Determination and Prediction of Digestible and Metabolizable Energy from the Chemical Composition of Chinese Corn Gluten Feed Fed to Finishing Pigs

Two experiments were conducted to determine the digestible energy (DE) and metabolizable energy (ME) contents of corn gluten feed (CGF) for finishing pigs and to develop equations predicting the DE and ME content from the chemical composition of the CGF samples, as well as validate the accuracy of the prediction equations. In Exp. 1, ten CGF samples from seven provinces of China were collected and fed to 66 finishing barrows (Duroc${\times}$Landrace${\times}$Yorkshire) with an initial body weight (BW) of $51.9{\pm}5.5$ kg. The pigs were assigned to 11 diets comprising one basal diet and 10 CGF test diets with six pigs fed each diet. The basal diet contained corn (76%), dehulled soybean meal (21%) and premix (3%). The ten test diets were formulated by substituting 25% of the corn and dehulled soybean meal with CGF and contained corn (57%), dehulled soybean meal (15.75%), CGF (24.25%) and premix (3%). In Exp. 2, two additional CGF sources were collected as validation samples to test the accuracy of the prediction equations. In this experiment, 18 barrows (Duroc${\times}$Landrace${\times}$Yorkshire) with an initial BW of $61.1{\pm}4.0$ kg were randomly allotted to be fed either the basal diet or two CGF containing diets which had a similar composition as used in Exp. 1. The DE and ME of CGF ranged from 10.37 to 12.85 MJ/kg of dry matter (DM) and 9.53 to 12.49 MJ/kg of DM, respectively. Through stepwise regression analysis, several prediction equations of DE and ME were generated. The best fit equations were: DE, MJ/kg of DM = 18.30-0.13 neutral detergent fiber-0.22 ether extract, with $R^2$ = 0.95, residual standard deviation (RSD) = 0.21 and p<0.01; and ME, MJ/kg of DM = 12.82+0.11 Starch-0.26 acid detergent fiber, with $R^2$ = 0.94, RSD = 0.20 and p<0.01. These results indicate that the DE and ME content of CGF varied substantially but the DE and ME for finishing pigs can be accurately predicted from equations based on nutritional analysis.