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A decision support system based on disease scoring enables dairy farmers to proactively improve herd health

Author

Listed:
  • Jan Saro

    (Department of Systems Engineering, Faculty of Economics and Management, Czech University of Life Sciences Prague, Prague - Suchdol, Czech Republic)

  • Luděk Stádník

    (Department of Animal Science, Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences Prague, Prague - Suchdol, Czech Republic)

  • Petra Bláhová

    (Department of Systems Engineering, Faculty of Economics and Management, Czech University of Life Sciences Prague, Prague - Suchdol, Czech Republic)

  • Simona Huguet

    (Department of Systems Engineering, Faculty of Economics and Management, Czech University of Life Sciences Prague, Prague - Suchdol, Czech Republic)

  • Helena Brožová

    (Department of Systems Engineering, Faculty of Economics and Management, Czech University of Life Sciences Prague, Prague - Suchdol, Czech Republic)

  • Jaromír Ducháček

    (Department of Animal Science, Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences Prague, Prague - Suchdol, Czech Republic)

Abstract

Decision support systems (DSSs) enable dairy farmers to make informed and timely decisions on herd health management. However, the lack of a disease scoring system by category and severity limits the application of this approach. In this study, we developed an innovative approach to dairy herd health management by establishing a novel scoring system for dairy herd health management aimed at providing a more nuanced understanding of disease impact. For this purpose, we retrieved 5-year data from 2 558 disease diary records of 798 primiparous and multiparous cows housed on a Czech farm and classified 125 production diseases into six categories, namely lameness, mastitis, postpartum diseases, digestive system, reproductive diseases and other diseases. Based on this metric, we developed a data-driven DSS for farm management. Using this DSS, we identified markers of disease categories for efficient veterinary monitoring on dairy farms. This DSS highlighted a decreasing trend of average monthly disease scores, yet the prevalence of postpartum and other diseases increased during the same period, due to changes in reproduction management within the herd. These findings underscore the need for data-driven targeted interventions for promoting the herd health. Therefore, our scoring model not only provides a comprehensive framework for dairy herd health monitoring and improvement but also advances dairy farming by providing a decision support system easily applicable to dairy farms based on available data recorded in disease diaries.

Suggested Citation

  • Jan Saro & Luděk Stádník & Petra Bláhová & Simona Huguet & Helena Brožová & Jaromír Ducháček, 2024. "A decision support system based on disease scoring enables dairy farmers to proactively improve herd health," Czech Journal of Animal Science, Czech Academy of Agricultural Sciences, vol. 69(5), pages 165-177.
    • Handle: RePEc:caa:jnlcjs:v:69:y:2024:i:5:id:53-2024-cjas
    DOI: 10.17221/53/2024-CJAS
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    References listed on IDEAS

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    1. J. Přibyl & P. Šafus & M. Štípková & L. Stádník & V. Čermák, 2004. "Selection index for bulls of Holstein cattle in the Czech Republic," Czech Journal of Animal Science, Czech Academy of Agricultural Sciences, vol. 49(6), pages 244-256.
    2. Amine Boudaoud, 2023. "Timed artificial insemination protocols in dairy cattle: Functioning, shortcomings, and improvements," Czech Journal of Animal Science, Czech Academy of Agricultural Sciences, vol. 68(6), pages 231-244.
    3. Ferchiou, Ahmed & Lhermie, Guillaume & Raboisson, Didier, 2021. "New standards in stochastic simulations of dairy cow disease modelling: Bio-economic dynamic optimization for rational health management decision-making," Agricultural Systems, Elsevier, vol. 194(C).
    4. Eva Kašná & Ludmila Zavadilová & Jan Vařeka & Jitka Kyselová, 2022. "General resilience in dairy cows: A review," Czech Journal of Animal Science, Czech Academy of Agricultural Sciences, vol. 67(12), pages 475-482.
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