Inference of a hidden spatial tessellation from multivariate data: application to the delineation of homogeneous regions in an agricultural field |
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Authors: | Gilles Guillot,Denis Kan-King-Yu,Joë l Michelin, Philippe Huet |
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Affiliation: | Institut National de la Recherche Agronomique, Paris, France, and Chalmers University of Technology, Göteborg, Sweden; UniversitéParis 6, France; Environnement et Grandes Cultures, Grignon, France |
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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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Keywords: | Bayesian modelling Clustering of spatial data Linear co-regionalization Multivariate geostatistics Non-stationarity Point processes Poisson–Voronoi tessellation Precision farming Soil types Spatial mixture Resistivity data |
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