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Combining expert knowledge and machine-learning to classify herd types in livestock systems
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 c...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7862359/ https://www.ncbi.nlm.nih.gov/pubmed/33542295 http://dx.doi.org/10.1038/s41598-021-82373-3 |
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author | Brock, Jonas Lange, Martin Tratalos, Jamie A. More, Simon J. Graham, David A. Guelbenzu-Gonzalo, Maria Thulke, Hans-Hermann |
author_facet | Brock, Jonas Lange, Martin Tratalos, Jamie A. More, Simon J. Graham, David A. Guelbenzu-Gonzalo, Maria Thulke, Hans-Hermann |
author_sort | Brock, Jonas |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7862359 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-78623592021-02-05 Combining expert knowledge and machine-learning to classify herd types in livestock systems Brock, Jonas Lange, Martin Tratalos, Jamie A. More, Simon J. Graham, David A. Guelbenzu-Gonzalo, Maria Thulke, Hans-Hermann Sci Rep Article 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. Nature Publishing Group UK 2021-02-04 /pmc/articles/PMC7862359/ /pubmed/33542295 http://dx.doi.org/10.1038/s41598-021-82373-3 Text en © The Author(s) 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Brock, Jonas Lange, Martin Tratalos, Jamie A. More, Simon J. Graham, David A. Guelbenzu-Gonzalo, Maria Thulke, Hans-Hermann Combining expert knowledge and machine-learning to classify herd types in livestock systems |
title | Combining expert knowledge and machine-learning to classify herd types in livestock systems |
title_full | Combining expert knowledge and machine-learning to classify herd types in livestock systems |
title_fullStr | Combining expert knowledge and machine-learning to classify herd types in livestock systems |
title_full_unstemmed | Combining expert knowledge and machine-learning to classify herd types in livestock systems |
title_short | Combining expert knowledge and machine-learning to classify herd types in livestock systems |
title_sort | combining expert knowledge and machine-learning to classify herd types in livestock systems |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7862359/ https://www.ncbi.nlm.nih.gov/pubmed/33542295 http://dx.doi.org/10.1038/s41598-021-82373-3 |
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