June 28 - July 1, 2010Columbia, MO

June 28 - July 1, 2010Columbia, MO

Proceedings of the Beef Improvement Federation

42nd Annual Research Symposium

& Annual Meeting

Proceedings of the Beef Improvement Federation

42nd Annual Research Symposium

& Annual Meeting

®

Program and proceedings publication produced with the support of The Beef Checko� Program.

Hosted by...

BIF Presidents

Brian McCulloh

Photo courtesy Angus Productions, Inc.

Jim Leachman 1992-1993Marvin Nichols 1994Glenn Brinkman 1995Burke Healey 1996Gary Johnson 1997Jed Dillard 1998Willie Altenburg 1999Galen Fink 2000Connee Quinn 2001Richard McClung 2002S R Evans, Jr 2003Jimmy Holliman 2004Lynn Pelton 2005Chris Christensen 2006Lora Rose 2007Tommy Brown 2008Brian McCulloh 2009

Frank Baker Initial ChairClarence Burch 1969-1970Doug Bennett 1971-1972David Nichols 1973-1974Ray Meyers 1975-1976Martin Jorgensen 1977-1978James Bennett 1979Mark Keffeler 1980Jack Farmer 1981Roger Winn 1982Steve Radakovich 1983Bill Borror 1984Gene Schroeder 1985Henry Gardiner 1986Harvey Lemmon 1987Bob Dickinson 1988-1989Jack Chase 1990-1991

2009-2010 BEEF IMPROVEMENT FEDERATION BOARD OF DIRECTORS

HistorianTwig MarstonUniversity of NebraskaNE Research & Extension601 E Benjamin Ave , Ste 104Norfolk, NE 68701402-370-4001 (O)402-649-2365 (C)402-370-4010 (F)tmarston2@unl edu

Breed Association RepsLarry KeenanRed Angus Association of

America4201 N I-35Denton, TX 76207940-387-3502larry@redangus org

Sally NorthcuttAmerican Angus Association3201 Fredrick Avenue St Joseph, MO 64506816-383-5157 (P)816-233-9703 (F)snorthcutt@angus org

Wade ShaferAmerican Simmental Associa-

tion1 Simmental WayBozeman, MT 59715406-556-9627 (O)406-587-9301 (F)wshafer@simmgene com

Jack WardAmerican Hereford AssociationPO Box 014059Kansas City, MO 64101816-842-3757 (O)816-842-6931 (F)jward@hereford org

Susan WillmonAmerican Gelbvieh Association10900 Dover StreetWestminster, CO 80021303-465-2333303-465-2339 (F)susanw@gelbvieh org

Producer RepsMark Cowan (at large)Cain Cattle Company4591 Attalla Road 4167Sallis, MS 39160662-289-3770 (O)662-582-2246 (C)markc@caincattle com

Troy Marshall (west)30649 Co Rd 53Burlington, CO 80807719-342-0201 (P)marshallcattlecompany@

hotmail com

Larry Mehlhoff (west)97 Duncan District RoadSheridan, MT 59749406-842-5693406-596-12045lranch@3rivers net

Gordon Stucky (central)Stucky Ranch421 NE 70 AvenueKingman, KS 67068620-532-4122gordon@stuckyranch com

Steve Whitmire (east)Ridgefield Farm1960 Brasstown RoadBrasstown, NC 28902828-837-6324828-835-8376swhitmire@aol com

Kevin Yon (east)Hwy 392P O Box 737Ridge Spring, SC 29129803-685-5048 (O)803-685-0548 (F)kyon@pbtcomm net

OthersTommy Brown 111 Charles StreetClanton, AL 35045205-351-1328 (C)205-755-5431 (O)tbrown205@bellsouth net

PresidentBrian McCulloh57589 Tainter Hollow RoadViroqua, WI 54665608-606-3238 (cell)608-675-3238 (H)woodhill@mwt net

Vice PresidentBen Eggers 3939 S Clark StreetMexico, MO 65265573-581-1225 (O)eggers@socket net

Executive DirectorJoe CassadyDepartment of Animal ScienceNorth Carolina State UniversityBox 7621Raleigh, NC 27695-7621919-513-0262 (O)919-515-6884 (F)joe_cassady@ncsu edu

Regional SecretariesMark Enns (west)Dept of Animal SciencesColorado State UniversityFort Collins, CO 80523-1171970-491-2722 (O)970-491-5326 (F)menns@lamar colostate edu

Jane Parish (east)Animal and Dairy SciencesBox 9815Mississippi State, MS 39762662-325-7466 (O)662-325-8873 (F)jparish@ads msstate edu

Bob Weaber (central)S134A ASRCUniversity of MissouriColumbia, MO 65211573-882-5479 (O)573-884-4545 (F)weaberr@missouri edu

Darrh Bullock804 W P Garrigus BuildingUniversity of KentuckyLexington, KY 40546-0215859-257-7514 (O)859-257-3412 (F)dbullock@uky edu

Tom FieldNCBA9110 E Nichols Ave #300Centennial, CO 80112303-850-3373 (O)303-770-7109 (F)970-217-6233 (Cell)tfield@beef org

Brian HouseSelect Sires, Inc 11740 US 42 NorthPlain City, OH 43064614-733-3420 (O)614-406-8449 (cell)ssbeef@selectsires com

Don MackenzieCanadian Beef Breeds Council320, 6715 – 8 Street N ECalgary, Alberta T2E 7H7CANADA403-938-7543 (O)403-938-7543 (F)info@canadianbeefbreeds com

Mike TessMontana State UniversityLinfield HallBozeman, MT 59717406-581-9071 (O)406-585-4685 (F)michaelwtess@gmail com

Mark ThallmanU S Meat Animal Research

CenterPO Box 166Clay Center, NE 68933-0000402-762-4261 (O) 402-762-4173 (F)mark thallman@ars usda gov

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BIF ProceedingsTable of Contents

How the Next Generation of Genetic Technologies Will Impact Beef Cattle Selection 46

Jerry Taylor, Ph.D., University of Missouri

Implementation and Deployment of Genomically Enhanced EPDs: Challenges and Opportunities 57

Sally Northcutt, Ph.D., American Angus Association

Technical Session Speaker Proceedings Papers

Understanding Cow Size and Efficiency 62Jennifer Johnson and J. D. Radakovich, King Ranch

Institute for Ranch Management, Texas A&M University, Kingsville

Across-Breed EPD Tables for the Year 2010 Adjusted To Breed Differences for Birth Year Of 2008 71

Larry A. Kuehn, Ph.D., L. Dale Van Vleck, Ph.D., R. Mark Thallman, Ph.D., and Larry V. Cundiff, Ph.D., USDA-ARS, MARC

Mean EPDs Reported By Different Breeds 93Larry A. Kuehn and R. Mark Thallman, USDA-ARS,

MARC

Value of DNA Marker Information for Beef Bull Selection 98

Alison Van Eenennaam, Ph.D., University of California-Davis

Use of BovineSNP50 Data for Feed Efficiency Selection Decisions in Angus Cattle 103

Megan Rolf, University of Missouri

Frank Baker Memorial Scholarship Award 118

Frank Baker Memorial Scholarship Past Recipients 120

BIF Presidents Inside front cover

2009-2010 BIF Board of Directors 1

Schedule of Events 4

Animal Breeding Then to Now: A Tribute to Dick Quass 8

General Session Speaker Biographies 9• Barry Dunn, Ph.D.• Matt Spangler, Ph. D.• Mike John• Imogene Latimer, DVM• Eric DeVuyst, Ph.D.• Tom Field, Ph.D.• Jerry Taylor, Ph.D.• Curt Van Tassell, Ph.D.• Sally Northcutt, Ph.D.• Larry Cundiff, Ph.D.

General Session Speaker Proceedings Papers

A Systems Approach to Beef Improvement 13Barry Dunn, Ph.D., Dean, College of Agricultural &

Biological Sciences, South Dakota State University

Production (And) or Profit? Focusing Our Breeding Objectives By Selecting For Profitable Genetics, Not Necessarily High Production Genetics 22

Matt Spangler, Ph.D., Extension Specialist, University of Nebraska-Lincoln

Adding Value to a Weaned Calf Marketing System 28

Mike John, MFA Inc. and John Ranch Inc.

Evaluation of Investment in Agricultural Technology 30

Eric DeVuyst, Ph.D., Oklahoma State University

Raising Beef in a First World Country: Science, Media and Politics 39

Tom Field, Ph.D., National Cattlemen’s Beef Association

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2010 Winning Frank Baker Memorial Scholarship Essay - Kent Gray . . . . . . . 121

Roy Wallace Scholarship . . . . . . . . . . . . 135

2010 Beef Improvement Federation Student Travel Fellowship Recipients . . . . . . . . . 136

Seedstock Producer Honor Roll of Excellence . . . . . . . . . . . . . . . . . . 137

Seedstock Producer of the Year Past Recipients . . . . . . . . . . . . . . . . 146

Seedstock Producer of the Year Nominees . . . 147

Commercial Producer Honor Roll of Excellence . . . . . . . . . . . . . . . . . . 152

Commercial Producer of the Year Past Recipients . . . . . . . . . . . . . . . . 159

Commercial Producer of the Year Nominees . . 160

Pioneer Award Past Recipients . . . . . . . . . 164

Continuing Service Award Past Recipients . . 167

Ambassador Award Past Recipients . . . . . . 169

2009 Frank Baker Memorial Scholarship Award . . . . . . . . . . . . . . . 170

Scott Speidel, Colorado State University, Fort Collins, Colorado

Lance D. Leachman, Virginia Tech, Christiansburg, Virginia

2009 Seedstock Producer of the Year . . . . . . 172Champion Hill, Inc., of Bidwell, Ohio

Harrell Hereford Ranch of Baker City, Oregon

2009 Commercial Producer of the Year . . . . 174JHL Ranch, Ashby, Nebraska

2009 Pioneer Award . . . . . . . . . . . . . . . 175Bruce Orvis of Orvis Cattle Company, Farmington, California

Bruce Golden, Ph.D., California Polytechnic State University, San Luis Obispo, California

Roy McPhee (posthumously), Lodi, California

2009 Continuing Service Award . . . . . . . . 179Darrh Bullock, Ph.D., Lexington, Kentucky

David A. Daley, Ph.D., Oroville, California

Renee Lloyd, McCormick Company, Johnston, Iowa

Mark R. Thallman, Ph.D., U.S. Meat Animal Research Center (USMARC), Clay Center, Nebraska

2009 Ambassador Award . . . . . . . . . . . . 183Kelli Meged Toledo,Trailhead Designs,Visalia, California

2010 BIF Sponsors . . . . . . . . . . . 184, 186-187

2010 BIF Pre-Conference Tour . . . . . . . . . 185

2010 BIF Post-Conference Tour . . . . . . . . . 185

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-Schedule of Events-All conference events will be at the Holiday Inn Select Executive Conference Center unless otherwise noted.

Monday, June 28, 2010

7:00 am – 5:00 pmPre-conference Tour Depart from hotel lobby at 7:00 am. Tour will drop

participants on MU Campus for final tour stops and Opening Reception.

Noon – 5:00 pmRegistration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Atrium Foyer Sponsored by Osborn and Barr1:00 – 4:30 pmBeef Improvement Federation Board of Directors

Meeting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Parliament II

5:00 – 9:00 pmBus Shuttle to MU Campus & Return to Hotel Sponsored by Boehringer Ingelheim Vetmedica, Inc.

5:30 – 7:00 pmOpening Reception . . . . . . . . . . . . . . . . . . . . . . . . . . .

Christopher S. Bond Life Sciences Center, Univer-sity of Missouri Campus

Sponsored by Land O’Lakes Purina Feeds7:00 – 8:30 pmAnimal Breeding Then to Now: A Tribute to

Dick Quaas

Tuesday, June 29, 2010

6:00 am – 6:00 pmRegistration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Atrium Foyer Sponsored by Osborn and Barr6:00 am – 9:00 pmMedia Room Open . . . . . . . . . . . . . . . . . . . . . . . . . . .

Polo Room Sponsored by Angus Productions, Inc.6:30 am – 8:00 amContinental Breakfast . . . . . . . . . . . . . . . . . . . . . . . . .

Expo Sponsored by University of Missouri Extension

General Session I: Genetic Selection to Achieve Your Profit Objective: Using Today’s Tools . . . . .Expo

Sponsored by Pfizer Animal Genetics8:00 – 8:30 amWelcome 8:35 – 9:25 amA Systems Approach to Beef Improvement Barry Dunn, Ph.D., Dean, College of Agricultural &

Biological Sciences, South Dakota State University9:30 – 10:30 amProduction (And) or Profit? Focusing Our Breed-

ing Objectives By Selecting For Profitable Genet-ics, Not Necessarily High Production Genetics

Matt Spangler, Ph.D., Extension Specialist, University of Nebraska-Lincoln

10:30 - 10:50 amBreak Sponsored by National Association of Animal

Breeders Beef Committee Members- ABS Global, Accelerated Genetics, Genex CRI, ORIgen, Select Sires

10:50 – 11:30 amMaking Selection Tools Work for Us: Producer

Panel Discussion Moderated by Brian McCulloh, BIF President Adding Value to a Weaned Calf Marketing

System Mike John, MFA Inc. and John Ranch Inc.

Adding Value to a Retained Ownership/End Product Marketing System

Imogene Latimer, DVM, NEMO Premier Beef Marketers

11:30 am – NoonHow to Evaluate Investment in New Technologies Eric DeVuyst, Ph.D., Oklahoma State University

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Noon – 2:30 pmBeef Improvement Federation Awards

Luncheon . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Expo Sponsored by BEEF Magazine

• Commercial Producer of the Year • Baker Scholarship Award • Roy Wallace Award • Ambassador Awards • BIF Continuing Service Awards • Graduate Fellowship Recipient Recognition

Concurrent Technical Keynote Sessions

2:30 – 5:30 pmJoint Session: Advancements in Cowherd Efficiency

And Live Animal, Carcass and Endpoint Windsor III & IV Chairmen: Mark Enns, Ph.D., Colorado State

University and Robert Williams, Ph.D., American International

Charolais Association Sponsored by GrowSafe Systems, Ltd.2:30 - 3:10 pm Evaluating Cow Maintenance John Evans, Ph.D., SWB Consulting Inc.3:10 - 3:50 pmGenetic Evaluation of Residual Gain as an

Alternative Measure of Efficiency of Feed Utilization

Mike MacNeil, Ph.D., USDA-ARS, LARRL3:50 - 4:10 pmBreak Sponsored by Missouri Show-Me-Select

Replacement Heifer Program4:10 - 4:50 pm Genetic Improvement of Feed Utilization in the

Swine Industry William Herring, Ph.D., Smithfield Premium

Genetics Group, Rose Hill, NC4:50 - 5:30 pmA Breed Association Perspective on Feed Efficiency Dan Moser, Ph.D., Kansas State University

2:30 – 5:30 pmAdvancements in Producer Applications

. . . . . . . . . . . . . . . . . . . . . . . . . . . .Windsor I & II Chairman: Jane Parish, Ph.D., Mississippi State

University Sponsored by California Beef Cattle Improvement

Association

2:30 - 3:10 pm Differences in Hair Coat Shedding and Effects

on Calf Weaning Weight And BCS Among Angus Dams

Trent Smith, Ph.D., Mississippi State University3:10 - 3:50 pmShow-Me Select Replacement Heifer Program

Update Mike Kasten, Kasten Ranch, Millersville, MO3:50 - 4:10 pmBreak Sponsored by Missouri Show-Me-Select

Replacement Heifer Program4:10 - 4:50 pmUnderstanding Cow Size and Efficiency Jennifer Johnson and J. D. Radakovich, King Ranch Institute for Ranch Management, Texas

A&M University, Kingsville4:50 - 5:30 pmAnimal Care Issues Are Here to Stay: Are You

Prepared to Deal with Them? Craig Payne, DVM, University of Missouri

5:30 -10:00 pmBus Shuttle to MU Campus & Return to Hotel Sponsored by Boehringer Ingelheim Vetmedica, Inc.6:00 - 9:30 pmEvening BBQ and Social Event with Music by

Becky Blackaby Venue: Trowbridge Livestock Arena, MU Campus Sponsored by IGENITY

Wednesday, June 30, 2010

6:00 am – 6:00 pmRegistration . . . . . . . . . . . . . . . . . . . . . .Atrium Foyer Sponsored by Osborn and Barr6:00 am – 9:00 pmMedia Room Open . . . . . . . . . . . . . . . . . .Polo Room Sponsored by Angus Productions, Inc.6:30 am – 8:00 amContinental Breakfast . . . . . . . . . . . . . . . . . . . . .Expo Sponsored by American Angus Association General Session II: The Future of Beef Cattle

Selection in the U .S . . . . . . . . . . . . . . . . . . Expo Sponsored by Pfizer Animal Genetics

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8:00 – 8:55 amRaising Beef in a First World Country: Science,

Media and Politics Tom Field, Ph.D., National Cattlemen’s Beef

Association9:00 – 9:30 amHow the Next Generation of Genetic Technologies

Will Impact Beef Cattle Selection Jerry Taylor, Ph.D., University of Missouri9:35 – 10:00 amThe Case and The Place for Genomic Tools in Beef

Cattle Selection: How Beef’s Implementation Model Will Be Different from the Dairy Industry’s

Curt Van Tassell, Ph.D., USDA-ARS-AIPL-BFGL10:00 – 10:30 amBreakSponsored by American Hereford Association/

Certified Hereford Beef10:30 – 11:00 amImplementation and Deployment of Genomically

Enhanced EPDs: Challenges and Opportunities Sally Northcutt, Ph.D., American Angus Association11:00 – 11:15 amBIF’s Role in Charting Our Future: Charge and

Session Wrap-Up Larry Cundiff, Ph.D., USDA-ARS, MARC (retired)11:15 am - NoonAnnual meeting, Regional Caucuses, Election of

DirectorsNoon - 2:30 pmBeef Improvement Federation Awards Luncheon

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Expo Sponsored by Missouri Beef Industry Council • Seedstock Producer of the Year Award • Pioneer Award • President’s Address • Afternoon Program Review • 2011 BIF Conference Invitation • International Guest Recognition

Concurrent Technical Keynote Sessions

2:30 – 5:30 pmAdvancements in Genetic Prediction . . Windsor III Chairman: Mark Thallman, Ph.D., USDA-ARS, MARC Sponsored by University of Missouri Extension

2:30 - 3:10 pm Advances Including DNA Tests in Genetic

Evaluations Mike MacNeil, Ph.D., USDA-ARS, LARRL3:10 - 3:50 pmGenetic Evaluation for Reproductive Traits Matt Spangler, Ph.D., University of Nebraska-

Lincoln3:50 - 4:10 pmBreak Sponsored by University of Missouri Extension4:10 - 4:50 pm 2010 Update to Across-breed EPD Adjustment

Factors Larry Kuehn, Ph.D., USDA-ARS, MARC4:50 - 5:30 pmInternational Bovine Genetics Collaboration Gary Bennett, Ph.D., USDA-ARS, MARC

2:30 – 5:30 pmAdvancements in Emerging Technologies

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Windsor IV Chairman: Jack Ward, American Hereford Association Sponsored by Sydenstricker Genetics2:30 - 3:10 pm 2000 Sires Project at USDA-MARC Larry Kuehn, Ph.D., USDA-ARS, MARC3:10 - 3:50 pmGenetics of Heifer Fertility Milt Thomas, Ph.D., New Mexico State University3:50 - 4:10 pmBreak Sponsored by University of Missouri Extension4:10 - 4:50 pm Genetics of Healthfulness of Beef Jim Reecy, Ph.D., Iowa State University4:50 - 5:30 pmGenetics of Feedlot Cattle Health Mark Enns, Ph.D., Colorado State University

2:30 – 5:30 pmAdvancements in Selection Decisions

. . . . . . . . . . . . . . . . . . . . . . . . . . Windsor I & II Chairman: Bob Weaber, Ph.D., University of

Missouri Sponsored by Canadian Beef Breeds Council

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2:30 - 3:10 pmEvaluation of Genetic by Environment Interactions

in Beef Cattle Using Reaction Norms Bill Lamberson, Ph.D., University of Missouri3:10 - 3:50 pmFactors Affecting the Selling Price of Arkansas

Feeder Calves and Implications on Selection Decisions

Brett Barham, Ph.D., University of Arkansas3:50 - 4:10 pmBreak Sponsored by University of Missouri Extension4:10 - 4:50 pm Value Of DNA Marker Information for Beef Bull

Selection Alison Van Eenennaam, Ph.D., University of

California-Davis4:50 - 5:30 pmUse Of BovineSNP50 Data for Feed Efficiency

Selection Decisions in Angus Cattle Megan Rolf, University of Missouri

5:30 – 6:30 pmBeef Improvement Federation Board of Directors

Meeting . . . . . . . . . . . . . . . . . . . . . . . .Parliament II5:30 pmEvening on your own, or departure

Thursday, July 1, 2010

7:00 am - 6:00 pmPost-conference Tour Depart from hotel lobby at 7:00 am. Tour will return

participants to hotel at approximately 6:00 pm

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­Animal­Breeding­Then­to­Now:­A­Tribute­to­Dick­Quaas

Richard“Dick”L.Quaas,Ph.D.,hasrecentlyretiredfromCornellUniversi-tyasProfessorofAnimalBreedingandGeneticsafteralonganddistinguishedcareer.Dickmadenumerouscontributionstothefieldofquantitativegeneticswherehisworkfocusedprincipallyonthegeneticevaluationandimprovementofbeefcattle.ItisontheoccasionofhisretirementthatwetaketimeduringtheBeefImprovementFederationmeetings,oftenattendedbyDick,topaytributetoamanwhoseworkasaresearchscientisthasenabledbeefproducersaroundtheworldtomakemeaningfulchangesinthegeneticmeritoftheirlivestock.

DickwasborntoRoyandGenevieveQuaas.Alongwithhisparentsandsiblings,MaxandMarry,DickwasamemberofafarmfamilyresidingnearAlburnett,Iowa.Asayoungman,heattendedIowaStateUniversityandearnedaBSdegreeinAnimalSciencein1966.Dickservedasvolunteermem-berofthePeaceCorpinGualaquiza,Ecuadorduring1966-67.HethenfarmedinLinnCountyIowaforayearbeforeenteringgraduateschoolin1968.Fol-lowinghisinterestinpopulationgeneticsandanimalbreedingstimulatedby

Dr.LanoyHazelatIowaStateUniversity,DickenrolledinColoradoStateUniversitytofurtherhistraininginthesefields.DickearnedaMSin1970andPhDin1973atColoradoStateUniversityunderthedirectionofDr.ThomasSutherland.

UponcompletionofhisPhD,DickjoinedthefacultyatCornellUniversityasanassistantprofessor.HewaspromotedtoAssociateProfessorin1980andProfessorin1989.Dicktaughtseveralgraduatecoursesinquan-titativegenetics,servedastheDirectorofGraduateStudiesfortheFieldofAnimalScienceandadvisedunder-graduatestudents.Dickistheauthororco-authorofmorethan75peerreviewedpublications.HewasafrequentinvitedspeakeratinternationalandnationalconferencessuchasASAS/ADSA,BIF,WCGALPandothers.Quaasservedasanad hocpeerreviewerforavarietyofpublicationsincludingtheJournalofAnimalScience,JournalofDairyScience,Genetics,GeneticsSelectionEvolution,Biometrics,andHeriditas.Dick’ssabbaticalandstudyleavesfromCornelltookhimtoCSIROTropicalCattleResearchCenter,Rockhampton,Queensland,Australia,theAnimalBreedingandGeneticsUnitattheUniversityofNewEngland,Armidale,NewSouthWales,AustraliaandtheAnimalBreedingGroupattheSwissFederalInstituteofTechnology.

DickworkedwiththeAmericanSimmentalAssociationfor25years.ManyofDick’smethodologiesandresearchresultswerefirstappliedinthegeneticevaluationofSimmentalcattle.Theevolutionof this leadinggeneticevaluationsystemfollowsachronologyofthediscoveriesmadebyDickandresearchpartner,E.JohnPollak.Dickwasinstrumentalinthedevelopmentofthefirstmulti-breedgeneticevaluationsystemandearlyincorporationofDNAmarkerdata intoageneticevaluation forWarner-BratzlerShearForce.Hechaired theNationalBeefCattleEvaluationConsortium’sQuantitativeTraitLocicommitteechargedwithvalidationofcom-mercialDNAmarkerpanelsanddevelopmentofstrategiesforincorporationofmarkerdataintonationalcattleevaluationsystems.

DickistherecipientofnumerousindustryandacademicawardsincludingtheRockefellerPrenticeMe-morialAwardinAnimalBreedingandGeneticsgivenbyAmericanSocietyofAnimalSciencein2006.In2002,hewas received theGoldenBookAward, thehighest honor from theWorldSimmentalFederation.TheBIFrecognizedDick’scontributiontobeefcattleimprovementbypresentinghimthePioneerAwardin1999.Here-ceivedtheJ.L.LushAwardinAnimalBreedingandGeneticsgivenbytheAmericanDairyScienceAssociationin1990andtheYoungScientistAwardin1982fromASAS/ADSA.

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General Session Speakers

Barry H. Dunn, Ph.D.Dean, College of Agriculture & Biological Sciences South Dakota State University

Barry Dunn’s family began ranching and raising cattle in South Dakota since the 1880’s. Educated at South Dakota State University, Barry received his B.S. in 1975 and

a M.S. in Animal Science in 1977. After two years managing a cow-calf research herd for North Dakota State University, Barry returned to South Dakota to manage his family’s ranch near Mission. Barry re-turned to academia in 1997 and received his Ph.D. in Animal Science in 2000. He served on the Animal and Range Science faculty at SDSU from 2000-2003. In 2004, Barry became the first Executive Director and Endowed Chair of the King Ranch Institute for Ranch Management at Texas A&M University-Kingsville. In May of 2010, he assumed the position of Dean of the College of Agriculture and Biological Sciences at SDSU.

Matt Spangler, Ph.D.Assistant Professor, University of Nebraska-Lincoln

Matt Spangler grew up on a diversified crop and livestock farm in south-central Kansas where his family still farms and has a cow/calf operation. After receiving his B.S. de-gree in Animal Science from Kansas State University (2001) he attended Iowa State University and received his M.S. degree in Animal Breeding and Genetics in 2003. He received his Ph.D. at the University of Georgia in Animal Breeding and Genetics (2006) and is currently an Assistant Professor and Extension Beef Genetics Specialist at the Univer-sity of Nebraska-Lincoln. Matt focuses on developing and delivering extension material related to the ge-netic improvement of beef cattle, within Nebraska and

nationwide. The majority of this effort is centered on the use of genomic tools. From a teaching perspective, he coordinates the Nebraska Beef Industry Scholars program and is responsible for the UNL teaching herd and annual bull sale. His research interests include the integration of molecular data into national cattle evalu-ations. He has served on the Ultrasound Guidelines Council, is a member of the National Beef Cattle Evalu-ation Consortium Producer Education Team, and serves as a member of the Editorial Board for the Journal of Animal Science.

Mike JohnDirector of MFA Health Track Operations Huntsville, Missouri

Mike John currently serves as the Director of MFA Health Track Operations. Health Track is a Vac 45 and ASV certified preconditioning pro-gram that has enrolled nearly 400,000 head since 2001. In addition, Mike is actively involved in the management of his family’s commer-cial cow/calf and retained ownership operation near Huntsville, Mo. He is a 1980 graduate of Kansas State University and holds BS in Animal Science. Mike is known across the U.S. through his leadership roles in the National Cattlemen’s Beef Association (NCBA) where he has served as President and was Chairman of the commission on animal ID and was Vice-Chair of the NCBA Mark of Quality Commission. He has also served NCBA as membership committee chairman, group chair for Industry and Member Services division and served as a board member for the US Meat Export Federation. Mike was Chairman of the Missouri Beef Industry Council, is a past President of the Missouri Cattlemen’s Association, and former board member and Scholarship Chairman of the Missouri Cattlemen’s Foundation.

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Imogene Latimer, DVMNEMO Premier Beef Marketers Hunnewell, MO

Dr.ImogeneLatimerhelpedorganizeNEMOPremierBeefMarketersin1999withthehelpofmembersoftheUniversityofMissouriExtensionBeefTeam.Shecurrentlykeepstherecordsandhelpsdistributetheproceedsforsaleofcattleforthegroup.Sheandherhusband,Kenny,farmandraisecattleinShelbyCounty,andaremembersofNEMOPremierBeefMarketers,participatingfromthebeginning.Dr.Lat-imeralsoisabovineveterinarianworkingparttimewiththeGeneralVeterinaryClinic,LLCinMonroeCitysince1987.

Eric DeVuyst, Ph D Associate Professor, Oklahoma State University

EricDeVuystwasraisedonafarmincentralMichigan.HeholdsaBSandMSfromMichiganStateUniversityinAgriculturalEconomics.HeearnedaPhDfromPurdueUniversity,alsoinAgriculturalEconomics.Hecurrentison

facultyatOklahomaStateUniversitywitharesearch/extensionappointment.Hehasheldpreviousappoint-mentsattheUniversityofIllinoisandNorthDakotaStateUniversity.Amongothertopics,Erichaspub-lishedscholarlyarticlesontheinfluenceofgeneticsonfedcattleprofitsandcalfweaningweights.Hisexten-sionworkislargelyfocusedondevelopfarmdeci-siontools.HeliveswithhiswifeCherylanddaughterMeganonasmallranchnorthofMorrisonOklahoma.

Thomas G Field, Ph D Executive Director of Producer Education National Cattlemen’s Beef Association

TomFieldisExecutiveDirec-torofProducerEducationfortheNationalCattlemen’sBeefAssociationinCentennial,Colorado.PriortojoiningNCBAin2008,DrFieldservedstudentsandindustryasafacultymemberintheDepartmentofAnimalSci-encesatColoradoStateUniversityfornearly20years.Theauthorof“BeefCattleManagementandDeci-sionMaking”and“ScientificFarmAnimalProduc-tion”,hehaspublishedwith44otherfacultymembersrepresenting7differentuniversitiesand8academicdepartments.AfrequentspeakeratbeefcattleeventsintheU.S.andabroad,hehasconsultedwithanum-berofbeefcattleandagriculturalorganizations,andhasservedonnumerousboardsrelatedtoeducation,agriculture,andathletics.

Tom,hiswifeLauraandthreesonsarepartnersinafamily-owned350headcommercialcow-calfopera-tioninwesternColorado.

FieldreceivedhisPh.D.,MSandBSdegreesfromCSU.

Jerry Taylor, Ph D Professor and Wurdack Chair in Animal Genomics, University of Missouri

JerryTaylorisaProfessorofAnimalSciencesandofGeneticsandholdstheWur-dackChairinAnimalGenom-icsintheDivisionofAnimalSciencesattheUniversityofMissouri-Columbia.Heisan

electedFellowoftheAmericanAssociationfortheAdvancementofScienceandisthe2008CelebrationofExcellenceDistinguishedResearcherAwardintheCollegeofAgriculture,FoodandNaturalResourcesattheUniversityofMissouri.HeisamemberoftheiBMACConsortiumwhichdevelopedtheIlluminaBovineSNP50assayforwhichtheteamwonthe2008USDATechnologyTransferAwardandthe2009FLC

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AwardforExcellenceinTechnologyTransfer.Hehasreceivedinexcessof$21millionincompetitiveresearchfundingasPIorco-PI,hasmentored11post-doctoralfellowsandhaschaired36M.S.andPh.D.graduateprograms.Alongwithhisco-investigatorsandgraduatestudentshehasauthored152peerre-viewedresearcharticles,4bookchapters,4patentsandhaseditedonebook.HehasassembledDNAsamplesandphenotypesonover18,000Angus,Lim-ousin,Charolais,Hereford,SimmentalandHolsteincattleandhisresearchfocusesontheidentificationofmutationsresponsibleforphenotypicvariationingrowth,carcasscomposition,feedefficiencyandmilkproduction.From1986to2000,hewasanAssociateandthenfullProfessorintheFacultyofGeneticsandDepartmentofAnimalScienceatTexasA&MUni-versity.HispostdoctoralresearchwasintheDepart-mentofAnimalScienceatCornellUniversitywherehestudiedthegeneticbasesoffertilityinmaleandfemaleHolsteincattle.JerryreceivedaB.Sc.degreeinMathematicsandaB.Sc.HonorsdegreeinMath-ematicalStatisticsfromtheUniversityofAdelaideandaPh.D.inQuantitativeGeneticsfromtheUniversityofNewEnglandinAustralia.

Sally Northcutt, Ph D Director of Genetic Research, American Angus Association

SallyNorthcuttisthegeneticresearchdirectorfortheAmer-icanAngusAssociationandAngusGeneticsInc.Herpri-maryresponsibilitiesincludeselectiontooldevelopment,beefcattledataanalysis,and

themodelingandapplicationoftheNationalCattleEvaluation.ShealsoworkswithuniversitiesandARSacrossthenationtocoordinatetheexpansiveresearchactivitiesoftheAssociation.

BeforecomingtotheAssociation,NorthcuttwasanExtensionbeefcattlebreedingspecialistfornineyearsatOklahomaStateUniversity,andshedirectedtheOkla-homaBeefInc.(OBI)centralbulltestatStillwater.Sheisactivelyinvolvedinindustryorganizations,suchastheBeefImprovementFederation,inwhichshehasservedinvariousleadershiprolesduringthepast12years.

AKentuckynative,Northcuttreceivedherbachelor’sandmaster’sdegreesfromtheUniversityofKentucky

andherdoctorateinbeefcattlebreedingandgeneticsfromIowaStateUniversity.

Curt Van Tassell, Ph D Research Geneticist, USDA-ARS-AIPL-BFGL

Dr.CurtVanTassellisaRe-searchGeneticistattheBovineFunctionalGenomicsLabora-toryandtheAnimalImprove-mentProgramsLaboratory,AgriculturalResearchService,U.S.DepartmentofAgricul-ture,inBeltsville,Maryland.Dr.VanTassell’sresearchprogramspansthreemajorareas:Developmentandimplementationofgenomeenabledselectionincattle;Improvementofsystemsusedinthenationalandinternationalgeneticevalu-ationofdairycattle;Developmentofbioinformatictoolstoacquire,store,andanalyzegenomicdata.Hebeganhiscareerin1994withARSasapostdoctoralresearcherinquantitativegenetics.DuringthattimehewassolelyresponsibleforthedevelopmentofaflexibleparameterestimationprogramcalledMultipleTraitGibbsSamplerofAnimalModels(MTGSAM).In1997hewasappointedtohiscurrentposition,withajointappointmentbetweenthegenomicsandgeneticevaluationgroupsinBeltsville.Inthatpositionhehascontinuedquantitativegeneticsresearch,includ-ingthedevelopmentoftherevisedUSnationalcalv-ingdifficultygeneticevaluationthataddedmaternalgeneticeffects.Hehasdevelopedabioinformaticsinfrastructureofcomputingandhumanresourcestofacilitategenomicsresearchinagriculturallyimportantspecies.Mostrecently,Dr.VanTassell’sactiveroleinthebovinegenomeprojecthasincludedidentifyingthebreedsandspecificanimalsselectedforSNPdis-covery,coordinatingfundingandDNAacquisitionfortheHolstein,Jersey,andBrownSwissbreeds.Finally,hehasledaconsortiumthatdevelopedahigh-densitygenotypingassayforuseincattleandledU.S.D.A.ef-fortstousethistoolforpredictionofgeneticmeritindairycattle.Dr.VanTassellhasbeeninstrumentalincommunicatingtheimportanceofintegrating,quanti-tativegeneticsandgenomicsresearchtothedairyandbeefcattleindustries,andfosteringtheirsupportforongoingresearch.Dr.VanTassellearnedaPh.D.inAnimalBreedingfromCornellUniversity.

