A dynamic mechanistic model was developed for growing and fattening pigs. The aim of the model was to predict growth rate and the chemical and anatomical body compositions from the digestible nutrient intake of gilts (20–105 kg live weight). The model represents the partitioning of digestible nutrients from intake through intermediary metabolism to body protein and body fat. State variables of the model were lysine, acetyl-CoA equivalents, glucose, volatile fatty acids and fatty acids as metabolite pools, and protein in muscle, hide–backfat, bone and viscera and body fat as body constituent pools. It was assumed that fluxes of metabolites follow saturation kinetics depending on metabolite concentrations. In the model, protein deposition rate depended on the availability of lysine and of acetyl-CoA. The anatomical body composition in terms of muscle, organs, hide–backfat and bone was predicted from the chemical body composition and accretion using allometric relationships. Partitioning of protein, fat, water and ash in muscle, organs, hide–backfat and bone fractions were driven by the rates of muscle protein and body fat deposition. Model parameters were adjusted to obtain a good fit of the experimental data from literature. Differential equations were solved numerically for a given set of initial conditions and parameter values. In the present paper, the model is presented, including its parameterisation. The evaluation of the model is described in a companion paper.

The objective of the present paper was to evaluate a dynamic mechanistic model for growing and fattening pigs presented in a companion paper. The model predicted the rate of protein and fat deposition (chemical composition), rate of tissue deposition (anatomical composition) and performance of pigs depending on nutrient intake. In the model evaluation, the predicted response of the pig to changes in model parameters and to changes in nutrient intakes is presented. As a result of the sensitivity analysis, changes in the maintenance energy requirements and the fractional degradation rate of muscle protein had the greatest impact on tissue deposition rates. The model was also highly sensitive to changes in the maximum velocity and steepness parameter of the lysine utilisation for muscle protein synthesis. The model was further tested by independent published results. The model successfully predicted the response of pigs to a wide range of variation in nutrient composition. Consequently, the model can be applied to develop feeding strategies to optimise pig production. It also enables prediction of the slaughter performance and the meat quality.