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基于R语言GGE双标图的芝麻区域试验分析

Analysis of Sesamum indicum Regional Test based on GGE Biplot of R Language
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摘要 为了全面比较芝麻新品种(系)田间表现和适应性、丰产性、稳产性,筛选优异芝麻品种(系),对2023年黄淮区域芝麻新品种(系)区域试验数据进行方差分析、相关性分析、灰色关联分析和GGE双标图分析。结果表明,13个参试芝麻品种(系)平均产量为1508.84 kg/hm^(2),产量最高的品种为商芝10号(1698.85 kg/hm^(2)),比对照增产19.74%;变异系数最大的品种是商芝11号(22.07%),变异系数最小的是项芝4号(8.76%);高稳系数最大的品种是商芝10号(87.52%),高稳系数最小的品种是峡芝1号(75.05%)。各试点平均产量最高的是安徽阜阳(1901.07 kg/hm^(2)),最低的是河南南阳(1351.05 kg/hm^(2))。参试芝麻新品种(系)主要性状中,空稍尖长度变异系数最大,为8.88%,含油量变异系数最小,为0.77%;芝麻品种(系)主要农艺和品质性状之间存在显著或极显著相关。产量与单株蒴数、单株重呈显著或极显著正相关关系;单株重与单株蒴数、含油量呈显著正相关关系,与蛋白质含量呈显著负相关关系。灰色关联度分析结果表明,各性状权重顺序从大到小为始蒴高度、单株蒴数、单株重、空稍尖长度、每蒴粒数、主茎果轴长度、有效果节数、产量、千粒重、株高、始蒴节位,对应的关联度为0.773、0.640、0.617、0.569、0.567、0.558、0.551、0.525、0.514、0.502、0.463。安徽阜阳试点的区分力和代表性最好,是理想试点。6个试点被分成3组,商芝10号在第1组试点产量最高,驻22B03在第2组试点产量最高,商芝11号在第3组试点产量最高。芝麻品种(系)适应性、丰产性和稳产性结果表明,13个芝麻品种在不同试点适应性存在差异,商芝10号、驻22B07、周芝22、信芝6号丰产性和稳产性较好。 In order to comprehensively compare the field performance,adaptability,high yield and stable yield of new sesame varieties(lines),and screen excellent sesame varieties(lines),the variance analysis,correlation analysis,grey correlation analysis and GGE biplot analysis on the regional test data of new sesame varieties(lines)in Huang-Hhuai-Hai Region were conducted in 2023.The results showed that the average yield of 13 Sesamum indicum varieties(lines)was 1508.84 kg/hm^(2),and the highest yield variety was Shangzhi No.10(1698.85 kg/hm^(2)),which was 19.74%higher than that of the control.The variety with the highest coefficient of variation was Shangzhi No.11(22.07%),and the lowest coefficient of variation was Xiangzhi No.4(8.76%).The variety with the largest high stability coefficient was Shangzhi No.10(87.52%),and the variety with the smallest high stability coefficient was Xiazhi No.1(75.05%).The highest average yield was in Fuyang,Anhui(1901.07 kg/hm^(2)),and the lowest was in Nanyang,Henan(1351.05 kg/hm^(2)).Among the main traits of new sesame varieties(lines),the coefficient of variation of empty tip length was the largest,which was 8.88%,and the coefficient of variation of oil content was the smallest,which was 0.77%.There were significant or extremely significant correlations between the main agronomic and quality traits of Sesamum indicum varieties(lines).The yield had significant or extremely significant positive correlation with capsule number per plant and weight per plant.The weight per plant was significantly positively correlated with capsule number per plant and oil content,and significantly negatively correlated with protein content.The results of grey correlation analysis showed that the weight order of each trait was initial capsule height,number of capsules per plant,weight per plant,empty tip length,number of grains per capsule,fruit axis length of main stem,number of effective nodes,yield,1000-grain weight,plant height from big to small,initial capsule node,and the corresponding correlation degrees were 0.773,0.640,0.617,0.569,0.567,0.558,0.551,0.525,0.514,0.502,0.463.Fuyang pilot in Anhui had the best discrimination and representativeness,and was an ideal pilot.The six pilots were divided into three groups.The highest yield of Shangzhi No.10 was in the first group,the highest yield of Zhu 22B03 was in the second group,and the highest yield of Shangzhi No.11 was in the third group.The results of adaptability,high yield and stable yield of Sesamum indicum varieties(lines)showed that there were differences in adaptability of 13 Sesamum indicum varieties in different pilot sites.The high yield and stable yield of Shangzhi No.10,Zhu 22B07,Zhouzhi No.22 and Xinzhi No.6 were better.
作者 周霞丽 隗正阳 阚跃峰 石明权 王文文 管少花 苗任重 陈宏 张少泽 崔向华 ZHOU Xiali;WEI Zhengyang;KAN Yuefeng;SHI Mingquan;WANG Wenwen;GUAN Shaohua;MIAO Renzhong;CHEN Hong;ZHANG Shaoze;CUI Xianghua(Zhumadian Academy of Agricultural Sciences,Zhumadian Henan 463000,China;Pingyu County Institute of Agricultural Sciences,Pingyu Henan 463400,China)
出处 《种子》 北大核心 2025年第7期186-194,共9页 Seed
基金 财政部和农业农村部国家现代农业产业技术体系(CARS-14-2-16) 河南省大豆油料产业技术体系(HARS-22-06-Z1)。
关键词 芝麻 新品种 灰色关联度分析 GGE双标图 Sesamum indicum new varieties grey correlation analysis GGE biplot
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