Accuracy of a real-time location system in static positions under practical conditions: Prospects to track group-housed sows

Social interaction between animals may influence disease transmission paths. Therefore, the usage of real-time location systems gains in importance for livestock farms and research institutes as this technology helps to simultaneously obtain positions of a large number of animals and to evaluate them automatically. Thus, the aim of the project was to specify the accuracy of the real-time location system under practical conditions with regard to a possible future application. In practice, ear tags have proven their worth because pigs manipulate and therefore destroy other objects applied to them in the long term. Therefore, a real-time location system was used providing the sending unit integrated in an ear tag. Ear tags were tested in a sows’ gestation stall in static positions. Measuring took place for 5 min per static position, whereas data was transmitted once per second (1 Hz) which led to 300 data points per position. Metal pen equipment led to lost or noisy positions. On average, 9% of data losses occurred and were inserted for the following data evaluation. A Haar wavelet was applied to reduce the noise. Filter settings were rated with the help of an error size consisting of the Euclidean error and an error for the variance of the filtered signal. An optimal filter setting could be achieved when only the 29 largest coefficients for the X axis and 20 largest coefficients for the Y axis were kept while all others were set to 0. Additionally, a t-test was performed to test whether an averaged number of coefficients over all ear tags and an optimal individual filtering of each single ear tag resulted in a significantly different filter result. P-values of the t-test were 0.15 (X coordinate) and 0.18 (Y coordinate) and therefore not significant. Thus, an averaged filter setting can be applied to all ear tags. The median accuracy of measured data described as Euclidean distance was 2.7 m before filtering and improved to 2.0 m after filtering. Considering the results of this system investigation, it shows that the system may be helpful for ensuing studies regarding e.g. animal behaviour, movement profiles, or social networks to uncover possible transmission paths for diseases.