Revisiting Past Research with Fresh Eyes
Associating Limb Movement Variables and Pain Scores: Objective Measures for the Presence of Lameness
Picture this: two cows are walking side by side in a dairy farm. One of them is limping, the other is not. You care about your animals. You want them to be healthy, happy, and productive. But sometimes, they suffer from lameness, a painful condition that affects their gait, their hooves, and their quality of life. How can you spot the early signs of lameness, and minimize its costly suffering?
This is the puzzle that we set out to solve in our groundbreaking study—entitled Comparison of Models to Identify Lame Cows Based on Gait and Lesion Scores, and Limb Movement Variables—published in the Journal of Dairy Science in 2006. We were engineers, and we had a passion for animal welfare. We wanted to find a better way to detect and treat lameness in cows, using science and technology.
Lameness is typically assessed by two methods: gait scores (GS) and lesion scores (LS). Gait scores are based on how the cow walks, and lesion scores are based on the wounds or abnormalities on the cow’s hooves. However, these methods are not always reliable, as different observers may see details differently and may miss some subtle signs of lameness.
That’s why we developed a new device that could measure the force applied by each limb of the cow on the ground. We called these measurements limb movement variables (LMV), and we hoped they could reveal hidden clues of lameness by giving us a more objective and accurate way to detect lameness. We also compared them with the traditional methods of gait scores (GS) and lesion scores (LS) to see how LMV relate to GS, LS, or both (GLS), and whether we could use them to predict lameness in cows.
We conducted our study at the University of Maryland Research Farm in Clarksville, Maryland, where we had access to a herd of about 90 milking cows. Out of these, 20 cows participated in our lameness study that lasted for 12 months. We collected data from our innovative split instrumented floor every day, and we examined the cows for GS and LS every week. We analyzed the data using various statistical models and compared their performance. Our results showed that LMV were indeed useful for identifying lame cows, and that they had a strong correlation with GS and GLS.
But looking back, we have realized that our study was not perfect, and that we could have improved it by adding another measurement: pain score (PS). This would be done by using a digital instrumented hoof tester, which could objectively measure how much pain the cow feels in her hooves and produce a granular sensitivity level. We may assume that pain is the primary reason that affects the cow’s gait, and that lesions that do not hurt may be disregarded in such analysis. So, PS might be a more trustworthy and specific indicator of lameness, and its correlation with LMV may be of great interest. By using LMV and PS together, we hope to create a more precise and objective method of finding and treating lameness in cows, which would benefit both the animals and the caretakers.
We are proud of our study and its contribution to the field of animal welfare. We believe it was a valuable step towards understanding and solving the problem of lameness in cows. But we also acknowledge that there is still more work to be done, and that our findings could be applied to other types of animals as well.
If you are curious about our study, we invite you to read our paper and learn more about our methods, results, and implications. You can find the abstract below, and the full text publication of Comparison of Models to Identify Lame Cows Based on Gait and Lesion Scores, and Limb Movement Variables in the Journal of Dairy Science.
We hope you enjoyed our tale of reflection, and that you share our passion for promoting animal wellbeing worldwide. This is more than just a study. This is a new way of seeing animals. This is engineering for a better future.
The views and opinions expressed in this post are those of Dr. Uri Tasch—one of the co-authors—and do not necessarily reflect the official position of all the authors or the journal.
Comparison of Models to Identify Lame Cows Based on Gait and Lesion Scores, and Limb Movement Variables
ABSTRACT
Bovine lameness results in pain and suffering in cattle and economic loss for producers. A system for automatically detecting lame cows was developed recently that measures vertical force components attributable to individual limbs. These measurements can be used to calculate a number of limb movement variables. The objective of this investigation was to explore whether gait scores, lesion scores, or combined gait and lesion scores were more effectively captured by a set of 5 limb movement variables. A set of 700 hind limb examinations was used to create gait-based, lesion-based, and combined (gait- and lesion- based) models. Logistic regression models were constructed using 1, 2, or 3 d of measurements. Resulting models were tested on cows not used in modeling. The accuracy of lesion-score models was superior to that of gait-score models; lesion-based models generated greater values of areas under the receiving operating characteristic curves (range 0.75 to 0.84) and lower mean-squared errors (0.13 to 0.16) compared with corresponding values for the gait-based models (0.63 to 0.73 and 0.26 to 0.31 for receiving operating characteristic and mean-squared errors, respectively). These results indicate that further model development and investigation could generate automated and objective methods of lameness detection in dairy cattle.
