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This report serves to give an overview of main procedures and standards in use at ISRIC – World Soil Information, regular member of the International Science Council (ISC) World Data System (WDS). These cover the whole data life cycle from field sampling to serving quality-assessed soil data to the world community through a range of web services, examples of which are provided. Consistent workflows, procedures and de facto standards are used to screen (QA/QC) and standardise respectively harmonise the wide range of soil-related data that have been shared with us for consideration in our world-covering databases and soil mapping work. Ultimately, these protocols and processes are aimed at facilitating global data interoperability and citability in compliance with FAIR principles: the data should be ‘findable, accessible, interoperable, and reusable’. A recent development at ISRIC has been the implementation of a community of practice (CoP) on soil data and information; the workflows, procedures and standards described in this report may be considered as resources for the evolving community of practice.

This document was prepared in the framework of ISRIC’s Community of Practice for soil information providers. It provides a compilation of resources required to compile a Soil and Terrain (SOTER) database at (sub)national scale using existing resources (i.e., no new field work is assumed). More detailed information on the various steps required for compiling the spatial (geographic) and attribute component of a SOTER database is provided in the cited documentation. Most of the materials cited here were created in the framework of the SOTER programme and related EU-projects. SOTER databases are typically compiled in the framework of international projects.

There is a growing demand for high-quality soil data. However, soil measurements are subject to many error sources. We aimed to quantify uncertainties in synthetic and real-world wet chemistry soil data through a linear mixed-effects model, including batch and laboratory effects. The use of synthetic data allowed us to investigate how accurately the model parameters were estimated for various experimental measurement designs, whereas the real-world case served to explore if estimates of the random effect variances were still accurate for unbalanced datasets with few replicates. The variance estimates for synthetic (Formula presented.) data were unbiased, but limited laboratory information led to imprecise estimates. The same was observed for unbalanced synthetic datasets, where 20, 50 and 80% of the data were removed randomly. Removal led to a sharp increase of the interquartile range (IQR) of the variance estimates for batch effect and the residual. The model was also fitted to real-world (Formula presented.) and total organic carbon (TOC) data, provided by the Wageningen Evaluating Programmes for Analytical Laboratories (WEPAL). For (Formula presented.), the model yielded unbiased estimates with relatively small IQRs. However, the limited number of batches with replicate measurements (5.8%) caused the batch effect to be larger than expected. A strong negative correlation between batch effect and residual variance suggested that the model could not distinguish well between these two random effects. For TOC, batch effect was removed from the model as no replicates were available within batches. Again, unbiased model estimates were obtained. However, the IQRs were relatively large, which could be attributed to the smaller dataset with only a single replicate measurement. Our findings demonstrated the importance of experimental measurement design and replicate measurements in the quantification of uncertainties in wet chemistry soil data. Highlights: Accurate uncertai

Volumetric Water Content at 1500kPa in 10-3 cm3cm-2 at 6 standard depths. Predictions were derived using a digital soil mapping approach based on Quantile Random Forest, drawing on a global compilation of soil profile data and environmental layers.

Volumetric Water Content at 33kPa in 10-3 cm3cm-2 at 6 standard depths. Predictions were derived using a digital soil mapping approach based on Quantile Random Forest, drawing on a global compilation of soil profile data and environmental layers.

SoilGrids produces maps of soil properties for the entire globe at medium spatial resolution (250 m cell size) using state-of-the-art machine learning methods to generate the necessary models. It takes as inputs soil observations from about 240 000 locations worldwide and over 400 global environmental covariates describing vegetation, terrain morphology, climate, geology and hydrology. The aim of this work was the production of global maps of soil properties, with cross-validation, hyper-parameter selection and quantification of spatially explicit uncertainty, as implemented in the SoilGrids version 2.0 product incorporating state-of-the-art practices and adapting them for global digital soil mapping with legacy data. The paper presents the evaluation of the global predictions produced for soil organic carbon content, total nitrogen, coarse fragments, pH (water), cation exchange capacity, bulk density and texture fractions at six standard depths (up to 200 cm). The quantitative evaluation showed metrics in line with previous global, continental and large-region studies. The qualitative evaluation showed that coarse-scale patterns are well reproduced. The spatial uncertainty at global scale highlighted the need for more soil observations, especially in high-latitude regions.

