Automated evaluation of cattle behavior under stable fly infestations

Stable flies (Stomoxys calcitrans L.) are one of the most severe pests of cattle worldwide. Historically, they have been associated primarily with confined livestock. More recently, their effects on pastured animals have become more evident. This pest directly affects animals with its annoying bite; decreasing weigh gains, feed efficiency, and milk production (Bruce and Decker 1958, Campbell et al. 2001). Estimates of economic losses due to stable flies in the U.S. exceed $2 billion per year (Taylor et al. 2012). In order to avoid their painful bites, cattle display defensive behaviors such as tail flicking, skin twitching, head throwing, foot stamping, and grouping (Mullens et al. 2006). These behaviors interrupt grazing and have metabolic costs. Quantifying these defensive behaviors and their impacts on cattle has proven to be difficult. Most studies have used direct observations to assess cattle behaviors (Dougherty et al. 1993, 1995, Mullens et al. 2006, Schole et al. 2011).  Direct observation methods lack objective thresholds, are difficult in large pastures, and depend on good weather conditions. In addition, the presence of the observer can modify the cattle’s behaviors. As an alternative to direct observations, remote sensing devices can objectively quantify cattle behaviors continuously, twenty-four hours per day seven days per week, for the entire fly season and monitor many animals simultaneously. Accelerometers can evaluate three-dimensional movements of animals and Global Positioning Systems (GPS) can provide absolute and relative positions as well as speed, direction, and extent of movement. The objectives of this study are two-fold. First, develop and calibrate a remote sensing device for livestock called CattleTracker and second, use that device to evaluate cattle behaviors in response to stable fly infestations.

Our remote sensing device is based upon the CraneTracker (Anthony et al. 2012) with capabilities for tracking and recording animal’s behavior with GPS and accelerometers. Currently, we are calibrating the device to translate sensor outputs into defensive behavior event counts. For calibration, we are video recording animals with the device, obtaining two types of data: video and sensors. Video and sensor data are aligned and behavioral events demarcated. Machine learning methods are being used to create algorithms to translate sensor outputs to event counts. Once calibration is complete, we will verify that the devices are recording event counts correctly by video recording animals with devices. This time, we will compare the device counts with manual counts derived from the videos. After validation, CattleTracker will be ready for the second objective, evaluate cattle behaviors in response to stable fly infestations. Stable fly adult population will be monitor with sticky traps. At the same time, twenty animals will be monitored with Cattletrackers. Cattle behavioral data will be analyzed relative to stable fly populations with mixed linear models. Spatial data derived from GPS will be analyzed for quantifying grouping behavior, and will be correlated with stable fly populations as well. Results will help us to understand and quantify the effects of stable flies on cattle.

References

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