Off-campus UNL users: To download campus access dissertations, please use the following link to log into our proxy server with your NU ID and password. When you are done browsing please remember to return to this page and log out.

Non-UNL users: Please talk to your librarian about requesting this dissertation through interlibrary loan.

Methods for detecting time lags in animal temperature regulation

Kismiantini, University of Nebraska - Lincoln


Time lags have been recognized as valuable measures of heat stress in animals. Some existing models such as the symmetric harmonic hysteresis and the asymmetric harmonic hysteresis models have been shown beneficial for obtaining time lags and describing the dynamics of thermally challenged animals. Both hysteresis models are based on the assumption that air temperature (forcing function) follows a sinusoidal pattern. This dissertation will present a simple and more general method for estimating time lags using a piecewise regression lags model that does not require sinusoidal air temperature trends. Time lags are estimated from taking the difference between the body temperature change point and the air temperature change point of the piecewise regression models for each response. The segmented method proposed by Muggeo (2003) is used to estimate parameters of a piecewise linear regression model including the change points and to develop a permutation test for testing the existence of the change points. The idea of a segmented method is then extended to a piecewise quadratic regression model for estimating the pseudo change points. The performance of the piecewise regression lags and hysteresis lags were assessed via simulations and applied to heat stress data on heifers with the conclusion that the piecewise regression lags models are efficient for obtaining the lags in comparison with hysteresis models and the animals’ response differently to heat stress.

Subject Area


Recommended Citation

Kismiantini, "Methods for detecting time lags in animal temperature regulation" (2017). ETD collection for University of Nebraska-Lincoln. AAI10261385.