title: Relationships between bovine hoof disorders, body condition traits, and test-day yields.
abstract: A designed field study was conducted on 1,962 first-lactation Holstein cows kept on 7 large-scale dairy farms in Germany, in loose-housing systems on standard slatted flooring. Records of hoof disorders at trimming composed of subclinical as well as clinical cases were combined with body measurements and body condition scores, test-day records, and further individual cow data. The 6 most frequent disorders were laminitis, also known as sole hemorrhage (57.3% of all cows), dermatitis digitalis (17.0%), dermatitis interdigitalis (7.9%), white line disease (12.6%), sole ulcer (7.1%), and interdigital hyperplasia (5.5%). The most important environmental effects were herd-visit date and stage of lactation. Heifers between 50 and 99 d in milk had the highest frequencies for laminitis. Lighter cows were less susceptible to laminitis and white line disease, but had a higher prevalence of interdigital hyperplasia. Relationships between stature and white line disease as well as relationships between body condition score and dermatitis digitalis were nonlinear, resulting in an improved disorder status of cows with an intermediate condition score and average stature. Increased biomechanical stress caused by different factors (weight, social rank, standing time) is a presumed effect that leads to an increased susceptibility for hoof diseases. From a management perspective, under- and overconditioning should be avoided. Milk production traits differed between cows with and without laminitis-related claw horn lesions. Fat percentage and fat-to-protein ratio in the first month before trimming were significantly decreased for cows with a positive laminitis status. Hence, this finding points to metabolic disorders being associated with a higher risk for hoof diseases. Estimates of heritabilities for laminitis, dermatitis digitalis, dermatitis interdigitalis, white line disease, and sole ulcer were 0.09, 0.14, 0.10, 0.11, and 0.06 when applying a threshold model with a probit link function.
authors.affiliation: Institute of Agricultural and Nutritional Sciences, University of Halle, 06099 Halle, Germany.
authors.forename: K
authors.initials: K
authors.lastname: Schöpke
authors.forename: S
authors.initials: S
authors.lastname: Weidling
authors.forename: R
authors.initials: R
authors.lastname: Pijl
authors.forename: H H
authors.initials: HH
authors.lastname: Swalve
mesh_terms: D000367:Age Factors; D000818:Animals; D002417:Cattle / Q000235:genetics; D002418:Cattle Diseases / Q000209:etiology / Q000235:genetics*; D003612:Dairying / Q000379:methods / Q000706:statistics & numerical data; D058066:Digital Dermatitis / Q000209:etiology / Q000235:genetics; D005534:Foot Diseases / Q000209:etiology / Q000235:genetics / Q000662:veterinary*; D006724:Hoof and Claw; D019655:Quantitative Trait, Heritable