Implementing the Corn Silage Trial Results on Your Farm. Dr. Jessica Williamson, Penn State Joe Lawrence, Cornell CALS PRO-DAIRY

Transcription

1 Implementing the Corn Silage Trial Results on Your Farm Dr. Jessica Williamson, Penn State Joe Lawrence, Cornell CALS PRO-DAIRY

2 Implementing the Corn Silage Trial Results on Your Farm 2018 PENNSYLVANIA DAIRY SUMMIT JESSICA WILLIAMSON, PENN STATE JOE LAWRENCE, CORNELL, PRODAIRY

3 PSU Data Presentation can be found at:

4

5

6

7 2016

8 2017

9 Picking the Best fit for your farm Hybrid Evaluation.not Hybrid Competition How consistent does a hybrid perform across locations? What hybrids perform well under conditions similar to your farm? Soil Type Length of Growing Season What factors impact predicted milk yields the most? Digestible Fiber What affects digestible fiber? Soil Type Weather Yield

10 Picking the Best fit for your farm Hybrid Evaluation.not Hybrid Competition What factors match your needs and feeding program? Yield Acreage constraints Starch Digestible Fiber What other forages are being fed?

11 Comparing Data It s all Relative Is this a good Yield? 26 tons/acre, 35% DM Compared to what? Plot Mean: 21 tons/acre, 35% DM OR Plot Mean: 31 tons/acre, 35% DM Source; Unknown

12 Comparing data to Plot Mean Plot Mean Yield: 25.2 tons/acre, 35% DM Starch: 32.3 % of DM

13 Collecting your own data Build your own database Tracking yield & quality from year to year to understand field and hybrid performance. Green Samples at Harvest Labeled by field and by hybrid Fiber Digestibility undf240

14 Using Public data as comparison Plot Means: 2017 NY & VT Corn Silage Hybrid Trials Watch units Yield, Dry 35% DM Matter Starch Crude 30 hr 240 hr 240 hr Lignin andfom Protein NDFDom undfom undfom tons/acre % % DM % DM % DM % DM % NDF % NDF % DM day RM Albion, NY Willsboro, NY Alburg, VT day RM Aurora, NY Madrid, NY Alburg, VT Plot Means Aurora, NY Madrid, NY Average digestibility numbers will vary by hybrid and year Public trials provide the range in expected values for a given growing season

15 Fiber Assessment undf 240 better describes indigestibility of the forage for use in cattle Improves prediction of energy from forages Helps improve predictions of intake and rumen function, microbial production etc. Can help explain performance issues with fiber digestibility in some cases NIRS prediction available from CVAS

16 undf Role in Corn Silage Not a new concept Forage and byproducts have undf Undigested NDF residue after specified time points 0, 24, 30, 48, 120, 240 h undf30 (fill?); undf240 (intake?) Fill: Density, passage, fndf, NDFD, PS, NDF type Intake: not undf alone; maybe rate & undf? Variation in NDF pool sizes = rate of NDFD Likely best to use undf within forage Affected by environment Field observation: responsible for cow variation [milk (R 2 =.38) rumen ph (R 2 =.45) ] Slide: Dr. Chris Canale

17 Slide: Dr. Chris Canale

18 Slide: Dr. Chris Canale

19 NY & VT Corn Silage Trials 2016&17

20 Forage undf, kd Slide: Dr. Chris Canale

21 Slide: Dr. Chris Canale

22 Slide: Dr. Chris Canale

23 Slide: Dr. Chris Canale

24 Slide: Dr. Chris Canale

25 undf240, % NDF Slide: Dr. Chris Canale

26 Role of undf in Rations Guidelines for undf still being developed: ~0.35% BW (Miner) fundf: 0.25 to 0.3% BW undf from corn silage seems to impact cow performance Not always intake; mostly milk undf not related to NDF, DM, lignin so analysis is needed NDFD time points provide kinetics (kd); path to undf seems useful Mix & match fundf sources to optimize cow performance Legumes, grasses, small grain silage Slide: Dr. Chris Canale

27 undf (% NDF) undf 2016 vs undf across Locations Bradford County (Canton) Centre County (Rock Springs) Chester County (West Grove) Lancaster County (Landisville)

28 Slide Credit: Dr. Randy Shaver, U. of Wisconsin

29 Investigating a new method Predicting Milk Yield Utilizing Cornell Net Carbohydrate and Protein System What is CNCPS? Dynamic model used to balance feed rations for cattle Under continuous development since 1980 s First applied to on-farm use in the early 1990 s Currently used as the basis for ration software programs that feed more than 40% of the cows in the US and many more cows worldwide.

30 Predicting Milk Yield Utilizing Cornell Net Carbohydrate and Protein System v (NDS platform) undf240 Adjustment Predicted ME Allowable Milk Yield, undf240 equivalent (lbs/day) Adjusted total ration DMI so the undf240 was equal to that in the base ration. Reflects how much the cow actually may be able to consume based on rumen fill.

31 Considerations and Potential Limitations In practice, a dairy nutritionist would make other adjustments to the diet when a new forage is being utilized in the ration. While past approaches may not fully acknowledge the differences in each hybrid, the undf240 adjustment may show a greater difference between hybrids than what will happen in practice. The analysis presented here does not address the potential differences in starch digestibility between hybrids.

32 Fiber Conclusions Continue with the development of undf 240 as a routine measurement for silage assessment. Standardize our reporting as %NDF Change NDFD 24 hour to NDFD 30 at Penn State to be consistent with Cornell to facilitate comparisons. Add NDFD 120 and NDFD240 to reports

33 Combined RM Data

34 Combined RM Data New charts Yield vs. undf240h Yield vs. NDFD30h

35 2016

36 Crop Yield Quartile Plots What s on the axis? Penn State Trials Q 1 Q 2 Q1: Crop Yield: Above Average undf and NDFD: Below Average Q2: Crop Yield: Above Average undf and NDFD: Above Average Q 3 Q 4 Q3: Crop Yield: Below Average undf and NDFD: Below Average undf 240h or NDFD 30h Q4: Crop Yield: Below Average undf and NDFD: Above Average

37

38

39 Starch Digestibility Hybrids with more floury endosperm are being marketed with the potential to improve starch digestibility How can differences be best assessed? How do differences interact with other factors such as particle size and time of ensiling?

40 Starch Digestibility Assessment Study Objective: characterize starch digestibility of hybrids over time. Assess hybrids with wet chem 7 h IVSD with a 1mm grind on 5 replicated studies CVAS: 7h IVSD wet chemistry

41 Starch Digestibility (StarchD) What we know: KPS has a big impact on StarchD Time in silo increases StarchD Estimates of rumen digestible starch (RDS) useful in diet formulation Various methods to determine RDS In Situ (8h) In Vitro (2, 4, 7h) NIR Grind size (1, 4mm) Slide: Dr. Chris Canale

42 IVSD Methods PDMP Slide: Dr. Chris Canale

43 Slide: Dr. Chris Canale

44 Slide: Dr. Chris Canale

45 2017 PDMP IVSD 1mm 4h Slide: Dr. Chris Canale

46 NDFD30h (%NDF) BMR Study: NDFD30h West Grove Bainbridge Landisville BMR10B27 BMR14B96 BMR12B75 BMR15B15

47 undf240h (%NDF) BMR Study: undf240h West Grove Bainbridge Landisville BMR10B27 BMR14B96 BMR12B75 BMR15B15

48 Thank You! QUESTIONS? COMMENTS?