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Larry V Cundiff, Ph D Research Geneticist (retired), USDA-ARS-MARC

Dr.LarryCundiffretiredinJanuary2007after40yearsserviceasaResearchGeneti-cistwiththeU.S.DepartmentofAgriculture,AgriculturalResearchService.Hewas

ResearchLeaderoftheGeneticsandBreedingRe-searchUnitattheU.S.MeatAnimalResearchCenterfrom1976until2005,whenheacceptedaninterim8-monthappointmentasActingCenterDirector.Hisresearcheffortshaveinvolvedevaluationandutiliza-tiondiversebreeds,effectsandutilizationofhetero-sisthroughalternativecrossbreedingsystems,andevaluationandeffectivenessofselectionfortraitsofeconomicimportanceinbeefproduction.Sincehisretirement,hehascontinuedserviceasacollaboratorattheU.S.MeatAnimalResearchCenterassistingwithpreparationofresearchreportsandspeakingatbeefindustrymeetingsandconferences.

Dr.CundiffhasservedaschairmanoftheBeefIm-provementFederation(BIF)CommitteeonGeneticPredictionfrom1973until2007,andastheAgricul-turalResearchService,USDArepresentativeontheBIFBoardofDirectorsfrom1981until2007.Recent-ly,hehasservedasEditoroftheBeefImprovementFederation’s9thEditionofGuidelinesforUniformBeefImprovementPrograms.

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A SYSTEMS APPROACH TO BEEF IMPROVEMENT

Barry H. Dunn1 and Jennifer J. Johnson2

1South Dakota State University and 2Texas A&M University-Kingsville

Introduction

“To obtain understanding, we need to supplement the quantitative techniques brought to us through the march of science, with the artistic understanding of qualities that our obsession with science has brushed aside.”

Roger Martin, 2010, Dean of the Rotman School of Management, University of Toronto

From many important perspectives, beef improvement efforts since World War II, particularly the Beef Improvement Federation’s work over the last 42 years, have resulted in tremendous progress. Cattle reproduce more frequently and grow faster and more efficiently than their ancestors of little more than half a century ago. In many respects, the meat products these cattle yield are not only healthier than in the past, they are well accepted in a complex, consumer driven world economy. To a large extent, these changes are the direct result of a linear, scientific approach. This reductionist approach might best be described as an elaborate attempt to solve a complex puzzle by searching for missing puzzle pieces. The puzzle is analogous to the quest for beef improvement. The pieces represent the genes identified and associated with economically important traits. The puzzle is generally accepted as solved by either increasing gene frequency for important traits or by increasing those traits’ dominant allele. The two primary tools used to accomplish this noted beef improvement have been the use of breeding systems and the application of the principles of animal selection. Over the last four decades, the popularity and use of breeding systems has waxed and waned. However, the application of the principles of animal selection has only gained momentum and prominence. Progress via animal selection is limited by three critical constraints. The first is selection differential. The second is generation turnover. The third is the heritability of the trait of interest. Since heritability is generally accepted as fixed, changes in gene frequency are due primarily to the identification of outstanding individuals and the accelerated diffusion of their genes into the population through various techniques and technologies. Therefore the success of this “puzzle” approach to beef improvement has been and remains dependent on the constant development of more advanced technologies. From simple ratios of key metrics to whole animal genomic panels, piece after piece has been added to the puzzle, whose success is widely understood to be change.

But what if beef improvement was approached not as a puzzle, whose solution was just a technology away, but as a mystery? In his provocative book What the Dog Saw, author Malcolm Gladwell challenges the reader with such a question (Gladwell, 2009). Gladwell suggests that puzzles are “transmitter dependent, they turn on what we are told.” That is to say, solutions to puzzles depend on “pieces.” In this case, in order to improve beef, the cattle industry has been dependent on the regular transmission of techniques and technologies that fall into one of two categories. The first are technologies that aid in the identification of individuals that possess unique characteristics in order to increase the selection differential of a breeding program. For

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example, ratios, breeding values, EPDs, genetic markers, and whole animal panels are all designed to aid in the identification of animals with traits that deviate from the average of the population. The second category of techniques and technologies speed the diffusion of a selected animal’s genes through a population. These techniques and technologies include rapid generation turnover, artificial insemination, estrus synchronization, sexed semen, embryo transfer, and cloning. And so, with this puzzle approach, beef improvement has been and remains dependent on the regular transmission of techniques and technology derived from science and industry.

In contrast, Gladwell (2009) says that mysteries are “receiver dependent, they turn on the skills of the listener.” In the case of beef improvement then, his message challenges an industry to be thoughtful and contemplative in its application of techniques and technologies by honing its skills of listening and understanding. For example, is the most economical and effective way of improving beef accomplished by manipulating the bovine genome to increase gene frequency? Or could it be the mechanical treatment of the meat itself? Could it lie with the emerging field of epigenetics? Or perhaps it lies “back to the future” in a learned application of the principles of breeding systems? Perhaps it lies in the thoughtful and strategic combination of multiple strategies. The successful development, evaluation, and application of complex strategies to unravel the “mystery” of improving beef requires knowledge and skills in fields not generally associated with the historic work of beef improvement. Examples are micro and macro economics, managerial accounting, system dynamics, and systems thinking; fields whose mastery is “receiver dependent.” Complex Systems

“Society has become so complex that traditional ways and means are not sufficient anymore. Approaches of a holistic or systems nature have to be introduced.”

Ludwig von Bertalanffy, (Laszlo, 1972)

Bertalanffy’s challenge resonates with a beef industry that struggles with profitability across its many segments and, perhaps even more importantly, shrinking cattle numbers that threaten its place in a dynamic economy. These powerful trends, that seem to have a life of their own, clearly represent a serious challenge to the stability of the entire beef industry. The choice then is to approach this challenge as either a puzzle or a mystery. If this daunting challenge is viewed as a mystery to be unraveled, it needs to be understood as a complex system. But what is a complex system? Hall and Fagen (1968) classically defined systems as “a set of objects together with relationships between the objects and between their attributes.” They define objects as “the parts or components of the system,” attributes as “properties of the objects,” and relationships are the things that “tie a system together.” Waldrop (1992) describes systems as networks of agents acting in parallel, but interacting with each other in a system, where nothing is fixed and control is highly dispersed. Providing deeper elucidation, MIT professor John Sterman (1998) described five fundamental characteristics of a complex system:

1. They are tightly coupled: everything influences everything else. 2. They are dynamic: change occurs on many scales. 3. They are policy resistant: obvious solutions to problems fail or actually make things

worse. 4. They are counterintuitive: cause and effect are distant in time and space.

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5. They exhibit tradeoffs: advantageous short-term behavior is often different, or even antagonistic, to advantageous long-term behavior.

Is the work of improving beef a complex system? Based on the definitions and

characteristics listed above, it clearly is. Evidence abounds. Many of the economically important genetic traits of beef cattle are correlated with each other. However, some are antagonistic. Phenotypic change may exhibit itself in the first calf crop following a mating, or many years later in the offspring of those calves. After generations of work to increase the percentage of “choice” cattle in the marketplace, their proportion in the population of the fed cattle marketed remains remarkably the same (Rhoades et al., 2008). The lethal genes associated with dwarfism in Hereford cattle in the 1950’s, and more recently with curly calf in Angus cattle, provides classical examples of how rational decisions related to animal selection may have unexpected and unpredictable consequences that exhibit themselves decades later. Increasing the growth traits of cattle through selection and breeding systems in the 1970s provided a short-term benefit for individual cattle producers as they increased the pounds of beef for sale from their ranches and farms. But as consumer demand for beef dropped precipitously in the 1980s and 1990s, the oversupply of beef only added to severely depressed prices for cattlemen. The question becomes; why does the beef industry and other complex systems behave the way they do?

MIT Professor Emeritus Jay Forrester described the following principles of complex systems that offer insight into this question (Sterman, 1998).

• The nature of “feedback” tends to mislead people into taking ineffective and even counterproductive action.

• People do not understand the complex interactions in a system and cannot correctly predict the outcome of their actions.

• Most difficulties are internally caused, even though there is an overwhelming tendency to blame outside forces.

• The actions people take, usually with the belief that the actions they take are a solution, are often the cause of the problems.

Continuing to approach complex systems like beef improvement as simply the cause and

effect of selected parts is naïve, misleading and problematic (Senge et al., 1994; Goodman 1994). There is important intrinsic value in viewing things like beef improvement as a complex system (Senge, 1990; Leibold et al., 2005; Haines, 2009). It clearly contains fundamental elements of several foundational sciences including biology, ecology, economics, and human behavior. To solve the mystery of beef improvement, the basic relationships between the fundamental elements of the system are as important, if not more important, than the foundational science of any of the component parts.

Beef Improvement as a Complex System

“The weather never settles down. It never repeats itself exactly. It’s essentially unpredictable more than a week or so in advance. And yet we can comprehend and explain almost everything that we see up there. We can identify important features such

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as weather fronts, jet streams, and high-pressure systems. We can understand their dynamics. We can understand how they interact to produce weather on a local and regional scale. In short, we have a real science of weather without full prediction. And we can do it because prediction isn’t the essence of science. The essence is comprehension and explanation.

Waldrop (1992)

Epigenetics. What might beef improvement look like if it were approached as a complex system? Perhaps it would include an application of the principles of epigenetics. The definition of the Greek prefix epi means “on, upon, over, above, after, near, beside, and after” (The American Heritage Stedman’s Medical Dictionary, 2010). Epigenetics are mechanisms that seem to allow an organism to respond to its environment through changes in gene expression (Jaenisch and Bird, 2003). They are the mechanisms that come into play “after” genetics. As a field of study, epigenetics is the exploration of how genes are expressed, and occurs both pre and postnatally as well as long into adulthood (Jaenisch and Bird, 2003). It is now widely held that gene expression is modulated by outside forces (Shenk, 2010). For example, the diet of a pregnant woman can affect her offspring’s susceptibility to disease (Hazani and Shasha, 2008). The diet of an individual can affect his or her susceptibility to cancer late in life (Jaenisch and Bird, 2003). Also, and contrary to current dogma, heritable variation can arise as a response to the environment, and is not always random (Shenk, 2010).

The study of epigenetics during pregnancy is often referred to as developmental programming or fetal programming. A growing body of work from the University of Nebraska (Stalker et al., 2006; Martin et al., 2007; Larson et al., 2009) provides clear and compelling evidence of fetal programming in beef cattle production systems. In particular, protein supplementation of pregnant cows improved weaning weights of calves (Stalker et al., 2006), the fertility of their female progeny (Martin et al., 2007), and the health and some carcass characteristics of male progeny in the finishing phase (Larson et al., 2008). In their review paper on the effects of maternal nutrition on fetal growth and the performance of their progeny, Funston et al. (2009) offers sophisticated explanations concerning how the mechanisms of fetal programming work. This is extremely important work, because a widely adopted strategy to improve profitability has been to lower winter fed costs by cutting or lowering supplementation levels. It also provides an excellent example of how, in a complex system, short-term results may be different than long-term consequences. In this case, lowering feed costs during pregnancy may improve profitability in the near-term, but decrease profitability in the long-term. It also provides evidence that, in complex systems like beef improvement, cause and effect are distant in time and space.

There is also compelling evidence of the role of epigenetics in growth and development after parturition. Long thought to occur late in the finishing phase, marbling is now recognized to begin at a relatively young age and progresses at a constant rate through the finishing phase (Bruns, 2004) and can be improved by creep feeding (Myers et al., 1999). However, in contrast to the effect of pre-partum nutrition on improving a heifer calf’s productivity as a cow, in a 21-year study, creep feeding adversely affected lifetime productivity of cows (Martin et al., 1981). It would seem then that improved nutrition affects animal performance differently depending on sex and whether supplementation occurs pre or post partum. Interestingly, aggressive implant treatments affect marbling differently, depending on the age of the animal when they are administered (Bruns et al., 2005). These examples are clear evidence, that regardless of an

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animal’s genetic propensity, its actual phenotype can be affected by not only by its physical environment, but by exogenous factors, like pre and post partum nutrition and hormones, that control the biochemical processes required for gene expression.

Learned Behavior. What if the observations made concerning animal production and performance were not due to the genetics of the animal, the environment in which it is living, or epigenetics, but instead to learned behavior? Clearly, in confined animal production, where an animal’s nutritional needs and immediate physical environment are controlled, this is not relevant. But consider production systems on pastures or rangeland. The science of herbivory is based on the assumption that all animals in the population behave predictably, as reflected by treatment means, which are then extrapolated to populations (Provenza, 2004). Variation within treatments is treated as a statistical problem to be dealt with mathematically, rather than with curiosity or opportunistic viewpoint. What if variation in response to pasture or rangeland treatments was viewed as a potential source of solutions to problems, much like residual feed intake is in the exploration of efficiency? Provenza (1995) argues that an animal’s diet while grazing pastures or rangelands reflects their ability to select from a smorgasbord of sedges, grasses, and forbs, and that much of that selection is learned (Provenza, 1995). He proposes that this learned behavior is based on aversion, which yields benefits like a balanced diet, and a reduction in the ingestion of toxic foods (Provenza, 1996). The fact that there is variation in diet selection, and that it is learned, provides fertile ground for research in the quest for optimization in forage based beef cattle production systems.

Micro-economics. If the goal of beef improvement is positive change in a trait of interest to beef cattle producers specifically, or the beef industry in general, then the application of the principles of economics should be applied. The basic micro-economic principle of marginality as related to technology in beef cattle production systems cannot be ignored (Dunn, 2004). The cost of producing more of anything is not fixed, but lies on a curve, referred to as a marginal cost curve. For example, the cost of producing each unit of a product is not equal. It decreases as the production function achieves efficiency but then increases as the point of diminishing returns is reached and surpassed. The cost of increasing the gene frequency of a trait falls on a marginal cost curve. When the cost of adding an additional unit exceeds its value, it is irrational to add more. Increasing the gene frequency of genes that affect a trait like marbling makes sense when the carcass premiums for choice versus select are large and exceeds the marginal cost. But consumer demand for beef is elastic, and carcass premiums reflect not only the elasticity of demand, but also of supply. Further, the elasticity of consumer demand for beef is much greater than the elasticity of the supply of beef, let alone for specific characteristics like marbling. This inelasticity of the supply of beef in general, and for specific traits in particular, is a function of the long production cycle associated with beef production and the relatively low hereditability and slow generation turnover associated with beef cattle genetics. A logical explanation of why the choice/select spread has narrowed in 2010 is because the protracted worldwide recession has caused a decline in consumer demand for choice beef in relation to its relatively inelastic supply.

If the principles of micro-economics are applied in a systems analysis of a trait like marbling, several key aspects need to be considered. First, the value of marbling would be determined or estimated in a dynamic economy, taking into account economic trends and the elasticity of demand for choice beef. Secondly, optimum levels of the frequency for genes associated with marbling would be established based upon the marginal cost of achieving each level. Thirdly, the marginal cost of alternatives like fetal programming or creep feeding would be

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determined. Finally, if the marginal cost of increasing marbling is below the projected return, a combination of technologies would be applied that keep the return on investment as high as possible.

Examples of System Thinking as Related to Beef Improvement. During the 37th Annual Research Symposium and Annual Meeting of the Beef Improvement Association, Dr. Bryan Melton challenged the beef cattle industry to include economics as part of multi trait selection indices and to follow those with economically weighted EPDs (Melton, 1995). While widely used in the pork and poultry industry, his 1995 challenge seemed novel to the struggling beef industry. Today, the major breed associations provide bioeconomic values based on economically weighted multi-trait selection indices to assist their members and customers in the genetic selection process. While not always transparent, they are an excellent example of solving the mystery of beef improvement in a thoughtful, multi-disciplinary, receiver dependent approach.

In 2002, Splan et al. (2002) published heritability estimates for weaning weight based on the records of 23,681 crossbred steers and heifers from the USDA-Meat Animal Research Center in Clay Center Nebraska to be 0.4 ± 0.02. Texts on beef cattle production and extension publications generally report that weaning weight is moderately heritable at 0.35 to 0.40. But is the heritability of weaning weight the same across all geographical locations? Could the environment in some locations be so extreme that it could reduce the heritability of this important trait? Although anecdotal, in 2008, John Genho analyzed the production records of over 20,000 Santa Gertrudis cattle from King Ranch, Kingsville, Texas. Using the Cornell Animal Model, he reported that the heritability estimate for weaning weight from these records to be 0.10 (Genho, 2008, Personal Communication). In terms of environmental conditions and nutritional availability, the subtropical environment in which these cattle were raised is extreme. Perhaps these animals from south Texas had a reduced opportunity to express their true genetic potential for weaning weight when compared to cattle raised in conditions that are more similar to those reported in the literature. Could this be true in other environments? If it is, it could have important economic implications for cattle producers from areas of outside the Midwest.

In conversation last winter Steve Radakovich, one of the early system thinkers in the beef industry as well as a longtime leader in the Beef Improvement Federation, posed a provocative question. “In beef cattle metabolism, maintenance energy is used for bodily function and repair. Since longevity requires the constant repair and maintenance of critical organ systems, and if efficiency is gained by lowering the portion of energy partitioned to maintenance, could we be selecting against longevity as we improve efficiency?” (Radakovich, 2010, Personal Communication)? This question is representative of the creative thought necessary to solve the complex challenges of beef improvement. It has at its core the fundamental principles of complex systems as outlined by Forrester (Sterman, 1998).

The pork and poultry industries have long been viewed as competition for beef. A general response of the cattle industry has been to find production systems that mimic theirs, and to find ways to compete with them from a feed efficiency basis. What would beef improvement look like if the environment was viewed not as a variable to be controlled, as it has been in the pork and poultry industries, but as a competitive advantage to exploit? Variation in wine is the backbone of the industry. Regional differences in fruits and vegetables are being promoted.

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This question is easy to dismiss in an industry that is singularly focused, but is worthy of discussion in an industry willing to look outside the box.

Answers to the questions above, and many more, will challenge the very best scientists and thought leaders in beef cattle research and industry. But the primary question centers on the beef industry’s willingness to ask them. Conclusion

“I doubt if we stand a good chance of achieving understanding of the components, and of the interactions among them, as long as we insist on maintaining the comfort of our specialist or discipline zone. All indications to me are that we need more integrations of our disciplinary efforts both within and among beef cattle problem areas if we are to make the greatest contribution to developing technology for maximizing the amount of edible beef of a given quality per unit of resource use.”

Gregory (1972)

The debate about beef improvement shouldn’t be centered on a validation of the past. The historic path chosen by the industry has achieved its goal of change. But the beef industry’s serious and compelling problems lie in the future. The challenge for the Beef Improvement Federation at its 42nd Annual Research Symposium and Annual Meeting concerns the path it chooses for tomorrow. Will it continue as a “transmitter dependent” organization in search of puzzle pieces? Or will it become a “receiver dependent” organization that accepts Keith Gregory’s challenge to break out of the “comfort of our specialist or discipline zone(s)” and expand its view as it unravels the mystery that is the complex system of beef production and improvement? Literature Cited Berlinski, D. 1976. On System Analysis: An Essay Concerning the Limitations of Some

Mathematical Methods in the Social, Political, and Biological Sciences. The MIT Press, Cambridge, Massachusetts.

Bruns, K. W., R. H. Pritchard and D. L. Boggs. 2004. The relationship among body weight, body

composition, and intramuscular fat content in steers. J. Anim. Sci. 82:1315-1322. Bruns, K. W., R. H. Pritchard and D. L. Boggs. 2005. The effect of stage growth and implant

exposure on performance and carcass composition in steers. J. Anim. Sci. 83:108-116. Dunn, B. H. 2004. Technology: Price tag and profit. Proceedings of the Beef Improvement

Federation’s 36th Annual Research Symposium and Annual Meeting. Sioux Falls, South Dakota.

Funston, R. N., D. M. Larson, and K. A. Vonnahme. 2009. Effects of maternal nutrition on

conceptus growth and offspring performance: Implications for beef cattle production. J. Anim. Sci. 88:E205-E215.

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Gladwell, M. 2009. What the Dog Saw: And Other Adventures. Little, Brown and Company. New York, New York.

Goodman, M. 1994. A Beginner’s Guide to Systems Thinking. Pegasus Communications.

Cambridge, Massachusetts. Gregory, K. E. 1970. Beef cattle type for maximum efficiency “putting it all together”. J. Anim.

Sci. 34:881-884. Haines, S. 2009. Strategic and Systems Thinking: The Winning Formula. Haines Centre for

Strategic Management. San Diego, California. Hall, A. D., and R. E. Fagen. 1968. Definition of System in N. Buckley, ed., Modern Systems

Research for the Behavioral Scientist. Aldine Press. Chicago, Illinois. Hazani, E. and S. M. Shasha. 2008. Effects of the Holocaust on the physical health of the

offspring of survivors. Israel Medical Assoc. J. 10:251-255. Jaenisch, R. and A. Bird. 2003. Epigenetic regulation of gene expression: how the genome

integrates intrinsic and environmental signals. Nature Genetics 33:245-254. Larson, D. M., J. L. Martin, D. C. Adams and R. N. Funston. 2008. Winter grazing system and

supplementation during late gestation influence performance of beef cows and steer progeny. J. Anim. Sci. 87:1147-1155.

Laszlo, E. (ed. 1972). The relevance of general systems theory: Papers Presented to Ludwig von

Bertalanffy on His Seventieth Birthday. Braziller. New York, New York. Leibold, M., G. Probst, and M. Gibbert. 2005. Strategic Management in the Knowledge

Economy: 2nd edition. Publicus KommunikationsAgentur. Erlangen, Germany. Martin, T. G., R. P. Lemenager, G. Srinivasan and R. Alenda. 1981. Creep Feed as a Factor

Influencing Performance of Cows and Calves. J. Anim. Sci. 53:33-39. Martin, J. L., K. A. Vonnahme, D. C.Adams, G. P. Lardy and R. N. Funston. 2007. Effects of

dam nutrition on growth and reproductive performance of heifer calves. J. Anim. Sci. 85:841-847.

Melton, B. E. 1995. Conception to consumption: the economics of genetic improvement.

Proceedings of the Beef Improvement Federation’s 27th Annual Research and Symposium and Annual Meeting. Sheridan, Wyoming.

Myers, S. E., D. B. Faulkner, F.A. Ireland, L. Berger and D. F. Parrett. 1999. Production systems

comparing early weaning to normal with or without creep feeding for beef steers. J. Anim. Sci. 77:300-310.

Provenza, F. D. 1995. Postingestive feedback as an elementary determinant of food preference

and intake in ruminants. J. Range Manage. 48:2-17.

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Provenza, F. D. 1996. Acquired aversions as the basis for varied diets of ruminants foraging on

rangelands. J. Anim. Sci. 74:2010-2020. Provenza, F. D. 2004. Adaptability, foraging, and the complexity of landscapes. 2004 HOLT

CATR Symposium on Excellence in Ranch Management. Texas A&M University: Kingsville, Kingsville, Texas.

Rhoades, R. D., J. E. Sawyer, A. D. Herring, PAS, and D. P. Anderson. 2008. Case Study: Has

Beef Quality Grade Declined? The Professional Animal Scientist 24:619-627. Senge, P. 1990. The Fifth Discipline. Currency Doubleday. New York, New York. Senge, P. 1994. The Fifth Discipline Fieldbook. Currency Doubleday. New York, New York. Shenk, D. 2010. The Genius in All of Us. Doubleday New York, New York. Splan, R. K., L. V. Cundiff, M. E. Dikeman, and L. D. Van Vleck. 2002. Estimates of parameters

between direct and maternal genetic effects for weaning weight and direct genetic effects for carcass traits in crossbred cattle. J. Anim. Sci. 2002. 80:3107–3111.

Stalker, L. A., D. C. Adams, T. J. Klopfenstein, D. M. Feuz and R. N. Funston. 2006. Effects of

pre-and postpartum nutrition on reproduction in spring calving cows and calf feedlot performance. J. Anim. Sci. 84:2582-2589.

Sterman, J. D. 1998. Introduction to systems dynamics. System Dynamics: Modeling for Organizational Learning. MIT Sloan. Cambridge, Massachusetts. The American Heritage Stedman’s Medical Dictionary: 2nd edition. 2004. Houghton Mifflin

Company. Wilmington, Massachusetts. Waldrop, M. M., 1992. Complexity; The Emerging Science At The Edge Of Order and Chaos.

Simon and Schuster, New York, New York.

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PRODUCTION (AND) OR PROFIT? FOCUSING OUR BREEDING OBJECTIVES BY SELECTING FOR PROFITABLE GENETICS ,NOT NECESSARILY HIGH

PRODUCTION GENETICS

Matt Spangler

University of Nebraska-Lincoln

Introduction Steep increasing genetic trends for growth traits (weaning and yearling) and mature cow weight can be seen in many breeds but perhaps more alarming are those producers that have dramatically increased the genetic potential for milk production in their cow herds. Conditional on the assumption that the Beef Cattle Industry is a For Profit organization, then it would seem logical that profit (Revenue – Expense) should drive our selection decisions. In order to actually do this, knowledge of environmental constraints, genetic antagonisms, and the selection tools that have the potential to measure profit are critical. Environmental Constraints The development of an obtainable breeding objective begins by clearly identifying environmental constraints and marketing goals. Table 1 illustrates levels of production that are suited for differing production environments. Table 1. Matching genetic potential for different traits to production environments1

Production Environment Traits

Feed Availability

Stress2 Milk Mature Size

Ability to store energy3

Resistance to stress4

Calving ease

Lean yield

High Low

High

M to H5

M

M to H

L to H

L to M

L to H

M

H

M to H

H

H

M to H

Medium Low

High

M to H

L to M

M

M

M to H

M to H

M

H

M to H

H

M to H

H

Low Low

High

L to M

L to M

L to M

L to M

H

H

M

H

M to H

H

M

L to M 1 Adapted from Gosey, 1994. 2 Heat, cold, parasites, disease, mud, altitude, etc. 3 Ability to store fat and regulate energy requirements with changing (seasonal) availability of feed. 4 Physiological tolerance to heat, cold, internal and external parasites, disease, mud, and other factors. 5 L = Low; M = Medium; H = High.

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If feed resources are limited in a stressful environment then selection for increased extreme output (high growth, milk, and red meat yield) could have negative impacts on the ability of cows to be successful breeders without the need for large quantities of harvested forage. The beginning of a profitable breeding objective is identifying what the environment will allow you to produce, at least until we have tools to apply direction selection to traits of adaptation.

Crossbreeding

At a BIF meeting in 2010, it hardly seems fit to even mention crossbreeding. Commercial producers who have not yet adopted it are a burden to the beef industry. However, it is an excellent example of selection for profitability. We know that the two primary benefits of crossbreeding are complementing the strengths of two or more breeds and heterosis, neither of which create trait maximums. If we think about it simplistically, crossbreeding for a trait like weaning weight leaves us with a calf crop that is better than the average of the parental lines, not better than both parental lines. Crossbreeding, if done correctly, seeks to optimize many traits through complementing breed strengths and produce animals that are better than the average of the parental lines that created them. The best tool that the commercial cattleman ever had is based on optimization, not the production of extremes. So, it would stand to reason that within breed selection should have the same goal, optimums and not maximums.

Genetic Correlations Unfortunately, all traits that might be included in a breeding objective are not independent of each other. Sometimes this is beneficial as we see a favorable correlated response, and other times these genetic correlations pit revenue against cost. A good example of this comes from the suite of weight traits. Depending on the targeted marketing endpoint either weaning weight (WW), yearling weight (YW) or carcass weight (CW) become a source of revenue and all are related to a major factor influencing the cost of production, mature cow weight (MW). Table 2 illustrates the genetic correlations between MW and WW, YW, and CW, respectively. Table 2. Genetic correlations between mature cow weight (MW) and weaning weight (WW), yearling weight (YW), and carcass weight (CW). WW1 YW1 CW2 MW 0.62 0.45 0.81 1 Estimates from Northcutt and Wilson, 1993. 2 Estimate from Nephawe et al., 2004.

Although it is not intuitive, literature results show that of the immature traits, WW has the highest genetic correlation with mature cow weight. Other similar estimates have been shown in the literature ranging from 0.65 to 0.82 in Red Angus field data (Williams et al., 2009). The same authors estimated the genetic correlation between postweaning gain and

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MW to range between 0.48 and 0.59. This is particularly relevant in the context of producers that sell some portion of calves but also keep back their own replacement females. Care should be given not to focus solely on the revenue portion, sale weight, but rather optimizing input costs associated with mature weight and revenue sources from calf sale weight. The mature sale weight, CW, shows a strong and positive relationship with MW and again care should be taken to optimize selection between the two.

Selection For Decreased Input   Traditionally, there have been few EPDs that could be used to directly select against input costs. However there has been one for some time, milk. Research has shown that cows with the genetic propensity to milk heavily require more energy for lactation and maintenance. The National Research Council (NRC) data shows that a cow who produces 25 lbs. of milk at peak lactation requires 10% more feed energy than a cow producing 15 lbs. of milk at peak lactation. To see a 10% difference in feed energy with regards to mature weight it would require moving from a 1,000 lb. cow to a 1,200 lb. cow, or a change of 200 lbs. of body weight. Moderating mature cow size and selecting for an optimal window of milk production is beneficial when it comes to cutting costs regardless of your production environment given that milk production has been estimated to explain 23% of the variation in maintenance requirements (Montano-Bermudez et al., 1990). However, in limited feed environments females with high maintenance energy requirements may also have difficulty maintaining an acceptable body condition score and rebreeding. Nugent et al. (1993) determined that with limited nutrient availability, breeds with a high genetic potential for milk production had longer anestrous periods which lead to lower conception rates during a fixed breeding season. Other researchers have concluded that selection for increased milk production past an adequate threshold is not economically or biologically efficient if the marketing endpoint was at either weaning or slaughter (van Oijen et al., 1993). While the lactation requirements may be intuitive, cows with a higher milk yield also tend to have increased visceral organ mass this increasing energy requirements even when the cow is not lactating (Solis et al., 1988). Other selection tools exist for decreasing input costs including mature weight EPDs and more recently the Maintenance Energy EPD published by the Red Angus Association of America (Evans, 2001; Williams et al., 2009). The study by Williams and others clearly depicts that selection for immature weights is occurring thus increasing MW. Furthermore, the study illustrates that without accounting for this prior selection in the development of ME predictions, and inherent bias is created. Most of the described tools focus on the cow-herd and not in the finishing phase. The American Gelbvieh Association publishes a Days-to-Finish (DtF) EPD designed to select for animals that reach slaughter earlier, as measured by a constant fat thickness of 0.4 inches. For producers that are rewarded for feedlot performance DtF can be an effective way to decrease input costs derived from a greater number of days on feed or feeding cattle past an optimal fat thickness. Brigham at al., (2006) estimated the genetic correlation between Dtf and WW to be -0.29, suggesting that larger weaning weights tend to be moderately associated with few days on feed.

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Bio-economic Index Values In order to mitigate genetic antagonisms in an effort to select for profit economic index values become the tool of choice. A bio-economic index (H) is simply a collection of EPDs that are relevant to a particular breeding objective, whereby each EPD is multiplied by an associated economic weight (a). For example, the economic index value H can be written H = EPD1a1 + EPD2a2 + EPD3a3 + ... where EPDs 1, 2, and 3 are multiplied by their corresponding economic weight and summed. Consequently, a high index value does not necessarily mean that an animal excels in all EPD categories given that superiority in trait can compensate for inferiority in other traits depending on how the EPDs are weighted in the index. A high index value should be thought of as excelling in the ability to meet a breeding objective. These index values do not have a measure of accuracy directly associated with them because each EPD is weighted differently in the index and it is not statistically possible to weight the accuracy values. Like EPDs, they can easily change overtime with the addition of new information (i.e. progeny records) as the component EPDs change. It is important to note, however, that before proper use of an index can be ensured, a breeding objective must be clearly identified. For example, the use of an index such as the American Angus Association’s Dollar Beef ($B) in an enterprise that retains replacement heifers can lead to adverse effects, given that sire selection pressure has been placed on terminal traits via $B. The majority of economic index values are rigid (i.e. not catered to individual enterprises). A much more desirable method would use individualized index values where the bull with the highest index value may differ from one herd to the next, depending on how the animal fits the specific needs of each enterprise. While this would lead to more accurate identification of parents for the next generation, it becomes a challenging metric to use for advertisement purposes in the seedstock industry, which probably explains why this more desirable method of multiple-trait selection has not been exploited by the majority of breed associations. For example, it is possible to advertise that a bull is in the top 1% of the breed for $B, but if an index parameters are partially defined by the prospective bull buyer or semen user the most desirable bull for that producer may not be the best for other producers. One example to the contrary would be the interactive Terminal Sire index produced by the International Charolais Association. New and Improved Tools Genomic tools hold the potential to provide predictions for hard to measure traits that focus on input costs such as feed intake. Ideally, genomic predictions for feed intake would be incorporated into an economic index as a key component of input cost. However, accurate genomic predictions will require phenotypes. The improvement of existing phenotypic databases for traits is also needed. It is critical that seedstock producers routinely turn in mature cow weights along with body condition scores to further aid in selecting for optimal weights and the development of tools

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such as the American Angus Associations Cow Energy value index ($EN) and the Red Angus Associations Maintenance Energy (ME) EPD. This will require participation in Whole (or Total) Herd reporting, a very necessary process for complete data collection and the development and delivery of genetic prediction tools.  Summary

Trends are rarely flat, as an industry we have measured ourselves by steep lines in one direction or the other. From a seedstock perspective this may have been perceived as necessary in order to differentiate themselves (either as breeders or as breeds) from others in the market place. Clearly identifying your production environment and realistic production goals given that environment are critical. Profit lies in the optimization of expense and revenue and optimization is always more challenging than maximizing outputs or minimizing inputs. It will require more effort, detailed financial records, and a structured breeding objective that builds a cow herd based on optimum values and not extremes. One final thought, extremely low maintenance cows will push the lower threshold of what is biologically possible for weight and produce virtually no milk. High output cows will represent the other extreme, weigh more than most mature bulls and milk heavier than the best Holstein. Both excel in some measure of the profit equation (i.e. lowest cost or highest revenue) but neither promises to be profitable. Literature Cited Brigham, B.W., S.E. Speidel, D.W. Beckman, D.J. Garrick, W. Vanderwert, S. Willmon, and

R.M. Enns. 2006. Parameter estimates and breeding values for days to a constant fat endpoint. In Proc. Western Section, Amercian Society of Animal Science, volume 57.

