178 Tag Your It: Use of Wearable Biometric and Remote Sensing Technology to Monitor Animal Growth, Performance, and Efficiency in Extensively Managed Landscapes

Abstract

Abstract Energy budgets in grazing cattle are notoriously difficult to study given complex environmental and physiological interactions between plants and animals, each operating according to individual grazing budgets. However, these synergistic energy pathways can be modeled using a series of mechanistic relationships describing forage resource distribution and animal behavior, each which can be observed with high levels of accuracy and precision using biometric sensors and remote sensing technology. The objective of this study is to demonstrate the coordination of data inputs into a coherent framework utilizing biometric and remote sensing technology. Crossbred steers (n = 10) were fitted with GPS and video tracking collars (Vectronic) and accelerometers (Wildbytes technologies), then managed for 286 days as part of a broader research grazing ecology project on pasture (~11.54 hectare) comprised of bermudagrass (Cynodon dactylon) and tall fescue (Festuca arundinacea) and equipped with an automated walk-over-weigh (TruTest) scale system. Distribution of forage quality was sampled monthly with a ½ m2 quadrat using a 20 x 20-meter grid (n = 297) and remotely assessed via an unmanned aerial vehicle (Matrice 100) mounted hyperspectral camera (MicaSense) at 8 cm resolution. Animal behavior was predicted utilizing a trained randomforest prediction algorithm, and linked to geo-location and forage quality at the spatial-temporal scale in Program R. We then constructed animal ethogram, distribution of forage quality, and spatial-temporal distribution of animal behavior across the grazing landscape for a test period. We found steers allocated the greatest proportion of time to grazing and resting behaviors, 30 and 60% respectively, with the remainder dedicated to ruminating and walking behaviors. A diurnal, circadian rhythm in animal behavior demonstrates that steers grazed between 0900 and 1000 in the morning, and again between 1300 and 2000 in the evening, while resting behavior dominated the hours between 0300 and 0900, with a slight increase in grazing behavior noted at 0600, or approximately sunrise. Animal behavior linked forage availability showed steers selectively grazed areas with higher NDVI signatures, while seeking sources of water and cover to practice resting behavior. Given the need to develop robust models predicting forage intake and energy in grazing animals to improve sustainability and environmental metrics, animal tracking and biometric technologies offer a vital solution to improving management of extensively managed grazing livestock.