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

Davood Poursina () and B Brorsen
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Davood Poursina: Oklahoma State University

Computational Statistics, 2025, vol. 40, issue 2, No 19, 1053-1069

Abstract: 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.

Keywords: Bayesian Kriging; Fertilizer; Gaussian spatial process; Linear plateau; Optimal nitrogen; Spatially varying coefficients (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s00180-024-01527-9

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