The objective of the current study was to develop Bayesian simultaneous equation models for modelling energy intake and partitioning in growing pigs. A key feature of the Bayesian approach is that parameters are assigned prior distributions, which may reflect the current state of nature. In the models, rates of metabolizable energy (ME) intake, protein deposition (PD) and lipid deposition (LD) were treated as dependent variables accounting for residuals being correlated. Two complementary equation systems were used to model ME intake (MEI), PD and LD. Informative priors were developed, reflecting current knowledge about metabolic scaling and partial efficiencies of PD and LD rates, whereas flat non-informative priors were used for the reminder of the parameters. The experimental data analysed originate from a balance and respiration trial with 17 cross-bred pigs of three genders (barrows, boars and gilts) selected on the basis of similar birth weight. The pigs were fed four diets based on barley, wheat and soybean meal supplemented with crystalline amino acids to meet or exceed Danish nutrient requirement standards. Nutrient balances and gas exchanges were measured at c. 25, 75, 120 and 150 kg body weight (BW) using metabolic cages and open circuit respiration chambers. A total of 56 measurements were performed. The sensitivity analysis showed that only the maintenance component was sensitive to the prior specification, and hence the maintenance estimate of 0·91 MJ ME/kg0·60 per day (0·95 credible interval (CrI): 0·78-1·09) should be interpreted with caution. It was shown that boars' ability to deposit protein was superior to that of barrows and gilts, as these had an estimated maximum PD (PDmax) of 250 g/day (0·95 CrI: 237-263), whereas the barrows and gilts had a PDmax of 210 g/day (0·95 CrI: 198-220). Furthermore, boars reached PDmax at 109 kg BW (0·95 CrI: 93·6-130), whereas barrows and gilts maximized PD at 81·7 kg BW (0·95 CrI: 75·6-89·5). At 25 kg BW, the boars partitioned on average 5-6% more of the ME above maintenance into PD than barrows and gilts, and this was progressively increased to 10-11% more than barrows and gilts at 150 kg BW. The Bayesian modelling framework can be used to further refine the analysis of data from metabolic studies in growing pigs. © Cambridge University Press 2012.

Nutrient loading and air emissions from swine operations raise environmental concerns. The objective of the study was to describe and evaluate a mathematical model (Davis Swine Model) of nutrient partitioning and predict manure excretion and composition on a daily basis. State variables of the model were AA, fatty acids, and a central pool of metabolites that supplied substrate for lipid synthesis and oxidation. The model traced the fate of ingested nutrients and water through digestion and intermediary metabolism into body protein, fat, water, and ash, where body protein and fat represented the body constituent pools. It was assumed that fluxes of metabolites follow saturation kinetics, depending on metabolite concentrations. The main inputs to the model were diet nutrient composition, feed intake, water-to-feed ratio, and initial BW. First, the model was challenged with nutrient partitioning data and then with excretion data. The data had 48 different feeding regimes with contrasting energy and lysine intakes at 2 different stages of growth. The overall observed and predicted mean were 109 and 112 g/d for protein deposition and 132 and 136 g/d for lipid deposition respectively, suggesting minor mean bias. Root mean square prediction error (RMSPE) was used in evaluation of the model for its predictive power. The overall RMSPE was 2.2 and 4.1 g/d for protein and lipid deposition, respectively. The excretion database used for evaluation of the model was constructed from 150 digestibility trials using growing-finishing pig diets that had a wide range of nutrient chemical composition. Nutrient and water excretion were quantified using the principle of mass conservation. The average daily observed and predicted manure production was 3.79 and 3.99 kg/d, respectively, with a RMSPE of 0.49 kg/d. There was a good agreement between observed and predicted mean fecal N output (9.9 and 9.8 g/d, respectively). Similarly, the overall observed and predicted mean urine N output was 21.7 and 21.3 g/d, respectively, suggesting minor mean bias. The RMSPE was 1.9 and 4.1 g/d for fecal and urinary N, respectively. Evaluation of the model showed that the model predicts manure excretion and N content well and can be used to assess environmental mitigation options from swine operations.