摘要
为了充分利用优质普通菜豆资源,以来自3个国家的72份普通菜豆资源为研究材料,采用隶属函数、Shannon-Wiener's多样性指数、主成分分析及聚类分析等方法对10个表型性状进行综合评价分析.结果表明:10个性状的变异系数在13.9%～52.1%之间,表型多样性指数在1.354～2.209之间;9个表型性状与小区产量的紧密程度表现为单株产量＞株高＞荚宽＞单株荚数＞单荚粒数＞荚长＞每果节荚数＞单株分枝数＞百粒重.UMAP聚类分析将72份普通菜豆资源按照3个国家进行分组,巴西距离墨西哥和危地马拉的距离相对较远.72份普通菜豆资源系统聚类划分为3个类群:第Ⅰ类群包括25份普通菜豆资源,其中,巴西10份、墨西哥10份、危地马拉5份;第Ⅱ类群包括25份普通菜豆资源,其中,巴西4份、墨西哥14份、危地马拉7份;第Ⅲ类群包括22份普通菜豆资源,其中,巴西3份、墨西哥14份、危地马拉5份;主成分评价及隶属函数评价筛选到10份综合表现优异的资源,包括YJ010459、YJ009992-A、YJ009764、YJ010450、YJ010490、YJ010092-A、YJ010270、YJ009749、YJ009789、YJ010012.
Abstract
In order to fully utilize high-quality common kidney bean resources,72 kidney bean resources from 3 countries were used as research materials,and membership function,Shannon-Wiener's diversity index,principal component analysis and cluster analysis were used to evaluate 10 phenotypic traits.The results showed that the coefficient of variation of the 10 traits ranged from 13.9%to 52.1%,and the phenotypic diversity index ranged from 1.354 to 2.209.The close degree between traits and plot yield was the yield per plant＞plant height＞pod width＞pod number per plant＞seed number per pod＞pod length＞pod number per node＞branch number per plant＞100-seed weight.The UMAP Cluster analysis grouped 72 kidney bean resources into three groups,with Brazil relatively far from Mexico and Guatemala,and 72 kidney bean resources were clustered into three groups,group Ⅰ includes 25 kidney bean resources,including 10 from Brazil,10 from Mexico and 5 from Guatemala,and Group Ⅱ includes 25 kidney bean resources,including 4 from Brazil,14 from Mexico and 7 from Guatemala.Group Ⅲ includes 22 resources,including 3 resources in Brazil,14 resources in Mexico and 5 resources in Guatemala.Ten excellent resources(YJ010459,YJ00992-A,YJ009764,YJ010450,YJ010490,YJ010092-A,YJ010270,YJ009749,YJ009789 and YJ010012)were selected by principal component analysis and membership function evaluation.
基金项目
国家现代农业产业技术体系——食用豆(CARS-08-Z17)
贵州省高层次创新型人才(毕科人才合字[2021]04号)
贵州省高层次创新型人才(毕科人才合字[2023]8号)
毕节市揭榜挂帅项目(毕科合重大专项[2022]3号)
毕节市揭榜挂帅项目(毕科合重大专项[2023]5号)
贵州省第七批人才基地项目(RCJD2020-30)
国家科技期刊平台
NSTL
万方数据