Distillers Grains Feeding & Beef Quality. G. Erickson, T. Klopfenstein V. Bremer & many students

Transcription

1 Distillers Grains Feeding & Beef Quality G. Erickson, T. Klopfenstein V. Bremer & many students

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3 Ethanol Plants & Fed Cattle Population Madrid 2 Sutherland 3 Trenton 4 McCook 5 Cambridge 6 Lexington 7 Minden 8 Ravenna 9 Hastings 10 Aurora 11 Central City 12 York 13 Columbus 14 Norfolk 15 Plainview

4 Abengoa Bioenergy, York, NE DRY MILLING WDG(+S) CORN GRIND, WET, COOK FERMENTATION YEAST, ENZYMES STILL ALCOHOL & CO 2 STILLAGE DISTILLERS GRAINS WDG, DDG WDGS DDGS DISTILLERS SOLUBLES

5 Cargill wet milling, Blair, NE WET MILLING CGF STEEP CORN GRIND WASH WATER STEEP SEPARATION STARCH, SWEETENER, ALCOHOL GLUTEN MEAL CORN OIL CORN BRAN SEM, screenings, dist solubles WET CORN GLUTEN FEED DRY CORN GLUTEN FEED

6 Byproducts WDGS, modified (45% DM) WDGS, traditional (35% DM) DDGS (25% solubles) Syrup, distillers solubles, CCDS WCGF (45% DM) WCGF Sweet Bran (60% DM) DCGF Steep new distillers grains

7 Use Inclusion < 15% (2 3 lb): protein Inclusion > 15% (4+ lb): energy

8 Traditional WDGS Performance y = x x R 2 = 0.89 y = x x R 2 = WDGS level ADG F:G Vander Pol et al., 2006 Nebraska Beef Rep; 2005 Midwest American Society of Animal Science

9 Traditional WDGS $ Profit, $/ steer WDGS level Buckner et al., 2008 Nebraska Beef Rep. (in press)

10 Dry DGS Performance y = x x R 2 = 0.70 y = x x R 2 = DDGS level ADG F:G Buckner et al., 2007 Nebraska Beef Rep; 2007 Midwest American Society of Animal Science

11 Dry DGS $ Profit, $/ steer DDGS level Buckner et al., 2008 Nebraska Beef Rep. (in press)

12 Modified WDGS Performance y = x R 2 = 0.77 y = x x R 2 = Modified WDGS level ADG F:G Huls et al., 2008 Nebraska Beef Rep. (in press)

13 Modified WDGS $ Profit, $/ steer Modified WDGS level Huls et al., 2008 Nebraska Beef Rep. (in press)

14 Issues Many types of byproducts Evaluated individually in many experiments Combining these (meta analysis) to gain insight into carcass quality is necessary All experiments done with same DOF Therefore, performance influences carcass quality All have been (should be) very economical

15 UNL Meta Analysis of WDGS Effect on Carcass Characteristics Virgil Bremer, Galen Erickson & Terry Klopfenstein

16 UNL Studies Used Experiment Year Diet DM % WDGS Hd/Tx Sindt et al , 5.2, 12.6, Larson et al , 5.2, 12.6, Ham et al , Fanning et al , Vander Pol et al , 20, Vander Pol et al , 10, 20, 30, 40, Buckner et al , Corrigan et al , 15, 27.5, Luebbe et al , 15, 30 32

17 Average Daily Gain ADG (lb) y = x x Diet DM % WDGS Predicted Values WDGS Level ADG (lb) Intercept cov. P = 0.03 L P < P < 0.01 Q P < 0.01

18 Feed Conversion F:G (lb/lb) y = x x Diet DM % WDGS Predicted Values WDGS Level F:G Intercept cov. P = 0.04 L P < P < 0.01 Q P = 0.09

19 12 th Rib Fat Depth 12 th Rib Fat (in) y = 8E 05x x Diet DM % WDGS Predicted Values WDGS Level FAT Intercept cov. P = 0.02 L P < P < 0.01 Q P = 0.04

20 Marbling Score Marbling Score y = x x Diet DM % WDGS Predicted Values WDGS Level Marbling = Small 0 Intercept Slope cov. P = 0.08 cov. P = 0.09 L P = P < 0.01 Q P = 0.05

