Ambiente termoacústico, qualidade do ar e bem-estar de suínos em instalações com diferentes tipologias de baias

• Autor(a) principal: Cecchin, Daiane
• Data de Publicação: 2016
• Tipo de documento: Tese
• Idioma: por
• Título da fonte: Repositório Institucional da UFLA
• Texto Completo: http://repositorio.ufla.br/jspui/handle/1/11513
• topic: Suíno – Comportamento; Suíno – Instalações; Conforto térmico; Amônia; Dióxido de carbono; Poluição sonora; Modelo computacional; Lógica Fuzzy; Swine – Behavior; Swine – Housing; Thermal comfort; Ammonia; Carbon dioxide; Noise pollution; Computational models; Fuzzy logic; Instalações para Produção Animal

The objectives of this study were to evaluate the quality of air based on gas concentration, thermal environment, and sound pressure (at animal and worker levels), and to develop and test a computational mathematical model based on fuzzy set theory to predict thermal comfort based on surface temperature and respiratory rate of the pigs. The predictions were to be derived from the age of the animals and the temperature and humidity index (THI). Thermal comfort was evaluated by means of environmental data, calculation of the temperature and humidity index (THI), Globe Temperature and Humidity Index (BGHI) and enthalpy (h) of the environment. The evaluation was also based on the average sound pressure of noise (in dB(A)). Measurements of the instantaneous concentrations of ammonia (NH3) and carbon dioxide (CO2) were taken at the level of the pigs and at 1.50 m above ground, inside three different stalls for growing and finishing pigs, them being: stall with water depth, stall with floor partially leaked on the ends and stall with floor partially leaked on the ends and in the center. We observed that the stall with water depth presented the highest average concentration of NH3. Despite was a significant difference in CO2 concentrations, the values of CO2 found are within the limits allowed by Brazilian standards, thus indicating that the facilities do not cause discomfort in relation to CO2 levels. The stall with water depth allowed a level of discomfort regarding NH3 concentration above the values that can cause problems to the animal, based on recommendations from literature. The type of the stall floor appeared to have no influence over the sound pressure at any time. To prepare the fuzzy, we used data of the thermal environment (THI) and physiological responses (Surface Temperature (ST) and respiratory rate (RR)), defining the age of the animals (in days) and temperature and humidity index (THI) as input variables, given that these variables directly influence the physiological responses of the animals. The fuzzy model developed based on the age of the pigs and the THI was adequate for predicting the response of respiratory rate of pigs in growth phase, considering different types of flooring. There was a low standard deviation and significant correlation of this variable with the data measured during the conduction of the field experiment. The model can be used as a tool to control the thermal environment in commercial production facilities.