四川农业大学学报 ›› 2007, Vol. 25 ›› Issue (03): 239-243,248.doi: 10.16036/j.issn.1000-2650.2007.03.002

• 研究论文 •    下一篇

栽培二粒小麦(Triticum dicoccum Schrank)主要农艺性状分析

王晓蓉1, 李伟1,2, 郑有良1   

  1. 1. 四川农业大学小麦研究所, 四川 都江堰 611830;
    2. 四川农业大学农学院, 四川 雅安 625014
  • 收稿日期:2007-03-19 出版日期:2007-09-30 发布日期:2017-03-04
  • 基金资助:
    四川省"十一五"作物育种攻关项目。

Principal Component and Cluster Analysis of Agronomic Characters in Triticum dicoccum Schrank

WANG Xiao-rong1, LI Wei1,2, ZHENG You-liang1   

  1. 1. Triticeae Research Institute, Sichuan Agricultural University, Dujiangyan 611830, Sichuan China;
    2. College of Agriculture, Sichuan Agricultural University, Yaan 625014, Sichuan, China
  • Received:2007-03-19 Online:2007-09-30 Published:2017-03-04

摘要: 利用相关、主成分及聚类分析对91份栽培二粒小麦(Triticum dicoccum S.)主要农艺性状进行了考察。结果表明,栽培二粒小麦农艺性状遗传变异丰富,具有单株有效穗多、千粒重较低和生育期较长等特点。其中株高与穗长、生育期,有效穗与抽穗期,穗长与小穗数、穗粒数,小穗数与穗粒数,穗粒数与生育期,抽穗期与生育期间相关和偏相关极显著。主成分分析中抽穗期、穗长、有效穗及粒重等4个主成分因子累积贡献率达88.16%,以抽穗期因子的贡献率最大(38.12%)。供试材料在遗传距离0.50水平上聚为4类,其中类Ⅰ为高秆大穗晚熟型,类Ⅱ为高秆寡分蘖晚熟型,类Ⅲ为矮秆粒多早熟型,类Ⅳ为粒多强分蘖早熟型,其聚类结果与供试材料地理来源间不存在必然联系。

关键词: 栽培二粒小麦, 农艺性状, 相关分析, 主成分分析, 聚类分析

Abstract: The agronomic characters of 91 cultivated emmer wheat were evaluated by correlation, principal component and cluster analysis. Higher variation of the eight agronomic characters was observed among all accessions. The result of correlation analysis indicated that there were significantly positive relationships between spike length, growing period and plant height, but significantly negative relationships between number of productive panicles and heading time. Four main components, which together made the contribution of 88.16% to variation, were obtained based on principal component analysis, the component of heading time making the greatest contribution of 38.12%. Furthermore, all the materials were clustered into four groups at 0.50 of genetic distance (GD) value. The clustered results of all accessions did not display direct relationship with the geographical origins of all materials.

Key words: Triticum dicoccum, agronomic characters, correlation, principal component analysis, cluster analysis

中图分类号: 

  • S512.19