ASSESSING REFINEMENTS IN MODELING SINUSOIDAL CONDITIONS USED TO DRIVE CATTLE BODY TEMPERATURES

Abstract

A model, termed the PET model, is used to estimate body temperature in cattle challenged by hot cyclic chamber temperatures. The model is based on Newton's law of cooling, driven by an estimated sinusoidal function. In practice, it is often difficult to maintain hot sinusoidal fluctuations in chamber temperatures. However, it is possible to model cyclic chamber temperatures using a discrete Fourier series. By increasing the precision in estimating the cyclic temperature driving function, we can more precisely estimate the parameters in the PET model. Simulation studies were performed to investigate the effect of under- and over-parameterization on accuracy of estimates, performance of a number of model selection criteria, and on nonlinear behavior such as intrinsic and parameter-effects curvature, bias, excess variance, and skewness. Our results will help researchers decide how to model ambient temperatures producing heat stress in cattle and improve estimates for evaluating management strategies.