bob weaber, ph.d.0 3 6 9 2 5 8 1 4 7 0 3 6 9 2 5 8 1 4 7) s) beef production total commercial cattle...

Robert L. Weaber, Ph.D. 6/02/2010

Beef Production and Recording Technical Session - 37th ICAR Session - Riga, Latvia 1

Bob Weaber, Ph.D.State Extension Specialist-Beef Genetics University of Missouri

[email protected]

US Beef Industry Inventories

Production

Structure Trends Genetic

Performance Recording

Breed Registrations

New trait development

New Technologies

06/02/2010 Beef Production and Recording Technical Session - 37th ICAR Session - Riga, Latvia 2

mailto:[email protected]



US beef production is a big business

Largest segment in Ag sector of US economy

$61 billion in 2007 (USDA, 2007)

32.8 million beef cows (USDA, 2007)

765,000 beef farms/ranches (USDA, 2009a)



42,65433,333

132,028

94,521

0

5,000

10,000

15,000

20,000

25,000

30,000

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

1950

1953

1956

1959

196

2

196

5

196

8

1971

1974

1977

198

0

198

3

198

6

198

9

199

2

199

5

199

8

200

1

200

4

200

7

Po

un

ds

(mil

lio

ns)

He

ad

(th

ou

san

ds)

Beef Production Total Commercial Cattle Slaughter Cattle and Calves Inventory

(USDA, 2010)




Segmented business Uncoordinated

▪ Marketing▪ Breeding decisions/objectives

Undifferentiated products and market channels

‘Island mentality’


Seedstock

Feedyard

Packer

Consumer

Commercial




0

50,000

100,000

150,000

200,000

250,000

300,000

350,000

400,000

198

919

90

199

119

92

199

319

94

199

519

96

199

719

98

199

920

00

200

120

02

200

320

04

200

520

06

200

720

08

An

nu

al R

eg

istr

ato

ins

ANGUS

HEREFORD

RED ANGUS

CHAROLAIS

SIMMENTAL

LIMOUSIN

CHIANINA

GELBVIEH

MAINE ANJOU

SALERS


-20

0

20

40

60

80

100

800

850

900

950

1000

1050

1100

1150

1200

1970 1975 1980 1985 1990 1995 2000 2005 2010

YW

EP

D

Bu

lls

Ad

just

ed

YW

(lb

)

Year of Birth

YW-Bulls YW EPD

Data Source: 2009 Am. Angus Sire Evaluation Report; Phenotypic and Genetic Trends




800850900950

100010501100115012001250

-20 0 20 40 60 80 100

YW

-Bu

lls

YW EPD

YW Line Fit Plot

YW-Bulls Predicted YW-Bulls


YWPheno = 910 + 3.38*YWEPD

R2 = 0.96


-3

-2

-1

0

1

2

3

65

67

69

71

73

75

77

79

81

83

85

1970 1975 1980 1985 1990 1995 2000 2005 2010

BW

EP

D

Ad

just

ed

BW

-B

ull

s

Year of Birth

BW-Bulls BW EPD





65

70

75

80

85

-3 -2 -1 0 1 2 3

BW

-Bu

lls

BW EPD

BW Line Fit Plot

BW-Bulls Predicted BW-Bulls


BWPheno = 75 + 2.68*BWEPD

R2 = 0.95

0

2

4

6

8

10

12

19

85

19

86

19

87

19

88

19

89

19

90

19

91

19

92

19

93

19

94

19

95

19

96

19

97

19

98

19

99

20

00

20

01

20

02

20

03

20

04

20

05

20

06

20

07

Me

an B

W E

PD

Across Breed EPD Genetic Trends-BIRTH WEIGHTAll Breeds Presented on ANGUS EPD Base

AN

AR

HH

CH

GV

LM

MA

SM

Weaber and Fennewald, 2009

06/02/2010 14Beef Production and Recording Technical Session - 37th ICAR Session - Riga, Latvia




