Breeding for improved perennial ryegrass (PRG) cultivars to support pastoral based production systems for milk and meat is a critically important goal. However, genetic gains for traits such as forage yield and quality have very much lagged behind genetic gain for agronomic traits in cereals. One reason for this is the long breeding cycle in a typical PRG breeding programme, where a single cycle of selection can take 5-6 years. Genomic selection (GS) is a form of marker assisted selection that simultaneously estimates all loci, haplotype, or marker effects across the entire genome to calculate Genomic Estimated Breeding Values (GEBVs). The main advantage that GS could offer PRG breeding is to enable multiple cycles of selection to be achieved in the same time it takes to do a single cycle of conventional selection, thereby increasing the rate of genetic gain. Improving digestibility of the forage leads to an increase in animal performance, and is therefore an important target trait for forage breeders. Furthermore, it has already been shown that increases in organic matter digestibility can reduce methane emissions. Reducing methane emissions is a key target of the EUs climate and energy policy. In this action I will focus on developing and validating GS equations for feed parameters that are being used as model inputs into the Cornell Net Carbohydrate and Protein System (CNCPS). This CNCPS is currently being adapted to predict nutritional value to the grazing animal in pasture based production systems, and it is envisaged that it will be able to identify feed parameters limiting milk-solid production and thereby direct future forage breeding efforts. The work of this action will lead to a novel and innovative forage breeding programme that can select for multiple feed parameters to develop the ideal forage cultivars for pasture production systems.

Plant viruses continue to be one of the main threats to European agriculture, exacerbated by climate change, evolution of pesticide resistance, and loss of crop protection chemistries. Europe has ambitions of creating a more sustainable food system with a goal of reducing pesticide usage by 50% by 2030 as part of the European Farm to Fork Strategy. Achieving these goals requires robust integrated pest-management (IPM) approaches and real-time decision support systems (DSS) for farmers. In disease management there is a strong reliance on rapid, sensitive, and specific diagnostic tools built on a clear understanding of viral diversity. HealthyPlants will use high-throughput sequencing to (i) complete the first systematic survey of viruses of cultivated plants in Ireland and establish a baseline upon which to build improved diagnostic tools, (ii) establish the importance of arable margins and hedgerows as cereal virus reservoirs, and (iii) identify viruses on newly imported crops with potential phytosanitary risks. HealthyPlants will deliver the first database of viral sequences of cultivated plants on the island of Ireland, an open-access platform to support development of robust diagnostics within Europe.