Statistical learning approaches to optimise the health, welfare, and productivity of calves on dairy farms - Hyde, Robert (2022)
The effective management of preweaned calves is one of the most important areas of dairy farm management, and can have substantial impacts in terms of health, welfare, and productivity. It is critical that veterinary advisors are able to implement proactive changes to housing and management likely to not only result in the largest improvements, but also be applicable to the majority of farms. The majority of statistical techniques are intended for a relatively low dimensional setting, and conventional statistical approaches are often poorly suited to problems where the number of variables exceed the number of observations. The number of potential variables available in today’s data rich environment is increasing, and the robust identification of causal variables is becoming increasingly important. Statistical learning techniques represent important tools in identifying factors associated with calf health and performance on dairy farms. In Chapter 2, over 21 million cattle deaths were analysed utilising national birth and death registrations from the national British Cattle Movement Service, to quantify the temporal incidence rate, distributional features, and factors affecting variation in mortality rates in calves in GB since 2011. Alongside providing a first benchmark for calf mortality rates in GB, factors associated with mortality rates were further explored utilising multivariate adaptive regression spline models and suggest that environmental conditions such as mean monthly environmental temperature and month of birth play a significant role in calf mortality rates at a national level. To identify the most important factors for optimal calf health and performance, the farm management practices were collected from 60 farms, resulting in a large number of potential housing and management variables affecting calf performance. In Chapter 3, an elastic net was used in combination with stability selection techniques, and these were utilised to identify which factors were most likely to improve calf performance on the majority of farms, and included management areas such as stocking demographics, milk/colostrum feeding, environmental hygiene and environmental temperature. Colostrum samples were also collected from enrolled farms, and in Chapter 4, a first benchmark of bacterial levels within colostrum on GB dairy farms was provided. Bacterial levels in were significantly higher when colostrum was collected using equipment than taken directly from the cow’s teat, suggesting interventions to reduce bacterial contamination should focus on the hygiene of collection and feeding equipment. An automated backwards stepwise mixed effects regression model was utilised in conjunction with stability selection to identify a small number of variables likely to have the largest effect on colostrum hygiene on the largest number of farms and suggest that the cleaning of colostrum collection and feeding equipment after every use should be performed with hot water as opposed to cold water, and hypochlorite or peracetic acid as opposed to water or parlour wash. It is important that associations identified during observational studies are not interpreted as causal, and the most important variables identified in Chapter 2, 3 and 4 were tested as a calf health plan intervention in a randomised controlled trial to elucidate causality, as described in Chapter 5. Health and performance outcomes were analysed for 60 dairy farms randomly allocated to receive the health plan as an intervention. Growth rates were higher for calves on farms receiving the plan for both male or beef and dairy heifer calves, and results from regression models suggest that male or beef calves had significant increased growth rates on farms receiving the plan than those that were not. Model predictions suggest that a farm with the highest number of interventions in place (15) compared to farms with the lowest number of interventions in place (4) would expect an improvement in mean growth rates from 0.65kg/d to 0.81kg/d for male or beef calves, from 0.73kg/d to 0.88kg/d for dairy heifers, a decrease in mortality rates from 10.9% to 2.8% in male or beef calves, and a decrease in diarrhoea rates from 42.1% to 15.1% in dairy heifers. Neonatal calves are relatively susceptible to heat loss, and Chapters 2 and 3 suggested that reduced environmental temperatures are associated with increased calf mortality, and reduced growth rates. The aim of Chapter 6 was to evaluate the impact of calf jackets and supplementary heat sources on the growth rates of preweaned calves in a randomised controlled trial. Seventy-nine calves from a single British dairy farm were randomly allocated to receive heat lamps or calf jackets in a factorial study design. Regression model results suggest 1kW heat lamp usage significantly improved growth rates by around 90g/d, and no effect of jacket were identified. A significant, positive impact of increased pen temperature on calf ADG was also identified in this study and was reinforced when including prior information from Chapter 3 within a Bayesian framework. The research presented in this thesis utilised a range of statistical learning techniques to identify factors associated with calf performance, which have been tested in a randomised controlled trial. To provide farmers and veterinarians with access to the calf health findings of the thesis in interactive form, the University of Nottingham Herd Health Toolkit (www.nottingham.ac.uk/herdhealthtoolkit) was created, including tools relating to the management of colostrum, prediction of mortality rates and ultimately a bespoke calf health plan based on user inputs. A number of statistical learning techniques within the field of stability selection were developed in parallel to this thesis, and the creation of the stabiliser R package to allow these techniques to be utilised by the wider research community.