Early signs of lameness in dairy cows revealed by milking and behavioral data using generalized additive mixed models.

Early detection of lameness is difficult in dairy farms as clinical signs may be subtle. This study aims to identify key changes in milking and behavioral data before and after lameness identification, to assess their potential as early-warning indicators. Data were collected on a dairy farm in Northern Italy, with 600 Holstein cows milked twice daily in a rotary parlor, equipped with sensors and pedometers. A total of 67 cows (days in milk: 177 ± 104; parity: 2.62 ± 1.57) with at least one farmer-detected lameness event between 2022 and 2023 were included. Daily data on milk yield, duration, flows, lying time, bouts, and steps were analyzed in a 60-d window around the detection day (Δ -30 to +30), using generalized additive mixed models (GAMMs) accounting for parity and stage of lactation. Daily milk yield began to decline 12-16 d before detection, with losses of up to 16% in multiparous cows. Before lameness detection, milking duration shortened, and average milk flow slowed in multiparous cows, particularly between 60 and 120 s after cluster attachment. Lying time increased by 8-11% in the days before detection, while steps differed by parity: primiparous cows reduced their activity, whereas multiparous cows showed increased steps. After hoof trimming, most of the traits returned toward pre-lameness levels. Although the magnitude of changes was moderate, timing and consistency indicate that deviations in milking performance and behavior can be detected before lameness becomes visible. These findings provide a basis for developing predictive algorithms to identify cows at risk earlier, using standard farm infrastructure, without additional sensors.