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    Leveraging accelerometer data for lameness detection in dairy cows:
    a longitudinal study of six farms in Germany (2023)

    Art
    Zeitschriftenartikel / wissenschaftlicher Beitrag
    Autoren
    Lavrova, Anastasia I. (WE 16)
    Choucair, Alexander (WE 18)
    Palmini, Andrea (WE 16)
    Stock, Kathrin F.
    Kammer, Martin
    Querengässer, Friederike (WE 16)
    Doherr, Marcus G. (WE 16)
    Müller, Kerstin E. (WE 18)
    Belik, Vitaly (WE 16)
    Quelle
    Animals
    Bandzählung: 13
    Heftzählung: 23
    Seiten: Artikelnummer: 3681
    ISSN: 2076-2615
    Sprache
    Englisch
    Verweise
    URL (Volltext): https://www.mdpi.com/2076-2615/13/23/3681
    DOI: 10.3390/ani13233681
    Kontakt
    Nutztierklinik

    Königsweg 65
    14163 Berlin
    +49 30 838 62261
    klauentierklinik@vetmed.fu-berlin.de

    Abstract / Zusammenfassung

    Lameness in dairy cows poses a significant challenge to improving animal well-being and optimizing economic efficiency in the dairy industry. To address this, employing automated animal surveillance for early lameness detection and prevention through activity sensors proves to be a promising strategy. In this study, we analyzed activity (accelerometer) data and additional cow-individual and farm-related data from a longitudinal study involving 4860 Holstein dairy cows on six farms in Germany during 2015–2016. We designed and investigated various statistical models and chose a logistic regression model with mixed effects capable of detecting lameness with a sensitivity of 77%. Our results demonstrate the potential of automated animal surveillance and hold the promise of significantly improving lameness detection approaches in dairy livestock.