Evans, J.L. 2001. Genetic prediction of mature weight and mature cow maintenance energy

requirements in Red Angus cattle. Ph.D. Colorado State University, Fort Collins. Gosey, J. 1994. Composites: A beef cattle breeding alternative. Proc. Beef Improvement

Federation Annual Meeting. June 1-4, W. Des Moines, IA. P. 93. Montano-Bermudez, M., M. K. Nielsen, and G. H. Deutscher. 1990. Energy requirements for

maintenance of crossbred beef cattle with different genetic potential for milk. J. Anim. Sci. 68:2279-2288.

Nephawe, K.A., L.V. Cundiff, M.E. Dikeman, J.D. Crouse, and L.D. Van Vleck. 2004. genetic relationships between sex-specific traits in beef cattle: Mature weight, weight adjusted for body condition score, height and body condition score of cows, and

carcass traits of their steer relatives. J. Anim. Sci. 82: 647-65. Northcutt, S.J., and D.E. Wilson. 1993. Genetic parameter estimates and expected progeny differences for mature size in Angus cattle. J. Anim. Sci. 71:1148-1153.

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NRC. 1996. Nutrient requirements of beef cattle 7th Ed. National Academy Press, Washington, D.C. Nugent, R.A., III, T.G. Jenkins, A.J. Roberts, and J. Klindt. 1993. Relationship of post- partum interval in mature beef cows with nutritional environment, biological type and serum IGF-I concentrations. Anim. Prod. 56:193-200. Solis, J. C., F. M. Byers, G. T. Schelling, C. R. Long, and L. W. Greene. 1988. maintenance

requirements and energetic efficiency of cows of different breed types. J. Anim. Sci. 66:764-773.

Van Oijen, M., M. Montano-Bermudez, and M.K. Nielsen. Economical and biological efficiencies of beef cattle differing in level of milk production. J. Anim. Sci. 71: 44- 50. Willimas, J.L., D.J. Garrick, and S.E. Speidel. 2009. Reducing bias in maintenance energy

progeny difference by accounting for selection on weaning and yearling Weights. J. Anim. Sci. 87:1628-1637.

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Adding Value to a Weaned Calf Marketing System

Mike John

June 2010

There has been much debate regarding the profitability of preconditioning calves.

The debate seems to be similar to the one surrounding creep feeding and the question is always “does it pay?” My answer to those questions has always been “well, that depends”. Sounds like a copout but here’s what I think it depends on. The factors I have come to understand both on our own operation and through nearly 400,000 head through the MFA Health Track program are season, genetics, critical mass, health, condition, shrink, and finally actually capturing any marketing program premiums. Season

In recent history in the Midwest USA, barring some exceptional corn price fluctuation, I would argue that the best you can do is to market an 850lb. steer around August 15th. From then until the end of the year the price spreads between 500 and 900 pounds narrow to negligible. Since feed cost trends downward through summer and many times through new crop corn harvest, this tells me that the ability to profit from added weight on spring born calves should be significant. Genetics

Calves that are bred to perform well in feedlots will also do well in a preconditioning program. Cross breeding with bulls that have some frame and muscle, as well as feed efficiency and weaning growth accuracies should pay off in this type of program. The more consistent a group of calves are the easier they are to feed. In other words, genetic similarity AND a 60 day calving period are worthwhile goals. Critical Mass

Since the average herd size in most of the country is less than 40 head, people tend to turn a deaf ear to this discussion. Even though we all know that a large draft size or truckload quantities increase efficiency and garner higher prices. Backgrounders figured this one out decades ago. Tighter calving periods, combining producers, preconditioning programs that allow pooling, are some of the options available to anyone looking to capture more market value. Health

Although I didn’t put this one first, it doesn’t mean it isn’t extremely important in a preconditioning program. We have kept records of Health Track calves that get sick during the 45 day preconditioning period. In our dataset, morbidity ranges from .35% to

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nearly 5% based more on when the vaccinations are given, than on what brand. Using MLV 4-5 way pre-weaning as a first round provides the absolute best protection. Adequate nutrition plays a key role in developing immunity. Condition

In the last 10 years I have worked with Health Track, there has been a significant change in the type of condition most buyers are looking for. Thin and Fleshy are both negative terms now. The right genetics can provide calves that can gain well and still not get fleshy. Medium fleshed calves that have frame and muscle will perform well and stay healthy, it’s as simple as that. Maximizing weight gain into the appropriate season’s market without getting them too fleshy is the best advice you’ll get. Shrink

Is it the opposite of compensatory gain? Maybe, but it can also be a harbinger of health wrecks. If you do the math 5% shrink on a 500 lb calf is 25 lbs. At $1.25/lb that’s $31.25. If you can save that much shrink, you can save that much money per head. Preconditioned calves shrink less than bawling calves, it is a fact and easy to understand. If you don’t believe me go to an auction and watch the behavior of both groups. Value Added Market Access

There are many practices that if they are properly documented provide access to market premiums. Notice I said access. There are no guarantees. We all know that the last person with their hand in the air gets the cattle and the price is only based on that. There are well documented premiums for ASV cattle these days but back verification is proof that cow/calf producers don’t always capture it. Weaned, Vaccinated, Vac 45 process verified, Natural, NHTC, Organic, are all examples of value added processes. There is a cost to all of them and you have to determine if you have any chance in your marketing scheme to capture enough premium to pay it. I will summarize this way. Weaning and vaccinating, and “Maximizing weight gain into the appropriate season’s market without getting them too fleshy” is still the best advice you’ll get.

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EVALUATION OF INVESTMENT IN AGRICULTURAL TECHNOLOGY

Eric A. DeVuyst

Department of Agricultural Economics, Oklahoma State University, Stillwater, OK 74078

Abstract

Agricultural producers are continually faced with decisions regarding the adoption of new technologies. This paper reviews the applied decision tools available for use in assessing technology adoption decisions with emphasis on livestock production decisions. Decisions and decision tools are categorized by relative scale, perceived risk and degree of reversibility. The tools required to assess decisions increase in sophistication and information requirements as the investment’s scale, risk and reversibility decrease. Tools discussed include partial budgeting, enterprise budgeting, whole farm budgeting, cashflow budgeting, capital budgeting, and real option pricing models. How investment in technology can increase value by increasing future options to producers is also discussed.

Introduction

Investment in agricultural technology can be expensive. New livestock production facilities can require investment of millions of dollars. New harvest equipment can run into the hundreds of thousands of dollars. Genetic testing of breeding stock can be equal or even exceed a year’s average return on a per head basis. Further, the returns from adopting new technology are usually uncertain. For example, will buyers pay for genetic information? What if a producer discovers through testing that his stock does not have genetics desired by the market? While theoretical-based tools are available to analyze even the most complex decision problems, the information requirements or training needed to utilize these tools are often too onerous to be practical for most real world decisions. Here, I discuss the practical shortcomings of theory-based decision analysis and review several applied alternatives. While these applied decision tools require numerous simplifications, they have lower informational requirements and are accessible to most real-world decision makers. These tools are discussed in relation to three criteria: relative scale of the decision, perceived riskiness of the decision and the degree of reversibility of the decision.

Theory versus Practice of Decision Making

Economic theory of investment under uncertainty utilizes the expected utility hypothesis (von Neumann and Morgenstern 1949). Under this framework, investors allocated limited funds between competing investment alternatives in order to maximize their individual expected utilities. In a multi-period model, expected utilities over time are discounted and summed. There are several well-known criticisms of this framework (see, e.g., Fishburn 1981). Despite the economists’ “rational man” assumption of economic behavior, there are several well-known inconsistencies (or paradoxes) with the expected utility hypothesis (see, e.g., Hirshleifer and Riley). Perhaps most relevant criticism in the context of this paper is the difficulty in obtaining subject joint probability distributions that characterize the randomness of returns from competing investment alternatives. If investments are mutually exclusive (e.g., technology A versus

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technology B), the difficulty is only somewhat mitigated as the joint distributions of returns associated with each new technologies and existing enterprises must still be determined.

To further mitigate the difficulties in applying the expected utility framework, assumptions regarding the utility function and independence of investments are often imposed. For example, by assuming constant absolute risk aversion and independence of return distributions, the model simplifies to analyzing the expected utility associated with one investment at a time while ignoring other on-going investments. This assumption is maintained for many applied decision frameworks. With this simplification, only the distributions of returns from the technologies being considered must be determined. While greatly simplifying the analysis, elicitation of subjective probability distributions is a non-trivial and potentially impossible task. It requires the decision maker to subjectively determine all possible outcomes from an investment and the relative likelihood of each outcome. For unproven technologies, asking a producer to assess the potential outcomes and likelihoods may result in highly unrealistic probability distributions. This is due to the presence of ambiguity, rather than risk1. While alternative decision models have been developed when ambiguity is present (Fox and Tversky 1995), these models are even more difficult to apply to practical decision making.

To illustrate some of the difficulties associated with probability distribution elicitation, consider a seed stock producer decision to adopt marker-assisted selection for tenderness. At the time of this writing, there are no large-scale marketing channels that reward tenderness. While it is not certain when or if market channels will develop to reward tender carcasses, it does seem likely that eventually packers or other market channels will pay premiums on carcasses that meet some tenderness criteria. However, when that will happen and what the level of premiums will be are both uncertain. In order to use the formal decision analysis method, a producer would need to subjectively assess 1) the probability that premiums will be paid for tenderness for all future marketing dates, 2) the probability distribution for returns paid for premiums in all future time periods, 3) the probability distribution for premiums paid to seed stock producers with verified markers for tenderness for all future time periods, and 4) the distribution of tenderness markers in his herd for all future time periods. The difficulty associated with these assessments means that formal decision analysis is not useful in most real world applications.

Given the difficulty in employing formal decision analysis for practical decision making, a wide variety of managerial tools have been developed for use by agricultural producers. Some are “back-of-the-envelop” calculations; others would likely require the assistance of an economist trained in their use. To help aid in the choice of decision tools, I suggest three criteria for aiding the determination of the tools required. These three criteria are relative scale of the

1 To illustrate risk and ambiguity, consider an urn containing 50 black balls and 50 red balls. Most people would correctly conclude that the odds of drawing a black ball is 50%. This scenario can be described as risky or uncertain. Now, consider an urn with a total of 100 black and red balls. What is the probability of drawing a black ball now? Subjective probability assessment may fail miserably to reflect reality. A “naïve” distribution of 50% black and 50% red balls might be significantly in error. If the person filling the urn put in 100 black balls, the naïve believe is highly inaccurate. This second scenario demonstrates ambiguity.

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investment, perceived riskiness of the investment and the degree of reversibility of the investment.

First is the relative scale of the investment. Relative scale might be in terms of percent of business being changed or in terms of dollars invested. For example, consider a US soybean producer considering changing 160 acres to a new variety. For a 2000-acre farm, this is probably a fairly minor change. In contrast, a 10-acre change for an African subsistence farmer might be a large percent of the farm’s acres.

Second is perceived risk. In the previous US farm example, a 160-acre change to a new variety is most likely a low-risk decision. Producers routinely make these decisions, seed companies and land grant universities routinely publish varietal trial results, and markets likely exist for the new variety. So, this decision has a low-level of perceived risk. While for the African subsistence farmer, a 10-acre change might have very significant consequences. Get the decision wrong and his family could suffer malnutrition and lose their farm and home. So, the decision might have a high-level of perceived risk.

Third is the degree of reversibility. Some decisions can be “un-done” at a low cost and in a short-time period. In the soybean example, the US producer can switch back to the old variety in the next growing season, a low-cost and short-time reversal. An example of a potential high-cost and long-time reversal is the decision to change hide color in a breeding herd. Individually and collectively, many US producers have selected for black-hided cattle in response to premiums associated with Certified Angus Beef (CAB). While a rational response to economic conditions, it would be costly and take several years to “undo” this decision. Few producers have the financial ability to sell off existing breeding animals and replace them in a short-time period, say one or two years. Perhaps most US producers would take eight to ten years or more to either replace their existing breeding herd by buying new bulls and breeding in the desired hide color or buying replacement females over several years.2 

Reversibility is essentially an option. It gives the producer the option to revert to previous production practices and has real economic value. Economists call these types of options “real options” as opposed to financial options (e.g., futures options), also called financial derivatives. However, determining the value of an option is difficult—especially for non-traded options. Financial options, such as puts and calls, are traded on exchanges and their market values readily observed. Real options are not traded in markets. Real option pricing models are mathematically complicated and, similar to decision analysis, are information intensive. Few people, other than a subset of economists, are trained in modeling real options. Given the complexity of modeling the value of real options, real option price models are not used by agricultural producers. Their usefulness is discussed later in the context of how investment in technology can increase options and, therefore, add value. 

Applied Decision Tools 

Given the practical difficulties of employing formal decision analysis, Extension specialists and farmers utilize several less formal decision making tools or aids. Only under the 2 This is not a criticism of black-hided cattle or the decision to select for black hides but to demonstrate the difficulty/costs associated with reversing or “undoing” some decisions.

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most severe assumptions are these tools compatible with the expected utility hypothesis. However, the reduced informational requirements and lack of sophistication can be viewed strengths in the practice of decision making. A number of these tools are discussed below. Most are variants of budgeting. Budgeting is used to test a production, marketing and/or investment plan on paper before real world implementation. These tools are used to identify bottlenecks to profitability, compare the profitability of alternative plans, and assess cashflow difficulties. 

Partial Budgeting 

The simplest form of applied decision tools is partial budgeting. This tool is useful when considering small-scale, low-risk and highly-reversible decisions. In a previous example, a 2000-acre US soybean producer was considering switching 160 acres to a new soybean variety. A partial budget is an appropriate tool for analyzing this decision.  

With a partial budget, only changes in revenues and expenses are included. In the soybean example, rent would not change with the adoption of a new variety. So, rent is not included as an item on the budget. Table 1 provides a suggested format for a partial budget. In the table, the left column lists the “cons,” reduced revenues and additional costs, from proposed change and the right column lists the “pros,” additional revenue and reduced costs, from the adoption. The columns as summed with the left column summing to A and the right column summing to B. If B-A > 0, then the change appears to be advisable. Given the simplicity of the method, there are other factors that might need to be considered. For example, are there impacts on labor or machinery constraints? Is convenience improved or reduced? Is the family’s lifestyle affected? Before implementing a change, a producer should assess these potential factors, even if the partial budget suggests that the change would be beneficial. 

Table 1. Partial budget format*  

Reduced revenues  

Additional revenues 

Additional expenses  

Reduced expenses 

Reduced revenues + Additional expenses = A  Additional expenses + Reduces expenses = B  *Adapted from Kay, Duffy and Edwards (2008).  

Partial budgeting has been used to analyze a wide range of beef production. Examples include preconditioning of calves (Dhuyvetter et al. 2005), testing for disease in feeder cattle (Larson et al. 2005), and estrus synchronization in beef heifers (Gaines et al. 1993). 

Enterprise Budgeting 

Similar to partial budgeting, enterprise budgeting is used to compare competing enterprises. An enterprise budget projects all revenues, variable expenses, and fixed (overhead) expenses that can be allocated to a given enterprise. Decisions cDonsidered may be less reversible, larger in scale and somewhat more risky. For example, consider switching acres to a

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new crop. There may be more than a few minor changes in revenues and expenses. The producer may need to purchase new equipment, incurring additional ownership expenses. This also reduces the degree of reversibility as the disposal of the additional equipment may require time. And, risk exposure may increase as the producer may not be as familiar with the production, handling, storage and marketing of the new crop. 

While risk is not formally incorporated into enterprise budgeting, ad hoc methods are frequently employed. Producers can analyze “what if” type questions. For example, what if yield is 25% below expectation? What if price falls by 15%? By assessing a wide range of yield, price and expense scenarios, producers can develop a sense of the range of possible returns from alternative levels of competing enterprises. 

Enterprise budgets are available for most crops, fruits, nuts, berries, and livestock. Many can be found at the website for the Digital Center for Risk Management Education, University of Minnesota (UM DCRME 2010). 

Whole Farm Budgeting 

Whole farm budgeting builds on enterprise budgeting. Enterprise budgets are aggregated, and unallocated expenses are subtracted from aggregate returns. With the focus on the whole farm, even larger scale decisions can be analyzed. These include, for example, the implications of discontinuing an entire enterprise or investment/disinvestment in facilities. Impacts on the farm’s bottom line are the focus of whole farm budgeting. While larger scale, higher-risk and less reversible decisions might be analyzed with this method, it is only slightly more illuminating than enterprise budgeting. Risk is again considered using ad hoc measures, and the degree of reversibility is not formally considered. 

Cashflow Budgeting 

The budgeting tools discussed up to this point are focused on profitability and the economic advisability of investments. In contrast, cashflow budgeting is focused solely on cash. This tool is used to project time periods where cash is short or in excess of current cash demands. It is particularly useful in assessing the feasibility of an investment rather than the advisability. 

An annual cashflow budget is typically developed using month to month sources and used of cash. Importantly, all projected sources and uses of cash are included. Unlike budgets focused on profitability, cash items such as projected capital asset purchases and sales, principal payments, planned new borrowings, withdrawals for family living expense and contributed capital from off-farm income are considered in a cashflow budget. Also unlike most other budgeting tools, non-cash expenses, e.g., depreciation, are not considered. 

Even if one of the profit-focused budgeting tools suggests that an investment will be profitable, it may not be self-liquidating. That is, the additional income generated may not be sufficient to cover debt service on the investment. For example, land investments often do not self-liquidate, i.e., cashflow. But, the land investment might still be economically advisable, i.e., profitable. Cashflow budgeting is useful in determining if additional sources of cash are needed to make an investment feasible. So, cashflow budgeting is recommended for all investments, even if they appear to be profitable. 

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Capital Budgeting 

Capital budgeting is concerned with investments that are long-lived. That is, revenues and expenses are incurred over multiple years. There are several tools that are used for capital budgeting. Three of the most commonly used tools are payback period, net present value (NPV), and internal rate of return (IRR) (Barry et al. 2000). The payback period calculates the number years required for the sum of annual net returns to equal the investment cost. This method ranks investments that quickly self-liquidate as most advisable. However, it ignores the potential differences in productive lives of alternative investments and the time value of money. 

The net present value method discounts future cashflows and sums them up. If the NPV is positive, an investment is projected to be profitable. When comparing mutually exclusive investments (e.g., investment A vs. investment B), the investment with the highest NPV is chosen. The NPV method is consistent with an objective of maximizing expected profits and is recommended by economists. 

The IRR method is similar to the NPV method, except that the discount rate is solved for rather than assumed. With the IRR method, the discount rate needed to make an investment’s net present value equal zero is calculated. The investments are ranked then using IRR, with a higher IRR preferred. In most cases, the IRR will rank investments the same as NPV, but there are cases where the IRR method will generate incorrect rankings (Barry et al. 2000). 

Since NPV is consistent with a profit maximizing, it is used for investment analysis. Net present values can be mathematically converted to annuities. An annuity is computed as the constant annual dollar amount that has the NPV as an investment. Using an annuity equivalent, it is possible to project the optimal timing of some investments. Perrin (1972) developed investment decision rules that utilize annuity equivalents. The rules are used to decide when to replace an existing investment (called the defender) with a new investment (called the challenger). Returns for the defender are projected several years into the future. In the year when the projected annuity equivalent return for the challenger exceeds the projected returns for the defender, the investment should be made. 

Capital budgeting techniques do not explicitly consider risk and irreversibility. Again, ad hoc approaches to risk analysis are often used. However, capital budgeting methods allow for more analysis of sophisticated investment decisions by considering the optimal timing of investments.  

Real Options 

As discussed above, real options are options related to future actions. Reversibility is a real option and has value to decision makers. Budgeting tools do not adequately account for the degree of reversibility of alternative investments. Real option pricing models explicitly model the value of real options, including reversibility (Dixit and Pindyck 1994). Using real options modeling, the optimal timing of investments can be found. However, this richness is not costless. These models are mathematically sophisticated and require more information than budgeting tools. Most challenging is assessing the variability of returns from investment over time. As was discussed in the introduction, probability elicitation is challenging. Few producers will be able to

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use real option modeling to evaluate investment in new technology, and few—if any—software tools are available to aid producers. Producers might be able to get assistance at a land grant university. 

Technology and real options 

There are cases where a budgeting tool does not project profitability from an investment, but the investment might still be advisable. New technology can create flexibility in future activities. In other words, new technology can create real options. Deterministic budgeting tools (partial budgeting, enterprise budgeting, whole farm budgeting, cashflow budgeting, and capital budgeting) do not adequately account for the value of these options. 

To illustrate, return to the example of a seed stock producer considering the use of tenderness marker-assisted selection. Since there are currently no large-scale markets for beef with verified tenderness genetics, simple budget tools would suggest that this is not a profitable investment. However, given there is some probability that markets will provide incentives to producers of tender beef, a real option can be created by using marker-assisted selection. If the seed stock producer begins using this selection tool, he will be in a position to market seed stock with desired genetics if and when the channels develop to reward tenderness. This is flexibility can have value. 

Note, however, there is risk associated with this strategy. The markets might not be created during the farmer’s productive time horizon. Lower-cost methods producing tender beef might be found, perhaps as simple as feed additives or post-slaughter treatment. Or worse, the US market for beef might collapse due to some unforeseen circumstance. So, the producer might invest in a technology that does not eventually result in improved profitability. 

The tools required to analyze this type of investment are real option pricing models and stochastic programming models. Both are likely more sophisticated than can be readily developed and used on farm or in an Extension context. The proof of this can be found in the lack of research literature utilizing these approaches. A few applied economics studies (Lusk 2007, DeVuyst et al 2007; Mitchell et al. 2009) have used analyzed the value of alternative markers in fed cattle. However, at the time of this writing, no economic studies have analyzed the investment in marker-assisted selection. 

Resources available to producers 

Most land grant universities provide enterprise budgets for a wide range of crops, livestock, fruits, nuts and vegetables. And, many of those budgets are available on the internet. For example, Oklahoma State University has enterprise budgets available on line (OSU Ag Econ 2010). The University of Minnesota maintains a farm management budget database (U of M DCRME 2010) with budgets from several states. Also, some land grant universities have the ability to work with producers to generate budgets for specialized investments. Again using OSU as an example, the Food and Agricultural Products Center (OSU FAPC 2010) provides services to individuals and companies considering investment in agricultural-related technology and businesses. Producers can contact their local Cooperative Extension Service office to find similar resources available in their home state. 

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Conclusion 

Decision making in the real world is complex, suggesting that complex decision models are appropriate. However, the information requirements of most complex decision models render them useless in an applied context. So, producers and Extension specialists rely on several readily available low-information requirement budgeting tools. Partial budgets, enterprise budgets, whole-farm budgets, cashflow budgets and capital budgeting tools are most often used to assess investment in new technology at the farm level. The cost of using these tools is a lack of ability to formal assess risk and the degree of reversibility associated with alternative investments. More sophisticated tools, including real option pricing models, do consider risk and reversibility but are not accessible to most agricultural producers. 

Literature Cited 

Barry, P.J., C.B. Baker, P.N. Ellinger, J.A. Hopkins. Financial Management in Agriculture 6th Edition. Danville, IL: Interstate Publishers. 2000. 

DeVuyst, E.A., J. Bullinger,, M. Bauer, P. Berg and D. Larson. 2007. “An economic analysis of genetic information: Leptin genotyping in fed cattle,” Journal of Agricultural and Resource Economics 32(2): 291-305. 

Dhuyvetter, K.C., A.M. Bryant, and D.A. Blasi. 1995. “Preconditioning beef calves: Are expected premiums sufficient to justify the practice?” Professional Animal Scientist 21(6):502-514.

Dixit, A.K. and R.S. Pindyck. Investment under uncertainty. Princeton, NJ: Princeton University Press. 1994. 

Fishburn, P.C. 1981. “Subjective expected utility: A review of normative theories,” Theory and Decision 13(2):139-199.

Fox, C.R. and A. Tversky. 1995. “Ambiguity aversion and comparative ignorance,” The Quarterly Journal of Economics 110(3):585-603.

Gaines, J.D., J. Galland, D. Schaefer, R. Nusbaum and D. Peschel. 1993. “The economic effect of estrus synchronization in beef heifers on average weaning weight of calves,” Theriogenology 39(3):669-675 .

Hirshleifer J. and Riley, J.G. The analytics of uncertainty and information. New York, NY: Cambridge University Press, 1992. 

Kay, R.D. W.M. Edwards, and P.A. Duffy. Farm Management, 6th Edition. New York, NY: McGraw-Hill, 2008. 

Larson, R.L, R.B. Miller, , S.B. Kleiboeker, M.A. Miller, B.J. White. 1995. “Economic costs associated with two testing strategies for screening feeder calves for persistent infection with bovine viral diarrhea virus,” JAVMA 226(2):249-254.

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Lusk, J. 2007. “Economic value of selecting and marketing cattle by leptin genotype,” Journal of Agricultural and Resource Economics 32(2):306-329.

Mitchell, J., E.A. DeVuyst, M.L. Bauer, and D.L. Larson. 2009. “Cow-calf profitability and leptin genotyping,” Agricultural Economics 40:113-118.

Oklahoma State University, Department of Agricultural Economics Budgets. http://agecon.okstate.edu/budgets/. Accessed 26-May-2010. 

Oklahoma State University, Food and Agricultural Products Center. http://www.fapc.okstate.edu/index.html. Accessed 26-May-2010. 

Perrin, R.K. “Asset replacement principles,” American Journal of Agricultural Economics 54:60-67. 

University of Minnesota Digital Center for Risk Management Education. http://www.agrisk.umn.edu/budgets/. Access 27-May-2010. 

von Neumann, J. and O. Morgenstern. Theory of Games and Economic Behavior. Princeton, NJ: Princeton University Press, 1944. 

38

RAISING BEEF IN A FIRST WORLD COUNTRY: SCIENCE, MEDIA AND POLITICS

Tom Field, PhD

Executive Director, Producer Education, National Cattlemen’s Beef Association

These are chaotic times for those who make their living from animal agriculture

enterprise. For a good portion of the last century, the industry was focused on improving productivity and it generated fantastic improvements in production per animal by systematically applying the principles of nutrition, genetics, animal health, reproductive physiology, and a host of other scientific disciplines. The impact has been to provide a bounty of animal-based protein capable of meeting the needs of U.S. consumers and key export markets while utilizing fewer resources in the process. As a result, the industry contracted as economies of scale and size favored larger enterprises that could deliver lower costs of production while better meeting the demands of an increasingly target-specific markets. Simultaneously, well fed U.S. consumers had the luxury of approaching their food choices from a life-style and often philosophical vantage point. Thus agriculture finds itself in a paradoxical situation framed by its own productive success, a smaller policy voice as the number of practicing agriculturalists declines, and the naivety and low level of agricultural knowledge of contemporary food system critics, governmental officials, educators, and reporters. As a result, many in agriculture feel unduly criticized and unfairly battered by the social and political climate.

In an affluent society with a reasonably functional free market, agriculture plays a significantly more complex role than it does in emerging economies where the cloud of chronic persistent hunger lingers at the vast majority of doorsteps. In the developing nations of the world, food production and distribution has a focused urgency – provide more calories to better meet the minimal dietary needs of the population. Achieving this objective typically requires improving infrastructure (transportation, access to capital, etc), introducing technologies as simple as fertilizers, pest management compounds, and basic irrigation and as complex as genetically modified plants and other biotechnologies. Yet, in developed nations the value of these technologies and protocols is being hotly debated. Norman Borlaug must have felt a certain degree of bewilderment, if not outright frustration, at the end of his long and fruitful career as he witnessed the demonization of the very technologies he championed to save millions from the scourge of hunger.

While the growing food needs of developing nations loom on the horizon, agriculture in more affluent societies finds itself under increasing levels of scrutiny from activists, social elitists, governmental regulators, and opinion influencers. These vocal critics of the U.S. agriculture system have been reasonably successful in framing the debate. Four trends have begun to shape the discussion about food value and quality, the processes used to grow, fabricate, and distribute foodstuffs; and the multi-faceted interactions between farmers/ranchers, grocery distributors and retailers, food service operators, consumers, and communities. These trends are as follows:

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• “Industrial” agriculture challenged on multiple fronts including concerns about food safety, environmental impact, animal welfare, human nutrition and wellness as well as local and regional economic effects. Consumers yearn for simplicity in the face of complex challenges and it is this disconnect that has been successfully leveraged by the food elitists such as Michael Pollan into celebrity status and an even broader platform from which to portray large scale, technology inclusive agriculture as “bad” and small, traditional agriculture as “good”.

• Food emerges as a social platform and flashpoint for change driven by ethanol policy, the

push for organic and local production by USDA and other governmental agencies, and the glamorization of the local food movement, farmer’s markets, and other unique niche markets. Food Foresight (2009) suggests that “private foundations, environmental, and public health groups, chefs, media, and the marketplace call for change in the name of healthier consumers, healthier farm animals, and a healthier planet.”

• The consumer mass market continues to fragment while experiencing an erosion of trust.

Concerns about economic instability, international unrest, and political turmoil add to the growing tension that is palpable in the consumer marketplace. Consumer markets cannot be categorized into several easily defined categories as the divergence of values expressed by the “baby boomers”, generation X, and the millennial generation has created a host of unique food marketing opportunities.

• Science becomes less of an authority as the web facilitates the rapid diffusion of

information and misinformation. Fueled by an increasingly opinion driven media and urban myths transmitted at the speed of light by the internet, contemporary consumers who are several generations removed from any agricultural experience find it increasingly difficult to make sense of the swirling messages about the food system.

Nuffer, et al, 2008

In light of these trends, growing consumer demand centers on the ability of agriculture and the food system to deliver several clear values to customers: • Transparency • Authenticity • Healthfulness of product and PROCESS • Experience

Nuffer, et al, 2008

In a survey of consumers from the United States, United Kingdom, Germany, Argentina, and China – taste (75%), quality (73%), and price (70%) were the dominant factors affecting food purchases (Ketchum, 2008). In the same survey, consumers were asked which factors would be their highest priority if they were CEO of a global food company. Their top three responses were to improve human nutrition (65%), improve food safety (64%), and make foods that taste great (52%). Results of the survey showed that while consumers indicated a preference

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for local foods, they were not willing to pay more for locally grown foods nor were they willing to sacrifice eating satisfaction in exchange.

Today’s consumer climate can best be characterized as uneasy and uncertain as the chaos of the times creates fear due to the lack of predictability in nearly all phases of American life. The economic crisis has affected consumer behavior in the short-term certainly and most likely for the foreseeable future. Food Foresight (2009) describes this emerging consumer as “one more mindful about desires versus necessities, more vigilant about spending, and more prone to make trade-offs to save money.” The report goes on to say that “interest in things such as green products and healthy foods will continue to grow in a post-crisis world, but consumer will be less willing to pay a premium for them, and will demand more value for their money when they do.”

Today’s consumers are the least knowledgeable about agriculture and food production in U.S. history. This knowledge gap creates the environment in which activists and special interests have the ability to exploit consumer ignorance and potentially implement public policy which puts American agriculture at risk in terms of its ability to sustain profitability and thus puts homeland security in an even more precarious position. Nonetheless, agriculturalists have no choice but to engage in the discussion and to develop strategies to successfully advocate for sustaining the opportunity for both consumers and producers to make choices from a range of viable options.

While the vortex created by consumer confusion, market chaos, and poorly implemented agricultural policy hurls itself at the industry an even more critical challenge is emerging as agriculture undergoes substantial concentration and down-sizing. Concentration has impacted every level of the beef supply and distribution chain. For example, Wal-Mart now has approximately 29% of the food retail trade while the second through fifth largest food retailers (Kroger Company, Costco Wholesale Company, SuperValu Stores, and Safeway) add an additional 23.2 percent share. Thus the top 5 grocers have just over one-half of all food retailing share. Add in the next five largest and the percent market share climbs to nearly 70% (Supermarket News, 2010). At the front end of the supply chain, cow-calf herds with less than 50 head of inventory account for almost 80% of operations but only 28% of the beef cow inventory. Herds with over 100 cows in inventory account for only 11% of the total enterprises but almost one-half of the nation’s beef cows (NASS, 2010).

Since 1987, nearly 250,000 beef cattle enterprises have ceased operations leaving approximately 760,000 producers managing a beef cow inventory of 32.5 million head and generating $49.2 billion in cash receipts. While the economic impact of the industry continues to be substantial, the cow herd has declined to its lowest level since World War II. Perhaps most concerning about the decline in numbers of producers and the beef herd is that the significant profitability of the cow-calf sector from 1999 to 2008 was not able to reverse the trend (LMIC, 2010). While drought played a role in herd reduction over the past 10-15 years, it is clear that producers voluntarily exited the business during a profitable time in the beef cycle. Age of producer, opportunities to sell land to capture equity, lack of interest by the next generation, and the trend for rising input costs likely played a role but the impacts of rising levels of regulatory activity, negative press, activist pressure, and lack of community support may have played a determining factor in many decisions. What results in the beef industry and in the vast majority

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of agricultural enterprises is a landscape of fewer but larger growers, processors, and distributors of food.

An important issue confronting the beef industry and all of agriculture are the declining resources allocated to agricultural research, development, outreach and education by the public sector. While space precludes a detailed discussion of this dilemma, the decline of federal agricultural research efforts, shrinking of land grant university budget allocations for agriculture, and the rapidly growing disconnect between land grant university faculty and the agricultural and food systems must be addressed if the beef industry and all of agriculture is to assure its long term health.

The market and economic chaos alone is sufficiently challenging but when the regulatory and political issues related to the environment, food safety, and animal well-being, are tossed into the fray; the scene takes on a decidedly brawl-like atmosphere. Take environmental regulation as an example of adding complexity to an already strained agricultural system. Here are just a few of the potential new regulations that loom on the horizon:

a. Redefinition of the “waters of the United States of America” would in effect place all wet areas of the U.S. under the jurisdiction of the federal Clean Water Act including lakes, rivers, streams (continuous and intermittent), mudflats, sand flats, sloughs, prairie potholes, wet meadows, playa lakes, and natural ponds with no recognized exclusions to cover ditches, stock tanks, manmade ponds, drain tiles, etc.

b. Regulation of dust and particulate matter created by tilling, planting and harvesting crops, feed mixing, cattle movement, driving on unpaved roads, and other agricultural uses under the authority of the Clean Air Act.

c. Regulation of ammonia under the Clean Air Act. Source: NCBA, 2008

In each case, if either Congress or the agency implements even part of the proposed rules,

the net effect will be to increase the level of industry concentration as only the largest enterprises will have the resources to comply with these new regulations which typically leads to additional criticism about the industrialization of agriculture and loss of family-farms.

Perhaps no issue is so fraught with emotional pitfalls, divergent philosophical positions, and misconceptions as the realm of animal well-being. This issue is bookended by two wildly divergent approaches – on one end of the spectrum are those who intentionally inflict pain and discomfort on animals out of cruelty or willful neglect while the opposite vantage point is characterized by those who would seek any means, including violence against property and humanity, to further an animal liberation agenda. Neither of these are appropriate or defensible positions in light of our social contract with the livestock under our care and stewardship and the larger community of human beings who are served by animal agriculture.

