Details zur Publikation

Referenztyp Zeitschriften
DOI / URL Link
Creative Commons Lizenz creative commons licence
Titel (primär) Combining expert knowledge and machine-learning to classify herd types in livestock systems
Autor Brock, J.; Lange, M.; Tratalos, J.A.; More, S.J.; Graham, D.A.; Guelbenzu-Gonzalo, M.; Thulke, H.-H.
Journal / Serie Scientific Reports
Erscheinungsjahr 2021
Department OESA
Band/Volume 11
Seite von art. 2989
Sprache englisch
Topic T5 Future Landscapes
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.
dauerhafte UFZ-Verlinkung
Brock, J., Lange, M., Tratalos, J.A., More, S.J., Graham, D.A., Guelbenzu-Gonzalo, M., Thulke, H.-H. (2021):
Combining expert knowledge and machine-learning to classify herd types in livestock systems
Sci. Rep. 11 , art. 2989