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Geography and Data Science

We explore soil patterns and soil properties at different scales in space and time. We develop methods for quantifying soil properties combining expert knowledge, data science, geostatistics, remote and proximal sensing, and process-based modelling. We explicitly quantify uncertainties.

Our Geography and Data Science projects

Project Assessing soil health on different spatial scales
Project Understanding and Mapping belowground functional biogeography
Project Developing a high-resolution 4-dimensional soil modelling and mapping platform for the Netherlands (BIS-4D)
Project Machine learning for fertilizer recommendation in Ghana
Project Management effects on soil health at the European scale
Project Modelling and mapping soil organic carbon in space and time using machine learning and mechanistic approaches
Project Modelling potato yield response to fertilizers and soil nutrients using semi-mechanistic models and machine learning
Project The development and use of a soil database with quantified uncertainties
Project Variability in maize yield and response to fertiliser in Ghana

Our Geography and Data Science senior scientists