摘要
通过对97份大麦种质资源农艺性状、生育期、抗性调查,筛选综合性状优异的种质,为大麦高产育种提供依据。运用相关分析、聚类分析和主成分分析等多元统计方法对6个性状进行遗传多样性分析。结果表明,6个性状平均变异系数为14.45%,主穗粒数和穗下节长的变异系数最大,蛋白质含量变异系数最小。通过系统聚类,将参试的97份材料分为4个类群,第Ⅰ类群属于矮杆组,第Ⅱ类群属于低蛋白组,第Ⅲ类群属于高杆高蛋白组,第Ⅳ类群包括48份材料,产量性状优异。在主成分分析中,可选取方差累计贡献率为89.9%的前4个主成分来评价97份大麦资源。本研究揭示了大麦不同资源的表型特异性和遗传多样性,筛选出了部分优异资源,为高产优质大麦新品种的选育提供重要的科学依据。
By investigating the agronomic traits,growth period and resistance of 97 barley germplasms,the germplasms with excellent comprehensive traits was selected to provide basis for high-yield breeding of barley.And,multivariate statistical methods such as correlation coefficient analysis,cluster analysis and principal component analysis were used to analyze the genetic diversity of six traits.The results showed that the average coefficient of variation of 6 traits was 14.45%,the variable coefficient of grain number and subpanicle node length was the largest,while the variable coefficient of protein content was the lowest.Further,the 97 materials were divided into 4 groups,namely groupⅠbelonging to group with short rod,groupⅡbelonging to group low protein content,groupⅢbelonging to group with high rod and high protein content,and groupⅣincluding 48 materials with excellent yield and traits by systematic clustering.In principal component analysis,the first four principal components with 89.9%cumulative variance contribution rate could be selected to evaluate 97 barley germplasms.In this study,phenotypic specificity and genetic diversity of different barley resources were revealed,and some excellent resources were screened out,which provided important scientific basis for breeding new barley varieties with high yield and good quality.
作者
牛小霞
柳小宁
潘永东
包奇军
张华瑜
赵锋
NIU Xiaoxia;LIU Xiaoning;PAN Yongdong;BAO Qijun;ZHANG Huayu;ZHAO Feng(Institute of Industrial Crops and Malting Barley,Gansu Academy of Agricultural Sciences,Lanzhou 730070,China)
出处
《种子》
北大核心
2021年第8期68-72,77,共6页
Seed
基金
甘肃省农业科学院农业科技创新专项(2020 GAAS 37)
甘肃省青年科技基金计划(20 JR 5 RA 101)
国家大麦青稞产业体系(CARS-05-01 A-08)。
关键词
大麦
农艺性状
相关分析
聚类分析
主成分分析
barley
agronomic traits
correlation analysis
cluster analysis
principle component analysis
