Identifying cow – level factors and farm characteristics associated with locomotion scores in dairy cows using cumulative link mixed models

Oehm, Andreas W.; Merle, Roswitha; Tautenhahn, Annegret; Jensen, K. Charlotte; Mueller, Kerstin-Elisabeth; Feist, Melanie; Zablotski, Yury; Abuelo, Angel (2022): Identifying cow – level factors and farm characteristics associated with locomotion scores in dairy cows using cumulative link mixed models. PLOS ONE, 17 (1): e0263294. ISSN 1932-6203

Abstract

Lameness is a tremendous problem in intensively managed dairy herds all over the world. It has been associated with considerable adverse effects on animal welfare and economic viability. The majority of studies have evaluated factors associated with gait disturbance by categorising cows into lame and non-lame. This procedure yet entails a loss of information and precision. In the present study, we extend the binomial response to five categories acknowledging the ordered categorical nature of locomotion assessments, which conserves a higher level of information. A cumulative link mixed modelling approach was used to identify factors associated with increasing locomotion scores. The analysis revealed that a low body condition, elevated somatic cell count, more severe hock lesions, increasing parity, absence of pasture access, and poor udder cleanliness were relevant variables associated with higher locomotion scores. Furthermore, distinct differences in the locomotion scores assigned were identified in regard to breed, observer, and season. Using locomotion scores rather than a dichotomised response variable uncovers more refined relationships between gait disturbances and associated factors. This will help to understand the intricate nature of gait disturbances in dairy cows more deeply.

• Dokumententyp: Artikel (LMU)
• Organisationseinheit (Fakultäten): 08 Tiermedizin > Zentrum für Klinische Tiermedizin > Klinik für Wiederkäuer mit Ambulanz und Bestandsbetreuung
• DFG-Fachsystematik der Wissenschaftsbereiche: Lebenswissenschaften
• DFG: Gefördert durch die Deutsche Forschungsgemeinschaft (DFG) - 491502892