摘要
为筛选出适宜绥化地区推广应用的优良籽用型工业大麻种质资源材料,对35份工业大麻种质资源的保苗率、株高、茎粗、分枝数、千粒重、干花叶产量和种子产量7个性状指标进行比较分析,应用主成分和系统聚类分析方法进行综合评价。结果表明,35份籽用工业大麻种质资源的种子产量变异最大,为50.54,株高变异最小,仅为6.38;各性状指标之间存在显著或极显著的相关性;通过主成分分析,提取了3个具有代表性且特征值大于1的主成分,累计贡献率为78.022%,并构建综合评价模型,对参试材料进行综合排序;最后应用系统聚类将35份籽用工业大麻种质分为3类,初步筛选出18份综合性状良好的育种材料。
In order to screen excellent seed-using industrial hemp germplasm resources suitable for popularization and application in Suihua Area,seven characters of 35 industrial hemp germplasm resources were compared and analyzed,including seedling survival rate,plant height,stem diameter,number of branches,1000-grain weight,dry mosaic yield and seed yield.At the same time,the resources were comprehensively evaluated by principal component analysis and systematic cluster analysis.The results showed that the coefficient of variation of seed yield of 35 seed industrial hemp germplasm resources was the largest,which was 50.54,and the coefficient of variation of plant height was the smallest,which was only 6.38.There was a significant or extremely significant correlation among all the traits.Through principal component analysis,three representative principal components with eigenvalues greater than 1 were extracted,with a cumulative contribution rate of 78.022%,and a comprehensive evaluation model was constructed to sort the tested materials comprehensively.Finally,35 seed-using industrial hemp germplasm were divided into 3 categories by systematic clustering,and 18 breeding materials with good comprehensive characteristics were preliminarily screened out.
作者
高佳缘
武新娟
唐贵
隋冬华
张冬雪
李鑫
孙伟
吴建忠
GAO Jiayuan;WU Xinjuan;TANG Gui;SUI Donghua;ZHANG Dongxue;LI Xin;SUN Wei;WU Jianzhong(Institute of Rural Revitalization Science and Technology,Heilongjiang Academy of Agricultural Sciences,Harbin 150023,China;Pratacultural Science Institute,Heilongjiang Academy of Agricultural Sciences,Harbin 150086,China)
出处
《黑龙江农业科学》
2023年第9期19-25,共7页
Heilongjiang Agricultural Sciences
基金
黑龙江省农业科学院“农业科技创新跨越工程”专项(HNK2019CX09)。
关键词
籽用工业大麻
综合评价
主成分分析
系统聚类
seed-using industrial hemp
comprehensive evaluation
principal component analysis
systematic clustering