Dairy farming accounts for approximately 18% of UK agricultural production by value, with 1.9 million cows having an annual production of around 13.3 billion litres of milk. In order to remain profitable and competitive the UK dairy industry has undergone a period of rapid change in recent years. This has involved increasing herd sizes accompanied by modifications to dairy cow genetics and feeding systems to raise average yields. The average cow currently produces about 7000 litres of milk per annum, but only survives in the herd for 3 lactations. This short lifespan threatens national herd sustainability due to limitations in the supply of replacement heifers, reduced profitability and increased environmental footprint. The negative impact of selecting solely on production traits has been recognised by some breeding companies, but there remains a lack of information on the economics of selection and the identification of critical decision points. Furthermore, traits included in UK selection indices only relate to the dairy cow once she has entered the milking herd, so do not account for traits relating to the growth and fertility of dairy heifers which impact on their subsequent survival and performance. The two partners have recently undertaken an observational cohort study to identify the timing and major causes of wastage in the dairy herd. This obtained detailed on-farm data for cohorts of Holstein-Friesian heifers on 19 commercial dairy farms from birth until culling. Data were collected on the individual heifers (growth parameters, metabolic and endocrine status, reproductive performance and milk production), their parentage, and the farm environment. The relative importance of genetic and environmental components during the rearing period in relation to subsequent survival, growth and fertility was determined. Databases generated during this initial study provide a strong basis for examining the true costs of heifer rearing. These will be supplemented with data derived from other recently published UK sources relating to heifer mortality, fertility, milk production and longevity under different management systems. This information will be used to compare the costs of selecting between reproductive efficiency, lactation yields and longevity when developing genetic selection indices. Economic analysis will include the direct and indirect costs of heifer rearing and the farm-level and national impacts of using current genetic selection indices for lactation yield versus longevity. The latter will be examined using a cost-effectiveness framework with the application of different outcomes. The possible benefits of including heifer growth and fertility traits in selection indices will be modelled. Initially a dynamic, deterministic herd model will be developed that will take into account mortality and culling rates, age at first calving, fertility rates and lactation yields. This will be used to estimate milk output from a herd and the ability for a herd to maintain its size without the need to buy in replacements. A decision tree structure will also be developed with node points based on breeding decisions and the outcomes in terms of the traits in the individual animal. The two models will be combined as a basis for the cost-effectiveness analysis. Sensitivity analysis will be performed on the models parameters. Those parameters that influence the outcomes greatly will be made stochastic within the overall model. The project will generate: (i) accurate data on the cost of heifer replacement in different management systems taking into account the costs of animals culled prematurely; (ii) estimation of break-even lactation number to cover the costs of heifer replacement in different management systems and (iii) cost-effectiveness analysis of the best selection trait(s) for lactation yield, longevity and overall herd sustainability. Finally the implications of the findings to national dairy policy will be assessed.

Bovine mastitis is the foremost endemic infectious disease of dairy cattle and remains a major challenge to the UK and worldwide dairy industries. Although there have been numerous studies reporting cow and herd risk factors for bovine mastitis, no research has been conducted to evaluate optimal decision making in different farm circumstances; this is a critical unknown element in mastitis control. The purpose of this research is to use a Bayesian decision analytic framework to investigate the hypothesis that mastitis control can be improved by adopting a 'best strategy' in given farm circumstances. To conduct this research, detailed data are available from a recently launched national mastitis control scheme, the DairyCo Mastitis Control Plan (www.mastitiscontrolplan.co.uk) that has been developed by our industrial partner DairyCo. We will use the rich data from at least 600 farms in the scheme which includes detailed information on herd size, farm facilities and manpower, management interventions and detailed records of clinical and subclinical mastitis. For the initial statistical analysis, sample size estimates indicate that differences in clinical and subclinical mastitis of 5% will be detectable for individual or groups of management interventions, and this is deemed to be a clinically important effect size. A Bayesian decision analytic framework will be constructed to allow synthesis of the initial multivariable data analysis of the National Scheme data with economic and production information, to explicitly represent the decision process associated with mastitis control. The Bayesian framework provides a structure that will allow synthesis of multiple sources of information and also for uncertainty (risk) to be evaluated in the cost benefit of different interventions. Such an approach, often termed probabilistic sensitivity analysis, is now required by the National Institute of Clinical Excellence (NICE) for the evaluation of human medical interventions, but is rarely used in animal health. In Year 1, collation and initial analysis of the data from 600 farms in the National Mastitis Control Scheme will occur. In Year 2, the Bayesian framework will be constructed to quantify the relative importance of, and uncertainty in, different preventive strategies for bovine mastitis. We will specifically predict the consequences of different interventions (and groups of interventions), in different farm circumstances on the 'Incremental Net Benefit' (net financial return) of each strategy. Therefore, given farm patterns of mastitis (farms in the national scheme are grouped into four categories according to the predominant pattern of mastitis on the unit) and a set of possible interventions, we will predict the optimum prevention strategy and the associated uncertainty of a financial return. We will employ a single integrated Bayesian procedure, using Markov chain Monte Carlo, which has the mathematical advantage of allowing all joint parameter uncertainty to be propagated through the model and is important because evaluation of the uncertainty associated with different decisions is a key area of investigation. In Year 3, the predictive value of models will be evaluated using both 'within' and 'out of model' posterior predictions (using new farms in the national scheme) and in the second 6 months of Year 3, the student will work with the industrial partner. In Year 4, results from the decision models will be incorporated into the DairyCo National Mastitis Control Scheme software (in collaboration with DairyCo partner QMMS Ltd) to inform on farm decision making. This software will allow scheme participants to identify the management interventions that, for specific farms, are most likely to provide the greatest health and financial benefits, and will ensure that the research has an immediate impact to improve mastitis in UK dairy cows.

