Using Gompertz model in comparison with artificial neural network to estimate the growth parameters of the broiler chickens fed with sodium bentonite

Document Type : Original Article

Authors
M Chaji 1
A Masoudi 2
H Damiri 3
M Boojarpour 4
A Azarfar 5
1 Associate Professor, Department of Animal Science, Faculty of Animal and Food Sciences, Ramin Agriculture and Natural Resources University of Khouzestan
2 PhD Student, Department of Animal Science, Faculty of Agriculture, Lorestan University
3 MSc Graduated Student, Department of Animal Science, Faculty of Animal and Food Sciences, Ramin Agriculture and National Resources University of Khuzestan
4 Assistant Professor, Department of Animal Science, Faculty of Animal and Food Sciences, Ramin Agriculture and Natural Resources University of Khouzestan
5 Associate Professor, Department of Animal Science, Faculty of Agriculture, Lorestan University
10.22077/jlr.2025.4064
Abstract
This experiment was conducted to determine the effects of sodium bentonite on broiler growth parameters, and to compare prediction ability of the Gompertz model and artificial neural network (ANN). A total of 288 one day-old Ross chicken were used in a completely randomized design with 6 treatments (6 diets) and each treatment were divided into 4 replicates. The treatments were diets including 0, 0.75, 1.5, 2.25, 3 and 3.75% sodium bentonite, which was used as top-dress. Gompertz growth function was used to estimate the growth characteristics and to compare the prediction ability of this function with artificial neural network, R square coefficient (R2), mean absolute deviation (MAD), root mean square error (RMSE) and bias were used as indicators. The treatments caused significant changes in the final weight (Wf), the initial weight of chicks (W0), the time for change of growth curve (T) and the weight in the change of growth curve (Wi) (P
Keywords
Growth parameters
Gompertz model
Sodium bentonite
Artificial neural network
Broiler chickens