Spatial variability of litter temperature, relative air humidity and skin temperature of chicks in a commercial broiler house

ArticleThe thermal environment inside a broiler house has a great influence on animal welfare and productivity during the production phase. Among the importance of the chicken litter is the function of absorbing moisture, provide thermal insulation and provide a soft surface for broilers. The skin temperature is an important physiological parameter to quantify the thermal comfort of animals, its variations may occur as a function of thermal variables. So, the aim of this work was to analyse the magnitude and spatial variability of chicken litter temperature and relative humidity of the air and to correlate them with the spatial distribution of chicks’ skin surface temperature throughout the broiler house during the 7th, 14th and 21st days of the chicks’ life, using geostatistical techniques. The experiment was performed in a commercial broiler house located in the western mesoregion of Minas Gerais, Brazil, where 28,000 male Cobb chicks were housed. The heating system consisted of an industrial indirect-fired biomass furnace. The heated air was inflated by an AC motor, 2,206 W of power, 1,725 RPM. Geostatistical techniques were used through semivariogram analysis and isochore maps were generated through data interpolation by kriging. The semivariogram was fitted by the restricted maximum likelihood method. The used mathematical model was the spherical one. After fitting the semivariograms, the data were interpolated by ordinary kriging. The semivariograms along with the isochore maps allowed identifying the non-uniformity of spatial distribution of the broiler litter temperature throughout the broiler house for 3 days of chicks’ life. It was observed that skin surface presented a positive correlation with the litter temperature and a negative correlation with the air humidity. The semivariograms along with the isochore maps allowed identifying the non-uniformity of spatial distribution of the litter temperature, air humidity and skin temperature of chicks throughout the broiler aviary for the three days. In addition, the use of geostatistics and distribution maps made possible to identify different environmental conditions in regions inside the broiler house that may harm the development of chicks

Efeito do ambiente de produção sobre frangos de corte sexados criados em galpão comercial

Avaliou-se o efeito do ambiente de produção sobre o desempenho produtivo e respostas fisiológicas de frangos de corte demarca comercial sexados, machos e fêmeas. Os frangos foram criados em duas alas separadas no interior de um galpão comercial com sistemas de ventilação convencional e nebulização. O ambiente produtivo foi avaliado por meio do índice de temperatura do globo negro e umidade, da intensidade de ruído e do nível de iluminância. A avaliação dos animais foi feita por meio das respostas fisiológicas - frequência respiratória, temperatura retal, temperatura da pele e temperatura da pena - e produtivas - ganho de peso semanal, massa corporal e mortalidade. Os machos apresentaram desempenho produtivo superior às fêmeas (P<0,05). A massa corporal média dos machos foi 214,6g maior que a das fêmeas aos 35 dias de vida, a qual se igualou à massa corporal dos machos somente aos 38,47 dias de vida. As respostas fisiológicas não se relacionaram com o ambiente

Physics-Informed Neural Networks for 1-D Steady-State Diffusion-Advection-Reaction Equations

This work aims to solve six problems with four different physics-informed machine learning frameworks and compare the results in terms of accuracy and computational cost. First, we considered the diffusion-advection-reaction equations, which are second-order linear differential equations with two boundary conditions. The first algorithm is the classic Physics-Informed Neural Networks. The second one is Physics-Informed Extreme Learning Machine. The third framework is Deep Theory of Functional Connections, a multilayer neural network based on the solution approximation via a constrained expression that always analytically satisfies the boundary conditions. The last algorithm is the Extreme Theory of Functional Connections (X-TFC), which combines Theory of Functional Connections and shallow neural network with random features [e.g., Extreme Learning Machine (ELM)]. The results show that for these kinds of problems, ELM-based frameworks, especially X-TFC, overcome those using deep neural networks both in terms of accuracy and computational time

Validação experimental de modelos matemáticos para a predição do volume e área superficial de ovos

