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Improving irrigation management by modelling the irrigation schedule

Author

Listed:
  • Van Aelst, P.
  • Ragab, R. A.
  • Feyen, J.
  • Raes, D.

Abstract

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  • Van Aelst, P. & Ragab, R. A. & Feyen, J. & Raes, D., 1988. "Improving irrigation management by modelling the irrigation schedule," Agricultural Water Management, Elsevier, vol. 13(2-4), pages 113-125, June.
    • Handle: RePEc:eee:agiwat:v:13:y:1988:i:2-4:p:113-125
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    Cited by:

    1. Mouatadid, Soukayna & Adamowski, Jan F. & Tiwari, Mukesh K. & Quilty, John M., 2019. "Coupling the maximum overlap discrete wavelet transform and long short-term memory networks for irrigation flow forecasting," Agricultural Water Management, Elsevier, vol. 219(C), pages 72-85.
    2. Elmaloglou, St. & Malamos, N., 2000. "Simulation of soil moisture content of a prairie field with SWAP93," Agricultural Water Management, Elsevier, vol. 43(2), pages 139-149, March.
    3. Liangfeng Zou & Yuanyuan Zha & Yuqing Diao & Chi Tang & Wenquan Gu & Dongguo Shao, 2023. "Coupling the Causal Inference and Informer Networks for Short-term Forecasting in Irrigation Water Usage," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(1), pages 427-449, January.
    4. Forouhar, Leila & Wu, Wenyan & Wang, Q.J. & Hakala, Kirsti, 2022. "A hybrid framework for short-term irrigation demand forecasting," Agricultural Water Management, Elsevier, vol. 273(C).
    5. R. Perea & E. Poyato & P. Montesinos & J. Díaz, 2015. "Irrigation Demand Forecasting Using Artificial Neuro-Genetic Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(15), pages 5551-5567, December.

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