Sensitivity analysis of an early egg production predictive model in broiler breeders based on dietary nutrient intake

Neural networks (NNs), especially the group method of data handling-type NN (GMDH-type NN), are new tools in modelling growth and production in poultry systems. In the present study, the GMDH-type NN was used to model early egg production (EEP, eggs/bird) in Ross 308 broiler breeders (BBs) from 24 to 29 weeks of age based on their dietary energy and nutrient intake. The selected input variables were intake levels of metabolizable energy (ME; MJ/bird/day), crude protein (CP; g/bird/day), methionine (Met; g/bird/day), and lysine (Lys; g/bird/day). A sensitivity analysis (SA) technique was utilized to evaluate the relative importance of input variables on model output. The GMDH-type NN revealed a high ability in the modelling of EEP based on the input variables investigated. The SA results indicated that the models developed showed most sensitivity to dietary intake of Met, followed by dietary intake of Lys, ME and CP, respectively. The maximum sensitivity of each input variable was considered as the optimum value for maximizing EEP in BB. The suggested optimum values for dietary nutrient intake were as follows: 1.9-2.1 MJ/bird/day for ME, 23 g/bird/day for CP, 0-65-0.8 g/bird/day for Met and 1.4-1.5 g/bird/day for Lys.