The provision of safe and wholesome food is an overarching objective for the entirety of the food and agricultural system. The responsibility for food safety is shared space occupied by the production and processing phase, the distribution and marketing phase, and the food

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preparation, service and storage components of the industry which includes consumers. While our attention has been largely focused on interventions to pathogens, some in society question the use of many production technologies such as antibiotics, growth enhancement technologies, pest management compounds, and feed additives out of fear that these technologies may result in a food supply that fails in terms of safety or lacks the ability to fulfill consumer expectations about the wholesomeness of their food supply.

With all of these factors at play coupled with the constant reminder that we must feed the equivalent population of an additional two Chinas over then next 4 to 5 decades, much is at stake – food security at home and abroad, the viability of rural economies, the sustainability of a vibrant and productive agricultural infrastructure, the livelihood of agricultural producers, and ultimately the well-being of consumers.

It would be naïve to suggest that there are simple solutions to these challenges given their complexity. The solutions will likely emerge in fits and starts as we grapple and struggle with these issues. So where do beef producers and industry leaders begin?

From a 10,000 foot perspective, the focus of the beef industry must be on sustaining an environment where profitability can be attained, market development domestically and abroad, and advocating for a free market, limited government landscape. On an individual enterprise level, the creation of a clearly thought out business plan has never been more important. While the cowherd continues to contract, the long term opportunities for those who can manage cost, access land resources though means beyond outright ownership, and build partnerships are bright.

At the more tactical level, participants in the live animal phase of the beef production chain should consider the following:

• Assessment - create the vision, determine the goals/objectives, and examine the prevailing attitudes and values of the enterprise. In many ways, this process is centered on detailing the legacy of a business and its leaders by determining core values upon which future activity will be founded. A question to help start the conversation – are you a craftsman or a technician?

• Evaluate – take a hard look at the specific processes and protocols (calving, branding,

weaning, handling, transporting, processing, and marketing) that affect the profitability of the ranch, the well-being of the people and livestock, and the health of the resources (natural, community, etc). In a business rich in tradition that has traditionally been managed in accordance with seasonal signals, the process of taking a fresh look at management activities can yield opportunities for improvements in profitability and quality of life. With respect to animal well-being, environmental stewardship, and food safety; each participant in the industry has a responsibility to be thoughtful and intentional in their activities. If our processes or work is not in line with core values or exposes the business and industry to rational criticism; action is required.

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• Commit - dedication and focus is required to seek continuous improvement. Implementation of best practices, measurement of progress, and commitment to continuous learning and discover are vital steps.

• Communicate - train people (family members, employees, day help) to meet the

expectations established in the previous steps. Take time to express the expectations, train people so they have the best opportunity to be successful, and then recognize performance that meets or exceeds the expectation.

• Partners – effective partnerships are central to profitability. Membership in local, state

and national cattlemen’s organizations is crucial to help protect private property rights, the opportunity to be profitable, and free of the excesses of government. Careful consideration must also be given to finding working relationships with non-traditional partners with whom common goals are shared.

• Educate – the need for industry participants to vigorously pursue knowledge and

understanding of the issues confronting the beef industry as well as its benefits and costs. Industry leaders and advocates must function from an informed position and with the skill set to communicate the impact of the beef industry on the community, natural landscape, economy, consumers, and food system.

• Engage – food and agricultural production have become center plate discussion items in

contemporary culture. Beef producers and others who tackle the difficult job of feeding a growing consumer population can not afford to stay on the sidelines. Engagement in the discussion that occurs in social and traditional media, the political and regulatory arena, in classrooms, boardrooms, and family rooms cannot be ignored.

Agriculture and the food supply chain is a complex system from which consumers,

opinion influencers, and policy makers are typically far removed. The future of the beef industry depends in large part on bridging that broadening gap. The late Jerry Garcia of Grateful Dead fame described our situation when he said “somebody has got to do something and it is just incredibly pathetic that it has to be us.” Literature Cited: Ketchum Global Food Practice. 2009. Food 2020: The Consumer as CEO. New York, NY. Livestock Marketing Information Center. 2010. Estimated average cow-calf returns.

Englewood, CO. Nuffer, Smith and Tucker. 2009. Food Foresight: Trends Intelligence for the Agri-food Chain –

2009. San Diego, CA. Nuffer, Smith and Tucker. 2008. Food Foresight: Trends Intelligence for the Agri-food Chain –

2008. San Diego, CA.

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National Agricultural Statistics Service. 2010. Agricultural Statistics – 2008. USDA,

Washington DC. National Cattlemen’s Beef Association. 2008. Environmental Regulation in the Beef Industry:

Special Report. Washington DC. Supermarket News. 2010. Top 75 Food Retailers. New York, NY.

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HOW THE NEXT GENERATION OF GENETIC TECHNOLOGIES WILL IMPACT BEEF CATTLE SELECTION

Megan M. Rolf, Stephanie D. McKay, Matthew C. McClure, Jared E. Decker, Tasia M. Taxis, Richard H. Chapple, Dan A. Vasco, Sarah J. Gregg, Jae Woo Kim, Robert D. Schnabel and

Jeremy F. Taylor

Division of Animal Sciences, University of Missouri, Columbia, MO 65211, USA

Abstract

Recent advancements in sequencing and genotyping technologies have enabled a rapid evolution in methods for beef cattle selection. The past three decades has seen the advancement from restriction fragment length polymorphism (RFLP) markers that were low-throughput, time-consuming and difficult to score to the newest high-density single nucleotide polymorphism (SNP) assays where marker genotypes are easily and inexpensively generated. The cattle genome sequence was published in 2009 and sequencing technologies have now advanced to the point that a complete genome can be resequenced to a relatively deep coverage for ~$30,000 on several different next generation sequencing platforms. While a reference genome to align the reads is currently required for this process, with read lengths increasing with each software or chemistry update, de novo sequence assemblies will become routine in the very near future. Once the analytical methodologies are developed and become widely available, animal scientists will begin to use them to develop cost-effective diagnostics for use in beef cattle production systems. As a result of this rapid expansion of technology, new tools will become available for beef producers to implement in the endeavor to efficiently produce high quality beef for today’s consumer. Tools such as high-density genotyping assays and next generation sequencing instruments will help to shorten the generation interval, aid in the identification of causal mutations, increase the accuracy of EPDs on young sires and dams, provide information on gene expression and enhance our understanding of epigenetic and gut microbiome effects on cattle phenotypes.

Introduction

There will be many changes in methodologies for the genetic evaluation of beef animals in the near future due to rapid technological advances. These advances provide the momentum for change in the industry and will enhance our ability to produce beef efficiently in today’s marketplace. The ability for beef producers to accurately select for genetically superior animals began over four decades ago when mixed model methods were first published by Henderson (1975). The first national cattle evaluation (NCE) was performed in 1974 (Willham, 1993), and since then, models have evolved from single-trait sire models to the multi-trait animal models used today. The next large step looming on the horizon will be the genomic revolution.

Current technologies are beginning to shape the next generation of genetic evaluation. One of the most useful advances has been the public availability of a published genome sequence for beef cattle. Baylor College of Medicine was the first to sequence the bovine genome and used a combination of bacterial artificial clone (BAC) methods as well as whole genome shotgun sequencing (The Bovine Genome Sequencing and Analysis Consortium, 2009). The University

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of Maryland quickly released their genome assembly based upon the sequences produced by the Baylor College of Medicine and this assembly has also been annotated (Zimin et al., 2009). These two assemblies differ slightly according to the methods used to assemble the sequence reads and the availability of both provides an invaluable resource for genomic studies in beef cattle.

Microarrays which are used to study the expression profiles of genes within specific tissues are available in two different forms: long oligonucleotide arrays (typically those spotted on glass slides that can be bought privately from researchers) and short oligonucleotide arrays (typically those commercialized by companies such as Affymetrix). Microarray technologies can allow the simultaneous profiling of very large numbers of genes and can be used to identify the pathways that are up- or down-regulated in different tissue types or disease states. This allows the identification of the key genes that regulate the behavior of entire pathways and possibly even phenotypes. These genes then become the targets of pharmocological intervention (drug targets) or even genetic manipulation. The greatest disadvantage of microarrays is that they can only query the genes for which probes are designed onto the array. Thus, we have to know the full complement of “genes” within a species genome to be able to design a comprehensive microarray, and unfortunately this is not the case even for humans, which have the most extensively studied genome. Microarrays also suffer from a loss of information in that, often, only probes are generated for one region of a gene and the gene may actually produce more than one type of transcript or protein. Finally, microarrays require quite a lot of technical skill and large numbers of replicates, normalization and dye swaps must be used to filter the true signal from the biological and technical noise.

The first high-density and high-throughput genotyping assay was the 10K single nucleotide polymorphism (SNP) chip commercialized by Affymetrix (The Bovine HapMap Consortium, 2009). However, the density of SNPs in this panel was insufficient for many genomic studies (including genomic selection (GS) and genome-wide association analyses (GWAS)) which led to the need for a higher density chip. The Illumina BovineSNP50 chip was developed by a consortium of animal scientists using SNP discovery populations in Holstein, Angus and mixed breeds of beef cattle (Van Tassell et al., 2008) and provided much higher density (~50,000 SNPs per animal) than previous high-throughput genotyping assays (Matukumalli et al., 2009). This assay has become the international standard for GS and GWAS in cattle and has even been applied to other species to resolve the evolutionary relationship among the horned ruminants (Decker et al., 2009; MacEachern et al., 2009), testing the number of SNPs needed to form a genomic relationship matrix (Rolf et al., 2010) and investigating the amount of introgression of cattle DNA into bison populations (Schnabel et al., unpublished data). While the Illumina BovineSNP50 assay has proven to be extremely useful for many different types of genomic studies, our current data suggest that even higher density assays will be needed to build models for GS with utility across breeds.

New Technologies

High Density SNP Genotyping Chips from Affymetrix and Illumina

Two new high-density SNP genotyping chips will be introduced in 2010. The first is an assay from Illumina that will utilize the same bead technology and single base extension

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chemistry that is used for the current BovineSNP50 50K chip. The Illumina assay will genotype approximately 800K SNPs per animal and should be available by the time of this BIF meeting. The second high density SNP chip will be marketed by Affymetrix and will also genotype approximately 800K SNPs. This chip uses a different chemistry to the Illumina chip, but the ligation-based assay should result in almost the same call rate (% of genotypes called per sample) and the produced genotypes should be very high quality (low intrinsic error rate). Best of all, the two companies will compete for business and the cost of these assays may end up as low as we are currently paying for the Illumina 50K assay! With 50K SNPs available per animal, why do we need 800K? There are several important applications and advancements that will be made possible with the addition of more SNPs. The first is that SNPs will be distributed much more closely together in the genome. With 50K SNPs in a genome of approximately 3 billion base pairs, we would expect 1 SNP about every 60 Kb, but with 800K SNPs, we would expect 1 SNP approximately every 3.8 Kb. This inter-marker distance provides much finer resolution for mapping the causal mutations that underlie variation within a trait and also allows a much greater opportunity for identifying SNPs that can predict genotype at these causal mutations when scored in animals of different or even mixed breed content for use in GS.

SNP discovery for the Illumina BovineSNP50 assay was performed using pools of DNA samples from Angus, Holsteins and a group of bulls sampled from the next most important US beef breeds. As a result, there is a bias inherent in the assay towards SNPs that have high minor allele frequency in Angus and Holsteins and the assay performs slightly better for GWAS and GS in these breeds. However, the SNP discovery for the design of the Illumina and Affymetrix 800K panels was performed by sequencing a large number of animals from many different breeds (including both Bos taurus and Bos indicus) to minimize the ascertainment bias in SNP informativeness across breeds. The end result should be a panel of SNPs that will have high average allele frequencies in almost all cattle breeds but will also contain many loci with low allele frequencies. This is especially important for performing GWAS, since common SNPs cannot be strongly associated with rare or low frequency variants within a population. The larger, more variable panel will contain some SNPs which are at low frequency in the population of interest to facilitate the detection of rare variants within that population.

Perhaps the greatest immediate value of the 800K chips will be the potential for implementing across-breed GS in the beef industry. The real advantage of GS is its ability to simultaneously select for desirable combinations at all loci responsible for genetic variation in a trait using panels of closely linked markers. Figure 1 provides a representation of the difference between traditional marker assisted selection (MAS) – the “single marker” tests that have been used in the industry to this point - and GS. MAS typically involves selecting for desirable genotypes at a small number of loci, which are usually of large effect, as these loci are usually the easiest to identify in association or linkage analyses. In contrast, GS allows the simultaneous selection for desirable genotypes genome-wide. The 50K SNP chip has been shown to be effective for GS within breeds of cattle such as for Net Merit in Holsteins (VanRaden et al., 2009). However, the computation of molecular breeding values with high accuracies requires that a large numbers of animals with high accuracy EPDs be genotyped and the lack of a centralized DNA repository (such as are utilized by the dairy breeds) has limited the numbers of animals available for genotyping within each of the beef breeds. Because of the shortage of DNA samples on animals with high accuracy EPDs, individuals from different breeds will need to be genotyped and the analysis performed across breeds. The assumption here is that the

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causal variants that create variation in traits are the same set of loci across breeds but they differ in frequency which leads to breed differences in the mean of these traits across breeds. However, SNP allele frequencies also differ across breeds and these differences in marker and causal variant frequencies mean that different SNPs are going to be more or less strongly associated with trait variation in different breeds. The density of SNPs on the 50K assay is not sufficient in an across breed analysis to arrive at a model for the prediction of molecular breeding values that will be highly accurate across breeds and the 800K chips will be vitally important for this application. With a SNP every 3.8 Kb, there will be a sufficient SNP density to surmount these issues and obtain accurate molecular estimates of genetic merit across breeds by identifying the markers that are very close to the causal mutations and that have the same SNP allele on the chromosomes harboring the trait enhancing allele at the causal mutation across all breeds. For example, in the Carcass Merit Project (CMP) 50K data for Warner-Bratzler shear force (WBSF) the correlations between SNP effects (Table 1) and molecular estimates of breeding value estimated from SNP effects produced within each of the breeds (Table 2) were low. However, in the region from 44,000,728-44,208,978 nucleotides on chromosome 29 which harbors the µ-calpain gene, we scored 37 additional SNPs to the 6 SNPs present on the BovineSNP50 assay to produce a mean marker spacing of 4.8 Kb. In the analysis of these data, we found one SNP was consistently associated with WBSF across breeds and that the same allele was predictive of increased tenderness across all 5 analyzed breeds.

Next Generation Sequencing

The ability to quickly, accurately, and inexpensively sequence the genomes of individual animals has the potential to revolutionize selection in beef cattle. Recent technological advancements have made great improvements in the affordability and accessibility of genomic sequence data. Two currently marketed platforms are the Illumina Genome Analyzer (or HiSeq 2000) and ABI SOLiD. Initially, read lengths for the Illumina and SOLiD were in the range 35-36 base pairs (bp) with a cost per million bases of sequence of approximately $2 (Shendure and Ji, 2008). However these platforms have been rapidly developed with improvements in chemistry and software allowing the Genome Analyzer to achieve reads of 125 bp and both technologies currently support paired-end reads in which each end of a 300 bp fragments are sequenced to a depth of 85 bp. More importantly, these instruments are now capable of producing up to 95 Gb of sequence in a single run of the instrument. After quality control processing of the data and mapping fragments to a genome assembly, this results in as much as a 15X coverage of animal genome. Two such runs at a cost of less than $30,000 will produce sufficient sequence data to allow a de novo assembly of an animal’s genome sequence.

The data obtained from next generation sequencing has many applications. One application is the identification of the actual expression level of all of the genes that are expressed in essentially any tissue or animal. RNA sequencing (RNA-Seq) allows the novel assembly of a transcriptome (the set of expressed genes) for any tissue and provides quantitative data to identify differences in gene expression between two samples. The approach also identifies if alternative exons of a gene are used to create different forms of a protein in different tissues or animals and also produces the DNA sequence of each transcript. Thus any sequence differences (SNPs within coding regions) that result in amino acid changes could produce phenotypic variation within a trait. RNA-Seq produces estimates of the actual number of transcripts of a particular mRNA from the counts of the number of reads that map to each gene.

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The top panel in Figure 2 shows the number of sequence reads observed for the PRP gene’s messenger RNA in the brain of a dog. The figure shows that there is a large number of sequence reads observed for PRP (location indicated by the box) and another mRNA shown to the right of PRP.

Both RNA-Seq and genomic DNA sequencing data provide insight into novel and causal polymorphisms within an individual. The bottom panel in Figure 2 shows a C/G SNP polymorphism identified in the PRP gene from the RNA-Seq data, which results in the change of the amino acid at this position from aspartic acid (D) to glutamic acid (E). The discovery of mutations which actually cause variation within traits will become increasingly important and their knowledge will allow testing across breeds which will drastically reduce the number of loci that need to be tested to explain variation within a trait. If we know the causal mutations, we only have to test for those mutations, rather than using 800K SNPs to estimate the effects of these variants. This will result in the development of more affordable, accurate panels of SNPs that work across breeds. It will also suggest the genes that should be screened across populations in the endeavor to understand all existing naturally occurring variation which may have important phenotypic effects. Information will also be gained that will suggest drug targets or targets for genetic modification, if this technology is deemed acceptable for use in animals by consumers.

A novel application of this technology that is becoming increasingly important in the human and mouse communities is the sequencing of gut microbiomes. Most work in humans and mice to this point has focused on profiling the 16S ribosomal RNA (rRNA) gene to identify the microbes present in the gut using long-read and low throughput (traditional Sanger) sequencing methods. However, this type of research has recently expanded to utilize next generation sequencing technologies (Qin et al., 2010). The study of “gut microbiomes” and their interactions with the genotype of the host is important because previous studies have shown that there is substantial genetic diversity in the species present within the gut microbiome (Li et al., 2009; Turnbaugh et al., 2006, 2009) and that the gut microbes have a significant impact on energy harvest and obesity in humans and mice (Backhed et al., 2004). One study observed that when germ-free mice were inoculated with a gut microbiome from either a lean or an obese individual, the mice that received the gut flora from the obese mice gained more weight than their counterparts which received the gut flora from the lean mice (Turnbaugh et al., 2006). Because of the way that nutrients (especially those from forages) are harvested in ruminants, it is likely that gut microbiomes have an even greater impact on energy metabolism than in human or mouse. Furthermore, the composition of these gut populations may also be related to feed efficiency, methane/greenhouse gas emissions and manure production; thus, it is imperative that we explore whether the host genotype has an effect on the composition of the gut flora, and if so, select for favorable gut populations. Currently, these relationships are poorly understood, and the host interactions that may be under nuclear genetic control are either confounded with additive genetic effects (which would be desirable) or are being placed in the residual component of our genetic models, where they are not selectable.

Epigenetics

Epigenetics is a field of rising importance in genetics and genomics. Epigenetics involves DNA and histone modifications which can influence gene expression and thus the

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genetic variation in a trait, even if animals have identical genotypes and DNA sequence. Some examples of these modifications include imprinting, X-inactivation, gene silencing and embryonic reprogramming (Sellner et al., 2007). Epigenetic effects such as methylation involve the addition of methyl groups to cytosines and if these occur in the promoters of genes, transcription machinery can be blocked from binding to the DNA. DNA methylation is influenced by both the genetics and environment of the individual, but has been shown to be stably transmitted from parents to offspring for several generations. Once the bovine epigenome (the set oft nucleotides that are methylated in the DNA that is present across different tissues) has been characterized, there is the potential to select or perhaps even induce favorable effects and include this information into breeding programs. Most of the new high-throughput sequencing instruments can elucidate whether nucleotides are methylated (however the new sequencer from Pacific Biosystems can detect methylation as a by-product of sequencing by measuring the time it takes to incorporate a new base while reading genomic sequence), which will allow rapid advances into the understanding of these effects and their influence on phenotypes in beef cattle.

Finally, technologies such as ChIP-Seq (which allows determination of which proteins, such as transcription factors, interact with DNA to influence gene expression and also allows examination of epigenetic chromatin modifications) and Bis-Seq (massively parallel sequencing of bisulfate-treated DNA, which converts unmethylated cytosines to uracils) are powerful new tools for providing insight into the nature and extent of epigenetic modifications within the genome.

Conclusions

The fantastic pace at which new technologies are being developed to study the genome make it an exciting time in the beef industry for producers and scientists alike. High-density genotyping assays will soon revolutionize the way we conduct genetic prediction and whole-genome sequencing of animals and their gut populations along with epigenetic profiling will lead to new tools to ethically and efficiently provide high quality beef that meets consumer demands in an increasingly competitive marketplace.

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Table 1:  Correlation coefficients between SNP effects estimated for Warner‐Bratzler shear force (WBSF) between five different breeds of animals involved in the NCBA sponsored Carcass Merit Project.  The number of animals used in the analysis are shown on the diagonal. 

WBSF SNP Effects 

ANGUS  CHAROLAIS  HEREFORD  LIMOUSIN  SIMMENTAL 

ANGUS  651  0.0267  0.0351  0.0134  0.0260 

CHAROLAIS    695  0.0135  0.0019  0.0081 

HEREFORD      1095  ‐0.0196  0.0251 

LIMOUSIN        283  ‐0.0047 

SIMMENTAL          516 

 Table 2:  Correlation coefficients between molecular estimates of breeding value (MBVs) estimated from SNP allele substitution effects for Warner‐Bratzler shear force (WBSF) in five breeds of animals involved in the NCBA sponsored Carcass Merit Project.  Elements in each row represent correlations between MBVs computed using the SNP effects for the breed in that row with MBVs computed for the breed in that row using SNP allele substitution effects for the breed in each column.  

WBSF Angus SNP effects 

Charolais SNP effects 

Hereford SNP effects 

Limousin SNP effects 

Simmental SNP effects 

Angus MBVs  1.0000  0.2229  0.2500  ‐0.0625  0.0661 

Charolais MBVs 

0.0442  1.0000  0.0407  0.0035  0.0715 

Hereford MBVs  0.2997  0.1100  1.0000  ‐0.3259  ‐0.0068 

Limousin MBVs  ‐0.0220  ‐0.0391  ‐0.1794  1.0000  ‐0.1875 

Simmental MBVs 

0.1502  0.1624  0.1160  ‐0.0255  1.0000 

 

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Figure 1:  Depiction of traditional marker assisted selection (MAS) versus genomic selection (GS).  The box represents the genome of an animal and the circles represent variation within the genome.  The size of the circles represents the amount of genetic variation explained at that locus.  The white circles represent variation that is not being selected due to a lack of a suitable closely‐linked marker and the filled circles represent the variation which is under selection using each of the approaches. 

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Figure 2: Dog RNA-Seq data in a NextGene viewer showing the PRP region. The top panel shows the number of copies on the Y axis and chromosomal position on the X axis. The center panel shows the reference sequence compared to the sample

sequence assembly and any detected amino acid change. The bottom panel shows the tiled sequences. A SNP can be

observed and is highlighted in the tiled sequence.

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Literature Cited

Backhed, F., H. Ding, T. Wang, L. V. Hooper, G. Y. Koh, A. Nagy, C. F. Semenkovich and J. I. Gordon. 2004. The gut microbiota as an environmental factor that regulates fat storage. Proc. Natl. Acad. Sci. 101(44):15718-23.

Decker, J. E., J. C. Pires, G. C. Conant, S. D. McKay, M. P. Heaton, K. Chen, A. Cooper, J.

Vilkki, C. M. Seabury, A. R. Caetano, G. S. Johnson, R. A. Brenneman, O. Hanotte, L. S. Eggert, P. Wiener, J. J. Kim, K. S. Kim, T. S. Sonstegard, C. P. Van Tassell, H. L. Neibergs, J. C. McEwan, R. Brauning, L. L. Coutinho, M. E. Babar, G. A. Wilson, M. C. McClure, M. M. Rolf, J. Kim, R. D. Schnabel and J. F. Taylor. 2009. Resolving the evolution of extant and extinct ruminants with high-throughput phylogenomics. Proc. Natl. Acad. Sci. 106(44):18644-9.

Henderson, C. R. Best linear unbiased estimation and prediction under a selection model. 1975.

Biometrics 31:423-47. Li, M., B. Wang, M. Zhang, M. Rantalainen, S. Wang, H. Zhou, Y. Zhang, J. Shen, X. Pang, M.

Zhang, H. Wei, Y. Chen, H. Lu, J. Zuo, M. Su, Y. Qiu, W. Jia, C. Xiao, L. M. Smith, S. Yang, E. Holmes, H. Tang, G. Zhao, J. K. Nicholson, L. Li and L. Zhao. 2009. Symbiotic gut microbes modulate human metabolic phenotypes. Proc. Natl. Acad. Sci. 105(6):2117-22.

MacEachern, S., J. McEwan, A. McCulloch, A. Mather, K. Savin, and M. Goddard. 2009. Molecular evolution of the Bovini tribe (Bovidae, Bovinae): is there evidence of rapid evolution or reduced selective constraint in Domestic cattle? BMC Genom. 10:179.

Matukumalli, L. K., C. T. Lawley, R. D. Schnabel, J. F. Taylor, M. F. Allan, M. P. Heaton, J. O’Connell, S. S. Moore, T. P. L. Smith, T. S. Sonstegard and C. P. Van Tassell. 2009. Development and characterization of a high density SNP genotyping assay for cattle. PLoS One 4:e5350.

Qin, J., R. Li, J. Raes, M. Arumugam, K. S. Burgdorf, C. Manichanh, T. Nielsen, N. Pons, F. Levenez, T. Yamada, D. R. Mende, J. Li, S. Li, D. Li, J. Cao, B. Wang, H. Liang, H. Zheng, Y. Xie, J. Tap, P. Lepage, M. Bertalan, J. M. Batto, T. Hansen, D. Le Paslier, A. Linneberg, H. B. Nielsen, E. Pelletier, P. Renault, T. Sicheritz-Ponten, K. Turner, H. Zhu, C. Yu, S. Li, M. Jian, Y. Zhou, Y. Li, X. Zhang, S. Li, N. Qin, H. Yang, J. Wang, S. Brunak, J. Doré, F. Guarner, K. Kristiansen, O. Pedersen, J. Parkhill, J. Weissenbach, MetaHIT Consortium, P. Bork, S. D. Ehrlich and J. Wang. 2010. A human gut microbial gene catalogue established by metagenomic sequencing. Nature 464(7285)59-65.

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.

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Sellner, E. M., J. W. Kim, M. C. McClure, K. H. Taylor, R. D. Schnabel and J. F. Taylor. 2007. BOARD-INVITED REVIEW: Applications of genomic information in livestock. J. Anim. Sci. 85:3148-3158.

Shendure, J., and H. Ji. 2008. Next-generation DNA sequencing. Nat. Biotech. 26:1135-1145.

The Bovine Genome Sequencing and Analysis Consortium. 2009. The genome sequence of taurine cattle: A window to ruminant biology and evolution. Science 324(5926):522-8.

The Bovine HapMap Consortium. 2009. Genome wide survey of SNP variation uncovers the genetic structure of cattle breeds. Science 324(5926):528-532.

Turnbaugh, P.J., M. Hamady, T. Yatsunenko, B. L. Cantarel, A. Duncan, R. E. Ley, M. L. Sogin, W. J. Jones, B. A. Roe, J. P. Affourtit, M. Egholm, B. Henrissat, A. C. Heath, R. Knight and J. I. Gordon. 2009. A core gut microbiome in obese and lean twins. Nature 457:480-485.

Turnbaugh, P. J., R. E. Ley, M. A. Mahowald, V. Magrini, E. R. Mardis and J. I. Gordon. 2006. An obesity-associated gut microbiome with increased capacity for energy harvest. Nature 444(7122):1027-31.

VanRaden P. M., C. P. Van Tassell, G. R. Wiggans, T. S. Sonstegard, R. D. Schnabel, J. F. Taylor and F. S. Schenkel. 2009. Invited review: reliability of genomic predictions for North American Holstein bulls. J. Dairy Sci. 92:16-24.

Van Tassell C. P., T. P. L. Smith, L. K. Matukumalli, J. F. Taylor, R. D. Schnabel, C. T. Lawley, C. D. Haudenschild, S. S. Moore, W. C. Warren and T. S. Sonstegard. 2008. Simultaneous SNP discovery and allele frequency estimation by high throughput sequencing of reduced representation genomic libraries. Nat. Meth. 5:247-52.

Willham, R. L. 1993. Ideas into action: a celebration of the first 25 years of the Beef Improvement Federation. University Printing Services, Oklahoma State University, Stillwater, OK.

Zimin, A. V., A. L. Delcher, L. L. Florea, D. R. Kelley, M. C. Schatz, D. Puiu, F. Hanrahan, G. Pertea, C. P. Van Tassell, T. S. Sonstegard, G. Marcias, M. Roberts, P. Subramanian, J. A. Yorke and S. L. Salzberg. 2009. A whole-genome assembly of the domestic cow, Bos Taurus. Gen. Bio. 10(4):R42.

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IMPLEMENTATION AND DEPLOYMENT OF GENOMICALLY ENHANCED EPDS: CHALLENGES AND OPPORTUNITIES

Sally L. Northcutt

Angus Genetics Inc., American Angus Association

Genomic-enhanced Expected Progeny Differences (EPDs) are calculated on animals using the American Angus Association® (AAA) database along with the results from the IGENITY® Profile for Angus cattle to provide more thorough characterization of economically important traits and improved accuracy on young animals. Angus breeders have become accustomed to the rapid feedback of this new endeavor, and updated weekly carcass EPDs have become the norm, to provide timely selection tools beyond the classic interim EPD concept. Implementation by the Association has been rapid. Challenges seem unending since the technology continues to evolve, but opportunities for future genetic improvement likewise seem endless.

Implementation Overview

In October 2009, the AAA released National Cattle Evaluation (NCE) genomic-enhanced EPDs for carcass traits. Nearly two years of research collaboration between Angus Genetics Inc.® (AGI) and IGENITY has resulted in an IGENITY genomic profile, specific to Angus cattle. The AAA leadership, through AGI, has a vision to provide Angus breeders with the most advanced solutions to their genetic selection and management needs. AGI is a subsidiary of the AAA and is involved in the development and implementation of new technology for use by the beef industry.

The Association’s weekly carcass EPDs are composed of the typical pieces one would expect in the NCE, but also include genomic results, or molecular breeding values, as available on animals. Molecular breeding values from IGENITY are derived from a High Density Whole Genome Scan with 50,000 markers (HD WGS). Every week, the full NCE for carcass traits is conducted for the most timely, up-to-date genetic predictions computed on nearly two million animals. Figure 1 illustrates how samples, animal identification, and genomic results move through channels to ultimately enter the American Angus Association NCE. Only the Angus-specific IGENITY profiles received through this data flow process are incorporated into the carcass EPDs.

Angus breeders submit the DNA sample directly to AGI, located within the parent company of the AAA in Saint Joseph, MO. The identity of the animal is recorded through the AGI system with a barcode. Through this process, the animal identity is known within the AAA records before the DNA sample is sent to IGENITY. An electronic file with this anonymous animal identification tracking and DNA samples are sent to IGENITY for genomic profiling. In three to four weeks, an electronic file of genomic results is returned to AGI for system upload and subsequent weekly carcass genetic evaluation.

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Figure 1. Information exchange between the breeder, AGI, and IGENITY

Carcass Evaluation Model

With the inclusion of genomic results, the carcass evaluation has an additional piece of information contributing to the genetic system. The weekly genetic predictions for carcass merit encompass carcass harvest records, ultrasound scans, and genomic results using methodology described in previous research (MacNeil and Northcutt, 2008; MacNeil et al., 2010a). The result of the integrated evaluation is a genomic-enhanced EPD for carcass weight, marbling score, ribeye area and fat thickness. The units of measure remain in carcass trait format, and ultrasound data and genomic results serve as indicator traits. Established genetic relationships between the indicator and carcass traits impact the EPDs and accuracy, with the genetic correlations between the molecular breeding values derived from HD WGS and the economically relevant carcass traits ranging from 0.50 to 0.65 (MacNeil et al., 2010b). In a weekly update, typically scheduled each Friday morning, the genomic-enhanced NCE EPDs are available at www.angus.org/Animal/EpdPedSearch.aspx.

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Opportunities

Incorporation of genomic results into NCE procedures has opened the door to new opportunities in selection tools. By itself, the impact of weekly carcass evaluations has been sizable, providing breeders with rapid selection tool feedback beyond a traditional evaluation every six months. Key benefits of generating NCE genomic-enhanced carcass EPDs on a weekly basis include:

- NCE EPDs are the best genetic predictions for carcass traits, surpassing ratios, interim EPDs, and profile scores as selection tools.

- Pedigree-estimated interim EPDs for young nonparent animals are short-lived or bypassed to provide the more informative NCE EPDs each week.

- Carcass NCE EPDs are available on Angus cattle in a rapid timeframe.

- Ultrasound, carcass and genomic databases with a four-generation pedigree are used simultaneously each week.

- Carcass genomic profile results are incorporated into EPDs without a six-month wait for biannual evaluations.

- Ultrasound-scanned animals receive NCE EPDs within a week of the scan results being processed by the AAA, for a comprehensive prediction beyond what is available from interim EPDs.

- On calves with ultrasound or genomic profiles, dams that had no carcass EPDs in the past now receive weekly NCE EPDs without the time lag of a biannual evaluation.

- Calves with genomic profile results have calculated NCE EPDs using all data contributing to the comprehensive EPD system.

- The carcass bio-economic indexes are updated with the change of associated carcass trait EPDs.

A frequent question to a breed association is the ‘equation’ to calculate EPDs. Mixed model methodology to generate EPDs is not trivial, but can be referenced in the guidelines for the Beef Improvement Federation (Beef Improvement Federation, 2002). With efficient software routines and high-speed computers, the computational process to generate weekly carcass EPDs is straightforward. Weekly carcass evaluations with genomic results included in the analysis are part of the evolution to provide Angus breeders with rapid, accurate selection tools for genetic improvement.

Animal Accuracy Example

The beauty of using the genomic data as an indicator trait is that animals at a young age can have carcass trait EPDs prior to ultrasound scanning. As an example, for an Angus calf out of registered parents with no ultrasound scan record and no genomic profile, the EPDs are simply a parental average EPD, or interim EPD, with a default 0.05 accuracy level. If this calf of any age has a genomic result reported through AGI/AAA, the weekly carcass evaluation produces an

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EPD with accuracies ranging from 0.28 to 0.38 depending on the carcass trait. Unlike the phenotypic data (carcass, ultrasound), the genomic result requires no contemporaries to enter the genetic evaluation. Thus, the genomic profile can be incorporated from animals of any age. If this calf is later scanned as a yearling and then accumulates progeny data later in life as a parent, each new piece of information is rolled into the weekly carcass evaluation.

For animals that already have an EPD in the carcass evaluation, the genomic results still have impact on the carcass traits. EPDs may move up, down, or stay the same, and the accuracies increase on animals in cases where there is not extensive data reported for the animal as a parent thus far. As another example, consider a dam with her own ultrasound scan record from a proper contemporary group and 11 scanned progeny. With the dam’s own scan record and progeny information in the evaluation initially, the marbling EPD accuracy is 0.25. After her profile results are included in the weekly NCE carcass evaluation, her marbling EPD accuracy improves to 0.37.

