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Modeling nitrogen dynamics and biomass production in rice paddy fields of cold regions with the ORYZA-N model

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  • Gao, Ya
  • Sun, Chen
  • Ramos, Tiago B.
  • Huo, Zailin
  • Huang, Guanhua
  • Xu, Xu

Abstract

This article introduces ORYZA-N as an enhanced soil-water-nitrogen-plant coupling model to simulate agro-hydrological processes and crop growth in rice paddy fields. The model is developed based on the ORYZA2000 (a widely-used rice model with an open-source code) with significant improvements in modeling the nitrogen (N) fate and related processes. Three new modules were designed in ORYZA-N for simulating N transport and transformation processes, including a soil temperature module, a solute module for ammonium nitrogen (NH4-N) and nitrate nitrogen (NO3-N) transport, and a soil nitrogen module for organic carbon and nitrogen turnover. The mechanics of root water uptake and soil water drainage also were improved to better describe the soil water movement and its effects on root N uptake. The ORYZA-N model was then tested and applied in simulating the fate of water and nitrogen and crop growth for Japonica rice in cold regions (JRC) in the Northeast China Plain (NECP), for a two-year field experiment (during 2018 and 2019). Model evaluation shows a good performance was achieved with the ORYZA-N model. The simulated values for pond water depths, N uptake and partitions, and various organ productions were in particularly good agreement with the observed values (R2 > 0.68, NSE > 0.50); meanwhile, the simulated fluctuation trends for NH4-N and NO3-N concentrations were reasonable as well. The calibrated/obtained nitrogen-related parameters for JRC were obviously different from those for rice varieties in warmer regions, which can efficiently complement the parameter dataset for rice modeling in cold regions. Finally, the N dynamics, balance, and utilization efficiency were interpreted and assessed for the two experimental years, and possible improvement measures for N use for JRC were proposed. The total aboveground N content was about 160 kg N ha−1 at harvest, while the N content in the stems, green leaves, dead leaves, and panicles accounted for about 15, 5, 25, and 55% of the total aboveground N content, respectively. Overall, rational simulation proved the enhanced functionality and practicality of ORYZA-N for modeling the N fate and utilization.

Suggested Citation

  • Gao, Ya & Sun, Chen & Ramos, Tiago B. & Huo, Zailin & Huang, Guanhua & Xu, Xu, 2023. "Modeling nitrogen dynamics and biomass production in rice paddy fields of cold regions with the ORYZA-N model," Ecological Modelling, Elsevier, vol. 475(C).
    • Handle: RePEc:eee:ecomod:v:475:y:2023:i:c:s0304380022002824
    DOI: 10.1016/j.ecolmodel.2022.110184
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    1. Jing, Qi & Bouman, Bas & van Keulen, Herman & Hengsdijk, Huib & Cao, Weixing & Dai, Tingbo, 2008. "Disentangling the effect of environmental factors on yield and nitrogen uptake of irrigated rice in Asia," Agricultural Systems, Elsevier, vol. 98(3), pages 177-188, October.
    2. Xin Tong & Xueqin He & Hongwei Duan & Lujia Han & Guangqun Huang, 2018. "Evaluation of Controlled Release Urea on the Dynamics of Nitrate, Ammonium, and Its Nitrogen Release in Black Soils of Northeast China," IJERPH, MDPI, vol. 15(1), pages 1-13, January.
    3. Raes, Dirk & Geerts, Sam & Kipkorir, Emmanuel & Wellens, Joost & Sahli, Ali, 2006. "Simulation of yield decline as a result of water stress with a robust soil water balance model," Agricultural Water Management, Elsevier, vol. 81(3), pages 335-357, March.
    4. Han, Huanhao & Gao, Rong & Cui, Yuanlai & Gu, Shixiang, 2021. "Transport and transformation of water and nitrogen under different irrigation modes and urea application regimes in paddy fields," Agricultural Water Management, Elsevier, vol. 255(C).
    5. Bouman, B.A.M. & Kropff, M.J. & Wopereis, M.C.S. & ten Berge, H.F.M. & van Laar, H.H., 2001. "ORYZA2000: modeling lowland rice," IRRI Books, International Rice Research Institute (IRRI), number 281825.
    6. McCown, R. L. & Hammer, G. L. & Hargreaves, J. N. G. & Holzworth, D. P. & Freebairn, D. M., 1996. "APSIM: a novel software system for model development, model testing and simulation in agricultural systems research," Agricultural Systems, Elsevier, vol. 50(3), pages 255-271.
    7. Wang, Weiguang & Yu, Zhongbo & Zhang, Wei & Shao, Quanxi & Zhang, Yiwei & Luo, Yufeng & Jiao, Xiyun & Xu, Junzeng, 2014. "Responses of rice yield, irrigation water requirement and water use efficiency to climate change in China: Historical simulation and future projections," Agricultural Water Management, Elsevier, vol. 146(C), pages 249-261.
    8. Gao, Ya & Xu, Xu & Sun, Chen & Ding, Shibo & Huo, Zailin & Huang, Guanhua, 2021. "Parameterization and modeling of paddy rice (Oryza sativa L. ssp. japonica) growth and water use in cold regions: Yield and water-saving analysis," Agricultural Water Management, Elsevier, vol. 250(C).
    9. Xu, Xu & Sun, Chen & Neng, Fengtian & Fu, Jing & Huang, Guanhua, 2018. "AHC: An integrated numerical model for simulating agroecosystem processes—Model description and application," Ecological Modelling, Elsevier, vol. 390(C), pages 23-39.
    10. Ren, Dongyang & Xu, Xu & Engel, Bernard & Huang, Guanhua, 2018. "Growth responses of crops and natural vegetation to irrigation and water table changes in an agro-ecosystem of Hetao, upper Yellow River basin: Scenario analysis on maize, sunflower, watermelon and ta," Agricultural Water Management, Elsevier, vol. 199(C), pages 93-104.
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