New technologies like cow-attached accelerometers could improve lameness detection and management, but underlying issues in analytics and algorithms could prevent farmers from adopting the new systems.
A recent article in the Journal of Dairy Science reviewed the viability of using accelerometers to detect lame cattle. Their review found that attaching one gait-measuring accelerometer per cow at a low resolution (less than 100 Hz) could potentially provide farmers with an accurate and low-cost method for automating lameness detection. However, the researchers didn’t give a full endorsement to this method. Based on the evidence, they concluded that the outstanding gaps in lameness detection algorithms and analysis mean that widespread adoption on-farm is years away.
Preventing, identifying and treating lame cattle is an ongoing challenge for farmers and the cattle industry. Existing prevention methods like improved walking surfaces and nutrition are only part of the solution. Mild and moderate cases of lameness are notoriously difficult to identify, leading to cases becoming more severe and costly to treat down the line. Most identification strategies use locomotion scoring to assess the entire herd. Routine hoof trimming is another identification and treatment method since the legs are lifted and inspected by farmers and veterinarians. Farmers also rely on ad hoc observations for diagnosis.
Though these detection strategies can work, there are some practical barriers to their application. Despite recommendations stating that locomotion scoring should happen on a monthly basis, this rarely happens. Some farmers don’t do it at all. Similar issues exist with hoof trimming.
For the most part, farmers tend to rely on ad hoc detection despite the well-documented drawbacks. Ad hoc observations for diagnosis are problematic for mild and moderate lameness cases. “Eyeballing” it is ineffective, and it doesn’t give farmers the herd-level statistics they need to manage their operations.
Finding ways to automate lameness detection would be hugely useful for farmers. Automatic and algorithm-based methods would allow for early detection and identify mild and moderate cases as they emerge. This early detection could also reduce the time between onset and treatment – preventing cases from becoming severe, speeding up recovery time and saving money on treatment.
Different researchers have suggested using wearable devices like accelerometers to collect herd-level statistics. Ideally, these devices would alert farmers to changes in the cows’ gait or behaviour and give progress reports on recovering cows. Though this is theoretically feasible, there are barriers to adopting automated systems.
Firstly, not all accelerometers are created equal. The data the devices collect requires analysis and responsive algorithms, and different companies would have unique measurement systems. Additionally, farmers may not be able to invest in the emerging technology. The economic returns from automating lameness detection could still be marginal, and since many farmers perceive mild and moderate lameness cases as non-urgent, the industry could maintain the status quo.
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