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Six hundred fifty-nine farms in three regions of Germany (North: n = 240, East: n = 247, and South: n = 172) were included in the study, which aims at determining the association of management-related risk factors with farm-level lameness in German dairy herds. For each risk factor, a generalised linear regression model with negative binomial distribution and logit link was built. Results showed that cows housed in deep-bedded cubicles had a lower risk of being lame than cows housed in other cubicle types. A larger cubicle width was associated with a lower risk of being lame. Feeding a total mixed ration was associated with lower lameness prevalence (compared to feeding a partial mixed ration or single components). For first lactation cows, lameness assessment performed daily (compared to less than daily) and during other work tasks (compared to lameness assessment as a separate work task) were associated with lower risk for lameness. Finally, the present study provided evidence for crucial associations of management-related risk factors with lameness in German dairy cows, especially in the fields of cubicle design, feeding management, and lameness assessment.

From 2016 to 2020, the "PraeRi" study, conducted by three German veterinary universities, was aimed at enhancing animal health and welfare in dairy farms. With 765 dairy farms visited and 101,307 animals examined, this study provided a basis for improving animal health and welfare. The study population comprised three different regions representing a broad variety of characteristics. To ensure representative estimates, a sample size of 250 farms was determined for each region, employing a stratified sampling plan based on farm size. According to the information provided by the farmers, the most commonly occurring disease in their herds was mastitis without general disorder (14.2% to 16.3% of the herd-depending on the region). For most disorders, prevalence data were lowest for the region South compared with the two remaining regions. Multivariable regression analyses were performed to identify risk factors for various target variables, and the results were communicated through individual reports and benchmarking flyers to participating farmers. The authors encountered challenges in management and communication due to the project's size in terms of personnel, data, and farms examined. Harmonizing data management and hypothesis testing across all involved parties added complexity.