Novel approaches to improving calf health: new technology and data analytics

Maintaining good calf health is important for both the productivity and sustainability of farming enterprises. A report by the Cattle Health and Welfare Group (GB)) showed that 3.6% of dairy bull calves and 2.5% of dairy heifer calves died under 1 month of age. The impacts of poor calf health include calf losses, costs of veterinary treatment and increased labour costs. Ill health in calves also leads to poorer growth in later life. Calf disease adversely affects the origin dairy farm if animals are kept as heifer replacements, but will also affect the beef-from-dairy sector when calves are sold to rearer units. Early detection of disease would help reduce these welfare, productivity and economic impacts and also reduce antibiotic use. Improving animal resilience to disease challenge is also important in reducing the impact on the animal and the farm.

The aim of this project is to explore a number of approaches to reducing calf mortality and morbidity.

Objective 1. Behavioural indicators of disease: coupling computer generated diagnoses with treatment strategies. Research has shown that in experimental settings, feeding and activity patterns can be used to detect ill health up to 2 days prior to farmer diagnosis (e.g. Bowen et al., 2019). However, it is not known whether using such systems will reduce overall treatment rates or how accurate these computer-generated diagnoses are when applied by farm staff. Health alerts will be created using established models of behavioural indicators of disease. A protocol for treatment groups of calves will define what treatment action should be taken after an alert and compared to control groups under standard farm health treatment practices. Environmental data, growth and intake data, and key attributes of the calf's history will also be gathered and advanced data analytics (including machine learning) will be used to assess the effectiveness of the model diagnosis coupled with a treatment programme on calf health outcomes.

Objective 2. Novel indicators of disease. There are a number of emerging technologies that could be applied in the early detection of disease. These include assessments of heart rate variability, blood oxygen saturation or lung function. Pilot trials will be used to assess the validity and practicality of these measures in disease detection. The most promising new monitoring technology(s) will be assessed in a trial comparing responses in sick and healthy calves. Additional data (as above) will be added and machine learning techniques used to assess the efficacy of diagnosis of these novel methods.

Objective 3. Effect of plane of nutrition on response to disease challenge.
Calves are typically fed a restricted milk allowance to encourage them move onto solid feed. However, a recent study indicated that calves on a low plane of nutrition (PON) show exaggerated responses to disease challenge (Sharon et al., 2019). The propsed study will investigate effects of different PONs on immunological and behavioural outcomes following natural disease challenge in calves.

References
Bowen et al (2019). BSAS Annual Meeting.
Sharon et al., (2019). J. Dairy Sci. 102:9082-9096