摘要
通过对不同来源的30份工业大麻种质资源的12个表型性状进行测定,计算各性状的变异系数,并进行相关性分析、主成分分析和聚类分析,以期揭示工业大麻各材料表型性状的遗传多样性,为工业大麻种质资源的合理利用及新品种选育等提供数据支撑。对参试材料的表型性状进行分析,结果表明,参试的30份工业大麻种质资源存在丰富的遗传变异,各表型性状的变异幅度为9.01%~34.00%,变异系数最大的是果实熟性,最小的是株高。相关分析表明,工业大麻各性状之间存在不同程度的相关性,且大多数性状之间呈正相关关系。主成分分析提取了前4个主成分,累计贡献率达80.80%。4个主成分分别反映了株型、分枝、节数、产量及其构成信息。采用离差平方和法对30份供试材料的表型性状进行聚类分析,供试种质资源分成三大类群,第Ⅰ类为细矮低产型,第Ⅱ类属于型大籽粒型,第Ⅲ类属于高产型。
12 agronomic traits of 30 industrial hemp germplasm resources were measured,coefficient of variation of each trait was calculated,and correlation analysis,principal component analysis and cluster analysis were conducted,in order to reveal the broad genetic diversity of phenotypic traits of industrial hemp,and provide data support for the rational use of industrial hemp germplasm resources and breeding new varieties.The phenotypic traits measured showed abundant variations among the investigated accessions,with the coefficient of variation ranging from 9.01%to 34.00%,the largest was maturity,and the smallest was plant height.Correlation analysis showed that varying degrees of correlation existed among the traits of industrial hemp,and most of the characters were positively correlated.Principal component analysis extracted four components explaining 80.80%of total variance.The four principal components reflected the plant morphology,branching,node number,yield and their composition information,respectively.Cluster analysis divided 30 industrial hemp germplasm resources into three categories.The comprehensive character of the first group was thin,short and low yield type,the second group was large grain type,the third group was high yield type.
作者
高金虎
吴晓宇
赵铭森
冯旭平
孔佳茜
薛红丽
孟晓康
康红梅
GAO Jinhu;WU Xiaoyu;ZHAO Mingsen;FEN Xuping;KONG Jiaqian;XUE Hongli;MENG Xiaokang;KANG Hongmei(Institute of Industrial Crops,Shanxi Agricultural University,Taiyuan 030031,China)
出处
《种业导刊》
2022年第3期7-13,共7页
Journal of Seed Industry Guide
基金
国家麻类产业技术体系建设专项(CARS-16S01)
山西农业大学生物育种工程项目(YZGC046)。
关键词
工业大麻
种质资源
表型性状
遗传多样性分析
Industrial hemp
Germplasm resources
Phenotypic traits
Genetic diversity analysis
