ORYZA2000 rice crop growth simulation model

Submitted by pepijn.vanoort on Fri, 09/05/2014 - 15:38
General
Name
ORYZA2000
Program type
ORYZA2000 rice crop growth simulation model
Available since
Description

ORYZA2000v2n14s1 subversion developed for Rice Climate Change study for Africa.

Supporting material for the following scientific publication:

van Oort, P.A.J., Zwart, S.J., 2017. Impacts of climate change on rice production in Africa and causes of simulated yield changes. Global Change Biology, http://dx.doi.org/10.1111/gcb.13967

ORYZA2000v2n13 subversions developed for Improved Climate Risk Simulations for Rice in Arid Environments.

Supporting material for the following scientific publication:

van Oort P.A.J., de Vries M.E., Yoshida H, Saito K., 2015. Improved Climate Risk Simulations for Rice in Arid Environments. PLoS ONE 10(3): e0118114. doi:10.1371/journal.pone.0118114

The ORYZA2000v2n13 model is fully documented in a book and website, see the references below

  1. Bouman BAM, Kropff MJ, Tuong TP, Wopereis MCS, ten Berge HFM, van Laar HH. 2001. ORYZA2000: modeling lowland rice. Los Baños (Philippines): International Rice Research Institute, and Wageningen: Wageningen University and Research Centre. 235 p.
  2. IRRI ORYZA2000 website

The "ORYZA2000" family. A recent history.

The ORYZA2000 model originates from the Wageningen SUCROS model. Since the publication of the ORYZA200 book (Bouman et al., 2001) a number of important updates have been made. The last major update of open source code is version 2 number 13 (v2n13).  Since then there have been at least four major developments:

  1. Version 3 (ORYZA2000v3) with many improvements has been released by IRRI. See Tao Li et al. (2017) for a recent overview.
  2. The physiology part of ORYZA2000 (the Bouman et al 2001 version, not the later v3 version) has been integrated in APSIM (= ORYZA2000 crop growing on an APSIM soil). This version can be downloaded from the APSIM website. See Gaydon et al. (2015) for a recent overview of APSIM-ORYZA.
  3. An improved rice phenology calibration program was developed (van Oort et al. 2011).
  4. New heat and cold sterility subroutines were developed, incorporated into version v2n13, resulting in a family of model subversion v2n13s1 to v2n13s26. These different subsversions were systematically tested and compare for two sites in Senegal (van Oort et al. 2015).
  5. A few more improvements were implemented in a later version ORYZA2000v2n14s1 used in a recent study on impacts of climate change on rice in Africa (van Oort et al, 2017).

The reason why these different model branches have emerged is mostly due to differences in opinion on whether source code should be open or not. Further improvements are still needed, all papers cited above highlight both the qualities and the remaining uncertainties in the different model versions.

Scale of application
field
Spatial resolution
Field
Key outputs

Growth and development of rice in different environments at different water and nitrogen levels

Time horizon
growing season
Time step of modeling
day
Required to run

executable can be run standalone. The control.dat file which points to the different input files must be placed in the same directory as the executable

Required to develop

For source code of v3 and beyond please contact IRRI, T.Li@irri.org or T.Li@cgiar.org at https://sites.google.com/a/irri.org/oryza2000/home

For latest versions of library files (TTUTIL, WEATHER, OP_OBS) please contact Kees Rappoldt at http://www.ecocurves.nl/

You need a fortran compiler. If you do not yet have a fortran compiler check out http://models.pps.wur.nl/content/gfortran-compiler

 

Database I/O
NA
Articles

Bouman BAM, Kropff MJ, Tuong TP, Wopereis MCS, ten Berge HFM, van Laar HH. 2001. ORYZA2000: modeling lowland rice. Los Baños (Philippines): International Rice Research Institute, and Wageningen: Wageningen University and Research Centre. 235 p.

 

Applications & Use

ORYZA2000 can be used for the following applications (and probably more!):

  1. Soil and physiological research: if the model does not reproduce your data well and you have systematic errors, then that can be the starting point for research, in which your formulate hypotheses for the cause of systematic error. See for example van Oort et al (2011, 2014)
  2. Cropping calendar optimisation
  3. Optimisation of irrigation
  4. Optimisation of fertiliser application
  5. Selection of desirable crop traits and management for different environments (TPE)
  6. Quantification of climatic risks (drought, heat, cold)

References

van Oort, P.A.J., Zhang, T., de Vries, M.E., Heinemann, A.B., Meinke, H. 2011. Correlation between temperature and phenology prediction error in rice (Oryza sativa L.). Agricultural and Forest Meteorology 151(12): 1545-1555.

van Oort, P.A.J., de Vries, M.E., Yoshida, H., Saito, K., 2014. Improved climate risk simulations for rice in arid environments. PLOS ONE. Paper currently in review

Author(s)
P.A.J. van Oort
Address
AfricaRice