Application of Artificial Neural Network for Modeling Benefit to Cost Ratio of Broiler Farms in Tropical Regions of Iran

The economics of poultry meat production depends on numerous factors, but most important is general economic policy. For the economic analyses, net profit, gross return, net return, Benefit to Cost Ratio (BCR), productivity, etc. have to be computed. In this study, various Artificial Neural Network (ANN) models were developed to estimate the BCR of broiler farms in tropical regions of Iran. To develop ANN models, data were obtained from growers, government officials as well as from relevant databases. The developed ANN was a Multilayer Feed Forward Network (MLFN) with five neurons in the input layer, one and two hidden layer(s) of various numbers of neurons and one neuron in the output layer. The MLFN were trained with the experimental data obtained from 44 broiler farms. Based on performance measures, (5-20-1)-MLFN, namely, a network having five neurons in its input layer and twenty neurons in the hidden layer resulted in the bestsuited model estimating the BCR. For the optimal model, the values of the model’s outputs correlated well with actual outputs, with coefficient of determination (R) of 0.978. For this configuration, MSE, MAE and MAPE values were 0.002, 0.037 and 2.695, respectively. Sensitivity analysis revealed that feed cost is the most significant parameter in modeling the BCR to cost ratio in the broiler production.