河南农业科学 ›› 2026, Vol. 55 ›› Issue (7): 11-19.DOI: 10.15933/j.cnki.1004-3268.2026.07.002

• 作物栽培·遗传育种 • 上一篇    下一篇

78 份玉米自交系抗倒伏性综合评价及核心指标筛选

李方杰1,史大坤1,卫晓轶1,魏锋1,刘俊恒2,梅涌现3   

  1. (1.新乡市农业科学院,河南 新乡 453003; 2.鹤壁禾博士晟农科技有限公司,河南 鹤壁 458000;3.驻马店市种业发展中心,河南 驻马店 463000)
  • 收稿日期:2026-03-26 接受日期:2026-05-13 出版日期:2026-07-15 发布日期:2026-07-30
  • 通讯作者: 卫晓轶,副研究员,博士,主要从事玉米遗传育种研究。E-mail:xiaoyi_919@ 126.com。梅涌现同为通信作者
  • 作者简介:李方杰,研究实习员,硕士,主要从事玉米遗传育种研究。E-mail:ymslfj2022@126.com
  • 基金资助:
    河南省重点研发专项(241111114300);河南省科技攻关项目(262102110288)

Comprehensive Evaluation of Lodging Resistance of 78 Maize Inbred Lines and Screening of Core Indexes

Li Fangjie1,Shi Dakun1,Wei Xiaoyi1,Wei Feng1,Liu Junheng2,Mei Yongxian3   

  1. (1.Xinxiang Academy of Agricultural Sciences,Xinxiang 453003,China; 2.Hebi Dr. He Shengnong Technology Co.,Ltd.,Hebi 458000,China; 3.Zhumadian City Seed Industry Development Center,Zhumadian 463000,China)
  • Received:2026-03-26 Accepted:2026-05-13 Published:2026-07-15 Online:2026-07-30

摘要: 为筛选具有优异抗倒伏潜力的玉米种质资源,以78份玉米自交系为试验材料,于2024—2025年连续2 a测定茎秆穿刺强度、茎秆压碎强度和地上第3—5节间长度、短轴直径、长轴直径、干质量、横截面积及单位体积干质量,综合运用相关性分析、主成分分析、隶属函数分析、聚类分析及多元线性回归分析对玉米自交系抗倒伏性进行综合评价,并筛选核心指标。结果表明,2 a 20个性状在不同玉米自交系间差异均达到显著或极显著水平;2 a 20个性状的平均变异系数介于13.53%~29.51%,表明性状遗传变异丰富,具备良好的选择潜力。茎秆穿刺强度和茎秆压碎强度与地上第3—5节间短轴直径、长轴直径、干质量、横截面积均呈极显著正相关。基于主成分分析将20个指标转换成5个主成分,累计贡献率92.124%;抗倒伏性综合评价值(D)排名前5的自交系为新LP269、Z33-1、H7875S、H7235、新4095。聚类分析将78 份玉米自交系分为3 类,Ⅰ类属于抗倒伏型,包括13 份自交系,分别为新5951、新LP269、H7235、H7875A、丹360、H7898、新4095、辽5263、辽5262、齐319、H7875S、698-3、Z33-1;Ⅱ类属于中间型,包括57份自交系;Ⅲ类属于易倒伏型,包括8份自交系。通过逐步回归分析建立抗倒伏性预测模型(R²=0.996 0,p<0.01),筛选出茎秆穿刺强度、压碎强度、地上第5节间长度、地上第4节间短轴直径、地上第4—5节间长轴直径、地上第3—5节间干质量共9个性状为鉴定玉米抗倒伏性的核心指标。

关键词: 玉米, 自交系, 抗倒伏性, 综合评价, 核心指标筛选

Abstract: To screen out maize germplasm resources with excellent resistance to lodging,78 maize inbred lines were used as the experimental materials. The stem puncture strength,stem crushing strength,and length,short axis diameter,long axis diameter,dry weight,cross⁃sectional area,dry weight per volume of the aboveground 3rd to 5th internodes were measured continuously for two years from 2024 to 2025.Comprehensive evaluation of lodging resistance of maize inbred lines was conducted using correlation analysis,principal component analysis,membership function analysis,cluster analysis,and multiple linear regression analysis,and core indexes were selected.The results showed that the 20 traits differed significantly or extremely significantly among different maize inbred lines in two years;the average variation coefficients of the 20 traits ranged from 13.53% to 29.51%,indicating that the genetic variation of the traits was rich and had good selection potential. The stem puncture strength and stem crushing strength were extremely significantly positively correlated with short axis diameter,long axis diameter,dry weight,and cross⁃sectional area of the aboveground 3rd to 5th internodes.Based on principal component analysis,20 indexes were converted into 5 principal components,with cumulative contribution rate of 92.124%; the top five inbred lines bosed on the comprehensive evaluation value of lodging resistance were XinLP269,Z33⁃1,H7875S,H7235,and Xin 4095.Cluster analysis divided 78 maize inbreds into three categories,category Ⅰ belonged to the lodging⁃resistant type,including 13 inbred lines,namely Xin 5951,XinLP269,H7235,H7875A,Dan 360,H7898,Xin 4095,Liao 5263,Liao 5262,Qi 319,H7875S,698⁃3,and Z33⁃1;category Ⅱ belonged to the intermediate type,including 57 inbred lines;category Ⅲ belonged to the lodging⁃prone type,including eight inbred lines. A prediction model for lodging resistance was established through stepwise regression analysis(R²=0.996 0,p<0.01),and nine traits,including stem puncture strength,stem crushing strength,length of the aboveground 5th internode,short axis diameter of the aboveground 4th internode,long axis diameter of the aboveground 4th to 5th internodes,and dry weight of the aboveground 3rd to 5th internodes,were selected as the core indexes for identifying maize lodging resistance.

Key words: Maize, Inbred line, Lodging resistance, Comprehensive evaluation, Core indexes selection

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