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As soil carbon has become a key component of climate-smart agriculture, the demand for high-resolution maps has increased drastically. Meanwhile, machine learning algorithms are becoming more widely used and are opening up new solutions in soil mapping. This paper shows which algorithms perform best, how soil inventory data can be most efficiently used for digital soil mapping, and the different available options and methods to derive high-resolution soil carbon data at the large regional scale.

Maps of soil properties made by machine learning techniques are increasingly applied in Earth surface process modelling and agronomy. Maps of the same area made by different methods appear quite different and also differ from field-based polygon soil survey maps. We explore these differences both visually and numerically, using methods that quantify the spatial patterns. Readers can apply the methods to their areas of interest in the USA with the supplied R Markdown scripts.

Digital soil mapping (DSM) allows us to regionalize soil properties by relating them to environmental covariates with the help of an empirical model. Legacy soil data provide a valuable basis to generate high-resolution soil maps with DSM. We studied the usefulness of data-clustering methods to tackle potential sampling bias in legacy soil data while applying DSM for soil texture regionalization. Clustering has proved to be useful in various steps of the DSM process.

Although present in food security key areas, Arenosols carbon stocks are barely studied. A 150-year-old land use change in a Mediterranean Arenosol showed a loss from 50 Gt C ha^-1 to 3 Gt C ha^-1 after grape cultivation. ¹⁴C showed that deep ploughing in a vineyard plot redistributed the remaining microbial carbon both vertically and horizontally. Despite the drastic degradation of the organic matter pool, Arenosols would have a high carbon storage potential, targeting the 4 per 1000 initiative.

Soil structure is of central importance for soil functions. It is, however, ill defined. With the increasing availability of X-ray CT scanners, more and more soils are scanned and an undisturbed image of the soil's structure is produced. Often, a qualitative description is all that is derived from these images. We provide now a web-based Soil Structure Library where these images can be evaluated in a standardized quantitative way and can be compared to a world-wide data set.

Soil ecological stoichiometry offers a tool to explore the distribution, cycling, limitation, and balance of chemical elements. This study improved the understanding of soil organic carbon and nutrient dynamics in tea plantation ecosystems and also provided supplementary information for soil ecological stoichiometry in global terrestrial ecosystems.