Determination and prediction of the energy content and amino acid digestibility of brewer's spent grain for growing pigs.

Two experiments were conducted to determine the energy content and amino acid (AA) digestibility of 10 brewer's spent grain (BSG) for growing pigs, with the goal of developing predictive models for these digestible nutrients based on their chemical composition. In Exp. 1, 66 crossbred barrows (initial body weight (BW): 35.5±4.5 kg) were randomly assigned to 1 of 11 diets, including a corn basal diet and 10 test diets in which 20% of the corn was replaced with BSG. Difference method was employed to calculate the digestible energy (DE) and metabolizable energy (ME) of BSG. In Exp. 2, 11 crossbred barrows (initial BW: 32.3±3.8 kg) were surgically fitted with T-cannulas in the distal ileum and randomly assigned to an 11×6 incomplete Latin square design with 11 diets and 6 experimental periods, including a nitrogen-free diet and 10 test diets formulated with BSG as the sole nitrogen source, with 0.4% titanium dioxide added as an indigestible marker to calculate the standardized ileal digestibility (SID) of AA. Results showed that there was considerable variation in the chemical composition of BSG, with all coefficient of variation exceeding 10%. On a dry matter basis, the mean DE and ME values were 2,771 and 2,610 kcal/kg, respectively. The best prediction equations for DE and ME were: DE=-1698+(1.21×GE)-(27.02×NDF) (R2=0.99, P<0.01), and ME=-1800+(1.18×GE)-(25.11×NDF) (R2=0.99, P<0.01). The mean SID values of Lys, Met, Thr, Trp, and Val was 63.1%, 73.4%, 63.8%, 77.7%, and 72.8%, respectively, both were positively correlated (P<0.05) with gross energy, ether extract and crude protein content, and negatively correlated (P<0.05) with fiber content. In conclusion, predictive models for DE, ME, and SID of AA can be developed based on the nutrient composition of BSG in growing pigs. However, considering the inherent variability in nutrient composition, the accuracy and applicability of these models in practical feed formulation require validation using samples from an independent data set.