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春用蚕品种数量性状主成分分析

Analysis on the Principal Components in Quantitative Characters of Silkworm Varieties for Spring Rearing
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摘要 [目的]探讨我国春用蚕品种的选育现状。[方法]选20世纪70、80年代育成的15个春用家蚕品种,对12个数量性状进行主成分分析,再根据主成分值进行聚类分析。[结果]方差分析表明12个数量性状差异显著。12个数量性状简化为产量因子、净纤因子、解舒率因子、茧层率因子4个主成分。4个主成分的信息量占全部信息量的84.50%。聚类分析发现15个品种的正态标准差均方根距离取值在0.315~1.191,可划分为各具特点的3大类群。产量因子的整体趋势是第1类群>第2类群>第3类群;净纤因子是第1类群>第3类群>第2类群;解舒率因子是第2类群>第3类群>第1类群;茧层率因子是第1类群>第2类群>第3类群。[结论]我国1970年以后育成的春用蚕品种多数性状相近。 Objective The study aimed to discuss the breeding status of silkworm varieties for spring rearing in China.Method 15 Bombyx mori varieties for spring rearing,which were bred in the 1970s and 1980s,were selected out and their 12 quantitative characters were conducted for principle components analysis and then the cluster analysis was done according to principle components values.Result The variance analysis showed that 12 quantitative characters had significant difference.The 12 quantitative characters were simplified into 4 principle components of yield,net fibre,reelability rate and cocoon layer rate factors.The information content of 4 principle components accounted for 84.50% of total information content.The cluster analysis showed that the selected-value at the distance of mean square root of normal standard deviation of 15 varieties were in 0.315~1.191,which could be classified into 3 large groups with their own characters.The whole trend of yield factor was the 1st group 〉 the 2nd group 〉 the 3rd group,that of net fibre factor was the 1st group 〉 the 3rd group 〉 the 2nd group,that of reelability rate factor was the 2nd group 〉 the 3rd group 〉 the 1st group and that of cocoon layer rate factor was the 1st group 〉 the 2nd group 〉 the 3rd group.Conclusion Most silkworm varieties for spring rearing,which were bred in China after 1970,had similar characters.
出处 《安徽农业科学》 CAS 北大核心 2007年第35期11481-11482,共2页 Journal of Anhui Agricultural Sciences
基金 "十一五"国家科技支撑计划(2006BAD06B03) 镇江市科技计划(NY2007036)
关键词 家蚕 育成的春用品种 主成分分析 聚类分析 Bombyx mori Bred silkworm variety for spring rearing Principal component analysis Cluster analysis
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