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马卡小麦主要农艺性状分析
引用本文:熊丽娟,李伟等.马卡小麦主要农艺性状分析[J].中国农学通报,2006,22(11):118-118.
作者姓名:熊丽娟  李伟等
作者单位:四川农业大学小麦研究所,四川,都江堰,611830
基金项目:教育部创新团队发展计划;高等院校博士专项
摘    要:对29份马卡小麦主要农艺性状进行了考察与分析。结果表明,马卡小麦具有植株高大、分蘖力强和小穗数多等特点。偏相关分析表明,分蘖数与有效穗数,成穗率与有效穗数,千粒重与小穗数偏相关均达极显著水平。单株产量与穗长偏相关显著,与有效穗数、穗粒重偏相关达极显著水平。蛋白质含量与穗长偏相关显著。主成分分析揭示了粒重、有效穗数、成穗率、株高和穗长对变异的贡献率达90.32%。基于因子得分与主成分贡献率的遗传距离在0.62水平上可将供试材料分为四类。这些结果为进一步利用马卡小麦基因资源提供了一定的理论依据。

关 键 词:荔枝  荔枝  生产特点  产业发展  
收稿时间:2006-06-07
修稿时间:2006-06-072006-06-10

Analysis in Principle Agronomic Traits of Triticum macha Dekaprel et Menabde
Xiong Lijuan,Li Wei,Zheng Youliang.Analysis in Principle Agronomic Traits of Triticum macha Dekaprel et Menabde[J].Chinese Agricultural Science Bulletin,2006,22(11):118-118.
Authors:Xiong Lijuan  Li Wei  Zheng Youliang
Institution:Triticeae Research Institute, Sichuan Agricultural University, Dujiangyan, Sichuan 611830, China
Abstract:Twenty-nine accessions of Triticum macha Dekaprel et Menabde were carried out by partial-correlation, principle component and clustering analysis. In general, T.macha accessions had higher plant height, strong tillering ability, and more spikelets. Partial-correlation analysis indicated that the correlations between the tiller numbers and the spike numbers per plant, forming spike percent and the spike numbers per plant, 1000-grain weight and the spikelet number per spike were significant positive significant, respectively. These accessions had a extremely significant positive partial-correlation between grains weight per plant and spikelet numbers per spike, grains weight per spike, respectively. There was a positive partial-correlation between protein content and spike length. Principle component analysis showed the first five main components (grain weight, spikelet numbers per spike, forming spikelet percent, plant height and spike length) had the contrition of 90.32% to variation. By clustering analysis, all the materials were divided into four groups. These results afforded theoretical proof for further utilizing genetic resource of T.maha.
Keywords:Triticum macha  Agronomic traits  protein content  Patial-correlation analysis  Principle component analysis  Clustering analysis
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