“The farmers of tomorrow will use their mobile phones as pocket soil laboratories. This will allow them to save on energy, eliminate fertilisers and produce food more efficiently while also getting paid for capturing atmospheric carbon” – Thomas Gumbricht, Stockholm University

This special podcast series is part of the AI 4 Soil Health project which aims to help the farmers and land managers of tomorrow by providing new tools to measure soil health without the need for laboratories. Using artificial intelligence to monitor and predict soil health for farmers and growers across Europe. 

We all depend on good soils. When healthy they capture carbon, improve yields, reduce flooding and boost biodiversity. But soils are under pressure from current farming practices, and the challenges are only increasing with the growing demand for food production.

Threats to soil health include loss of organic matter, loss of biodiversity, soil compaction from large machinery and loss of soil itself due to erosion. It is estimated that between 60 and 70% of EU’s soils are unhealthy.

If we want to reverse this trend, we need to understand which practices work and which do not. The climate crisis does not leave us much room for mistakes, and soil is complex.  So we need to be able to map changes into the future so we can make the right plan for the right locality.

For this people need the right measurement tools. The right tools will give land managers and policy makers the confidence to make changes to their farming practices which improve soil health and resilience.  

For more information about the project visit

Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Research Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.

This work has received funding from UK Research and Innovation (UKRI) under the UK government’s Horizon Europe funding guarantee [grant numbers 10053484, 1005216, 1006329].

This work has received funding from the Swiss State Secretariat for Education, Research and Innovation (SERI).