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Abstract. 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.

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Background, Aims, and Scope. Historically, built areas were ignored in soil mapping and in studies of soil formation and behaviour. It is now recognized that these areas, and therefore their soils, are of prime importance to human populations. Another trend is the large increase in reclaimed lands and new uses for old industrial areas. In several countries there are active projects to map such areas, either with locally-developed classification systems or ad-hoc names. Soil classification gives unique and reproducible names to soil individuals, thereby facilitating correlation of soil studies; this should be possible also for urban soils. The World Reference Base for Soil Resources (WRB) is the soil classification system endorsed by the International Union of Soil Science (IUSS). The 2006 edition has important enhancements which allow urban and industrial soils to be described and mapped, most notably a new reference group, the Technosols.Main Features. Urban soils are first defined, followed by the philosophical basis of soil classification in general and the WRB in particular. WRB 2006 added a new Technosols reference soil group for soils whose properties and function are dominated by technical human activity as evidenced by either a substantial presence of artefacts, or an impermeable constructed geomembrane, or technic hard rock. Technosols are one of Ekranic, Linic, Urbic, Spolic or Garbic; further qualifiers are added to show intergrades to other groups as well as specific soil properties. Soils from fill are recognized as Transportic Regosols or Arenosols. Toxic soils are specifically recognized by a qualifier.Discussion. The limit between Technosols and other groups may be difficult to determine, because of the requirement that the technic nature dominate any subsequent pedogenesis. Recommendations and Perspectives.The WRB should certainly be used in all urban soil studies to facilitate communication and correlation of results. In the period leading up to the next revision in 2010, the quantitative results from urban soil studies should be used to refine class definitions.

The potential economic and agronomic impacts of gradual climate warming are examined at the farm level. Three models of the relevant climatic, agronomic, and economic processes are developed and linked to address climate change impacts and agricultural adaptability. Several climate warming scenarios are analyzed, which vary in severity. The results indicate that grain farmers in southern Minnesota can effectively adapt to a gradually changing climate (warmer and either wetter or drier) by adopting later maturing cultivars, changing crop mix, and altering the timing of field operations to take advantage of a longer growing season resulting from climate warming.

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The paper evaluates spreading of observations in feature and geographical spaces as a key to sampling optimisation for spatial prediction by correlation with auxiliary maps. Although auxiliary data are commonly used for mapping soil variables, problems associated with the design of sampling strategies are rarely examined. When generalised least-squares estimation is used, the overall prediction error depends upon spreading of points in both feature and geographical space. Allocation of points uniformly over the feature space range proportionally to the distribution of predictor (equal range stratification, or ER design) is suggested as a prudent sampling strategy when the regression model between the soil and auxiliary variables is unknown. An existing 100-observation sample from a 50 by 50 km soil survey in central Croatia was used to illustrate these concepts. It was re-sampled to 25-point datasets using different experimental designs: ER and 2 response surface designs. The designs were compared for their performance in predicting soil organic matter from elevation (univariate example) using the overall prediction error as an evaluation criterion. The ER design gave overall prediction error similar to the minmax design, suggesting that it is a good compromise between accurate model estimation and minimisation of spatial autocorrelation of residuals. In addition, the ER design was extended to the multivariate case. Four predictors (elevation, temperature, wetness index, and NDVI) were transformed to standardised principal components. The sampling points were then assigned to the components in proportion to the variance explained by a principal component analysis and following the ER design. Since stratification of the feature space results in a large number of possible points in each cluster, the spreading in geographical space can also be maximised by selecting the best of several realisations.

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