Combining expert knowledge and machine-learning to classify herd types in livestock systems

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Title: Combining expert knowledge and machine-learning to classify herd types in livestock systems
Authors: Brock, JonasLange, MartinTratalos, Jamie A.More, Simon JohnGraham, David A.Guelbenzu Gonzalo, MariaThulke, Hans-Hermann
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Date: 4-Feb-2021
Online since: 2021-06-25T14:48:41Z
Abstract: A detailed understanding of herd types is needed for animal disease control and surveillance activities, to inform epidemiological study design and interpretation, and to guide effective policy decision-making. In this paper, we present a new approach to classify herd types in livestock systems by combining expert knowledge and a machine-learning algorithm called self-organising-maps (SOMs). This approach is applied to the cattle sector in Ireland, where a detailed understanding of herd types can assist with on-going discussions on control and surveillance for endemic cattle diseases. To our knowledge, this is the first time that the SOM algorithm has been used to differentiate livestock systems. In compliance with European Union (EU) requirements, relevant data in the Irish livestock register includes the birth, movements and disposal of each individual bovine, and also the sex and breed of each bovine and its dam. In total, 17 herd types were identified in Ireland using 9 variables. We provide a data-driven classification tree using decisions derived from the Irish livestock registration data. Because of the visual capabilities of the SOM algorithm, the interpretation of results is relatively straightforward and we believe our approach, with adaptation, can be used to classify herd type in any other livestock system.
Funding Details: Department of Agriculture, Food and the Marine
Funding Details: Projekt DEAL
Type of material: Journal Article
Publisher: Springer
Journal: Scientific Reports
Volume: 11
Issue: 1
Keywords: Herd production typeInfections diseasesSelf-organising maps (SOMs)Kohonen mapsCattle
DOI: 10.1038/s41598-021-82373-3
Language: en
Status of Item: Peer reviewed
ISSN: 2045-2322
This item is made available under a Creative Commons License:
Appears in Collections:Veterinary Medicine Research Collection
CVERA Research Collection

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