Evaluation of an ear tag based accelerometer for detecting bovine respiratory disease in dairy calves
The aim of the study is to investigate current socioeconomic issues concerning applied agricultural and veterinary research. In the last few years, technologies were developed on livestock farms that can be summarized as 'precision livestock farming'. The characteristics of precision livestock farming are, e.g. an increased use of information and communication technologies, in particular wireless technology, an increasing technical surveillance, the use of sensors at critical control points during production, and an intensified observation of animals in order to detect diseases in early stages in individuals or on herd level. Miniaturization of tools in combination with current wireless technologies allows the development and use of sensors for continuous monitoring of physiological and pathological conditions in animals.Healthy and high performing calves and young bovines are of major economic importance on farm. Furthermore, the consumers demand in terms of animal welfare and minimal drug (in particular antibiotic) use on food producing farms increases. The most important disease in calves and young stock on different types of bovine farms is bovine respiratory disease. In this context, early disease detection is of major importance. Delayed diagnosis and consequently therapy may lead to prolonged use of antibiotics, a high recurrence rate, development of refractory sequalae, decreased overall performance, and endemic herd disease. The aim of the planned dissertation is to evaluate 'Precision livestock farming'-methods, in particular a wireless sensor technology for a reliable detection of bovine respiratory disease in calves and young stock, with regard to efficiency and new fields of application. A sensor system that has already been introduced successfully for activity monitoring and positioning in dairy cows will be used to develop and test an algorithm to identify bovine respiratory disease in calves and young stock. The sensor captures movement data of animals that are sent permanently to receivers installed in the barn. In a first step, activity patterns and information that were recorded by a video monitoring system as well as results of clinical examinations will be matched. Based on this, an algorithm will be developed to detect early state of bovine respiratory disease. In a second step, the accuracy of this technology will be evaluated on a larger number of animals in the field.