Challenges

The opportunity to provide rapid, more accurate carcass EPDs to Angus breeders has been accompanied with some challenges. Each time the genomic panels are improved, the correlation between the molecular breeding value and the trait of interest must be re-estimated. With carcass traits this process has become more straightforward; however, time must still be allotted to implement any new improvements to the carcass evaluation model.

Also, animals may have existing molecular breeding values in the evaluation and then additional genomic profiles are subsequently purchased by other breeders. This results in the need for database storage and evaluation procedures to handle multiple molecular breeding values on a single animal.

Association databases must be flexible to receive varying amounts of genomic results on animals, track the source of such data pieces, and check for duplicate records as well. Breeder access to information relating to the timeframe for which samples are submitted, results received, and evaluation procedures conducted needs to be flexible. Much of this occurs through the breeder AAA Login website.

As the advances in characterizing Angus genetics with genomic technology continue to accelerate, Angus breeders are faced with sale catalog and print advertisement deadlines, and commercial bull buyer questions as to why EPDs change from one evaluation to the next. While the most current EPDs are available online and breeders are encouraged to use those tools for up-to-date information, the deadlines for printed material and the understanding of the new technology still generate demand for additional outreach from the AAA to its clientele.

Producer uptake and education is a critical challenge. Genomic values presented to breeders that are outside the realm of EPDs derived from NCE create confusion as to which selection tools are best for genetic improvement decisions. The Beef Improvement Federation commission on DNA markers released “Guidelines for Combining Molecular and Quantitative Approaches in Genetic Evaluation” in December 2008 (Tess, 2008) with the following statements regarding the reporting of DNA test results:

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“It is important the DNA test results be reported to [the] beef industry in a consistent, understandable format. Further, the format should be compatible with NCE methods.”

“BIF recommends that DNA test results be reported in the form of an EPD, in the units of the trait, on a continuous scale, and with a corresponding BIF accuracy.”

“Guiding Philosophy. BIF believes that information from DNA tests 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. For some economically important traits, information other than DNA tests may not be available. Selection tools based on these tests should still be expressed as EPD within the normal parameters of NCE.”

Associations will continue to be challenged to direct breeders to use NCE EPDs as the seamless route for genomic-enhanced selection. This will be particularly important as genomic results are available for traits in which phenotypes are more difficult to collect and quantify.

Literature Cited

Beef Improvement Federation. 2002. Guidelines for Uniform Beef Improvement Programs. (8th Ed.). Athens, GA.

MacNeil, M. D., and S. L. Northcutt. 2008. National cattle evaluation system for combined

analysis of carcass characteristics and indicator traits recorded by using ultrasound in Angus cattle. J. Anim. Sci. 86:2518-2524.

MacNeil, M. D., J. D. Nkrumah, B. W. Woodward, and S. L. Northcutt. 2010a. Genetic evaluation of Angus cattle for carcass marbling using ultrasound and genomic indicators. J. Anim. Sci. 88: 517-522.

MacNeil, M. D., S. L. Northcutt, R. D. Schnabel, D. J. Garrick, B.W. Woodward and J. F. Taylor. 2010b. Genetic correlations between carcass traits and molecular breeding values in Angus cattle. Proc. 9th World Congr. Genet. Appl. Livest. Prod. (in press).

Tess, M. W. 2008. Guidelines for Combining Molecular and Quantitative Approaches in Genetic Evaluation. Proceedings of the 9th Genetic Prediction Workshop, Beef Improvement Federation. pp 76-82.

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UNDERSTANDING COW SIZE AND EFFICIENCY

Jennifer J. Johnson1, Barry H. Dunn2, and J.D. Radakovich1

1Texas A&M University-Kingsville and 2South Dakota State University Introduction

Cattlemen have debated cow size and efficiency since the early days of the business. While efficient cattle production has been researched for over a century, it remains remarkably misunderstood. This misunderstanding can be costly for the industry as well as individual cattle operations because important and expensive management decisions are erroneously made based on misinformation or lack of understanding. However, a more productive way to frame the efficiency question is “which cattle are most efficient in a specific environment and production system?” In nature, different breeds of the same species can appear markedly different because they have adapted differently to best fit their specific environment. Similarly, different cattle are efficient in different environments and production systems. Gaining a better understanding of the interrelated components of efficiency is critical for cattlemen seeking to maximize profit in their specific operations. The Interrelated Components

Defining Efficiency. Achieving optimal efficiency is an important goal in all businesses. In a food animal production system, overall efficiency is best measured by the ratio of total costs to total animal product from females and their progeny over a given period of time (Dickerson, 1970). It is important to note that this ratio is the inverse of the output/input ratio most commonly used in business. Despite this concise definition, defining optimum efficiency in the cattle business is complicated. Overall efficiency of a cattle production system is a combination of biological efficiency, or feed consumed to beef produced, and economic efficiency, or dollars spent to dollars returned. Though related, biological and economic efficiency may not be identical. Optimizing the relationship between them is a complicated process, and doing so requires understanding and managing the genetic potential of cattle, the environment in which cattle are asked to perform, and decisions about when and what product a producer is marketing.

The Efficiency Conundrum. Dickerson (1970) noted that on the ranch, an efficient cowherd exhibits early sexual maturity, a high rate of reproduction, low rates of distochia, longevity, minimum maintenance requirements, and the ability to convert available energy (native or nonnative forage) into the greatest possible pounds of weaned calves. He stated that to maximize efficiency in the cow calf context, the objective is lean growth and earlier sexual maturity with minimum increase in mature weight. For a cow on a ranch, the ability to reproduce is by far the most important contributor towards efficiency, and the ability to reproduce in a given feed environment is related to its mature size.

Cundiff (1986) reported that in comparison to cattle in a ranch environment, cattle that excel in the production of retail product typically produce heavier birth weights, reach puberty at older ages, have lower propensities to marble, and have higher maintenance requirements due to

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heavier mature weights and greater visceral mass. Continental breeds of cattle with these characteristics were introduced in the United States beginning in the 1970’s. Their importation was a reaction to both the “green revolution” of the 1960’s, which reduced the cost per unit feed in the feedlot industry, and to new industry-changing technologies which favored heavier slaughter weights for packers (Ferrell and Jenkins, 2006). Essentially, a market was developed to reward cattle with the genetic potential to take full advantage of low cost feed. Furthermore, the packing industry continues to reward large framed cattle. Hanging the greatest pounds of carcass, which yield the largest amount of meat possible in the assembly line, is what is most efficient for that segment of the industry. It is relatively easy to recognize that efficiency in the feedlot and packing plants is the driving force behind the market signals incentivizing cattlemen to select for increased growth traits and carcass weight. Selecting for increased weaning weight leads to an increase in mature cow size, which, depending on feed availability, may or may not be efficient in a grass environment (Kelley, 2002). However, in the last several decades, ranchers have successfully mitigated the increased cost of larger cows with low cost supplemental feed. Doing so is a rational response to market signals, as long as supplemental feed remains inexpensive and readily available.

Making the efficiency conundrum even more complicated are the differences that also exist in how economic efficiency is achieved. On a ranch, the goal is to have the highest percentage of calf crop at the heaviest weight without causing dystocia, and therefore maximum total pounds of calves, with the minimum amount of investment and costs. In a feedlot, the goal is simply to produce the most pounds of beef possible in order to profit at a margin above feed costs. Because the drivers behind the cost structures are different, the solution to the puzzle, an efficient animal, may also be different.

It is evident then that biological and economic efficiency for cattle production are not always positively correlated due to the segmentation of the beef cattle industry, which has logically, and economically, separated itself into three highly competitive segments. The first is the ranch, where cattle must be efficient in what is often a limited energy, forage-based, high investment per unit business. The second segment is the feedlot, where cattle must be efficient in a high energy, grain-based, low investment per unit, margin based business. The third is the packing segment, which has the lowest investment per unit and is also a margin based business. The reality is that biological traits supporting efficient use of grazed forages in the first segment of the industry are markedly different from biological traits supporting efficient use of harvested concentrates in the second (Notter, 2002). Nationwide, only a small number of cow-calf producers maintain ownership of their cattle through the backgrounding, yearling, or feeding segments (Melton, 1995). The price received for weaned calves follows prevailing market prices and is adjusted for a number of factors including weight, lot size, uniformity, health, horns, condition, fill, breed, muscling, and frame size. Feeder cattle buyers prefer larger framed, heavier muscled cattle (Schroeder et al., 1998). A cow-calf producer that selects solely for smaller framed cattle based on the assumption that they are more biologically efficient may find their cattle heavily discounted in the market place, which, by definition, would decrease the economic efficiency of the operation.

The Interplay between Genetic Potential and the Environment. Biological efficiency depends upon the interaction between genetic potential and the environment; specifically the

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availability and variability of feed resources. Cattle partition food energy in the following order: maintenance, growth, lactation, and reproduction. Essentially, a cow takes care of herself, then the calf on the ground, then the calf to come. Energy required for maintenance varies. Ritchie (2001) described high maintenance cows as those that tend to have high milk production, high visceral organ weight, high body lean mass, low body fat mass, high output, and high input. High maintenance cattle also tend to reach puberty at a later age, unless they have been selected for milk production (Arango, 2002). Low maintenance cows tend to be low in milk production, low in visceral organ weight, low in body lean mass, high in body fat mass, low output and low input (Ritchie, 2001). However, it is very important not to confuse maintenance requirements with efficiency. Efficiency is a ratio of input to output, and maintenance energy is an input, but not an indication of output.

In one of the most comprehensive experiments conducted on cow efficiency, researchers at the USDA Meat Animal Research Center (MARC) studied the biological efficiency of nine different breeds of cattle over a range of feed energy intakes (Jenkins and Ferrell, 1994). Ranking for efficiency among the breeds, three British and six Continental, depended on feed intake. At lower feed energy intake, the MARC researchers found that breeds that were more moderate in genetic potential for growth and milk production (Angus, Red Poll, and Pinzgauer) were more efficient because of higher conception rates. This clearly underscores the overriding importance of reproduction in a discussion about efficiency. At lower energy intakes, and because of their greater maintenance requirements, the breeds with greater growth and milk potentials had less energy to commit to reproduction. However, at high energy intakes, the Continental breeds with greater genetic potential for milk production and growth were more efficient than the British breeds because they were able to reproduce and the extra available energy was converted into milk, resulting in heavier calves. At high energy intakes those breeds with lower genetic potential for growth and milk production could not convert the additional energy into milk and therefore the cows themselves, rather than their calves, got fatter, essentially an unproductive use of energy.

In another study, efficiency was investigated in three calvings of small, medium, and large Brahman cattle. The small and medium framed cattle were more efficient for the first two calvings, but by the third, when the large framed cattle had reached their full growth potential, the large cattle were more biologically efficient (Vargas, 1999). These results reiterate that, both between and within breeds, maximum efficiency occurs at a level of feed intake that does not limit reproduction and also provides sufficient energy for milk production to meet the growth potential of the breed as expressed in the calf (Jenkins and Ferrell, 2002). Alternatively, if nutritional input exceeds genetic potential for either reproduction or production, efficiency declines (Jenkins and Ferrell, 1994).

Matching growth and milk production to the feed resources available is key to creating efficient cows (Greiner, 2009). The natural availability of feed resources varies greatly across the United States; Iowa and Georgia are vastly different environments than the arid Great Plains and the high deserts of Nevada. Utilizing cattle with different genetic potentials for production is a logical response to environmental variation.

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In a analysis of a 165,000 cow database, the authors of this paper found a statistical relationship between cow maintenance energy EPDs and calf weaning weights. As maintenance energy EPDs increase, so does cow weight; bigger cows generally have higher maintenance energy requirements. Furthermore, as cow maintenance energy EPDs increase, and, so does calf weaning weight; bigger cows generally have bigger calves. The important application of these relationships is in the calculation of how much additional maintenance energy requirements cost relative to how much additional profit is realized through additional weaning weight. An increase in 12 additional required Megacalories per year for cow maintenance, which is roughly two pounds of corn, equates to an additional three pounds of weaning weight. When corn and calf prices are adjusted for inflation, the additional profit from the extra pounds has exceeded the additional cost of corn every year since 1975 by at least $2.50. The practical implications of these findings are that the increase in the nation’s average cow size is a rational response to inexpensive feed, and, if a cow will get bred in her environment, the additional maintenance energy requirements of a larger cow is more than paid for by the additional weight of her calf.

Metabolic Weight versus Live Weight. The average elephant weights 220,000 times as much as the average mouse, but requires only about 10,000 times as much energy in the form of food calories to sustain itself. This is because of the mathematical and geometric relationship between body surface area and volume, which in biology is articulated by Kleiber’s Theory. It states that metabolic weight = live weight^.75 (Kleiber, 1932). Essentially, the bigger the animal, the more efficiently it uses energy. For instance, eighty seven 1200 lb cows require the same amount of food energy for maintenance as one hundred 1000 lb cows (Table 1).

Live Weight

Metabolic Weight

Animal Unit Equivalent (% of

1000 lbs)

Equivalent Herd Size (Baseline: one hundred 1000

lb cows) 800 150 85% 118 850 157 89% 113 900 164 92% 108 950 171 96% 104 1000 178 100% 100 1050 184 104% 96 1100 191 107% 93 1150 197 111% 90 1200 204 115% 87 1250 210 118% 85 1300 216 122% 82 1350 223 125% 80 1400 229 129% 78 1450 235 132% 76 1500 241 136% 74 1550 247 139% 72 1600 253 142% 70

Table 1: Metabolic weight, animal unit equivalent, and equivalent herd size for each fifty pound live weight class interval in beef cattle.

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An understanding of Klieber’s Theory is of practical importance when calculating

equivalent herd sizes (Table 1). The biology of maintenance energy requirements dictates that while a larger cow will consume more food than a smaller cow, its additional feed requirements, as a percentage, are less than its additional weight, as a percentage. For example, though a 1200 pound cow weighs 20% more than a 1000 pound cow, the 1200 pound cow’s feed requirements are only 13% more. Knowing equivalent herd sizes based on Kleiber’s Theory is the only way to accurately compare the efficiency of different sizes of cattle. However, a biological understanding of how maintenance energy varies with size is not useful unless paired with an economic understanding of how herd size impacts profitability.

If herd size is adjusted correctly, switching from a larger to smaller cattle will not increase total fixed costs or feed costs, but will increase variable costs, depreciation costs, and investment costs in terms of cattle inventory. Therefore, the gross income generated by selling a greater number of lighter calves must outweigh these additional variable, depreciation, and investment costs in order to justify the decrease in cow size. Alternatively, switching from smaller to larger cattle will decrease variable, depreciation, and investment costs, with no change to fixed costs or feed costs. However, producers in highly variable feed environments may benefit from a greater number of smaller cattle because of the economic risk associated with low reproduction rates of larger cows if supplemental feed is unavailable or expensive. Tools for Approaching Cow Efficiency

If a producer has decided that the current size of their cows is not right for their production system, the following discussion of both ineffective and effective tools to increase efficiency provides valuable insight for making a profitable adjustment.

The Problem with Calf Weight/Cow Weight as a Measure of Efficiency. The ratio most commonly used to quantify efficiency is fundamentally flawed in several respects. Weaning weight divided by cow weight results in a ratio in which the numerator indicates output and the denominator assumes a level of input through a commonly accepted association of cow weight and feed requirements. Several studies have found that this ratio is inferior to weaning weight as an estimation of efficiency (Dinkel and Brown, 1978, Cartwright, 1979). This is because using the ratio as a selection measure results in selecting based on two phenotypes of different individuals and the consequent confounding of direct and maternal genetic effect on these phenotypes (MacNeil, 2005). For instance, milk production potential, though unaccounted for directly in the ratio, has a great impact on both the numerator and the denominator.

Using weaning weight divided by cow weight to differentiate between two cows on a ranch as a measure of efficiency is tenuous at best, for several important reasons. First of all, blanket estimates and assumptions of feed intake may not be accurate. Feed intake depends on body condition score, sex, stage of production, age, quality of forage, and environmental stress (Cartwright, 1979). What makes Jenkins and Ferrell’s (1994) nine breed study of efficiency so meaningful is that efficiency was not based on assumed or estimated feed intake, but on actual energy intake, which was measured at every feeding. Secondly, the calf weight/cow weight ratio dilutes the impact of the most important component, which is reproduction. Both cows are much

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more efficient than their open counterparts. A fifty pound difference in weaning weight is minimal compared to a four hundred pound calf versus no calf. Thirdly, the cow with the heavier calf (assumed heavier milk production) will have greater visceral mass and therefore greater intake even when dry. Fourthly, the cow with greater milk production may be at greater risk for not re-breeding because of the order in which feed energy is partitioned. Finally, pasture observations of calf weight as a percentage of cow weight can be misleading because of differences in calf age, sex, and other variables.

Though it does not reflect individual cow efficiency, the ratio of total pounds weaned divided by number of cows exposed is the best measure of efficiency for the entire herd. This ratio recognizes the most important maternal trait of efficiency, reproduction, without confounding variables. Increasing this ratio without increasing input costs will result in increased net profit.

The Problem with Culling for Efficiency. Selecting for genetic change in a cow herd through female culling is not an effective method for changing the overall efficiency of a commercial cowherd for several reasons. First, cattle in commercial herds have long generation intervals, which makes progress in genetic change extremely slow. Secondly, the selection differential for efficiency within the same herd is probably smaller than is commonly held and, as has been previously discussed, cannot be effectively and reliably measured. Third, culling based on traits with low heritability is ineffective. Finally, since an individual cow contributes little to the overall genetic makeup of a calf crop, it is much more effective to select for efficiency through bulls.

Optimizing a Breeding System. For a profit-driven producer, no matter the environment and market end point, the goal is to produce as much product as possible through a cow herd. Setting up a breeding system to capture genetic potential in a given environment and given market will optimize efficiency. Cross breeding programs take advantage of breed complementarity and breed differences, making them an ideal way to positively and relatively quickly produce genetic change for efficiency. For example, a terminal sire bred to a cross bred female will wean approximately 28% more pounds of beef per exposed female than a single breed (Field and Taylor, 2003).

Optimizing a Production System. Indigenous feed resources vary dramatically by geographic location. The natural variation of animals of the same species around the world speaks to the fact that nature defines the “right” genetics for efficiency differently in different environments. Jenkins and Ferrell’s (1994) study concurs with this natural phenomenon. Forage production west of the 100th meridian is vastly different than ranching in areas with high annual precipitation, not only in amount but also in frequency and reliability. Availability of low cost feed also varies by region and even by ranch within a region, and should impact decisions about efficiency. Price and availability of feed may be a good indicator of whether or not a ranch is in a high feed environment or not. Furthermore, environments can be categorized not only by level of feed availability, but also by levels of stress, which include cold, heat, parasites, disease, mud, and altitude (Bourdon, 1988). For instance, the efficiency of Bos indicus cattle in tropical and sub-tropical environments is due to their heat tolerance, an advantage the British breeds do not have (Field and Taylor, 2003).

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Besides environment, market end point is the other paramount factor impacting the

efficiency of a beef cow-calf production system. Increased milk potential is most beneficial when calves are sold at weaning and maximum pre-weaning growth is rewarded in the marketplace. In a retained ownership scenario, calf growth due to maternal milk production is less critical because the calf’s own growth potential has a longer period of time to capture profit for the rancher. Furthermore, when selling cattle by the head, as is the case with seed stock or replacement heifer operations, number of head, not pounds, is the key metric.

In a traditional production system where a rancher sells calves at weaning, the most efficient cow is the one with the highest milk potential that can, without reducing the percentage of calves successfully weaned, repeatedly produce a calf by bulls with the growth and carcass characteristics valued most in the marketplace. Such a cowherd fits with their environment, native forage, while producing calves best suited for their eventual environment, unlimited grain. This is why crossbreeding systems that exploit heterosis and complementarity and match genetic potential with market targets, feed resources and climates provide the most effective means of breeding for production efficiency (Cundiff, 1993).

Optimizing Herd Size. The optimal herd size for any ranch varies greatly depending upon its rainfall, infrastructure, investment, and manpower. An efficient cow herd is one that nets the most profit by keeping marginal revenue above marginal cost. Because of Kleiber’s Law, cow size, in relation to available feed resources, determines herd size. A rancher can increase herd size by reducing cow size up to a certain point without increasing feed and fixed costs, but doing so does increase investment and variable costs. The cost structure of each ranch is unique and can vary over time, as will profit margins. Each producer must evaluate their unique system and determine, based upon biological and economic determinants of herd size, what is most profitable for them. Conclusion

Efficiency in animal production is a measure of input costs to total animal product. Determining the right size of cow for any specific production system necessitates understanding how beef industry segmentation affects the interaction of biological and economic efficiency. Antagonisms exist between ideal genetic traits at different stages in the chain of cattle production, and in different environments. Maintenance energy should not be confused with efficiency and must be calculated and discussed in terms of the animal’s metabolic weight.

Improving efficiency requires measurement, and though popular, literature does not support calf weight/cow weight as a better measurement of efficiency than weaning weight. Improving efficiency of a cowherd through culling is ineffective compared to prudent bull selection. Market end points have a profound impact on efficiency. For the majority of cow calf producers in the nation, the most efficient cow is the one with the highest milk potential that can, without reducing the percentage of calves successfully weaned, repeatedly produce a calf by bulls with the growth and carcass characteristics valued most in the marketplace. Size, of cow, through the biology of metabolic weight, should dictate herd size, and optimal herd size varies with the cost structure of a specific production system.

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No one breed or size category of cattle excels in all traits or is most efficient in all

environments. Any “one size fits all” approach will result in un-captured profit, and therefore suboptimal efficiency. The question of efficiency needs to be discussed in the context of a specific system, which requires careful analysis of the environment, market, and goals of that system. Literature Cited Arango, J. A, and L. D. Van Vleck. 2002. Size of beef cows: early ideas, new developments.

Genet. Mol. Res. 1:51-63. Bourdon, R. M. 1988. Bovine Nirvana – from the perspective of a modeler and purebred breeder.

J. Anim. Sci. 66:1892-1898. Cartwright, T. C. 1979. Size as a component of beef production efficiency: cow-calf production.

J. Anim. Sci. 48(4): 974. Cundiff, L. V., S. Szabo, K. E. Gregory, R. M. Koch, M. E. Dikeman, and J. D. Crouse. 1993.

Breed comparisons in the germplasm evaluation program at MARC. Proceedings Beef Improvement Federation. Asheville, NC.

Dickerson, G. E. 1970. Efficiency of animal production - molding the biological components. J.

Anim. Sci. 30:849. Dinkel, C. A., and M. A. Brown. 1978. An evaluation of the ratio of calf weaning weight to cow

weight as an indicator of cow efficiency. J. Anim. Sci. 46:614–617. Ferrell, C. L. and T. G. Jenkins. 2006. Matching genetics with production environment.

Proceedings Beef Improvement Federation. Choctaw, MS. Field, T. G., and R. E. Taylor. 2003. Beef production and management decisions. 4th ed.

Prentice Hall Publishing Company, New Jersey. Greiner, S. P. 2009. Beef cow size, efficiency, and profit.

http://pubs.ext.vt.edu/news/livestock/old/aps-200904_Greiner.html Accessed Dec. 10, 2009.

Jenkins, T. G. and C. L. Ferrell. 1994. Productivity through weaning of nine breeds of cattle

under varying feed availabilities: I. initial evaluation. J. Anim. Sci. 72:2787. Jenkins, T. G. and C. L. Ferrell. 2002. Beef cow efficiency revisited. Proceedings Beef

Improvement Federation. Omaha, NE. Kelley, A. L. 2002. The relationship of genetics and nutrition and their influence on animal

performance. Proceedings Beef Improvement Federation. Omaha, NE.

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Kleiber, M. 1932. Body size and metabolism. Hilgardia. 6:315-349 MacNeil, M. D. 2005. Genetic evaluation of the ratio of calf weaning weight to cow weight. J.

Anim. Sci. 83:794-802. Melton, B. E. 1995. Profiting from change in the U.S. beef industry: genetic balance for

economic gains. Technical Report of the National Cattlemen’s Association, Englewood, CO.

Notter, D. R. 2002. Defining biological efficiency of beef production. Proceedings Beef

Improvement Federation. Omaha, NE. Ritchie, H. D. 1995. The optimum cow – what criteria must she meet? Proceedings Beef

Improvement Federation. Sheridan, WY. Schroeder, T. C., J. Mintert, F. Barzle, O. Gruenwald. 1988. Factors affecting feeder cattle price

differentials. West. J. of Agri. Econ. 13:71. Vargas, C. A., T. A. Olson, C. C. Chase Jr, A. C. Hammond, and M. A. Elzo. 1999. Influence of

frame size and body condition score on performance of Brahman cattle. J. Anim. Sci. 77(12):314-3149.

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ACROSS-BREED EPD TABLES FOR THE YEAR 2010 ADJUSTED TO BREED DIFFERENCES FOR BIRTH YEAR OF 2008

L. A. Kuehn, L. D. Van Vleck, R. M. Thallman and L. V. Cundiff

Roman L. Hruska U.S. Meat Animal Research Center, USDA-ARS,

Clay Center and Lincoln, NE 68933 Summary Factors to adjust the expected progeny differences (EPD) of each of 18 breeds to the base of Angus EPD are reported in the column labeled 6 of Tables 1-7 for birth weight, weaning weight, yearling weight, maternal milk, marbling score, ribeye area, and fat thickness, respectively. An EPD is adjusted to the Angus base by adding the corresponding across-breed adjustment factor in column 6 to the EPD. It is critical that this adjustment be applied only to Spring 2010 EPD. Older or newer EPD may be computed on different bases and, therefore, could produce misleading results. When the base of a breed changes from year to year, its adjustment factor (Column 6) changes in the opposite direction and by about the same amount. Breed differences are changing over time as breeds put emphasis on different traits and their genetic trends differ accordingly. Therefore, it is necessary to qualify the point in time at which breed differences are represented. Column 5 of Tables 1-7 contains estimates of the differences between the averages of calves of each breed born in year 2008. Any differences (relative to their breed means) in the samples of sires representing those breeds at the U.S. Meat Animal Research Center (USMARC) are adjusted out of these breed difference estimates and the across-breed adjustment factors. The breed difference estimates are reported as progeny differences, e.g., they represent the expected difference in progeny performance of calves sired by average bulls of two different breeds (born in 2008) and out of dams of a third, unrelated breed. In other words, they represent half the differences that would be expected between purebreds of the two breeds. Introduction This report is the year 2010 update of estimates of sire breed means from data of the Germplasm Evaluation (GPE) project at USMARC adjusted to a year 2008 basis using EPD from the most recent national cattle evaluations. The 2008 basis year is chosen because yearling records for weight and carcass traits should have been accounted for in EPDs for progeny born in 2008 in the Spring 2010 EPD national genetic evaluations. Factors to adjust Spring 2010 EPD of 18 breeds to a common base were calculated and are reported in Tables 1-3 for birth weight (BWT), weaning weight (WWT), and yearling weight (YWT) and in Table 4 for the maternal milk (MILK) component of maternal weaning weight (MWWT). Tables 5-6 summarize the factors for marbling score (MAR), ribeye area (REA), and fat thickness (FAT). The across-breed table adjustments apply only to EPD for most recent (spring, 2010) national cattle evaluations. Serious errors can occur if the table adjustments are used with earlier EPD which may have been calculated with a different within-breed base.

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The following describes the changes that have occurred since the update released in 2009 (Kuehn et al., 2009): The most significant changes continue to relate to the new sampling in the USMARC GPE program. Progeny from 16 of the 18 breeds involved in the across-breed EPD process have been born (approximately 50/yr) and improve the accuracy in predicting the differences between these breeds. These 16 breeds are the breeds that register the most cattle and have national genetic evaluations for production traits. Sires are sampled on a continuous basis (every 2 years). The first progeny of this new sampling were born in Fall 2007. Last year Santa Gertrudis and Chiangus adjustment factors were estimated for the first time for birth and weaning weight. This year, these breeds had sufficient progeny numbers for yearling weight and carcass traits to be included as well. Maternal milk for these breeds will also be reported in future iterations of this report as daughters from these matings begin to have calves of their own. As numbers of progeny increase in these breeds, some significant changes can occur. The number of direct progeny with birth and weaning weight increased by over 30% for Santa Gertrudis and Chiangus and by up to 20% in breeds such as Braunvieh and Salers. Each of these breeds had relatively large changes in their USMARC breed of sire estimates (labeled column 3 in Tables 2 and 3) for weaning or yearling weights or both compared to last year. Yearling weight sire breed differences were particularly prone to change as progeny from new GPE sampling born in Spring 2008 and Fall 2008 were included in the analysis for the first time. These seasons were the first in which progeny were compared directly to Hereford- and Angus-sired progeny (breeds with the most data) in over 20 years for many of these breeds. Changes in national cattle evaluation can also cause across breed adjustment factors to change relative to previous years. Salers EPDs were put on a new base this year which causes their adjustment factor (labeled column 6; Tables 1-7) to change relative to last year, though their sire breed differences (labeled column 5) remained relatively constant. Additionally, Tarentaise conducted a new national cattle evaluation (last evaluation was in 2006). This evaluation showed significant genetic trends in weaning and yearling weights causing their sire breed differences and their adjustment factors to increase substantially from last year’s update. As a last change relative to national cattle evaluations, we received carcass EPDs for several South Devon sires (8 of 15) that did not have carcass EPDs before this year; therefore, their progeny are now included in the evaluation. Materials and Methods All calculations were as outlined in the 2002 BIF Guidelines. The basic steps were given by Notter and Cundiff (1991) with refinements by Núñez-Dominguez et al. (1993), Cundiff (1993, 1994), Barkhouse et al. (1994, 1995), Van Vleck and Cundiff (1997–2006), and Kuehn et al. (2007-2009). Estimates of variance components, regression coefficients, and breed effects were obtained using the MTDFREML package (Boldman et al., 1995). All breed solutions are reported as differences from Angus. The table values of adjustment factors to add to within-breed EPD are relative to Angus.

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Models for Analysis of USMARC Records An animal model with breed effects represented as genetic groups was fitted to the GPE data set (Arnold et al., 1992; Westell et al., 1988). In the analysis, all AI sires (sires used via artificial insemination) were assigned a genetic group according to their breed of origin. Due to lack of pedigree, dams mated to the AI sires and natural service bulls mated to F1 females were also assigned to separate genetic groups (i.e., Hereford dams were assigned to different genetic groups than Hereford AI sires). Cows from Hereford selection lines (Koch et al., 1994) were used in Cycle IV of GPE and assigned into their own genetic groups. Through Cycle VIII, most dams were from Hereford, Angus, or MARCIII (1/4 Angus, 1/4 Hereford, 1/4 Pinzgauer, 1/4 Red Poll) composite lines. In order to be considered in the analysis, sires had to have an EPD for the trait of interest. All AI sires were considered unrelated for the analysis in order to adjust resulting genetic group effects by the average EPD of the sires. Fixed effects in the models for BWT, WWT (205-d), and YWT (365-d) included breed (fit as genetic groups) and maternal breed (WWT only), year and season of birth by GPE cycle by age of dam (2, 3, 4, 5-9, >10 yr) combination (190), sex (heifer, bull, steer; steers were combined with bulls for BWT), a covariate for heterosis, and a covariate for day of year at birth of calf. Models for WWT also included a fixed covariate for maternal heterosis. Random effects included animal and residual error except for the analysis of WWT which also included a random maternal genetic effect and a random permanent environmental effect. For the carcass traits (MAR, REA, and FAT), breed (fit as genetic groups), sex (heifer, steer) and slaughter date (213) were included in the model as fixed effects. Fixed covariates included slaughter age and heterosis. Random effects were animal and residual error. To be included, breeds had to report carcass EPD on a carcass basis using age-adjusted endpoints. The covariates for heterosis were calculated as the expected breed heterozygosity for each animal based on the percentage of each breed of that animal’s parents. In other words, it is the probability that, at any location in the genome, the animal's two alleles originated from two different breeds. Heterosis is assumed to be proportional to breed heterozygosity. For the purpose of heterosis calculation, AI and dam breeds were assumed to be the same breed and Red Angus was assumed the same breed as Angus. For purposes of heterosis calculation, composite breeds were considered according to nominal breed composition. For example, Brangus (3/8 Brahman, 5/8 Angus) × Angus is expected to have 3/8 as much heterosis as Brangus × Hereford. Variance components were estimated with a derivative-free REML algorithm with genetic group solutions obtained at convergence. Differences between resulting genetic group solutions for AI sire breeds were divided by two to represent the USMARC breed of sire effects in Tables 1-7. Resulting breed differences were adjusted to current breed EPD levels by accounting for the average EPD of the AI sires of progeny/grandprogeny, etc. with records. Average AI sire EPD were calculated as a weighted average AI sire EPD from the most recent within breed genetic evaluation. The weighting factor was the sum of relationship coefficients between an individual sire and all progeny with performance data for the trait of interest relative to all other sires in that breed.

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For all traits, regression coefficients of progeny performance on EPD of sire for each trait were calculated using an animal model with EPD sires excluded from the pedigree. Genetic groups were assigned in place of sires in their progeny pedigree records. Each sire EPD was ‘dropped’ down the pedigree and reduced by ½ depending on the number of generations each calf was removed from an EPD sire. In addition to regression coefficiencts for the EPDs of AI sires, models included the same fixed effects described previously. Pooled regression coefficients, and regression coefficients by sire breed were obtained. These regression coefficients are monitored as accuracy checks and for possible genetic by environment interactions. The pooled regression coefficients were used as described in the next section to adjust for differences in management at USMARC as compared to seedstock production (e.g., YWT of males at USMARC are primarily on a slaughter steer basis, while in seedstock field data they are primarily on a breeding bull basis). For carcass traits, MAR, REA, and FAT, regressions were considered too variable and too far removed from 1.00. Therefore, the regressions were assumed to be 1.00 until more data is added to reduce the impact of sampling errors on prediction of these regressions. However, the resulting regressions are still summarized. Records from the USMARC GPE Project are not used in calculation of within-breed EPD by the breed associations. This is critical to maintain the integrity of the regression coefficient. If USMARC records were included in the EPD calculations, the regressions would be biased upward. Adjustment of USMARC Solutions The calculations of across-breed adjustment factors rely on breed solutions from analysis of records at USMARC and on averages of within-breed EPD from the breed associations. The basic calculations for all traits are as follows: USMARC breed of sire solution (1/2 breed solution) for breed i (USMARC (i)) converted to an industry scale (divided by b) and adjusted for genetic trend (as if breed average bulls born in the base year had been used rather than the bulls actually sampled): Mi = USMARC (i)/b + [EPD(i)YY - EPD(i)USMARC]. Breed Table Factor (Ai) to add to the EPD for a bull of breed i: Ai = (Mi - Mx) - (EPD(i)YY - EPD(x)YY). where, USMARC(i) is solution for effect of sire breed i from analysis of USMARC data, EPD(i)YY is the average within-breed 2010 EPD for breed i for animals born in the base year

(YY, which is two years before the update; e.g., YY = 2008 for the 2010 update), EPD(i)USMARC is the weighted (by total relationship of descendants with records at

USMARC) average of 2010 EPD of bulls of breed i having descendants with records at USMARC,

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b is the pooled coefficient of regression of progeny performance at USMARC on EPD of sire (for 2008: 1.10, 0.84, 1.06, and 1.18 BWT, WWT, YWT, and MILK, respectively; 1.00 was applied to MAR, REA, and FAT data),

i denotes sire breed i, and

x denotes the base breed, which is Angus in this report.

