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Multi-element soil extractions such as Mehlich 3 (M3) have gained popularity in recent years, but comparing outcomes to other soil testing methods is not always straightforward. In this study, extraction mechanisms of M3, Olsen and neutral 1 M ammonium acetate (AA) soil tests were explored and transfer functions were derived between P-Olsen and P-M3 as well as between K-AA and K-M3. Soils from tropical and temperate areas were used to derive these P and K transfer functions and were evaluated separately. The application of these transfer functions for tropical soils was evaluated by using them as input for the Quantitative Evaluation of the Fertility of Tropical Soils (QUEFTS). AA and M3 generally extracted similar amounts of K, but relations between K-AA and K-M3 were different for tropical and temperate soils. For tropical soils, the transfer function did not require additional parameters besides K-M3 to predict K-AA, but for temperate soils inclusion of clay content and pH was needed. This difference between tropical and temperate soils was explained by clay mineralogy. The relation between P-Olsen and P-M3 in tropical soils was found to be dependent on pH, Al-M3, Fe-M3 and Ca-M3. P-Olsen and K-AA values, calculated with their respective transfer functions, were used as input for QUEFTS. The yields predicted with measured P-Olsen and Exch. K were used as benchmark. For 63 out of 81 soil samples, predicted maize yields with transfer functions deviated less than 10% from the benchmark. The largest deviations from the benchmark were found for low P-Olsen and K-AA values, which corresponds to QUEFTS maize yield predictions up to 3000 kg ha−1. We conclude that a M3 extraction results and soil pH can reliably be transferred to, and thus replace P-Olsen and K-AA determinations with the functions developed for tropical soils. The transfer functions can be used to generate input for the QUEFTS model with minor effects on yield predictions, thus expanding its applicability

Pillar 4 of the Global Soil Partnreship (GSP) identified the need to develop/support national level databases, based on common de facto standards, that contain a list of predefined, commonly required, soil properties for geo-referenced soil profiles (site and layers), and this within a federated structure. Originally, two types of soil profile databases (called Tier 1 and Tier 2) were envisaged for this in the implementation plan (GSP and FAO 2016). However, this would imply creation of a central GLOSIS database in contradiction to the federated, bottom-up structure envisaged by the GSP. Consequently, the foreseen GLOSIS spatial data infrastructure does not accommodate centralized data bases (de Sousa et al. 2019). Soil data providers will share (parts of) their data via web services that are registered by the discovery hub. Consequently, we propose to speak of Tier 1 and Tier 2 type data (hereafter referred to as T1 and T2) in the context of GLOSIS and not T1 and T2 databases. A T1 can accommodate all soil profile data that have been collated in a given country and standardised in accord with (emerging) GLOSIS protocols. Alternatively, T2 are subsets of the national T1’s that can be used to address specific issues (i.e. soil functions and threats as required for SoilSTAT). The document was endorsed by INSII in June 2020, see https://www.fao.org/fileadmin/user_upload/GSP/eighth_PA/GSPPA_VIII_2020_8.pdf.

This study compares the performance of five popular equal-area projections supported by Free and Open Source Software for Geo-spatial (FOSS4G)—Sinusoidal, Mollweide, Hammer, Eckert IV and Homolosine. A set of 21,872 discrete distortion vindicatrices were positioned on the ellipsoid surface, centred on the cells of a Snyder icosahedral equal-area grid. These indicatrices were projected on the plane and the resulting angular and distance distortions computed, all using FOSS4G. The Homolosine is the only projection that manages to minimise angular and distance distortions simultaneously. It yields the lowest distortions among this set of projections and clearly outclasses when only land masses are considered. These results also indicate the Sinusoidal and Hammer projections to be largely outdated, imposing too large distortions to be useful. In contrast, the Mollweide and Eckert IV projections present trade-offs between visual expression and accuracy that are worth considering. However, for the purposes of storing and analysing big spatial data with FOSS4G the superior performance of the Homolosine projection makes its choice difficult to avoid.

Soils have critical relevance to global issues, such as food and water security, climate regulation, sustainable energy, desertification and biodiversity protection. As a consequence, soil is becoming one of the top priorities for the global environmental policy agenda. Conventional soil maps suffer from large limitations, i.e. most of them are static and often obsolete, are often generated at coarse scale, and can be uneasy to handle. Digital Soil Mapping has been developed as a solution to generate high-resolution maps of soil properties over large areas. Two projects, GlobalSoilMap and SoilGrids, presently aim at delivering the first generation of global, high-resolution soil property fine grids. In this paper, we briefly describe the GlobalSoilMap history, its present status and present achievements, and illustrate some of these with (mainly) French examples. At given moment there is still an enormous potential for forthcoming research and for delivering products more helpful for end users. Key here is the continuous progress in available co-variates, in their spatial, spectral and temporal coverage and resolution through remote sensing products. All over the world, there is still a very large amount of point soil data still to be rescued and this effort should be pursued and encouraged. Statistically advances are expected by exploring and implementing new models. Especially relevant are spatial-temporal models and contemporary Artificial Intelligence for handling the complex big data. Advances should be made and research efforts are needed on estimating the uncertainties, and even on estimating uncertainties on uncertain-ties. Attempts to merge different model strategies and products (for instance deriving from different covariates, spatial extents, soil data sources, and mod-els) should be made in order to get the most useful information from each of these predictions, and to identify how controlling factors may change depending on scales.

