Project description
Better pastures for high productivity, low methane emissions
The development of new forage plant varieties to support milk and meat production is critical. High digestibility of the forage has positive effects on animal production and can limit methane emissions – a key goal of the EU's climate policy. Despite this, improvements in perennial ryegrass (PRG) digestibility have been modest due to its long breeding cycle. However, using information in the DNA to estimate a plants digestibility presents an opportunity to reduce time taken to complete a cycle of selection by at least a factor of five. The EU-funded GenSPaD project will develop and test approaches to use DNA information to predict nutritional value in forage breeding programmes.
Objective
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.
Fields of science
- natural sciencesbiological sciencesbiochemistrybiomoleculescarbohydrates
- agricultural sciencesagriculture, forestry, and fisheriesagricultureindustrial cropsfodder
- natural scienceschemical sciencesorganic chemistryaliphatic compounds
- natural sciencesbiological sciencesgeneticsgenomes
- agricultural sciencesagriculture, forestry, and fisheriesagriculturegrains and oilseedscereals
Keywords
Programme(s)
Funding Scheme
MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)Coordinator
R93 Carlow
Ireland