中国农业科技导报 ›› 2019, Vol. 21 ›› Issue (3): 34-41.DOI: 10.13304/j.nykjdb.2018.0337

• 生物技术 生命科学 • 上一篇    下一篇

苎麻种质资源农艺性状主成分及聚类分析

李林林1,黄敏升1,崔国贤1,2*,白玉超1,3,李雪玲1,刘楠楠1,崔丹丹1,苏小惠1,王继龙1   

  1. 1.湖南农业大学苎麻研究所, 长沙 410128; 2.中国农业科学院麻类研究所, 长沙 410205; 3.芭田生态工程股份有限公司, 深圳 518000
  • 收稿日期:2018-05-31 出版日期:2019-03-15 发布日期:2018-08-16
  • 通讯作者: *通信作者:崔国贤,教授,主要从事麻类栽培育种、生理生态及植物营养生理研究。E-mail:gx-cui@163.com
  • 作者简介:李林林,硕士研究生,主要从事麻类作物栽培生理研究。E-mail:1846680327@qq.com。
  • 基金资助:
    现代农业产业技术体系项目(CARS-16-E11)资助。

Principal Component and Cluster Analysis of the Main Agronomic Characters of Ramie Germplasm

LI Linlin1, HUANG Minsheng1, CUI Guoxian1,2*, BAI Yuchao1,3, LI Xueling1, LIU Nannan1, CUI Dandan1, SU Xiaohui1, WANG Jilong1   

  1. 1.Ramie Research Institute of Hunan Agricultural University, Changsha 410128; 2.Institute of Bast Fiber Crops, Chinese Academy of Agricultural Sciences, Changsha 410205; 3.Batian Ecological Engineering Co. Ltd., Shenzhen 518000, China
  • Received:2018-05-31 Online:2019-03-15 Published:2018-08-16

摘要: 为了揭示我国丰富的苎麻种质资源,充分发掘利用其有益基因,利用主成分分析和聚类分析方法对收集的94份苎麻种质的7个主要农艺性状进行评价分析。利用主成分分析将苎麻的7个性状简化为4个主成分因子,其累积贡献率高达90.35%;其中第一主成分以株高、茎粗、有效株率的影响为主;第二主成分各个性状系数均为正,可以看作是苎麻种质农艺性状的综合反映;第三主成分以分株数、总株数的影响为主;第四主成分以有效株率的影响为主。采用系统聚类分析,将94份苎麻种质材料在阈值为3.79时聚为三个大类,可划分为高株细茎型、矮株粗茎型和1个特殊型。上述结果将苎麻种质资源农艺性状简化为4个主成分因子,并将94个苎麻品种分为3种类型,为苎麻优质品种选育和多功能应用提供参考依据。

关键词: 苎麻, 农艺性状, 主成分分析, 聚类分析

Abstract: The 7 agronomic characters of 94 ramie germplasm from China were investigated through principal component analysis and cluster analysis in order to identify the diversity of ramie germplasm and find the benefit genes. The results of principal components analysis showed that the 7 traits were simplified into 4 principal components (over 90.35% accumulated contribution). The first principal component was given priority to plant height, stem diameter, effective strain rate. The coefficients of the second principal component were all positive, which could be regarded as the comprehensive reflection of the agronomic characters of ramie germplasm. The third principal component was given priority to single stump points number and total number. The fourth principal component was given priority to the influence of the effective strain rate. Using system clustering analysis, 94 ramie varieties were clustered into three categories at the genetic distance 3.79, including high plant and thin stem, short plant and thick stem, and a special type. In this study, the agronomic traits of ramie germplasm resources were simplified into 4 principal component factors, and 94 ramie varieties were divided into three categories, which provided a reference for the breeding of high quality ramie varieties and their multi-functional application.

Key words: ramie, agronomic character, principal component analysis, clustering analysis