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

Authors: Guillot, Gilles¹; Kan-King-Yu, Denis²; Michelin, Joël³; Huet, Philippe³

Source: Journal of the Royal Statistical Society: Series C (Applied Statistics), Volume 55, Number 3, May 2006 , pp. 407-430(24)

Publisher: Wiley-Blackwell

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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.

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

Document Type: Research article

DOI: http://dx.doi.org/10.1111/j.1467-9876.2006.00544.x

Affiliations: 1: Institut National de la Recherche Agronomique, Paris, France, and Chalmers University of Technology, Göteborg, Sweden 2: Université Paris 6, France 3: Environnement et Grandes Cultures, Grignon, France

Publication date: 2006-05-01