Comparison of the Accuracy of Nonlinear Models and Artificial Neural Network in the Performance Prediction of Ross 308 Broiler Chickens

Document Type : Original Paper

Authors

1 Department of Animal Science, Science and Research Branch, Islamic Azad University, Tehran, Iran

2 Animal Science Research Institute of Iran, Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran

Abstract

This study aimed to investigate and compare nonlinear growth models (NLMs) with the predicted performance of broilers using an artificial neural network (ANN). Six hundred forty broiler chicks were sexed and randomly reared in 32 separate pens as a factorial experiment with 4 treatments and 4 replicates including 20 birds per pen in a 42-day period. Treatments consisted of 2 metabolic energy levels (3000 and 3100 kcal/kg), 2 crude protein levels (22 and 24%) and two sexes. Ten birds in each pen tagged and their weekly BW records were collected individually to evaluate the accuracy of predicted BW by ANN as an alternative to nonlinear regression models (Logistic, Gompertz, Von Bertalanffy, and Brody). Based on the goodness of fit criteria and error measurement statistics, the NLMs fitted the age-weight data better than ANN. The findings indicated that the performance prediction of broiler chicks using the Gompertz model (R2 = 0.9989) was more accurate than other NLMs (R2 = 0.9628 to 0.9988) and ANN (R2 = 0.95839). Therefore, the application of the Gompertz model is suggested to predict the BW changes of Ross 308 broiler chicks over time.

