Murray State Theses and Dissertations

Abstract

Climate change has had devastating effects globally, most commonly talked about during natural disasters and rising temperatures. Notably, the climate concern is turning towards agriculture and livestock. With rising temperatures, the prolonged amount of heat stress put on animals, specifically cattle, is becoming more apparent. Heat stress has been linked to a reduction in cattle growing and fattening, feed intake, productivity, reproduction, and fertility; increased heart rates and respiration; changes in behavior; and mortality in severe cases. There are abatement strategies put in place to lower heat stress in cattle, such as improvements in shading and cooling, nutritional management, and genetic modification during reproduction. In cattle, body temperature is affected by a culmination of environmental variables, such as relative humidity, temperature heat index, air temperature, wind speed, soil surface temperature, and incoming and outgoing short and long wave radiation. Various methods have been used to fit 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. In this thesis, Bayesian Structural Time Series (BSTS) methods are implemented as a better alternative to model and forecast tympanic temperature in heat-stressed animals and compare the results with classical time series methods.

Year manuscript completed

2023

Year degree awarded

2023

Degree Awarded

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Thesis Advisor

Manoj Pathak

Committee Chair

Manoj Pathak

Committee Member

Chris Mecklin

Committee Member

Thomas Powell

Document Type

Thesis

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