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Soil organic carbon (SOC) accumulation in agroecosystems is a promising solution to simultaneously improve food security and mitigate climate change. Indeed, because of their large carbon deficit, cropland soils can potentially sequester a substantial amount of atmospheric carbon (C). To estimate the soil C-sequestration potential, it is critical to derive reliable estimations of the current soil C-saturation level. This step is essential to obtain an accurate quantification of C-deficits in cultivated soils. In addition, it is important to identify agricultural practices that favor SOC accumulation in order to reduce the soil C-deficit. Based on a 30-year old soil monitoring network of multiple cropland (CR) and permanent grassland (PG) sites established in western Switzerland, we (i) quantified the C-deficit in croplands, (ii) identified the factors driving the C-deficit and (iii) evaluated the assumption that grasslands can be used as C-saturated reference sites. We demonstrated that SOC in CR were depleted by a third compared to PG. The main factor affecting C-deficit in CR was the proportion of temporary grasslands (TG) within the crop rotation. We also showed that PG have not reached their C-saturation level in the study area and that additional C could be stored in PG soil under optimal management. When accounting for pedo-climatic differences, the C-deficit of CR that do not include TG in the rotation was equivalent to 3 kg C m-2 down to 50 cm depth. The relationship between the proportion of TG in the rotation and SOC stocks in the topsoil (0-20 cm) and subsoil (20-50 cm) was linear and similar at both depths, revealing the strong potential of the subsoil to sequester C.

We present methods to evaluate the spatial patterns of the geographic distribution of soil properties in the USA, as shown in gridded maps produced by Predictive Soil Mapping (PSM) at global (SoilGrids v2), national (Soil Properties and Class 100 m Grids of the USA), and regional (POLARIS soil properties) scales, and compare them to spatial patterns known from detailed field surveys (gSSURGO). The methods are illustrated with an example: topsoil pH for an area in central New York State. A companion report examines other areas, soil properties, and depth slices. A set of R Markdown scripts is referenced so that readers can apply the analysis for areas of their interest. For the test case we discover and discuss substan- tial discrepancies between PSM products, as well as large differences between the PSM products and legacy field surveys. These differences are in whole-map statistics, visually-identifiable landscape features, level of detail, range and strength of spatial autocorrelation, landscape metrics (Shannon diversity and evenness, shape, aggregation, mean fractal dimension, co-occurence vectors), and spatial patterns of property maps classified by histogram equalization. Histograms and variogram analysis revealed the smoothing effect of machine-learning models. Property class maps made by histogram equalization were substantially different, but there was no consistent trend in their landscape metrics. The model using only national points and covariates was not better than the global model, and in some cases introduced artefacts from a lithology covariate. Uncertainty (5–95% confidence intervals) provided by SoilGrids and POLARIS were unrealistically wide compared to gSSURGO low and high estimated values and show substantially different spatial patterns. We discuss the potential use of the PSM products as a (partial) replacement for field-based soil surveys.

Increasing soil organic carbon (SOC) in agro-ecosystems enables to address simultaneously food security as well as climate change adaptation and mitigation. Croplands represent a great potential to sequester atmospheric C because they are depleted in SOC. Hence, reliable estimations of SOC deficits in agro-ecosystems are crucial to evaluate the C sequestration potential of agricultural soils and support management practices. Using a 30-year old soil monitoring networks with 250 sites established in western Switzerland, we identified factors driving the long-term SOC dynamics in croplands (CR) and permanent grasslands (PG) and quantified SOC deficit. A new relationship between the silt + clay (SC) soil particles and the C stored in the mineral-associated fraction (MAOMC) was established. We also tested the assumption about whether or not PG can be used as carbon-saturated reference sites. The C-deficit in CR constituted about a third of their potential SOC content and was mainly affected by the proportion of temporary grassland in the crop rotation. SOC accrual or loss were the highest in sites that experienced land-use change. The MAOMC level in PG depended on the C accrual history, indicating that C-saturation level was not coincidental. Accordingly, the relationship between MAOMC and SC to determine soil C-saturation should be estimated by boundary line analysis instead of least squares regressions. In conclusion, PG do provide an additional SOC storage capacity under optimal management, though the storage capacity is greater for CR.

