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Genetic and management solutions for lameness-associated endemic diseases in dairy cattle

Objective

We will continue our effort to create a national database of UK foot trimmers' records. This will build upon work conducted by the University of Liverpool (UoL). The UoL will spend the first year of this project helping a large number of foot trimmers and farmers to share their data. Daily mobility scores performed automatically on approximately 15,000 cows by CattleEye will also be included in this dataset. Genotypes for a subset of approximately 8,000 animals are also available. Rapid qualitative approaches will be used to accelerate widespread engagement within the first year. Individual phenotypes, pedigree and genotypes in the project database will then be jointly analysed to estimate genetic parameters and determine genetic variants associated with the various lameness-related phenotypes. Phenotypic and genomic outcomes will be integrated in a series of simulation studies to determine ways to inform future breeding programmes aiming to control cattle lameness while continuing the improvement of other important animal traits associated with cow production, health, fertility, and longevity. Foot trimming lesion data and mobility scores (human or CattleEye generated) will be used to develop novel KPIs for lesion incidence or lameness, temporal patterns of prevalence of lameness, and a herd level diagnosis of the predominant epidemiological pattern of diseases associated with lameness within the herd. We will then utilise a range of traditional statistical methods and machine learning methods to predict several key animal events relevant to lameness control, namely the probability and time to; a new (first) case of lameness; of curing once lame; and of recurrence of lameness once recovered. We will also quantify the association of lameness prediction scores with future milk production and reproductive performance. Widespread and rapid impact will be achieved via an extensive knowledge exchange programme underpinned by implementation science research.

Investigators
Professor Georgios Oikonomou; Dr Helen Higgins, Dr John Tulloch
Institution
University of Liverpool
Start date
2023
End date
2026
Project number
BB/X017451/1