Comparison of Gompertz and artificial neutral network models of broiler growth received Artichoke extract in their drinking water

Document Type : Research Paper

Authors

1 M. Sc. Student, Department of Animal Science, Faculty of Agriculture, Lorestan University, Khorramabad, Iran

2 Ph.D. Student, Department of Animal Science, Faculty of Agriculture, Lorestan University, Khorramabad, Iran

3 Assistant Professor, Department of Animal Science, Faculty of Agriculture, Lorestan University, Khorramabad, Iran

4 Associate Professor, Department of Animal Science, Faculty of Agriculture, Lorestan University, Khorramabad, Iran

Abstract

An experiment was carried out to estimate growth parameters of broiler chickens received Artichoke extract in their drinking water using Gompertz non-linear model and to compare Gompertz non-linear regression equation and artificial neural network modeling in terms of their ability to predict body weight of broiler chicks at day 42 of age. A total number of 250 one- day- old Ross 308 broiler chicks were used. The chicks received Artichoke extract in their drinking water at the doses of 0, 100, 200, 300 and 500 mg per liter. The birds were weighed at days 1, 2, 7, 14, 21, 24, 27, 30, 33, 35 and 42 of experimental period after 3 hours of fasting. Estimated mature body weight was significantly higher in birds received 200 mg/liter Artichoke extract in drinking water than the other birds (P<0.05). The chicks received 200 mg/liter Artichoke extract in their drinking water had a significantly higher coefficient of relative growth compared to the control group and those received 300 mg/liter Artichoke extract (P<0.05). The goodness of fit in terms of R2 values of the artificial neural network (ANN) model showed a higher accuracy of prediction for body weight of broiler chicks at day 42 of age than the equation established by Gompertz model (0.998 vs. 0.997, respectively). Because mean square error (MSE), mean absolute deviation (MAD), mean absolute percentage error (MAPE) was lower in the ANN than in Gompertz model, it estimated body weight of birds at day 42 of age better than did Gompertz model.

Keywords


  1. Abdo, Z. M. A., Radwan, N.L. & NessrinSelim, A. )2007(. The Effect of Artichoke leaves meal on the utilization of dietary energy for broiler chicks. Journal of Poultry Science, 6 (12), 973-982.
  2. Amiri, S., Montazer Torbati, M. B., Khasheai Siuky, A. & Arshi, E. (2014). Comparison of multivariable regression and artificial neural network in predicting weight trout using morphometric data. In: 6th Iranian National Iranian Congress on Animal Science, 27-28 Aug. Tabriz University, Tabriz, Iran, pp.476.
  3. Darmani, K.H., Kebreab, E., Lopez, S. & France, J. (2003). An evaluation of different growth functions for describing the profile of live weight with time (age) in meat and egg strains of chicken. Journal of Poultry Science, 82(10), 1536-1543.
  4. Ebrahimpour, R., Nikoo, H., Masoudnia, S., Yousefi, M.R. & Ghaemi, M.S. (2011). Mixture of MLP-experts for trend forecasting of time series: A case study of the Tehran stock exchange. International Journal of Forecasting, 27(3), 804-816.
  5. Fritsche, J. & Beindorff, C.M. (2002). Isolation, characterization and determination of minor Artichoke (Cynara scolymus L.) leaf extract compounds. European Journal ofFood Research and Technology, 215, 149-157.
  6. Goliomytis, M., Panopoulou, E. & Rogdakis, E. (2003). Growth Curves for Body Weight and Major Component Parts, Feed Consumption, and Mortality of Male Broiler Chickens Raised to Maturity. Journal of Poultry Science,81, 932-938.
  7. Lopez, S. (2008). Non-linear functions in animal nutrition. In France J, Kebreab E (eds) Mathematical modelling in animal nutrition, CABI, USA. pp. 47-88.
  8. Nikkhah, M., Motaghitalab, M. & Zavare, M. (2008). Comparison of Hyperbolastic Models with classic models for describing broiler chicken growth curves. Iranian Animal Science Journal, 40(4), 71-78. (In Farsi).
  9. Rahimi, S., Teymorizadeh, Z., Torshizi, K., Omidbaigi, R. & Rokni, H. (2011). Effetct os three herbal extract on growth, performance, immune system, blood factors and intestinal selected bacterial population in broiler chickens. Journal of Agricultural Science and Thechnology, 13, 537-539.
  10. Rezvanejad, A. & Amozegar, M. (2014). Comparison non-linear regression models with artificial neural networks to determine the growth curve in Japanese quail. In: 6th Iranian National Iranian Congress on Animal Science, 27-28 Aug. Tabriz University, Tabriz, Iran, pp.463.
  11. Roush, W.B. & Branton, S.L. (2005). A Comparison of fitting growth models with a genetic algorithm and nonlinear regression. Journal of Poultry Science, 84, 494-502.
  12. Roush, W.B., Dozier, W.A. & Branton, S.L. (2006). Comparison of Gompertz and neutral network models of broiler growth. Journal of Poultry Science, 85, 794-797.
  13. SAS Institute Inc. (2003). SAS/STAT User’s Guide Version 9. SAS Institute Inc., Cary, NC.
  14. Tzeng, R.Y. & Becker, W.A. (1981). Growth patterns of body and abdominal fat weights in male broiler chickens. Poultry Science, 60, 1101-1106.
  15. Wilson, B.J. (1977). Growth curves: Their analysis and use. Pages 89–115 in Growth and Poultry Meat Production. K. N. Boorman and B. J. Wilson, ed. British Poultry Science Ltd. Edinburgh.
  16. Windisch, W., Schedle, K., Plitzner, C. & Kroismayr, A. (2008). Use of photogenic products as feed additives for swine and poultry. Journal of Animal Science, 86, 140-146.
  17. Yee, D., Prior, M.G. & Florence, L.Z. (1993). Development of predictive models of laboratory animal growth using artificial neural networks. Computer Applications in the Biosciences, 9, 517-522.
  18. Ziai, S.A., Dastpak, A., NaghdiBadi, H., Poorhoseini, L., Hemati, A.R. & GharaviNaene, M. (2003). Review on Cynara scolymus L. Journal of Medicinal Plants, 4 (13), 1-13. (In Farsi).