Keywords


Abbas AA, Yosif AA, Shukur AM & Ali FH. 2014. Effect of Genotypes, Storage Periods and Feed Additives in Broiler Breeder Diets on Embryonic and Hatching Indicators and Chicks Blood Parameters. Scientia Agriculturae, 7: 44-48. DOI: 10.15192/PSCP.SA.2014.3.1.4448
Aggrey SE. 2002. Comparison of Three Nonlinear and Spline Regression Models for Describing Chicken Growth Curves. Poultry Science, 81: 1782-1788. DOI: 10.1093/ps/81.12.1782
Ahmad, HA. 2009. Poultry Growth Modeling Using Neural Networks and Simulated Data. Journal of Applied Poultry Research, 18: 440-446. DOI: 10.3382/japr.2008-00064
Ahmadi H & Mottaghitalab M. 2007. Hyperbolastic Models as a New Powerful Tool to Describe Broiler Growth Kinetics. Poultry Science, 86: 2461-2465. DOI: 10.3382/ps.2007-00086
Cravener TL & Roush WB. 2001. Prediction of Amino Acid Profiles in Feed Ingredients: Genetic Algorithm Calibration of Artificial Neural Networks. Animal Feed Science and Technology, 90: 131-141. DOI: 10.1016/S0377-8401(01) 00219-X
Duncan, D. B. 1955. Multiple Range and Multiple F Tests. Biometrics, 11: 1-42. DOI: 10.2307/ 3001478
Darmani Kuhi 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. Poultry Science, 82: 1536-1543. DOI: 10.1093/ps/82.10.1536
Eleroglu H, Yildirim A, ┼×ekero─člu A, Çoksöyler FN & Duman M. 2014. Comparison of Growth Curves by Growth Models in Slow Growing Chicken Genotypes Raised the Organic System. International Journal of Agriculture and Biology, 16: 529-535
Golian A & Ahmadi H. 2008. Non-linear Hyperbolastic Growth Models for Describing Growth Curve in Classical Strain of Broiler Chicken. Research Journal of Biological Sciences, 3: 1300-1304.
Kaewtapee C, Khetchaturat C & Bunchasak C. 2011. Comparison of Growth Models between Artificial Neural Networks and Nonlinear Regression Analysis in Cherry Valley Ducks. Journal of Applied Poultry Research, 20: 421-428. DOI: 10.3382/japr.2010-00223
Kum D, Karakus K & Ozdemir T. 2010. The Best Nonlinear Function for Body Weight at Early Phase of Norduz Female Lambs. Trakia Journal of Sciences, 8: 62-67.
Lopez S, France J, Gerrits WJJ, Dhanoa MS, Humphries DJ & Dijkstra, J. 2000. A Generalized Michaelis-Menten Equation for the Analysis of Growth. Journal of Animal Science, 78: 1816-1828. DOI: 10.2527/2000.7871816x
Masoudi A & Azarfar A. 2017. Comparison of Nonlinear Models Describing Growth Curves of Broiler Chickens Fed on Different Levels of Corn Bran. International Journal of Avian & Wildlife Biology, 2: 34-39, DOI: 10.15406/ ijawb.2017.02.00012
Mohammed FA. 2015. Comparison of Three Nonlinear Functions for Describing Chicken Growth Curves. Scientia Agriculturae, 9: 120-123. DOI: 10.15192/PSCP.SA.2015.9.3.120123
Nahashon SN, Aggrey SE, Adefope NA, Amenyenu A & Wright D. 2006. Growth Characteristics of Pearl Gray Guinea Fowl as Predicted by the Richards, Gompertz, and Logistic Models. Poultry Science, 85: 359-363. DOI: 10.1093/ps/85.2.359
Narinc D, Karaman E, Ziya Firat M & Aksoy T. 2010. Comparison of Nonlinear Growth Models to Describe the Growth in Japanese Quail. Journal of Animal and Veterinary Advances, 9: 1961-1966. DOI: 10.3923/javaa.2010.1961.1966
Norris D, Ngambi JW, Benyi K, Makgahlela ML, Shimelis HA & Nesamvuni EA. 2007. Analysis of Growth Curves of Indigenous Male Venda and Naked Neck Chickens. South African Journal of Animal Science, 37: 21-26. DOI: 10.4314/ sajas.v37i1.4021
Porter T, Kebreab E, Darmani Kuhi H, Lopez S & Strathe AB. 2010. Flexible Alternatives to the Gompertz Equation for Describing Growth with Age in Turkey Hens. Poultry Science, 89: 371-378. DOI: 10.3382/ps.2009-00141
Prestes AM, Garnero ADV, Marcondes CR, Damé MC, Janner EA & Rorato PRN. 2012. Estudo da Curva de Crescimento de Bubalinos da Raça Murrah Criados no Estado do Rio Grande do Sul. Anais do 9º Simpósio Brasileiro de Melhoramento Animal. João Pessoa: ABMA.
Roush WB, Dozier WA & Branton SL. 2006. Comparison of Gompertz and Neural Network Models of Broiler Growth. Poultry Science, 85: 794-797. DOI: 1093/ps/85.4.794
Rumelhart DE & McClelland JL. 1986. Parallel Distributed Processing: Explorations in the Microstructure of Cognition: Foundations. Vol. 1: MIT Press. Cambridge. UK.
Sabbioni A, Superchi P, Bonomi A, Summer A & Boidi G. 1999. Growth Curves of Intensively Reared Ostriches Struthio Camelus in Northern Italy. Proceedings of 50thEAAP Congress, July 2000.
Saghi DA, Aslaminejad A, Tahmoorespur M, Farhangfar H, Nassiri M, Dashab GR. 2012. Estimation of Genetic Parameters for Growth Traits in Baluchi Sheep Using Gompertz Growth Curve Function. Indian Journal of Animal Science, 82: 889-892.
SAS (Statistical Packages for the Social Sciences). 2013. SAS/STAT® 9.4. User’s Guide. SAS Institute Inc. Cary, North Carolina.
Sengul T & Kiraz S. 2005. Non-linear Models of Growth Curves in Large White Turkeys. Journal of Veterinary and Animal Science, 29: 331-337.
Sogut B, Celik S, Ayasan T & Inci H. 2016. Analyzing Growth Curves of Turkeys Reared in Different Breeding Systems Intensive and Free-Range with Some Nonlinear Models. Brazilian Journal of Poultry Science, 18: 619-628. DOI: 10.1590/1806-9061-2016-0263
Tariq MM, Iqbal F, Eyduran E, Bajwa MA, Huma ZE & Waheed A. 2013. Comparison of Non-Linear Functions to Describe the Growth in Mengali Sheep Breed of Balochistan. Pakistani Journal of Zoology, 45: 661-665.
The R Foundation for Statistical Computing. 2017. Training of Neural Network: Package ‘neuralnet’. https://cran.r-project.org/web/packages/neuralnet/ neuralnet.pdf. Accessed on February 7. 2019.
The Ross 308 Broiler: Nutrition Specification. 2014. Aviagen Publications. http://tmea.staging. aviagen. com /assets/ Tech_Center/Ross_Broiler/ Ross-308-Broiler-Nutrition-Specs-2014r17-EN. pdf
Topal M & Bolukbasi SC. 2008. Comparison of Nonlinear Growth Curve Models in Broiler Chickens. Journal of Applied Animal Research, 34: 149-152. DOI: 10.1080/09712119.2008. 9706960
Vitezica ZG, Marie-Etancelin C, Bernadet MD, Fernandez X & Granie RC. 2010. Comparison of Nonlinear and Spline Regression Models for Describing Mule Duck Growth Curves. Poultry Science, 89: 1778-1784. DOI: 10.3382/ps.2009-00581
Yakuboglu C & Atil H. 2001. Comparison of Growth Curve Models on Broilers Growth Curves I: Parameters Estimations. Online Journal of Biological Science, 1: 680-681. DOI: 10.3923/jbs.2001.682.684
Yang Y, Mekki DM, Lv SJ, Wang LY & Wang JY. 2006. Analysis of Fitting Growth Models in Jinghai Mixed-Sex Yellow Chicken. International Journal of Poultry Science, 5: 517-521. DOI: 10.3923/ijps.2006.517.521