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Abstract 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 CO 2 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) CO 2 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. Graphical abstract Unlabelled Image Highlights • A space-for-time substitution model was used to predict current and future SOC stocks in Wisconsin. • Land use, climate and soil parent material were the key drivers of SOC distribution. • By the end of 2050, the soils would store an additional 20 Mg SOC ha−1 on top of the baseline stock of 90 Mg ha−1. • Predicted SOC change by 2050 varies by soil order, land use and ecological zones. [ABSTRACT FROM AUTHOR]

Abstract Soil is recognized as the largest carbon reservoir in the terrestrial ecosystem. Soil organic carbon (SOC) is vulnerable to changes in land use and climate. For a better understanding of the SOC dynamics and its driving factors, we collected data of the 1980s and 2000s in the North and Northeast China and conducted the digital soil mapping for spatial variation of SOC for the respective period. In the 1980s, 585 soils were sampled and the area was resampled in 2003 and 2004 (1062 samples) in a 30-km grid. The main land use in the area was cropland, forest and grassland. The random forest was used to predict the SOC concentration and its temporal change using land use, terrain factors, vegetation index, vis-NIR spectra and climate factors as predictors. The average SOC concentration in 1985 was 10.0 g kg−1 compared to 12.5 g kg−1 in 2004. The SOC variation was similar over the two periods, and levels increased from south to north. The estimated SOC stock was 1.68 Pg in 1985 and 1.66 Pg in 2004, but the SOC changes were different under different land uses. Over the twenty-year period, average temperatures increased and large areas of forests and grassland were converted to cropland. SOC under cropland was increased by 0.094 Pg (+9%) whereas 0.089 Pg SOC was lost under forests (−25%) and 0.037 Pg in the soils under grassland (−25%). It is concluded that land use is the main drivers for SOC changes in this area while climate change had different contributions in different regions. SOC loss was remarkable under the land use conversion while cropland has considerable potential to sequester SOC. Graphical abstract Unlabelled Image Highlights • Digital soil mapping was efficient for evaluation of the SOC changes at large scale with limited data. • Topsoil organic carbon increased in North China and rapidly decreased in Northeast China, however its stock remained neutral. • Land use is the predominant driving factor of the SOC changes in North and Northeast China. [ABSTRACT FROM AUTHOR]

Accurate data are needed to effectively monitor environmental condition, and develop sound policies to plan for the future. Globally, current estimates of soil total phosphorus (P) stocks are very uncertain because they are derived from sparse data, with large gaps over many areas of the Earth. Here, we derive spatially explicit estimates, and their uncertainty, of the distribution and stock of total P in Australian soil. Data from several sources were harmonized to produce the most comprehensive inventory of total P in soil of the continent. They were used to produce fine spatial resolution continental maps of total P in six depth layers by combining the bootstrap, a decision tree with piecewise regression on environmental variables and geostatistical modelling of residuals. Values of percent total P were predicted at the nodes of a 3-arcsecond (approximately 90 m) grid and mapped together with their uncertainties. We combined these predictions with those for bulk density and mapped the total soil P stock in the 0–30 cm layer over the whole of Australia. The average amount of P in Australian topsoil is estimated to be 0.98 t ha − 1 with 90% confidence limits of 0.2 and 4.2 t ha − 1 . The total stock of P in the 0–30 cm layer of soil for the continent is 0.91 Gt with 90% confidence limits of 0.19 and 3.9 Gt. The estimates are the most reliable approximation of the stock of total P in Australian soil to date. They could help improve ecological models, guide the formulation of policy around food and water security, biodiversity and conservation, inform future sampling for inventory, guide the design of monitoring networks, and provide a benchmark against which to assess the impact of changes in land cover, land use and management and climate on soil P stocks and water quality in Australia. [ABSTRACT FROM AUTHOR]