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Uncertainty analysis of an irrigation scheduling model for water management in crop production

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Listed:
  • Mun, S.
  • Sassenrath, G.F.
  • Schmidt, A.M.
  • Lee, N.
  • Wadsworth, M.C.
  • Rice, B.
  • Corbitt, J.Q.
  • Schneider, J.M.
  • Tagert, M.L.
  • Pote, J.
  • Prabhu, R.

Abstract

Irrigation scheduling tools are critical to allow producers to effectively manage water resources for crop production. To be useful, these tools need to be accurate, complete, and relatively reliable. The current work presents an uncertainty analysis and its results for the Mississippi Irrigation Scheduling Tool (MIST) model, showing the margin of error (uncertainty) of the resulting irrigation advice arising solely from the propagation of measurement uncertainty through the MIST calculations. The final relative uncertainty in the water balance value from MIST was shown to be around 9% of that value, which is in the normal range of the margin of error and acceptable for agronomic systems. The results of this research also indicate that accurate measurements of irrigation and rainfall are critical to minimizing errors when using MIST and similar scheduling tools. While developed with data from Mississippi, the results of this uncertainty analysis are relevant to similar tool development efforts across the southern and southeastern United States and other high-rainfall areas, especially for locations lacking high-quality co-located weather stations.

Suggested Citation

  • Mun, S. & Sassenrath, G.F. & Schmidt, A.M. & Lee, N. & Wadsworth, M.C. & Rice, B. & Corbitt, J.Q. & Schneider, J.M. & Tagert, M.L. & Pote, J. & Prabhu, R., 2015. "Uncertainty analysis of an irrigation scheduling model for water management in crop production," Agricultural Water Management, Elsevier, vol. 155(C), pages 100-112.
    • Handle: RePEc:eee:agiwat:v:155:y:2015:i:c:p:100-112
    DOI: 10.1016/j.agwat.2015.03.009
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    References listed on IDEAS

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    1. Fortes, P.S. & Platonov, A.E. & Pereira, L.S., 2005. "GISAREG--A GIS based irrigation scheduling simulation model to support improved water use," Agricultural Water Management, Elsevier, vol. 77(1-3), pages 159-179, August.
    2. Snyder, R.L. & Pedras, C. & Montazar, A. & Henry, J.M. & Ackley, D., 2015. "Advances in ET-based landscape irrigation management," Agricultural Water Management, Elsevier, vol. 147(C), pages 187-197.
    3. Allen, Richard G. & Pereira, Luis S. & Howell, Terry A. & Jensen, Marvin E., 2011. "Evapotranspiration information reporting: I. Factors governing measurement accuracy," Agricultural Water Management, Elsevier, vol. 98(6), pages 899-920, April.
    4. Dagdelen, Necdet & Yilmaz, Ersel & Sezgin, Fuat & Gurbuz, Talih, 2006. "Water-yield relation and water use efficiency of cotton (Gossypium hirsutum L.) and second crop corn (Zea mays L.) in western Turkey," Agricultural Water Management, Elsevier, vol. 82(1-2), pages 63-85, April.
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    1. Galioto, Francesco & Battilani, Adriano, 2021. "Agro-economic simulation for day by day irrigation scheduling optimisation," Agricultural Water Management, Elsevier, vol. 248(C).
    2. Mondaca-Duarte, F.D. & van Mourik, S. & Balendonck, J. & Voogt, W. & Heinen, M. & van Henten, E.J., 2020. "Irrigation, crop stress and drainage reduction under uncertainty: A scenario study," Agricultural Water Management, Elsevier, vol. 230(C).
    3. Niu, G. & Li, Y.P. & Huang, G.H. & Liu, J. & Fan, Y.R., 2016. "Crop planning and water resource allocation for sustainable development of an irrigation region in China under multiple uncertainties," Agricultural Water Management, Elsevier, vol. 166(C), pages 53-69.
    4. Feki, Mouna & Ravazzani, Giovanni & Ceppi, Alessandro & Mancini, Marco, 2018. "Influence of soil hydraulic variability on soil moisture simulations and irrigation scheduling in a maize field," Agricultural Water Management, Elsevier, vol. 202(C), pages 183-194.

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