Abstract

A hot thermal environment is one factor that causes loss of activity, productivity and even death in cows. One reason may be the effect of hair coat (black or white) on body temperature during different activities and environment conditions. In this study, we attempt to characterize the relationship between body temperature over time using activity, physiological and environmental effects with a nonlinear regression model. The fixed and mixed versions of models are examined. We also examine the effect of the measuring device, anchor length (long or short). Environmental effects, such as, air temperature, thermal heat index (THI), relative humidity, solar radiation, and wind speed, For PM Milking activity, differences in hair coat and anchor length tested as treatment effects. We present a detailed approach focusing on the rate of change in animal body temperature using the nonlinear regression model and investigate how the dynamics between the random-effects changes.

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Apr 27th, 3:30 PM

NONLINEAR MIXED MODELS TO EVALUATE EFFECTS OF ENVIRONMENT CONDITIONS, HAIR COAT, AND ANCHOR LENGTH ON BODY TEMPERATURE DURING AFTERNOON MILKING OF HOLSTEIN COWS IN HAWAII

A hot thermal environment is one factor that causes loss of activity, productivity and even death in cows. One reason may be the effect of hair coat (black or white) on body temperature during different activities and environment conditions. In this study, we attempt to characterize the relationship between body temperature over time using activity, physiological and environmental effects with a nonlinear regression model. The fixed and mixed versions of models are examined. We also examine the effect of the measuring device, anchor length (long or short). Environmental effects, such as, air temperature, thermal heat index (THI), relative humidity, solar radiation, and wind speed, For PM Milking activity, differences in hair coat and anchor length tested as treatment effects. We present a detailed approach focusing on the rate of change in animal body temperature using the nonlinear regression model and investigate how the dynamics between the random-effects changes.