0

10

20

30

40

50

60

70

19

85

19

86

19

87

19

88

19

89

19

90

19

91

19

92

19

93

19

94

19

95

19

96

19

97

19

98

19

99

20

00

20

01

20

02

20

03

20

04

20

05

20

06

20

07

Me

an W

W E

PD

Across Breed EPD Genetic Trends-WEANING WEIGHTAll Breeds Presented on ANGUS EPD Base

AN

AR

HH

CH

GV

LM

MA

SM



0

10

20

30

40

50

60

70

80

90

19

85

19

86

19

87

19

88

19

89

19

90

19

91

19

92

19

93

19

94

19

95

19

96

19

97

19

98

19

99

20

00

20

01

20

02

20

03

20

04

20

05

20

06

20

07

Me

an Y

W E

PD

Across Breed EPD Genetic Trends- YEARLING WEIGHTAll Breeds Presented on ANGUS EPD Base

AN

AR

HH

CH

GV

LM

MA

SM





-10

-5

0

5

10

15

20

25

19

85

19

86

19

87

19

88

19

89

19

90

19

91

19

92

19

93

19

94

19

95

19

96

19

97

19

98

19

99

20

00

20

01

20

02

20

03

20

04

20

05

20

06

20

07

Me

an M

ilk E

PD

Across Breed EPD Genetic Trends-MILKAll Breeds Presented on ANGUS EPD Base

AN

AR

HH

CH

GV

LM

MA

SM



(Am. Angus Association, 2009; Am. Simmental Association, 2009)




Breed Bir

th W

eig

ht

Wean

ing

Weig

ht

Milk

Yearl

ing

Weig

ht

To

tal M

ate

rnal

Yearl

ing

Heig

ht

Matu

re H

eig

ht

Matu

re W

eig

ht

Scro

tal C

ircu

mfe

ren

ce

Gesta

tio

n L

en

gth

Calv

ing

Ease D

irect

Calv

ing

Ease M

ate

rnal

Heif

er

Pre

gn

an

cy

Carc

ass W

eig

ht

Rib

eye A

rea

Fat

Th

ickn

ess

Marb

lin

g

Reta

il P

rod

uct

Yie

ld G

rad

e

Ten

dern

ess

Perc

en

t In

tram

uscu

lar

Fat

Rib

eye A

rea

Fat

Th

ickn

ess

Reta

il P

rod

uct

Sta

yab

ilit

y

Main

ten

an

ce E

nerg

y

Do

cilit

y

Angus X X X X X X X X X X X X X X X X X X X X X X X

Charolais X X X X X X X X X X X X

Gelbvieh X X X X X X X X X X X X X

Hereford X X X X X X X X X X

Limousin X X X X X X X X

Maine Anjou X X X X X X X X X X

Red Angus X X X X X X X X X X X X X

Simmental X X X X X X X X X X X X X X X X X

Growth Reproduction Carcass Ultrasound Other


0%

20%

40%

60%

80%

100%

120%

140%

0

50,000

100,000

150,000

200,000

250,000

300,000

350,000

400,000

450,000

196

2

196

5

196

8

1971

1974

1977

198

0

198

3

198

6

198

9

199

2

199

5

199

8

200

1

200

4

200

7

WW

Re

cord

s a

s P

erc

en

tag

e o

f R

eg

istr

ati

on

s

Re

cord

Co

un

t

YearAAA Registrations Weaning Weights Recorded WW Records as Percentage of Registrations

(Am. Angus Association, 2010b)




0

1,000,000

2,000,000

3,000,000

4,000,000

5,000,000

6,000,000

7,000,000

Cu

mu

lati

ve R

eco

rd C

ou

nt

Year

Birth Wt.

Weaning Wt.

Yearling Wt.

Ultrasound



0

50,000

100,000

150,000

200,000

250,000

300,000

350,000

400,000

0%

50%

100%

150%

200%

250%

AA

A R

eg

istr

ati

on

s

Re

cord

co

un

t a

s P

erc

en

tag

e o

f p

revi

ou

s y

ea

rs r

eg

istr

ati

on

s

Year

Calving Ease

Birth Wt.

Weaning Wt.

Yearling Wt

Yearling Ht.

