Estimation of carbon contents and stocks are important for carbon sequestration, greenhouse gas emissions and national carbon balance inventories. For Denmark, we modeled the vertical distribution of soil organic carbon (SOC) and bulk density, and mapped its spatial distribution at five standard soil depth intervals (0−5, 5−15, 15−30, 30−60 and 60−100 cm) using 18 environmental variables as predictors. SOC distribution was influenced by precipitation, land use, soil type, wetland, elevation, wetness index, and multi-resolution index of valley bottom flatness. The highest average SOC content of 20 g kg⁻¹ was reported for 0−5 cm soil, whereas there was on average 2.2 g SOC kg⁻¹ at 60−100 cm depth. For SOC and bulk density prediction precision decreased with soil depth, and a standard error of 2.8 g kg⁻¹ was found at 60−100 cm soil depth. Average SOC stock for 0−30 cm was 72 t ha⁻¹ and in the top 1 m there was 120 t SOC ha⁻¹. In total, the soils stored approximately 570 Tg C within the top 1 m. The soils under agriculture had the highest amount of carbon (444 Tg) followed by forest and semi-natural vegetation that contributed 11% of the total SOC stock. More than 60% of the total SOC stock was present in Podzols and Luvisols. Compared to previous estimates, our approach is more reliable as we adopted a robust quantification technique and mapped the spatial distribution of SOC stock and prediction uncertainty. The estimation was validated using common statistical indices and the data and high-resolution maps could be used for future soil carbon assessment and inventories.

Soil mapping in Denmark has a long history and a series of soil maps based on conventional mapping approaches have been produced. In this study, a national soil map of Denmark was constructed based on the FAO–Unesco Revised Legend 1990 using digital soil mapping techniques, existing soil profile observations and environmental data. This map was developed using soil-landscape models generated with a decision tree-based digital soil mapping technique. As input variables in the model, more than 1170 soil profile data and 17 environmental variables including geology, land use, landscape type, area of wetlands, digital elevation model and its derivatives were compiled. The predicted map showed that Podzols and Luvisols were the most frequent soil groups, covering almost two-thirds of the area of Denmark. Geographically, Podzols occupied a major portion of western Denmark, where the soils have developed on sandy parent material, whereas eastern Denmark mostly contained Luvisols developed on loamy basal till. The occurrence of the predicted soil groups was assigned using several variables, of the most important was clay content in the topsoil and subsoil, elevation, geology and landscape type. The overall prediction accuracy based on a 20% hold-back validation data was 60%, but increased to 76% when prediction accuracy of similar soil groups was considered. Podzoluvisols and Alisols were among the weakly predicted groups (70%). Overall, the average prediction uncertainty was less than 34%. Compared to the existing conventional soil map, the new map showed promising predictions. Validation of the predicted map with different techniques (point validation, prediction confidence analysis, and map-to-map comparison) confirmed that the output is reliable and can be used in various soil and environmental studies without major difficulties. This study also verified the importance of GlobalSoilMap products and a priori pedological information that improved prediction performance and quality of the new FAO soil map of Denmark

Carbon stored in soils contributes to a variety of soil functions, including biomass production, water storage and filtering, biodiversity maintenance, and many other ecosystem services. Understanding soil organic carbon (SOC) spatial distribution and projection of its future condition is essential for future CO₂ emission estimates and management options for storing carbon. However, modeling SOC spatiotemporal dynamics is challenging due to the inherent spatial heterogeneity and data limitation. The present study developed a spatially explicit prediction model in which the spatial relationship between SOC observation and seventeen environmental variables was established using the Cubist regression tree algorithm. The model was used to compile a baseline SOC stock map for the top 30 cm soil depth in the State of Wisconsin (WI) at a 90 m × 90 m grid resolution. Temporal SOC trend was assessed by comparing baseline and future SOC stock maps based on the space-for-time substitution model. SOC prediction for future considers land use, precipitation and temperature for the year 2050 at medium (A1B) CO₂ emissions scenario of the Intergovernmental Panel on Climate Change. Field soil observations were related to factors that are known to influence SOC distribution using the digital soil mapping framework. The model was validated on 25% test profiles (R²: 0.38; RMSE: 0.64; ME: -0.03) that were not used during model training that used the remaining 75% of the data (R²: 0.76; RMSE: 0.40; ME: -0.006). In addition, maps of the model error, and areal extent of Cubist prediction rules were reported. The model identified soil parent material and land use as key drivers of SOC distribution including temperature and precipitation. Among the terrain attributes, elevation, mass-balance index, mid-slope position, slope-length factor and wind effect were important. Results showed that Wisconsin soils had an average baseline SOC stock of 90 Mg ha^-1 and the distribution was highly variable (CV: 64%). It was estimated that WI soils would have an additional 20 Mg ha^-1 SOC by the year 2050 under changing land use and climate. Histosols and Spodosols were expected to lose 19 Mg ha^-1 and 4 Mg ha^-1, respectively, while Mollisols were expected to accumulate the largest SOC stock (62 Mg ha^-1). All land-use types would be accumulating SOC by 2050 except for wetlands (-34 Mg C ha^-1). This study found that Wisconsin soils will continue to sequester more carbon in the coming decades and most of the Driftless Area will be sequestering the greatest SOC (+63 Mg C ha^-1). Most of the SOC would be lost from the Northern Lakes and Forests ecological zone (-12 Mg C ha^-1). The study highlighted areas of potential C sequestration and areas under threat of C loss. The maps generated in this study would be highly useful in farm management and environmental policy decisions at different spatial levels in Wisconsin.