Optimisation of on-farm technologies to predict health and resilience in dairy calves

Currently there are no accurate digital tools with decision support to predict calf health and production. Our approach is highly novel as it uses cutting-edge data techniques to develop and use novel features from various calf behaviours (various activities, social networks, feeding, play) and physiology (temperature both core and eye) captured by technologies (automatic feeders, activity location sensors, bolus, thermal cameras) and on-farm data to predict health and production and welfare indicator as play. We will optimise the use of technologies by identifying which information is of value and by conducting a comparative evaluation of the technologies w.r.t their predictive accuracy. Our approach is different and extends the use of technologies for the first-time to accurately measure and quantify dynamic indicators of resilience in 3 states (behavioural, physiological and production) in calves.