The liver fluke is a highly damaging and common parasite that infects a high proportion of sheep flocks and cattle herds globally. In Europe, it is estimated that this parasite costs the livestock industry 635 million annually due to decreased milk yields, fertility and growth rates, and increases in mortality and veterinary treatment costs. Sustainable control of liver fluke is extremely challenging because of climate change and increasing rates of parasite resistance to treatment. Liver fluke must infect a mud snail before infecting livestock, a trait which means that specific infection risk areas are present on farms where this snail resides. However, our understanding of mud snail distribution is poor which further hinders a farmer's ability to manage infection risk in animals through grazing and land management. Furthermore, limited tools are available to farmers and vets to diagnose liver fluke infection. This makes accurately treating liver fluke infections difficult and has led to the overuse of fluke treatment drugs and subsequent resistance development in liver fluke populations. Our research will aim to develop new solutions for liver fluke control by developing and deploying environmental DNA and protein tools that can determine liver fluke infection risk areas on farms. We will initially assess farmer understanding of liver fluke infection risk areas though a series of interviews, which will also highlight knowledge gaps for future education programmes. We will then use eDNA surveys to map the distribution of mud snails on farmland, which will inform study farmers of fluke infection risk areas and will enhance our fundamental understanding of mud snail ecology, mud snail habitats and their typical characteristics. This aspect of the project will directly engage and collaborate with farmers and veterinarians who will co-create a farmer/veterinarian education programme that will be driven by our research findings and initially identified knowledge gaps. This project will also identify animal behaviour and performance traits associated with liver fluke infection status. We will monitor the behaviour of lambs experimentally infected and naturally infected with liver fluke using wearable behaviour sensor technologies. Behaviour changes monitored will include activity and motion, as well as lying rates and time. We hypothesise that these behaviours will vary between infected and non-infected lambs and that infected lamb behaviour will change as liver fluke infection progresses. We will also monitor the growth rates of lambs naturally infected with liver fluke with the aim of identifying if lamb performance can be indicative of the need to treat against liver fluke. We hypothesise here that by controlling for grass availability and quality, that lambs falling below a certain threshold of performance will be the ones that benefit from treatment against liver fluke. Finally, we will investigate which proteins are secreted by various liver fluke stages that are found in the environment. These life stages include eggs, miracidia (larvae that infect mud snails) and cercariae and metacercariae (larval stages that can infect livestock). We hypothesise that unique proteins will be secreted by infective liver fluke larvae and that we can detect these proteins in water samples collected from mud snail habitats on farms. The presence and ability to detect these unique infective fluke larva proteins would allow immediate liver fluke infection risk on pastures to be determined, subsequently informing best practise and enhancing our understanding of fluke infection risk. This research will benefit farmers by developing tools and knowledge that will enhance the control of liver fluke on their farms. The project will also enhance our fundamental understanding of parasite biology, animal behaviour and mud snail ecology, information that can assist researchers to further optimise parasite and disease control globally.

In the UK, dairy milk is a key part of the economy and an important source of nutrition. There are several diseases that regularly develop in UK dairy cows which compromise health and welfare, and lead to economic losses for the farmer and industry. Ill cows have also been found to contribute disproportionately to methane emissions and hence the environmental sustainability of the sector. In addition, high welfare is more important than ever to satisfy societal demands for food production. To help farmers detect and treat these diseases, numerous solutions for automated monitoring of dairy cattle are now available to farmers. A critical disadvantage of all these technologies is that they are focussed on detecting the observable symptoms of later stage disease, when treatment options may be limited, reduction of milk production persistent and animal welfare more severely compromised. A cow's response to infection and trauma is to de-prioritise behaviours not immediately essential to survival and recovery - such as social interactions - in favour of those that remain critical for longer, In a recent study we have found that social exploration, the grooming of others and receiving headbutts were lower in individuals with early stage mastitis. We hence hypothesise that social behaviour changes could be early predictors of disease. Detecting social behaviour changes is difficult for the busy farmer, but is possible by monitoring them at key focal points, such as when queueing for milking or feeding at the feed bunk, using video cameras and artificial intelligence (AI). We have developed highly robust AI that can track the motion of cows in video and recognises each individual through their distinctive coat pattern. Others have now demonstrated good classification of affiliative and agonistic social interactions from video and hence we now propose combining the two ideas to track changes in activities and social behaviours over time for each identified cow in a herd. From collecting two years of video from 64 cameras covering the main barn at our John Oldacre Centre dairy farm, we will train a model that learns what types of behaviours change over time that are indicative of different early stage diseases. We will focus on mastitis and lameness, as these diseases have the greatest incidence in our data and are the most important for the UK dairy industry. At the same time, we will sample the saliva of a subset of our herd so we can determine general levels of inflammation, enabling us to see how specific our behavioural predictors are to particular diseases. Dairy farmers are specialists in the behaviour and personalities of their cattle and their input will be vital to helping understand vagaries in farm data and how our system is functioning. We will test our system by deploying it at a network of recruited farms, and will conduct in-depth semi-structured interviews with the farmers regarding their experiences of camera placement (including intrusiveness and social acceptance by farm workers), operation and any other perceived impacts to their farms, farm workers or animal management, health and welfare. It is also critical that we design the system with all facets of industry, to engage their diverse insights and expertise in setting alert levels, designing user-friendly interfaces that will be well placed to be uptaken and discussing additional routes to market such as for disease surveillance. We have therefore assembled a consortium of partners covering all key areas from farmers to vets, the supply chain, data/diagnostic service providers and business development, all of whom we have a proven track record of successful engagement and impact with. Through consultation we will develop a sustainable strategy for meaningful lay stakeholder and public involvement with our system and results, helping to promote a widespread understanding and public/stakeholder acceptance of the system.