ABSTRACT This study aimed to determine a model to predict mean surface temperature of broiler chicks and live load microclimate conditions during transport by using neural networks. The research was conducted in the state of São Paulo, Brazil, by monitoring nine shipments with different density of boxes using an air-conditioned truck with an average capacity of 380 boxes. Fourteen chick boxes were chosen on each shipment, assessing five chicks per box. The mean surface temperature of chicks (MST) was measured with an infrared thermometer in both loading and unloading. By assessing the container microclimate (center and inside boxes), air temperature (T), relative humidity (RH) and specific enthalpy (h) were recorded; thereby, seventeen data loggers were placed, one per box (14), and three along the container. MST and truck microclimate were analyzed using artificial neural networks with a single layer and seven neurons, which were trained with the least mean square (LMS) algorithm. MST in the unloading showed a better prediction of MST during transport. The best prediction of microclimatic conditions was obtained inside the boxes during the shipment.

RESUMO O objetivo deste trabalho foi avaliar a resposta termorregulatória de búfalas leiteiras nos ambientes de pré-ordenha (sala de espera) e pós-ordenha. Para identificar a capacidade termorregulatória dos animais, coletou-se a temperatura de superfície da pele, utilizando um termômetro de infravermelho (TMPT) e uma câmera termográfica (TMPC), e a frequência respiratória (FR) dos animais. Para a caracterização do ambiente térmico, foram calculados o índice de temperatura de globo e umidade (ITGU), a carga térmica radiante (CTR) e a entalpia (H). Os índices e os dados fisiológicos dos animais foram analisados utilizando-se de Redes Neurais Artificiais com uma única camada, treinadas com o algoritmo mínimo quadrado médio (LMS - Least mean square). Os resultados indicaram que os ambientes de pré e pós-ordenha alcançaram valores de ITGU, FR, TMPT e TMPC acima dos limites relacionados à zona de termoneutralidade dos bubalinos, sendo a temperatura superficial da pele influenciada mais intensamente pela variação das condições ambientais em detrimento da frequência respiratória. Conclui-se que os bubalinos são sensíveis às variações ambientais, sendo os valores de temperatura de superfície da pele melhores indicadores de conforto térmico em relação à frequência respiratória.

ABSTRACT The objective of this study was to evaluate the thermoregulatory response of dairy buffaloes in pre-milking and post-milking. To identify animal thermoregulatory capacity, skin surface temperatures were taken by an infrared thermometer (SST), a thermographic camera (MTBP) as well as respiratory rate records (RR). Black Globe and Humidity Index (BGHI), radiating thermal load (RTL) and enthalpy (H) were used to characterize the thermal environment. Artificial Neural Networks analyzed those indices as well as animal physiological data, using a single layer trained with the least mean square (LMS) algorithm. The results indicated that pre-milking and post-milking environments reached BGHI, RR, SST and MTBP values above thermal neutrality zone for buffaloes. In addition, limits of surface skin temperatures were mostly influenced by changing ambient conditions to the detriment of respiratory rates. It follows that buffaloes are sensitive to environmental changes and their skin temperatures are the best indicators of thermal comfort in relation to respiratory rate.