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

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.

A machine learning approach using partitioning around medoids clustering and random forest classification to model groups of farms in regard to production parameters and bulk tank milk antibody status of two major internal parasites in dairy cows

“…Lame animals are not only impaired in their natural behavioral patterns but also experience severe, often chronic pain [ 64 , 65 ]. The association of lameness with production related parameters has been demonstrated by previous research [ 17 , 25 , 66 , 67 ]. It is hence reasonable to assume that lameness also has an association with the milk composition parameters used as input variables in the current cluster analysis.…”

“…In the latter, lameness prevalence was the most important criterion to classify clusters. Lameness is a very common, major problem in dairy production that impinges upon animal welfare, physiological integrity, and economic viability [ 17 , 63 , 64 ]. Lame animals are not only impaired in their natural behavioral patterns but also experience severe, often chronic pain [ 64 , 65 ].…”

“…It is hence reasonable to assume that lameness also has an association with the milk composition parameters used as input variables in the current cluster analysis. In addition, lameness is a condition that is strongly associated with housing and management practices on the farm [ 17 , 25 , 68 ]. As a consequence, farm-level lameness prevalence may reflect a certain type of on farm setting regarding management elements and housing conditions which may translate into additional animal health and welfare related issues.…”

“…Apart from that, SCC is associated with other input variables of the cluster analysis such as milk yield [ 80 ]. Furthermore, SCC has been associated with lameness [ 17 ], the top ranking variable discriminating clusters in study region North. Together, both factors are very common in dairy production and may act as a representation of certain type of farm management and housing conditions.…”

“…Farm selection process and sample calculation are elaborated on in [ 16 , 17 ]. Briefly, different scenarios were calculated given a power of 80% and a level of significance of 5% in order to calculate an optimal and feasible sample size.…”