Document Type : Original Article


1 In order, 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


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


1.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
2.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.
3.Monjah, M, B, 2000. Calculating brain. Publication of Amirkabir Industrial University.
4. Bahreini Behzadi, M.R. and A.A. Aslaminejad, 2010. A comparison of neural network and nonlinear regression predictions of sheep growth. Journal of animal and veterinary advances. 9(16): 2128-2131.
5. Fernandez, C., E. Soria, J. D. Martin, and A. J Serrano. 2006. Neural Networks for animal science applications: Two case studies. Expert systems with Applications. 31:444-450.
6. Roush, W.B., W.A. Dozier, and S.L. Branton, 2006. Comparison of Gompertz and neural network models of broiler growth. Poult. Sci., 85: 794-797
7. Tahmoorespur, M. and H. Ahmadi. 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.
8. Yee, D., M.G. Prior and L.Z. Florence, 1993. Development of predictive models of laboratory animal growth using artificial neural networks. Bioinformatics, 9: 517-522.