In swine operations, greenhouse gas emissions are mostly from stored manure. Accurate prediction of manure composition is required to estimate environmental footprint from swine operations. Pig growth models are often used to optimize profitability of swine production facilities; however, their application may be more valuable through assessment of environmental footprint from swine production. The study aims to describe and evaluate nutrient partitioning and excretion in a pig growth model to be used in predicting manure volume and composition. From a biological perspective, nutrient excretion can be viewed as the “residual” and hence prediction of nutrient partitioning between protein (PD) and lipid deposition (LD) is central. The model represents the partitioning of digestible nutrients from intake through intermediary metabolism to body protein and body fat. The model contained 3 state variables: Amino acids, fatty acids and a central pool of metabolites that supplies substrate for lipid synthesis and oxidation. Body protein and fat represented the body constituent pools. It was assumed that fluxes of metabolites follow saturation kinetics depending on metabolite concentrations. The feed intake can either be defined by the user or ad libitum intake may be simulated by means of an algorithm for metabolic regulation. The model was developed using the open source software R. The data of Bikker et al. (1994, 1995 and 1996) [J. Anim. Sci. 72:1744–1753, 73:2355–2363 and 74:817–826] was used to evaluate model predictions. The data had 48 different feeding regimens with contrasting energy and lysine intakes at 2 different stages of growth. The overall observed and predicted mean were 109, 112, and 132 and 136 g/d for PD and LD, respectively, suggesting minor mean bias. The overall mean square prediction error was 2.2 g/d and 4.1 g/d for PD and LD, respectively.