Use este identificador para citar ou linkar para este item: http://repositorio.ufla.br/jspui/handle/1/29867
Título: Índice fuzzy para o conforto térmico de suínos na fase de crescimento e terminação com base na temperatura superficial e frequencia respiratória
Palavras-chave: Ambiência térmica
Instalações para suínos
Modelo fuzzy
Lógica fuzzy
Teoria fuzzy
Suinocultura
Comportamento animal
Thermal environment
Swine housing
Fuzzy model
Fuzzy logic
Fuzzy theory
Swine farming
Animal behavior
Data do documento: 2016
Editor: Universidade Estadual Paulista "Júlio de Mesquita Filho" (UNESP)
Faculdade de Ciências Agronômicas (FCA)
Citação: CECCHIN, D. et al. Índice fuzzy para o conforto térmico de suínos na fase de crescimento e terminação com base na temperatura superficial e frequencia respiratória. Energia na Agricultura, Botucatu, v. 31, n. 4, p. 334-341, 2016.
Resumo: The objective of this study was to develop and test a computational mathematical model, based on the fuzzy theory to predict the surface temperature and respiratory rate of pigs in function of their age and the temperature and humidity index (THI). Pigs were observed in the growth-finishing phase. The data of the thermal environment (THI) and physiological responses (surface temperature (ST) and respiratory rate (RR)) were used in developing the fuzzy model. The age of the animals (days) and the temperature and humidity index (THI), were defined as input variables because they are variables that directly influence the physiological responses of the animals. The average standard deviation of the fuzzy model observed values was 0.61% and 1.38% for TS and FR, respectively. The highest standard deviation was 1.30% and 4.16% (for TS and RR, respectively), and the lowest standard deviation was of 0.01% and 0.14% (for TS and FR respectively). The coefficients of determination (R2%) showed that 58.72% and 83.55% (for TS and FR respectively) of the variation can be explained by the model. Therefore, the fuzzy model was more suitable for respiratory response prediction than for predicting surface temperature of pigs in the growing phase, with low standard deviation and significant correlation between this variable and the data measured during the field experiment. Therefore, this model can be used as a control tool of the thermal environment in commercial production facilities.
URI: http://revistas.fca.unesp.br/index.php/energia/article/view/2491
http://repositorio.ufla.br/jspui/handle/1/29867
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