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涩里番.CA / Animal Science / Faculty & Staff

Kevin M. Wade

Kevin Wade, Animal Science, 涩里番

Associate Professor - Information Systems

Director, Dairy Information Systems Group听

T: 514-398-7973听 |听kevin.wade [at] mcgill.ca (Email) |听 Barton Building, B1-020A听 |听

Degrees

BAgrSc, MAgrSc (Dublin)
PhD (Cornell)

Short Bio

Kevin Wade was born in Ireland, educated in Agricultural Sciences (BSc, MSc) at University College Dublin, and obtained his PhD in Animal Breeding and Genetics from Cornell University (1990). Following a post-doctoral fellowship at the University of Guelph, where he implemented national genetic evaluations for Calving Ease in Dairy cattle, he was hired in 涩里番鈥檚 Department of Animal Science to fill the NSERC-Semex position in Dairy Information Systems (1992). Wade leads a group of researchers dedicated to the improvement of dairy-herd management through the exploitation of collected data. This principally involves collaboration with Valacta 鈥� the Dairy Production Centre of Expertise for Quebec and Atlantic Canada. In addition to leading this research group, Wade has served at various levels of administration at 涩里番 鈥� University Senate, Director of Continuing Professional Development (AES), and Chair of the Department of Animal Science from (2007 鈥� 2018). He is a past Chair of the Macdonald Campus Committee on Information Technologies, and represents the Faculty on the University Teaching and Learning Services Committee. As the Faculty's Dairy Academic Lead, Wade continues to spearhead, and be involved in, large-scale research initiatives in dairy-production research, teaching, and infrastructure.

Professional activities

  • Director, Dairy Information Systems Group
  • Member,
    • Comit茅 de formation continue
    • Comit茅 des 茅quivalences
  • Board member (涩里番 Representative),
  • Board member (涩里番 Representative),
  • Participant,
  • Collaborating Member,
  • Member,

Research interests

Research in Applied Artificial Intelligence: various applications (artificial neural networks; case-based reasoning; decision-tree analyses; etc.) have been used in the development of prediction tools for milk production and incidence of disease in dairy cattle.

Big Data Analyses: through the use of data mining and the investigation of cube database technologies, the large amounts of milk-recording data are being examined with a view to discovering potential relationships among easily-recorded data and traits of economic interest.

On-farm Management Systems: the development of dairy-cattle lifetime models, helped by advances in data visualization, is allowing producers and advisors to better understand the profitability aspects of their enterprises through the identification of outliers and the impact of poor management decisions.

Current Research

  • Use of machine learning to determine the effect of forage quality on milk production in dairy cattle.
  • The effect of heifer growth on early-life fertility in dairy cattle.
  • Use of Knowledge Graphs for predictive analyses
  • Information from robotic milking systems

Courses

ANSC 250. Introduction to Livestock Management

Credits: 3
Offered by: Animal Science (Faculty of Agric Environ Sci)
Terms offered: Fall 2025
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Description

Introduction to the scientific principles underlying animal livestock production as it relates to the consumer food chain. The world- wide demand for animal products, various areas of management (reproduction, nutrition, breeding, health, and welfare) that are used to provide those products by examining both conventional means as well as new and evolving technologies. How these techniques relate to some of the major production systems (dairy, beef, pig, and broiler and egg production) 鈥� primarily in a Provincial/Canadian context.
  • Fall
  • 3 lectures and one 2-hour lab

Most students use Visual Schedule Builder (VSB) to organize their schedules. VSB helps you plan class schedules, travel time, and more.


ANSC 565. Applied Information Systems.

Credits: 3
Offered by: Animal Science (Faculty of Agric Environ Sci)
This course is not offered this catalogue year.

Description

Introduction to concepts of an Information System and subsequent application to various scenarios in agriculture. Industry analysis in terms of users, goals, available data/information, communication, delivery structure, decision making, feedback, exploitation of technology and possible improvements using the Internet. Individual case studies and familiarisation with cutting-edge computer applications.
  • Prerequisite: ABEN 251 or demonstrated equivalency
  • Winter
  • 3 lectures and one 2-hour lab

Most students use Visual Schedule Builder (VSB) to organize their schedules. VSB helps you plan class schedules, travel time, and more.

Publications

To view a list of current publications, please .

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