Predictive Soil Mapping (PSM) is based on applying statistical and/or machine learning techniques to fit models for the purpose of producing spatial and/or spatiotemporal predictions of soil variables i.e. maps of soil properties and classes at different resolutions. It is a multidisciplinary field combining statistics, data science, soil science, physical geography, remote sensing, geoinformation science and a number of other sciences. Predictive Soil Mapping with R is about understanding the main concepts behind soil mapping, mastering R packages that can be used to produce high quality soil maps, and about optimizing all processes involved so that also the production costs can be reduced. The online version of the book is available at: https: //envirometrix.github.io/PredictiveSoilMapping/ Pull requests and general comments are welcome. These materials are based on technical tutorials initially developed by the ISRIC's Global Soil Information Facilities (GSIF) development team over the period 2014-2017

The humid highlands in sub-Saharan Africa (SSA) are characterized by high population densities and require intensification. The Consortium for Improving Agriculture-based Livelihoods in Central Africa (CIALCA) has set up a research for development platform in various mandate areas in DR Congo, Burundi, and Rwanda, aiming to identify improved production, market, and nutrition options and facilitating the access for development partners to these options. This platform is supported by capacity building, multi-stakeholder dialogue, and monitoring and evaluation efforts. The conference, facilitated by CIALCA, aimed to (i) take stock of the state-of the art in agricultural intensification in the highlands of SSA and (ii) chart the way forward for agricultural research for development in the humid highlands of SSA, and more specifically in the recently launched Humidtropics Consortium Research Programme, through keynote, oral and poster presentations, and strategic panel discussions.