Energy utilization of reduced oil-dried distillers grains with solubles (RO-DDGS) in swine

Transcription

1 Energy utilization of reduced oil-dried distillers grains with solubles (RO-DDGS) in swine Brian J. Kerr,* Thomas E. Weber,* and Michael E. Persia *USDA-ARS-NLAE, Ames, Iowa 011; and Iowa State University, Ames, Iowa 011 INTRODUCTION The ethanol industry produces a variety of co-products, ranging from dehydrated corn germ meal to corn bran, with typical DDGS being approximately 31% CP, 12% ether extract (EE), and 44% neutral detergent fiber (NDF) on a DM basis. With the ethanol industry seeking alternate profit streams from ethanol plants and the biodiesel industry seeking alternatives to high-priced soybean oil, one potential product is corn oil extracted from existing ethanol plants. In extracting the oil in DDGS, crude fat levels can drop from approximately 12% to 6% EE (as-is basis), while the concentration of other nutrients in the resultant OE-DDGS are increased. Based upon past research, the increases in CP, NDF, and ash, would likely contribute to a lower metabolizable energy (ME) value to growing pigs. To date, however, there are only 2 reports on the energy value of an OE-DDGS (2,976 and 3,6 kcal ME/kg DM) which are less than the estimated 3,812 kcal ME/ kg DM for DDGS and the 3,953 kcal ME/kg DM for corn. Because it is not known if the decrease in EE in OE-DDGS results in a linear drop in ME, research is needed to quantify the impact of reducing oil in DDGS on the ME in growing pigs. OBJECTIVES Determine the impact on ME due to graded reductions on crude fat in OE- DDGS fed to growing pigs. 1. Analyze 3 samples of OE-DDGS (10.5, 7.5, and 5.5% oil, as-is basis) for CP, EE, starch, NDF, acid detergent fiber (ADF), total dietary fiber (TDF), ash, and minerals. (Based on past research, this compositional information needed to predict DE and ME.) 2. Conduct energy (DE and ME) digestibility study with pigs fed OE-DDGS. 3. Correlate DE and ME to OE-DDGS composition. 4. Compare DE and ME to previously determined regression equations and data. TREATMENTS Swine: Utilizing substitution methodology for metabolism studies, a basal diet will be formulated to contain 97% corn with added vitamins and minerals. Oil extracted-ddgs (4) samples will be obtained with approximate crude fat concentrations of 10.5, 7.5, and 5.5 (and 4.5)% from which to evaluate the impact of graded levels of oil reduction on DE and ME content. DESIGN Swine: For the determination of energy digestibility, 75 kg gilts will be randomly assigned to either a basal diet or a diet containing 70% basal and 30% OE-DDGS. Pigs will be fed twice daily at approximately 3.0% of their body weight with water provided ad libitum and housed individually in metabolism crates designed for total, but separate collection of feces and urine. Pigs will be allowed to adapt to the experimental diet for 9 d, followed by a 4-d total collection period. Energy balance will be conducted by typical nutrient balance procedures. Metabolism crates (24 available) will allow the use of a basal diet and 3 samples of OE-DDGS, resulting in 6 replications per treatment per metabolism group. From a single batch of pigs at the ISU Swine Nutrition Farm, 2 groups of 24 pigs will be sequestered resulting in 12 replications per treatment. CRITERIA Digestible and metabolizable energy determination.

