Numerical optimization of nitrogen application to rice. Part I. Description of MANAGE-N

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• 作者：ten Berge ; H.F.M. ; Thiyagarajan ; T.M. ; Qinghua ; Shi ; Wopereis ; M.C.S. ; Drenth ; H. ; Jansen ; M.J.W.
• 关键词：Rice ; Nitrogen ; Fertilizer ; Timing ; Numerical optimization ; Simulation ; Leaf nitrogen use coefficient ; Global radiation use coefficient
• 刊名：Field Crops Research
• 出版年：1997
• 出版时间：March, 1997
• 年：1997
• 卷：51
• 期：1-2
• 页码：29-42
• 全文大小：926 K

文摘

A tool (MANAGE-N) to identify optimal nitrogen (N) management in irrigated rice is presented. It combines a dynamic crop growth model (ORYZA-0) with a numerical optimization procedure and a user interface reading local weather and soil and crop characteristics as inputs to generate site-tailored recommendations for N-fertilizer management. These have the form of generalized logistic curves expressing the ideal temporal pattern to apply any user-defined total N dose. ORYZA-0 simulates crop N uptake, N allocation, growth and yield with only seven equations and a number of empirical constraint parameters. A single exponential relation describes crop growth rate as a function of daily incident global radiation (R, MJ m⁻² d⁻¹), bulk leaf nitrogen (N_L, g N per m² ground area) and a site calibration factor center border=0 SRC=/images/glyphs/BHE.GIF>_sv. Leaf area and light interception are not explicitly distinguished. Harvest index is calculated from biomass at flowering and cumulative incident radiation after flowering. Values of model parameters are provided based on 16 data sets from China, India, The Philippines and Australia covering 94 nitrogen management treatments (doses, timing). The corresponding 94 growth curves are used to evaluate three aspects of the growth equation, using observed time series of R and N_L as boundary conditions: (i) goodness of fit of growth curves after calibration of center border=0 SRC=/images/glyphs/BHE.GIF>_sv; (ii) variation in center border=0 SRC=/images/glyphs/BHE.GIF>_sv within and across locations; and (iii) errors in predicted crop biomass at flowering and harvest, using cross-validation between treatments within each site. All curves were fitted with correlation coefficients from 0.97 to 0.99. Parameter center border=0 SRC=/images/glyphs/BHE.GIF>_sv varied more between sets (experiments) than within sets (p < 0.001), with preflowering values between 0.68 and 0.97 and postflowering values 0.38 to 0.96. Root mean squared errors of biomass prediction were 230 to 1600 kg/ha at flowering and 280 to 3000 kg/ha at harvest, or 5 to 15 % of observed values in most cases. Further tests of the model are presented in Part II (Ten Berge et al., Field Crops Research, this issue).