Welcome to Journal of Agriculture,

Journal of Agriculture ›› 2022, Vol. 12 ›› Issue (8): 27-34.doi: 10.11923/j.issn.2095-4050.cjas2021-0099

Special Issue: 生物技术 小麦

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The Agronomic Traits of 24 Broomcorn Millet Materials: Principal Component Analysis and Cluster Analysis

YANG Fang(), LIANG Haiyan, LIN Fengxian, SONG Xiaoqiang, DENG Yarui, LI Hai()   

  1. Alpine Crops Research Institute, Shanxi Agricultural University, Datong 037008, Shanxi, China
  • Received:2021-05-06 Revised:2021-08-13 Online:2022-08-20 Published:2022-09-22
  • Contact: LI Hai E-mail:460091450@qq.com;lihaihai2005@126.com

Abstract:

To screen excellent breeding materials for broomcorn millet and provide reference for its local breeding, the agronomic traits of 24 broomcorn millet materials were analyzed by principal component analysis and cluster analysis, and a comprehensive evaluation was conducted in this experiment. The results showed that the cumulative variance contribution rate of the four extracted principal components was 84.9%, which reflected most of the information of the original indexes. ‘201-8’ and ‘06-D69’ had the highest comprehensive scores among the 24 materials. The test materials were grouped into four categories, the score and yield of the first and second categories were better, the average score was 41.78 and 38.48, respectively, and the average yield was 251.1 kg/hm2 and 210.6 kg/hm2, respectively. These materials can be given priority in variety breeding. The score and yield of the third category was poor, 37.69 and 185.1 kg/hm2, respectively, but the plant height, spike length, basal stem diameter and other indicators were better, which could be considered in breeding lodging-resistant varieties. The materials of the fourth category scored the least and had the lowest yield, 33 and 169 kg/hm2, respectively, and other indexes were not ideal too. Therefore, the materials of the fourth category are not recommended.

Key words: broomcorn millet, agronomic traits, principal component analysis, cluster analysis, comprehensive evaluation

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