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A review of traditional and machine learning methods applied to animal breeding

Part of: Big Data

Published online by Cambridge University Press:  18 December 2019

Shadi Nayeri ,
Mehdi Sargolzaei  and
Dan Tulpan Open the ORCID record for Dan Tulpan [Opens in a new window]
Shadi Nayeri
Affiliation:
Department of Animal Biosciences, Centre for Genetic Improvement of Livestock, University of Guelph, Guelph, Ontario, N1G 2W1, Canada
Mehdi Sargolzaei
Affiliation:
Select Sires Inc., Plain City, Ohio, 43064, USA Department of Pathobiology, University of Guelph, Guelph, Ontario, N1G 2W1, Canada
Dan Tulpan*
Affiliation:
Department of Animal Biosciences, Centre for Genetic Improvement of Livestock, University of Guelph, Guelph, Ontario, N1G 2W1, Canada
*
Author for correspondence: Dan Tulpan, Department of Animal Biosciences, Centre for Genetic Improvement of Livestock, University of Guelph, Guelph, Ontario, N1G 2W1, Canada. E-mail: dtulpan@uoguelph.ca
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Abstract

The current livestock management landscape is transitioning to a high-throughput digital era where large amounts of information captured by systems of electro-optical, acoustical, mechanical, and biosensors is stored and analyzed on a daily and hourly basis, and actionable decisions are made based on quantitative and qualitative analytic results. While traditional animal breeding prediction methods have been used with great success until recently, the deluge of information starts to create a computational and storage bottleneck that could lead to negative long-term impacts on herd management strategies if not handled properly. A plethora of machine learning approaches, successfully used in various industrial and scientific applications, made their way in the mainstream approaches for livestock breeding techniques, and current results show that such methods have the potential to match or surpass the traditional approaches, while most of the time they are more scalable from a computational and storage perspective. This article provides a succinct view on what traditional and novel prediction methods are currently used in the livestock breeding field, how successful they are, and how the future of the field looks in the new digital agriculture era.

Keywords

Animal breedinganimal healthmachine learningpredictionregression
Type
Review Article
Information
Animal Health Research Reviews , Volume 20 , Issue 1 , June 2019 , pp. 31 - 46
Copyright
Copyright © Cambridge University Press 2019

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