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

Gilles Guillot, Denis Kan-King-Yu, Joël Michelin, Philippe Huet

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

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.
Original languageEnglish
JournalJournal of the Royal Statistical Society, Series C (Applied Statistics)
Volume55
Issue number3
Pages (from-to)407-430
ISSN0035-9254
DOIs
Publication statusPublished - 2006
Externally publishedYes

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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