Due to magnitude of geometric characteristics of the egg in agribusiness, this research was performed aiming to experimentally validate various mathematical models of volume and surface area of eggs, using adjusted and literature empirical equations in order to improve the accuracy of calculation of these parameters. For this reason, 450 samples of eggs were collected: 150 chicken eggs (white), 150 chicken eggs (brown) and 150 quail eggs. Each egg was weighed and its dimensions (length and width) were measured using a digital caliper. The real volume of each egg was determined by immersion in water. The surface area and volume, the samples were obtained using adjusted empirical equations, and from computational methods and the literature. Based on the results, the brown eggs have weight, volume and surface area significantly higher than white eggs and quail eggs (p<0.05; Scott Knott). Overall, all models to estimate the volume and surface area of eggs, proposed in this paper and from published research, showed mean values close to real. The image analysis eggs, which is neither invasive nor destructive, a good alternative to the conventional destructive methods.Em razão da grande importância das características geométricas do ovo na agroin- dústria, objetivou-se, com o presente estudo, validar experimentalmente diversos modelos matemáticos do volume e área superficial de ovos, utilizando equações empíricas ajustadas e da literatura, visando a melhoria na precisão do cálculo destes parâmetros. Para isso, 450 amostras de ovos foram coletadas, sendo 150 ovos brancos, 150 ovos vermelhos e 150 ovos de codorna. Cada ovo foi pesado e suas dimensões (comprimento e largura) foram medidas utilizando um paquímetro digital. O volume real de cada ovo foi determinado pelo método de imersão em água. A área superficial, bem como o volume, das amostras foram obtidos por equações empíricas ajustadas, método computacional e por meio de equações da literatura. Com base nos resultados, os ovos vermelhos possuem peso, volume e área superficial estatisticamente maiores em relação os ovos brancos e de codorna (p<0,05; Scott-Knott). Todos os modelos para estimativa do volume e da área superficial de ovos, propostos neste trabalho e oriundos da literatura, apresentaram valores médios próximos ao real. A análise de imagem dos ovos, que é não invasiva e não destrutiva adequada, é uma boa alternativa aos métodos destrutivos convencionais

Prediction of free-stall occupancy rate in dairy cattle barns through fuzzy sets.

ABSTRACT - The goal of this study was to develop a fuzzy model to predict the occupancy rate of free-stalls facilities of dairy cattle, aiding to optimize the design of projects. The following input variables were defined for the development of the fuzzy system: dry bulb temperature (Tdb, °C), wet bulb temperature (Twb, °C) and black globe temperature (Tbg, °C). Based on the input variables, the fuzzy system predicts the occupancy rate (OR, %) of dairy cattle in free-stall barns. For the model validation, data collecting were conducted on the facilities of the Intensive System of Milk Production (SIPL), in the Dairy Cattle National Research Center (CNPGL) of Embrapa. The OR values, estimated by the fuzzy system, presented values of average standard deviation of 3.93%, indicating low rate of errors in the simulation. Simulated and measured results were statistically equal (P>0.05, t Test). After validating the proposed model, the average percentage of correct answers for the simulated data was 89.7%. Therefore, the fuzzy system developed for the occupancy rate prediction of free-stalls facilities for dairy cattle allowed a realistic prediction of stalls occupancy rate, allowing the planning and design of free-stall barns. RESUMO - Objetivou-se com o presente trabalho desenvolver um modelo fuzzy para predizer a taxa de ocupação de baias em instalações para gado de leite do tipo free-stall, auxiliando na otimização do dimensionamento de projetos. Para o desenvolvimento do sistema fuzzy, foram definidas como variáveis de entrada: a temperatura de bulbo seco (Tbs, °C); temperatura de bulbo úmido (Tbu, °C), e temperatura de globo negro (Tgn,ºC). Com base nas variáveis de entrada, o sistema fuzzy prediz a taxa de ocupação (TO,%) de vacas leiteiras em instalações do tipo free-stall. Para a validação do modelo, coletas de dados foram realizadas nas instalações do Sistema Intensivo de Produção de Leite (SIPL), do Centro Nacional de Pesquisa de Gado de Leite (CNPGL) da Embrapa. Os valores de TO estimados pelo sistema fuzzy apresentaram valores de desvio-padrão médio de 3,93%, indicando baixo índice de erros na simulação. Os resultados simulados foram, estatisticamente, iguais aos medidos (P>0,05, Teste t). Após a validação do modelo proposto, a porcentagem de acerto médio para os dados simulados foi de 89,7%. Portanto, o sistema fuzzy, desenvolvido para a predição da taxa de ocupação de baias em instalação free-stall para bovinos leiteiros, possibilitou a predição realística da taxa de ocupação de baias, propiciando o planejamento e o projeto de instalações free-stall