|URRIOLA, PEDRO - University Of Minnesota|
|LI, MU - University Of Minnesota|
|SHURSON, GERALD - University Of Minnesota|
Submitted to: Animal Feed Science and Technology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/12/2014
Publication Date: 11/17/2014
Publication URL: http://handle.nal.usda.gov/10113/59863
Citation: Urriola, P., Li, M., Kerr, B.J., Shurson, G. 2014. Evaluation of prediction equations to estimate gross, digestible, and metabolizable energy content of maize dried distillers grains with solubles (DDGS) for swine based on chemical composition. Animal Feed Science and Technology. 198:196-202.
Interpretive Summary: The expansion of the ethanol biofuel industry has generated a variety of co-products, which due to availability and price, have become available for use as a potential feedstuff for swine. Ethanol companies have been extracting a portion of the oil from distillers dried grains with solubles (DDGS) resulting in a product called reduced oil-DDGS. Although prediciton equations on the impact of this oil extraction on the caloric value to growing pigs has been published, these equations have not been validated. This research demonstrated that the most precise and accurate DE equation included GE, NDF, and EE as equation variables, while the most precise and accurate ME equation included GE, CP, NDF, and EE as equation variables. The data also suggests that cross-validation of equations that require PS, BD, or TDF as inputs was not possible because these inputs were only measured in 1 of the 5 published studies used in this evaluation. This information is important for nutritionists at universities, feed companies, and swine production facilities for the determination of the energy value of RO-DDGS for use in feed formulations, and provides a basis from which to assess its economic value.
Technical Abstract: The objective of this study was to cross-validate prediction equations to estimate the concentration of gross energy (GE), digestible energy (DE), and metabolizable energy (ME) among sources of corn distillers dried grains with solubles (DDGS) with variable chemical composition in growing pigs. Published concentrations (dry matter basis) of GE, CP, ether extract (EE), neutral detergent fiber (NDF), and total dietary fiber (TDF) along with particle size (PS, µm), bulk density (BD, g/cm3) and in vivo determinations of DE and ME from 45 sources of DDGS samples were obtained from 5 published studies. Prediction equations for GE (5 equations), DE (20 equations), and ME (19 equations) from published studies were used to calculate the concentration of GE, DE, and ME among DDGS sources and compare with experimentally determined in vivo values. Each equation was evaluated using the entire data set, and data sets that excluded data from which the equation was developed (cross-validation). Equations were compared for their overall explanation of variance (R-squared), precision for reduction in prediction error (PE, kcal/kg DM), and accuracy in deviation of the predicted mean from the overall observed mean (bias, kcal/kg DM). Prediction of GE concentrations among DDGS sources was poor (PE < 200 and biases > 150) despite moderate explanation of overall variance (R-squared < 0.6). Therefore, we tested DE and ME equations that included GE as an input using the actual analyzed GE concentration of samples. Under this condition, the most precise (PE = 144) and accurate (bias = 19) DE equation was DE = -2,161 + (1.39 × GE) – (20.7 × NDF) – (49.3 × EE). The most precise (PE = 149) and accurate (bias = -82) ME equation was ME = -261 + (1.05 × GE) – (7.89 × CP) + (2.47 × NDF) – (4.99 × EE). Predicting GE with equation GE = 4,583 + (50.6 × EE) – (0.1 × PS), and using this estimate in the equation of ME = -261 + (1.05 × GE) – (7.89 × CP) + (2.47 × NDF) – (4.99 × EE), resulted in moderate precision (PE = 134) and accuracy (bias = 48). Cross-validation of equations that require PS, BD, or TDF as inputs was not possible because these inputs were only measured in 1 of the 5 published studies used in this evaluation.