Soils have critical relevance to global issues, such as food and water security, climate regulation, sustainable energy, desertification and biodiversity protection. All these examples require accurate national soil property information and there is a need to scientific support to develop reliable baseline soil information and pathways for measuring and monitoring soils. Soil sustainable management is a global issue, but effective actions require high-resolution data about soil properties. Two projects, GlobalSoilMap and SoilGrids, aim at delivering the first generation of high-resolution soil property grids for the globe, the first one by a bottom-up approach (from country to globe), the latter by top-down (global). The GLobAl Digital SOIL MAP (GLADSOILMAP) consortium brings together world scientific leaders involved in both projects. The consortium aims at developing and transferring methods to improve the prediction accuracy of soil properties and their associated uncertainty, by using legacy soil data and ancillary spatial information. This approach brings together new technologies and methods, existing soil databases and expert knowledge. The consortium aims at transferring methods to achieve convergence between top-down and bottom-up approaches, and to generate methods for delivering maps of soil properties. These maps are essential for communities from climate and environmental modeling to decision making and sustainable resources management at a scale that is relevant to soil management. The consortium will ensure links with the numerous actors in geosciences of the world, and will contribute to improving their skills in digital mapping and their national and international legibility. The actions include 4 main Work Packages (WP) subdivided into several tasks that are summarized below: WP0 Management of the projectWP1 Legacy and ancillary data for Digital Soil Mapping (DSM)Test the potential of new ancillary data for DSMExplore methodologies to merge and/or harmonize different productsPropose methods for harmonizing products to a common date WP2 Methods for sampling, modelling and mapping soils in space and timeTesting and developing new methods/models for predictionTesting methods for estimating complete probability distribution WP3 Methods for estimating model and map uncertaintyDevelop methods of uncertainty spatial assessmentDevelop methods do deal with censored data/soft dataSolve the question of influence on the age of the rescued soil data on predictions WP4 Scientific outreach and capacity buildingProduce an exhaustive review of GlobalSoilMap initiatives and results all over the worldRevise and update the GlobalSoilMap specifications by keeping them at the state-of-the-art levelShow relevance of gridded, Global, DSM by use cases and communication to end users The added value of the consortium is to allow a direct scientific exchange between members that should result in synthesis papers, in the identification of the major knowledge gaps, and in extending, deepening and disseminating knowledge of DSM, with the final aim to contribute to the achievement of global soil maps. Another added value of the consortium will certainly be to foster the creation of new ideas. Acknowledgements: the Consortium GLADSOILMAP is supported by LE STUDIUM Loire Valley Institute for Advanced studies.

In the 21st century, soils are at the crossroads of global issues (i.e., food security, water security, biodiversity protection, climate change, and ecosystem services) and essential to achieve some of the Sustainable Development Goals. Although soils are central to these global issues, their management requires local actions and knowledge, which requires fine-resolution soil information. With an emphasis on broad-scale studies (>10,000 km2), this review outlines recent progress in the development of GlobalSoilMap, an initiative to provide a global fine-resolution grid of soil properties with quantified uncertainties using the bottom-up approach. This review provides an overview related to the soil data source, environmental covariates, spatial prediction, modelling and mapping techniques, uncertainty qualification, and target soil properties. The main findings of this review are: (1) A great increase of publication was observed after 2012, reaching a peak in recent years; (2) Australia and China were the most active countries; (3) Geoderma was the most frequent journal that was preferred by authors to publish related studies; (4) More than a half of the studies did not report soil sampling design; (5) Data splitting was the most frequent strategy for model evaluation, and independent validation was rarely used; (6) Nonlinear predictive model (i.e., machine learning) was becoming popular than ever before; (7) Relief, organisms and climate were the top three SCORPAN factors used in modelling; (8) Soil organic carbon (or soil organic matter) was the top soil property of interest.This review also highlights the perspectives of GlobalSoilMap for further improving the quality of soil information globally and making it practical in decision making.