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.

Adapting selection tools and objectives to efficiently manage French cattle meat and milk productions is a major challenge of the next years. Genomic selection provides a fantastic opportunity to reorient bovine selection towards a more sustainable breeding. The Gembal project aims at developing a multi-breed genomic selection to extend its use to all beef and dairy breeds, including the small ones. Special attention will be paid for functional traits and maternal traits: calving ease, fertility and longevity of cows in both beef and dairy breeds. At national level, this project should be a common foundation for all breeding schemes, thus avoiding a multiplication of too small and inefficient initiatives. The core of the project is the making-up of the technical basis for the development of multi-breed genomic selection in beef and dairy cattle. The basic idea is that a sample - so-called imputation population - will be genotyped with a high density chip in each breed, whereas most other individuals will be genotyped at a lower cost for a medium density chip. The condition required to build the imputation populations is an extensive use of a new molecular tool, a high density chip with 800,000 SNP developed by Illumina with a consortium including INRA and UNCEIA. Task 2 is dedicated to this technical part of the project. In Task 3, the large multi-breed resource cattle population generated in Task 2 will be the basis for academic researches aimed at characterizing the genetic diversity across breeds and the history of each population submitted to its own context, i.e. drift and selection. This task will also be useful to detect the conserved chromosomal segments across breeds that can be used in multi-breed genomic selection as it will be envisioned in Task 5. Task 4 corresponds to imputation, i.e the statistical procedure to infer missing genotypes in most individuals from the complete genotype information in a limited imputation sample. We will study the quality of the imputation according to breed effective and imputation sample size. We will also develop more computationally efficient algorithms, as imputation will be very demanding with the fast development of genomic selection. Then, a genomic prediction model, using linkage disequilibrium information across breeds, will be developed in Task 5. The methodological challenges are the development of powerful and robust statistical approaches as well as and computing tools for the prediction in a multi-breed context, especially for functional traits with correlated direct and maternal genetic effects. The applications regarding functional traits will be carried out in Task 6 and Task 7 for dairy and beef breeds, respectively. In Task 6, the existence of three breeds in France for which reference populations of reasonable to very large size are available and for which genomic selection programs are already implemented will allow us to undertake reliable comparisons of within vs multi-breed genomic evaluations, hopefully revealing what are the underlying conditions for a successful implementation of multi-breed evaluation. An alternative strategy will consist in checking whether the conserved genome fragments corresponding to favourable haplotypes of QTL detected in any large breed are also segregating in the smaller breeds. Then a genomic evaluation based on these haplotypes could be implemented for the smaller breeds. In Task 7, the multi-beef breed reference population will be composed of the 2,300 bulls that also constitute the beef imputation populations. If a sufficient number of QTL are commonly detected across beef and dairy breeds, a QTL detection and a computation of prediction equations from the beef and dairy pooled reference populations will be undertaken for maternal functional traits.

Agriculture provides humans with necessary food and raw materials, but its environmental impact is unacceptably high. Much effort is underway to make agriculture more sustainable, for example through precision agriculture or organic agriculture. In all of agriculture, controlling weeds is a major issue. Weed control with herbicides leads to a large environmental impact. Manual weed control leads to back-breaking labour, high labour costs, and difficulties of finding sufficient labour. The main objective of GALIRUMI is to deliver robot weeding for herbicide-free weed control in dairy farming. Robotic weeding will reduce the environmental impact of dairy farming by eliminating herbicide use and reducing exposure of farm workers to herbicides. It will also remove an important obstacle for dairy farmers to switch to organic production, thereby contributing to an increase in production of organically produced milk and higher incomes for farmers. GALIRUMI will develop and demonstrate a number of innovative technologies in weed detection, weed degradation, autonomous vehicles and robot-as-a-service for precision dairy farming based on precise navigation provided by EGNSS. Several areas of expertise are needed for successful development of practical robotic weeding: • EGNSS • Computer vision for weed detection • Construction of farm machinery • Providing services to farmers • Weed science • Dairy research The robotic weeding control system is the end product that GALIRUMI is aiming to put into practice and commercialise, proposed by dock weeding as a service either by: • Laser defoliation system using small robot that requires frequent application in the field. • Medium sized robot system with an electrocution tool that requires single season application in the field. GALIRUMI will significantly reduce manual labour for weeding, weed management cost, damage to grassland from the application of herbicide, impact of dairy farming on the environment and dairy cow discomfort

Farmers, veterinarians and other animal health managers in the livestock sector are currently missing information on prevalence and burden of non-EU-regulated contagious animal diseases. They are in need of adequate tools for risk assessment and for prioritisation of control measures for these diseases. The DECIDE project will develop data-driven decision support tools, which present (i) robust and early signals of disease emergence and options for diagnostic confirmation; and (ii) options for controlling the disease along with their implications in terms of disease spread, economic burden and animal welfare. DECIDE will focus on respiratory and gastro-intestinal syndromes in the three most important terrestrial livestock species (pigs, poultry, cattle) and on growth reduction and mortality in salmonids, the most important aquaculture species. For each of these, we will (i) identify the stakeholder needs; (ii) determine the burden of disease and costs of control measures; (iii) develop data sharing frameworks based on federated data access and federated learning; (iv) build multivariate and multi-level models for creating an early warning system. Together, all of this will form the decision support tools to be integrated in existing farm management systems wherever possible and to be evaluated in several pilot implementations in farms across Europe. To achieve these ambitious goals, DECIDE has assembled a unique multidisciplinary consortium of experts in veterinary epidemiology and diagnostics, data science, mechanistic and predictive modelling, economics, animal welfare and social sciences. The consortium also includes several representatives of stakeholders with ample access to data, such as national animal health agencies, providers of veterinary services or farm equipment suppliers. The results of DECIDE will lead to improved decisions on disease control to increase animal health and welfare and protect human health and the food chain in Europe and beyond.