2 STATS Swine: Data will be analyzed as a completely randomized design with pig as the experimental unit and the energy concentration of the basal diet as a covariate between groups of pigs. Peer-reviewed journal manuscripts. PUBLICATION GENERAL Three groups of 24 finishing gilts (n = 72, BW = kg) were housed individually in metabolism crates ( m) that allowed for separate, but total collection of feces and urine. Crates were equipped with a stainless steel feeder and a nipple waterer, to which the pigs had ad libitum access. Gilts were randomly assigned to 1 of 4 test diets or the basal diet with the basal diet replicated 4 times and each test diet replicated 5 times within each group, resulting in 12 and 15 replications for the basal and each test diet, respectively, over the entire experiment. The basal diet contained 96.7% corn and vitamins and minerals with corn being the sole energy containing ingredient (Table 1). Test diets contained 70% of the basal diet and 30% of each test ingredient. All diets were fed in a meal form. Feed was provided to the gilts twice daily (1.5 kg/meal) during the 9 d of adaptation and the 4 d collection period. Total feed offered and unconsumed feed were weighed and recorded at the end of the 4 d collection period. Because feed intake may affect subsequent nutrient digestibility and DE and ME determinations, 3 pigs which refused > 20% of their diets relative to pigs within the same group were removed from the study (1 pig each for low, medium, and high crude fat products). During the time-based 4 d total fecal and urine collection period, stainless steel wire screens were placed under each metabolism crate for total fecal collection, while stainless steel buckets containing 30 ml of 6N HCl were placed under each crate for the total urine collection. Feces and urine were collected twice daily and stored at 0 C until the end of the collection period. At the end of the collection period, feces were pooled over the 4 d period, dried in a 70 C forced air oven, weighed, ground through a 1-mm screen, and a subsample was taken for analysis. Likewise, urine samples were pooled over the 4 d period, thawed at the end of the collection period, weighed, and a subsample collected for analysis. All RO-DDGS products were ground through a 1-mm screen prior to chemical analysis. Samples were analyzed for various nutritional and physical components as summarized in Table 2. Samples were also analyzed for mycotoxin contamination as summarized in Table 3. Nutrient and energy intakes were calculated based upon actual feed intake over the 4-d collection period. The nutrient and energy digestibility (including ME) of each test ingredient was calculated by subtracting the nutrient or energy contributed by the basal diet from the nutrient or energy of the diet containing that particular test ingredient and then dividing the result by the inclusion rate of the test ingredient in the diet. Using the individual pig as the experimental unit, data were subjected to ANOVA with treatment in the model (SAS Inst. Inc., Cary, NC), and treatment means are reported as least-square means. The experiment was conducted as a completely randomized design with nutrient or energy digestibility (including ME) of the basal diet used as a covariate to nutrient and energy digestibility values among all groups of pigs. Final BW and feed intake were also tested as covariates, but they were not found to be significant, and thus they were not included in the final model. Stepwise regression was used to determine the effect of the feedstuff composition on apparent nutrient and energy digestibility with variables having P-values 0.15 being retained in the model. INTERPRETATIVE SUMMARY In our previous research, we utilized a commercial laboratory for ingredient analysis. The USDA-ARS laboratory also has some of this analytical capacity, and as a consequence, we were interested in analytical variation noted between these two laboratories and their effect on prediction equations. Even though each laboratory utilizes approved methodology, each laboratory resulted in different nutrient values of the test ingredients (Table 2). While this was expected, because this research relates digestibility coefficients determined in the animal to ingredient analysis, this has a large impact on the industry-wide application of this research. This laboratory variation is bothersome as in a past review of lab variation; we predicted a difference in 400 kcal/ kg DM for a single DDGS sample depending upon which laboratory was used for analysis. In the current project, we were also interested in the ability to relate ash, GE, fiber, and CP relative to changes

