Haskard, K.Cullis, B.Verbyla, A.2008-05-132008-05-132007Journal of Agricultural, Biological, and Environmental Statistics, 2007; 12(2):147-1601085-71171537-2693http://hdl.handle.net/2440/43957The Matérn correlation function provides great flexibility for modeling spatially correlated random processes in two dimensions, in particular via a smoothness parameter, whose estimation allows data to determine the degree of smoothness of a spatial process. The extension to include anisotropy provides a very general and flexible class of spatial covariance functions that can be used in a model-based approach to geostatistics, in which parameter estimation is achieved via REML and prediction is within the E-BLUP framework. In this article we develop a general class of linear mixed models using an anisotropic Matérn class with an extended metric. The approach is illustrated by application to soil salinity data in a rice-growing field in Australia, and to fine-scale soil pH data. It is found that anisotropy is an important aspect of both datasets, emphasizing the value of a straightforward and accessible approach to modeling anisotropy. © 2007 American Statistical Association and the International Biometric Society.enGeometric anisotropyKrigingModel-based geostatisticsResidual maximum likelihoodSpatial correlationJournal article002007086110.1198/108571107X1960040002468707000012-s2.0-3444730917448791