Results Heterosis Heterosis was included in the statistical model as a covariate for all traits. Maternal heterosis was also fit as a covariate in the analysis of weaning weight. Resulting estimates were 1.44 lb, 12.84 lb, 16.62 lb, 0.032 marbling score units (i.e. 4.00 = Sl00, 5.00 = Sm00), 0.26 in2, and 0.043 in for BWT, WWT, YWT, MAR, REA, and FAT respectively. These estimates are interpreted as the amount by which the performance of an F1 is expected to exceed that of its parental breeds. The estimate of maternal heterosis for WWT was 17.34 lb. Across-breed adjustment factors Tables 1, 2, and 3 (for BWT, WWT, and YWT) summarize the data from, and results of, USMARC analyses to estimate breed of sire differences and the adjustments to the breed of sire effects to a year 2008 base. The column labeled 6 of each table corresponds to the Across-breed EPD Adjustment Factor for that trait. Table 4 summarizes the analysis of MILK. Tables 5, 6, and 7 summarize data from the carcass analyses (MAR, REA, FAT). Breed of sire differences and adjustments for MAR, REA, and FAT are reported in Tables 5-7. Because of the accuracy of sire carcass EPDs and the greatest percentage of data being added to carcass traits, sire effects and adjustment factors are more likely to change for carcass traits in the future. Column 5 of each table represents the best estimates of sire breed differences for calves born in 2008 on an industry scale. These breed difference estimates are reported as progeny differences, e.g., they represent the expected difference in progeny performance of calves sired by average bulls (born in 2008) of two different breeds and out of dams of a third, unrelated breed. In each table, breed of sire differences were added to the raw mean of Angus-sired progeny born 2006 through 2009 at USMARC (Column 4) to make these differences more interpretable to producers on scales they are accustomed to. Across-breed EPD Adjustment Factor Example Adjustment factors can be applied to compare the genetic potential of sires from different breeds. Suppose the EPD for birth weight for a Limousin bull is +0.5 (which is below the year 2008 average of 1.5 for Limousin) and for a Red Angus bull is +2.0 (which is below the year 2008 average of 0.3 for Red Angus). The across-breed adjustment factors in the last column of

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Table 1 are 2.6 for Red Angus and 4.2 for Limousin. Then the adjusted EPD for the Limousin bull is 4.2 + 0.5 = 4.7 and for the Red Angus bull is 2.6 + 2.0 = 4.6. The expected birth weight difference when both are mated to another breed of cow, e.g., Angus, would be 4.7 – 4.6 = 0.1 lb. The differences in true breeding value between two bulls with similar within-breed EPDs are primarily due to differences in the genetic base from which those within-breed EPDs are computed. Birth Weight The range in estimated breed of sire differences for BWT ranged from 0.8 lb for Red Angus to 7.7 lb for Charolais and 12.2 lb for Brahman. Angus continued to have the lowest estimated sire effect for birth weight (Table 1, column 5). The relatively heavy birth weights of Brahman-sired progeny would be expected to be offset by favorable maternal effects reducing birth weight if progeny were from Brahman or Brahman cross dams which would be an important consideration in crossbreeding programs involving Brahman cross females. As this is the second year in which newly sampled bulls for GPE were used in the calculation of sire breed differences, changes in breed of sire effects were generally small, less than 1 lb for all except Brahman and Chiangus, relative to last year’s update (Kuehn et al., 2009). Weaning Weight

Breed effects on weaning weight remained fairly similar to Angus for most breeds—16 of the 17 sire breed differences were within 10 lb of the values in Kuehn et al. (2009). The average Tarentaise sire breed effect was predicted 15.4 heavier than in Kuehn et al. (2009) relative to Angus. This change was primarily due to a realized genetic trend in Tarentaise from a new national cattle evaluation. Sire breed effects of Santa Gertrudis and Braunvieh were 8-9 lb heavier relative to Angus as compared to last year. These changes can largely be attributed to larger number of progeny and increased progeny comparisons to Angus- and Hereford-sired progeny. Yearling Weight Santa Gertrudis and Chiangus were reported for yearling weight for the first time this year. Most other breeds (13 of 15) differences were similar (less than 5.5 lb) relative to Angus compared to Kuehn et al. (2009). Braunvieh and Salers both changed relative to Angus by +22.4 and -10.8 lb, respectively, primarily due to increased numbers of progeny as summarized for yearling weight. Most breeds (all except Charolais and Simmental) were lighter than Angus as has been typical with recent reports (Kuehn et al., 2007-2009). The genetic trend for Angus yearling weight continues to increase (2008 average EPD 1.5 lb higher than 2009 average EPD). Maternal Milk The changes from last year for milk for the current base year (Table 4, column 5) were generally small. Differences will likely be more substantial in the 2011 update due to heifers from the most recent GPE cycle reaching calving age. The genetic trend for milk for Angus, like that for yearling weight, has been steep relative to breeds such as Simmental and Gelbvieh. Thus

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sire breed differences between Simmental or Gelbvieh and Angus are relatively small compared to estimates 15 to 30 years ago. Marbling Marbling score was estimated to be highest in Angus (Table 5, column 5) with Shorthorn and Red Angus being the most similar (~0.4 score units lower). Santa Gertrudis and Chiangus were reported for the first time for marbling and other carcass traits this year. In general, Continental breeds were estimated to be one-half to a full marbling score lower than Angus with the exception of Salers. Progeny from Hereford sires were predicted to have the lowest marbling score relative to other British breeds. Ribeye Area Continental breeds had higher ribeye area estimates relative to the British breeds (Table 6, column 5) as would be expected. The estimates of sire breed differences were similar to last year for almost all breeds. South Devon changed relative to Angus because of an increase of the number of sires with EPDs reported by the association. Fat Thickness Progeny of Continental breeds had 0.1 to 0.2 in less fat at slaughter than British breeds (Table 7, Column 5). All other breeds were leaner than Angus. Charolais, Salers, and Simmental were predicted to be the leanest breeds among the 12 breeds analyzed for carcass traits. Limousin was not included in the FAT analysis because they do not report an EPD for FAT. Changes in breed of sire effects relative to Angus were all minor compared to the previous year (Kuehn et al, 2009). Accuracies and Variance Components Table 8 summarizes the average Beef Improvement Federation (BIF) accuracy for bulls with progeny at USMARC weighted appropriately by average relationship to animals with phenotypic records. South Devon bulls had relatively small accuracy for all traits as did Hereford and Brahman bulls. Charolais and Gelbvieh bulls had low accuracy for yearling weight and milk. Accuracies for carcass traits, as expected, were considerably lower than accuracies for growth traits in general. The sires sampled recently in the GPE program have generally been higher accuracy sires, so the average accuracies should continue to increase over the next several years. Table 9 reports the estimates of variance components from the animal models that were used to obtain breed of sire and breed of MGS solutions. Heritability estimates for BWT, WWT, YWT, and MILK were 0.58, 0.17, 0.46, and 0.17, respectively. Heritability estimates for MAR, REA, and FAT were 0.42, 0.47, and 0.39, respectively.

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Regression Coefficients Table 10 updates the coefficients of regression of records of USMARC progeny on sire EPD for BWT, WWT, and YWT which have theoretical expected values of 1.00. The standard errors of the specific breed regression coefficients are large relative to the regression coefficients. Large differences from the theoretical regressions, however, may indicate problems with genetic evaluations, identification, or sampling. The pooled (overall) regression coefficients of 1.11 for BWT, 0.84 for WWT, and 1.06 for YWT were used to adjust breed of sire solutions to the base year of 2008. These regression coefficients are reasonably close to expected values of 1.0. Deviations from 1.00 are believed to be due to scaling differences between performance of progeny in the USMARC herd and of progeny in herds contributing to the national genetic evaluations of the 16 breeds. Breed differences calculated from the USMARC data are divided by these regression coefficients to put them on an industry scale. A regression greater than one suggests that variation at USMARC is greater than the industry average, while a regression less than one suggests that variation at USMARC is less than the industry average. Reasons for differences in scale can be rationalized. For instance, cattle, especially steers, are fed at higher energy rations than some seedstock animals in the industry. Also, in several recent years, calves have been weaned earlier than 205 d at USMARC, likely reducing the variation in weaning weight of USMARC calves relative to the industry. The coefficients of regression for MILK are also shown in Table 10. Several sire (MGS) breeds have regression coefficients considerably different from the theoretical expected value of 1.00 for MILK. Standard errors, however, for the regression coefficients by breed are large except for Angus and Hereford. The pooled regression coefficient of 1.18 for MILK is reasonably close to the expected regression coefficient of 1.00. Regression coefficients derived from regression of USMARC steer progeny records on sire EPD for MAR, REA, and FAT are shown in Table 11. Each of these coefficients has a theoretical expected value of 1.00. Compared to growth trait regression coefficients, the standard errors even on the pooled estimates are high, though they have decreased from the previous year. Each coefficient deviates from the expected value of 1.00 more than the growth trait coefficients with the exception of REA. Therefore, the theoretical estimate of 1.00 was used to derive breed of sire differences and EPD adjustment factors. The pooled regression estimates would cause USMARC differences to be larger on an industry scale for MAR and smaller on an industry scale for FAT. These regressions will change considerably in upcoming across-breed analyses as more data is added to the GPE program and new sires from most of these breeds are sampled. Prediction Error Variance of Across-Breed EPD Prediction error variances were not included in the report due to a larger number of tables included with the addition of carcass traits. These tables did not change substantially from those reported in previous proceedings (Kuehn et al., 2007; available online at http://www.beefimprovement.org/proceedings.html). An updated set of tables is available on request (Larry.Kuehn@ars.usda.gov).

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Implications Bulls of different breeds can be compared on a common EPD scale by adding the appropriate across-breed adjustment factor to EPD produced in the most recent genetic evaluations for each of the 18 breeds. The across-breed EPD are most useful to commercial producers purchasing bulls of two or more breeds to use in systematic crossbreeding programs. Uniformity in across-breed EPD should be emphasized for rotational crossing. Divergence in across-breed EPD for direct weaning weight and yearling weight should be emphasized in selection of bulls for terminal crossing. Divergence favoring lighter birth weight may be helpful in selection of bulls for use on first calf heifers. Accuracy of across-breed EPD depends primarily upon the accuracy of the within-breed EPD of individual bulls being compared.

References Arnold, J. W., J. K. Bertrand, and L. L. Benyshek. 1992. Animal model for genetic evaluation of

multibreed data. J. Anim. Sci. 70:3322-3332. Barkhouse, K. L., L. D. Van Vleck, and L. V. Cundiff. 1994. Breed comparisons for growth and

maternal traits adjusted to a 1992 base. Proc. Beef Improvement Federation 26th Research Symposium and Annual Meeting, Des Moines, IA, May, 1994. pp 197-209.

Barkhouse, K. L., L. D. Van Vleck, and L. V. Cundiff. 1995. Mixed model methods to estimate

breed comparisons for growth and maternal traits adjusted to a 1993 base. Proc. Beef Improvement Federation 27th Research Symposium and Annual Meeting, Sheridan, WY. May 31-June 3, 1995. pp 218-239.

Boldman, K. G., L. A. Kriese, L. D. Van Vleck, and S. D. Kachman. 1993. A Manual for Use of

MTDFREML (DRAFT). A set of programs to obtain estimates of variances and covariances. USDA-ARS, Roman L. Hruska U.S. Meat Animal Research Center, Clay Center, NE. (120 pp).

Cundiff, L. V. 1993. Breed comparisons adjusted to a 1991 basis using current EPD’s. Proc.

Beef Improvement Federation Research Symposium and Annual Meeting, Asheville, NC. May 26-29, 1993. pp 114-123.

Cundiff, L. V. 1994. Procedures for across breed EPD's. Proc. Fourth Genetic Prediction

Workshop, Beef Improvement Federation, Kansas City, MO. Jan. 1994. Koch, R. M., L. V. Cundiff, and K. E. Gregory. 1994. Cumulative selection and genetic change

for weaning or yearling weight or for yearling weight plus muscle score in Hereford cattle. J. Anim. Sci. 72:864-885.

Kuehn, L. A., L. D. Van Vleck, R. M. Thallman, and L. V. Cundiff. 2007. Across-breed EPD

tables for the year 2007 adjusted to breed differences for birth year of 2005. Proc. Beef

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Improvement Federation 39th Annual Research Symposium and Annual Meeting, Fort Collins, CO. June 6-9, 2007. pp 74-92.

Kuehn, L. A., L. D. Van Vleck, R. M. Thallman, and L. V. Cundiff. 2008. Across-breed EPD

tables for the year 2008 adjusted to breed differences for birth year of 2006. Proc. Beef Improvement Federation 40th Annual Research Symposium and Annual Meeting, Calgary, AB. June 30-July 3, 2008. pp 53-74.

Kuehn, L. A., L. D. Van Vleck, R. M. Thallman, and L. V. Cundiff. 2009. Across-breed EPD

tables for the year 2009 adjusted to breed differences for birth year of 2007. Proc. Beef Improvement Federation 41th Annual Research Symposium and Annual Meeting, Sacramento, CA. April 30-May 3, 2009. pp 160-183.

Notter, D. R., and L. V. Cundiff. 1991. Across-breed expected progeny differences: Use of

within-breed expected progeny differences to adjust breed evaluations for sire sampling and genetic trend. J. Anim. Sci. 69:4763-4776.

Núñez-Dominguez, R., L. D. Van Vleck, and L. V. Cundiff. 1993. Breed comparisons for growth

traits adjusted for within-breed genetic trend using expected progeny differences. J. Anim. Sci. 71:1419-1428.

Van Vleck, L. D. 1994. Prediction error variances for inter-breed EPD's. Proc. Fourth Genetic

Predication Workshop, Beef Improvement Federation, Kansas City, MO. Jan. 1994. Van Vleck, L. D., and L. V. Cundiff. 1994. Prediction error variances for inter-breed genetic

evaluations. J. Anim. Sci. 71:1971-1977. Van Vleck, L. D., and L. V. Cundiff. 1995. Assignment of risk to across-breed EPDs with tables

of variances of estimates of breed differences. Proc. Beef Improvement Federation 27th Research Symposium and Annual Meeting, Sheridan, WY. May 31-June 3, 1995. pp 240-245.

Van Vleck, L. D., and L. V. Cundiff. 1997. Differences in breed of sire differences for weights

of male and female calves. Proc. Beef Improvement Federation Research Symposium and Annual Meeting, Dickinson, ND. May 14-17, 1997. pp 131-137.

Van Vleck, L. D., and L. V. Cundiff. 1997. The across-breed EPD tables adjusted to a 1995 base.

Proc. Beef Improvement Federation Research Symposium and Annual Meeting, Dickinson, ND. May 14-17, 1997. pp 102-117.

Van Vleck, L. D., and L. V. Cundiff. 1998. Across-breed EPD tables for 1998 adjusted to a 1996

base. Proc. Beef Improvement Federation Research Symposium and Annual Meeting, Calgary, Alberta, Canada. July 2, 1998. pp 196-212.

Van Vleck, L. D., and L. V. Cundiff. 1998. Influence of breed of dam on across-breed

adjustment factors. Midwestern Section ASAS and Midwest Branch ADSA 1998 Meeting, Des Moines, IA. Abstract # 10. p 31.

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Van Vleck, L. D., and L. V. Cundiff. 1999. Across-breed EPD tables for 1999 adjusted to a 1997

base. Proc. Beef Improvement Federation 31th Annual Research Symposium and Annual Meeting, Roanoke, VA. June 15-19, 1999. pp 155-171.

Van Vleck, L. D., and L. V. Cundiff. 2000. Across-breed EPD tables for 2000 adjusted to a 1998

base. Proc. Beef Improvement Federation 32th Annual Research Symposium and Annual Meeting, Wichita, KS. July 12-15, 2000. pp 98-116.

Van Vleck, L. D., and L. V. Cundiff. 2001. Across-breed EPD tables for 2001 adjusted to breed

differences for birth year 1999. Proc. Beef Improvement Federation 33th Annual Research Symposium and Annual Meeting, San Antonio, TX. July 11-14, 2001. pp 44-63.

Van Vleck, L. D., and L. V. Cundiff. 2002. Across-breed EPD tables for 2002 adjusted to breed

differences for birth year of 2000. Proc. Beef Improvement Federation 34th Annual Research Symposium and Annual Meeting, Omaha, NE. July 10-13, 2002. pp 139-159.

Van Vleck, L. D., and L. V. Cundiff. 2003. Across-breed EPD tables for the year 2003 adjusted

to breed differences for birth year of 2001. Proc. Beef Improvement Federation 35th Annual Research Symposium and Annual Meeting, Lexington, KY. May 28-31, 2003. pp 55-63.

Van Vleck, L. D., and L. V. Cundiff. 2004. Across-breed EPD tables for the year 2004 adjusted

to breed differences for birth year of 2002. Proc. Beef Improvement Federation 36th Annual Research Symposium and Annual Meeting, Sioux Falls, SD. May 25-28, 2004. pp 46-61.

Van Vleck, L. D., and L. V. Cundiff. 2005. Across-breed EPD tables for the year 2005 adjusted

to breed differences for birth year of 2003. Proc. Beef Improvement Federation 37th Annual Research Symposium and Annual Meeting, Billings, MT. July 6-9, 2005. pp 126-142.

Van Vleck, L. D., and L. V. Cundiff. 2006. Across-breed EPD tables for the year 2006 adjusted

to breed differences for birth year of 2004. Proc. Beef Improvement Federation 39th Annual Research Symposium and Annual Meeting, Choctaw, MS. April 18-21, 2006. Available online at: http://www.beefimprovement.org/proceedings/06proceedings/

2006-bif-vanvleck-cundiff.pdf. Van Vleck, L. D., L. V. Cundiff, T. L. Wheeler, S. D. Shackelford, and M. Koohmaraie. 2007.

Across-breed adjustment factors for expected progeny differences for carcass traits. J. Anim. Sci. 85:1369-1376.

Westell, R. A., R. L. Quaas, and L. D. Van Vleck. 1988. Genetic groups in an animal model. J.

Dairy Sci. 71:1310-1318.

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Table 1. Breed of sire solutions from USMARC, mean breed and USMARC EPD used to adjust for genetic trend to the year 2008 base and factors to adjust within breed EPD to an Angus equivalent – BIRTH WEIGHT (lb)

Ave. Base EPD Breed Soln BY 2008 BY 2008 Factor to Number Breed USMARC at USMARC Sire Breed Sire Breed adjust EPD AI Direct 2008 Bulls (vs Ang) Average Differencea To Angus

Breed Sires Progeny (1) (2) (3) (4) (5) (6) Angus 122 1626 2.1 1.8 0.0 91.5 0.0 0.0 Hereford 127 2067 3.6 2.1 4.1 96.4 4.9 3.4 Red Angus 36 480 0.3 -1.3 -0.5 92.3 0.8 2.6 Shorthorn 42 304 2.3 1.4 6.6 98.1 6.6 6.4 South Devon 15 153 2.6 1.9 5.4 96.8 5.3 4.8 Beefmaster 25 229 0.5 1.2 7.5 97.2 5.7 7.3 Brahman 43 562 1.8 0.6 12.5 103.7 12.2 12.5 Brangus 24 225 -0.4 0.9 4.4 93.9 2.4 4.9 Santa Gertrudis 15 119 0.5 1.1 7.4 97.3 5.8 7.4 Braunvieh 21 306 -0.1 0.6 6.7 96.6 5.0 7.3 Charolais 90 911 0.6 0.3 8.6 99.3 7.7 9.3 Chiangus 14 132 1.2 2.3 6.0 95.6 4.1 5.0 Gelbvieh 63 834 1.3 1.1 4.0 95.0 3.5 4.3 Limousin 53 902 1.5 0.9 3.7 95.2 3.6 4.2 Maine Anjou 34 307 1.9 4.4 8.2 96.1 4.6 4.8 Salers 44 298 1.8 2.5 3.8 93.9 2.3 2.6 Simmental 64 870 1.2 2.1 6.1 95.8 4.3 5.2 Tarentaise 7 199 1.9 1.9 2.6 93.6 2.0 2.2 Calculations: (4) = (3) / b + [(1) – (2)] + (Recent Raw Angus Mean: 91.2 lb) with b = 1.11 (5) = (4) – (4, Angus) (6) = (5) – (5, Angus) – [(1) – (1, Angus)] aThe breed difference estimates represent half the differences that would be expected between purebreds of the two breeds.

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Table 2. Breed of sire solutions from USMARC, mean breed and USMARC EPD used to adjust for genetic trend to the year 2008 base and factors to adjust within breed EPD to an Angus equivalent – WEANING WEIGHT (lb)

Ave. Base EPD Breed Soln BY 2008 BY 2008 Factor to Number Breed USMARC at USMARC Sire Breed Sire Breed adjust EPD AI Direct 2008 Bulls (vs Ang) Average Differencea To Angus

Breed Sires Progeny (1) (2) (3) (4) (5) (6) Angus 122 1496 44.5 25.5 0.0 601.1 0.0 0.0 Hereford 125 1910 42.0 24.7 -0.2 599.1 -2.0 0.5 Red Angus 36 465 30.7 26.3 -1.3 584.9 -16.1 -2.3 Shorthorn 42 289 15.1 11.9 5.9 592.2 -8.8 20.6 South Devon 15 134 40.6 23.4 2.1 601.8 0.7 4.6 Beefmaster 25 222 8.0 16.1 26.4 605.6 4.5 41.0 Brahman 43 481 14.0 6.6 19.3 612.6 11.5 42.0 Brangus 24 217 21.0 21.6 14.3 598.5 -2.6 20.9 Santa Gertrudis 15 116 4.0 9.1 9.2 588.0 -13.0 27.5 Braunvieh 21 291 5.9 5.2 4.5 588.1 -13.0 25.6 Charolais 89 818 24.0 12.0 23.8 622.5 21.4 41.9 Chiangus 14 124 42.0 43.2 0.9 581.9 -19.2 -16.7 Gelbvieh 63 784 41.0 33.4 11.4 603.2 2.2 5.7 Limousin 53 826 42.7 26.9 2.3 600.6 -0.4 1.4 Maine Anjou 34 282 40.1 42.7 6.8 587.5 -13.6 -9.2 Salers 44 283 40.9 31.4 6.8 599.7 -1.4 2.2 Simmental 63 790 31.1 25.0 23.3 616.1 15.0 28.4 Tarentaise 7 191 16.0 -5.6 2.6 606.7 5.7 34.2 Calculations: (4) = (3) / b + [(1) – (2)] + (Raw Angus Mean: 582.0 lb) with b = 0.84 (5) = (4) – (4, Angus) (6) = (5) – (5, Angus) – [(1) – (1, Angus)] aThe breed difference estimates represent half the differences that would be expected between purebreds of the two breeds.

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Table 3. Breed of sire solutions from USMARC, mean breed and USMARC EPD used to adjust for genetic trend to the year 2008 base and factors to adjust within breed EPD to an Angus equivalent – YEARLING WEIGHT (lb)

Ave. Base EPD Breed Soln BY 2008 BY 2008 Factor to Number Breed USMARC at USMARC Sire Breed Sire Breed adjust EPD AI Direct 2008 Bulls (vs Ang) Average Differencea To Angus

Breed Sires Progeny (1) (2) (3) (4) (5) (6) Angus 116 1357 81.5 47.3 0.0 1020.2 0.0 0.0 Hereford 122 1763 70.0 41.6 -22.4 993.2 -27.0 -15.5 Red Angus 33 404 55.9 46.0 -7.1 989.2 -31.1 -5.5 Shorthorn 41 255 25.0 18.8 20.0 1011.1 -9.1 47.4 South Devon 15 134 76.1 50.3 -1.0 1010.9 -9.4 -4.0 Beefmaster 22 157 12.0 23.3 20.1 993.7 -26.6 42.9 Brahman 41 416 23.0 11.2 -35.3 964.4 -55.9 2.6 Brangus 21 152 41.3 38.0 12.0 1000.6 -19.6 20.6 Santa Gertrudis 13 90 6.0 11.6 -12.4 968.6 -51.6 23.9 Braunvieh 19 267 11.5 11.1 -10.0 977.0 -43.2 26.8 Charolais 84 716 42.2 22.7 27.7 1031.7 11.5 50.8 Chianina 13 89 77.0 79.2 -7.9 976.3 -43.9 -39.4 Gelbvieh 60 728 75.0 60.1 2.8 1003.5 -16.7 -10.2 Limousin 49 755 80.2 54.8 -22.7 989.9 -30.4 -29.1 Maine Anjou 31 264 78.8 85.7 14.2 992.6 -27.7 -25.0 Salers 43 254 78.1 59.2 6.8 1011.4 -8.9 -5.5 Simmental 54 678 55.7 45.4 27.9 1022.7 2.5 28.3 Tarentaise 7 189 28.6 -3.6 -29.0 990.7 -29.5 23.4 Calculations: (4) = (3) / b + [(1) – (2)] + (Raw Angus Mean: 986.0 lb) with b = 1.06 (5) = (4) – (4, Angus) (6) = (5) – (5, Angus) – [(1) – (1, Angus)] aThe breed difference estimates represent half the differences that would be expected between purebreds of the two breeds.

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Table 4. Breed of maternal grandsire solutions from USMARC, mean breed and USMARC EPD used to adjust for genetic trend to the year 2008 base and factors to adjust within breed EPD to an Angus equivalent – MILK (lb)

Ave. Base EPD Breed Soln BY 2008 BY 2008 Factor to Number Breed USMARC at USMARC Sire Breed Sire Breed adjust EPD AI Direct Direct 2008 Bulls (vs Ang) Average Differencea To Angus

Breed Sires Gpr Progeny (1) (2) (3) (4) (5) (6) Angus 104 2704 559 21.0 11.4 0.0 591.6 0.0 0.0 Hereford 108 3485 743 16.0 8.4 -24.3 569.0 -22.6 -17.6 Red Angus 21 529 119 16.5 14.1 -1.8 582.9 -8.7 -4.2 Shorthorn 26 269 74 2.3 4.9 18.8 595.3 3.7 22.4 South Devon 14 373 70 21.2 19.3 -0.1 583.8 -7.8 -8.0 Beefmaster 20 247 51 2.0 -2.1 -12.1 575.8 -15.8 3.2 Brahman 32 768 176 6.0 3.4 19.4 601.0 9.4 24.4 Brangus 19 229 43 7.2 1.9 -6.9 581.4 -10.2 3.6 Braunvieh 9 544 94 0.3 -1.0 21.9 601.9 10.2 30.9 Charolais 68 1282 260 6.6 3.8 -5.2 580.4 -11.3 3.1 Gelbvieh 47 1256 262 18.0 17.4 16.9 597.0 5.3 8.3 Limousin 40 1404 273 21.4 17.2 -11.4 576.6 -15.1 -15.5 Maine Anjou 20 533 91 20.2 24.5 12.8 588.5 -3.1 -2.3 Salers 27 364 91 19.8 23.0 13.6 590.4 -1.3 -0.1 Simmental 47 1392 267 4.4 8.1 10.1 586.9 -4.8 11.8 Tarentaise 6 367 80 0.6 5.3 19.7 594.0 2.3 22.7 Calculations: (4) = (3) / b + [(1) – (2)] + (Raw Angus Mean: 582.0 lb) with b = 1.18 (5) = (4) – (4, Angus) (6) = (5) – (5, Angus) – [(1) – (1, Angus)] aThe breed difference estimates represent half the differences that would be expected between purebreds of the two breeds.

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Table 5. Breed of sire solutions from USMARC, mean breed and USMARC EPD used to adjust for genetic trend to the year 2008 base and factors to adjust within breed EPD to an Angus equivalent – MARBLING (marbling score unitsa)

Ave. Base EPD Breed Soln BY 2008 BY 2008 Factor to Number Breed USMARC at USMARC Sire Breed Sire Breed adjust EPD AI Direct 2008 Bulls (vs Ang) Average Differenceb To Angus

Breed Sires Progeny (1) (2) (3) (4) (5) (6) Angus 97 591 0.35 0.13 0.00 5.62 0.00 0.00 Hereford 115 817 0.03 -0.01 -0.47 4.97 -0.65 -0.33 Red Angus 31 117 0.06 0.15 -0.04 5.27 -0.35 -0.06 Shorthorn 38 135 -0.02 0.02 -0.20 5.15 -0.47 -0.10 South Devon 13 49 0.30 -0.02 -0.18 5.54 -0.08 -0.03 Santa Gertrudis 12 39 0.00 -0.03 -0.76 4.67 -0.95 -0.60 Braunvieh 19 130 0.01 -0.01 -0.45 4.96 -0.65 -0.31 Charolais 29 121 0.03 -0.04 -0.59 4.88 -0.74 -0.42 Chiangus 13 39 0.14 0.05 -0.56 4.93 -0.69 -0.48 Limousin 46 278 0.01 -0.08 -0.96 4.52 -1.09 -0.75 Maine Anjou 28 127 0.20 0.17 -0.84 4.59 -1.03 -0.88 Salers 38 119 0.10 -0.24 -0.57 5.17 -0.45 -0.20 Simmental 52 294 0.13 0.07 -0.61 4.85 -0.77 -0.55 Calculations: (4) = (3) / b + [(1) – (2)] + (Raw Angus Mean: 5.40) with b = 1.00 (5) = (4) – (4, Angus) (6) = (5) – (5, Angus) – [(1) – (1, Angus)] a4.00 = Sl00, 5.00 = Sm00 bThe breed difference estimates represent half the differences that would be expected between purebreds of the two breeds.

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Table 6. Breed of sire solutions from USMARC, mean breed and USMARC EPD used to adjust for genetic trend to the year 2008 base and factors to adjust within breed EPD to an Angus equivalent – RIBEYE AREA (in2)

Ave. Base EPD Breed Soln BY 2008 BY 2008 Factor to Number Breed USMARC at USMARC Sire Breed Sire Breed adjust EPD AI Direct 2008 Bulls (vs Ang) Average Differencea To Angus

Breed Sires Progeny (1) (2) (3) (4) (5) (6) Angus 97 592 0.18 0.03 0.00 12.58 0.00 0.00 Hereford 115 817 0.20 -0.05 -0.22 12.46 -0.12 -0.14 Red Angus 31 117 0.06 -0.16 -0.26 12.40 -0.18 -0.06 Shorthorn 38 135 0.06 -0.01 0.16 12.66 0.08 0.20 South Devon 13 49 0.21 0.22 0.29 12.72 0.14 0.11 Santa Gertrudis 12 40 0.00 -0.03 -0.36 12.10 -0.48 -0.30 Braunvieh 19 130 0.01 0.00 0.86 13.30 0.72 0.89 Charolais 29 122 0.18 0.09 0.81 13.33 0.75 0.75 Chiangus 13 40 -0.08 0.05 0.61 12.92 0.34 0.60 Limousin 47 279 0.37 0.27 1.29 13.82 1.24 1.05 Maine Anjou 28 127 0.16 0.10 1.12 13.62 1.04 1.06 Salers 38 120 0.02 0.02 0.79 13.22 0.64 0.80 Simmental 52 295 0.11 -0.05 0.86 13.45 0.87 0.94 Calculations: (4) = (3) / b + [(1) – (2)] + (Raw Angus Mean: 12.43 in2) with b = 1.00 (5) = (4) – (4, Angus) (6) = (5) – (5, Angus) – [(1) – (1, Angus)] aThe breed difference estimates represent half the differences that would be expected between purebreds of the two breeds.

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Table 7. Breed of sire solutions from USMARC, mean breed and USMARC EPD used to adjust for genetic trend to the year 2008 base and factors to adjust within breed EPD to an Angus equivalent – FAT THICKNESS (in)

Ave. Base EPD Breed Soln BY 2008 BY 2008 Factor to Number Breed USMARC at USMARC Sire Breed Sire Breed adjust EPD AI Direct 2008 Bulls (vs Ang) Average Differencea To Angus

Breed Sires Progeny (1) (2) (3) (4) (5) (6) Angus 97 592 0.013 0.002 0.000 0.538 0.000 0.000 Hereford 115 817 0.002 -0.003 -0.054 0.477 -0.061 -0.050 Red Angus 31 117 0.000 -0.009 -0.061 0.474 -0.064 -0.051 Shorthorn 38 135 -0.014 0.016 -0.144 0.353 -0.185 -0.158 South Devon 13 49 0.010 0.009 -0.111 0.417 -0.121 -0.118 Santa Gertrudis 12 40 0.000 0.002 -0.137 0.388 -0.150 -0.137 Braunvieh 19 130 0.001 -0.013 -0.180 0.361 -0.177 -0.165 Charolais 29 122 0.001 -0.002 -0.236 0.293 -0.245 -0.233 Chiangus 13 40 0.020 0.008 -0.149 0.390 -0.148 -0.155 Maine Anjou 28 127 0.000 -0.005 -0.215 0.317 -0.221 -0.208 Salers 38 120 0.000 -0.007 -0.222 0.312 -0.227 -0.214 Simmental 52 295 0.010 0.010 -0.216 0.311 -0.227 -0.224 Calculations: (4) = (3) / b + [(1) – (2)] + (Raw Angus Mean: 0.527 in) with b = 1.00 (5) = (4) – (4, Angus) (6) = (5) – (5, Angus) – [(1) – (1, Angus)] aThe breed difference estimates represent half the differences that would be expected between purebreds of the two breeds.

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Table 8. Mean weighteda accuracies for birth weight (BWT), weaning weight (WWT), yearling weight (YWT), maternal weaning weight (MWWT), milk (MILK), marbling (MAR), ribeye area (REA), and fat thickness (FAT) for bulls used at USMARC

Breed BWT WWT YWT MILK MAR REA FAT

Angus 0.77 0.74 0.68 0.66 0.48 0.47 0.45

Hereford 0.62 0.58 0.58 0.52 0.22 0.36 0.26

Red Angus 0.91 0.90 0.90 0.88 0.70 0.68 0.58

Shorthorn 0.80 0.78 0.72 0.74 0.59 0.57 0.49

South Devon 0.37 0.41 0.37 0.44 0.02 0.05 0.04

Beefmaster 0.84 0.88 0.84 0.73

Brahman 0.64 0.65 0.58 0.55

Brangus 0.83 0.81 0.70 0.73

Santa Gertrudis 0.87 0.84 0.77 0.33 0.52 0.46

Braunvieh 0.85 0.86 0.83 0.79 0.44 0.30 0.46

Charolais 0.77 0.71 0.61 0.62 0.47 0.50 0.44

Chiangus 0.82 0.79 0.78 0.48 0.48 0.53

Gelbvieh 0.80 0.75 0.60 0.59

Limousin 0.92 0.89 0.83 0.85 0.72 0.72

Maine Anjou 0.76 0.75 0.75 0.73 0.39 0.39 0.39

Salers 0.83 0.83 0.76 0.83 0.20 0.26 0.28

Simmental 0.94 0.94 0.93 0.93 0.79 0.79 0.79

Tarentaise 0.96 0.95 0.95 0.94 aWeighted by relationship to phenotyped animals at USMARC for BWT, WWT, YWT, MAR, REA, and FAT and by relationship to daughters with phenotyped progeny MILK.

