Informace o projektu

Building the mission's knowledge repository and advancing the European Soil Observatory (SoilWise)

Kód projektu
Období řešení
4/2023 - 3/2027
Investor / Programový rámec / typ projektu
Evropská unie
Fakulta / Pracoviště MU
Přírodovědecká fakulta

Soil is crucial for life as it provides us food and fibre, regulates water and climate, and hosts thousands of organisms. A recent assessment states that 60-70% of soils in Europe can be currently considered as unhealthy due to different soil degradation processes (A Soil Deal for Europe (European Commission, 2022a)). In light of this worrying figure, the soil health and food (SH&F) mission set the goal to have 75% of European soils healthy or significantly improved by 2030. Such an ambition should be based on reliable existing data and knowledge collected at local, national and EU levels to develop and agree on a baseline for EU soils, provide a basis for the measures needed at all scales and land uses to allow informed decision making to reach the ambition. Then through monitoring campaigns, research projects or citizens involvement, new data will be gathered to assess changes and identify trends. Soil data and knowledge are a vital pillar of the future EU soil strategy. At the same time, the current soil data landscape in Europe is scattered, many existing data are not FAIR (Findable, Accessible, Interoperable and Reusable) and trust, willingness and ability to share soil data and knowledge is limited, thereby paralysing the roll-out of the Mission ‘A Soil Deal for Europe’ (from now on ‘the Mission’). Several efforts aimed at FAIRification of the soil data and knowledge exist, from the soil scientific domain (e.g. e-SOTER, GS Soil, ISO 28258), at national and European level (INSPIRE), as a global unification (e.g. OGC Soil IE, GloSIS), and others, but implementation is low. A gap remains in (1) bridging these activities and linking them with relevant data and knowledge assets to see the greater cross-domain picture of soil data and knowledge sharing and (2) further supporting data and knowledge FAIRness by innovative techniques of Artificial Intelligence (AI) and Machine Learning (ML).

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