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60Co-γ射线诱变连麦2号M3代主要农艺性状的变异及多元分析
引用本文:张从宇,王敏,李文阳.60Co-γ射线诱变连麦2号M3代主要农艺性状的变异及多元分析[J].激光生物学报,2011,20(6):747-751.
作者姓名:张从宇  王敏  李文阳
作者单位:1. 安徽科技学院,安徽凤阳,233100
2. 安徽农业大学,安徽合肥,230036
基金项目:安徽省教育厅自然科学项目
摘    要:用60Co-γ射线诱变小麦连麦2号,对M3代292个穗行进行了主要农艺性状的变异及多元分析.结果表明:各性状的变异幅度是不同的,变异系数从大到小依次为:穗数>千粒重>穗长>小穗数>株高>行粒重>退化小穗数>穗粒数>穗粒重;变异率从大到小依次为:穗数>千粒重>株高=行粒重>穗粒数>穗长>穗粒重>小穗数>退化小穗数.穗数、...

关 键 词:60Co-γ射线  小麦  农异性状  变异  多元分析

Variation and Multivariate Analysis in Agronomic Traits of Wheat Lianmai 2 Hao' s M3 Induced by 60Co-γ, Rays
ZHANG Congyu,WANG Min,LI Wenyang.Variation and Multivariate Analysis in Agronomic Traits of Wheat Lianmai 2 Hao' s M3 Induced by 60Co-γ, Rays[J].ACTA Laser Biology Sinica,2011,20(6):747-751.
Authors:ZHANG Congyu  WANG Min  LI Wenyang
Institution:1.Anhui Science and Technologe University,Fengyang 233100,Anhui,China; 2.Anhui Agricultural University,Hefei 230036,Anhui,China )
Abstract:With wheat Lianmai2 reduced by (60)Co-γrays,variation and multivariate analysis in agronomic traits of 292 M_3 ear row were made.The results showed that:The range of variation was different,the descending order of coefficient of variation was spike number1000 grain weightspike lengthpanicle numberplant heightear row grain weightdegradation spike numberspike grain numberspike grain weight;The descending order of mutation rate was spike number1 000 grain weightplant height = ear row grain weightspike grain numberspike lengthspike grain weightpanicle numberdegradation spike number.There was significant correlation between spike number,spike length,plant height,panicle number,spike grain number,spike grain weight and ear row grain weight.Principal component analysis showed that 5 main components were selected.The cumulative contribution rate was 85.49%.When the genetic distance was 25.38,292 car row could be divided into 7 different characteristic groups.Tigh-yield type should be selected in GroupⅣand Group V,which had 27 materials;dwarf types should be selected in GroupⅢwhich had 59 materials.
Keywords:~(60)Co-γrays  wheat  agronomic traits  variation  multivariate analysis
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