Abstract: •Soils contain 1062 Pg organic C (SOC) in 0-1 m depth based on the adjusted Harmonized World Soil Database. Different estimates of bulk density of Histosols cause an uncertainty in the range of -56/+180 Pg. We also report the frequency distribution of SOC stocks by continent, wetland type, and permafrost type. Using additional estimates for frozen and deeper soils, global soils are estimated to contain 1325 Pg SOC in 0-1m and ca. 3000 Pg, including deeper layers. The global soil organic carbon (SOC) mass is relevant for the carbon cycle budget and thus atmospheric carbon concentrations. We review current estimates of SOC stocks and mass (stock × area) in wetlands, permafrost and tropical regions and the world in the upper 1 m of soil. The Harmonized World Soil Database (HWSD) v.1.2 provides one of the most recent and coherent global data sets of SOC, giving a total mass of 2476 Pg when using the original values for bulk density. Adjusting the HWSD’s bulk density (BD) of soil high in organic carbon results in a mass of 1230 Pg, and additionally setting the BD of Histosols to 0.1 g cm−3 (typical of peat soils), results in a mass of 1062 Pg. The uncertainty in BD of Histosols alone introduces a range of −56 to +180 Pg C into the estimate of global SOC mass in the top 1 m, larger than estimates of global soil respiration. We report the spatial distribution of SOC stocks per 0.5 arcminutes; the areal masses of SOC; and the quantiles of SOC stocks by continents, wetland types, and permafrost types. Depending on the definition of “wetland”, wetland soils contain between 82 and 158 Pg SOC. With more detailed estimates for permafrost from the Northern Circumpolar Soil Carbon Database (496 Pg SOC) and tropical peatland carbon incorporated, global soils contain 1325 Pg SOC in the upper 1 m, including 421 Pg in tropical soils, whereof 40 Pg occurs in tropical wetlands. Global SOC amounts to just under 3000 Pg when estimates for deeper soil layers are included. Variability in estimates is due to variation in definitions of soil units, differences in soil property databases, scarcity of information about soil carbon at depths > 1 m in peatlands, and variation in definitions of “peatland”.

Abstract: Field-scale crop models are increasingly applied at spatio-temporal scales that range from regions to the globe and from decades up to 100 years. Sufficiently detailed data to capture the prevailing spatio-temporal heterogeneity in weather, soil, and management conditions as needed by crop models are rarely available. Effective sampling may overcome the problem of missing data but has rarely been investigated. In this study the effect of sampling weather data has been evaluated for simulating yields of winter wheat in a region in Germany over a 30-year period (1982-2011) using 12 process-based crop models. A stratified sampling was applied to compare the effect of different sizes of spatially sampled weather data (10, 30, 50,100, 500, 1000 and full coverage of 34,078 sampling points) on simulated wheat yields. Stratified sampling was further compared with random sampling. Possible interactions between sample size and crop model were evaluated. The results showed differences in simulated yields among crop models but all models reproduced well the pattern of the stratification. Importantly, the regional mean of simulated yields based on full coverage could already be reproduced by a small sample of 10 points. This was also true for reproducing the temporal variability in simulated yields but more sampling points (about 100) were required to accurately reproduce spatial yield variability. The number of sampling points can be smaller when a stratified sampling is applied as compared to a random sampling. However, differences between crop models were observed including some interaction between the effect of sampling on simulated yields and the model used. We concluded that stratified sampling can considerably reduce the number of required simulations. But, differences between crop models must be considered as the choice for a specific model can have larger effects on simulated yields than the sampling strategy. Assessing the impact of sampling soil and crop management data for regional simulations of crop yields is still needed.

Abstract: Continuous crop rotation modeling is a prospective trend that, compared to 1-crop or discrete year-by-year calculations, can provide more accurate results that are closer to real conditions. The goal of this study was to compare the water balance and yields estimated by the HERMES crop rotation model for present and future climatic conditions in the Czech Republic. Three locations were selected, representing important agricultural regions with different climatic conditions. Crop rotation (spring barley, silage maize, winter wheat, winter rape) was simulated from 1981-2080. The 1981-2010 period was covered by measured meteorological data, while 2011-2080 was represented by a transient synthetic weather series from the weather generator M& Rfi. The data were based on 5 circulation models, representing an ensemble of 18 CMIP3 global circulation models, to preserve much of the uncertainty of the original ensemble. Two types of crop management were compared, and the influences of soil quality, increasing atmospheric CO2 and adaptation measures (i. e. sowing date changes) were also considered. Results suggest that under a ‘dry’ scenario (such as GFCM21), C-3 crops in drier regions will be devastated for a significant number of seasons. Negative impacts are likely even on premium-quality soils regardless of flexible sowing dates and accounting for increasing CO2 concentrations. Moreover, in dry conditions, the use of crop rotations with catch crops may have negative impacts, exacerbating the soil water deficit for subsequent crops. This approach is a promising method for determining how various management strategies and crop rotations can affect yields as well as water, carbon and nitrogen cycling.