Application of machine learning to genomic selection of dairy cattle through improved feed efficiency complex prediction

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The sustainability and profitability of the dairy livestock sector is dependent on a balanced interplay between the production of produce that can meet consumer expectations, the maintenance of animal health and wellbeing, and the efficient management of resources and inputs. Feed efficiency (FE) complex in animals, as defined by the production of the same quantity of dairy product using fewer feeding resources, is a key productivity indicator with an estimated 60% of the total costs of dairy production being associated with feeding. Given current international concerns regarding greenhouse gas emissions, nutrient losses, and water quality, facilitating improvements to FE complex is an important focus of dairy-based production systems. FE complex is affected by many factors, including pedigree, physiological, environmental, and genomics variations, and therefore traditional methods based on linear approaches that quantify FE complex have limited statistical power and accuracy. Alternative more comprehensive approaches such as machine learning (ML)-based methods offer the potential to integrate heterogeneous variables and utilise high dimensional data more efficiently. ML techniques can highlight the most important factors influencing FE complex and these selected variables can be used to develop improved models that can more accurately predict FE complex and provide new knowledge on the biological processes underlying the FE complex. This project will seek to utilise a data-driven approach using ML by combining pedigree, physiological, environmental, and genomics information to provide improved prediction accuracy in the FE complex. High precision datasets, captured within a specially constructed database, collated at the Agri-Food and Bioscience Institute (AFBI) over the last 20 years will be made accessible to this work through the Northern Ireland Farm Animal Biobank (NIFAB). These datasets, containing pedigree, weekly feed intake and feed analysis, management and other environmental data, daily production records, and genomics information, present a unique opportunity to better understand the FE complex in dairy cattle, and improve our knowledge on genomic influence on FE complex by identifying key genes and associated biological pathways. This information will ultimately allow dairy farmers in the UK to select the most efficient cows within the herd from which to breed replacements, and the most food efficient sires to use on these cows. As a consequence, feed costs will be reduced and profitability improved, mitigating inefficiencies in production systems and nutrient losses. As a CASE studentship, this project will provide a unique opportunity to work directly with AFBI and learn about the needs and research gaps of the agri-food sector.