期刊文献+

北方粳稻不同穗型品种冠层特征与干物质生产研究 被引量:2

Studies on canopy properties and its relation to dry matter producton in japonica rice varieties with different panicle types in north
在线阅读 下载PDF
导出
摘要 本文对北方粳稻不同穗型品种的特征与干物质生产和产量的关系进行了研究 ,结果表明 ,不同穗型品种 ,对N肥高低反应不同 ,冠层发展动态与干物质生产速率也有明显差异 ,随着施N水平的提高 ,不同穗型品种抽穗前叶面积增长速度增大 ,抽穗期的LAI与抽穗后群体干物质生产速度和产量间呈二次曲线回归关系 ,获得最高产量的最适LAI与获得最大干物质生产速度的最适LAI接近 . Using japonica rice varieties with different panicle types,studies were made on canopy properties in relation to dry matter that varieties responsiveness to nitrogen supply varied with panicle types,and there were significant differences in the canopy development and the rate of dry matter production among varieties with different pinicle types.The more was the nitrogen supply,the more differences of declining rate of leaf area after heading would be,and the less would be the differeces of increasing rate of leaf area before heading among varieties with different panicle types.The LAI at the heading stage had a significant quadratic regression relationship with yield and the rate of dry matter production after heading.The results also indicate that the LAI leading to maximum yield was basically consistent with the LAI conductive to maximum rate of dry matter production,and the yield was positively corelated with the rate of dry matter production after heading.
出处 《延边大学农学学报》 2000年第1期41-43,共3页 Agricultural Science Journal of Yanbian University
基金 国家科委攻关项目! (96C0 1 - 0 1 - 0 1 )
关键词 粳稻 穗型 冠层特征 干物质生产 产量 品种 japonica rice panicle types camopy properties LAI dry matter production yield
  • 相关文献

参考文献3

二级参考文献37

  • 1Cortes P, Kazmierkowski M, Kennel R, et al. Predictive control in power electronics and drives[J]. IEEE Trans. on Industrial Electronics, 2008, 55(12): 4312-4324.
  • 2Rodriguez J, Kennel R, Espinoza J, et al. High-performance control strategies for electrical drives: An experimental assessment[J]. IEEE Trans. on Industrial Electronics, 2012, 59(2): 812-820.
  • 3Mariethoz S, Domahidi A, Morari M. High-bandwidth explicit model predictive control of electrical drives[J]. IEEE Trans. on Indtistrial Electronics, 2012, 48(6): 1980-1992.
  • 4Miranda H, Cortes P, Yuz J, et al. Predictive torque control of induction machines based on state-space models[J]. IEEE Trans. on Industrial Electronics, 2009, 56(6): 1916-1924.
  • 5Geyer T, Papafotiou G, Morari, M. Model predictive direct torque control; part I. Concept, algorithm, and analysis[J]. IEEE Trans. on Industrial Electronics, 2009,56(6):1894-1905.
  • 6Cort6s P, Kouro S, La Rocca B, et al. Guidelines for weighting factors design in model predictive control of power converters and drives[C]//IEEE ICIT' 2009. Gippsland, VIC: IEEE, 2009: 1-7.
  • 7Zhang Y, Yang H. Model predictive torque control of induction motor drives with optimal duty cycle control[J]. IEEE Trans. on Power Electronics, 2014, in press.
  • 8Davari S A, Khaburi D A, Kennel R. An improved FCS-MPC algorithm for an induction motor with an imposed optimized weighting factor[J]. IEEE Trans. on Power Electronics, 2012, 27(3): 1540-1551.
  • 9Rojas C, Rodriguez J, Villarroel F, et al. Predictive torque and flux control without weighting factors[J]. IEEE Trans. on lndustrialElectronics, 2013, 60(2): 681-690.
  • 10Villarroel F, Espinoza J, Rojas C, et al. Multiobjective switching state selector for finite states model predictive control based on fuzzy decision making in a matrix converter[J]. IEEE Trans. on Industrial Electronics, 2013, 60(2): 589-599.

共引文献73

同被引文献27

引证文献2

二级引证文献3

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部