1) was common. In addition, each farm tended to have a consistent RISsatellite ranking across years, indicating the role of farmers’ irrigation management practice in RIS. ICA from different datasets contributed most to the random variation in RIS, while satellite-based ICA presented more reliable estimates with a median absolute percentage error of 5 %, which is half the error for survey-based ICA. We conclude that our satellite-based benchmarking system is effective in overcoming data limitations and is likely to be more reliable than systems using survey data. This system can support improved irrigation performance monitoring in this district and is transferable to other irrigation areas with irrigation supply information available."> 1) was common. In addition, each farm tended to have a consistent RISsatellite ranking across years, indicating the role of farmers’ irrigation management practice in RIS. ICA from different datasets contributed most to the random variation in RIS, while satellite-based ICA presented more reliable estimates with a median absolute percentage error of 5 %, which is half the error for survey-based ICA. We conclude that our satellite-based benchmarking system is effective in overcoming data limitations and is likely to be more reliable than systems using survey data. This system can support improved irrigation performance monitoring in this district and is transferable to other irrigation areas with irrigation supply information available.">
[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
IDEAS home Printed from https://ideas.repec.org/a/eee/agiwat/v302y2024ics037837742400297x.html
   My bibliography  Save this article

An integrated approach for multi-year irrigation benchmarking using satellites, surveys and on-farm measured data

Author

Listed:
  • Gao, Zitian
  • Guo, Danlu
  • Ryu, Dongryeol
  • Western, Andrew W.
Abstract
Benchmarking is an effective management tool to improve irrigation performance through comparison with other irrigation management units; however, its application is often limited by the data available to support the analysis. This study developed a benchmarking system that reduces reliance on local data by using satellite-based estimates and quantifies benchmarking uncertainty due to alternative data sources. We benchmarked the relative irrigation supply (RISsatellite) (i.e. the ratio of irrigation supply to crop net irrigation demand) for more than 300 farms growing corn/maize, cotton and rice crops in the Coleambally Irrigation Area in Australia from 2011 to 2019. Three key inputs to RISsatellite, namely irrigated cropping area (ICA), start and end dates of a season (SOS/EOS) and crop coefficient (Kc), were derived from Landsat time series data. To understand the uncertainty in satellite-based benchmarking, RISsatellite was compared with RISnon-satellite estimates derived from non-satellite datasets. The variation in RIS with respect to input data sources and the reliability of data sources were also quantified. RISsatellite was highly variable across farms in any particular year, and over-irrigation (RISsatellite>1) was common. In addition, each farm tended to have a consistent RISsatellite ranking across years, indicating the role of farmers’ irrigation management practice in RIS. ICA from different datasets contributed most to the random variation in RIS, while satellite-based ICA presented more reliable estimates with a median absolute percentage error of 5 %, which is half the error for survey-based ICA. We conclude that our satellite-based benchmarking system is effective in overcoming data limitations and is likely to be more reliable than systems using survey data. This system can support improved irrigation performance monitoring in this district and is transferable to other irrigation areas with irrigation supply information available.

Suggested Citation

  • Gao, Zitian & Guo, Danlu & Ryu, Dongryeol & Western, Andrew W., 2024. "An integrated approach for multi-year irrigation benchmarking using satellites, surveys and on-farm measured data," Agricultural Water Management, Elsevier, vol. 302(C).
    • Handle: RePEc:eee:agiwat:v:302:y:2024:i:c:s037837742400297x
    DOI: 10.1016/j.agwat.2024.108962
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S037837742400297X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.agwat.2024.108962?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:302:y:2024:i:c:s037837742400297x. 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.