Spatial variability of greenhouse gas (GHG) emissions from agricultural lands is not well
known although it has a great impact on the accuracy of GHG budget. The objectives of this …

We give an overview of existing approaches for the analysis of geostatistical multivariate
data, namely spatially indexed multivariate data where the indexing is continuous across …

The modified t test presented by Dutilleul [1993. Modifying the t test for assessing the
correlation between two spatial processes. Biometrics 49, 305–314] to assess the simple …

Given the high costs of soil sampling, low and extra-low sampling densities are still being
used. Low-density soil sampling usually does not allow the computation of experimental …

In this and a second article, we propose 'coregionalization analysis with a drift'(CRAD), as a
method to assess the multi-scale variability of and relationships between ecological …

In two articles, we present 'coregionalization analysis with a drift'(CRAD), a method to
assess the multi-scale variability of and relationships between ecological variables from a …

The variability of the relative soil gas diffusion coefficient (Ds/Do) in space and time is not
well known but is important for root respiration, microbial activities, and greenhouse gas …

We consider the framework in which vectors of variables are observed at different points in a
region. Such data are typically characterized by point-wise correlations among the variables …

In the linear model of coregionalization (LMC), when applicable to the experimental direct
variograms and the experimental cross variogram computed for two random functions, the …

Consider matrices Y and X containing 2-D spatial, continuous-quantitative data collected in
the same n locations at a given time, to explain p Y-variables (eg plant diversity indices) by q …