Digestible and metabolizable energy concentrations in cereal grains and byproduct ingredients fed to growing pigs

Abstract

The objectives were 1) to determine digestible energy (DE) and metabolizable energy (ME) in cereal grains and byproducts fed to pigs, 2) to validate the accuracy of prediction equations for feed ingredients, and 3) to develop prediction equations for DE and ME based on chemical compositions of ingredients. Twelve barrows [Duroc × (Landrace × Yorkshire)] with average body weight of 37.1 ± 1.1 kg were allotted to one of 12 diet groups in a 12 × 12 Latin square design with 12 periods. A basal diet contained corn and soybean meal (SBM). Ten diets were formulated by replacing 300 g/kg or 400 g/kg of corn and SBM with a test ingredient in the basal diet. A wheat diet contained 976 g/kg of wheat as a sole energy source. Based on measured energy values, prediction equations in the literature were validated and new prediction equations were developed. The DE and ME concentrations in corn gluten meal were the highest (P < 0.05) among 11 feed ingredients followed by lupin kernel, rice bran, and SBM. The DE and ME concentrations in wheat were higher (P < 0.05) compared with those in barley and wheat bran. The DE and ME concentrations did not differ between 2 sources of corn gluten feed. The energy in the ingredients ranged from 3999 to 5182 kcal/kg for gross energy, 988–4509 kcal/kg for DE, and 890–4266 kcal/kg for ME on an as-is basis. The results of comparisons between measured and predicted values indicated that previous equations underestimated DE and ME, especially for low-energy ingredients. The most suitable equations newly developed were: DE, kcal/kg dry matter = 4055 – 3.09 × neutral detergent fiber + 1.61 × crude protein + 6.32 × ether extract – 6.47 × ash (R2 = 0.949, nutrients in g/kg dry matter); ME, kcal/kg dry matter = 3975 – 2.99 × neutral detergent fiber + 1.38 × crude protein + 6.62 × ether extract – 7.45 × ash (R2 = 0.942, nutrients in g/kg dry matter). Overall, energy concentrations in various ingredients and newly developed prediction equations might be useful for accurate diet formulations.