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Table 9. Estimates of variance components (lb2) for birth weight (BWT), weaning weight (WWT), yearling weight (YWT), maternal weaning weight (MWWT), marbling (MAR), ribeye area (REA), and fat thickness (FAT) from mixed model analyses

Direct

Analysis

BWT

WWTa

YWT

Direct Animal within breed (19) 70.29 446.48 3606.15 Maternal genetic within breed (17) 450.53 Maternal permanent environment 676.35 Residual 50.50 1206.21 4157.08

Carcass Direct MAR REA FAT

Animal within breed (12) 0.239 0.633 0.0095 Residual 0.332 0.711 0.0151

aDirect maternal covariance for weaning weight was -89.44 lb2

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Table 10. Pooled and within-breed regression coefficients (lb/lb) for weights at birth (BWT), 205 days (WWT), and 365 days (YWT) of F1 progeny and for calf weights (205 d) of F1 dams (MILK) on sire expected progeny difference and by sire breed BWT WWT YWT MILK Pooled 1.11 ± 0.04

0.84 ± 0.04

1.06 ± 0.05

1.18 ± 0.09

Sire breed

Angus 0.98 ± 0.10 0.77 ± 0.08 1.21 ± 0.08 1.12 ± 0.16

Hereford 1.16 ± 0.07 0.76 ± 0.05 1.03 ± 0.06 1.11 ± 0.16

Red Angus 0.85 ± 0.14 0.78 ± 0.22 0.72 ± 0.25 1.85 ± 0.40

Shorthorn 0.78 ± 0.29 0.67 ± 0.25 0.89 ± 0.31 0.88 ± 0.95

South Devon -0.25 ± 0.63 0.03 ± 0.56 -0.01 ± 0.47 -0.31 ± 1.57

Beefmaster 1.98 ± 0.50 1.16 ± 0.31 0.99 ± 0.47 3.86 ± 0.70

Brahman 2.22 ± 0.23 1.00 ± 0.21 1.12 ± 0.24 0.64 ± 0.57

Brangus 1.85 ± 0.36 0.59 ± 0.39 0.95 ± 0.44 0.59 ± 0.74

Santa Gertrudis 5.83 ± 1.71 1.00 ± 0.44 -0.16 ± 0.48

Braunvieh 0.86 ± 0.31 1.23 ± 0.36 1.30 ± 0.40 2.72 ± 1.31

Charolais 1.08 ± 0.13 0.91 ± 0.12 0.71 ± 0.13 1.09 ± 0.31

Chiangus 1.70 ± 0.42 0.76 ± 0.37 0.25 ± 0.53

Gelbvieh 0.96 ± 0.15 0.89 ± 0.17 1.06 ± 0.18 0.99 ± 0.50

Limousin 0.83 ± 0.11 0.94 ± 0.11 1.14 ± 0.13 1.65 ± 0.30

Maine Anjou 1.78 ± 0.31 0.47 ± 0.30 0.74 ± 0.37 0.88 ± 0.55

Salers 0.98 ± 0.28 0.81 ± 0.33 0.52 ± 0.33 1.90 ± 0.56

Simmental 1.21 ± 0.17 1.56 ± 0.15 1.40 ± 0.15 0.82 ± 0.44

Tarentaise 1.49 ± 1.35 0.70 ± 0.60 1.49 ± 0.82 1.01 ± 0.93

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Table 11. Pooled and within-breed regression coefficients marbling (MAR; score/score), ribeye area (REA; in2/in2), and fat thickness (FAT; in/in) of F1 progeny on sire expected progeny difference and by sire breed MAR REA FAT Pooled 0.70 ± 0.07 0.98 ± 0.09 1.27 ± 0.11

Sire breed

Angus 0.96 ± 0.12 1.09 ± 0.22 1.46 ± 0.19

Hereford 0.63 ± 0.21 0.48 ± 0.17 1.00 ± 0.21

Red Angus 1.03 ± 0.24 1.81 ± 0.37 2.06 ± 0.69

Shorthorn 1.64 ± 0.38 0.51 ± 0.73 2.35 ± 0.62

South Devon 0.10 ± 0.91 1.21 ± 4.12 1.63 ± 5.29

Santa Gertrudis -1.43 ± 1.79 1.03 ± 0.73 1.41 ± 0.78

Braunvieh 3.68 ± 1.71 0.53 ± 0.87 0.09 ± 0.38

Charolais 1.17 ± 0.37 1.30 ± 0.40 1.80 ± 0.83

Chiangus 0.75 ± 0.36 -0.85 ± 0.79 -0.04 ± 1.42

Limousin 1.15 ± 0.45 1.43 ± 0.22

Maine Anjou 1.26 ± 1.46 -1.01 ± 0.85 2.59 ± 1.37

Salers 0.05 ± 0.14 3.64 ± 1.01 -0.38 ± 0.96

Simmental 0.42 ± 0.19 0.66 ± 0.20 2.08 ± 0.49

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MEAN EPDs REPORTED BY DIFFERENT BREEDS

Larry A. Kuehn and R. Mark Thallman Roman L. Hruska U.S. Meat Animal Research Center, USDA-ARS,

Clay Center, NE 68933

Expected progeny differences (EPDs) have been the primary tool for genetic improvement of beef cattle for over 35 years beginning with evaluations of growth traits. Since that time EPDs have been added for several other production traits such as calving ease, stayability, and carcass merit and conformation. Most recently, several breed associations have derived economic indices from their EPDs to increase profit under different management and breeding systems.

It is useful for producers to compare the EPDs of potential breeding animals with their breed average. The current EPDs from the most recent genetic evaluations of 23 breeds are presented in this report. Mean EPDs for growth traits are shown in Table 1 (23 breeds), for other production traits in Table 2 (14 breeds), and for carcass and composition traits in Table 3 (19 breeds). Several breeds also have EPDs that are unique to their breed; these EPDs are presented in Table 4.

Average EPDs should only be used to determine the genetic merit of an animal relative to its breed average. To compare animals of different breeds, across breed adjustment factors should be added to animals’ EPDs for their respective breeds (see Across-breed EPD Tables reported by Kuehn et al. in these proceedings).

This list is likely incomplete; evaluations for some breeds are not widely reported. If you see a breed missing and would like to report the average EPDs for that breed, please contact Larry (Larry.Kuehn@ars.usda.gov) or Mark (Mark.Thallman@ars.usda.gov).

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Table 1. Birth year 2008 average EPDs from 2010 evaluations for growth traits

Breed Birth

Weight (lb) Weaning

Weight (lb) Yearling

Weight (lb) Maternal Milk (lb)

Total Maternal (lb)

Angus 2.1 44.5 81.5 21 Hereford 3.6 42 70 16 37 Murray Grey 3.2 19 29 3 13 Red Angus 0.3 30.7 55.9 16.5 Shorthorn 2.3 15.1 25 2.3 9.9 South Devon 2.6 40.6 76.1 21.2 42 Beefmaster 0.5 8 12 2 6 Braford 1.2 8 13 3 7 Brahman 1.8 14 23 6 Brangus -0.4 21 41.3 7.2 17.8 Red Brangus 1.5 12.5 19.9 5.5 11.8 Santa Gertrudis 0.5 4.0 6.0 0.0 2.0 Senepol 1.1 9.0 13.4 4.1 8.5 Simbrah 2.7 26.8 43.5 2.6 16 Braunvieh -0.14 5.9 11.5 0.3 0.6 Charolais 0.6 24 42.2 6.6 18.6 Chianina 1.2 42 77 9.5 30.5 Gelbvieh 1.3 41 75 18 38 Limousin 1.5 42.7 80.2 21.4 Maine-Anjou 1.9 40.1 78.8 20.2 40.2 Salers 1.8 40.9 78.1 19.8 Simmental 1.2 31.1 55.7 4.4 20.0 Tarentaise 1.9 16 28.6 0.6

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Table 2. Birth year 2008 average EPDs from 2010 evaluations for other production traits

Breed Calving Ease

Direct (%) Calving Ease Maternal (%)

Scrotal Circumference

(cm) Docility

Score Stayability

(%) Murray Grey -0.7 -0.3 0.10 Angus 5 6 0.40 Hereford 0.1 0.7 0.6 Red Angus 5.4 3.4 9.3 Shorthorn -1.7 -1.5 South Devon 0.1 Beefmaster 0.10 Brangus 0.69 Charolais 2.6 3.5 0.58 Gelbvieh 105 104 0.4 4 Limousin 7.7 3.3 0.4 15.3 17.3 Salers 0.2 0.2 0.3 7.9 22.9 Simmental 6.9 2.6 17.6 Tarentaise -1.2 0.6

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Table 3. Birth year 2008 average EPDs from 2010 evaluations for carcass and composition traits Carcass Ultrasound

Breed Carcass Wt (lb)

Retail Product

(%) Yield Grade

Marbling Score

Ribeye Area (in2)

Fat Thick-ness (in) IMF (%)

Ribeye Area (in2)

Fat Thick-ness (in)

WBSF (lb)

Angus 12.0 0.345 0.18 0.013 Hereford 0.03 0.20 0.002

Red Angus 0.06 0.06 -0.000a Shorthorn 5.07 -0.016 0.055 -0.014 South Devon 25.1 0.8 0.3 0.21 0.01 Beefmaster 0.000 0.04 0.000 Braford 4.6 0.002 0.040 0.001 Brahman 5.1 0.01 -0.01 0.05 -0.002 0.0 Brangus 0.026b 0.36b -0.001b Santa Gertrudis 0.0 0.00 0.00 0.00 Simbrah -7.4 0.05 -0.01 -0.17 0.01 -0.05 Braunvieh 1.1 0.01 0.01 0.001 Charolais 13.7 0.03 0.18 0.001 Chianina 3.0 -0.20 0.14 -0.08 0.02 Gelbvieh 8.3c -0.03c 0.10c Limousin 20.0 -0.02 0.01 0.37 Maine-Anjou 0.2 0.29 0.20 0.16 0.00 Salers 20.4 0.0 0.1 0.02 0.00 Simmental -2.7 -0.01 0.13 0.11 0.01 -0.17 aCalculated using only actual carcass data (no ultrasound data); all other carcass scale evaluations for Red Angus use a multi-trait model bReported on an ultrasound scale but calculated using ultrasound and carcass data in a multi-trait model cAdjusted to a fat-constant endpoint

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Table 4. Birth year 2008 average EPDs from 2010 evaluations for other traits unique to individual breeds

Angus Mature

Weight (lb) Mature

Height (in) Yearling

Height (in)

Cow Energy

Value ($)

Weaned Calf

Value ($) Feedlot

Value ($) Grid

Value ($) Beef

Value ($) 30 0.4 0.4 2.16 24.83 23.66 22.14 41.29

Hereford

Baldy Maternal Index ($)

Brahman Influence Index ($)

Certified Hereford Beef Index ($)

Calving Ease Index ($)

15 14 18 14

Red Angus Heifer Pregnancy

(%) Mature Cow Maintenance

(Mcal/mo)

9.3 4.0 Gelbvieh Feedlot

Merit ($) Carcass

Value ($) Gestation Length (d)

Days to Finish (d)

8.82 6.74 -1.4 3.5 Limousin Mainstream Terminal

Index ($)

44.4 Simmental All Purpose

Index ($) Terminal Index ($)

Simbrah All Purpose Index ($)

Terminal Index ($)

101.4 61.2 70 45

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VALUE OF DNA MARKER INFORMATION FOR BEEF BULL SELECTION1

A.L. Van Eenennaam

University of California, Davis, CA, 95616 INTRODUCTION

There is clear value associated with using DNA information to identify animals that are carriers of recessive alleles. Tests are now available for specific genetic defects, color, and horned/polled status. Prior to the advent of DNA tests, the only way to test if a bull was a carrier of a genetic defect was to do progeny testing. Even then, definitive conclusions could only be drawn if he sired an afflicted calf. DNA-marker technology can also be used to verify or assign parentage, and this has value in terms of pedigree integrity or assigning paternity to calves conceived in multi-sire breeding pastures. Recently, a range of genetic tests have been developed to test for production traits ranging from fertility and longevity to growth and carcass merit. A question that often arises in conversations with producers is “What is the value of these tests?”

The answer to that question depends on what the tests are being used for. Some breeders

are testing animals and listing the results as an additional source of information in sale catalogs. If this adds value, increasing the animal’s sale price beyond the cost of the test, then this makes economic sense. Other people are using tests to make culling or selection decisions on traits that are not currently in breed EPDs (e.g. feed efficiency or tenderness). Working out whether this pays is a little more complicated. While these traits have obvious value, without more information, it is not possible to decide how much emphasis should be placed on these traits versus other important traits. For example, should you eliminate animals from your herd based solely on a poor feed efficiency DNA test result? That depends on how accurate the test is at predicting superior versus inferior animals. The more accurate a test is, the more opportunity there is to accelerate genetic improvement. It also depends on the importance of feed efficiency versus all the other traits contributing to your overall profitability. One way to make this decision is to develop a “selection index” that weights all traits on their relative economic importance. Indexes consider the "input" or expense side of selection decisions and enable cattle producers to make balanced selection decisions, taking into account the economically-relevant growth, carcass and fertility attributes of each animal to identify which animals are the most profitable for their particular commercial enterprise.

From the perspective of a seedstock breeder, the response to selection and therefore the

value associated with the use of a DNA test is dependent upon how much the DNA information improves the accuracy of genetic evaluations at the time of selection, and the value of a unit of 1 Based on a paper authored by Van Eenennaam*, A. L., J.H.J. van der Werf,† and M.E. Goddard‡,§ entitled “Value of DNA information for beef bull selection”, given at 9th World Congress of Genetics Applied to Livestock Production, Leipzig, Germany. August 1-6, 2010. *University of California, Davis, CA, 95616 †University of New England, Armidale, Australia, 2351 ‡Victorian Department of Primary Industries, Bundoora, Australia, 3086 §University of Melbourne, Parkville, Australia, 3054

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genetic improvement. To determine the value of DNA testing I recently did a simulation study with a hypothetical multi-trait DNA test and asked “What is the value of DNA tests to increase the accuracy of beef bull selection in the seedstock sector?” Structure of the seedstock herd. A simple two-tier industry example was modeled where the seedstock breeder was incurring the costs of DNA testing to improve the accuracy of bull selection. In this example the seedstock tier consisted of a closed nucleus of 600 breeding females (Table 1). It was assumed that in the absence of DNA test information, breeding value estimates on young, untested bulls were informed by their own performance records on selection criteria (Table 2) along with those of their sire, dam and 20 paternal-half sibs. Each year the top 8 bulls were selected to be stud sires, and 125 (remaining bulls from the top half of the calf crop) were made available for sale to commercial producers. Commercial sires were then used to sire four calf crops at a mating ratio of 25 females: 1 male (i.e. they were exposed to a total of 100 cows if they were in the herd for 4 breeding seasons).

Parameters Value Number of live stud calves available for sale/selection per exposure 0.89

Stud cow:bull ratio 30 Number of stud cows 600 Number of bulls calves available for sale/selection 267 Number of stud bulls selected each year 8 (~3%; i = 2.27) Number of bulls sold for breeding (annual) 125 (~50%; i = 0.8) Cull for age threshold of cow 10 Age structure of breeding cow herd (2-10 yr) 0.2, 0.18, 0.17,0.15, 0.12, 0.09, 0.05, 0.03, 0.01 Bull survival (annual) 0.8 Age structure of bulls in stud herd (2-4 yr) 0.41, 0.33, 0.26 Age structure of bulls in commercial herd (2-5 yr) 0.34, 0.27, 0.22, 0.17 Planning horizon 20 years Discount rate for returns 7% Maximum age of commercial sire 5 (4 breeding seasons) Commercial cow:bull ratio 25 Number of commercial females 9225 Table 1. Attributes of the modeled seedstock and commercial herd structure.

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Breeding objectives and index accuracy. Breeding objectives were developed for both maternal (self-replacing) and terminal herds targeting either the domestic Australian market where steers are finished on pasture (GRASS), or a high value market where steers are finished on concentrate rations in feedlots and marbling has

a high value (FEEDLOT). The proportion of trait genetic variation explained by the DNA test (r2) was set to the h2 of ALL selection criteria (Table 2). Selection index theory was used to predict index accuracy. Discounted gene flow methodology was used to calculate the value derived from the use of superior bulls. These values were then compared to selection based on performance recording alone as a baseline. It was assumed that all of the bulls in the annual cohort were DNA tested to enable selection of the best 3% as stud sires, and 50% as sale bulls. The extra cost of using DNA testing was assumed to be only the cost of the test, and resulting benefits were expressed on a per DNA test basis.

Table 2. Selection criteria available from performance recording, and heritabilities (h2).

Results and discussion.

Table 3. Improvement in selection response (%) resulting from a DNA-test enabled increases in index accuracy as compared to performance recording alone, value of genetic gain (ΔG) in commercial and stud sires, and value derived per DNA test used to increase the accuracy of male selection in a closed seedstock breeding program.

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DNA test information was combined with performance records to increase the accuracy of EPDs. This increased selection response 20-41% over that obtained with performance recording alone, depending upon the index (Table 3). Because DNA information is particularly useful to improve genetic predictions for traits that cannot be measured on juvenile individuals, the DNA-enabled selection response was highest for the maternal feedlot index, because the delayed measurement of maternal and carcass traits means that these traits are hard to improve based on phenotypic measurement The value of DNA-tests to enable more accurate selection of genetically-superior commercial bulls ranged from AU$61-135 for commercial bulls, and AU$3,631-6,359 for stud bulls. Assuming that the entire bull calf crop (n = 267) was tested and that the top 3% (n=8) bulls were selected as stud sires, and the remaining top half of the bulls (n=125) were sold as commercial sires, the breakeven value of the genetic gain derived from DNA testing ranged from AU$143-258 per test.

It is important to understand that these values assumed commercial producers were willing to pay a price premium for genetically-superior bulls, and some form of industry vertical integration or profit sharing between sectors such that benefits realized by downstream sectors (e.g. feedlot, processor) of the beef cattle supply chain were efficiently transferred back to the seedstock producer incurring the expense of DNA testing. The value of DNA tests to increase the accuracy of selection criteria to improve traits of direct value to commercial cattle enterprises (e.g. maternal traits like cow weaning rate or mature cow weight) would be less than that calculated for the total industry merit indexes modeled in this study. For example, 69% of the returns from including DNA data in commercial sire selection for the terminal feedlot index were derived from improved dressing %, saleable meat %, and marbling score; traits that generate to the processing sector.

These results were based on using a relatively powerful hypothetical DNA test panel that

predicted ALL of the selection traits with relatively high accuracies2. The accuracy of DNA-based predictions of breeding value is dependent on trait heritability and the size of the training set used to develop the test. A DNA test like the one modeled in this simulation study might be expected if it was developed using a relatively large (~2,500 animals) genotyped training population.

The values obtained in this study assumed that the commercial bull:cow ratio was 1:25. A

20% increase in this ratio (i.e. increasing it from 1:25 to 1:30) would increase the values in Table 3 by 20%. A major determinant of seedstock profitability is the proportion of young bulls that can be sold for breeding, and eliminating half of possible sale bulls from contention based on DNA testing may be unrealistic. Some seedstock breeders may only be interested in using DNA information to improve the accuracy of replacement stud sire selection for their own herd, and

2 Note that the term accuracy here is referring to the genetic correlation (r) between the test result and the true breeding value, not the “BIF” accuracy.

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not to additionally select the better half of the commercial bulls for sale as was modelled in this study.

If a breeder instead chose to sell all physically-sound bull calves, the value associated with testing commercial sire candidates would disappear. However, it would increase the value of replacement stud bulls due to the larger number of marketable descendants each stud bull would produce. For example, selling 80% of the bull crop as commercial sires, assuming 20% were culled for non-genetic reasons, would increase the value of a stud bull selected based on performance records for the terminal feedlot market from $14,579 to $24,143. If the DNA information from the hypothetical test modeled in this study was additionally used to select those replacement stud bulls, the value derived from each stud bull selected would also increase ~ 66% to $30,157. The value per DNA test in this case would depend upon what proportion of the bull crop was tested to select replacement stud bulls. If the seedstock operator continued to test 100% of the bull calves, this value would be ~ $180/test.

Until recently, commercialized DNA tests for beef cattle targeted only a handful of traits (e.g. marbling score, tenderness and feed efficiency). As DNA testing becomes more comprehensive and encompasses a larger number of traits, it will become increasingly important to integrate this information into national cattle evaluations. The incorporation of this DNA test information into carcass trait evaluations by the American Angus Association (www.angus.org/AGI/GenomicEnhancedEPDs.pdf) represents an important milestone in the application of DNA testing in beef cattle. It is difficult to make optimal selection decisions or even estimate the value of these multi-trait DNA tests in the absence of information on their accuracy, and the incorporation of DNA test results and target traits into genetic evaluations. However these developments will require the availability of additional genotyped, phenotyped populations to obtain the required genetic parameter estimates. Further, breeds may need to develop their own populations that are distinct from the original discovery populations to develop breed-specific estimates of the genetic parameters that will be required for the inclusion of DNA information into genetic evaluations. Although DNA information clearly has the potential to provide value to seedstock producers, making optimal use of this information will likely require the concurrent development of multi-trait selection indexes for breeding objectives of relevance to U.S. beef production systems.

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USE OF BOVINE SNP50 DATA FOR FEED EFFICIENCY SELECTION DECISIONS IN ANGUS CATTLE

Megan M. Rolf*, Jeremy F. Taylor *, Robert D. Schnabel*, Stephanie D. McKay*, Matthew C. McClure*, Sally L. Northcutt§, Monty S. Kerley* and Robert L. Weaber*

*Division of Animal Sciences, University of Missouri, Columbia, MO 65211, USA

§American Angus Association, St. Joseph, MO 64506, USA

Abstract

The past few years have led to a rapid increase in the use of molecular genetic technologies in the beef industry. With any new technological advance, the methods for implementation must be characterized and tested in populations of beef cattle. Recently, large panels of single nucleotide polymorphism (SNP) markers have become available and a multitude of animals have been genotyped. The best use of these data will likely be in the form of genomic selection, where the marker information is incorporated into the current system of genetic prediction and EPDs (expected progeny differences) will continue to be reported by the breed associations. Genomic selection methods will be exceptionally valuable for traits that are difficult and expensive to measure (such as residual feed intake, or RFI) or that are measured late in life (such as longevity/stayability). One method to utilize information from reduced marker panels is to utilize genomic relationship matrices (GRMs) in place of traditional pedigree-derived relationship matrices in genetic evaluation. Traditional pedigree derived matrices (numerator relationship matrices, NRM) contain a number for each pair of animals describing the average proportion of DNA two animals share identical by descent, or their “relatedness”. The data in a GRM may more accurately reflect the kinship between two animals because it is calculated directly from genomic data. This method is particularly useful for animals that have missing pedigree data, such as in populations of commercial cattle. We used a GRM to test genomic selection for feed efficiency traits, quantified the number of markers needed to calculate a GRM, performed a genome scan for regions influencing feed efficiency and tested model predicted feed intakes against individual feed intake data in a commercial Angus cattle population.

Introduction

The beef industry has made enormous strides in improving genetic merit for economically relevant traits (ERTs) such as calving ease, growth and carcass quality over the last several decades. Much of this improvement has been made possible by the availability of EPDs published by almost every purebred beef breed association (Crews, 2005), which are based on best linear unbiased prediction (BLUP) methods outlined by Henderson (1975). Most of these ERTs focus on outputs from the production system. However, production inputs, such as feed inputs, can have a significant influence on profitability and these traits have remained essentially unselected.

Feed efficiency is a trait with enormous economic importance, but selection for efficiency has remained elusive due to the difficulty and expense of gathering phenotypic data (Archer et al., 1997). In the past, increased growth rate has been selected for in the beef industry because growth and efficiency are correlated (Koch et al., 1963). Because of the correlated

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selection response, selection for increased growth rates can result in unintended consequences like increases in mature size and increased maintenance requirements in the cowherd (Archer et al., 1999; Okine et al., 2004).

Model predicted feed intakes from programs such as the Cornell Value Discovery System (CVDS; Guiroy et al., 2001) and the Decision Evaluator for the Cattle Industry (DECI; Williams et al., 2003a,b) have the potential to increase the number of feed efficiency observations produced on progeny of registered animals. These models are designed to predict the differences in intake for cattle that are fed in pens by allocating the total feed fed to the entire pen to individual animals based on their performance for traits related to growth and carcass composition. A study by Williams et al. (2006) showed high phenotypic (0.947-0.933) and genetic (0.97-0.99) correlations between CVDS and DECI predicted dry matter required (pDMR) values, so only the CVDS model was used in our study. These model predicted intakes have the potential to serve as an indicator trait for feed efficiency, much the same way that ultrasound data are indicator traits for carcass quality and yield. Indicator traits such as these do not necessarily impact revenue or risk themselves, but are easier and more cost effective to record and are genetically correlated with the ERT of interest (Crews, 2005). As more records are obtained on an indicator or causal trait, it will become more effective to incorporate these data into genetic evaluation systems in the beef industry.

Since the first national genetic evaluation in 1974 (Willham, 1993), the beef industry has been collecting phenotypes and incorporating them into genetic evaluation systems. The incorporation of feed intake data into genetic evaluation has the potential to dramatically influence selection on maintenance efficiency and genomic selection has the potential to make the most of limited data for genetic prediction on a large number of animals using either large marker panels (such as the 50K or 800K chips) or smaller panels of markers associated with ERTs. Genomic selection is the ability to (theoretically) select for desirable alleles at all genes in the genome that influence a trait by using markers spread throughout the entire genome. This approach has several significant advantages over marker assisted selection. It explains a larger portion of the genetic variance than a single marker, provides an easy, familiar interface (EPDs) and the danger suggested by Spangler et al. (2007) whereby producers select only for a few markers and disregard EPDs is entirely avoided.

Feed Intake Data

Individual feed intake records were collected for average daily feed intake (AFI), residual feed intake (RFI) and average daily gain (ADG) on 862 commercial Angus steers born between 1998 and 2005 at either the Circle A Ranch (Iberia, Stockton and Huntsville, MO) and research farms participating in the MFA Inc. feeding trials (Thompson and Greenley, MO). Intake data were collected using Calan gates (Circle A Ranch steers) or GrowSafe feeding systems (MFA steers, fed at the University of Missouri) and live weights were taken three times (beginning, mid-test and final) during the course of the feeding trial. DNA was available for genotyping and analysis on 698 of the steers as no blood was collected during the first year of the trial. Cryopreserved semen units were obtained on 1,721 Angus AI sires born between 1956 and 2003 that were used in artificial insemination (AI) within the United States. These animals included the sires of the steer calves and their male ancestors. Complete 62-generation pedigrees were provided by the American Angus Association. Half-sib family sizes derived from sire

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information ranged from 1 to 81 progeny. Due to the fact that this is a population of commercial animals, dams were unregistered and available pedigree information was determined to be unreliable based on attempts to phase chromosomes and infer missing genotypes. As not all maternal grandsires had been genotyped, correct parentage could not be assigned. This population structure is suitable for testing methods of genomic selection on commercial populations.

Data Acquisition

Residual feed intake was calculated as the difference between observed and expected feed intake

( ), which was predicted from the regression of average daily feed intake (AFI) on ADG and

metabolic midweight (MMW: mid-weight0.75) as follows:

RFI = AFI –

= b0 + b1ADG + b2MW0.75

Weights were taken at three different times during the feeding trial (first day of the test, mid-test and end of test). These cattle were commercially owned and the specific ration composition is unknown, however all of the animals within a feeding group were fed the same ration. RFI was calculated individually for each feeding group and the mean R2 value for the regression models was 0.49.

Genotypes were acquired using the Illumina BovineSNP50 assay and were screened for Mendelian inheritance to verify the accuracy of the sire pedigrees. Genotypes for nine sires were found to be inconsistent with their paternal pedigree and an additional two animals were split embryos (identical twins), so these animals were removed from the dataset. Quality control was performed for genotypes so that the minor allele frequency (MAF) was ≥0.05 and call rate was >95%. Quality control constraints resulted in 41,028 SNPs being retained for analysis on 698 steers and 1,707 AI sires. Missing genotypes (0.58%) were imputed using fastPHASE (Scheet and Stephens, 2006) with Btau4.0 positions.

Additive Effects Analysis

A numerator relationship matrix (NRM) was generated using pedigree information on 862 Angus steers, their dams (where available) and 34,864 identified parental ancestors. Variance components, breeding values and residuals were estimated using an animal model in the multiple trait derivative free restricted maximum likelihood (MTDFREML; Boldman et al., 1995) program. Convergence for models using a NRM was assumed when the variance of the -2*log-likelihood was <1x10-12. The model fit feeding pen as a fixed effect (year and season of birth were nested within pen, so only one effect was included) and breeding values and residuals were assumed to be uncorrelated.

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Genomic Relationship Matrix

The method of calculating a genomic relationship matrix (GRM) used in this analysis was proposed by VanRaden (2008). It is a regression method that uses a fraction of the population with complete and accurate pedigree data (in the form of a NRM on those individuals) to calibrate the allele sharing to the expected value of the relationship matrix, in this case, E[G]=A. In this dataset, the NRM was generated on 1,707 Angus AI sires with complete and accurate pedigree data. Complete genotypes for 698 Angus steers and 1,707 AI sires were assembled into a 2,405 x 41,028 genotype matrix (M) with animals in rows and SNPs in columns. The elements in M are -1, 0 and 1 for AA, AB and BB genotypes, respectively. The GRM was calibrated by finding the regression of the upper triangular elements of MM’ on the corresponding elements of A for the 1,707 AI sires only. The estimated slope and intercept were used to calibrate the GRM for all 2,405 animals as:

Estimates of these parameters were 9,731.9±0.65 and 15,198±7.26 for g0 and g1, respectively. The mean molecular inbreeding coefficient over all animals was 0.079. We estimated variance components, fixed effects, breeding values and residuals using restricted maximum likelihood under an animal model where the NRM was replaced by the GRM. Convergence was assumed on models including the GRM when heritability estimates had converged from above and below to three significant figures.

Variance component estimates and heritabilities are shown in Table 1. Estimated breeding values (EBVs) and residuals were retained for further analysis. The estimated heritabilities (AFI 0.14; RFI 0.14; ADG 0.09) reported here were much lower than literature estimates (AFI 0.45; RFI 0.39; ADG 0.28; MacNeil et al., 1991; Arthur et al., 2001) and standard errors were fairly high, possibly due to sampling effects resulting from the small number of animals used in this study. The mean accuracy for all 2,405 animals was 0.32 for AFI and RFI, while the mean accuracy for ADG was 0.23. Mean accuracies for steers (NRM 0-0.46 vs GRM 0.36-0.43) and sires of steers (NRM 0-0.45 vs 0.37-0.44) indicate that similar accuracies were achieved when using either the NRM or GRM in this dataset, however the GRM accuracy was achieved given an approximately 20% loss in phenotypic data. This is most likely the result of the ability of the GRM to extract information from the genotypes related to the identity by descent information among the steers due to the relationships among their dams, which was missing in the NRM analysis. Accuracies for the GRM analyses were lower than those previously reported for genomic selection (Hayes et al., 2009; VanRaden et al., 2009; VanRaden, 2008; Schaeffer, 2006; Meuwissen et al., 2001), presumably due to the small number of animals with phenotypic observations and the lower heritabilities estimated in this study. Even though there are limitations within this dataset, it would be possible to combine datasets from other Angus research populations in an effort to increase the heritability estimates and obtain more accurate EBVs.

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Marker Panel Subsets

MATLAB (The Mathworks, Natick, MA) was used to test the number of markers necessary to precisely estimate the GRM using the approach outlined above. Subsets of n markers were randomly sampled with replacement (see Figure 1) from the full set of 41,028 markers. This approach ensures full representation of the entire genome within the marker subsets. For each of the 50 replicates (i=1,…,50) for each subset of n markers, a GRM (Gni) was estimated using the regression approach proposed by VanRaden (2008). Correlations were estimated between the upper triangular elements of Gni and G (the full GRM estimated from all available SNPs) for all 2,405 animals and between Gni and A for all 1,707 AI sires and averages were produced across replicates. Mean correlation of the NRM and the full GRM was approximately 0.86 when considering the 1,707 Angus AI sires. The mean correlation of the full GRM and the Gni exceeded 0.86 when between 1,000 and 2,500 markers were utilized for the calculation of the GRM. Minimal increases in the correlation coefficients were seen when greater than 10,000 SNPS were included in the calculation of the GRM, which is illustrated in Figure 1. Tables containing the complete correlation results for this analysis can be found in Rolf et al. (2010).

It is likely that smaller panels of 384 or 1,536 markers will be utilized in the beef industry until genotyping costs decrease to more affordable levels for most producers. Consequently, we performed 200 bootstrap replicates of 384 or 1,536 randomly sampled SNPs from the full set of 41,028 markers. Minimum, mean and maximum correlations between the bootstrap samples and the full GRM were 0.60, 0.65 and 0.68 and 0.85, 0.87 and 0.88 for 384 SNP and 1,536 SNP panels, respectively. The mean correlation using 1,536 randomly sampled markers met the mean correlation between the NRM and full GRM, indicating that in the absence of pedigree data in commercial herds, a GRM constructed from a panel of 1,536 SNPs may be a viable alternative to calculate EBVs for genetic selection. One potential caveat of this approach is that the panels used in the beef industry will not be randomly sampled SNPs, but rather panels of SNPs that are associated with various economically relevant traits. The efficacy of this approach will depend on the distribution of linkage disequilibrium among the markers and the minor allele frequencies.

Genome-Wide Association Analysis

Estimated breeding values (EBVs) and residuals obtained previously from mixed model estimation with a GRM were utilized for genome-wide association analysis (GWAS). Traits (RFI, AFI and ADG) were analyzed on 698 animals as either EBVs or phenotypes (EBV + residual). The same SNP set was utilized for the GWAS analysis and included 41,028 SNP with an average MAF of 0.28 and an average spacing of 65.73±68.45 kb for the 39,484 autosomal and 487 X chromosome loci. SNP that mapped to unassigned contigs (ChrUn; n=1,057) were also included in the analysis.

Multiple hypothesis testing is always an issue when performing GWAS studies, so a permutation analysis (Churchill and Doerge, 1994) was performed to obtain a genome-wide significance threshold of 0.05 to control the rate of type I error in this study. The permutation analysis consisted of 10,000 dependent variable permutations per trait accompanied by a GWAS on the permuted dependent variable to determine the largest F-statistics obtained by chance with the data provided.

