Johne's disease has been rated by dairy farmers in the UK as the number one endemic disease affecting productivity. It causes chronic illness, which progressively, worsens and can spread throughout the herd. To tackle the disease effectively, vet practices and farmers need to optimise the use of existing data, whilst also making evidence-based risk assessments about their herds. Our multi-disciplinary project aims to make use of existing data sources and trial environmental sampling for risk assessments with the aim of enhancing Johne's Disease control. Our specific questions are: 1. What factors explain the differences in the success of Johne's control between herds? (WP1) 2. What are the major bottlenecks to farmer and veterinarian engagement in using disease test data and what are the solutions? (WP1) 3. Why are some veterinary practices markedly more successful in controlling the disease in their client base than other practices? (WP1) 4. What measures undertaken by farmers are most likely to be associated with successful control in infection? (WP1) 5. What risk factors identified in on-farm risk assessments are associated with the presence of infection? (WP2) 6. What level of confidence would environmental sampling give as a means of estimating the probability of infection or freedom from infection? (WP2) This proposal brings together a uniquely multidisciplinary team from across the UK to tackle Johne's disease. It combines a farmer (Abi Reader, project partner) with veterinary expertise in Johne's disease control (Peter Orpin, sub-contractor), specialists in data management (James Hanks, subcontractor), a stakeholder engagement specialist (David Rose), a veterinary epidemiologist (Abel Ekiri) and a veterinary microbiologist (Nick Wheelhouse). Within Northern Ireland AHWNI leads on the control of Johne's Disease. The proposal will work in each country of the United Kingdom. Strain (subcontractor and project partner), CEO of AHWNI has a long-standing involvement with Johne's Disease control through managing the NI control programme and his involvement in the all-island (Ireland) Technical Working Group for the infection. Findings from this study will identify relevant herd risk factors and biomarkers to use for prediction of Johne's disease risk. Subsequently, in the next phase after the 12 months, these data will be used to develop prediction models and a practical and cost-effective surveillance tool for Johne's risk assessment at the herd level.

Currently there are no accurate digital tools with decision support to predict calf health and production. Our approach is highly novel as it uses cutting-edge data techniques to develop and use novel features from various calf behaviours (various activities, social networks, feeding, play) and physiology (temperature both core and eye) captured by technologies (automatic feeders, activity location sensors, bolus, thermal cameras) and on-farm data to predict health and production and welfare indicator as play. We will optimise the use of technologies by identifying which information is of value and by conducting a comparative evaluation of the technologies w.r.t their predictive accuracy. Our approach is different and extends the use of technologies for the first-time to accurately measure and quantify dynamic indicators of resilience in 3 states (behavioural, physiological and production) in calves. Through implementation of a "Living Lab" (LL; first for dairy), a user-centric research methodology for prototyping, refining and validating IoT solutions, the results will inform decision support for farmers. It's timely as results allow optimal and novel use of current technologies and through our consortium involving multiple stakeholders, including commercial partners, we are best placed to exploit these outcomes. Translation and applicability: The algorithms we will develop in the project will help farmers by providing early disease detection for calves, measures of positive welfare (play) for the herd and predicting production outcomes - these will be of value to both farmers and vets for calf management decisions. The outcome and knowledge of feature importance from different technologies in prediction and their comparative evaluation is of huge value to farmers, vets (for choice and adoption) and wider industry (for innovation). Routes to translation and impact will be via our consortium and hosting of LL workshops during the project lifetime with various stakeholders and through our extensive existing networks. Using technologies to measure resilience has the added value in that it could promote their embedment in decision support and drive the uptake of technology on farms. This can help farmers and vets to identify animals that are vulnerable and predict how they are likely to respond to a future stressor and have a measure of herd resilience. Our results have applicability to other livestock sectors with digital tools. Next steps: Our longer-term aim (5 yr) plan will be to further validate the findings from this study, link to lifetime resilience and improve our understanding of early-life conditions that support the development and expression of these markers of resilience in calves. To understand which management interventions enhance resilience and how these markers could be incorporated in breeding programmes. A comprehensive validated resilience index will support a paradigm shift and move the focus from mere disease management to a more holistic and dynamic view of animal health.