土壤质量评价指标体系的构建及评价方法

doi:10.3864/j.issn.0578-1752.2021.14.010

Abstract

Abstract:

【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

LI Xin,ZHANG WenJu,WU Lei,REN Yi,ZHANG JunDa,XU MingGang. Advance in Indicator Screening and Methodologies of Soil Quality Evaluation[J].Scientia Agricultura Sinica, 2021, 54(14): 3043-3056.

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Figures/Tables 6

Fig. 1

Fig. 2

Fig. 3

Table 1

Principles, advantages and disadvantages of the MDS construction method"

构建方法 Construction method	原理 Principle	优点 Advantage	缺点 Disadvantage	参考文献 Reference
主成分分析 PCA	根据荷载大小进行筛选,可结合Norm值与指标间相关性 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	因子载荷的符号有正负性,综合评价函数意义不够明确 Unclear signs of factor loads, unclear meaning of comprehensive evaluation function	[25-27]
聚类分析 CA	通过R型聚类,将评价指标分类 Classifying indicators through R-type cluster	直观,结论形式简明 Intuitive and concise conclusion	评价指标较多时,不易获得结果 Hard to get result with numberous indicators	[28]
主成分-逐步回归分析 PC-SRA	将PCA筛选的指标引入回归分析,通过显著性检验进行筛选 Regression analysis with indicators screened by PCA, selecting by significance test	可保留影响最显著的指标,预测精度较高 Retaining indicators with the most significant impact, high prediction accuracy	当变量对因变量影响小时,结果不稳定 Hard to get stable result when independent variables have a small influence on the dependent variable	[29]
典范对应分析 CCA	将对应分析与多元回归分析相结合,每一步计算均与环境因子进行回归 Combining correspondence analysis and multiple regression analysis, regressioning with environmental factors in each step of calculation	可将样方、对象与环境因子的排序结果表示在同一排序图上 Displaying the sorting results of plots, objects and environmental factors on the same sorting chart	多应用于土壤指标对植物群落组成的影响,应用范围较小 Mostly used in the influence of soil indicators on the of plant communities, a small application range	[30-31]
偏最小二乘回归分析 PLSRA	通过典型相关分析来筛选自变量,提取偏最小二乘因子 Selecting independent variables through canonical correlation analysis and extracting partial least squares factors	可提供更合理回归模型,直观体现原始变量信息 providing a more reasonable regression model to directly reflect the original variable information	指标较少时不适用 Not applicable with few indicators	[32]
专家经验法 Expert experience	根据经验和研究区域实际情况进行筛选 Selecting based on experience and actual situation of the research area	筛选的指标的综合反映性较强 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	[33]

Table 1

Fig. 4

Table 2

Advantages and disadvantages of the soil quality assessment method"

评价方法 Assessment method	原理 Principle	优点 Advantage	缺点 Disadvantage	参考文献 Reference
多变量指标克立格法 Multiple-variable indicator kring method	利用多变量指标转换生成土壤质量结合GIS完成评价 Using multiple-variable transformation to integrate soil quality combined with GIS to complete the evaluation	自动评价、动态监测,扩展评价范围 Automatic evaluation, dynamic monitoring, expanding the scope of evaluation	运算过程复杂,模型选择缺乏依据 Complicated calculation process, lack of basis for model selection	[8], [34]
土壤质量动力学方法 Dynamics method	利用系统动力学的方法描述土壤质量的变化 Using system dynamic method to describe changes in soil quality	可动态、持续监测土壤质量 Dynamic and continuous monitoring of soil quality	无法进行大空间尺度下土壤质量的比较 Unable to compare soil quality at large spatial scales	[5]
土壤质量综合评分法 Integrated scoring method	通过生产量、土壤侵蚀量、地下水、地表水、大气和食物质量评价土壤质量 Evaluating soil quality through production, soil erosion, groundwater, surface water, air and food quality	可全方位、多角度评价土壤质量 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	[35]
土壤相对质量法 Relative quality method	以假设的理想土壤为标准,评估研究区土壤的相对质量 Evaluating relative quality of the soil in the study area with hypothetical ideal soil as the standard	可定量评估土壤质量,能够进行比较 Quantitative assessment of soil quality, able to compare	需选择理想土壤,若选择不当造成评价偏差 Evaluation bias caused improper selection of ideal soil	[8], [36]
土壤质量指数法 Soil quality index method	针对土壤功能选取指标,将指标定量化并综合生成土壤质量指数 Quantifying indicators and integrating a soil quality index selected for soil function	适用于土壤可持续管理,应用范围较广 Suitable for sustainable soil management with a wide range of applications	评价过程中部分受主观影响,易出现偏差 Partly influenced by subjectivity, prone to deviation	[25], [37]
灰色关联分析法 Grey relational analysis method	通过关联度和关联序反映各评价对象与理想对象的接近次序,对评价对象进行比较 Evaluating close order and comparing the evaluation objects between objects and ideal object by correlation degree and order	方法简便,评价结果客观 Easy to get an objective evaluation result	若指标值离散,会丢失部分信息 Prone to lose information with discrete values of indicators	[38]
物元法 Matter-element method	以物元理论和可拓集合理论为基础,建立多指标性能参数的质量评定模型 Establish a quality evaluation model for multi- index performance parameters based on matter- element and extension set theory	解决单项指标评价的不相容性 Solving the incompatibility of individual index evaluation	计算过程相对复杂 Relatively complicated calculation process	[39]
人工神经网络法 Artificial neural network method	通过样本训练使模型进行自学习、自适应,再利用模型对土壤质量进行评价 Training the model for achieving self-learning and adaptive through samples to evaluate soil quality	自动化程度高,避免主观干扰 Avoiding subjective interference with high degree of automation	若学习样本选取不当,易造成评价偏差 Evaluation bias caused improper selection of learning samples	[40]
TOPSIS法 TOPSIS method	计算样本与最优解和最劣解的相对距离来评价样本的优劣 Calculating the relative distance between samples and the optimal/worst solution to evaluate the quality of samples	排除主客观权重导致的误差 Eliminating errors caused by subjective and objective weights	量化指标较难,灵敏度不高 Difficult to quantify the index, low sensitivity	[41]

Table 2

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