Tag Archives: black globe temperature

900–906 H.H.R. Zanetoni, I.F.F. Tinôco, M. Barbari, L. Conti, G. Rossi, F.C. Baêta, M.O. Vilela, C.G.S. Teles Junior and R.R. Andrade
Alternative form to obtain the black globe temperature from environmental variables
Abstract |
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Alternative form to obtain the black globe temperature from environmental variables

H.H.R. Zanetoni¹*, I.F.F. Tinôco¹, M. Barbari²*, L. Conti², G. Rossi², F.C. Baêta¹, M.O. Vilela¹, C.G.S. Teles Junior¹ and R.R. Andrade¹

¹Federal University of Viçosa, Department of Agricultural Engineering, Av. Peter Henry Rolfs, s/n Campus University of Viçosa CEP: 36570-900, Viçosa, Minas Gerais, Brazil
²University of Florence, Department of Agricultural, Food, Environmental and Forestry Science, Via San Bonaventura, 13, IT50145 Firenze, Italy
*Correspondence: matteo.barbari@unifi.it; hiago.zanetoni@ufv.br

Abstract:

Reaching thermal comfort conditions of animals is essential to improve well-being and to obtain good productive performance. For that reason, farmers require tools to monitor the microclimatic situation inside the barn. Black Globe-Humidity Index (BGHI) acts as a producer management tool, assisting in the management of the thermal environment and in decision making how protect animals from heat stress. The objective of this work was to develop a mathematical model to estimate the black globe temperature starting from air temperature, relative humidity and air velocity. To reach this goal, data of air temperature and humidity were collected, with the aid of recording sensors. The black globe temperature was measured with a black copper globe thermometer and the air velocity was monitored with a hot wire anemometer. Data were analysed using a regression model to predict the black globe temperature as a function of the other variables monitored. The model was evaluated, based on the significance of the regression and the regression parameters, and the coefficient of determination (R²). The model proved to be adequate for the estimation of the black globe temperature with R2 = 0.9166 and the regression and its parameters being significant (p < 0.05). The percentage error of the model was low (approximately 2.2%). In conclusion, a high relation between the data estimated by the model with the data obtained by the standard black globe thermometer was demonstrated.

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