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瓠瓜种质资源主要果实性状的主成分与聚类分析
引用本文:张兆辉,姜玉萍,陈春宏.瓠瓜种质资源主要果实性状的主成分与聚类分析[J].中国蔬菜,2018,1(8):38-43.
作者姓名:张兆辉  姜玉萍  陈春宏
作者单位:(;1.上海市农业科学院庄行综合试验站,上海 201415;;2. 上海市设施园艺技术重点实验室,上海 201403)
基金项目:上海市市级农口系统青年人才成长计划项目〔沪农青字(2016)第1-5 号〕
摘    要:对119份瓠瓜种质的8个主要果实性状进行了主成分分析与聚类分析。结果表明,119份瓠瓜种质的ShannonWeaver多样性信息指数在0.394 8~2.063 6之间,8个果实性状存在较广泛的遗传多样性,其中以果实形状、皮色和单瓜质量的多样性最丰富。主成分分析结果表明,第一、第二、第三、第四主成分的贡献率分别为37.24%、23.33%、14.71%、9.44%,其累计贡献率达到84.72%。聚类分析结果表明,可以将119份瓠瓜种质分为两大类群,分别包含62份和57份种质,其中第一大类群在品质和外观方面均表现较好,可作为瓠瓜育种的基础材料。

关 键 词:瓠瓜  果实性状  主成分分析  聚类分析  

Principal Components and Cluster Analysis of Main Fruit Characters of Bottle Gourd Germplasm Resources
ZHANG Zhao-hui,JIANG Yu-ping,CHEN Chun-hong.Principal Components and Cluster Analysis of Main Fruit Characters of Bottle Gourd Germplasm Resources[J].China Vegetables,2018,1(8):38-43.
Authors:ZHANG Zhao-hui  JIANG Yu-ping  CHEN Chun-hong
Institution:(;1.Zhuanghang Integrated Experimental Station,Shanghai Academy of Agricultural Sciences,Shanghai 201415,China;;2.Shanghai Key Laboratory of Protected Horticultural Technology,Shanghai 201403,China)
Abstract:This paper carried out principal components and cluster analysis of 8 main fruit characters from119 bottle gourd germplasm resources.The results showed that Shannon-Weaver diversity information index was between 0.394 8 - 2.063 6.There were wide genetic diversities in 8 fruit characters,among them fruit shape,peel color and single fruit weight had the most abundant genetic diversities.The results of principal components analysis showed that the first,second,third and fourth principal component contribution rates were 37.24%,23.33%,14.71% and 9.44%,respectively and their cumulative contribution rate reached 84.72%.The results of cluster analysis indicated that 119 materials of bottle gourd germplasm resources could be divided into 2 groups,including 62 and 57 germplasm resources,respectively.The first group had better quality and appearance,and could be used as basic material for breeding of bottle gourd.
Keywords:Bottle gourd  Fruit characters  Principal components analysis  Cluster analysis  
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