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Application of the WRF-DSSAT Modeling System for Assessment of the Nitrogen Fertilizer Used for Improving Rice Production in Northern Thailand

Author

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  • Teerachai Amnuaylojaroen

    (Department of Environmental Science, School of Energy and Environment, University of Phayao, Phayao 56000, Thailand
    Atmospheric Pollution and Climate Change Research Unit, School of Energy and Environment, University of Phayao, Phayao 56000, Thailand)

  • Pavinee Chanvichit

    (Department of Agriculture, School of Agriculture and Natural Research, University of Phayao, Phayao 56000, Thailand)

Abstract

The cultivation of rice under irrigation provides fundamental sustenance for nearly half of the world’s population. Rice yields need to increase in order to maintain the rapidly growing population and meet growing food requirements. In this research, we applied the coupled atmospheric–crop model, which is based on the WRF and CERES-Rice models, to find the appropriate nitrogen fertilizer level for improving rice yield in northern Thailand. The model was conducted from June to December in 2011 and 2015. To evaluate the model’s capability, the output from the model, including meteorological data (i.e., precipitation and temperature) and rice production, was compared to actual observation data. The modeling system showed an acceptable level of output for statistical examination; for example, the R 2 values were 0.93, 0.76, and 0.97 for precipitation, temperature, and rice production, respectively. To assess the optimization of the nitrogen fertilizer level, we designed nine experiments: control cases and other cases that were multiplied by a factor of 2–10 times the nitrogen fertilizer levels. The model suggested that we can produce substantial rice yields by increasing nitrogen fertilizer levels by 12 kg/ha.

Suggested Citation

  • Teerachai Amnuaylojaroen & Pavinee Chanvichit, 2022. "Application of the WRF-DSSAT Modeling System for Assessment of the Nitrogen Fertilizer Used for Improving Rice Production in Northern Thailand," Agriculture, MDPI, vol. 12(8), pages 1-15, August.
  • Handle: RePEc:gam:jagris:v:12:y:2022:i:8:p:1213-:d:887201
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    References listed on IDEAS

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    1. Anna Gloria Billé & Marco Rogna, 2022. "The effect of weather conditions on fertilizer applications: A spatial dynamic panel data analysis," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(1), pages 3-36, January.
    2. Daxini, Amar & Ryan, Mary & O’Donoghue, Cathal & Barnes, Andrew P., 2019. "Understanding farmers’ intentions to follow a nutrient management plan using the theory of planned behaviour," Land Use Policy, Elsevier, vol. 85(C), pages 428-437.
    3. Teerachai Amnuaylojaroen & Pavinee Chanvichit & Radshadaporn Janta & Vanisa Surapipith, 2021. "Projection of Rice and Maize Productions in Northern Thailand under Climate Change Scenario RCP8.5," Agriculture, MDPI, vol. 11(1), pages 1-15, January.
    4. Teerachai Amnuaylojaroen & Pavinee Chanvichit, 2019. "Projection of near-future climate change and agricultural drought in Mainland Southeast Asia under RCP8.5," Climatic Change, Springer, vol. 155(2), pages 175-193, July.
    5. Sohail Abbas & Zulfiqar Ali Mayo, 2021. "Impact of temperature and rainfall on rice production in Punjab, Pakistan," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(2), pages 1706-1728, February.
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    Cited by:

    1. Himanshu, Sushil K. & Ale, Srinivasulu & Bell, Jourdan & Fan, Yubing & Samanta, Sayantan & Bordovsky, James P. & Gitz III, Dennis C. & Lascano, Robert J. & Brauer, David K., 2023. "Evaluation of growth-stage-based variable deficit irrigation strategies for cotton production in the Texas High Plains," Agricultural Water Management, Elsevier, vol. 280(C).

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