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‘Towards’ using grazing markers to determine grazing intake

‘Towards’ using grazing markers to determine grazing intake. Ron Lewis Department of Animal and Poultry Sciences. 2013 “Brown Bagger” Webinar Series October 30, 2013. Cow efficiency. An efficient cow herd has: high reproductive rates early sexual maturity longevity

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‘Towards’ using grazing markers to determine grazing intake

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  1. ‘Towards’ using grazing markers to determine grazing intake Ron Lewis Department of Animal and Poultry Sciences 2013 “Brown Bagger” Webinar Series October 30, 2013

  2. Cow efficiency An efficient cow herd has: • high reproductive rates • early sexual maturity • longevity • minimum maintenance requirements • ability to convert available energy from forage into calf weaning weight (Dickerson, 1970)

  3. Maintenance costs • ~ 65% total beef production costs due to feed • ~ 70% total energy consumed by cow-calf sector • ~ 75% cow’s total annual energy requirement for maintenance • varies appreciably (Ferrell and Jenkins, 1985)

  4. Challenge

  5. Today’s talk • Plant-wax markers • Measurement • Prediction • Our process • Validation • Additional markers • Extension to pasture • Summing up

  6. Plant cuticular wax • Wax on external surface of plants • Complex mixture with chemical composition that differs appreciably among plant species • n-alkanes (hydrocarbons) • Over 90% have odd-numbers of carbons (C29, C31 and C33predominant) • Relatively inert and ‘easy’ to assess (Dove and Mayes, 2005)

  7. Measurement • Plant • Assess n-alkane profiles of plants • Animal (fecal sample) • Diet composition • Assess n-alkane profile of fecal sample • Feed intake (and whole-diet digestibility) • In addition, dose with even-chain n-alkane (C32)

  8. Prediction • Diet composition • Match n-alkane concentrations in feces with combinations of plant profiles • Feed intake (I )

  9. Our process • Validation (n-alkanes) • Characterize plants • Predict diet composition • Additional markers • Extension to pasture • Fecal sampling • Dosing

  10. Characterize plants (simple mixture) n-alkanes LCOH

  11. Characterize plants (simple mixture)

  12. Characterize plants (simple mixture)

  13. Predictions (simple mixture)

  14. Characterize plants (complex mixture) Forb Curly dock Dandelion Lambsquarter Forage • Alfalfa • Clover • Red • White • Fescue • Orchard grass • Smooth brome

  15. Characterize plants (complex mixture) Red clover C27, C29 Lambsquarter Curly dock Smooth brome Orchard grass Dandelion White clover C31 Alfalfa Fescue C33

  16. Prediction (complex mixture)

  17. Additional markers • Long-chain fatty acids • Even-numbers of carbons • C20 – C32 exclusive to plants with high fecal recoveries • Long-chain alcohols (LCOH) • Primarily even-number of carbons with high fecal recoveries • Wide variation in patterns across plants (Dove and Mayes, 2005)

  18. Characterize plants (simple mixture) n-alkanes (Vargas Jurado, 2012) LCOH

  19. Characterize plants (simple mixture)

  20. Extension to pasture: dosing

  21. Extension to pasture: sampling • Need to link fecal sample to an animal Day 2 Day 3

  22. Extension to pasture: sampling

  23. Summing up • Understanding cow efficiency would benefit from measures of diet composition and intake at pasture • Plant-wax markers, with refinements, offers opportunities to achieve that aim

  24. Summing up • If scalable, such information may contribute to • pasture management systems • animal selection decisions

  25. Summing up • National sire (bull) testing program • Progeny tests within feedlot and pasture-based systems • Evaluate ‘sensitivities’ in feed efficiency relative to production system

  26. Thanks for listening Virginia Tech The Hutton Institute Bob Mayes Faculty/Staff Sarah Blevins David Fiske Harold McNair Terry Swecker Amy Tanner USMARC Harvey Freetly Heidi Hillhouse John Keele Larry Kuehn Sam Nejezchleb Graduate student Napo Vargas Jurado Undergraduate students Amy Brandon Patricia Helsel Annie Laib Jaime Rutter

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