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Inference of a hidden spatial tessellation from multivariate data: application to the delineation of homogeneous regions in an agricultural field

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  • Gilles Guillot
  • Denis Kan‐King‐Yu
  • Joël Michelin
  • Philippe Huet

Abstract

Summary. In a precision farming context, differentiated management decisions regarding fertilization, application of lime and other cultivation activities may require the subdivision of the field into homogeneous regions with respect to the soil variables of main agronomic significance. The paper develops an approach that is aimed at delineating homogeneous regions on the basis of measurements of a categorical and quantitative nature, namely soil type and resistivity measurements at different soil layers. We propose a Bayesian multivariate spatial model and embed it in a Markov chain Monte Carlo inference scheme. Implementation is discussed using real data from a 15‐ha field. Although applied to soil data, this model could be relevant in areas of spatial modelling as diverse as epidemiology, ecology or meteorology.

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  • Gilles Guillot & Denis Kan‐King‐Yu & Joël Michelin & Philippe Huet, 2006. "Inference of a hidden spatial tessellation from multivariate data: application to the delineation of homogeneous regions in an agricultural field," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 55(3), pages 407-430, May.
    • Handle: RePEc:bla:jorssc:v:55:y:2006:i:3:p:407-430
    DOI: 10.1111/j.1467-9876.2006.00544.x
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    Cited by:

    1. Marchetti, Yuliya & Nguyen, Hai & Braverman, Amy & Cressie, Noel, 2018. "Spatial data compression via adaptive dispersion clustering," Computational Statistics & Data Analysis, Elsevier, vol. 117(C), pages 138-153.

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