This paper describes the technical development and accuracy assessment of the most recent and improved version of the SoilGrids system at 250m resolution (June 2016 update). SoilGrids provides global predictions for standard numeric soil properties (organic carbon, bulk density, Cation Exchange Capacity (CEC), pH, soil texture fractions and coarse fragments) at seven standard depths (0, 5, 15, 30, 60, 100 and 200 cm), in addition to predictions of depth to bedrock and distribution of soil classes based on the World Reference Base (WRB) and USDA classification systems (ca. 280 raster layers in total). Predictions were based on ca. 150,000 soil profiles used for training and a stack of 158 remote sensing-based soil covariates (primarily derived from MODIS land products, SRTM DEM derivatives, climatic images and global landform and lithology maps), which were used to fit an ensemble of machine learning methods-random forest and gradient boosting and/or multinomial logistic regression-as implemented in the R packages ranger, xgboost, nnet and caret. The results of 10-fold cross-validation show that the ensemble models explain between 56% (coarse fragments) and 83% (pH) of variation with an overall average of 61%. Improvements in the relative accuracy considering the amount of variation explained, in comparison to the previous version of SoilGrids at 1 km spatial resolution, range from 60 to 230%. Improvements can be attributed to: (1) the use of machine learning instead of linear regression, (2) to considerable investments in preparing finer resolution covariate layers and (3) to insertion of additional soil profiles. Further development of SoilGrids could include refinement of methods to incorporate input uncertainties and derivation of posterior probability distributions (per pixel), and further automation of spatial modeling so that soil maps can be generated for potentially hundreds of soil variables. Another area of future research is the development of methods for multiscale merging of SoilGrids predictions with local and/or national gridded soil products (e.g. up to 50 m spatial resolution) so that increasingly more accurate, complete and consistent global soil information can be produced. SoilGrids are available under the Open Data Base License.

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SoilGrids250m: Global gridded soil information based on machine learning

Hydrologic analyses often involve the evaluation of soil water infiltration, conductivity, storage, and plant-water relationships. To define the hydrologic soil water effects requires estimating soil water characteristics for water potential and hydraulic conductivity using soil variables such as texture, organic matter (OM), and structure. Field or laboratory measurements are difficult, costly, and often impractical for many hydrologic analyses. Statistical correlations between soil texture, soil water potential, and hydraulic conductivity can provide estimates sufficiently accurate for many analyses and decisions. This study developed new soil water characteristic equations from the currently available USDA soil database using only the readily available variables of soil texture and OM. These equations are similar to those previously reported by Saxton et al. but include more variables and application range. They were combined with previously reported relationships for tensions and conductivities and the effects of density, gravel, and salinity to form a comprehensive predictive system of soil water characteristics for agricultural water management and hydrologic analyses. Verification was performed using independent data sets for a wide range of soil textures. The predictive system was programmed for a graphical computerized model to provide easy application and rapid solutions and is available at http://hydrolab.arsusda. gov/soilwater/Index.htm.

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Soil Water Characteristic Estimates by Texture and Organic Matter for Hydrologic Solutions

Black Carbon (BC) may significantly affect nutrient retention and play a key role in a wide range of biogeochemical processes in soils, especially for nutrient cycling. Anthrosols from the Brazilian Amazon (ages between 600 and 8700 yr BP) with high contents of biomassderived BC had greater potential cation exchange capacity (CEC measured at pH 7) per unit organic C than adjacent soils with low BC contents.Synchrotron-based near edge X-ray absorption fine structure (NEXAFS) spectroscopy coupled with scanning transmission X-ray microscopy (STXM) techniques explained the source of the higher surface charge of BC compared with non-BC by mapping crosssectional areas of BC particles with diameters of 10 to 50 mm for C forms. The largest cross-sectional areas consisted of highly aromatic or only slightly oxidized organic C most likely originating from the BC itself with a characteristic peak at 286.1 eV, which could not be found in humic substance extracts, bacteria or fungi. Oxidation significantly increased from the core of BC particles to their surfaces as shown by the ratio of carboxyl-C/aromatic-C. Spotted and non-continuous distribution patterns of highly oxidized C functional groups with distinctly different chemical signatures on BC particle surfaces (peak shift at 286.1 eV to a higher energy of 286.7 eV) indicated that non-BC may be adsorbed on the surfaces of BC particles creating highly oxidized surface. As a consequence of both oxidation of the BC particles themselves and adsorption of organic matter to BC surfaces, the charge density (potential CEC per unit surface area) was greater in BC-rich Anthrosols than adjacent soils. Additionally, a high specific surface area was attributable to the presence of BC, which may contribute to the high CEC found in soils that are rich in BC.

