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

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2026
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Abstract
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 parlour, 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 analysed in a 60-day 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 days 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 60-120 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 behaviour 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.
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Authors Sara Mondini, Chris Major Ncho, Giulia Gislon, A. Sandrucci, Stefano Cossa, Maddalena Zucali
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Journal italian journal of animal science
Year 2026
DOI
10.1093/jas/skag289
URL
https://doi.org/10.1093/jas/skag289
Keywords Keywords not found

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