Laboratoire Bioinformatique

Precision Diary Farming

Objectives

The main goal of this project is to provide tools to support Canadian dairy farmers for daily decision making about each animal and to iterate rapidly the complete cycle of production\season. Here we propose to develop tools to model the dairy production in a way that will provide the extraction of the non-genetic features that will drive the development of new management practices and allowing a precise lifetime productivity estimate. To this end, we will develop data mining and machine learning toolkits to deliver advanced tools to producers and their counsellors to allow better decision.

Milestones

  1. To deliver a useful platform for dairy production scientists, we fixed the following

specific objectives:

1. Provide toolkits and predictive models allowing dairy producers to assess, compare and monitor cows with the appropriate indicators integrating genotype-phenotype-environment.

2. Provide tools to allow farm comparisons with peers.

3. Conceive predictive models and tools allowing the inclusion of genetic profitability in the lifetime profit estimate for early decision making.

4. Deliver an easy to use services and controlled access data/results to each farm.

Benefits

With advances in genomics, an increasingly detailed genetic profile can be established
for each individual dairy cow, at a relatively low cost. Dairy cows are heavily phenotyped making the
calculation of the environmental component available through the integration of the different datasets
and the current knowledge of management practices (phenotype = genome + environment). Integrating
the data to generate an interactive model of dairy production will serve as a basis for the development of
new management tools for dairy producers to optimize management decisions (e.g., feeding,
reproduction, therapeutic treatments and caregiving). It will also be a stepping stone for the inclusion of
new selection traits such as the animal response to various housing conditions and management practices,
like lameness, heifer growth and adaptation to robotized milking systems.

Stakeholders

Genome Canada’s role is to see possibilities and seize opportunities for Canada in the emerging bioeconomy+, while facilitating the sound integration of genomics into society.

Find out more about genomics, its value to Canada and to specific sectors, societal and policy implications, and the role that Genome Canada plays to harness the transformative power of genomics for the benefit of Canadians.

Making genomics work for Citizens, Industry and Society! 
DNA: When the infinitely small generates big possibilities.

Valacta, the dairy production centre of expertise, has for  mission to improve the profitability and sustainability of Quebec and Atlantic dairy farms by offering herd management tools and advice. We share our expertise internationally with dairy production collaborators in several countries.

Canadian dairy farmers work every day to make Canadian milk better in every way. Learn about the people behind 100% Canadian milk.

Team

The project will have the collaborative support of important laboratories: 1. in UQAM: a. LACIM the world-class laboratory in Combinatory, Informatics and Mathematics to address the metrics and a simulation technologies; b. GDAC the center for knowledge acquisition and diffusion will contribute on knowledge extraction and transformation. 2. Valacta and the Canadian Dairy Network [see LCS]: will provide all the domain expertise needed to translate the Quebec expertise in dairy modeling. They will update the datasets. 3. MIMs: will support the implementation and user experiences of the developed tools. 4. IVADO: the prestigious institute of Artificial Intelligence will collaborate with us to integrate the Valacta data into predictive model systems. They will provide the cross check of the implemented data mining and machine learning technologies. 5. Hamidou Tembine: one of the world expert in game theory will counsel on the metrics to compare objects and how to integrate these metrics into learning. 6. Edith Charbonneau: will help cross-validating the results obtained in this project, she is a regular user of Valacta raw data and Quebec dairy production