Evaluating Corn Silage Management. Dr. Jessica Williamson, Penn State Joe Lawrence, Cornell CALS PRO-DAIRY
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1 Evaluating Corn Silage Management Dr. Jessica Williamson, Penn State Joe Lawrence, Cornell CALS PRO-DAIRY
2 Evaluating Corn Silage Management 2018 PENNSYLVANIA DAIRY SUMMIT JESSICA WILLIAMSON, PENN STATE JOE LAWRENCE, CORNELL, PRODAIRY
3 Penn State Testing Program Partner with Professional Dairy Managers of Pennsylvania Provide input w/advisory team Solicit hybrids Develop initiatives
4 Advisory Board Inputs Do hybrids vary in starch digestibility? Should we be evaluating fiber digestibility in more detail such as undf? Are there alternatives to Milk 2006?
5 Leadership Team Provides insights into analytical issues and trends in the industry Helps to work toward some consistency in evaluation and interpretation Greg Roth Chris Canale Alex Hristov Jessica Williamson
6 Evaluation Program Principles Replicated, multiple environment evaluations Sort hybrids by DM within test Provide maturity subcategories at each location Conduct tests under typical crop conditions Conduct separate test for BMRs Goal: report data to producers by November 1
7 2017 PDMP/PSU Silage Hybrid Evaluation Program Evaluated 200 hybrids 85-94d (16) d (28) d (12) d (36) d (10) d (31) d (44) d (19) Ten locations Blair (2), Bradford (2), Cambria, Centre, Franklin, Lancaster (2), and Chester Counties Three replications at each site Separate BMR evaluation at 3 locations
8 Overall goals of our program Test hybrids Improve Characterization Methods Broaden Collaborations
9 NYS & VT Corn Silage Trials Collaboration of: Cornell University PRO-DAIRY, Department of Animal Science Department of Plant Breeding & Genetics University of Vermont With support from: NYS Dairy Producers Seed Corn Industry Cornell Cooperative Extension
10 NYS & VT Corn Silage Trials Program Oversight Joe Lawrence, Cornell PRO-DAIRY Tom Overton, Cornell Animal Science Margaret Smith, Cornell Plant Breeding and Genetics Heather Darby, University of Vermont Allison Kerwin, Cornell Animal Science Mike Van Amburgh, Cornell Animal Science With contributions from many more on the team Mike Davis Jerry Cherney Sherrie Norman Keith Payne Dan Fisher Paul Stackowski Roger Rainville
11 2017 NYS & VT Corn Silage Trials 73 hybrids entered Locations Day Relative Maturity Albion, NY Willsboro, NY Alburgh, VT Day Relative Maturity Aurora, NY Madrid, NY Alburgh, VT Thank you to host Jon Greenwood Hugh Dudley
12 Additional Goals Utilize network of locations to study impact of growing environment on fiber digestibility and other quality parameters Rainfall Heat units Soil type Insect Damage & Mycotoxins Disease Pressure NY & VT Corn Silage Trials 2016&17
13 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
14 PSU Data Presentation can be found at:
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18 2016
19 2017
20 Production Details / Plot Background
21 undf (% NDF) undf 2016 vs undf across Locations Bradford County (Canton) Centre County (Rock Springs) Chester County (West Grove) Lancaster County (Landisville)
22 NY & VT Corn Silage Trials 2016&17
23 Slide Credit: Dr. Randy Shaver, U. of Wisconsin
24 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
25 Interpretation methods We have used Milk/ton based on the Milk 2006 spreadsheet developed by Univ. of WI to interpret yield and quality data Advisors express concern about value of fiber digestibility in Milk/ton ratings. New advances in understanding of forage quality New laboratory analyses becoming available
26 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.
27 Predicting Milk Yield Utilizing Cornell Net Carbohydrate and Protein System v (NDS platform) Base diet formulated for an ME & MP allowable milk yield of 100 lbs/day 2 nd lactation, 110 DIM, 1585 BW, 3.80% milk fat, 3.20% milk true protein High Corn Silage Diet 28 lbs DM from Corn Silage ~60% Forage in Ration Target: 60 lbs Dry Matter Intake (DMI) CNCPS Feed Library Values Used for base ration NDF digestibility values from 2 year average in Cumberland Valley Analytical Services database for: Corn Silage, Alfalfa Haylage, Straw
28 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.
29 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.
30 2016 CNCPS Data
31 2016 CNCPS Data Aurora Relative Maturity Yield, 35% DM Dry Matter Predicted ME Allowable Milk Yield, DMI Equivalent undf240 Intake, DMI Equivalent Adjusted TMR DMI, DMI Equivalent Predicted ME Allowable Milk Yield, undf240 tons/acre % lbs/day lbs/day lbs/day lbs/day CNCPS value CNCPS value CNCPS value CNCPS value day day day Madrid Relative Maturity Yield, 35% DM Dry Matter Predicted ME Allowable Milk Yield, DMI Equivalent undf240 Intake, DMI Equivalent Adjusted TMR DMI, DMI Equivalent Predicted ME Allowable Milk Yield, undf240 tons/acre % lbs/day lbs/day lbs/day lbs/day CNCPS value CNCPS value CNCPS value CNCPS value day day day
32 Combined RM Data
33 Combined RM Data New charts Yield vs. undf240h Yield vs. NDFD30h
34 2016
35 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
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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 NDFD30h (%NDF) BMR Study: NDFD30h West Grove Bainbridge Landisville BMR10B27 BMR14B96 BMR12B75 BMR15B15
42 undf240h (%NDF) BMR Study: undf240h West Grove Bainbridge Landisville BMR10B27 BMR14B96 BMR12B75 BMR15B15
43 Summary Working on updating our assessment of hybrids to reflect current issues in dairy nutrition Some development of approaches is necessary and underway Working together we hope to develop some consistency and better collaboration Testing programs can be more than just hybrid evaluation
44 Thank You! QUESTIONS? COMMENTS?
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