Abstract

Heat stress can be a serious problem for cattle. Body temperature (Tb) is a good measure of an animal’s thermo-regulatory response to an environmental thermal challenge. Previous studies found that Tb increases in response to increasing ambient temperature in a controlled chamber. However, when animals are in an uncontrolled environment, Tb is subject to many uncontrolled environmental factors, such as sunshade, wind, and humidity, that increase variation in the data. Hence, functional data analysis (FDA) was applied to analyze the data with uncontrolled environmental factors as curves in the whole series of days in this study. Breed (Angus, MARCIII, MARC-I, Charolais) and availability of shade (access versus no access to sunshade) were included as treatment factors in the statistical model. This study illustrates the potential of FDA to retain all information in the curves. The specific objectives are to use FDA to smooth Tb with large noise, to detect treatment effects on Tb, and to assess the interactions between breed and availability of shade with functional regression coefficients. The results show that FDA can be used to detect significant treatment interactions that may otherwise remain undetected using regular linear or nonlinear models. Significant interactions were found, indicating that access to sun-shade influences the way animals respond to a thermal challenge. Overall, it was found that breeds of cattle with dark-hides were more affected by temperature changes and peak temperatures than breeds of cattle with light-hides. Angus cattle (black) had the highest body temperatures in both shade and no shade areas, while Charolais (white) had the lowest body temperatures in the no shade area. However, MARC III (dark red) experienced the largest temperature differential between shade and no shade. Therefore, breed and availability of shade interactions are important considerations when making predictions to aid in management decisions involving feedlot cattle.

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Apr 28th, 4:30 PM

USING FUNCTIONAL DATA ANALYSIS TO EVALUATE EFFECT OF SHADE ON BODY TEMPERATURE OF FEEDLOT HEIFERS DURING ENVIRONMENTAL HEAT STRESS

Heat stress can be a serious problem for cattle. Body temperature (Tb) is a good measure of an animal’s thermo-regulatory response to an environmental thermal challenge. Previous studies found that Tb increases in response to increasing ambient temperature in a controlled chamber. However, when animals are in an uncontrolled environment, Tb is subject to many uncontrolled environmental factors, such as sunshade, wind, and humidity, that increase variation in the data. Hence, functional data analysis (FDA) was applied to analyze the data with uncontrolled environmental factors as curves in the whole series of days in this study. Breed (Angus, MARCIII, MARC-I, Charolais) and availability of shade (access versus no access to sunshade) were included as treatment factors in the statistical model. This study illustrates the potential of FDA to retain all information in the curves. The specific objectives are to use FDA to smooth Tb with large noise, to detect treatment effects on Tb, and to assess the interactions between breed and availability of shade with functional regression coefficients. The results show that FDA can be used to detect significant treatment interactions that may otherwise remain undetected using regular linear or nonlinear models. Significant interactions were found, indicating that access to sun-shade influences the way animals respond to a thermal challenge. Overall, it was found that breeds of cattle with dark-hides were more affected by temperature changes and peak temperatures than breeds of cattle with light-hides. Angus cattle (black) had the highest body temperatures in both shade and no shade areas, while Charolais (white) had the lowest body temperatures in the no shade area. However, MARC III (dark red) experienced the largest temperature differential between shade and no shade. Therefore, breed and availability of shade interactions are important considerations when making predictions to aid in management decisions involving feedlot cattle.