Abstract Number:
2515
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Poster
Participants:
Manoj Pathak (1), Lacey Quandt (2)
Institutions:
(1) Murray State University, N/A, (2) N/A, N/A
Co-Author:
First Author:
Presenting Author:
Abstract Text:
In the summer cattle body temperature is affected by a culmination of various weather and environmental variables, such as air temperature, soil surface temperature, temperature heat index, relative humidity, wind speed, and incoming and outgoing short and long wave radiation. With rising temperatures in summer, the prolonged amount of thermal stress put on animals, specifically farm cattle, at risk. Various methods have been used to model the cattle body temperature including but not limited to multiple regression with correlated error and transfer function methods. However, these models are not suitable to reveal various components with known structures that jointly affect the dynamic of cattle body temperature such as linear local trend and seasonality. The objectives of this study are two folds. First, to implement the Bayesian Structural Time Series methods as a better alternative to model and forecast the dynamic of core body temperature in heat-stressed animals and compare the results with classical time series methods. Second, to detect thermal stress in animal by decomposing the observed body temperature to its various components.
Keywords:
Bayesian Structural Time Series (BSTS)|Heat Stress|Environmental Variables|Hysteresis| |
Sponsors:
Biometrics Section
Tracks:
Model/Variable Selection
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