寒旱农业科学2024，Vol.3Issue(11)：1019-1024,6.DOI:10.3969/j.issn.2097-2172.2024.11.008
基于主成分和系统聚类分析法对23个观赏向日葵株系农艺性状的综合评价
Comprehensive Evaluation on Agronomic Characters of 23 Ornamental Sunflower Lines Based on Principal Component and Systematic Cluster Analysis
摘要
Abstract
To evaluate ornamental sunflower lines based on quality and quantity characters,so as to provide reference for breeding new varieties of ornamental sunflowers,high ornamental value sunflower lines were used as materials,the seedling and flowering phenotypic traits were analyzed,and the quality traits were graded by assigning values.On the basis of correlation analysis of all phenotypic traits,principal component analysis and systematic cluster analysis were performed on the data.Results showed that the diversity index and variation degree of ornamental sunflower lines were high,the diversity index of quality traits was 0.614 to 1.818,and the quantitative traits were 1.240 to 1.794.The variation coefficients of lingual flower width and lingual petal were 60.32%and 42.99%,respectively.Principal component analysis revealed that the eigenvalues of the first 5 principal components were 4.431,2.447,1.807,1.322,and 1.054,respectively,with corresponding variance contribution rates of 27.696%,15.293%,11.297%,8.261%and 6.585%,respectively.The cumulative contribution rate reached a significant value of 69.132%.The ornamental sunflower lines were grouped into 3 groups by systematic clustering analysis.Stem color,petal type,tongue flower color and anther color were the main phenotypic traits to distinguish different groups.Line gsk3014 were with the longest flowering days and line gsk3013,gsk3014,gsk3019 and gsk3020 were with earlier flowering stage.关键词
观赏向日葵/农艺性状/相关性分析/主成分分析/聚类分析Key words
Ornamental sunflower/Agronomic character/Correlation analysis/Principal component analysis/Cluster analysis分类
农业科技引用本文复制引用
王兴珍,卯旭辉,贾秀苹,谢亚萍,齐燕妮..基于主成分和系统聚类分析法对23个观赏向日葵株系农艺性状的综合评价[J].寒旱农业科学,2024,3(11):1019-1024,6.基金项目
国家特色油料产业技术体系(CARS-14-2-22) （CARS-14-2-22）
甘肃省农业科学院青年基金(2022GAAS52) （2022GAAS52）
2024年甘肃省重点人才项目(甘组通字[2024]4号). （甘组通字[2024]4号）
