Abstract
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.
Highlights
IntroductionThe use of intelligent systems for decision-making is necessary to obtain a maximum index of market performance and competitiveness (Pandorfi et al, 2012), in addition to mitigating or even eliminating the harmful effects of a thermal environment unsuitable for the physiological demands of birds (Nascimento et al, 2014)
In the current poultry scenario, changes in management techniques are indispensable
The evaluation of the thermal comfort of birds can be measured by cloacal temperature, which is altered when the bird is subjected to thermal stress (Yanagi Junior et al, 2012; Mayes et al, 2014)
Summary
The use of intelligent systems for decision-making is necessary to obtain a maximum index of market performance and competitiveness (Pandorfi et al, 2012), in addition to mitigating or even eliminating the harmful effects of a thermal environment unsuitable for the physiological demands of birds (Nascimento et al, 2014). Several studies have verified only the influence of different thermal stress intensities, without varying the duration of the thermal stress (Al-Zghoul et al, 2015; Cândido et al, 2016: Zhang et al, 2016). Analyzing the intensity and duration together makes it possible to investigate possible occurrences of adaptation of the bird to the stressful environment, depending on the exposure time, or to verify how stressor intensity can aggravate productivity losses due to longer or shorter exposure times. Discomfort influences water consumption (Lopes et al, 2015), feed intake, and weight gain, affecting feed conversion (Boiago et al, 2013)
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