Phenotype and pedigree informed genetic improvement in livestock using techniques such as Best Linear Unbiased Prediction (BLUP) has seen rates of improvement of between 1% to 3% per annum in many livestock populations. Over the past 50 or so years we have seen that genetics research, including molecular and statistical developments, has been applied by many operational plant and livestock breeding programmes. The availability of reference sequences for many species has resulted in the discovery of very many thousands (and higher) of single-nucleotide polymorphisms (SNPs) leading to the on-going development of low-cost SNP arrays and being used around the globe in many livestock species - genomic selection. Through research and industry (nationally and internationally) the UK dairy industry implemented genomic selection for industry traits (milk production, fertility, longevity) in April 2012 using a pooled collaborative SNP genotype file (predominately bulls, now over 100,000 individuals). This will lead to an expected increase in the annual rate of genetic improvement of approximately 30-50%. The next horizon for research and its translation into genetic improvement tools is the inclusion of sequence data alongside the tools that the industry have already invested in. These developments provide exciting opportunities for the research community to explore the more readily available and vast amounts of genomic data to create new knowledge and drive innovation in the field, as we have seen historically in plant and livestock genetic research. Because the UK dairy industry and research sectors are collectively likely to invest heavily in sequence information in the coming years, a collaborative strategy to generate, store and process sequence information efficiently is needed to enable its effective used in animal breeding research and for use in next generation genetic improvement tools. The rate of change we are now experiencing in ready availability of genomic and sequence information means there a real need to take a community based approach to utilising these data, including the involvement of the end user as well as basic research. In the case of this proposal, the end-user focus is the animal breeding industry as well as the biosciences research community. A DNA sequence captures the complete genome of an individual. If available for sufficient individuals, it will provide a range of benefits 1) greater livestock improvement through more accurate and more persistent genomic selection, 2) the identification of targets for genome editing, 3) detection and breeding management of rare variants, including recent mutations, and 4) greater biological knowledge. More powerful biological discovery will be enabled because the causal nucleotides are contained within the sequence, unlike the case of markers such as single nucleotide polymorphism (SNP). Its exploitation in animal breeding programmes is expected to create a paradigm shift that will greatly enhance the production of food from farmed livestock through both increased output and reduced wastage. However, it is expensive to collect sequence data at the high read rates (essentially accuracy) needed generally for research and this has led to small islands of sequence data at research institutes. Commercial breeding companies are beginning to assimilate some sequence data but this is usually IP protected. Some have large genotype datasets that can be imputed to full sequence level. The aim of this project is to develop the methodology(s) to optimise the distribution of sequencing effort across, and in key populations within, the UK dairy cattle population. The hypothesis that will be studied is that the inclusion of optimal sequence data will improve the results for genome wide association studies for novel traits and genomic selection in the wider UK dairy cattle population compared to widespread SNP chips alone.