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22 UNL Meta Analysis of WCGF (Sweet Bran) Effect on Carcass Characteristics Virgil Bremer, Galen Erickson & Terry Klopfenstein

23 UNL Studies Used Experiment Year Diet DM % Sweet Bran Hd/Tx Richards et al , Scott et al , 10, 21, Herold et al , Scott et al , Scott et al , Buckner et al , Losa et al , 30 72

24 Dry Matter Intake DMI lb/d y = x Predicted Values WCGF Level DMI (lb) % Sweet Bran (DM basis) Linear P < 0.01 Quadratic P = 0.35

25 Average Daily Gain 5 ADG, lb y = x Predicted Values WCGF Level ADG (lb) % Sweet Bran (DM basis) Linear P < 0.01 Quadratic P = 0.67

26 Feed Conversion F:G y = x Predicted Values WCGF Level F:G % Sweet Bran (DM basis) Linear P = 0.03 Quadratic P = 0.48

27 12 th Rib Fat Depth Fat Depth, in y = x Predicted Values WCGF Level FAT % Sweet Bran (DM basis) Linear P < 0.01 Quadratic P = 0.87

28 Marbling Score Marbling Score y = x Predicted Values WCGF Level Marbling % Sweet Bran (DM basis) Linear P < 0.01 Quadratic P = 0.78

29 Sweet Bran and quality grade Control WCGF Over 2.3 million hd over 5 yrs not fed Sweet Bran Over 1.4 million hd over 5 yrs fed Sweet Bran in 2002

30 UNL Meta Analysis of WCGF (Traditional) Effect on Carcass Characteristics Virgil Bremer, Galen Erickson & Terry Klopfenstein

31 DMI, lb/d % WCGF A (DM basis) Linear P = 0.38 Quadratic P = 0.48

32 Average Daily Gain ADG, lb/d y = x Predicted Values WCGF Level ADG (lb) % WCGF A (DM basis) Linear P = 0.10 Quadratic P = 0.90

33 F:G % WCGF A (DM basis) Linear P = 0.59 Quadratic P = 0.60

34 Fat Depth, in % WCGF A (DM basis) Linear P = 0.46 Quadratic P = 0.46

35 Marbling Score Marbling Score y = x Predicted Values WCGF Level Marbling % WCGF A (DM basis) Linear P = 0.09 Quadratic P = 0.60

36 Dry DGS Performance y = x x R 2 = 0.70 y = x x R 2 = DDGS level ADG F:G Buckner et al., 2007 Nebraska Beef Rep; 2007 Midwest American Society of Animal Science

37 Dry DGS Quad HCW Marb LM fat Buckner et al., 2007 Nebraska Beef Rep; 2007 Midwest American Society of Animal Science

38 Conclusion Intermediate Levels of byproduct (10 40%) DRC & HMC Diets Equal DOF Treatments that improve performance (i.e. better ADG and F:G) Get fat quicker More marbling

39 PUFA and Trans Fat with 0, 15, 30% WDGS c 5.91 b 6.23 a 0% WDGS 15% WDGS 30 % WDGS 3.07 c 3.83 b 5.13 a 1 0 PUFA TRANS FAT abc Mean values within a response variable and followed by the same letter are not significantly different (P > 0.05).

40 Marbling and Fat % with 0, 15, 30% WDGS % Fat y = 0.014x R 2 = y = x R 2 = y = x R 2 = USDA Marbling Score Trt 0 Trt 15 Trt 30

41 Why the confusion (rumors)? Many types of byproducts Evaluated individually in many experiments Byproducts are not all equal, yet all done together Interaction with grain processing Feeding very large amounts (> 40% of DM), then may be a challenge more information needed Research on meat characteristics needed

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43 Beef Extension Page Beef Reports

44 CONTACT: Galen Erickson; PH: ; Acknowledge: Nebraska Center for Energy Sciences Research / NPPD Abengoa Bioenergy Nebraska Corn Board Cargill Wet Milling UNL Foundation GARD Dakota Gold Research Chief Ethanol Nebraska Beef Council