The study aims to explore the rational application of mung bean germplasm resources and lay the foundation for the breeding of mung bean varieties.Using 25 mung bean germplasms from the main mung bean producing areas in Hebei Province in recent years as experimental materials,genetic diversity analysis,cluster analysis,and principal component analysis were conducted on 10 major agronomic and economic traits.The results showed that the coefficient of variation for 10 agronomic and economic traits ranged from 2.12％to 16.57％,with the highest coefficients of variation observed for the number of main stem nodes(16.57％)and the entire growth period(12.82％).According to the correlation analysis,there was a significant positive correlation between individual grain yield,hundred grain weight,and yield;the yield of grains per plant was significantly positively correlated with the number of grains per pod(P＜0.01),and significantly positively correlated with the length of a single pod and the weight of one hundred grains.Cluster analysis showed that 25 materials were divided into 6 groups,among which Group Ⅰ(7 samples)and Group Ⅵ(3 samples)had significantly higher yield,hundred grain weight,and pod length traits than other groups,and could be used as parent materials for high-yield mung bean introduction or hybrid breeding.Principal component analysis concentrated 10 agronomic and economic trait indicators into 3 principal components with a cumulative contribution rate of 70.16％,which could effectively explain the total variation of mung bean agronomic traits.The second principal component was closely related to pod length and 100 grain weight,with a contribution rate of 23.563％.Research has found that 25 mung bean germplasm resources had great potential for variation.Comprehensive evaluation using multiple analysis methods shows that'Yingge 2','Jilv HN0802-1-2-1-1-4','Zhanglv 2','Jilv 0911-1-2',and'Langlv 2'can be used as excellent parental materials forbreeding.
mung beagermplasm resourcesbreedingagronomic traitseconomic traitsgenetic diversitycluster analysisprincipal component analysis
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