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Black Carbon Increases Cation Exchange Capacity in Soils

Minerals and organic matter (OM) may form intricate associations via myriad interactions. In soils, the associations of OM with mineral surfaces are mainly investigated because of their role in determining the long-term retention of OM. OM “must decay in order to release the energy and nutrients that drive live processes all over the planet” ( Janzen, 2006 ). Thus, the processes and mechanisms that retain OM in soil are a central concern to very different branches of environmental research. An agronomist may want to synchronize periods of high nutrient and energy release with the growth stages of a crop. An environmental chemist may wish to either immobilize an organic soil contaminant or enhance its decomposition into less harmful metabolites, while climate scientists need to understand the processes that mediate the production of potent greenhouse gases from decomposing OM. Associations of OM with pedogenic minerals (henceforth termed mineral–organic associations (MOAs)) are known to be key controls in these and many other processes. Here we strive to present an overview of the current knowledge on MOAs and identify key questions and future research needs.

Chapter One – Mineral–Organic Associations: Formation, Properties, and Relevance in Soil Environments

Ecosystem mycorrhizal type is shown to have a stronger effect on soil carbon storage than temperature, precipitation, clay content and primary production; ecosystems dominated by ectomycorrhizal and ericoid mycorrhizal fungi contain 70% more soil carbon per unit nitrogen than do ecosystems dominated by arbuscular mycorrhizal fungi. Ecosystems differ in the type of plant-associated mycorrhizal fungi (root symbionts associated with nearly all land plants) that dominate. Ectomycorrhiza and ericoid mycorrhizal (EEM) fungi produce nitrogen-degrading enzymes, whereas arbuscular mycorrhiza do not, leading to the prediction that plants in the EEM ecosystems will compete with decomposers for soil nitrogen and therefore increase soil carbon storage. These authors assemble a global data set to show that this is indeed the case, with 70% more carbon storage in EEM ecosystems than in ecosystems dominated by arbuscular mycorrhiza, and that mycorrhizal type is more important than other determinants of soil carbon storage levels. Soil contains more carbon than the atmosphere and vegetation combined1. Understanding the mechanisms controlling the accumulation and stability of soil carbon is critical to predicting the Earth’s future climate2,3. Recent studies suggest that decomposition of soil organic matter is often limited by nitrogen availability to microbes4,5,6 and that plants, via their fungal symbionts, compete directly with free-living decomposers for nitrogen6,7. Ectomycorrhizal and ericoid mycorrhizal (EEM) fungi produce nitrogen-degrading enzymes, allowing them greater access to organic nitrogen sources than arbuscular mycorrhizal (AM) fungi8,9,10. This leads to the theoretical prediction that soil carbon storage is greater in ecosystems dominated by EEM fungi than in those dominated by AM fungi11. Using global data sets, we show that soil in ecosystems dominated by EEM-associated plants contains 70% more carbon per unit nitrogen than soil in ecosystems dominated by AM-associated plants. The effect of mycorrhizal type on soil carbon is independent of, and of far larger consequence than, the effects of net primary production, temperature, precipitation and soil clay content. Hence the effect of mycorrhizal type on soil carbon content holds at the global scale. This finding links the functional traits of mycorrhizal fungi to carbon storage at ecosystem-to-global scales, suggesting that plant–decomposer competition for nutrients exerts a fundamental control over the terrestrial carbon cycle.

Dwayne R. Buxton editor-in-chief Asa publications

Papers

Methods of soil analysis.