Impact of digital dermatitis on feedlot cattle behaviour

The welfare of ill and injured feedlot cattle: a review of the literature and implications for managing feedlot hospital and chronic pens

By definition, ill and injured animals are on the negative valence of animal welfare. For beef cattle kept in feedlot settings, advances in cattle health management have resulted in a greater understanding and prevention of illness and injury. However, the management of cattle once they become ill and injured is an understudied area, and there are gaps in knowledge that could inform evidence-based decision-making and strengthen welfare for this population. The aim of this review is to provide a comprehensive overview of the acquired knowledge regarding ill and injured feedlot cattle welfare, focusing on existing knowledge gaps and implications for hospital and chronic pen management and welfare assurance. Ill and injured feedlot cattle consist of acutely impaired animals with short-term health conditions that resolve with treatment and chronically impaired animals with long-term health conditions that may be difficult to treat. A literature search identified 110 articles that mentioned welfare and ill and injured feedlot cattle, but the population of interest in most of these articles was healthy cattle, not ill and injured cattle. Articles about managing ill and injured cattle in specialized hospital (n = 12) or chronic (n = 2) pens were even more sparse. Results from this literature search will be used to outline the understanding of acutely and chronically ill and injured feedlot cattle, including common dispositions and welfare considerations, behavior during convalescence, and strategies for identifying and managing ill and injured cattle. Finally, by working through specific ailments common in commercial feedlot environments, we illustrate how the Five Domains Model can be used to explore feelings and experiences and subsequent welfare state of individual ill or injured feedlot cattle. Using this approach and our knowledge of current industry practices, we identify relevant animal-based outcomes and critical research questions to strengthen knowledge in this area. A better understanding of this overlooked topic will inform future research and the development of evidence-based guidelines to help producers care for this vulnerable population.

Machine learning approaches to predict and detect early-onset of digital dermatitis in dairy cows using sensor data

The present study aimed to employ machine learning algorithms based on sensor behavior data for (1) early-onset detection of digital dermatitis (DD) and (2) DD prediction in dairy cows. Our machine learning model, which was based on the Tree-Based Pipeline Optimization Tool (TPOT) automatic machine learning method, for DD detection on day 0 of the appearance of the clinical signs has reached an accuracy of 79% on the test set, while the model for the prediction of DD 2 days prior to the appearance of the first clinical signs, which was a combination of K-means and TPOT, has reached an accuracy of 64%. The proposed machine learning models have the potential to help achieve a real-time automated tool for monitoring and diagnosing DD in lactating dairy cows based on sensor data in conventional dairy barn environments. Our results suggest that alterations in behavioral patterns can be used as inputs in an early warning system for herd management in order to detect variances in the health and wellbeing of individual cows.

An experimental model to induce digital dermatitis in beef calves

Background

Digital dermatitis (DD) is a multifactorial infectious disease affecting the skin on feet of cattle causing erosion and inflammation above the heel bulbs. Some cases of DD cause lameness and significantly impact animal welfare and productivity. While DD has emerged as a concern for the beef industry, key information regarding early detection and its impact on cattle behaviour is lacking. The primary objective of this study was to determine if an established DD experimental model for dairy calves could be used to induce DD lesions in beef calves. A secondary objective was to describe changes in behaviour and pain associated with induction of DD lesions. Eight beef calves acquired from a single cow-calf operator were enrolled in the study. Upon enrolment, calves were evaluated and determined to be free of foot lesions. Within the experimental environment, calves were housed in individual pens and assigned to two groups (mock-inoculated and inoculated). Both hind feet of each calf were enrolled. Within calf, inoculation protocol was consistent, and a 28-day experimental protocol was employed. Two days prior to inoculation, both hind feet of each calf were abraded (area above the heel bulbs and below the dewclaws), moistened, and wrapped to facilitate an anaerobic condition. Feet were inoculated with macerated DD lesion material or mock inoculum and remained wrapped until clinical signs of DD or protocol endpoint.

Results

After a period of 14 to 18 days post inoculation, three of five inoculated calves developed clinical signs (lameness), and upon close inspection, DD lesions were present on at least one hind foot. Two of five inoculated calves did not develop lesions within 28 days. Zero of three mock-inoculated calves developed DD. Treponema spp. were detected by quantitative polymerase chain reaction from biopsies of induced lesions. Measurements of behaviour prior to disease induction were numerically different between DD affected and mock-inoculated calves.

Conclusions

An experimental infection model established for dairy cattle was used to successfully induce acute DD lesions in three of five inoculated beef calves. This model can provide a framework to study intervention protocols and to evaluate the impact of DD on behaviour and pain.