“We reckon every case of mastitis we get costs us £300, so every case you reduce, you are reducing your costs."
"As a dairy farmer our goal is to reduce the amount of antibiotics we use, and the welfare of the cows will increase."
John Torrance, Farmer
“The system could be applied to all farms, being relatively cheap, particularly the more intensive larger farms where it is harder for farmers to monitor the welfare of the cows themselves.”
Dr Jonathan Amory, Principal Lecturer in Animal Behaviour and Welfare at Writtle College
Omnisense has been selected to supply the Series 500 cluster positioning system to a landmark research project under an £820,000 government grant award from the Biotechnology and Biological Sciences Research Council (BBSRC).
Started in November 2012, the 39-month joint project involves Writtle College and the University of Essex working with experts from University of Exeter and Royal Veterinary College.
Animal welfare is still a major concern in a society, particularly the well-being of livestock used in food production such as the modern dairy cow.
There is currently a lack of reliable methods for the early detection of disease by farm staff, but research has shown that the study of animal behaviour may provide solutions. However, analysis of dairy cow behaviour is still heavily reliant on direct observation and video analysis or the use of 3D accelerometer devices that give useful, but very restricted information such as locomotion or posture.
The project will utilise recent advances in local positioning technology made by Omnisense, that was successfully piloted in 2012. It is being used to record positional data on a large group of cows (100+) simultaneously to provide new insights into 24 hour time budgets, location, activity and highly detailed information regarding social behaviour including social facilitation (group behaviour), social bonding and agonistic behaviour.
These new welfare indices will allow the development of novel predictive models for important diseases, such as lameness, mastitis and metritis. The research will allow for development of an on-farm automated ‘early warning’ system for disease detection.
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