In recent years, egg production has had an intense growth in Brazil, and Brazilian egg consumption per capita has significantly increased in the last decade. To reduce sanitary and financial risks, decisions regarding the production and health status of the flock must be made based on objective criteria. Our aim was to determine the main "input" variables for the prediction of egg production performance in commercial laying breeder flocks using an ANN model. The software NeuroShellClassifier and NeuroShell Predictor were used to build the ANN. A total of 26 egg-production traits were selected as input variables and eight as output variables. A database of 44,120 Excel cells was generated. For the training and validation of the models, 74.9% and 25.1% of the data were used, respectively. The accuracy of the ANN models was calculated and compared using the analysis of coefficient of multiple determination (R2), mean squared error (MSE), and an assessment of uniform scatter in the residual plots. The models for the outputs "weekly egg production," "weekly incubated egg,", "accumulated commercial egg," and "viability" showed an R2 greater than 0.8. Other models yielded R2 values lower than 0.8. The ANN predicts adequately eight egg-production traits in the breeders of commercial laying hens. The method is an option for data management analysis in the egg industry, providing estimates of the relative contribution of each input variable to the outputs.
Due to the genetic similarity of pathogenic Escherichia coli isolated from birds and pathotypes of human origin, it is suggested that they have a common ancestor and may exchange virulence-associated genes. This study aimed to detect virulence-associated genes in E. coli strains isolated from the Red-browed Amazon parrot (Amazona rhodocorytha) kept at a conservation institute in Brazil. High genetic variability in virulence was observed, since 12 virulence profiles were found among 14 strains. The number of virulence-associated genes of single strains ranged from 5 to 22 out of 33 genes tested, and only one strain did not present any virulence genes. Regarding adhesion genes, most strains presented from two to five genes, and crlA (85.7%) and fimC (85.7%) were the most frequent. Frequencies were similar for invasion and iron acquisition genes. Variations among genes were observed for serum resistance and toxin-related genes. Some of the E. coli strains isolated from parrots presented virulence genes that are commonly associated with pathotypes of human origin, including newborn meningitis E. coli, uropathogenic E. coli, and sepsis-associated E. coli. It is noteworthy that some of these genes were present in the majority of the analyzed strains. Our results indicate that these strains detected in clinically healthy parrots can be potential reservoirs of several virulence-associated genes. These genes can be transmitted to other E. coli strains, including those that affect humans. These E. coli strains present a high pathogenic potential of virulence-associated genes in extraintestinal pathogenic E. coli strains.
Resumo O objetivo deste trabalho foi predizer os indicadores de produção e determinar o seu potencial impacto econômico em um sistema de integração utilizando as redes neurais artificiais (RNA). Quarenta parâmetros zootécnicos e de produção de granjas de matrizes e de frango de corte, um incubatório e um abatedouro foram selecionados como variáveis. Os modelos de RNA foram estabelecidos para quatro variáveis de saída (“eclosão vendável”, “peso ao final da quinta semana”, “condenações parciais” e “condenações totais”) e foram analisados em relação ao coeficiente de determinação múltipla (R2), coeficiente de correlação (R), erro médio (E), erro quadrático médio (EQM) e raiz do erro quadrático médio (REQM). Os cenários produtivos foram simulados e os impactos foram estimados. Os modelos de RNA gerados foram adequados para simular diferentes cenários produtivos após o treinamento. Para “eclosão vendável”, o modelo de incubadora e o período de incubação aumentaram os ganhos financeiros. Para “peso ao final da quinta semana”, a linhagem também demonstrou influencia no retorno financeiro, o que não aconteceu com o peso ao final da primeira semana. O sexo do lote possui influência nas taxas de “condenação parcial”, ao contrário do peso do frango no primeiro dia. As taxas de mortalidade e o peso do frango apresentaram influência na “condenação total”, mas o sexo do lote e o tipo de pinto não tiverem influência.
The aim of this study was to predict production indicators and to determine their potential economic impact on a poultry integration system using artificial neural networks (ANN) models. Forty zootechnical and production parameters from broiler breeder farms, one hatchery, broiler production flocks, and one slaughterhouse were selected as variables. The ANN models were established for four output variables: “saleable hatching”, “weight at the end of week 5,” “partial condemnation,” and “total condemnation” and were analyzed in relation to the coefficient of multiple determination (R2), correlation coefficient (R), mean error (E), mean squared error (MSE), and root mean square error (RMSE). The production scenarios were simulated and the economic impacts were estimated. The ANN models were suitable for simulating production scenarios after validation. For “saleable hatching”, incubator and egg storage period are likely to increase the financial gains. For “weight at the end of the week 5” the lineage (A) is important to increase revenues. However, broiler weight at the end of the first week may not have a significant influence. Flock sex (female) may influence the “partial condemnation” rates, while chick weight at first day may not. For “total condemnation”, flock sex and type of chick may not influence condemnation rates, but mortality rates and broiler weight may have a significant impact.
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