Quantitative Methods for Real Time Auto-Monitoring of Livestock

The use of automated sensors has grown rapidly in recent years, with sensor data now routinely used for monitoring in a wide range of situations, including human health and behaviour, the environment, wildlife, and agriculture. There is the potential to massively increase use of empirical data for decision making in real time, but in many areas, development and validation of quantitative approaches is still needed in order to move from the developmental stage to this intended practical application. Whilst there is wide-ranging literature in this area, in livestock farming there is a paucity that provides fair robust statistical comparisons of alternative quantitative methods and evidence that resulting decision making performs adequately in practice on farms. That is, it must be practically feasible to repeatedly apply the method dynamically in real time on farms, and optimise decisions made. We discuss alternative quantitative approaches that could be used for real time decision making from automatic monitoring, which we refer to as prediction, as well as approaches to rigorous statistical evaluation of the resulting decision-making process, which we refer to as prediction validation. Associated research challenges are discussed in detail with reference made to livestock farming, but many issues discussed are widely applicable.