Carcass

Ultrasound

AAA Registrations




Traditional – registration and performance systems separate ($ for each) – Pay as you go

Cow or herd based systems One fee for performance record processing,

registration and transfer

Cow based system, per cow charge on entire inventory (AR, SM, GV)

Hybrid systems (decoupling of registration and performance record charges)

Objectives – more performance record density and fertility/longevity data


Calving Ease (Direct and Maternal)

DONE!

Growth (BW, WW, YW, CW)

DONE!

Carcass (Marbling, Yield, Fat Thickness, REA)

DONE!




Maternal/Reproduction

Heifer Pregnancy

Stayability/LPL

▪ SP(5|1)

▪ S(5| 1,2,3,4)

Temperament/Docility

Chute Scores

Feed Intake/Efficiency

FCR, FE, RFI, FI


Difficult?

Lots of EPDs

Some for Economically Relevant Trait (ERT) some for Indicator Traits

Important?

More than one trait is important for enterprise, operation or industry profitability

Beef Production and Recording Technical Session - 37th ICAR Session - Riga, Latvia 2606/02/2010



A trait that has a direct cost or return associated with it is an Economically Relevant Trait (ERT).

Traits that are correlated to ERTs are indicator traits.

Example: Is Birth Weight or Calving East the ERT? Why??

Weaning Weight or Yearling Weight?

Beef Production and Recording Technical Session - 37th ICAR Session - Riga, Latvia 2706/02/2010

Reproduction:Growth:End Product

2:1:1(Melton, 1995)




$W = Weaning Value ($ per head) BW, WW, Milk, Mature Wt.

$F = Feedlot Value ($ per head) WW, YW and correlations

$ QG = Quality Grade ($ per head) $ YG = Yield Grade ($ per head) $G = Grid Value ($ per head) Grade and yield components

$B = Beef Value ($ per head) $F and $G adjusted for weight and costs

$EN = Cow Energy (savings/cow/year) Milk and Mature Wt. as they relate to maintenance energy

requirementsBeef Production and Recording Technical Session - 37th ICAR Session - Riga, Latvia 2906/02/2010

New tools Sequencing

SNP detection and characterization

High throughput genotyping platforms▪ 3K, 20K, 50K, 800K SNP per chips

Bovine Genome Sequence SNP identification

Additional SNP discovery to build BovineSNP50 chip




Increased utility Markers will describe a larger portion of additive

genetic merit▪ Inclusion of many more marked QTL

Panels will be generated for a larger number of ERT and qualitative traits

Enhance selection response and elimination of deleterious genes

Incorporation of genomic data into genetic evaluation systems

Rapid proliferation over next 12-18 months


n=1,707 animals


(Adapted from Rolf et al., 2010)



0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

100 500 1000 2500 5000 10000 15000 20000 25000 30000 35000 40000

Co

rrela

tion

No. SNPs in Subset of G

Correlation between NRM and subsets of GRM

Correlation between subsets of GRM and full GRM

0.9147


(Adapted from Rolf et al., 2010)

=35


2 2 20.20;% 50;% 10A Ph

(Rolf et al., 2009)



Large marker panels or whole genome selection system

Incorporate marker data into EPD calculation Am. Simmental used WBSF markers in computation

of EPD

Am. Angus Association debut of Genome Assisted Carcass EPDs

Improves accuracy for young animals/selection candidates

Reduces need to collect expensive phenotypes Eliminates discrepancies in MBV and EPD



MARB IMF MBV

MARB ~0.45 ~ 0.60 ??IMF ~0.35 ??

MBV ~ 1.0

Heritability on diagonal, genetic correlations off diagonal



“The BIF Commission believes that information from DNA test only has value in selection when incorporated with all other available forms of performance information for economically important traits in NCE, and when communicated in the form of an EPD with a corresponding BIF accuracy.”

