EXPLORE THE DATA ECOSYSTEM

Grasslands are vitally important for European agriculture. The 20 partners of Inno4Grass gather farmers’ organisations, extension services, education and research in eight countries (Germany, Belgium, France, Ireland, Italy, the Netherlands, Poland & Sweden) where grasslands contribute a major share of the agricultural area. The overall objective of the project is to bridge the gap between practice and science to ensure the implementation of innovative systems on productive grasslands to achieve profitability while providing environmental services. The associated animal productions are dairy and beef cattle and sheep.

Inno4Grass will set up a Facilitator Agents network, capture novelties from innovative farms scrutinized via 85 case studies, discuss and synthesize them in electronic farm networks and through cognitive mapping. It will upgrade this capital via multi-actor approaches and science dialogue, transfer innovation capital and boost collaboration and exchanges beyond the borders of regions and among Member States (MS). Dedicated dissemination approaches and events like national and European Wikimedia, decision support systems and grassland awards are designed and applied to convey innovations to practice with highest acceptance by practitioners and beyond the project term. Inno4Grass will ensure delivery and training of grassland knowledge at operational, tactical and strategic levels for farmers, advisors, and students (specific syllabus, materials for existing MOOCs) and for the value chain mobilizing key actors within the collaborating MS. At least 100 practice abstracts and 104 video clips describing innovative practices will be provided. The project strongly contributes to the implementation of the EIP and many consortium members are involved in their national contact points. This supports the establishment and cross linkage of Operational Groups on grasslands.

Acronym
Inno4Grass
Website
Address
DE
Scientific discipline(s)
Geographic coverage
Type
Data science categories
Data science categories