‘towards’ using grazing markers to determine grazing intake

‘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

400Red cloverFescue

mg/

kg D

M

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

1.0Test diet composition: fescue proportion

ABC

Actual proportion (kg/kg)

Pre

dict

ed p

ropo

rtion

(kg/

kg)

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)

Obs

erve

d pr

opor

tion

(kg/

kg)

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 d

iet

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

500Red cloverFescue

mg/

kg D

M

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

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