IDEAS home Printed from https://ideas.repec.org/a/spr/compst/v40y2025i2d10.1007_s00180-024-01527-9.html
   My bibliography  Save this article

Site-specific nitrogen recommendation: fast, accurate, and feasible Bayesian kriging

Author

Listed:
  • Davood Poursina

    (Oklahoma State University)

  • B. Wade Brorsen

    (Oklahoma State University)

Abstract

Bayesian Kriging (BK) provides a way to estimate regression models where the parameters are smoothed across space. Such estimates could help guide site-specific fertilizer recommendations. One advantage of BK is that it can readily fill in the missing values that are common in yield monitor data. The problem is that previous methods are too computationally intensive to be commercially feasible when estimating a nonlinear production function. This paper sought to increase computational speed by imposing restrictions on the spatial covariance matrix. Previous research used an exponential function for the spatial covariance matrix. The two alternatives considered are the conditional autoregressive and simultaneous autoregressive models. In addition, a new analytical solution is provided for finding the optimal value of nitrogen with a stochastic linear plateau model. A comparison among models in the accuracy and computational burden shows that the restrictions significantly reduced the computational burden, although they did sacrifice some accuracy in the dataset considered.

Suggested Citation

  • Davood Poursina & B. Wade Brorsen, 2025. "Site-specific nitrogen recommendation: fast, accurate, and feasible Bayesian kriging," Computational Statistics, Springer, vol. 40(2), pages 1053-1069, February.
    • Handle: RePEc:spr:compst:v:40:y:2025:i:2:d:10.1007_s00180-024-01527-9
    DOI: 10.1007/s00180-024-01527-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00180-024-01527-9
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s00180-024-01527-9?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:spr:compst:v:40:y:2025:i:2:d:10.1007_s00180-024-01527-9. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.