Endemic diseases are core drivers of medicine use in sheep. Resistance is driven by pathogen exposure to drugs within the animal and residues or metabolites in the environment. A reduction in the burden of endemic disease would reduce pressure on the existing medicines, decrease selection for resistance and reduce medicinal residues in food and the environment. Guidelines exist for the management of both parasitic disease and lameness, yet endemic disease continues to cause the industry significant challenges; negatively impacting sheep and farmer welfare. This proposal aims to address key knowledge gaps concerning endemic diseases of sheep in Northern Ireland. Farm data will be used to co-develop, with farmers, realistic and practical, evidence based, on-farm solutions to mitigate against key endemic diseases of sheep as well as provide a case study for other UK sheep producing regions. One pillar of this work will be the application of diagnostic testing to underpin a "Test don't treat approach". In doing so this project addresses two of the strategic themes of the call: evidence-based on-farm management and the development of novel approaches to endemic disease control. This initial one-year project aims to exploit sheep scab as an exemplar disease upon which a research framework for the control of endemic disease can be developed. Sheep scab, caused by infestation with the parasitic mite, Psoroptes ovis, is endemic in NI and represents a significant economic and welfare concern for the NI sheep industry, which consists of a national flock of ~ two million sheep at the 2019 census. There is a paucity of information on the distribution of sheep scab in NI. While some lessons could be extrapolated from previous work in Great Britain, given the highly fragmented land use by interconnected farms, driven in part by the traditional conacre system of land rental, specific data is required on the transmission and distribution of the disease in NI. This data will also serve as the basis for a model of other transmissible endemic diseases in the NI flock. This project is centred around the farm and farmer. A series of knowledge transfer events will be held. These will include cross industry representation to allow farmers and prescribers, together, to understand the diseases and their control options. In addition, pre-intervention data will be obtained about farmer and prescriber knowledge and belief regarding endemic disease and its cost among the NI flock. Specific training will be given to local, private veterinary surgeons (vets) on current best practice for the diagnosis, prevention and where necessary, treatment of sheep scab. A system will be designed to allow farmers to self-report if they believe sheep scab is present in their flock. The project will pay for the vet's farm visit and for diagnostic testing. Standardised data will be collected and, where indicated, support given for the treatment of sheep scab. Data collected will be used to determine the distribution and transmission dynamics of scab in the NI flock, as well as assessing the behavioural economic constraints and barriers to control and the environmental impact (GHG emissions) of the disease. Project findings will be shared with industry stakeholders. A process of co-development will be used to determine the future steps needed to control, and where applicable, eradicate disease from the national flock. This will include the optimisation of, diagnostic testing, treatment and prevention, as well as considering behavioural change for example in regard to stock movements and boundary management. Outputs from the project will be presented to government and industry policy makers in order to inform future endemic disease strategies with a focus on ensuring the legacy from this project can lead to longer term improved animal health and farmer welfare as well as contribute to the UK farming industry's stated carbon net zero aspiration.

Resilience in animals refers to their capacity to cope with short-term environmental disturbances, with a fast return to normal status and it is an acknowledged beneficial trait of farmed livestock. Resilient cows are considered those with a high probability of completing multiple lactations, with good reproductive performance, that encounter few health problems which they overcome easily, and that are efficient and consistent in their milk production. A major contributor to an individuals' ability to be robust or resilient over their life-course is the "Developmental Origins of Health and Disease" (DOHaD), whereby insults to the developing embryo/foetus (e.g. nutritional insults, inflammatory response to disease/toxins, therapeutics, elevated concentrations of hormones) at specific developmentally sensitive time points, can alter an individuals' susceptibility to disease. Resilience in dairy cattle at both individual and herd level is therefore considered critical to optimise health, welfare, and productivity and to reduce the environmental footprint of dairy farming as the industry targets net-zero. Rather than considering health or welfare according to individual diseases, traits or syndromes, enhanced resilience allows the possibility of a wide-ranging enhancement of health and wellbeing. Therefore, enhancing resilience could provide a step change to reduce endemic disease in dairy cows. The aim of this 12-month study is to quantify in-utero environmental factors that contribute to post-partum lifetime resilience in dairy cows, using a very large set of data (>30,000 cow lifetime records). Our hypothesis for the research is that perturbations to dairy cows during developmentally sensitive stages of early pregnancy influence lifetime resilience of their offspring. We will quantify and predict resilience using a large dataset containing detailed lifetime records for the offspring that can be mapped back to a wide of maternal-mediated stressors experienced by the offspring at specific stages of pregnancy. We will measure the effect of known on-farm stressors during specific stages of pregnancy and evaluate how these underpin lifetime resilience. During the 12 month project we will; 1) Produce an optimised, validated predictive model of lifetime resilience for dairy cows from events that occur while in utero. 2) Identify and quantify the major factors and events during pregnancy that impact on lifetime resilience and thereby evaluate the extent to which resilience can be enhanced through optimised herd management. Outcomes: i) A method to predict lifetime resilience for dairy cows at birth, co-developed by farmers and vets, to inform selective breeding programmes on-farm. ii) Identification of major factors during pregnancy (and their relative importance) that impact the lifetime resilience of the offspring to inform management strategies to optimise resilience on-farm. HOW WILL THIS HELP FARMERS? With an accurate knowledge of lifetime resilience for dairy heifers at birth, a farmer will be able to; i) avoid breeding from replacements with low resilience (evidence indicates that in utero insults can be transmitted via genetic changes, giving transgenerational effects), ii) minimise the factors during pregnancy that have a deleterious impact on resilience (success being monitored by an overall herd resilience score), iii) in the short term, use improved management strategies for the subset of animals with low scores, to mitigate their low resilience. Translation to farmer: Our industry partner has developed a software platform to house the models, construct the resilience predictions real time and deliver results direct to farmers - therefore the route to translate research findings to practice is already in place.

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.

Parasites, if they are not controlled effectively, have a major impact on the productivity, health and welfare of sheep and cattle. Cattle and sheep pick up parasites, such as roundworms, whilst they graze. These parasites are normally controlled by treating the whole flock or herd with a worming medicine that either prevents infection or that kills the parasites before they cause damage. However, this type of approach to parasite control has resulted in the development of resistance to the medicines used, meaning the parasites are no longer killed by the treatment. In this project we will identify ways in which the control of parasitic diseases could be improved to reduce the impact and spread of resistance. We will focus on roundworm parasites found in the stomach and intestines of sheep, since anthelmintic resistance is prevalent in sheep roundworm populations, and it is a growing threat to the sheep industry in the UK. To achieve this aim we will first establish in partnership with farmers, vets and Registered Animal Medicine Advisors, what farmers need enable them to integrate new parasite control strategies on their farms. Secondly, using mathematical models, we will identify new options that could be used to improve parasite control on farms. These models will build on existing mathematical models and develop them further to include farm and stock management data together with parasite burdens within individual animals. In this way the impact on parasite burden of specific interventions such as selecting heavily parasitised individual animals to treat or changing to a mob-based pasture management system can be assessed before they are rolled out onto farms. The outputs from the project will be a better understanding of what inhibits farmers from adopting new approaches to parasite control and secondly will present new options that could be used on farms to improve parasite control.

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.