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• W3048935632 abstract "Sensor technologies allow ethologists to continuously monitor the behaviors of large numbers of animals over extended periods of time. This creates new opportunities study livestock behavior in large-scale commercial settings, but also new methodological challenges. Densely sampled behavioral data from large heterogenous groups can contain a range of complex stochastic structures that may be difficult to visualize using conventional exploratory data analysis techniques. The goal of this research was to assess the efficacy of unsupervised machine learning tools in recovering complex behavioral patterns from such datasets to better inform subsequent statistical modeling. This methodological case study was carried out using records on milking order, or the sequence in which cows arrange themselves as they enter the milking parlor. Data was collected over a 6-month period from a closed group of 200 mixed-parity Holstein cattle on an organic dairy. Cows at the front and rear of the queue proved more consistent in their entry position than animals at the center of the herd, a systematic pattern of heterogeneity seen more clearly using entropy estimates, a scale-free alternative to variance that is robust to outliers. Dimension reduction techniques were then used to visualize relationships between cows. No evidence of social cohesion was recovered, but Diffusion Map embeddings proved more adept than PCA at revealing the underlying linear geometry of this data. Median parlor entry positions from the pre- and post-pasture subperiods were highly correlated (R2=0.91), suggesting a surprising degree of temporal stationarity. Data Mechanics visualizations, however, revealed heterogenous nonstationary among subgroups of animals in the center of the group and herd-level temporal outliers. A repeated measures model recovered inconsistent evidence of a relationships between entry position and cow attributes. Mutual conditional entropy tests, a permutation-based approach to assessing bivariate correlations robust to nonindependence, confirmed a significant but nonlinear association with peak milk yield, but revealed the age effect to be potentially confounded by health status. Finally, queueing records were related back to behaviors recorded via ear tag accelerometers using linear models and mutual conditional entropy tests. Both approaches recovered consistent evidence of differences in home pen behaviors across subsections of the queue." @default.
• W3048935632 created "2020-08-18" @default.
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• W3048935632 date "2020-08-13" @default.
• W3048935632 modified "2023-10-17" @default.
• W3048935632 title "Mind the Queue: A Case Study in Visualizing Heterogeneous Behavioral Patterns in Livestock Sensor Data Using Unsupervised Machine Learning Techniques" @default.
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• W3048935632 doi "https://doi.org/10.3389/fvets.2020.00523" @default.
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