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大麦产量构成模型

邹薇1,3;刘铁梅2;孔德艳1;汤亮1;曹卫星1   

  1. 1南京农业大学江苏省信息农业高技术研究重点实验室, 南京 210095 ; 2华中农业大学植物科技学院, 武汉 430070; 3云南农业大学农学与生物技术学院, 昆明 650201

  • 收稿日期:2008-07-09 修回日期:1900-01-01 出版日期:2009-02-20 发布日期:2009-02-20

Simulation model on barley yield formation.

ZOU Wei1,3;LIU Tie-mei2;KONG De-yan1;TANG Liang1;CAO Wei-xing1   

  1. 1Jiangsu Key Laboratory for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China; 2College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China;3College of Agronomy and Biological Technology, Yunnan Agricultural University, Kunming 650201, China
  • Received:2008-07-09 Revised:1900-01-01 Online:2009-02-20 Published:2009-02-20

摘要: 通过产量构成法构建了适用于不同地区不同品种的大麦产量模拟模型.以南京、昆明、武汉3个地区各试验处理中不同大麦品种最适条件下的产量因素为基础,建立了最适条件下每株穗数相对值、每穗粒数相对值、千粒重相对值与累积光合有效辐射的回归方程,构建了实际条件下的不同大麦品种每株穗数、每穗粒数和千粒重与这三者在最适条件下的潜在值和实际条件下的水肥丰缺因子等变量的函数关系.模型较为全面地考虑了大麦品种生长发育的内外因素,内因主要体现为品种遗传特性(潜在的每株穗数、潜在的每穗粒数、潜在的千粒重和灌浆期因子),外因包括光合有效辐射和水肥丰缺因子.运用武汉、昆明、扬州3个地区不同品种、不同播期的田间试验资料对模型进行了测试和检验.结果表明,模型对大麦产量构成因子及理论产量的模拟效果较好,模拟值与观测值吻合度高,每株穗数、每穗粒数和千粒重相对误差(RE)绝对值的平均值分别为1.96%、1.88%和1.67%,对这三者及理论产量的模拟值和观测值进行y=x回归分析,相关系数(r)在0.9464~0.9987,相关性显著,说明模型具有较高的预测性和适用性.

关键词: 防护林, Landsat TM, SPOT5, 三北地区

Abstract: By the method of yield component, a simulation model on barley yield f ormation was established, which could be available for different barley cultivar s in different areas. The establishment process of the model was as the followin g. With the yield components of different cultivars under optimal conditions in Wuhan, Yangzhou, and Kunming as the bases, the regression equations of the relat ive values of ears per plant, kernels per ears, and thousand-grain weight with the accumulated photosynthetic effective radiation (∑PAR) were established, and the functional relations between the ears per plant, kernels per ears, and thou sand-grain weight under actual conditions as well as their potential values und er optimal conditions and the variables soil moisture regime and nutrient conten ts under actual conditions were formed. The model more comprehensively considere d the internal and external factors of barley growth and development. The intern al factors reflecting the genetic properties of cultivar included potential ears per plant, potential kernels per ear, potential thousand grain weight, and grai n-filling duration; while the external factors included ∑PAR and soil moistur e regime and nutrient contents. Field experiments with different barley cultivar s at different sowing dates were conducted in Wuhan, Kunming, and Yangzhou, and the data obtained were used to calibrate and validate the model. The results sho wed that the model better simulated the yield components and theoretical yield, with the average absolute values of relative error (RE) of ears per plant, kerne ls per ear, and thousand-grain weight being 196%, 188% and 167%, respe ctively. The correlation coefficient from y=x regression analysis on the sim ulated and observed values of the three yield components and theoretical yield w as between 09464 and 09987, indicating that this model was more predictab le and applicable.

Key words: shelter forest,  , Landsat TM, SPOT5, Three-North region.