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基于6个重要农艺性状的四川地区大蒜资源表型评价 被引量:12

Phenotype Evaluation of Garlic(Allium sativum L.)Germplasm based on the Six Main Agronomic Traits in Sichuan Area
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摘要 采用主成分分析法和聚类分析法,对四川地区81份大蒜种质资源的株高、叶长、叶宽、假茎长、假茎粗和鳞茎质量6个农艺性状进行多样性评价,同时结合海拔高度开展相关性分析。结果表明:四川地区大蒜资源的遗传变异丰富,6个农艺性状的变异系数为19.13%~73.43%,其中大蒜鳞茎质量的变异系数高达73.43%。6个农艺性状可综合为3个主成分,累计贡献率达87.804 6%,第1主成分反映植株株型,第2、3主成分反映鳞茎质量。在主成分分析的基础上,将81份大蒜资源在欧氏距离169.8的水平上聚为4个类群,类群Ⅱ适合作为选育独头蒜品种的重要资源,类群Ⅲ适合作为选育高产大蒜品种的重要资源。海拔高度与大蒜鳞茎质量呈显著正相关,与株高、假茎长呈极显著负相关。 This paper carried out diversity evaluation on the 6 agronomic traits of plant height,leaf length,leaf width,pseudostem length and diameter,bulb weight of the 81 accessions of garlic germplasm resources collected from Sichuan area;and at the same time conducted correlation analysis combined with altitude by principal component analysis and clustering analysis. The results showed that the garlic resources in Sichuan area had rich genetic variation. The variable coefficient of the above 6 agronomic traits were 19.13%-73.43%,among which the variable coefficient of the bulb weight was as high as 73.43%. The 6 agronomic traits could be integrated into 3 principal components and the cumulative contribution rates accounted for 87.804 6%. The first principal component represented the plant type,the second and the third principal components represented bulb weight. Based on the principal component analysis,the 81 accessions of garlic resources were classified into 4 groups at the level of Euclidean distance 169.8 by clustering analysis. As important resources,group Ⅱ was the important resource for breeding one-clove garlic variety,and group Ⅲ was important resource for high-yield variety. The altitude was significant positive correlation with garlic bulb weight,while was extremely significant negative correlation with plant height and pseudostem length.
出处 《中国蔬菜》 北大核心 2018年第3期63-68,共6页 China Vegetables
基金 国家特色蔬菜产业技术体系专项(CARS-24-G-19) 现代农业产业技术体系四川蔬菜创新团队专项 科研条件平台建设-蔬菜轮作专项
关键词 大蒜 种质资源 农艺性状 主成分分析 聚类分析 相关性分析 Garlic Germplasm resources Agronomic trait Principal component analysis Clustering analysis Correlation analysis
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