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

Jonas Brock1,2, Martin Lange3, Jamie A Tratalos4

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This study introduces a novel machine learning approach using self-organising-maps (SOMs) to classify livestock herd types. This method aids animal disease control and surveillance by providing a data-driven understanding of different cattle farming systems in Ireland.

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Area of Science:

  • Veterinary Science
  • Agricultural Science
  • Data Science

Background:

  • Effective animal disease control and surveillance require a clear understanding of livestock herd types.
  • Current methods may lack the granularity needed for precise epidemiological studies and policy development.
  • The Irish cattle sector, like others, benefits from refined herd classification for disease management.

Purpose of the Study:

  • To develop and validate a new method for classifying livestock herd types.
  • To apply this method to the Irish cattle population for improved disease control insights.
  • To demonstrate the utility of the self-organising-maps (SOMs) algorithm in livestock systems analysis.

Main Methods:

  • Combined expert knowledge with the self-organising-maps (SOMs) machine-learning algorithm.
  • Utilized Irish livestock registration data, including bovine birth, movement, disposal, sex, and breed information.
  • Identified 17 distinct herd types using 9 key variables.

Main Results:

  • Successfully classified Irish cattle herds into 17 distinct types using the SOMs algorithm.
  • Developed a data-driven classification tree based on livestock registration data.
  • Demonstrated the straightforward interpretation of results due to the visual nature of SOMs.

Conclusions:

  • The novel SOMs-based approach provides a robust method for classifying livestock herd types.
  • This data-driven classification enhances understanding for animal disease control and surveillance in cattle.
  • The methodology is adaptable for classifying herd types in diverse global livestock systems.