Many infectious diseases affect livestock, impacting not only on the health and welfare of the animals but also on the economic sustainability of the agricultural industry and future food security. Reproductive failure in cattle is one area of great concern to the agricultural sector, as it has a major impact on productivity in UK cattle herds. While there are many factors contributing to reduced rates of reproduction in livestock systems, infection plays a key role, with 77% of diagnosed cases of bovine fetal death reported as resulting from infectious causes. However, diagnosis of the infectious causes of pre-natal death in cattle is poor, with 80% of cases remaining undiagnosed (according to DEFRA's Veterinary Investigation Surveillance reports for 2002-09). This can be explained in part by a failure to detect the presence of other unidentified disease causing organisms. In recent years, there has been an increase in the identification of a group of new emerging bacterial organisms that are found in the environment and have been shown to be associated with a variety of conditions in humans, such as pneumonia and miscarriage. These organisms, which share similar biological characteristics to Chlamydia species that are known to cause a broad range of infections in humans and animals, such as sexually-transmitted infections, pneumonia, blindness and fetal death, are referred to as Chlamydia-like organisms. These Chlamydia-like organisms are also increasingly becoming recognized as potential disease causing organisms of livestock, being particularly associated with the pre-natal death of calves. Indeed, they have been found in over a quarter of the cases analyzed in the UK, and thus could account for some of the 80% unaccounted, undiagnosed cases reported by DEFRA. To date, the only studies that have been carried out have relied upon the analysis of tissue samples, which have been submitted to veterinary laboratories for disease diagnosis. While these studies have been vital in demonstrating the presence of the organisms in samples for which no alternative diagnoses could be reached, they have been performed on a relatively small number of samples. In addition, in general, little information is obtained on the disease and production histories of the farms from which the animals originated. Thus, this study aims to investigate the presence of these organisms on dairy farms across the UK and how this relates to animal production performance. We will isolate the organisms from clinical samples to allow us to characterise and assess how many different types of Chlamydia-like organisms are present in the UK cattle herds, as well as investigate their potential spread from animal to animal by analyzing environmental samples, such as drinking water and bedding. We will also develop experimental model systems to allow us to investigate how the organisms cause infection and disease, and determine the immune response to infection. The combination of these studies will greatly increase our understanding of the disease causing potential and role of this group of emerging bacteria in cattle reproduction. The outcomes will lead to improved diagnoses of cattle reproductive failure, inform and educate the industry to the presence of these organisms, lead to improved management systems and allow an evaluation of the potential benefits of future vaccine strategies to prevent disease incidence.

Liver fluke is a common parasite that affects sheep and cattle in the UK. It is found throughout the world and in some countries it affects humans too, causing serious and sometimes fatal disease. Fluke infected cattle lose weight, become anaemic, lethargic and stop being productive. This has a serious effect on the welfare of the animal and serious economic consequences for the farmer. It is thought that fluke costs UK agriculture at least £300million pounds a year through direct losses, but real costs are probably much higher. Fluke has become much more common over the past 10 years, due in part to our changing weather patterns, wet summers and mild winters favour the development of the parasite and its vector - a mud snail, found commonly throughout Britain. In a recent study we found 75% of dairy herds had evidence of fluke infection. Future climate change is predicted to have a significant impact on prevalence of infection, changing the epidemiology and increasing incidence of disease. Increased cattle movements and changes to both farm management and environmental schemes are exacerbating the problem. A limited range of drugs is available to control fasciolosis. Only one drug - triclabendazole (TCBZ), is effective against early and late juvenile and adult stages of the parasite and is used extensively for prophylaxis and treatment of disease. There is growing evidence of resistance to TCBZ in fluke populations, moreover the European Medicines Agency has recently revised its advice on drugs used to treat fluke such that they are now contra-indicated in dairy animals. Targeted use of drugs, at specific times of year will slow the development of drug resistance and reduce the overall quantity of drug used, but a better understanding of the epidemiology and transmission of disease is vital if we are to develop control programmes that rely on improved on farm management practises rather than depending solely on drugs. This ultimately will be a sustainable and cost-effective way to control both clinical and sub-clinical disease in cattle and is the express desire of the livestock industry. Specifically requested by the farming industry, the purpose of this project is to produce new, sustainable, bespoke control programmes for beef and dairy farms, to reduce losses associated with fluke infection. In order to achieve this we must first develop diagnostic tests to identify infected herds. We already have good tools that we can use on milk samples to detect infected dairy herds but we need similar tests that are appropriate for beef herds. In addition we are aware of a newly emerging parasite problem, the rumen fluke. It is not clear if this parasite causes disease but it has the potential to interfere with the diagnostic tests we are developing for fluke. Therefore we will also develop a molecular test for rumen fluke. Secondly we will develop a system to categorise snail habitats that can be used to analyse satellite maps on a regional geographic scale to obviate the need to visit every farm to investigate snail habitat. We will also investigate how cow behaviour affects how the parasite gets to a snail host and from the snail host back to the cow. These are risk factors for fluke infection on a farm. Other risk factors, particularly husbandry practices, physical and environmental factors will be obtained from a study of 250 farms and these data fed into statistical and mathematical models to determine theoretically which of these factors are the most important in determining whether a farm has fluke or not. Concurrently we will assess the cost-benefit of changing these practices. Finally we will conduct a trial to evaluate if changing farm practice is effective in reducing levels of infection. We are working in partnership with the Agricultural Levy boards of the UK to implement improved control of fluke infection to benefit animal health, welfare and profitability of livestock farming in the UK.