IDEAS home Printed from https://ideas.repec.org/a/taf/cijwxx/v36y2020i2-3p528-546.html
   My bibliography  Save this article

Sustainability of water and energy use for food production based on optimal allocation of agricultural irrigation water

Author

Listed:
  • Mo Li
  • Vijay P. Singh

Abstract

Food security is inextricably linked with water and energy use in irrigated agriculture. This article develops an optimization model to evaluate the sustainability of water and energy use for food production, and the coordination among water, energy and carbon footprints. A case study of Heping Irrigation District, China, demonstrates the applicability of the model. We find that 87.47, 86.12, and 83.67 million m3 of irrigation water allocation are sustainable for high, normal, and low flow levels, respectively, considering economic, social and environmental benefits. The structure of surface water and groundwater allocation remains consistent for different subareas.

Suggested Citation

  • Mo Li & Vijay P. Singh, 2020. "Sustainability of water and energy use for food production based on optimal allocation of agricultural irrigation water," International Journal of Water Resources Development, Taylor & Francis Journals, vol. 36(2-3), pages 528-546, March.
    • Handle: RePEc:taf:cijwxx:v:36:y:2020:i:2-3:p:528-546
    DOI: 10.1080/07900627.2019.1649129
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/07900627.2019.1649129
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/07900627.2019.1649129?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.

    Citations

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


    Cited by:

    1. Li Wang & Yong Zhou & Qing Li & Tao Xu & Zhengxiang Wu & Jingyi Liu, 2021. "Application of Three Deep Machine-Learning Algorithms in a Construction Assessment Model of Farmland Quality at the County Scale: Case Study of Xiangzhou, Hubei Province, China," Agriculture, MDPI, vol. 11(1), pages 1-23, January.
    2. Jue Wang & Keyi Ju & Xiaozhuo Wei, 2022. "Where Will ‘Water-Energy-Food’ Research Go Next?—Visualisation Review and Prospect," Sustainability, MDPI, vol. 14(13), pages 1-19, June.

    More about this item

    Statistics

    Access and download statistics

    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:taf:cijwxx:v:36:y:2020:i:2-3:p:528-546. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/cijw20 .

    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.