Jennifer Salau

Feasibility of automated body trait determination using the SR4K time-of-flight camera in cow barns

As herd sizes have increased in the last decades, computerized monitoring solutions, which provide fast, objective and accurate evaluations of the herd status, gain more and more importance. This study analyzes the feasibility of a Time-of-Flight-camera-based system for gathering body traits in dairy cows for use under cow barn conditions. Recording, determination of body condition score on a 5 point scale by visual and manual inspection, and measuring the backfat thickness with ultrasound took place from July 2011 to May 2012 at the dairy research farm Karkendamm of the Institute of Animal Breeding and Husbandry, Christian-Albrechts-University Kiel (Germany) and between August 2010 and July 2012 at the Institute for Agricultural Engineering and Animal Husbandry of Bavarian State Research Center for Agriculture in Grub (Germany). The two breeds Holstein Friesian cows (Karkendamm) and Fleckvieh (Grub) were considered in this study. Software for recording, image sorting and evaluation, determining the body parts needed, and extracting traits from the images was written and assembled to an automated system. Sorting the images and finding ischeal tuberosities, base of the tail, and dishes of the rump, backbone, and hips had error rates of 0.2%, 1.5%, 0.1%, and 2.6%, respectively. 13 traits were extracted and compared to backfat thickness and body condition score as well as between breeds. All traits depend significantly on the animal and showed very large effect sizes. Coefficients of determination restricted to individual animals were reaching up to 0.89. The precision in measuring the traits and gathering backfat thickness was comparable. Results indicated that the application of Time-Of-Flight in determination of body traits is feasible.

Quantification of the effects of fur, fur color, and velocity on Time-Of-Flight technology in dairy production

With increasing herd sizes, camera based monitoring solutions rise in importance. 3D cameras, for example Time-Of-Flight (TOF) cameras, measure depth information. These additional information (3D data) could be beneficial for monitoring in dairy production. In previous studies regarding TOF technology, only standing cows were recorded to avoid motion artifacts. Therefore, necessary conditions for a TOF camera application in dairy cows are examined in this study. For this purpose, two cow models with plaster and fur surface, respectively, were recorded at four controlled velocities to quantify the effects of movement, fur color, and fur. Comparison criteria concerning image usability, pixel-wise deviation, and precision in coordinate determination were defined. Fur and fur color showed large effects (η2=0.235 and η2=0.472, respectively), which became even more considerable when the models were moving. The velocity of recorded animals must therefore be controlled when using TOF cameras. As another main result, body parts which lie in the middle of the cow model’s back can be determined neglecting the effect of velocity or fur. With this in mind, further studies may obtain sound results using TOF technology in dairy production.

Quality assessment of static aggregation compared to the temporal approach based on a pig trade network in Northern Germany.

Recent analyses of animal movement networks focused on the static aggregation of trade contacts over different time windows, which neglects the systems temporal variation. In terms of disease spread, ignoring the temporal dynamics can lead to an over- or underestimation of an outbreaks speed and extent. This becomes particularly evident, if the static aggregation allows for the existence of more paths compared to the number of time-respecting paths (i.e. paths in the right chronological order). Therefore, the aim of this study was to reveal differences between static and temporal representations of an animal trade network and to assess the quality of the static aggregation in comparison to the temporal counterpart. Contact data from a pig trade network (2006-2009) of a producer community in Northern Germany were analysed. The results show that a median value of 8.7 % (4.6-14.1%) of the nodes and 3.1% (1.6-5.5%) of the edges were active on a weekly resolution. No fluctuations in the activity patterns were obvious. Furthermore, 50% of the nodes already had one trade contact after approximately six months. For an accumulation window with increasing size (one day each), the accumulation rate, i.e. the relative increase in the number of nodes or edges, stayed relatively constant below 0.07% for the nodes and 0.12 % for the edges. The temporal distances had a much wider distribution than the topological distances. 84% of the temporal distances were smaller than 90 days. The maximum temporal distance was 1000 days, which corresponds to the temporal diameter of the present network. The median temporal correlation coefficient, which measures the probability for an edge to persist across two consecutive time steps, was 0.47, with a maximum value of 0.63 at the accumulation window of 88 days. The causal fidelity measures the fraction of the number of static paths which can also be taken in the temporal network. For the whole observation period relatively high values indicate that 67% of the time-respecting paths existed in both network representations. An increase to 0.87 (0.82-0.88) and 0.92 (0.80-0.98), respectively, could be observed for yearly and monthly aggregation windows. The results show that the investigated pig trade network in its static aggregation represents the temporal dynamics of the system sufficiently well. Therefore, the methodology for analysing static instead of dynamic networks can be used without losing too much information.

