Horizon Soil Mission: AI Decision Tools for Soil Management
Funds one €9M project turning long-term field-trial data into AI decision-support tools for farmers, advisors and land managers.
Long-term field experiments are some of the best data agriculture has on soil health — some have run for over 20 years — but the records are scattered, inconsistently collected, and hard to combine with other datasets. Meanwhile, farmers and advisors looking for soil-management advice often get generic recommendations, because nobody has built the tools to turn all that scattered data into somethi…
This funds building a shared data and AI infrastructure for soil health, not a single lab study. The winning project has to link a network of at least 50 long-term field experiments (LTEs) across at least 7 owning institutions into one open, interoperable data system, merge that with other soil datasets (including Mission Soil project results and open repositories like CORDIS, SoilWise and Zenodo), and train AI and machine-learning models on the combined data to produce decision-support tools and mobile apps for farmers, advisors and land managers. The project also has to publish its AI components openly and run outreach — hackathons, workshops, training — so outside developers can build on them, and can pass some of its budget to third-party developers to build or improve AI-powered farm-advisory apps, capped at €60,000 per third party.
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