Analysis and Comparative Study of Different Comprehensive Evaluation Methods on the Integrated Agronomic Traits of SoybeanVarieties
To provide a scientific and rational method for comprehensive evaluation of soybean agronomic traits,five common compre-hensive evaluation methods,including the DTOPSIS,grey relational analysis,fuzzy evaluation method,similarity and difference analy-sis,and principal component analysis,were applied to conduct a comprehensive analysis of 14 varieties from the summer soybean re-gional trial in Henan Province in 2020.The advantages and disadvantages of different methods in comprehensive evaluation of soy-bean agronomic traits were compared.The results showed that there was a very significant positive correlation between the comprehen-sive scores of the varieties calculated by the five analysis methods.Among them,the DTOPSIS method had the best discrimination for the comprehensive traits of the varieties,followed by grey relational analysis,similarity and difference analysis,principal component analysis,and fuzzy function method.The similarity and difference analysis had the highest accuracy in evaluating comprehensive traits,followed by the fuzzy function method,grey relational analysis,principal component analysis,and DTOPSIS.The grey relational analysis had the largest grey relational degree with yield among the comprehensive scores of the five analysis methods,followed by DTOPSIS,similarity and difference analysis,fuzzy function method,and principal component analysis.The results indicated that the grey relational analysis method have a high degree of accuracy and discrimination in the comprehensive evaluation of agronomic traits of a large number of soybean materials,and the analysis results are closely related to yield,which can provide effective decision-making references for breeders in the identification and screening of soybean varieties.
soybeangrey relational analysisDTOPSISfuzzy function methodsimilarity and difference analysisprincipal compo-nent analysis
国家科技期刊平台
NSTL
万方数据
