Eficiência energética de máquinas de aquecimento e seus efeitos no ambiente térmico e no desempenho de frangos de corte

• Ano de defesa: 2021
• Autor(a) principal: Jahn, Diandra Masurana
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Tecnológica Federal do Paraná; Dois Vizinhos; Brasil; Programa de Pós-Graduação em Zootecnia; UTFPR
• Palavras-chave em Português: Frango de corte; Madeira como combustível; Construções rurais - Aquecimento e ventilação; Broilers (Chickens); Fuelwood; Farm buildings - Heating and ventilation; CNPQ::CIENCIAS AGRARIAS::ZOOTECNIA; ZOOTECNIA (50400002)
• Link de acesso: http://repositorio.utfpr.edu.br/jspui/handle/1/31429

The high demand for products of animal origin made the chain need to improve production and understand the factors that affect it. In broiler production, evolution is constant, so the use of statistical methodologies allows us to assess the behavior of these factors. The study aimed to evaluate four different heating machines, seeking to link the variables of climatic conditions and zootechnical performance with the consumption of pellets and electricity. The work was carried out using a griller broiler production database, from a poultry integrator, located in the Southwest of Paraná, containing information from August 2019 to July 2020. This database was composed of 12 batches of mixed birds, housed one day old, where each flock contained approximately 120,000 birds. The birds were subjected to four treatments, that is, four different heating machines (machine A, machine B, machine C and machine D) and the conditions provided to the birds were the same for all aviaries. The variables analyzed that made up the database were: temperature and relative humidity of the internal and external environment, concentration of internal carbon dioxide, feed conversion, weight gain and consumption of electricity and pellets. For the statistical analysis of the data, analysis of variance, Tukey test and Principal Component Analysis were used, using the R Software. Principal component analysis allows for better data interpretation, providing a study with minimal loss of information, even with large amount of data, being able to eliminate variables with little importance for the analysis and reduce data redundancy, reducing dimensionality. Thus, we can conclude that machine D presented better efficiency, and with the analysis of main components, 4 components were selected, which explained approximately 80% of the data variability, and the climatic variables interfered the most in the performance of the heating machines.