--Tess 2008


Development of ERTs

Phenotypic record collection is difficult and expensive

Expansion of herd/cow based recording systems to capture fertility/longevity data

Expansion of participation in performance system by seed stock and commercial producers

Implementation of genomic selection tools

ERT/defect focused

Adequately sized resource populations for training/QTL discovery

Evaluation and delivery platforms to handle genotype/MBV data

Cost vs. response to selection




Am. Angus Association. 2010a. 2009 Angus Sire Evaluation report. http://www.angus.org/Nce/AHIRAvg.aspx. Accessed May 20, 2010. Am. Angus Association. 2010b. Personal communication: Historic performance record and registration counts time series. Bourden, R. M. 1998. Shortcomings of current genetic evaluation systems. J. Anim. Sci. 76:2308-2323 Garcia, L. G., K. L. Nicholson, T. W. Hoffman, T. E. Lawrence, D. S. Hale, D. B. Griffin, J. W. Savell, D. L. VanOverbeke, J. B. Morgan, K. E. Belk, T.

G. Field, J. A. Scanga, J. D. Tatum, and G. C. Smith. 2008. National Beef Quality Audit–2005: Survey of targeted cattle and carcass characteristics related to quality, quantity, and value of fed steers and heifers. J. Anim. Sci. 86:3533-3543.

Golden, B. L., D. J. Garrick, S. Newman, and R. M. Enns. 2000. Economically Relevant Traits A Framework for the Next Generation of EPDs. Proc. 32nd Annual Research Symposium and Annual Meeting, Beef Improvement Federation, Wichita, Kansas.

Hazel, L. N. 1943. The genetic basis for constructing selection indexes. Genetics 28:476-490. Hazel L. N. and J. L. Lush. 1943. The efficiency of three methods of selection. Journ. Hered. 33:393-399. Henderson, C. R. 1951. Mimeo published by Cornell University. Ithaca, NY. Koots, K. R., J. P. Gibson, and J. W. Wilton. 1994. Analyses of published genetic parameter estimates for beef production traits. 2. Phenotypic and

genetic correlations. An Breeding Abstr 62:825. MacNeil, M. D., J. D. Nkrumah, B. W. Woodward, and S. L. Northcutt. 2010. Genetic evaluation of Angus cattle for carcass marbling using

ultrasound and genomic indicators. J Anim Sci 2010 88: 517-522. National Cattlemen’s Beef Association (NCBA). 2000. Executive Summary of the 2000 National Beef Quality Audit. Accessed on May 7, 2007 at:

http://www.texasbeef.org/files/publication_4_NBQA2000.pdf Roeber, D. L., D. R. McKenna, P. K. Bates, T. B. Schmidt, K. E. Belk, T. G. Field, J. A. Scanga, J. W. Savell, J. B. Morgan, T. H. Montgomery, and G. C.

Smith. 2002. The 2000 National Beef Quality Audits: Views of producers, packers, and merchandisers on current quality characteristics of beef. Prof. An. Sci. 18:112-119.

Rolf, M. M., J. F. Taylor, R. D. Schnabel, S. D. McKay, M. C. McClure, S. L. Northcutt, M. S. Kerley and R. L. Weaber. 2010. Impact of reduced marker set estimation of genomic relationship matrices on genomic selection for feed efficiency in Angus cattle. BMC Genet. 11:24.

Rolf, M. M. 209. M.S. Thesis, University of Missouri. USDA. 2009a. 2007 Census of Agriculture: United States Summary and State Data. Vol. 1, Geographic Series, Part 51, Pages 7,19. USDA. 2009b. National Weekly Direct Slaughter Cattle - Formulated and Forward Contract – Domestic. Accessed on 07/08/2009 at:

http://mpr.datamart.ams.usda.gov/menu.do?path=\Species\Cattle\Weekly Cattle\(LM_CT151) National Weekly Direct Slaughter Cattle -Formulated and Forward Contract - Domestic

USDA. 2009c. National Weekly Direct Slaughter Cattle - Negotiated Purchases. Accessed on 07/08/2009 at: http://mpr.datamart.ams.usda.gov/menu.do?path=\Species\Cattle\Weekly Cattle\(LM_CT154) National Weekly Direct Slaughter Cattle -Negotiated Purchases


http://www.angus.org/Nce/AHIRAvg.aspx

bob weaber, ph.d.0 3 6 9 2 5 8 1 4 7 0 3 6 9 2 5 8 1 4 7) s) beef production total commercial cattle...

Documents