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.

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.