淮北地区晚播小麦产量构成分析及抗晚播品种筛选研究 |
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引用本文: | 乔玉强,曹承富,杜世州,赵 竹,李 玮,陈 欢.淮北地区晚播小麦产量构成分析及抗晚播品种筛选研究[J].中国农学通报,2015,31(15):17-21. |
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作者姓名: | 乔玉强 曹承富 杜世州 赵 竹 李 玮 陈 欢 |
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作者单位: | 安徽省农科院作物所/安徽省农作物品质改良重点实验室,安徽省农科院作物所/安徽省农作物品质改良重点实验室,安徽省农科院作物所/安徽省农作物品质改良重点实验室,安徽省农科院作物所/安徽省农作物品质改良重点实验室,安徽省农科院作物所/安徽省农作物品质改良重点实验室,安徽省农科院作物所/安徽省农作物品质改良重点实验室 |
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基金项目: | 国家科技支撑计划项目(2012BAD04B09,2011BAD16B06,2013BAD07B08);安徽省农科院学科建设项目(14A0203)。 |
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摘 要: | 为筛选适合安徽淮北地区种植的晚播小麦品种,以20份淮北地区主栽小麦品种为材料,研究了晚播(11月14日)条件下小麦品种的产量构成,并分析了不同小麦品种的抗晚播能力特性。结果表明,晚播小麦品种的产量变幅为462.62~540.85 kg/667 m2,产量构成间的变异表现为穗粒数>穗数>千粒重;在晚播小麦品种的产量构成中,穗数和穗粒数与产量呈正相关,其中穗粒数与产量间的相关极显著(r=0.578),千粒重与产量间呈负相关,但未达显著水平;多元回归分析表明,单位面积穗数、穗粒数和千粒重对单位面积产量的形成具有真实的回归关系,所构建的回归方程为Y=71.964 3.483X1 5.327X2 2.533X3;聚类分析表明,20个主栽品种在遗传距离为1.206处被划分为4类,同时根据聚类结果,筛选了一批抗晚播能力较强的品种,如皖科06290、许科1号、宿553和周麦27等,这些品种可作为安徽淮北地区晚播条件下种植的理想品种。
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关 键 词: | 层次分析法 层次分析法 社会-经济-自然复合生态系统 可持续发展 干旱绿洲农业区 |
收稿时间: | 2014/11/19 0:00:00 |
修稿时间: | 2014/12/18 0:00:00 |
Study on Yield Components and Screening of Varieties Resistance to Late-sowing in Huaibei Area |
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Abstract: | With 20 main wheat varieties of huaibei area as the test materials, a field experiment was carried out to study the yield components and the characteristics of resistance to late-sown in the condition of late-lowing. The results indicated that the average yield range of late-sown wheat was from 462.62 to540.85 kg/667 m2,and the variances of yield components was kernels per spike>numbers of spike>1000-grain weight. Correlation analysis showed that yield was correlated with number of spike(r=0.259),kernels per ear(r=0.578**) and 1000-grain weight (r=-0.360);Multiple regression equation of yield with its components was Y=71.964 3.483X1 5.327X2 2.533X3.According to the cluster analysis, all of these varieties were divided into four main and some secondary groups, wanke 06290,xuke 1,su 553 and zhoumai 27 which could be used as ideal varieties for late-sown planting in huaibei area of Anhui province were screened. |
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Keywords: | Huaibei area Late-sown wheat Yield components Index of resistance to late-sown Cluster analysis Variety screening |
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