Re-parametrization of a swine model to predict growth performance of broilers

The aim of the study was to investigate whether a pig growth model is suitable to be modified and adapted forbroilers. As monogastric animals, pigs and poultry share many similarities in their digestion and metabolism, many structures (body protein and lipid stores) and the nutrient flows of the underlying metabolic pathways are similar among species. For that purpose, the InraPorc model was used as a basis to predict growth performance and body composition at slaughter in broilers. Firstly, the backbone of InraPorc was translated to Excel and examined whether the equations were suitable for growing birds by evaluating the parameters and model behavior. After determining of differences the model was modified for predicting growth in broilers in relation to the nutrient supply. The model core is very generic in terms of representing the most important nutrient flows and the transformation from absorbed nutrients to protein and fat accretion during growth. The idea of nutrient utilization is mainly based on concepts used in net energy and ideal protein systems. The model is driven by feed intake, the partitioning of energy between protein and lipid deposition, and availability of dietary protein and amino acids. Parameters of the Gompertz function were adjusted using literature data to express maximum feather-free body protein deposition. A separate Gompertz equation was used to estimate feather growth and protein content of feather was assumed to be a nonlinear function of age. The amino acid loss with feathers was considered as a part of the maintenance requirement and the fasting heat production was used as the NE requirement for maintenance (460 kJ/(kg BW)0.70/day). Feed intake for broilers was expressed on a NE bases and estimated by a Gamma-function (which expresses the NE intake as multiples ofmaintenance), as in InraPorc. Parameters of the Gamma function were adjusted to experimental data from different broiler studies. This study is part of the Feed-a-Gene project and received funding from the European Union’s H2020 program under grant agreement no. 633531.