1  Introduction

Digital Soil Mapping (DSM) uses statistical and machine-learning methods to model and map the spatial distribution of soil properties. It uses soil observations along with geospatial environmental data—such as climatic variables, terrain properties, remote sensing indices, land cover and geology—as inputs.

The Soils4Africa project, funded under the European Union’s Horizon 2020 programme (Grant Agreement 862900), aims to provide a comprehensive assessment of the state of the soil on Africa’s agricultural land, serving as a baseline for future soil monitoring and sustainable land management. The project achieves this by:

  1. Collecting topsoil and subsoil samples from 15,000 locations from agricultural landscapes using a robust, continent-wide sampling framework and standardized field survey protocols.
  2. Analysing all samples in a single reference laboratory in Africa, following uniform laboratory procedures to ensure comparability of soil properties and indicators across coutries.
  3. Developing an open‐access Soil Information System (SIS) that provides soil quality indicators and underlying data, and detailed Standard Operating Procedures (SOPs) and manuals.

This Digital Soil Mapping Manual is an integral component of the Soils4Africa SIS. It provides a hands-on, reproducible tutorial - implemented in the statistical software R - that shows how to produce digital soil maps from the Soils4Africa soil data together with a set of covariate layers. The manual is organized as follows:

By following this manual, soil scientists, GIS specialists, and data analysts will gain a well-documented, clear workflow for creating reproducible digital soil maps. The workflow is suitable for use at various scale levels.

Important note: This manual uses the Soils4Africa soil data from Ghana for demonstration purposes only. Because of the continental design and sampling strategy of the Soils4Africa project, the Soils4Africa soil data is best suited for soil assessement at broader regional (e.g. West Africa) to continental levels. It is not recommended for (sub)national-level assessments, particularly in smaller countries withs relatively sparse sampling coverage.

The Soils4Africa data can serve, however, as a valuable complement to existing national soil datasets and support soil mapping efforts at the national level.