数字农科院2.0

Evaluation of the Kernel Test Weight and Selection of Identification Indexes of Maize Inbred Lines

文献类型： 外文期刊

作者： Shen, Tao;Li, Jianping;Wang, Chao;Fan, Haihong;Zheng, Yunxiao;Liu, Yifan;Zhang, Shuzhen;Zhu, Liying;Jia, Xiaoyan;Zhao, Yongfeng;Song, Wei;Guo, Jinjie

作者机构：

关键词： comprehensive evaluation;PCA;cluster analysis;stepwise regression

期刊名称： AGRONOMY-BASEL

ISSN：

年卷期： 2025 年 15 卷 8 期

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收录情况： SCIE（2025版）

摘要： Kernel test weight (KTW) is one of the important assessment indexes of maize quality grade and one of the important influencing factors of yield. This study analyzed 12 traits related to KTW in 321 maize inbred lines using multivariate methods. The principal component analysis (PCA) indicated that the four PCs covered 78.176% of the information of the 12 traits in 321 maize inbred lines. Cluster analysis categorized the maize lines into six groups, identifying 16 elite inbred lines with the highest KTW. A stepwise regression model for KWT evaluation was developed using four PCA traits: starch content, amylopectin content, 100-kernel weight, and kernel circumference. The findings of this study serve as a valuable reference point for the genetic improvement of maize germplasm re-sources in kernel test weight and the creation of high kernel test weight maize resources.

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