Respiration rate (RR) is 1 of the physiological responses used to gauge the level of heat stress in cattle. Respiration rate is usually measured by counting chest movement of the animal. This procedure has some disadvantages including that the person who is doing the counting must be trained to ensure accurate results, the animals must be preconditioned to the presence of the observer, and the presences of the observer may influence the behavior and activity of the animals or their position in the pen. In this study, a device that continuously records RR without restraining the animal was developed. The device is lightweight, cheap, easy to install, and more importantly, does not interfere with the activities of the animal. The device is mounted in a halter and is placed around the neck of the subject. The device measures air temperature near the nostrils of the animal and RR is calculated as the number of oscillations of the temperature. The RR measured by the device were compared against RR observed by counting the flank movement (for 60 s, repeated every 10 min) of 5 Nellore cattle, 1 animal per d, and the results show no statistical difference ( = 0.45) between the 2 methods. This demonstrates that this device can be used to continuously measure RR of cattle under field conditions.

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A B S T R A C TInternal-body (core) and surface temperatures of livestock are important information that indicate heat stress status and comfort of animals. Previous studies focused on developing mechanistic and empirical models to predict these temperatures. Mechanistic models based on bioenergetics of animals often require parameters that may be difficult to obtain (e.g., thickness of internal tissues). Empirical models, on the other hand, are databased and often assume linear relationships between predictor (e.g., air temperature) and response (e.g., internal-body temperature) variables although, from the theory of bioenergetics, the relationship between the predictor and the response variables is non-linear. One alternative to consider non-linearity is to use machine learning algorithms to predict physiological temperatures. Unlike mechanistic models, machine learning algorithms do not depend on biophysical parameters, and, unlike linear empirical models, machine learning algorithms automatically select the predictor variables and find non-linear functions between predictor and response variables. In this paper, we tested four different machine learning algorithms to predict rectal (T r ), skin-surface (T s ), and hair-coat surface (T h ) temperatures of piglets based on environmental data. From the four algorithms considered, deep neural networks provided the best prediction for T r with an error of 0.36%, gradient boosted machines provided the best prediction for T s with an error of 0.62%, and random forests provided the best predictions for T h with an error of 1.35%. These three algorithms were robust for a wide range of inputs. The fourth algorithm, generalized linear regression, predicted at higher errors and was not robust for a wide range of inputs. This study supports the use of machine learning algorithms (specifically deep neural networks, gradient boosted machines, and random forests) to predict physiological temperature responses of piglets.

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