Assessment examine of the modeling ability of ORYZA2000 for rice cultivars in Guilan province (Iran)

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Research Paper 01/02/2014
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Assessment examine of the modeling ability of ORYZA2000 for rice cultivars in Guilan province (Iran)

Ebrahim Azarpour, Maral Moraditochaee, Hamid Reza Bozorgi
J. Biodiv. & Environ. Sci. 4(2), 195-204, February 2014.
Copyright Statement: Copyright 2014; The Author(s).
License: CC BY-NC 4.0

Abstract

Various crop growth simulation models exist for rice but thorough validation and evaluation reports are scarce. We present the model ORYZA2000, which simulates the growth and development of rice under conditions of potential production and water and nitrogen limitations. In order to evaluated model, an experiment as factorial in RCBD with three replications was conducted during 2009 year in the Rice Research Institute, Iran, and Roudsar, East of Guilan. Factors were cultivar (Khazar, Ali Kazemi and Hashemi), and nitrogen fertilizer levels (0, 30, 60, and 90 Kg N/ha). Two programs DRATES and PARAM provided by ORYZA2000 are performed for the calibration using the experimental. Evaluation assimilate and measured partitioning factors to leaves, stems, storage and above ground total (FLV, FST, FSO and FAGT respectively); leaf area index (LAI); total biomass and grain yield for each cultivar with determination R2, p(t), α, β, CRM, RMSE and RMSEn. With respect to in both locations the difference between simulated and measured total biomass of the varieties was +2 to +13% and the difference between simulated and measured grain yield was +3 to +12% (Tables 2, 3, 4), it can be concluded that the model has a high capability to simulate total biomass and grain yield of the cultivars in Guilan climate condition.

Azarpour E, Moradi M. 2013. A comparative study on  energy  use  and  cost  analysis  of  Rice  Varieties under Traditional and Semi-mechanized Farming Systems  in  North  of  Iran.  Biomass,  InTech-Open Access Company, 1-37. http://dx.doi.org/10.5772/51165

Bouman BAM, Van Keulen H, Van Laar HH, Rabbinge R. 1996.  The  ‘School  of  De  Wit’  crop growth simulation models: A pedigree and historical overview. Agricultural Systems 52, 171-198. http://dx.doi.org/10.1016/0308-521X(96)00011-X

Bouman BAM, Krop MJ, Tuong YP, Wopereis MCS, Ten Berge HFM, Van Laar HH. 2001. ORYZA2000: Modelling Lowland Rice. International Rice Research Institute, Wageningen University and Research Centre, Los Ban os, Philippines, Wageningen, Netherlands.

Bouman BAM, Van Laar HH. 2006. Description and evaluation of the rice growth model ORYZA2000 under nitrogen-limited conditions. Agricultural Systems 87, 249-273. http://dx.doi.org/10.1016/j.agsy.2004.09.011

Caton BP, Cope AE, Mortimer M. 2003. Growth traits of diverse rice cultivars under severe competition: implications for screening for competitiveness. Field crop research 83, 157-172. http://dx.doi.org/10.1016/S0378-4290(03)00072-8

Fageria NK,  Slaton NA,  Baligar  VC.  2003. Nutrient  management  for  improving  lowland  rice productivity and sustainability. Advances in Agronomy 80, 63-152. http://dx.doi.org/10.1016/S0065-2113(03)80003-2

Fageria NK. 2007. Yield physiology of rice. Journal of Plant Nutrition 30, 843–879. http://dx.doi.org/10.1080/15226510701374831

Feng LP, Bouman BAM, Tuong RJ, Cabangon YL, Li Lu GA, Feng YH. 2007. Exploring options to grow rice under water short conditions in northern China using a modeling approach. I: Field experiments and model evaluation. Agriculture Water Management 88, 1-13. http://dx.doi.org/10.1016/j.agwat.2006.10.006

Gauch HG, Hwang JTG, Fick GW. 2003. Model evaluation by comparison of model-based predictions and measured values. Agronomy Journal 95, 1442– 1446. http://dx.doi.org/10.2134/agronj2003.1442

Grafius JG. 1959. Heterosis in barley. Agronomy journal 51, 551-554. http://dx.doi.org/10.2134/agronj1959.000219620051 00090013x

Peykani GR, Kavoosi Kelashemi M, Sadat Barikani SH, Sasouli MR. 2008. Comparison of Production Productivity of 3 Rice Varieties Including Long Grain Good Quality, Long Grain High Yielding and Hybrid Rice in Iran (Case Study: Gilan Province) American-Eurasian Journal Agricultural & Environment Science 4, 625-632.

RinaldiM, Losavio N, Flagella Z. 2003. Evaluation of OILCROP-SUN model for sunflower in southern Italy. Agricultural Systems 78, 17-30. http://dx.doi.org/10.1016/S0308-521X(03)00030-1

Welbank PJ, French SAW, Witts KJ. 1966. Dependence of yield of wheat varieties on their leaf area. Annals of Botany 30, 291-299.

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