浙江农业学报

• 作物科学 •    下一篇

玉米品种多环境测试主成分评价模型的构建及应用

  

  1. (1. 毕节市农业科学研究所,贵州 毕节 551700;2. 遵义师范学院 生命科学学院,贵州 遵义 563000)
  • 出版日期:2016-02-25 发布日期:2016-03-10

Construction of evaluation model of principal component analysis on multi environment testing in maize varieties and its application

  1. (1. Bijie Institute of Agricultural Sciences, Bijie 551700, China; 2. College of Life Science, Zunyi Normal University, Zunyi 563000, China)
  • Online:2016-02-25 Published:2016-03-10

摘要: 为了探讨玉米多环境试验中环境对品种的影响以及品种在不同试点的适应性,对8个品种在5个不同试点的9个性状(生育期、株高、穗位高、穗长、穗行数、秃尖长、百粒重、单穗粒重和产量)进行主成分评价模型的构建及应用研究,根据模型提取了5个主成分因子,计算相应主成分因子得分矩阵。结果表明,威丰2号在5个试点综合表现较好;除毕试1201在盘县试点表现相对较好、毕单17号在威宁试点较好外,威丰2号、W4503、胜玉2号、罗单601、金发玉201205和荷玉1201均在大方试点表现最好。8个品种在5个试点的不同效应均可由相应主成分因子及综合因子得分直观地展现出来,与传统分析方法相比,结果具有较好的一致性。该模型明确了不同试点对品种的综合效应以及相应品种在不同试点的适应性,为玉米多环境试验的综合评价提供一种新的途径。

关键词: 玉米, 模型, 多环境, 主成分分析

Abstract: To study the impact of environments on maize varieties and the adaptability of maize varieties in multiple environments, a model based on principal component analysis method was constructed and used to research eight varieties in five sites with nine traits (growth period, plant height, ear height, ear length, ear row number, bare tip length, hundred\|grain weight, grain weight per ear and yield). According to the model, five principal component factors were extracted and the score matrices of the corresponding principal component factors were obtained. The result showed that Weifeng 2 was the best according to the comprehensive performance in all environments. Additionally, Bishi 1201 had a better performance in Panxian, Bidan 17 had a better performance in Weining, and Weifeng 2, W 4503, Shengyu 2, Luodan 601, Jinfayu 201205 and Heyu 1201 all grew well in Dafang. The different effects of eight varieties in five sites could be displayed by the corresponding principal component factors and comprehensive factor scores, the results were in good agreement with the traditional method. This model defined the comprehensive effect and adaptability of maize varieties in different environments, and provided a new way for the comprehensive evaluation of maize in multiple environment test.

Key words: maize, model, multiple environments, principal components analysis