3 in the ether extract (EE). As shown in Figure 1, many components were relatively well related to the change in the EE of RO-DDGS. As expected, as the percent EE in RO-DDGS was reduced, GE decreased while TDF, CP, and ash increased. In contrast, NDF appeared to decrease as oil was extracted. We cannot explain this apparent decrease as it goes against the logic that as oil is removed, other components would be concentrated. We also measured mycotoxin contamination in the basal diet and each RO-DDGS sample, and although some mycotoxins were detected, they were well below limits of concern (Table 3). Digestibility of various components in the RO-DDGS samples are presented in Table 4 and Figure 2. Digestibility coefficients differed between treatments, but were not helpful in predicting DE or ME of the RO-DDGS samples. This was not expected based upon previous literature, and warrants further evaluation of the data. The main focus of this experiment was to relate DE and ME to the percent EE in the RO-DDGS sample. The GE, DE, and ME of the basal diet and each RO-DDDS sample is reported in Table 5. As expected, GE was well related to EE content (see also Figure 1), but the simple linear relationship between DE or ME and percent EE is not clear. As shown in Figure 3, the relationship between DE or ME (kcal/kg DM) and percent EE in the RO-DDGS (DM basis) could be graphed, suggesting a change in DE and ME by 32 and 46 kcal, respectively, per kg DM for each one percentage unit change in EE, but the R2 (coefficient of determination) is poor, 0.22 and 0.32 for DE and ME, respectively. This lack of relationship can also be denoted by regression analysis demonstrating that the P value for DE and ME are nonsignificant, being 0.54 and 0.43, respectively. Calculating an expected change in DE and ME and using corn oil (DE = 8755, ME = 8405) and corn grits (DE = 3355, ME = 3210) as component equivalents for a RO-DDGS, suggests that a decrease in one percentage unit of EE relates to a loss of approximately 54 and 52 kcal/kg of DE an ME, respectively, slightly higher than what we found in the current experiment. Additional regression analysis relating DE and ME to ingredient composition (using either the analytical data from the commercial laboratory or our USDA- ARS laboratory) are depicted in Table 6. Regardless of where the samples were analyzed, no combination of nutrient or energy analyses could be used to predict DE. This was a bit surprising given that others (Just et al., 1984; Noblet and Perez, 1993; Pedersen et al., 2007) and our recent data on corn co-products (Anderson et al., 2012) have shown that various parameters (GE, TDF, NDF, EE, etc.) could be useful in such predictions. In those papers, however, a wider range in ingredients relative to EE and fiber were utilized compared to the current study, which has been suggested to provide a more robust approach to energy modeling. Relative to ME, only hemicellulose (USDA-ARS lab) and/or GE could be related to the ME of the RO-DDGS samples. In contrast, no analysis obtained from the commercial laboratory was found to be significant. This supports our statement that lab-to-lab variation can have a profound impact on ingredient values and subsequent data interpretation. However, given the results of the current experiment relative to the importance of GE and a fiber component in ME prediction, and data from previous research indicting that EE and ash are also important in energy evaluation systems for swine, the distillers and nutrition community need to develop better ways of predicting the concentrations of these nutrients (and energy) in feedstuffs as well as how to reduce the variation in analyses between laboratories. Lastly, no nutrient parameters were found to be significant relative to DE as a percent of GE while CP and fiber were important relative to ME as a percent of DE; and in no case was the percent EE in the RO-DDGS found to be a significant variable for these two measures (Table 6 and Figure 4). In summary, relating DE and ME to only the percent EE in RO- DDGS is not a statistically sound method of analysis, with the data in the current experiment suggesting that additional parameters such as GE and fiber are necessary for improved prediction equations. Table 1. Ingredient composition of basal diet, as-fed basis 1 Ingredient Concentration, % Corn Monoammonium phosphate 0.75 Limestone 1.30 Sodium chloride 0.35 Titanium dioxide 0. Vitamin mix Trace mineral mix Basal diet formulated to contain 0.% Ca and 0.45% P. 2 Provided the following per kilogram of diet: vitamin A, 7,716 IU; vitamin D 3, 1,929 IU; vitamin E, 39 IU; vitamin B 12, 0.04 mg; riboflavin, 12 mg; niacin, 58 mg; and pantothenic acid, 31 mg. 3 Provided the following per kilogram of diet: Cu (oxide), 35 mg; Fe (sulfate), 3 mg; I (CaI), 4 mg; Mn (oxide), 120 mg; Zn (oxide), 300 mg; and Se (Na 2 SeO 3 ), 0.3 mg.

