  We propose a model-based geostatistical approach to deal with regionalized
compositions. We combine the additive-log-ratio transformation with
multivariate geostatistical models whose covariance matrix is adapted to take
into account the correlation induced by the compositional structure. Such
specification allows the usage of standard likelihood methods for parameters
estimation. For spatial prediction we combined a back-transformation with the
Gauss-Hermite method to approximate the conditional expectation of the
compositions. We analyze particle size fractions of the top layer of a soil for
agronomic purposes which are typically expressed as proportions of sand, clay
and silt. Additionally a simulation study assess the small sample properties of
the maximum likelihood estimator.
