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农学学报 ›› 2026, Vol. 16 ›› Issue (5): 1-7.doi: 10.11923/j.issn.2095-4050.cjas2025-0006

• 农艺科学 生理生化 •    下一篇

玉米农艺性状的多尺度关联分析与综合分类研究

付华(), 李猛, 刘兴舟, 马桂美, 王培, 张建(), 周言虎   

  1. 宿州市农业科学院, 安徽宿州 234000
  • 收稿日期:2025-01-10 修回日期:2025-03-03 出版日期:2026-05-20 发布日期:2026-05-15
  • 通讯作者:
    张建,男,1980年出生,安徽濉溪人,副研究员,主要从事玉米育种及栽培研究。通信地址:234000 安徽省宿州市埇桥区学府大道2156号 宿州市农业科学院,Tel:0557-2226816,E-mail:
  • 作者简介:

    付华,女,1985年出生,安徽宿州人,助理研究员,硕士,主要从事玉米育种及栽培研究。通信地址:234000 安徽省宿州市埇桥区学府大道2156号 宿州市农业科学院,Tel:0557-2226816,E-mail:

  • 基金资助:
    国家现代农业产业体系建设专项(CARS-02-71)

Research on Multi-Scale Correlation Analysis and Comprehensive Classification of Maize Agronomic Traits

FU Hua(), LI Meng, LIU Xingzhou, MA Guimei, WANG Pei, ZHANG Jian(), ZHOU Yanhu   

  1. Suzhou Academy of Agricultural Sciences, Suzhou, Anhui 234000
  • Received:2025-01-10 Revised:2025-03-03 Online:2026-05-20 Published:2026-05-15

摘要:

为系统评价玉米品种农艺性状的综合表现,本研究以2011—2023年参加安徽省夏玉米区域试验的(高密度组)玉米品种为研究对象,测定株高、穗位高、穗长、穗粗、秃尖长、千粒重、产量等13项关键农艺性状,通过多维度统计分析方法构建综合评价体系。首先利用相关性分析揭示性状间关联性,结果表明产量与穗粗、行粒数呈极显著正相关(r=0.869、r=0.836),与穗长、出籽率呈显著正相关(r=0.626、r=0.573),与秃尖长呈显著负相关(r=-0.558)。进一步采用主成分分析提取出5个主成分(累计贡献率达87.96%),分别反映产量潜力、形态特征和抗逆性,构建主成分综合评价模型。基于聚类分析将品种划分为高产稳产型(Ⅰ类)及适应性广型(II类)。结合灰色关联度分析量化各性状与理想品种的关联度(关联序:产量>穗行数>穗长>穗粗>生育期),筛选出综合表现最优年份的品种2023年、2017年、2019年(关联度>0.82)。本研究通过多方法融合建立了玉米品种农艺性状评价模型,为品种选育与生产推广提供理论依据。

关键词: 玉米, 相关性分析, 主成分分析, 聚类分析, 灰色关联度分析

Abstract:

In order to systematically evaluate the comprehensive performance of the agronomic traits of maize varieties, this study took the maize varieties (in the high-density group) participating in the summer maize regional trials in Anhui Province from 2011 to 2023 as the research objects. Thirteen key agronomic traits, including plant height, ear height, ear length, ear diameter, bald tip length, 1000-grain weight, and yield, were measured, and a comprehensive evaluation system was constructed through multi-dimensional statistical analysis methods. Firstly, correlation analysis was used to reveal the correlations among traits. The results showed that the yield had an extremely significant positive correlation with the ear diameter and the number of grains per row (r=0.869, (r=0.836), a significant positive correlation with the ear length and the grain yield rate (r=0.626, (r=0.573), and a significant negative correlation with the bald tip length (r=-0.558). Furthermore, principal component analysis was adopted to extract five principal components (with a cumulative contribution rate of 87.96%), which respectively reflected the yield potential, morphological characteristics, and stress resistance, and a comprehensive evaluation model of principal components was constructed. Based on cluster analysis, the varieties were divided into high-yield and stable-yield type (Type I) and wide-adaptability type (Type II). Combined with the grey relational analysis, the relational degrees of various traits with the ideal variety were quantified (relational order: yield > number of rows per ear > ear length > ear diameter > growth period), and the varieties in the years with the best comprehensive performance, namely 2023, 2017, and 2019 (relational degree > 0.82), were screened out. Through the integration of multiple methods, this study established an evaluation model for the agronomic traits of maize varieties, providing a theoretical basis for variety breeding and production promotion.

Key words: corn, correlation analysis, principal component analysis, cluster analysis, grey correlation analysis

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