Adaption of the temporal correlation coefficient calculation for temporal networks (applied to a real-world pig trade network)

The average topological overlap of two graphs of two consecutive time steps measures the amount of changes in the edge configuration between the two snapshots. This value has to be zero if the edge configuration changes completely and one if the two consecutive graphs are identical. Current methods depend on the number of nodes in the network or on the maximal number of connected nodes in the consecutive time steps. In the first case, this methodology breaks down if there are nodes with no edges. In the second case, it fails if the maximal number of active nodes is larger than the maximal number of connected nodes. In the following, an adaption of the calculation of the temporal correlation coefficient and of the topological overlap of the graph between two consecutive time steps is presented, which shows the expected behaviour mentioned above. The newly proposed adaption uses the maximal number of active nodes, i.e. the number of nodes with at least one edge, for the calculation of the topological overlap. The three methods were compared with the help of vivid example networks to reveal the differences between the proposed notations. Furthermore, these three calculation methods were applied to a real-world network of animal movements in order to detect influences of the network structure on the outcome of the different methods.

Temporal correlation coefficient for directed networks

Previous studies dealing with network theory focused mainly on the static aggregation of edges over specific time window lengths. Thus, most of the dynamic information gets lost. To assess the quality of such a static aggregation the temporal correlation coefficient can be calculated. It measures the overall possibility for an edge to persist between two consecutive snapshots. Up to now, this measure is only defined for undirected networks. Therefore, we introduce the adaption of the temporal correlation coefficient to directed networks. This new methodology enables the distinction between ingoing and outgoing edges. Besides a small example network presenting the single calculation steps, we also calculated the proposed measurements for a real pig trade network to emphasize the importance of considering the edge direction. The farm types at the beginning of the pork supply chain showed clearly higher values for the outgoing temporal correlation coefficient compared to the farm types at the end of the pork supply chain. These farm types showed higher values for the ingoing temporal correlation coefficient. The temporal correlation coefficient is a valuable tool to understand the structural dynamics of these systems, as it assesses the consistency of the edge configuration. The adaption of this measure for directed networks may help to preserve meaningful additional information about the investigated network that might get lost if the edge directions are ignored.

Effects of data quality in an animal trade network and their impact on centrality parameters

Abstract Dealing with the analysis of animal trade networks always faces the challenge of imperfect data sets mainly due to country borders or different producer communities. In the present study, the network robustness, i.e. the point at which false positive nodes or edges may influence the network structure and the results of the centrality parameters, were analysed for a pork supply chain of a producer community in Northern Germany. The analysis of animal trade networks mainly focusses on disease transmission and the development and implementation of targeted prevention and intervention strategies based on centrality parameters. Here, the inclusion criteria may impact the prediction of disease transmission as well as the outcome of the applied control measures. Thus, four different removal scenarios all based on the boundary specification problem (removal of arcs according to their frequency of appearance, removal of nodes according to their general frequency of appearance and according to their frequency of appearance as supplier or purchaser) were established to analyse the network robustness. In order to evaluate the changes in the rank order of the nodes a Spearman Rank Correlation Coefficient (rs) was calculated between the original network and each removal step. The removal of nodes according to their frequency of appearance showed the most robust results. The values of rs stayed above the threshold of 0.70 for at least a fraction of 80% removed arcs. For the other removal scenarios the centrality parameters under investigation showed various robust results concerning the ranking of the nodes. Therefore, the exclusion of farms that trade infrequently in the network would not be associated with significant change in network structure and centrality parameters. For targeted disease prevention and intervention strategies based on centrality parameters, it is of great relevance to be able to evaluate the influence of inclusion criteria on the network structure and thus on the speed and the extent of possible disease transmission.