IDEAS home Printed from https://ideas.repec.org/a/eee/agiwat/v271y2022ics0378377422002888.html
   My bibliography  Save this article

A combined model approach to optimize surface irrigation practice: SWAP and WinSRFR

Author

Listed:
  • Alavi, Seyed Abdollah
  • Naseri, Abd Ali
  • Ritzema, Henk
  • van Dam, Jos
  • Hellegers, Petra

Abstract

Surface irrigation worldwide exhibits low efficiency due to excessively deep percolation and runoff. To optimize surface irrigation practice, two questions must be answered simultaneously: when to irrigate and how to irrigate (what stream size to use and for how long) to meet crop water requirements. Current surface irrigation optimization models are one-dimensional, meaning they can only simulate surface water flow, neglecting subsurface flow. They therefore indicate only how to irrigate, not when to irrigate. Furthermore, the required depth of irrigation is needed as input data for optimization, though this parameter is difficult to establish accurately. In addition, the effects of different irrigation practices on crop transpiration and yield, and variations in these across an irrigated field, are unknown. The present study adopts two one-dimensional simulation models – WinSRFR (for surface flow) and SWAP (for subsurface flow) – to determine when and how best to irrigate in a blocked-end furrow irrigation system for sugarcane cultivation in southwestern Iran. SWAP is used to determine irrigation schedule, required irrigation depth and crop transpiration along furrows, while WinSRFR is used to determine the soil infiltration function and evaluate and optimize irrigation practice. The results of the combined model approach indicate that some 2.7 times more water is applied than required in current practice, imposing high waterlogging stress on sugarcane crops. According to the SWAP simulations, the required irrigation depth is some 75 mm and the number of irrigation applications can be reduced from 26 to 19. The irrigation optimization in WinSRFR indicates that irrigation depth can be reduced from 162 mm to 81 mm, resulting in application efficiency increasing from 47% to 92%. Furthermore, as furrow slope increases from 0.02% to 0.12%, optimized stream size decreases from 3.0 to 1.6 l/s and optimized irrigation cutoff times increase from 3.2 to 6.5 hrs. Overall, optimization of irrigation practice using the combined model approach could result in 63% water conservation and 22% higher sugarcane yield.

Suggested Citation

  • Alavi, Seyed Abdollah & Naseri, Abd Ali & Ritzema, Henk & van Dam, Jos & Hellegers, Petra, 2022. "A combined model approach to optimize surface irrigation practice: SWAP and WinSRFR," Agricultural Water Management, Elsevier, vol. 271(C).
    • Handle: RePEc:eee:agiwat:v:271:y:2022:i:c:s0378377422002888
    DOI: 10.1016/j.agwat.2022.107741
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378377422002888
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.agwat.2022.107741?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Mazarei, Reza & Mohammadi, Amir Soltani & Naseri, Abd Ali & Ebrahimian, Hamed & Izadpanah, Zahra, 2020. "Optimization of furrow irrigation performance of sugarcane fields based on inflow and geometric parameters using WinSRFR in Southwest of Iran," Agricultural Water Management, Elsevier, vol. 228(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Costabile, Pierfranco & Costanzo, Carmelina & Gangi, Fabiola & De Gaetani, Carlo Iapige & Rossi, Lorenzo & Gandolfi, Claudio & Masseroni, Daniele, 2023. "High-resolution 2D modelling for simulating and improving the management of border irrigation," Agricultural Water Management, Elsevier, vol. 275(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jovanovic, N. & Pereira, L.S. & Paredes, P. & Pôças, I. & Cantore, V. & Todorovic, M., 2020. "A review of strategies, methods and technologies to reduce non-beneficial consumptive water use on farms considering the FAO56 methods," Agricultural Water Management, Elsevier, vol. 239(C).
    2. Pazouki, Ehsan, 2021. "A practical surface irrigation design based on fuzzy logic and meta-heuristic algorithms," Agricultural Water Management, Elsevier, vol. 256(C).
    3. Costabile, Pierfranco & Costanzo, Carmelina & Gangi, Fabiola & De Gaetani, Carlo Iapige & Rossi, Lorenzo & Gandolfi, Claudio & Masseroni, Daniele, 2023. "High-resolution 2D modelling for simulating and improving the management of border irrigation," Agricultural Water Management, Elsevier, vol. 275(C).
    4. Mehri, Akbar & Mohammadi, Amir Soltani & Ebrahimian, Hamed & Boroomandnasab, Saeid, 2023. "Evaluation and optimization of surge and alternate furrow irrigation performance in maize fields using the WinSRFR software," Agricultural Water Management, Elsevier, vol. 276(C).
    5. Pazouki, Ehsan, 2021. "A practical surface irrigation system design based on volume balance model and multi-objective evolutionary optimization algorithms," Agricultural Water Management, Elsevier, vol. 248(C).
    6. Pazouki, Ehsan, 2023. "A smart surface irrigation design based on the topographical and geometrical shape characteristics of the land," Agricultural Water Management, Elsevier, vol. 275(C).
    7. Mazarei, Reza & Soltani Mohammadi, Amir & Ebrahimian, Hamed & Naseri, Abd Ali, 2021. "Temporal variability of infiltration and roughness coefficients and furrow irrigation performance under different inflow rates," Agricultural Water Management, Elsevier, vol. 245(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:agiwat:v:271:y:2022:i:c:s0378377422002888. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/agwat .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.