4 Table 2. Composition of reduced oil-ddgs, DM basis Item Basal UL L M H Bulk density, g/cc Particle size, µm Dry matter, % Dry matter, % GE, kcal/kg CP, % CP, % Total starch, % TDF, % NDF, % NDF, % ADF, % ADF, % Hemicellulose, % Hemicellulose, % EE, % EE, % Ash, % Phosphorus, g/kg Sulfur, g/kg Analysed by USDA-ARS, Ames, IA. 2 Analyzed by University of Missouri, Columbia, MO. 3 Analyzed by Eurofins, DesMoines, IA. Figure 1. Composition of RO-DDGS as affected by crude fat content (U of MO database) Percentage or 1/100 GE GE, 0.01 kcal/kg = (0.542 x %EE) R² = 0.99 %TDF = (0.333 x %EE) R² = 0.72 %NDF = (0.154 x %EE) R² = 0.08 %CP = (0.337 x %EE) R² = 0.86 % Ash (0.102 x %EE) R² = 0.73 GE CP M TDF NDF M Ash Table 3. Mycotoxin composition of reduced oil-ddgs, DM basis Item 1 Basal UL L M H Aflatoxin B1, ppb ND ND ND ND ND Aflatoxin B2, ppb ND ND ND ND ND Aflatoxin G1, ppb ND ND ND ND ND Aflatoxin G2, ppb ND ND ND ND ND Deoxynivalenol, ppm Fumonisin B1, ppm ND Fumonisin B2, ppm ND Fumonisin B3, ppm ND 0.1 ND ND ND Ochratoxin A, ppb ND ND ND ND ND T-2 Toxin, ppb ND ND ND ND ND Zearalenone, ppb ND 51.2 ND ND ND 1 Aflatoxin B1, B2, G1, G2, AOAC , 1 ppb detection limit; deoxynivalenol, JAOAC #4, 0.1 ppm detection limit; fumonisin B1, B2, B3, AOAC , 0.1 ppm detection limit; ochratoxin A, AOAC , 1 ppb detection limit; T-2 toxin, J. Ag. Food Chem #4, 100 ppb detection limit; zearalenone, JAOAC #6, ppb detection limit. Analyzed by Trilogy Analytical Laboratory, Washington, MO. Table 4. Digestibility of reduced oil-ddgs Statistics Item 1 Basal UL L M H SD P Acid detergent fiber Carbon Dry matter Energy Ether extract Neutral detergent fiber Nitrogen Phosphorus Sulfur Digestibility of the basal diet used as a covariate for subsequent digestibility values. Final BW and ADFI averaged kg and 2,693 g/d, respectively. Table 5. Energy concentration of reduced oil-ddgs, DM basis Statistics Item 1 Basal UL L M H SD P Gross energy Digestible energy Metabolizable energy Digestible and metabolizable energy value of the basal diet used as a covariate for subsequent DE and ME values of each reduced oil-ddgs sample. Final BW and ADFI averaged kg and 2,693 g/d, respectively.

5 Figure 2. Nutrient digestibility of RO-DDGS as affected by crude fat content Digestibility, % DMD CD ED ND NDFD ADFD EED SD Figure 3. DE and ME of RO-DDGS as affected by crude fat content DE or ME, kcal/kg DM DE DE, kcal/kg DM = ( x %EE) R² = 0.22 (P = 0.54) ME, kcal/kg DM = (46.23 x %EE) R² = 0.32 (P = 0.43) ME Table 6. Regression Analysis of Reduced Oil DDGS Digestible Energy, kcal/kg DM (all analyses) SE R 2 Metabolizable Energy, kcal/kg DM (University of Missouri analyses) Metabolizable Energy, kcal/kg DM (USDA ARS analyses) Intercept Hemicellulose Gross Energy SE R DE as a percent of GE, kcal/kg DM (all analyses) ME as a percent of DE, kcal/kg DM (University of Missouri analyses) Intercept Crude Protein SE R ME as a percent of DE, kcal/kg DM (USDA ARS analyses) Intercept Hemicellulose NDF SE R Figure 4. DE:GE and ME:DE relationship in RO-DDGS as affected by crude fat content 100 DE:GE ME:DE Percentage