Document Type : Original Article

Authors

1 In order, M.sc. student and faculty member of Agricultural Science University of Zanjan, Zanjan, Iran

2 Department of Animal Science, Faculty of Agriculture and Natural Resources, University of Gorgan, Iran

3 Department of Water Engineering, Gorgan University of Agricultural Sciences and natural Resources, Gorgan, Iran

10.26655/ijabbr.2017.6.6

Abstract

Traditional poultry production has changed to a considerable industry after few decades. Now, poultry industry is one of the main sectors to obtain the required protein for human consumption. Prediction of the weight and number of eggs according to economic traits can improve the efficiency of production and the profit of producers. In present study, the weight and number of eggs in Mazandaran native fowl were predicted using artificial neural network (ANN). The information of BW at birth, 8 and 12 weeks of age, weight and age at sexual maturity and the polymorphism of prolactin gene were used for the prediction. The results showed that ANN is reliable method for predicting the weight and number of eggs based on available information

Keywords

Jeiran, E., Mohammadian, M., Mehrabanian, A., 2005. A review of supporting policies in chicken in selected countries and an analysis on process of adjusting chicken market and egg market. Ministry of agricultural Jihad,
planning and economic ministry, institute of programming and agriculture economy. P.59
Tayebi, S.K., Azarbayjani, K., Byari, L., 2009. predicting egg price in Iran; comparing ARCH methods and artificial neural network. Magazine of development ad agriculture economy. Year seventeen. Number 65.
Monjah, M.B., 2000. Calculating brain. Publication of Amirkabir Industrial University.
Bahreini Behzadi, M.R., Aslaminejad, A.A., 2010. A comparison of neural network and nonlinear regression predictions of sheep growth. Journal of animal and veterinary advances. 9(16): 2128-2131.
Fernandez, C.E., Soria, J. D., Martin, Serrano. A.J., 2006. Neural Networks for animal science applications: Two case studies. Expert systems with Applications. 31:444- 450.
Roush, W.B., W.A. Dozier, W.A., Branton, S.L., 2006. Comparison of Gompertz and neural network models of broiler growth. Poult. Sci., 85: 794-797
Tahmoorespur, M., Ahmadi. H., 2011. A neural network model to describe weight gain of sheep from genes polymorphism, birth weight and birth type. 3rd International Conference on Sustainable Animal Agriculture for Developing Countries , 08-23.
Yee, D., Prior M.G., Florence, L.Z., 1993. Development of predictive models of laboratory animal growth using artificial neural networks. Bioinformatics, 9: 517-522.
Gendrel, D., Raymond, J., Oste, J., et al. 1999. Comparisson of procalcitonin with C-reactive protein, interleukin 6 and interferon-alpha for differentiation of bacterial vs. Viral infections. Pediatr Infect Dis J;18 (10):875-81.
Jadali, F., Sharifi, M., Jarollahi, A., Nahidi, S., 2009. CReactive protein And Lactate Dehydrogenase in Serum and Cerebrospinal Fluid in Rapid and Early Diagnosis of Childhood Meningitis. Iranian Journal of Child Neurology., 1(4), 37-46.
Sormunen, P., Kallio, M.J., Kilpi, T., Peltola, H., 1999. Creactive protein is useful in distinguishing Gram stainnegative bacterial meningitis from viral meningitis in children. J Pediatr. 134(6):725-9.
Snyder, R.D., 2003. Bacterial meningitis: diagnosis and treatment. Curr Neurol Neurosci Rep; 3(6):461-9.
Sharma, M., Nand, N., Evaluation of Enzymes in Pyogenic and Tuberculous meningitis. JAPI, Vo. 54, Feb. 2006.
Moshe Nussinovitch, et al : Cerebrospinal fluid lactate dehydrogenase isoenzyme in children with bacterial and aseptic meningitis. Accepted June 2009.
Lending, M., Slobody, L.B., Mestern, J., 1964. Cerebrospinal fluid glutamic oxalacetic transaminase and lactic dehydrogenase activities in children with
neurologic disorders. J Pediatr; 65: 415–21
Wroblewski, F., Decker B., Wroblewski R., 1958. The clinical implications of spinal fluid LDH activity. N Engl J Med; 258:635–9
Aicardi, J., 1992. Disease of the nervous system in childhood. Clin Dev Med; 1115/118: 1132–5.