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苦荬菜种质资源表型变异分析 被引量:6

Phenotypic Variation Analysis of Ixeris polycephala
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摘要 以30份苦荬菜种质材料为研究对象,对11个表型性状进行相关性分析、聚类分析、主成分分析,结果表明:(1)不同苦荬菜种质资源表型特征存在广泛变异,其中三级生殖枝数变异幅度最大(38.04%),生殖枝高度和小花数次之,叶长变异幅度最小(11.87%),不同类型间以野生材料变异幅度最大(46.37%);(2)苦荬菜各性状间显著相关,叶片长又宽的苦荬菜其茎秆粗壮,植株高大,花序长,二级和三级生殖枝数多;(3)聚类分析表明,不同苦荬菜种质资源可划分为4组,第1组与第2组属于叶匍匐型,第3组属于中等宽叶直立型,第4组属于阔叶直立型;(4)主成分分析显示,前3个主成分累计贡献率达71.904%,不同材料间差异来源主要是株高、茎粗、叶长、叶宽、小花数。 The correlation analysis,cluster analysis and principal component analysis of 1 1 phenotypic characters of 30 Ixeris polycephala materials were conducted. The results were as follows:1)there were wide range of variation within phenotypic characters of Ixeris polycephala ,the third reproductive branch had the most significant variance coefficient of 38.04%,followed by reproductive height and flower num-ber,while leaf length had the least variance coefficient of 1 1.87%,the variation of wild type was the high-est (46.37%). 2)There were significant correlation among these phenotypic characters. The wider and longer the leave of Ixeris polycephala were the tall plants with strong stem,longer inflorescence,more reproductive branches were.3)Cluster analysis showed that the materials could be divided into four groups with similar phenotypes,the first and second group belonged to narrow leaved creeping type,the third group belonged to mid-wide leaved erect type,and the forth group belonged to wide leaved erect type. 4) Principal component analysis showed that the first three principal components accounted for 71.904%,and indicated that the differences among Ixeris polycephala materials derived from plant height,stem diame-ter,the size of leaves and flower number. Thus,leaf number,third reproductive branch number,floret number of Ixeris polycephala had rich genetic variation,can provide important reference for phenotypic variation index.
出处 《中国草地学报》 CSCD 北大核心 2016年第3期34-41,共8页 Chinese Journal of Grassland
基金 国家现代牧草产业技术体系项目(CARS-35-05) 国家科技支撑计划项目(2011BAD17B03)资助
关键词 苦荬菜 表型变异 聚类分析 主成分分析 Ixeris polycephala Phenotypic variation Cluster analysis Principal component analysis
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