References
Automated detection of lameness in cattle: an update
Abstract
Lameness is common in the dairy industry, with an estimated prevalence of approximately 30%. Performing regular mobility scoring is recommended on farms to monitor the prevalence of herd lameness and identify (and subsequently treat) lame cows promptly, but mobility scoring can be subjective and may be difficult to regularly implement on farm. Accordingly, there is increasing interest in the automation of lameness detection with direct and indirect methods described. This article provides an overview of automated detection of lameness with a focus on direct methods.
Lameness is an abnormal gait typically considered to be an expression of foot or limb pain. A meta-analysis found that the reported prevalence of lameness in UK dairy cows is approximately 30% (Afonso et al, 2020), meaning that lameness is a leading welfare concern in the dairy industry, although it should be remembered that beef cattle are also affected (Tunstall et al, 2019).
In addition to the negative welfare effects of lameness, the economic impacts can be considerable. In 2009, Willshire and Bell estimated that the ‘average’ lameness case cost individual dairy farms just over £320 and the collective cost to the UK dairy industry was estimated to exceed £120 000 000 per year (Willshire and Bell, 2009), but it is likely that these costs have increased since this work was published. A tool to calculate the cost of lameness on individual farms has been made available by the Agriculture and Horticulture Development Board (AHDB, 2023a) to help veterinary surgeons and farmers understand the economic impact that lameness is having on their farms. Entering example figures provides an illustration of the current economic effect of lameness: if average UK figures regarding herd size and productivity (AHDB, 2022; 2023b) are entered with a lameness prevalence of 10%, lameness is calculated to cost the example farm almost £11 000 per annum, primarily as a result of reduced productivity (Figure 1).
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