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ARS Home » Midwest Area » Ames, Iowa » National Laboratory for Agriculture and The Environment » Agroecosystems Management Research » Research » Publications at this Location » Publication #303541
Title: Validation of prediction equations for apparent metabolizable energy of corn distillers dried grains with solubles in broiler chicks

Author
item MELOCHE, KATHRYN - Auburn University
item Kerr, Brian
item BILLOR, NEDRET - Auburn University
item SHURSON, GERALD - University Of Minnesota
item DOZIER, WILLIAM - Auburn University

Submitted to: Poultry Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/1/2014
Publication Date: 6/1/2014
Publication URL: https://handle.nal.usda.gov/10113/58905
Citation: Meloche, K., Kerr, B.J., Billor, N., Shurson, G., Dozier, W. 2014. Validation of prediction equations for apparent metabolizable energy of corn distillers dried grains with solubles in broiler chicks. Poultry Science. 93:1428-1439.

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 broilers. 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 birds has been published, these equations have not been validated. This research demonstrated that validation is necessary to quantify the expected error associated with practical application of each individual prediction equation to external data. This information is important for nutritionists at universities, feed companies, and broiler 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: An experiment consisting of 3 nearly identical trials was conducted to determine the apparent metabolizable energy (AMEn) content of distillers dried grains with solubles (DDGS) in order to validate 4 previously published prediction equations for AMEn of corn DDGS in broilers. In addition, prior research data were utilized to generate a best-fit equation for AMEn based on proximate analysis. Fifteen samples of DDGS ranging in ether extract (EE) from 4.98 to 14.29% (dry matter-basis) were collected from various dry-grind ethanol plants and were subsequently fed to broiler chicks to determine AMEn content. A corn-soybean meal control diet was formulated to contain 15% dextrose and test diets were created by mixing the control diet with 15% DDGS at the expense of dextrose. In each trial, male Ross × Ross 708 chicks were housed in grower battery cages and received a common starter diet until the experimental period. Each cage was randomly assigned to 1 of the dietary treatments (Trial 1 and Trial 2: Control + 6 test diets, 13 replicates per diet; Trial 3: Control + 3 test diets, 12 replicates per diet). Experimental diets were fed over a 6-d acclimation period, followed by a 48 h total excreta collection period. On a dry matter-basis, AMEn of the 15 DDGS samples ranged from 1,975 to 3,634 kcal/kg. Analyses were conducted to determine gross energy, crude protein, ether extract, dry matter, starch, total dietary fiber, neutral detergent fiber, crude fiber, acid detergent fiber, and ash content of the DDGS samples. All results were reported on a DM basis. Application of the 4 equations to the validation data resulted in root mean square error (RMSE) values of 335, 381, 488, and 502 kcal/kg, respectively. Least absolute shrinkage and selection operator (LASSO) technique was applied to proximate analysis data for 30 corn co-products adapted from prior research and resulted in the following best-fit equation: [AMEn (kcal/kg) = 3,673 – (121.35 × crude fiber) + (51.29 × ether extract) – (121.08 × ash); P < 0.01; R-squared = 0.70; R-squared-adj = 0.67; RMSE = 270 kcal/kg]. The RMSE values obtained through validation were not consistent with the expectation of predictive performance based on internal measures of fit for each equation. These results indicated that validation is necessary to quantify the expected error associated with practical application of each individual prediction equation to external data.