Studies to date utilising these tools have failed to use a wide range of behavioural and/or physiological features from monitored data; for example, social and play behaviour, animals coping style (consistent behaviour patterns1 (all generated from single sensor) have been linked to disease; as shown in our work temperature signal can be utilised to give temperature as well drinking. Furthermore, use of these technologies have so far narrowly focussed on disease detection and mostly utilising static features; dynamic features of continuous time series that technologies provide could predict resilience. Insights from use of complex dynamics systems theory in ecology, has shown that as system becomes less resilient, system variables show increasing delays in their recovery from internal or external perturbations. Thus, dynamic features such as (variance, temporal autocorrelation, cross -correlations) of high-resolution data can predict a system’s resilience. It is highly novel application for the use of these tools. These data, combined with advanced machine learning algorithms, can be used to automatically monitor health and resilience of calves and provide decision support tools to farmers to predict prevent calf diseases and improve resilience through adaptation of management strategies.

Using unique dataset detailed time series data on animal behaviours, physiology and production, we will generate algorithms that will predict health, production and resilience. Our hypotheses for this 12-month research are:

H1) Behavioural and physiological features captured from technologies can be optimised to predict health and production in calves

H2) Calves’ varying response to natural perturbations and key commercial stressors, measured via dynamic features in their time series of behaviour, physiology and production, can be used to quantify resilience

H3) Dynamic features of resilience in H2 will be correlated between states and to calves cumulative health outcomes.

Contact details:

Professor Jasmeet Kaler

Professor in Epidemiology and Precision Livestock Informatics

Nottingham Vet School