Ruminant Population Health

Precision livestock, big data and animal health and welfare analytics

Here in the Ruminant Population Health Research Group, we have track record in animal health and welfare data analysis using various advances statistical and mathematical modelling techniques.

The term big data refers to extremely large sets of information which require specialised computational tools to enable their analysis and exploitation. We’re working with industry partners to develop and validate innovative technology that can be used to capture big data on ruminant health, empowering farmers to improve welfare, production and sustainability.

The areas we’re researching include capturing and monitoring precision livestock health data; developing wearable sensors (such as accelerometers and temperature sensors) for animals which can detect lameness and other health and welfare parameters; and an internet of things for livestock farming.

We’re using advanced data-mining techniques, including machine-learning tools, to develop novel algorithms for animal health and welfare. Our team is also collaborating widely with global industry partners, including Intel, Hewlett Packard Enterprise, Farm Wizard, Dunbia and Prognostix Ltd.  


Research that’s making an impact

EL4L: Early Lameness Detection for Lameness Control

Creating an accurate, novel system for detecting the early signs of lameness in sheep.


SPILaMM: Sheep Performance Improvement through Lameness Monitoring and Management

Furthering understanding of the causes of sheep lameness and the ability of technology to help farmers counteract them.


Y-Ware: Young-stock Welfare & Performance – Decision Making Platform

Using technology to more accurately track the health of beef and dairy young stock and prevent unnecessary early deaths.

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Ruminant Population Health

School of Veterinary Medicine and Science
University of Nottingham
Sutton Bonington Campus
Leicestershire, LE12 5RD