Spatially resolved estimates of change in soil organic carbon (SOC) stocks are necessary for supporting national and international policies aimed at achieving land degradation neutrality and climate change mitigation. In this work we report on the development, implementation and application of a data-driven, statistical method for mapping SOC stocks in space and time, using Argentina as a pilot. We used quantile regression forest machine learning to predict annual SOC stock at 0–30 cm depth at 250 m resolution for Argentina between 1982 and 2017. The model was calibrated using over 5,000 SOC stock values from the 36-year time period and 35 environmental covariates. We preprocessed normalized difference vegetation index (NDVI) dynamic covariates using a temporal low-pass filter to allow the SOC stock for a given year to depend on the NDVI of the current as well as preceding years. Predictions had modest temporal variation, with an average decrease for the entire country from 2.55 to 2.48 kg C m−2 over the 36-year period (equivalent to a decline of 211 Gg C, 3.0% of the total 0–30 cm SOC stock in Argentina). The Pampa region had a larger estimated SOC stock decrease from 4.62 to 4.34 kg C m−2 (5.9%) during the same period. For the 2001–2015 period, predicted temporal variation was seven-fold larger than that obtained using the Tier 1 approach of the Intergovernmental Panel on Climate Change and United Nations Convention to Combat Desertification. Prediction uncertainties turned out to be substantial, mainly due to the limited number and poor spatial and temporal distribution of the calibration data, and the limited explanatory power of the covariates. Cross-validation confirmed that SOC stock prediction accuracy was limited, with a mean error of 0.03 kg C m−2 and a root mean squared error of 2.04 kg C m−2. In spite of the large uncertainties, this work showed that machine learning methods can be used for space–time SOC mapping and may yield valuable information to lan

There is growing international interest in better managing soils to increase soil organic carbon (SOC) content to contribute to climate change mitigation, to enhance resilience to climate change and to underpin food security, through initiatives such as international ‘4p1000’ initiative and the FAO's Global assessment of SOC sequestration potential (GSOCseq) programme. Since SOC content of soils cannot be easily measured, a key barrier to implementing programmes to increase SOC at large scale, is the need for credible and reliable measurement/monitoring, reporting and verification (MRV) platforms, both for national reporting and for emissions trading. Without such platforms, investments could be considered risky. In this paper, we review methods and challenges of measuring SOC change directly in soils, before examining some recent novel developments that show promise for quantifying SOC. We describe how repeat soil surveys are used to estimate changes in SOC over time, and how long‐term experiments and space‐for‐time substitution sites can serve as sources of knowledge and can be used to test models, and as potential benchmark sites in global frameworks to estimate SOC change. We briefly consider models that can be used to simulate and project change in SOC and examine the MRV platforms for SOC change already in use in various countries/regions. In the final section, we bring together the various components described in this review, to describe a new vision for a global framework for MRV of SOC change, to support national and international initiatives seeking to effect change in the way we manage our soils.

The World Soil Information Service (WoSIS) provides quality-assessed and standardised soil profile data to support digital soil mapping and environmental applications at broadscale levels. Since the release of the first "WoSIS snapshot", in July 2016, many new soil data were shared with us, registered in the ISRIC data repository and subsequently standardised in accordance with the licences specified by the data providers. Soil profile data managed inWoSIS were contributed by a wide range of data providers; therefore, special attention was paid to measures for soil data quality and the standardisation of soil property definitions, soil property values (and units of measurement) and soil analytical method descriptions. We presently consider the following soil chemical properties: organic carbon, total carbon, total carbonate equivalent, total nitrogen, phosphorus (extractable P, total P and P retention), soil pH, cation exchange capacity and electrical conductivity. We also consider the following physical properties: soil texture (sand, silt, and clay), bulk density, coarse fragments and water retention. Both of these sets of properties are grouped according to analytical procedures that are operationally comparable. Further, for each profile we provide the original soil classification (FAO, WRB, USDA), version and horizon designations, insofar as these have been specified in the source databases. Measures for geographical accuracy (i.e. location) of the point data, as well as a first approximation for the uncertainty associated with the operationally defined analytical methods, are presented for possible consideration in digital soil mapping and subsequent earth system modelling. The latest (dynamic) set of quality-assessed and standardised data, called "wosis-latest", is freely accessible via an OGC-compliant WFS (web feature service). For consistent referencing, we also provide time-specific static "snapshots". The present snapshot (September 2019) is comprised of

