This research has the potential to improve animal welfare, farm productivity, and environmental sustainability by targeting individual animal needs rather than the herd as a group.

What challenge(s) does the project address?

Meeting the projected 70% increase in global demand for livestock products by 2050 without increasing the environmental footprint will require a 50% increase in the efficiency of livestock farming. Farm animals are typically managed in groups where the target is an ‘average’ animal. This limits our ability to precision feed and manage individuals.

How will this research address the challenge(s)?

This proposal aims to develop novel digital technologies for livestock management that will be high impact, catalytic and transformational, focusing on integrating existing bio-monitoring systems and laboratory data to develop the tools to optimize precision feeding.

Why does this research matter?

Precision feeding in livestock production enables farmers to meet individual animal needs instead of those of a group and has the potential to increase whole-farm feed conversion efficiencies up to 26% or up to half of the desired 50% increase.

What are the (new) methods (techniques, technologies, etc.) that the project team will use during the research?

This research will apply machine learning, artificial intelligence, and mechanical models to optimize herd and individual animal health and production while minimizing greenhouse gas emissions.

What impact will the project have on agriculture?

The precision feeding tools to be developed in this research will improve feed efficiency, animal health and longevity, environmental sustainability, and farm profitability in Canada’s commercial dairy and beef farms.

Collaborators & Students

Dr. Trevor DeVries, Dr. John Cant, Dr. Katie Wood, Dr. Vern Osborne, Dr. Dan Tulpan, Dr. Jan Dijkstra, Dr. David Innes, Jihao You, Carolina Reyes, Patty Kedzierski, and Maureen Sahar