山西省不同生态区玉米品种数量性状多样性分析

王 怡; 刘守渠; 郭 峰; 任小燕; 段运平

文章摘要

王怡，刘守渠，郭峰，任小燕，段运平.山西省不同生态区玉米品种数量性状多样性分析[J].广东农业科学,2023,50(5):11-20

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Diversity Analysis of Quantitative Traits of Maize Varieties in Different Ecological Regions of Shanxi Province

DOI：10.16768/j.issn.1004-874X.2023.05.002

中文关键词: 玉米品种数量性状主成分分析相关性分析聚类分析

英文关键词: maize varieties quantitative character principal component analysis correlation analysis cluster analysis

基金项目:山西省农业科学院作物科学研究所青年基金项目（ZQ2004）

作者	单位
王怡，刘守渠，郭峰，任小燕，段运平	山西农业大学农学院，山西晋中 030801

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中文摘要:

【目的】充分挖掘并利用山西省不同生态区优质玉米品种。【方法】对山西省 4 个生态区的 75 个玉米品种的 13 个数量性状进行相关性分析、主成分分析和聚类分析。【结果】13 个数量性状中出籽率和粗淀粉含量变异系数较小，分别为 1.78%、1.92%，说明玉米品种的这两个性状能够稳定遗传；穗位和粗脂肪的变异系数较大，分别为 15.06%、13.78%，说明玉米品种的穗位和粗脂肪具有较大的选择潜力。产量与生育期、总叶片数、株高、穗位、行粒数、百粒质量以及出籽率间均呈现极显著正相关，相关系数分别为 0.591、0.520、0.630、0.57、0.315、0.461、0.380；与粗脂肪间呈现极显著负相关，相关系数为 -0.438。主成分分析发现，前 4 个主成分累计贡献率为 71.35%，其中第 1 主成分主要反映产量、粗脂肪、穗位、总叶片数；第 2 主成分主要反映生育期、行粒数、粗淀粉；第 3 主成分主要反映粗蛋白、粗淀粉、穗长以及行粒数；第 4 主成分主要反映容重。聚类分析显示，75 个玉米品种的 13 个数量性状最终划分为 3 个类群，初步明确了各个类群特征，其中第Ⅰ类群适合筛选容重、粗蛋白以及粗脂肪含量较高的玉米品种，第Ⅱ类群适合筛选产量高及粗淀粉含量高的玉米品种，第Ⅲ类群适合筛选株高、穗位以及穗长数值较大的玉米品种。【结论】75 个玉米材料有着比较丰富的遗传多样性，且数量性状之间均存在不同程度的相关性。主成分分析一共提取出 4 个主成分，累计贡献率为 71.36%，分别是产量因子、行粒数因子、粗蛋白因子、容重因子。聚类分析将 75 个玉米品种划分为了 3 个类群，这 3 个类群差异表现在容重、产量、株高等特征上。本研究为山西省玉米亲本的选配和性状改良奠定了研究基础。

英文摘要:

【Objective】 To fully explore and utilize high quality maize varieties in different ecological regions of Shanxi Province. 【Method】 Correlation analysis, principal component analysis and cluster analysis were performed on 13 quantitative traits of 75 maize varieties from 4 ecological regions in Shanxi Province. 【Result】 Among the 13 quantitative characters, the variation coefficients of seed yield and crude starch content were small, which were 1.78% and 1.92%, respectively, indicating that these two characters could be inherited stably. The coefficient of variation of ear position and ether extract was 15.06% and 13.78%, respectively, indicating that ear position and ether extract of maize varieties had greater potential for selection. Yield was significantly positively correlated with growth period, total leaf number, plant height, ear position, row number, 100-grain weight and seed production rate, and the correlation coefficients were 0.591, 0.520, 0.630, 0.57, 0.315, 0.461 and 0.380, respectively. The yield was significantly negatively correlated with crude fat, and the correlation coefficient was -0.438. The results of principal component analysis showed that the cumulative contribution rate of the first four principal components was 71.35%. The first principal component mainly reflected the yield, crude fat, ear position and total leaf number. The second principal component mainly reflected growth period, row number and crude starch. The third principal component mainly reflected the crude protein, crude starch, ear length and row number. The fourth principal component mainly reflects the bulk density. Cluster analysis showed that 13 quantitative characters of 75 maize varieties were divided into 3 groups, and the characteristics of each group were preliminarily defined. Group Ⅰ was suitable for screening maize varieties with higher bulk density, crude protein and crude fat content, group Ⅱ was suitable for screening maize varieties with high yield and high crude starch content. Group Ⅲ was suitable for screening maize varieties with higher plant height, ear position and ear length. 【Conclusion】 The 75 maize materials had rich genetic diversity, and the quantitative characters were correlated with each other to different degrees. A total of 4 principal components were extracted by principal component analysis, with a cumulative contribution rate of 71.36%, which were yield factor, row number factor, crude protein factor and bulk density factor. The 75 maize varieties were divided into three groups by cluster analysis. The differences of these three groups were shown in the characteristics of bulk density, yield and plant height. This study laid a foundation for the selection and character improvement of maize parents in Shanxi Province.

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