Search

Simple Summary: Lameness is a common problem among dairy cows, significantly impacting both their well-being and productivity. It not only hampers the mobility of cows but also exerts notable effects on behavioral aspects, like grooming and feed bunk visits. Typically, dairy cows are equipped with accelerometers primarily for estrus detection. Our goal was to formulate a model for detecting lameness using accelerometer data collected from six farms in Germany. Various statistical models were developed for lameness detection, including variables influenced by lameness, such as rumination, feeding and movement patterns, milk production, days in milk, and weight. The aim was to identify lameness in cows through a comprehensive analysis. We explored multiple models, considering different variables affected by lameness, and ultimately selected the most suitable model. Lameness in dairy cows poses a significant challenge to improving animal well-being and optimizing economic efficiency in the dairy industry. To address this, employing automated animal surveillance for early lameness detection and prevention through activity sensors proves to be a promising strategy. In this study, we analyzed activity (accelerometer) data and additional cow-individual and farm-related data from a longitudinal study involving 4860 Holstein dairy cows on six farms in Germany during 2015–2016. We designed and investigated various statistical models and chose a logistic regression model with mixed effects capable of detecting lameness with a sensitivity of 77%. Our results demonstrate the potential of automated animal surveillance and hold the promise of significantly improving lameness detection approaches in dairy livestock. [ABSTRACT FROM AUTHOR]