Climate change and land degradation are of increasing societal and governmental concern. For this reason, several international programs have been initiated in the last decade, such as the 4 per 1000 initiative and the Sustainable Development Goals of United Nations. The soil science community is actively working under different national and international organizations to provide regional and global soil information to support these programmes. Digital Soil Mapping (DSM), a relatively new methodology to create soil maps based on (geo)statistical methods, has became operational during the last fifteen years and has now been adopted by several organizations. It is defined as computer-assisted production of digital maps of soil type and soil properties, by use of mathematical and statistical models that combine information from soil observations with information contained in correlated environmental variables. Most studies in DSM spatially predict soil properties or classes from either new or legacy laboratory data and spatially exhaustive environmental covariates (GIS layers of biophysical land surface properties), typically using empirical statistical methods. These methods have shown to result in accurate maps at different scales, but do not provide knowledge about the interrelationships between the soil properties and the functioning of the soil and soil-landscape system. We not only need to properly describe or map soil spatial variation, but also to understand soil behaviour. This is needed to answer questions such as: which are the dominant soil processes in a certain region? How will the soil react under increased productivity pressure? How vulnerable is the soil to erosion or pollution? How much organic carbon can we store in the soil at a given location? Mechanistic soil-landscape models do include process-knowledge but cannot be applied easily for soil mapping because of their high complexity and large uncertainty. A solution could be to use structural equat

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).

In a previous study we introduced structural equation modelling (SEM) for digital soil mapping in the Argentine Pampas. An attractive property of SEM is that it incorporates pedological knowledge explicitly through a mathematical implementation of a conceptual model. Many soil processes operate within the soil profile; therefore, SEM might be suitable for simultaneous prediction of soil properties for multiple soil layers. In this way, relations between soil properties in different horizons can be included that might result in more consistent predictions. The objectives of this study were therefore to apply SEM to multi-layer and multivariate soil mapping, and to test SEM functionality for suggestions to improve the modelling. We applied SEM to model and predict the lateral and vertical distribution of the cation exchange capacity (CEC), organic carbon (OC) and clay content of three major soil horizons, A, B and C, for a 23 000-km2 region in the Argentine Pampas. We developed a conceptual model based on pedological hypotheses. Next, we derived a mathematical model and calibrated it with environmental covariates and soil data from 320 soil profiles. Cross-validation of predicted soil properties showed that SEM explained only marginally more of the variance than a linear regression model. However, assessment of the covariation showed that SEM reproduces the covariance between variables much more accurately than linear regression. We concluded that SEM can be used to predict several soil properties in multiple layers by considering the interrelations between soil properties and layers.

80% of arable land in Africa has low soil fertility and suffers from physical soil problems. Additionally, significant amounts of nutrients are lost every year due to unsustainable soil management practices. This is partially the result of insufficient use of soil management knowledge. To help bridge the soil information gap in Africa, the Africa Soil Information Service (AfSIS) project was established in 2008. Over the period 2008–2014, the AfSIS project compiled two point data sets: the Africa Soil Profiles (legacy) database and the AfSIS Sentinel Site database. These data sets contain over 28 thousand sampling locations and represent the most comprehensive soil sample data sets of the African continent to date. Utilizing these point data sets in combination with a large number of covariates, we have generated a series of spatial predictions of soil properties relevant to the agricultural management—organic carbon, pH, sand, silt and clay fractions, bulk density, cation-exchange capacity, total nitrogen, exchangeable acidity, Al content and exchangeable bases (Ca, K, Mg, Na). We specifically investigate differences between two predictive approaches: random forests and linear regression. Results of 5-fold cross-validation demonstrate that the random forests algorithm consistently outperforms the linear regression algorithm, with average decreases of 15–75% in Root Mean Squared Error (RMSE) across soil properties and depths. Fitting and running random forests models takes an order of magnitude more time and the modelling success is sensitive to artifacts in the input data, but as long as quality-controlled point data are provided, an increase in soil mapping accuracy can be expected. Results also indicate that globally predicted soil classes (USDA Soil Taxonomy, especially Alfisols and Mollisols) help improve continental scale soil property mapping, and are among the most important predictors. This indicates a promising potential for transferring pedological knowled