IDEAS home Printed from https://ideas.repec.org/a/eee/agiwat/v294y2024ics0378377424000507.html
   My bibliography  Save this article

Exploring key factors driving farm-level seasonal irrigation water usage with Bayesian hierarchical modelling

Author

Listed:
  • Gao, Zitian
  • Guo, Danlu
  • Ryu, Dongryeol
  • Western, Andrew W.

Abstract

Farm-level seasonal irrigation water usage is often highly variable across time and space in an irrigation district. Identifying the driving factors of this variation can help researchers and managers understand the underlying efficiency of water usage, identify the sources of water waste and develop best irrigation practices to facilitate the development of more efficient and sustainable irrigated cropping systems. This study explored driving factors for the seasonal irrigation water usage across an irrigation district in south-eastern Australia, using extensive climate, soil, cropping and water use data from 312 farms (a total of 1099 annual crop water use records). It focused on three irrigated crops (corn/maize, cotton and rice) for 2011–2019. Factors considered included climate, soils, irrigation practices and water allocation, from which key driving factors and their effects on the seasonal irrigation water usage were identified. A Bayesian hierarchical model averaging approach was developed to determine key predictors and estimate seasonal irrigation water usage. The Bayesian hierarchical modelling framework allowed seasonal irrigation water usage to be estimated at all crop-farm-year observation points simultaneously while also enabling different key predictors to be selected for different crop types. Results showed that the seasonal irrigation water usage was mainly driven by irrigation practices and soil, but the water use of different crop types was driven by different subsets of irrigation and soil predictors. Modelling was undertaken in natural log space and the model showed reasonable accuracy in estimating the seasonal irrigation water usage (R2 = 0.62). Our model is considered useful in 1) identifying drivers of variation of the seasonal irrigation water usage across time and space, and 2) suggesting potential adjustments to irrigation practices and quantifying their benefits to reduce irrigation water usage.

Suggested Citation

  • Gao, Zitian & Guo, Danlu & Ryu, Dongryeol & Western, Andrew W., 2024. "Exploring key factors driving farm-level seasonal irrigation water usage with Bayesian hierarchical modelling," Agricultural Water Management, Elsevier, vol. 294(C).
    • Handle: RePEc:eee:agiwat:v:294:y:2024:i:c:s0378377424000507
    DOI: 10.1016/j.agwat.2024.108715
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378377424000507
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.agwat.2024.108715?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.

    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:294:y:2024:i:c:s0378377424000507. 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: 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.