‘towards’ using grazing markers to determine grazing intake ron lewis department of animal and...
TRANSCRIPT
‘Towards’ using grazing markers to determine grazing intake
Ron LewisDepartment of Animal and Poultry Sciences
2013 “Brown Bagger” Webinar SeriesOctober 30, 2013
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)
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 maintenancevaries appreciably
(Ferrell and Jenkins, 1985)
Challenge
Plant-wax markersMeasurementPrediction
Our processValidationAdditional markersExtension to pasture
Summing up
Today’s talk
Plant cuticular wax
Wax on external surface of plantsComplex mixture with chemical
composition that differs appreciably among plant species
n-alkanes (hydrocarbons)Over 90% have odd-numbers of
carbons (C29, C31 and C33 predominant)
Relatively inert and ‘easy’ to assess
(Dove and Mayes, 2005)
Measurement
PlantAssess n-alkane profiles of plants
Animal (fecal sample)Diet composition
Assess n-alkane profile of fecal sampleFeed intake (and whole-diet digestibility)
In addition, dose with even-chain n-alkane (C32)
Prediction
Diet compositionMatch n-alkane concentrations in feces
with combinations of plant profiles Feed intake (I )
𝐼=Dose rate 𝑗
( 𝐹 𝑗×𝑅 𝑖
𝐹 𝑖×𝑅 𝑗)× (𝐻 𝑖−herbage content 𝑗 )
𝑖−odd −chain n −alkane𝑗−odd−chain n−alkane
Our process
Validation (n-alkanes)Characterize plantsPredict diet composition
Additional markers Extension to pasture
Fecal samplingDosing
Characterize plants (simple mixture)
C27 C29 C31 C330
100
200
300
400
Red cloverFescue
mg
/kg
DM
n-alkanes LCOH
Characterize plants (simple mixture)
Characterize plants (simple mixture)
0.0 0.2 0.4 0.6 0.8 1.00.0
0.2
0.4
0.6
0.8
1.0
Test diet composition: fescue proportion
ABC
Actual proportion (kg/kg)
Pre
dic
ted
pro
po
rtio
n (
kg/k
g)
𝑦=𝑥
0.2 0.3 0.4 0.5 0.6 0.70.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8Cattle diet composition: red clover
Estimated proportion (kg/kg)
Ob
serv
ed
pro
po
rtio
n (
kg/k
g)
Predictions (simple mixture)
𝑦=𝑥
Forb Curly dock Dandelion Lambsquarter
Forage Alfalfa Clover
Red White
Fescue Orchard grass Smooth brome
Characterize plants (complex mixture)
Characterize plants (complex mixture)
-1.0 -0.5 0.0 0.5 1.0 1.5 2.0-1.5
-1.0
-0.5
0.0
0.5
1.0
PC1
PC
2
C27, C29
C31
C33
White clover
Alfalfa
Red clover
Fescue
Orchard grass
Smooth brome
Curly dock
Dandelion
Lambsquarter
Prediction (complex mixture)
Avg.
1 2 3 4 5 6 7 8 9 100%
20%
40%
60%
80%
100%Cattle diet composition: mixed plants
White CloverRed CloverAlfalfaFescueSmooth bromeOrchard Grass
Animal
Per
cent
of
diet
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)
Characterize plants (simple mixture)
C27 C29 C31 C33 C26-OH
C28-OH
C30-OH
0
100
200
300
400
500
Red cloverFescue
mg
/kg
DM
n-alkanesLCOH(Vargas Jurado, 2012)
Characterize plants (simple mixture)
Extension to pasture: dosing
Extension to pasture: sampling
Need to link fecal sample to an animal
Day 2
Day 3
Extension to pasture: sampling
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
Summing up
If scalable, such information may contribute topasture management systemsanimal selection decisions
Summing up
National sire (bull) testing program Progeny tests within feedlot and pasture-
based systems Evaluate ‘sensitivities’ in feed efficiency
relative to production system
Thanks for listening
Faculty/Staff Sarah Blevins David Fiske Harold McNair Terry Swecker Amy Tanner
The Hutton Institute Bob Mayes
Graduate student Napo Vargas Jurado
Undergraduate students Amy Brandon Patricia Helsel Annie Laib Jaime Rutter
USMARC Harvey Freetly Heidi Hillhouse John Keele Larry Kuehn Sam Nejezchleb
Virginia Tech