Soil carbon has been measured for over a century in applications ranging from understanding biogeochemical processes in natural ecosystems to quantifying the productivity and health of managed systems. Consolidating diverse soil carbon datasets is increasingly important to maximize their value, particularly with growing anthropogenic and climate change pressures. In this progress report, we describe recent advances in soil carbon data led by the International Soil Carbon Network and other networks. We highlight priority areas of research requiring soil carbon data, including (a) quantifying boreal, arctic and wetland carbon stocks, (b) understanding the timescales of soil carbon persistence using radiocarbon and chronosequence studies, (c) synthesizing long-term and experimental data to inform carbon stock vulnerability to global change, (d) quantifying root influences on soil carbon and (e) identifying gaps in model-data integration. We also describe the landscape of soil datasets currently available, highlighting their strengths, weaknesses and synergies. Now more than ever, integrated soil data are needed to inform climate mitigation, land management and agricultural practices. This report will aid new data users in navigating various soil databases and encourage scientists to make their measurements publicly available and to join forces to find soil-related solutions.

Reported potentials for sequestration of carbon in soils of agricultural lands are overly optimistic because they assume that all degraded cropland and grassland can be subjected to best management practices. Two approaches for estimating this potential are presented. Method 1 (M1) considers literature-derived best estimates for annual soil organic carbon (SOC) gains (Mg C ha−1) by bioclimatic zone; Method 2 (M2) assumes an annual C increase of 3 to 5 promille with respect to present SOC mass (similar to the French ‘4 pour mille’ initiative). Four management scenarios are considered, capturing the varying level of plausibility of meeting the full technological potential. According to M1, achievable gains range from 0.05–0.12 Pg C yr−1 to 0.14–0.37 Pg C yr−1, with a technological potential of 0.32–0.86 Pg C yr−1. For M2, these are 0.07–0.12 Pg C yr−1, 0.21–0.35 Pg C yr−1, and 0.60–1.01 Pg C yr−1. Consideration of the technological potential only and use of a proportional annual increase in SOC (M2), rather than using best estimates for soil carbon gains by bioclimatic zone (M1), will provide too ‘bright a picture’ in the context of rehabilitating degraded lands and mitigating/adapting to climate change. Further, M2 assumes that possible C gains will be greatest where present SOC stocks are highest, which is counter-intuitive. Although all measures aimed at increasing SOC content should be encouraged due to the creation of win-win situations, it is important to create a realistic picture of the amount of SOC gains that are feasible based on bioclimatic and management implementation constraints.

In this paper we present effects of four paired agricultural management practices (organic matter (OM) addition versus no organic matter input, no-tillage (NT) versus conventional tillage, crop rotation versus monoculture, and organic agriculture versus conventional agriculture) on five key soil quality indicators, i.e., soil organic matter (SOM) content, pH, aggregate stability, earthworms (numbers) and crop yield. We have considered organic matter addition, no-tillage, crop rotation and organic agriculture as “promising practices”; no organic matter input, conventional tillage, monoculture and conventional farming were taken as the respective references or “standard practice” (baseline). Relative effects were analysed through indicator response ratio (RR) under each paired practice. For this we considered data of 30 long-term experiments collected from 13 case study sites in Europe and China as collated in the framework of the EU-China funded iSQAPER project. These were complemented with data from 42 long-term experiments across China and 402 observations of long-term trials published in the literature. Out of these, we only considered experiments covering at least five years. The results show that OM addition favourably affected all the indicators under consideration. The most favourable effect was reported on earthworm numbers, followed by yield, SOM content and soil aggregate stability. For pH, effects depended on soil type; OM input favourably affected the pH of acidic soils, whereas no clear trend was observed under NT. NT generally led to increased aggregate stability and greater SOM content in upper soil horizons. However, the magnitude of the relative effects varied, e.g. with soil texture. No-tillage practices enhanced earthworm populations, but not where herbicides or pesticides were applied to combat weeds and pests. Overall, in this review, yield slightly decreased under NT. Crop rotation had a positive effect on SOM content and yield; rotation with le

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 D random forest and gradient boosting and/or multinomial logistic regression D 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 method