describe changes in soil quality
Dynamic and continuous monitoring of soil quality
Unable to compare soil quality at large spatial scales
Integrated scoring method
Evaluating soil quality through production, soil erosion, groundwater, surface water, air and food quality
Comprehensive and multi-angle evaluation of soil quality
权重影响因素较多,计算较为 困难
Difficult to calculate weight with numberous factors influencing weight
Relative quality method
Evaluating relative quality of the soil in the study area with hypothetical ideal soil as the standard
Quantitative assessment of soil quality, able to compare
Evaluation bias caused improper selection of ideal soil
[8], [36]
Soil quality index method
Quantifying indicators and integrating a soil quality index selected for soil function
Suitable for sustainable soil management with a wide range of applications
Partly influenced by subjectivity, prone to deviation
[25], [37]
Grey relational analysis method
Evaluating close order and comparing the evaluation objects between objects and ideal object by correlation degree and order
Easy to get an objective evaluation result
Prone to lose information with discrete values of indicators
Matter-element method
Establish a quality evaluation model for multi- index performance parameters based on matter- element and extension set theory
Solving the incompatibility of individual index evaluation
Relatively complicated calculation process
Artificial neural network method
Training the model for achieving self-learning and adaptive through samples to evaluate soil quality
Avoiding subjective interference with high degree of automation
Evaluation bias caused improper selection of learning samples
TOPSIS method
Calculating the relative distance between samples and the optimal/worst solution to evaluate the quality of samples
Eliminating errors caused by subjective and objective weights
Difficult to quantify the index, low sensitivity
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