Lameness is the most visible animal welfare issue of the modern North American dairy industry and arguably the second most important cause of economic losses.1 Dairy cows affected with lameness are less profitable because of increased likelihood of culling and death, decreased reproductive performance, and decreased milk production.2,3 Consumer awareness about the welfare of food animals has created a demand for food-animal products from animal-friendly production systems. To genuinely meet this consumer demand, North American dairy farmers will have to substantially decrease the prevalence and incidence of lameness in dairy cows. Sole ulcers (pododermatitis circumscripta) and white line diseases are widespread in dairy cattle, and once an animal is affected, lameness can persist for > 30 days.4,5 Therefore, efforts should be directed toward decreasing the incidence of these diseases.
The ability to predict the incidence of diseases is an invaluable tool for their prevention. In human medicine, predictive models are commonly used to quantify the risk of disease as well as the probability of success of specific treatments. The risk of coronary heart disease is predicted by use of a multivariable-adjusted model that includes blood pressure values and cholesterol concentrations.6 With regard to dairy cattle, prevention of lameness is the most important step to reduce its welfare implications and associated economic losses.7 Hence, it is important to develop a model that accurately predicts the occurrence of lameness, allowing farmers to apply preventive strategies on targeted high-risk groups.
The objective of the study reported here was to develop a parsimonious statistical model that could accurately predict the incidence of lameness in the subsequent lactation by use of information available during hoof trimming at the cessation of lactation in dairy cows. The hypothesis was that DCT, BCS, age, and CHDLs at cessation of lactation are associated with the incidence of sole ulcers and white line disease in the subsequent lactation.
Body condition score
Claw horn disruption lesion
Digital cushion thickness
Receiver operating characteristic
Dairy Comp 305, Valley Agricultural Software, Tulare, Calif.
Aquila Vet ultrasound machine, Esaote Europe BV, The Netherlands.
Stata, StataCorp LP, College Station, Tex.
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