Comparison of Gompertz and neural network models of broiler growth

Poultry Science
W B RoushS L Branton

Abstract

Neural networks offer an alternative to regression analysis for biological growth modeling. Very little research has been conducted to model animal growth using artificial neural networks. Twenty-five male chicks (Ross x Ross 308) were raised in an environmental chamber. Body weights were determined daily and feed and water were provided ad libitum. The birds were fed a starter diet (23% CP and 3,200 kcal of ME/kg) from 0 to 21 d, and a grower diet (20% CP and 3,200 kcal of ME/ kg) from 22 to 70 d. Dead and female birds were not included in the study. Average BW of 18 birds were used as the data points for the growth curve to be modeled. Training data consisted of alternate-day weights starting with the first day. Validation data consisted of BW at all other age periods. Comparison was made between the modeling by the Gompertz nonlinear regression equation and neural network modeling. Neural network models were developed with the Neuroshell Predictor. Accuracy of the models was determined by mean square error (MSE), mean absolute deviation (MAD), mean absolute percentage error (MAPE), and bias. The Gompertz equation was fit for the data. Forecasting error measurements were based on the difference between the model and the obser...Continue Reading

Citations

Dec 13, 2016·Journal of Veterinary Pharmacology and Therapeutics·J HenriP Sanders
Jun 26, 2020·Animal : an International Journal of Animal Bioscience·A RemusC Pomar
Feb 19, 2020·Tropical Animal Health and Production·Asli Akilli, Ozkan Gorgulu
Sep 11, 2019·Journal of Animal Science·Herman MollenhorstClaudia Kamphuis

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