Research Axes
Research will assess how modifications to the cow's physical environment impact welfare and longevity outcomes, and how the digital environment enhances end users' ability to improve the animal’s physical and psychological experience. Our research hinges on four axes.
- Animal welfare and herd management
- Animal welfare and longevity
- Efficient and energy-efficient collection of predictive and monitoring data
- Big data, data analysis and artificial intelligence
Animal welfare and herd management
This axis focuses on the cow’s environment and its impact on welfare and relies on metrics assessing cows’ emotions, biomechanics, as well as biomarkers.
The team will assess:
- Improvements to current tie-stall systems via enrichment at the stall, impacts of handling, and access to exercise
- Impacts of and solutions for transitioning from tie-stall to no-stall systems to help both cows and producers adapt
Animal welfare and longevity
This axis measures both the long-term profitability of herds and the survivability of dairy cows and aims to develop predictors of longevity and welfare.
Predictive models rely on:
- AI-based early indicators of welfare concerns
- Machine learning to remotely detect herds at risk of welfare deterioration
Efficient and energy-efficient collection of predictive and monitoring data
This axis focuses on data collection and on ways to ensure faster, more efficient and reliable data across our network of partner farms.
Objectives:
- Develop infrastructure and software to optimize data collection and analysis from barn ecosystems
- Address current issues with collection and standardization of data originating from commercial databases
- Refine existing infrastructure to support research axes #1 and #2
Big data, data analysis and artificial intelligence
This axis addresses massive data processing, data analysis and artificial intelligence. It also looks at how to tackle challenges resulting from automated data handling and processing from sensors and cow activity detectors. It aims to automatize measures and inform the development of proxy measures to simplify decision-making for end users.
Objectives:
- Address heterogeneous data integration through the modelling of ontologies and knowledge graphs to represent multi-source data
- Develop prediction and prescription-based algorithms to support dairy producers’ decisions with their livestock
- Evaluate decision-making tools based on in-barn prediction indicators
- Ensure that tools developed for dairy producers adhere to the most stringent privacy laws