  Soil organic carbon (SOC) plays a major role in the global carbon budget. It
can act as a source or a sink of atmospheric carbon, thereby possibly
influencing the course of climate change. Improving the tools that model the
spatial distributions of SOC stocks at national scales is a priority, both for
monitoring changes in SOC and as an input for global carbon cycles studies. In
this paper, we compare and evaluate two recent and promising modelling
approaches. First, we considered several increasingly complex boosted
regression trees (BRT), a convenient and efficient multiple regression model
from the statistical learning field. Further, we considered a robust
geostatistical approach coupled to the BRT models. Testing the different
approaches was performed on the dataset from the French Soil Monitoring
Network, with a consistent cross-validation procedure. We showed that when a
limited number of predictors were included in the BRT model, the standalone BRT
predictions were significantly improved by robust geostatistical modelling of
the residuals. However, when data for several SOC drivers were included, the
standalone BRT model predictions were not significantly improved by
geostatistical modelling. Therefore, in this latter situation, the BRT
predictions might be considered adequate without the need for geostatistical
modelling, provided that i) care is exercised in model fitting and validating,
and ii) the dataset does not allow for modelling of local spatial
autocorrelations, as is the case for many national systematic sampling schemes.
