Abstract. Soil organic carbon plays a major role in the global carbon budget, and can act as a source or a sink of atmospheric carbon, thereby possibly influencing the course of climate change. Changes in soil organic carbon (SOC) stocks are now taken into account in international negotiations regarding climate change. Consequently, developing sampling schemes and models for estimating the spatial distribution of SOC stocks is a priority. The French soil monitoring network has been established on a 16 km × 16 km grid and the first sampling campaign has recently been completed, providing around 2200 measurements of stocks of soil organic carbon, obtained through an in situ composite sampling, uniformly distributed over the French territory.We calibrated a boosted regression tree model on the observed stocks, modelling SOC stocks as a function of other variables such as climatic parameters, vegetation net primary productivity, soil properties and land use. The calibrated model was evaluated through cross-validation and eventually used for estimating SOC stocks for mainland France. Two other models were calibrated on forest and agricultural soils separately, in order to assess more precisely the influence of pedo-climatic variables on SOC for such soils.The boosted regression tree model showed good predictive ability, and enabled quantification of relationships between SOC stocks and pedo-climatic variables (plus their interactions) over the French territory. These relationships strongly depended on the land use, and more specifically, differed between forest soils and cultivated soil. The total estimate of SOC stocks in France was 3.260 ± 0.872 PgC for the first 30 cm. It was compared to another estimate, based on the Correspondence to: M. P. Martin (manuel.martin@orleans.inra.fr) previously published European soil organic carbon and bulk density maps, of 5.303 PgC. We demonstrate that the present estimate might better represent the actual SOC stock distributions of France, and consequently that the previously published approach at the European level greatly overestimates SOC stocks.

Pedotransfer functions (PTFs) are used to estimate certain soil properties that are difficult and costly to measure from others more easily available. Bulk density is one important soil property. Although not requiring complex analysis, its measurement remains time consuming and is lacking in many soil surveys. For several decades, PTFs have been developed for predicting soil bulk density. Most of these PTFs are suited only for specific agro‐pedo‐climatic conditions, however, and can be applied only within a limited geographic area. In this study, we derived and experimented with two new PTFs based on a multiple additive regression trees (MART) method, and assessed their performance compared with existing PTFs when applied to a country‐level soil database, the Réseau de Mesures de la Qualité des Sols (RMQS) survey network. This database was designed to include the major soil types and land uses in France. The first proposed PTF (Model m) involves only three predictors typically found in the existing PTFs for bulk density (C content and texture) and the second one (Model M) includes eight easily accessible quantitative and qualitative predictors (e.g., soil taxon). Both models significantly outperformed existing PTFs. Without arbitrarily partitioning the data set before fitting the model, the m and M MART models yielded R2 values of 0.83 and 0.94, respectively. The predictive quality on independent data, assessed using cross‐validation, was also improved compared with published PTFs, with R2 reaching 0.62 and 0.66 and root mean square prediction errors of 0.123 and 0.117 Mg m−3 for the two MART models.

Soil organic carbon plays a major role in the global carbon budget, and can act as a source or a sink of atmospheric carbon, whereby it can influence the course of climate change. Changes in soil organic soil stocks (SOCS) are now taken into account in international negotiations regarding climate change. Consequently, developing sampling schemes and models for estimating the spatial distribution of SOCS is a priority. The French soil monitoring network has been established on a 16 km × 16 km grid and the first sampling campaign has recently been completed, providing circa 2200 measurements of stocks of soil organic carbon, obtained through an in situ composite sampling, uniformly distributed over the French territory. <br><br> We calibrated a boosted regression tree model on the observed stocks, modelling SOCS as a function of other variables such as climatic parameters, vegetation net primary productivity, soil properties and land use. The calibrated model was evaluated through cross-validation and eventually used for estimating SOCS for the whole of metropolitan France. Two other models were calibrated on forest and agricultural soils separately, in order to assess more precisely the influence of pedo-climatic variables on soil organic carbon for such soils. <br><br> The boosted regression tree model showed good predictive ability, and enabled quantification of relationships between SOCS and pedo-climatic variables (plus their interactions) over the French territory. These relationship strongly depended on the land use, and more specifically differed between forest soils and cultivated soil. The total estimate of SOCS in France was 3.260 ± 0.872 PgC for the first 30 cm. It was compared to another estimate, based on the previously published European soil organic carbon and bulk density maps, of 5.303 PgC. We demonstrate that the present estimate might better represent the actual SOCS distributions of France, and consequently that the previously published approach at the European level greatly overestimates SOCS

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