ABSTRACT Due to a number of factors involving the thermal environment of a broiler cutting installation and its interaction with the physiological and productive responses of birds, artificial intelligence has been shown to be an interesting methodology to assist in the decision-making process. For this reason, the main aim of this work was to develop an artificial neural network (ANN) to predict feed conversion (FC), water consumption (Cwater), and cloacal temperature (tclo) of broilers submitted to different air dry-bulb temperatures (24, 27, 30, and 33°C) and durations (1, 2, 3, and 4 days) of thermal stress in the second week of the production cycle. Relative humidity and wind speed were fixed at 60% and 0.2 ms−1, respectively. The experimental data were used for the development of an ANN with supervised training using the Levenberg-Marquardt backpropagation algorithm. The ANN consisted of three input layers one hidden, and three output with sigmoidal tangent transfer functions with values between −1 and 1. The developed ANN has adequate predictive capacity, with coefficients of determination (R2) for tclo, FC, and Cwater of 0.79, 0.87, and 0.97, respectively. In this way, the proposed ANN can be used as a support for decision-making to trigger poultry heating systems for broiler breeding.

Resumen El desempeño de pollos de engorde sometidos a estrés térmico en las primeras semanas de vida puede ser perjudicado, una vez que el sistema termorregulador del ave no está completamente desarrollado. El objetivo de este trabajo fue analizar los efectos de diferentes desafíos térmicos sobre el desempeño de pollos de engorde. Doscientos cuarenta pollos de engorde fueron sometidos a diferentes temperaturas de bulbo seco del aire deseada (tbs: 24, 27, 30 y 33°C) y a duraciones de estrés (DE: 1, 2, 3 y 4 días) en la segunda semana de vida. El consumo de ración (CR), ganancias de peso (GP) y conversión alimenticia (CA) fueron evaluadas a los 21 días de edad de las aves. A pesar de que las diferencias fueron verificadas para CR (p<0,05), ninguna diferencia fue observada para GP (p>0,05). Reducciones de CA para las tbs de 24, 27 y 33°C fueron de 14,8, 8,6, y 10,1%, respectivamente. El CA más pequeño se observó a 30°C, lo cual se caracterizó como condición de confort.

Abstract The performance of broilers submitted to thermal discomfort in the first weeks of life may be impaired, since the thermoregulatory system of the bird is not fully developed. The objective of this study was to analyze the effects of different thermal challenges on the performance of broilers. Two hundred and forty broilers were submitted to different desired dry-bulb temperatures (tdb: 24, 27, 30 and 33°C) and stress duration (SD: 1, 2, 3 and 4 days) in the second week of life. Feed intake (FI), weight gain (WG) and feed conversion (FC) were evaluated at 21 days of age. Although differences were verified for FI (p <0.05), none were observed for WG (p> 0.05). Reductions of FC for the 24, 27 and 33°C tdb were 14.8, 8.6 and 10.1%, respectively. The smallest FC was observed at 30°C, indicating comfort condition.

ABSTRACT Broiler chickens submitted to different intensities and durations of thermal stress are subject to variation in cloacal temperature and, consequently, to a decrease in performance. Given the complexity of these interactions, artificial intelligence is a useful methodology for decision-making. Thus, this study aimed to assess and predict, by means of a fuzzy model, the cloacal temperature of broiler chickens submitted to thermal stress in the second week of life, with varying durations and intensities, in climatized wind tunnels. Mamdani's inference and defuzzification methods by means of the center of gravity were used. One hundred and twenty rules were elaborated. The developed fuzzy model was used to predict the cloacal temperature of broiler chickens on the day of thermal stress as a function of air dry-bulb temperature (°C), stress duration (days), and the number of days after thermal stress. Cloacal temperature values simulated by the fuzzy model showed standard deviations and percentage errors lower than 0.02 and 0.08%, respectively, when compared to those obtained experimentally.

ABSTRACT This study aimed to compare fuzzy systems with different configurations to predict the surface temperature (ts) of broiler chickens subjected to different intensities and durations of thermal challenges in the second week of life. Data on the ts of broiler chickens aged 8 to 11 days were acquired by infrared thermography and subjected to combinations of four dry-bulb temperatures (tdb) (24, 27, 30, and 33 °C) and four durations of thermal challenges (DTC) (1, 2, 3, or 4 days). The input variables of the fuzzy systems were tdb and DTC, and the output variable was ts. The Mamdani inference method involving five defuzzification methods [center of gravity (centroid), bisector of the area (bisector), largest of maximum (lom), middle of maximum (mom), and smallest of maximum (som)], and Sugeno inference with two defuzzification methods [weighted average (wtaver) and weighted sum (wtsum)] were evaluated. For both inference methods, triangular and Gaussian pertinence functions were tested for input and output variables, except for Sugeno inference, which used singletons functions as output variables. While developing fuzzy systems, different configurations must be compared, and the system with smaller simulation errors should be selected.

ABSTRACT: The cloacal and surface temperatures are important physiological parameters to quantify the thermal comfort of animals, its variations may occur as a function of thermal variables. In this context, the goal of the present study was to evaluate the cloacal (tclo) and surface (tsur) temperatures of Cobb 500® broilers, in the second week of life, subjected to different air dry-bulb temperature (24, 27, 30 and 33 °C) and stress durations (1, 2, 3 and 4 days). The experiment was carried out in four climate-controlled wind tunnels. Air relative humidity and air velocity were set at 60% and 0.2 m s-1, respectively. The tclo and tsur were measured daily through digital thermometer and thermographic camera, respectively. Analysis of variance and Scott Knott mean test at 5% probability were applied to the data. The results indicate that the stress by low air temperature (24 and 27 °C) resulted decreased of tclo and tsur (p<0.05, Scott-Knott test). When subjected to high air temperature stress (33 °C), there was no significant difference (p>0.05, Scott-Knott test) compared to the control treatment (30 °C). Acclimation of broilers to thermal stress occurred from the second day of stress.