ICT in large and small dairy systems

Project information

ICT in large and small dairy systems

Call: ICT and Automation for a Greener Agriculture

Id: 14306

Acronym: DairyICT

1 April, 2013 to 31 March, 2016

No Partner Contact Country Total
1 Coord.Department of Veterinary Clinical and Animal Sciences
Department of Large Animal Sciences
Faculty of Health and Medical Sciences
University of Copenhagen
Chris KnightDenmark86.015.0Danish AgriFish Agency
Ministry of Food, Agriculture and Fisheries
2Department of Animal Science
Aarhus University
Klaus L. IngvartsenDenmark270.0142.0Danish AgriFish Agency
Ministry of Food, Agriculture and Fisheries
3Veterinary Physiology
Vetsuisse Faculty
Department of Engineering and Information Technology
University of Bern
Rupert BruckmaierSwitzerland120.070.0Federal Office for Agriculture - Bundesamt für Landwirtschaft
4Newcastle University
School of Agriculture Food and Rural Development
Ilias KyriazakisUnited Kingdom11.00.0Department for Environment, Food and Rural Affairs
5Centre for Intelligent Dynamic Communications (CIDCOM)
Department of Electronic and Electrical Engineering
University of Strathclyde
Ivan AndonovicUnited Kingdom13.00.0Department for Environment, Food and Rural Affairs
6INRA Research Unit Systemic Modelling applied to Ruminants
INRA - Joint research unit on Dairy Production
Nicolas FriggensFrance289.0202.0French National Research Agency
7Department of Animal Medicine, Production and Health,
University of Padua
Paolo BerzaghiItaly21.05.0Ministry of Agriculture Food, Forestry & Tourism Policies
David RobertsUnited Kingdom14.00.0Department for Environment, Food and Rural Affairs
Research Directorate
Moorepark Dairy Production Research
TEAGASC - Agriculture and Food Development Authority
Riona SayersIreland150.05.0TEAGASC - Agriculture and Food Development Authority


This multidisciplinary project seeks to integrate and extend existing state of the art technologies to ensure sustainable and responsible management of dairy units, with focus on cow health, milk quality and reduced emissions. We shall focus on milk metabolomic methods for determination of metabolic health, biomarker technologies for assessment of systemic health and accelerometer collars for measuring various activities including feeding behaviour, and hence intake. We shall also have access to NIR technology for feed quality assessment and rumen-bolus technology for measurment of rumen pH. We have advanced teleonomic technologies that will enable us to integrate these input data into decision support tools. Our technologies will monitor animals and environment, detect deviations from the normal state and either respond automatically to restore the normal state or issue an alert to husbandry staff. We have access to a range of dairy units for evaluation of the technologies.


Partially derived from the outputs obtained from the DairyICT project, the start-up company Silent Herdsman from the University of Strathclyde, Glasgow, was created. Silent Herdsman is an internet of things (IoT)- inspired decision support platform that supports the optimisation of animal husbandry within dairy farms and in turn maximises productivity (milk production, milk quality, health) and welfare. Silent Herdsman developed a tracking system consisting of a neck-mounted collar, which is very accurate, and highly scalable. In 2016, the company Afimilk Ltd (a global leader in farm management software and milk analysis tools for dairy operations in 50 countries) acquired the neck collar developed by Silent Herdsman. The collar allows the tracking of each cow individually and provides alerts related to changes associated with the onset of heat and/or the health of the animal in real time. By analysing the time spent eating and ruminating behaviour patterns for individual animals, Silent Herdsman also provides early indications of illness. The deployment of the collars has proven to decrease the calving index (CI) from 40 days (for poorly performing farms) to 20 days (for high-performing farms). For example, on a UK farm, collar deployment has yielded a 40-day improvement in the CI for a 500-cow herd, which translates to >£100k additional revenue and represents a return on investment within seven months.

  • Establishment of a COST Action in Dairy Animal Health and Welfare: FA1308 DairyCare www.dairycareaction.org
  • Identification of milk-borne biomarkers indicative of rumen dysfunction
  • Demonstration of the potential utility of stress biomarker determination in multiple matrices for assessment of acute and chronic stress
  • Development of multi-sensor based predictive models linking lameness, feeding behaviour and rumination time
  • Dairy
  • Sensing
  • Data integration
  • Smart systems

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