Comparing piecewise regression and hysteresis models in assessing beef cattle heat stress

Authors: KISMIANTINI - YOGYAKARTA STATE UNVIERSITY; ZHANG, SHUNPU - UNIVERSITY OF CENTRAL FLORIDA; ESKRIDGE, KENT - UNIVERSITY OF NEBRASKA; KACHMAN, STEPHEN - UNIVERSITY OF NEBRASKA; QIU, YUMOU - UNIVERSITY OF NEBRASKA; BROWN-BRANDL, TAMI

Submitted to: Transactions of the ASABE
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/2/2019
Publication Date: 4/30/2019
Citation: Kismiantini, Zhang, S., Eskridge, K.M., Kachman, S.D., Qiu, Y., Brown-Brandl, T. 2019. Comparing piecewise regression and hysteresis models in assessing beef cattle heat stress. Transactions of the ASABE. 62(2):549-559. https://doi.org/10.13031/trans.12910.
DOI: https://doi.org/10.13031/trans.12910

Interpretive Summary: Climate change may generate more frequent heat waves resulting in substantial cattle production losses through increased heat stress. Understanding cattle stress and the difference between individual animals can be used to help select heat tolerant animals. However, there are few phenotypes available to quantify heat stress of a given individual. The time differences between maximum ambient temperature and maximum body temperature can be defined as a lag. A second lag can be calculated as the difference between minimum ambient temperature and body temperature. The lengths of duration of these two lags were calculated using several different types of mathematical models. It was found that using a piecewise regression model worked best for body temperature of heat stressed cattle. A piecewise regression model is a mathematical model, which divides the data into sections, and then uses a simple linear model fit the data. The calculation of these two lags can be used to assess the stress level of feedlot cattle in warm and hot environments. The timing of these two lags could be used to quantify an individual animal’s heat stress level.

Technical Abstract: Climate change may generate more frequent heat waves resulting in substantial cattle production losses through increased heat stress. Time lags between air temperature and an animal’s body temperature have been recognized as valuable measures of heat stress, and developing methods for detecting time lags is important. Existing hysteresis models are useful for estimating air-body temperatures time lags, especially when air temperatures follows a consistent diurnal sinusoidal function, such as when animals are housed in a controlled environment. However, in cattle feedlot or pasture operations, consistent sinusoidal air temperature patterns are not realistic and a more flexible approach would be useful. In this article, piecewise regression models (linear and quadratic) are developed to estimate time lags under more general temperature trend conditions. Both piecewise regression and hysteresis models were fit to heat stress data of feedlot cattle. Simulations were conducted to compare the estimated time lags using both types of models. In the simulations, the asymmetric harmonic hysteresis model estimated time lags best followed by the piecewise linear regression model, while the piecewise regression models were generally more efficient for both simulated and actual data. It was concluded that the piecewise regression models are more appropriate than hysteresis models when applied to heat-stressed cattle in production environments.