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The GWAS was performed using custom code developed and implemented in MATLAB (MathWorks, Natik, MA) and was comprised of three steps. The first step consisted of individual one-way analysis of variance (ANOVA) tests for each SNP, trait and dataset. Analyses using EBVs were weighted by their corresponding accuracies and fitted additive effects only and analyses utilizing phenotypes fit either additive effects alone or additive and dominance effects simultaneously. Genotypes were coded 1, 0 or -1 for additive effects and 0, 1 or 0 for dominance values, corresponding to AA, AB and BB genotypes, respectively. The second step included all SNPs that met or exceeded the pre-determined genome-wide significance threshold previously described. These SNPs were included in a forward-selection analysis which was performed on a chromosome-by chromosome basis. The SNP with the highest F-statistic was sequentially added to the ANOVA model for each chromosome until no more SNPs could be added that met or exceeded the significance threshold. All of the SNPs selected in the chromosome-by-chromosome analysis were then combined into a final model to estimate the amount of variance explained by the selected SNPs in the third step.

GWAS utilizing phenotypes and modeling only additive effects yielded either few or zero SNPs in the final analysis models for all traits. Because of this, modeling both additive and nonadditive effects explained a larger portion of the phenotypic variance (AFI, 49.802%; RFI, 25.494%; ADG, 27.093%). As a result, any further discussion of results will pertain only to EBV analyses or analyses of phenotypes including both additive and dominance effects. Analysis of steer EBVs yielded a larger number of SNPs in the final model for RFI and ADG. The markers included into the final analysis for all three traits explained a fairly large portion of the additive genetic variance; however, because of the reduced power of this dataset, the amount of phenotypic variance explained was less than optimal. To facilitate comparison of results between phenotype and EBV analyses, Figure 2 shows a side-by-side comparison of the regions of the genome detected for AFI from the analysis of both steer EBVs (presented in panel A) and phenotypes (presented in panel B).

The number of SNPs included in the final analyses and the concordance between different traits in the analysis can be found in Tables 2 (EBV) and 3 (phenotypes). SNPs were considered concordant if they fell within the range of ±0.5 Mb of the position of the selected SNP to better account for the linkage disequilibrium in cattle populations (McKay et al., 2007) as well as selection of SNPs that are pleiotropic or closely linked and could show correlated responses with one another when used in selection. Of particular interest is the percentage of SNPs that were included in the final model for one trait, but also above the significance threshold (forward selected) for another trait. The concordance of SNPs between traits was fairly consistent with the magnitude of expected genetic correlations between traits. Interestingly, there was a slight concordance between AFI and RFI, evidenced by the fact that in the EBV analysis approximately 13% of SNPs included in the RFI model were forward selected for ADG and 16% of SNPs in the ADG final model were forward selected in the RFI analysis. Similar results were also observed in the phenotypic analysis, but of a smaller magnitude. This suggests that despite the phenotypic independence between RFI and ADG, genetic independence is not guaranteed between these two traits, as suggested by Kennedy et al. (1993). A high concordance was observed between AFI and ADG in the EBV analysis (30% of SNPs in the AFI model and 20% of SNPs in the ADG model), which was expected based on the moderate genetic correlation between these two traits. It appears to be possible to identify QTL for feed intake which are independent of ADG in this dataset. Selection on only these QTL would theoretically

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allow genetic improvement for feed efficiency without a problematic correlated response in the growth rate of the growing animal or in mature size in the cowherd.

Pathway Analysis

Even with appropriate consideration for multiple hypothesis testing, it is possible to select spurious SNPs in GWAS analyses. Many previous studies of feed efficiency in beef cattle have used linkage analysis resulting in large confidence intervals, or the SNP positions were not reported. Due to the lack of ability to accurately compare results from previous studies with those achieved in this study, a pathway analysis was conducted. The purpose of the pathway analysis is to have a form of independent validation of the GWAS results. If regions of the genome are identified that perform functions related to growth and metabolism, then it is likely that a real association has been discovered. Due to the larger number of annotations of human genes, we mapped human annotations to the bovine genome using the UCSC genome browser. Because of the limited range of LD in beef cattle (McKay et al., 2007), we identified regions of interest surrounding the SNPs using a 1 Mb window (SNP position±0.5 Mb). Genes within these regions were identified in the Database for Annotation, Visualization and Integrated Discovery (DAVID; Huang et al., 2009, Dennis et al., 2003) and queried against the KEGG Pathway Database (Kanehisa et al., 2010, 2006; Kanehisa and Goto, 2000). Results from the KEGG database were summarized into global pathways and their corresponding sub-categories using the KEGG Atlas. A summary of the KEGG pathway analysis findings is provided in Table 4. The analysis was most successful utilizing those traits with the highest heritabilities (AFI and RFI), suggesting that in well-powered studies with large numbers of animals and large SNP lists, pathway analysis is very useful. Many of the regions detected in the GWAS appear to harbor genes which are involved in growth or metabolic functions (69% AFI and 85% RFI as a percentage of the total number of identified pathways).

Model-Predicted Feed Intakes

The Cornell Value Discovery System (CVDS) was used to predict the dry matter required (pDMR) for the 862 (698 with DNA) Angus steers used in this study using a growth and maintenance model. No ration information was available, so animals were all assumed to have eaten a diet with an equivalent composition and nutrient density. Pen feed intakes were obtained by pooling the average individual feed intakes of each animal within the pen. Sex, growth promotant implant status, date on test, carcass data (ribeye muscle area, yield grade, hot carcass weight, fat thickness and marbling score) and weight data were input into CVDS and were used to specify growth and maintenance model parameters and account for composition of gain in the calculation of pDMR. The phenotypic correlation between pDMR and AFI was 0.78 (p<0.0001) in this dataset, which is consistent with that reported by Williams et al. (0.784; 2006).

Breeding values and residuals were estimated for pDMR on all animals using the previously outlined procedure. Variance components for pDMR were 0.1648 and 4.0933 for additive and phenotypic variance, respectively. Heritability was 0.04, which was lower than literature estimates, but consistent with the rest of the traits.

The largest concordance between SNPs in the final model for pDMR and SNPs forward selected in the other analyzed traits was found between pDMR and ADG (EBV 73%, phenotypes

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77%). This result was expected given the dependence of pDMR on growth data. SNPs in the final model for pDMR also showed concordance with SNPs forward selected for AFI (EBV 21%, phenotypes 22%) and RFI (EBV 15%, phenotypes 7%). These results indicate that while there may be significant overlap between pDMR and ADG, the moderate concordance of these predicted measurements with AFI and RFI merit further exploration of this trait as an indicator trait in genetic selection procedures.

Conclusions

These data on commercial Angus steers were useful for implementing a method of genomic selection for feed efficiency, not only in the 698 steers with observations, but also for generation of EBVs with moderate accuracies on 1,707 of the most widely used Angus AI sires in the United States. We suggest that studies utilizing GRM for producing breeding value estimates utilize at least 1,500 SNPs and preferably, 10,000 SNPs per animal. Inclusion of additional SNPs into the calculation of the GRM yielded only marginal improvement over a GRM calculated with 10K SNPs.

A large number of SNPs have been identified in these analyses which could be included in commercial marker panels for use in Angus cattle for selection on feed efficiency traits. These models account for large amounts of genetic (AFI 54%, RFI 62%, ADG 54% and pDMR 56%) or phenotypic (AFI 49%, RFI 25%, ADG 27% and pDMR 30%) variation in these populations. The estimates of the variance explained and the SNP effects are biased due to population sampling, as the SNPs most strongly associated in this population may not be representative of the Angus breed as a whole. A pathway analysis was the first step towards validation of these SNP associations; however these studies should be repeated and compared using an independent population of animals to produce an unbiased estimate of the amount of genetic variation explained by these SNPs in feed efficiency traits.

To the authors’ knowledge, this is the first work to examine the use of a predicted feed efficiency phenotype in a genome wide association analysis to compare model predictions to observed phenotypic records in beef cattle. Additional comparisons of pDMR with results using actual feed intake data, gain and RFI in studies with larger numbers of animals and larger heritabilities will be essential to further explore the use of these data for genetic evaluation and selection decisions in commercial beef cattle populations.

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Table 1:  Descriptive statistics and estimated variance components for NRM and GRM analyses of three feed efficiency traits (adapted from Rolf et al., 2010).  

Traita N Mean Min Max Var σ σ h2

AFI 862 11.0326 6.0599 15.2116 3.0323 0.1436 0.7786 0.16

RFI 862 0.0026 -3.3386 4.9952 0.7626 0.1147 0.4364 0.21

ADG 862 1.5363 0.0231 2.3443 0.1077 0.000002 0.552 0.00

AFI 698b 10.8943 6.0599 15.2116 3.1608 0.1404 0.8680 0.14

RFI 698b -0.0201 -3.3412 4.9952 0.8255 0.0849 0.5286 0.14

ADG 698b 1.5175 0.0231 2.2941 0.1105 0.0053 0.0528 0.09

aAverage daily feed intake, AFI; residual feed intake, RFI; and average daily gain, ADG; all measured in units of kg/d. bDNA samples were available on only 698 of the 862 phenotyped steers. Variance components for these three analyses were estimated using the GRM.

    Table 2:  Number of SNPs included in the final models or above the significance threshold (Forward Selected) for GWAS analysis of feed efficiency EBVs.  Numbers to the right are the number and percentage of SNPs in the final model for the trait in the row that were above the significance threshold for the trait in the column.  

Forward Selected

No. in Model

No. Fwd Selected AFI RFI ADG

AFI 53 178 - 37 a 69.81%

16 a 30.19%

RFI 66 281 35 a 53.03% - 9 a

13.64%

ADG 68 274 14a 20.59%

11a 16.18% -

 

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 Table 3:  Number of SNPs included in the final models or above the significance threshold (Forward Selected) for GWAS analysis of feed efficiency phenotypes.  Numbers to the right are the number and percentage of SNPs in the final model for the trait in the row that were above the significance threshold for the trait in the column.  

Forward Selected

No. in Model

No. Fwd

Selected AFI RFI ADG

AFI 65 83 - 11a 16.92%

3a 4.62%

RFI 18 21 10a 55.56% - 1a

5.56%

ADG 24 33 3a 12.50%

3a 12.50% -

 

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 Table 4:  Results from DAVID and KEGG Atlas for pathway analyses. 

Number of Pathways/No Genesa Global Pathway 

Sub Category  AFI EBVb 

AFI Phenb 

RFI EBVb 

RFI Phenb 

ADG EBVb 

ADG Phenb 

pDMR EBVb 

pDMR Phenb 

Carbohydrate Metabolism*          1/1    2/2   

Energy Metabolism*              1/1   

Lipid Metabolism*    3/9  4/4  2/8         

Nucleotide Metabolism*  2/2               

Amino Acid Metabolism*  1/1    4/5           

Metabolism of Other Amino Acids* 

    2/2           

Glycan Biosynthesis and Metabolism* 

    3/3    2/4    4/4   

Metabolism of Cofactors and Vitamins* 

  1/1             

Biosynthesis of Secondary Metabolites* 

  2/4  2/2  2/4         

Metabolism 

Xenobiotics Biodegradation and Metabolism* 

  1/4  3/3  1/4         

Translation  1/1               Genetic Information Processing 

Folding, Sorting and Degradation* 

2/3               

Signal Transduction*  1/2  6/12  2/4  4/5  3/3    4/4   Environmental Information Processing 

Signaling Molecules and Interaction* 

  1/5      1/3    1/3   

Transport and Catabolism*      1/1           

Cell Motility      1/2    1/1       

Cell Growth and Death*  1/1  1/4  1/1  1/1         

Cell Communication    3/5  4/7  1/1      1/8   

Endocrine System*    4/6  4/5  2/2      2/2   

Immune System  1/1  5/9  3/3  1/1    1/1    1/1 

Nervous System    1/2  2/3    1/1    1/1   

Cellular Processes 

Development*    1/1    1/1         

Cancers*  1/1  5/8  5/5  4/4         

Neurodegenerative Diseases 

  3/5    1/1  2/3       

Metabolic Disorders*    2/6  1/1           

Human Diseases 

Infectious Diseases      1/1        2/2   

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Figure 2:  Concordance between EBV and phenotype analyses for AFI. 

Figure  1:    Correlation  between  the  Gni  estimated  from  bootstrap  samples  of  reduced marker  panels versus  the  full  GRM  calculated  with  all  markers  available  (n=41,028).    The  red  line  indicates  the correlation  between  relationship  coefficients  between  full  and  reduced  marker  sets  for  all  2,405 animals.    The  blue  line  indicates  the  correlation  between  relationship  coefficients  between  reduced marker sets and the NRM for all 1,707 AI sires (adapted from Rolf et al., 2010).    

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Literature Cited

Archer, J. A., P. F. Arthur, R. M. Herd, P. F. Parnell and W.S. Pitchford. 1997. Optimum postweaning test for measurement of growth rate, feed intake, and feed efficiency in British breed cattle. J. Anim. Sci. 75:2024-32.

Archer, J.A., E. C. Richardson, R. M. Herd and P. F. Arthur. 1999. Potential for selection to improve efficiency of feed use in beef cattle: A review. Aust. J. Exp. Agr. 50:147-61.

Arthur, P.F., J. A. Archer, D. J. Johnston, R. M. Herd, E. C. Richardson and P. F. Parnell. 2001. Genetic and phenotypic variance and covariance components for feed intake, feed efficiency and other postweaning traits in Angus cattle. J. Anim. Sci. 79:2805-11.

Boldman, K. G., L. A. Kriese, L. D. Van Vleck, C. P. Van Tassell and S. D. Kachman. 1995. A manual for use of MTDFREML. A set of programs to obtain estimates of variance and covariance. USDA, Agriculture Research Service, Clay Center, NE.

Churchill, G.A. and R. W. Doerge. 1994. Empirical threshold values for quantitative trait mapping. Genetics 138:963-71.

Crews, D.H., Jr. 2005. Genetics of efficient feed utilization and national cattle evaluation: A review. Genet. Mol. Res. 4:152-65.

Dennis, G., Jr., B. T. Sherman, D. A. Hosack, J. Yang, W. Gao, H. C. Lane and R. A. Lempicki. 2003. DAVID: Database for Annotation, Visualization and Integrated Discovery. Genome Biol. 4(5):P3.

Guiroy, P.J., D. G. Fox, L. O. Tedeschi, M. J. Baker and M. D. Cravey. 2001. Predicting individual feed requirements of cattle fed in groups. J. Anim. Sci. 79:1983-95.

Hayes, B.J., P. J. Bowman, A. J. Chamberlain and M. E. Goddard. 2009. Invited Review: Genomic selection in dairy cattle: progress and challenges. J. Dairy Sci. 92:433-43.

Henderson, C. R. 1975. Best linear unbiased estimation and prediction under a selection model. Biometrics 31:423-47.

Kanehisa, M., S. Goto, M. Furumichi, M. Tanabe and M. Hirakawa. 2010. KEGG for representation and analysis of molecular networks involving diseases and drugs. Nucl. Acids Res. 38:D355-D360.

Kanehisa, M., S. Goto, M. Hattori, K. F. Aoki-Kinoshita, M. Itoh, S. Kawashima, T. Katayama, M. Araki and M. Hirakawa. 2006. From genomics to chemical genomics: New developments in KEGG. Nucl. Acids Res. 34:D354-D357.

115

Kanehisa, M., and S. Goto. 2000. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucl. Acids Res. 28:27-30.

Huang, D. W., B. T. Sherman and R. A. Lempicki. 2009. Systematic and integrative analysis of large gene lists using DAVID Bioinformatics Resources. Nat. Protoc. 4(1):44-57.

Kennedy, B. W., J. H. van der Werf and T. H. Meuwissen. 1993. Genetic and statistical properties of residual feed intake. J. Anim. Sci. 71:3239-50.

Koch, R. M., L. A. Swiger, D. Chambers and K. E. Gregory. 1963. Efficiency of feed use in beef cattle. J. Anim. Sci. 22:486-94.

MacNeil, M. D., D. R. Bailey, J. J. Urick, R. P. Gilbert and W. L. Reynolds. 1991. Heritabilities and genetic correlations for postweaning growth and feed intake of beef bulls and steers. J. Anim. Sci. 69:3183-9.

McKay, S. D., R. D. Schnabel, B. Murdoch, L. K. Matukumalli, J. Aerts, W. Coppieters, D. H. Crews, Jr., E. D. Neto, C. A. Gill, C. Gao, H. Mannen, P. Stothard, Z. Wang, C. P. Van Tassell, J. L. Williams, J. F. Taylor and S. S. Moore. 2007. Whole genome linkage disequilibrium maps in cattle. BMC Genet. 8:74-85.

Meuwissen, T. H., B. J. Hayes and M. E. Goddard. 2001. Prediction of total genetic value using genome-wide dense marker maps. Genetics 157:1819-29.

Okine, E. K., J. Basarab, L. A. Goonewardene and P. Mir. 2004. Residual feed intake and feed efficiency: Difference and implications. In: Florida Ruminant Nutrition Symposium, pp. 27-38.

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.

Schaeffer, L.R. 2006. Strategy for applying genome-wide selection in dairy cattle. J. Anim. Brdg. Genet. 123:218-23.

Scheet P and M. Stephens. 2006. A Fast and Flexible Statistical Model for Large-Scale Population Genotype Data: Applications to Inferring Missing Genotypes and Haplotypic Phase. Am. J. Hum. Genet. 78:629-644.

Spangler, M.L., J. K. Bertrand and R. Rekaya. 2007. Combining genetic test information and correlated phenotypic records for breeding value estimation. J. Anim. Sci. 85:641-649.

VanRaden, P. M. 2008. Efficient Methods to Compute Genomic Predictions. J. Dairy Sci. 91:4414-23.

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VanRaden, P. M., C. P. Van Tassell, G. R. Wiggans, T. S. Sonstegard, R. D. Schnabel, J. F. Taylor and F. S. Schenkel. 2009. Invited Review: Reliability of genomic predictions for North American Holstein bulls. J. Dairy Sci. 92:16-24.

Williams, C.B., G. L. Bennett, T. G. Jenkins, L. V. Cundiff and C. L. Ferrell. 2006. Using simulation models to predict feed intake: Phenotypic and genetic relationships between observed and predicted values in cattle. J. Anim. Sci. 84:1310-6.

Williams, C.B. and T. G. Jenkins. 2003a. A dynamic model of metabolizable energy utilization in growing and mature cattle. I. metabolizable energy utilization for maintenance and support metabolism. J. Anim. Sci. 81:1371-81. 103

Williams, C.B. and T. G. Jenkins. 2003b. A dynamic model of metabolizable energy utilization in growing and mature cattle. II. metabolizable energy utilization for gain. J. Anim. Sci. 81:1382-9.

Willham, R. L. 1993. Ideas into action: A celebration of the first 25 years of the beef improvement federation. University Printing Services, Oklahoma State University, Stillwater, OK.

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FRANK H. BAKER

Born: May 2, 1923, Stroud, Oklahoma

Died: February 15, 1993, Little Rock, Arkansas

Dr. Frank Baker is widely recognized as the “Founding Father” of the Beef Improvement Federation (BIF). Frank played a key leadership role in helping establish BIF in 1968, while he was Animal Science Department Chairman at the University of Nebraska, Lincoln, 1966-74. The Frank Baker Memorial Scholarship Award Essay competition for graduate students provides an opportunity to recognize outstanding student research and competitive writing in honor of Dr. Baker. Frank H. Baker was born May 2, 1923, at Stroud, Oklahoma, and was reared on a farm in northeastern Oklahoma. He received his B.S. degree, with distinction, in Animal Husbandry from Oklahoma State University (OSU) in 1947, after 2½ years of military service with the US Army as a paratrooper in Europe, for which he was awarded the Purple Heart. After serving three years as county extension agent and

veterans agriculture instructor in Oklahoma, Frank returned to OSU to complete his M.S. and Ph.D. degrees in Animal Nutrition.

Frank’s professional positions included teaching and research positions at Kansas State University, 1953-55; the University of Kentucky, 1955-58; Extension Livestock Specialist at OSU, 1958-62; and Extension Animal Science Programs Coordinator, USDA, Washington, D.C., 1962-66. Frank left Nebraska in 1974 to become Dean of Agriculture at Oklahoma State University, a position he held until 1979, when he began service as International Agricultural Programs Officer and Professor of Animal Science at OSU. Frank joined Winrock International, Morrilton, Arkansas, in 1981, as Senior Program Officer and Director of the International Stockmen’s School, where he remained until his retirement.

Frank served on advisory committees for Angus, Hereford, and Polled Hereford beef breed associations, the National Cattlemen’s Association, Performance Registry International, and the Livestock Conservation, Inc. His service and leadership to the American Society of Animal Science (ASAS) included many committees, election as vice-president and as president, 1973-74. Frank was elected an ASAS Honorary Fellow

Frank H. Baker photograph of portrait in Saddle and Sirloin Club

Gallery – Everett Raymond Kinstler, Artist

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in 1977, he was a Fellow of the American Association for the Advancement of Science, and served the Council for Agricultural Science and Technology (CAST) as president in 1979.

Frank Baker received many awards in his career, crowned by having his portrait hung in the Saddle and Sirloin Club Gallery at the International Livestock Exposition, Louisville, Kentucky, on November 16, 1986. His ability as a statesman and diplomat for the livestock industry was to use his vision to call forth the collective best from all those around him. Frank was a “mover and shaker” who was skillful in turning “Ideas into Action” in the beef cattle performance movement. His unique leadership abilities earned him great respect among breeders and scientists alike. Frank died February 15, 1993, in Little Rock, Arkansas.

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BIF Past Recipients of the Frank Baker Memorial Scholarship Award Kelly W. Bruns Michigan State University .............................................1994 William Herring University of Georgia ....................................................1994 D. H. “Denny” Crews, Jr. Louisiana State University ............................................1995 Dan Moser University of Georgia ....................................................1995 D. H. “Denny” Crews, Jr. Louisiana State University ............................................1996 Lowell S. Gould University of Nebraska..................................................1996 Rebecca K. Splan University of Nebraska..................................................1997 Robert Williams University of Georgia ....................................................1997 Patrick Doyle Colorado State University .............................................1998 Shannon M. Schafer Cornell University .........................................................1998 Janice M. Rumph University of Nebraska..................................................1999 Bruce C. Shanks Montana State University ..............................................1999 Paul L. Charteris Colorado State University .............................................2000 Katherine A. Donoghue University of Georgia ....................................................2000 Khathutshelo A. Nephawe University of Nebraska..................................................2001 Janice M. Rumph University of Nebraska..................................................2001 Katherina A. Donoghue University of Georgia ....................................................2002 Khathutshelo A. Nephawe University of Nebraska..................................................2002 Fernando F. Cardoso Michigan State University .............................................2003 Charles Andrew McPeake Michigan State University .............................................2003 Reynold Bergen University of Guelph .....................................................2004 Angel Rios-Utrera University of Nebraska..................................................2004 Matthew A. Cleveland Colorado State University .............................................2005 David P. Kirschten Cornell University .........................................................2005 Amy Kelley Montana State University ..............................................2006 Jamie L. Williams Colorado State University .............................................2006 Gabriela C. Márquez Betz Colorado State University .............................................2007 Yuri Regis Montanholi University of Guelph .....................................................2007 Devori W. Beckman Iowa State University ....................................................2008 Kasey L. DeAtley New Mexico State University .......................................2008 Scott Speidel Colorado State University .............................................2009 Lance Leachman Virginia Polytechnic Institute and State University ......2009

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INCREASING BEEF CATTLE PRODUCTION EFFICIENCY THROUGH SELECTION IN FEED UTILIZATION

Kent Gray

North Carolina State University

Introduction

Beef production is a business and like all businesses it is the relationship between the cost of inputs and value of outputs which determine profitability. Decreasing the cost of inputs while maintaining the value of outputs can increase profitability. Approximately 60 – 65% of overall production costs can be attributed to maintaining the cow herd, and about 80% of genetic change within a beef herd can be attributed to bull selection. Therefore, if one could identify bulls which will produce daughters that optimize efficiency of feed utilization then profitability would be expected to increase. Recent studies performed by Arthur et al. (2001) showed that genetic variation exists in feed efficiency of beef cattle signifying that selection for feed efficiency can be successful.

Different methods have been proposed to measure feed efficiency among livestock species. The two most common methods to measure feed utilization include gross efficiency or its inverse FCR and residual feed intake (RFI). Residual feed intake (RFI) was first proposed by Koch et al (1963) which is defined as the difference between predicted DMI and observed DMI. Predicting DMI can be accomplished by using NRC prediction equations based on predicted energy requirements given body weight, gain and energy available in the diet (Fan et al., 1995). Another method of predicting DMI uses a multivariable regression approach adjusting for factors associated with energy for maintenance and energy for growth. The most common adjustment factors are ADG, weight and body composition in growing cattle.

All measures of efficiency currently require collection of individual DMI. This is

labor intensive and expensive. Our objective is to identify both genetic and phenotypic relationships that may exist among the three main measures of feed utilization and phenotypic correlations of feed utilization that may exist among other economic traits. This can provide evidence for an indirect method for selection of feed efficiency or identify how selection may affect other traits.

Literature Review Measures of Energy

All feeds have some amount of energy that contributes to the maintenance requirement for energy. In order to determine actual value of a ration, energy lost due to excretion of feces, urine, heat, and gases should be subtracted from the total energy available. (Maynard and Loosli, 1969). Digestible energy (DE) is a measure of energy in which fecal

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energy is subtracted from the net energy. Ruminants that are fed a high forage diet will lose most of their energy through defecation (Maynard and Loosli, 1969). This makes DE highly useful when comparing feeds due to its ease to measure. Digestibility is available for most feed commonly fed to cattle and sheep. Total Digestible Nutrients (TDN) is calculated by multiplying a constant to DE and is used in a similar manner as DE (Maynard and Loosli, 1969). The drawback in using this measure of energy is that it ignores some other important energy losses that occur during digestion and metabolism of food (Maynard and Loosli, 1969).

Metabolizable energy (ME) is defined as energy provided by a feedstuff after fecal energy (FE), urinary energy (UE), and gaseous energy (GE) is subtracted from gross energy available (Maynard and Loosli, 1969). This measurement of energy provides more accurate information on the amount of energy that is available for the animal use but is hard to measure (NRC, 1996). It is difficult and expensive to collect gaseous energy that is lost (Maynard and Loosli, 1969).

Net energy (NE) represents the fraction of gross energy that is actually utilized. This

energy is defined as the sum of FE, UE, GE, and heat loss subtracted from gross energy. Net energy can be broken up into two portions, NE for gain (NEg) and NE for maintenance (NEm) (NRC, 1996). Some processes comprised in NEm include body temperature regulation, essential metabolic processes and physical activity (Maynard and Loosli, 1969). Mathematical models have been developed to estimate these energy components so experiments can be accomplished in a more industry-like environment (NRC, 1996).

Variations in energy requirements for maintenance exist among sex, age, season,

temperature, physiological state, previous nutrition and genotype (NRC, 1996). Recognizing which sires produce progeny with a superior genotype, requiring less NEm, per unit of mature body weight will decrease production costs through increased efficiency.

The methods of measuring feed that have been discussed all assume that animals are

in some type of penned off area. This is highly unrealistic when determining feed intake on cows within the breeding herd. These cows are most likely on a high forage grazing diet. Other methods have been developed for this type of feeding regimen. Some common methods for measuring feed intake in grazing animals include the pulse-dose marker method, the animal performance method, and the herbage disappearance method (Macoon et al., 2003).

Feed intake can be estimated by calculating total organic matter intake based on fecal

output and diet digestibility. Fecal output is estimated by using a pulse-dose marker such as chromium-mordanted fiber. By collecting fecal grabs at different times after the initial dose, marker concentrations can be analyzed and fit onto a recommended model (Macoon et al., 2003). An issue with using this technique for measuring feed intake is that it is a measurement that explains intake for a short period of time that must be extrapolated over a longer grazing period. It is unlikely that an animal will consistently eat in the same manner over the whole grazing period. Thus, this method of calculating feed intake may have substantial error (Macoon et al., 2003).

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Herbage disappearance method of calculating feed intake is found by determining the

difference in pre-grazing and post-grazing herbage mass (Macoon et al., 2003). This method seems to be the most logical but can be very labor intensive. One must measure the height of the grass and estimate how much grass was consumed. By determining nutritional content of the forage consumed net energy of the forage the animal has access to can be established. One advantage this method has is that it can, account for different pastures and pasture conditions. It is labor intensive, however and chance for measurement error increases significantly. Cattle are also herd animals and this method requires that animals be singled off, which may cause undo stress affecting feeding behavior. It is difficult to be confident that the animal would consume the same amount as normal.

The final method for determining feed intake is by estimating the energy

requirements of the animal, called the animal performance method. By using prediction equations found in the NRC (2000) requirements, maintenance can be calculated for lactation, body weight changes, walking and grazing activity (Macoon et al., 2003). Parity is also taken into account (Macoon et al., 2003). The main drawback of using this method is the assumption that all animals are similar in physiology and use the same amount of energy for the same activities. This is highly unlikely, it is probable that there are significant physiological differences among animals and this is the very issue that is in question.

When comparing the three methods used to predict feed intake in grazing animals we

find that animal performance and herbage disappearance methods are correlated insinuating that they are measuring the same trait (Macoon et al., 2003). The pulse-marker method is not correlated with the other two methods, and thus, that method may be measuring something else (Macoon et al., 2003).

Each method of measuring feed intake, whether it is on growing animals or mature

grazing animals, has its advantages and disadvantages which should be considered when using each system. The method used in a project is mostly influenced by resources available. By knowing the method used, results collected can more accurately be interpreted. Test duration for post weaning animals

Methods to measure and calculate feed efficiency take time. The amount of time

needed when measuring feed efficiency in post weaning animals using the Growsafe® system for ADG, DMI, FCR, and RFI are 63, 35, 42 and 63 days respectfully (Wang et al., 2006) when weight is measured weekly. Recommended test durations given in other sources for ADG are 112d (Brown et al., 1991), 84d (Liu and Makarechian, 1993a, b; Swiger and Hazel, 1961), and 70d (Archer et al., 1997). These recommended test durations are probably longer because weight was not measured weekly and the mode of collecting feed intake data was different. One must also remember that this time period does not take into account the time it takes for the animals to acclimate to the equipment used. A period of 84d should be sufficient for the acclimation and test period combined.

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Feed Efficiency as a Trait Feed Conversion Ratio or Gross Efficiency The most common method described in the literature when calculating feed efficiency is gross efficiency or its inverse feed conversion ratio (FCR). This efficiency measurement can be measured either in a certain time interval, weight interval, maturity interval, or subcutaneous fat interval. It is then calculated by either dividing the interval of interest by amount of feed consumed (gross efficiency) or by inverting the ratio (FCR).

Feed conversion ratio is found to be highly correlated with growth rate estimated

values range from -.61 to -.95 (Arthur et al., 2001; Klosterman, 1972; Nkrumah et al., 2004). Improving herd efficiency through decreasing FCR of growing cattle can actually be detrimental to whole herd productivity. It has been shown that FCR is a function of rate of maturation (Salmon et al., 1990). This type of selection will increase mature size of the breeding animals which in turn increases maintenance feed costs. This does not mean that realized economic efficiencies of the slaughter generation cannot supersede those costs therefore making the herd as a whole more economically efficient; however, it is unlikely that this would be the case. It has been shown that when mature size is increased that reproductive rates decrease and age of puberty occurs at a later date. This all makes an increase in mature body size which contributes little to the feed efficiency of a herd. One must keep in mind that feed efficiency of the herd encompasses all aspects of a beef production system (Dickerson, 1978; Fitzhugh, 1978). Feed Conversion Ratio or Gross Efficiency The most common method described in the literature when calculating feed efficiency is gross efficiency or its inverse feed conversion ratio (FCR). This efficiency measurement can be measured either in a certain time interval, weight interval, maturity interval, or subcutaneous fat interval. It is then calculated by either dividing the interval of interest by amount of feed consumed (gross efficiency) or by inverting the ratio (FCR).

Feed conversion ratio is found to be highly correlated with growth rate estimated values range from -.61 to -.95 (Arthur et al., 2001; Klosterman, 1972; Nkrumah et al., 2004). Improving herd efficiency through decreasing FCR of growing cattle can actually be detrimental to whole herd productivity. It has been shown that FCR is a function of rate of maturation (Salmon et al., 1990). This type of selection will increase mature size of the breeding animals which in turn increases maintenance feed costs. This does not mean that realized economic efficiencies of the slaughter generation cannot supersede those costs therefore making the herd as a whole more economically efficient; however, it is unlikely that this would be the case. It has been shown that when mature size is increased that reproductive rates decrease and age of puberty occurs at a later date. This all makes an increase in mature body size which contributes little to the feed efficiency of a herd. One must keep in mind that feed efficiency of the herd encompasses all aspects of a beef production system (Dickerson, 1978; Fitzhugh, 1978).

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Feed conversion ratio (FCR) as a measurement of efficiency is beneficial for operations that are centered on growing cattle such as feedlot production systems, or breeds that are used primarily for producing terminal sires. These types of cattle operations mainly acquire revenue through increased weight at slaughter. Improvement of FCR when used as an efficiency measurement increases size while intake remains constant (Nkrumah et al., 2004) which will increase profits. Maintenance Efficiency Maintenance efficiency is another method in which feed efficiency can be calculated. This method is defined as the feed required for an animal to gain no weight yet have enough energy to allow for all body components to receive the nourishment it needs to function properly (Ferrell and Jenkins, 1985). An alternative definition that is sometimes used is ratio of body weight to feed intake when there is no change in body weight (Ferrell and Jenkins, 1985). It is estimated that 70 - 75% of the total energy that is required in beef production is allocated to maintaining the herd (Ferrell and Jenkins, 1984). Of that maintenance energy the cow herd uses approximately 65 – 75% of it (Ferrell and Jenkins, 1985; Gregory, 1972; Klosterman, 1972; Montano-Bermudez et al., 1990). That translates into about 50% of the total energy used in an average beef operation as being used by the cow for maintenance alone. Although this measurement may seem like the obvious method to determine maintenance costs it cannot be measured on growing cattle very easily. Proxy measurements for maintenance can be calculated in growing animals by measuring fasting heat production. The problem with this measurement is that it can be affected by growth that occurred before the measurement is collected therefore measuring more than just maintenance (Koong and Ferrell, 1990). To accurately measure maintenance requirements for a particular animal can be very expensive and lengthy. In cattle it can take up to two years of maintaining a cow at a constant weight to reach any substantial conclusions on her maintenance energy requirements (Taylor et al., 1981). Partial Efficiency of growth

Partial efficiency of growth is the ratio of weight gain to feed after expected maintenance requirements have been subtracted. Predicted maintenance requirements are calculated, in this method of determining feed efficiency, by using feeding tables based on average body weight or metabolic studies of the energy balance of the animal. The assumption is made that there is no variation in maintenance requirements for individual animals. This is highly unlikely (Veerkamp and Emmans, 1995). Residual Feed Intake – Regression method (RFI(reg))

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Using residual feed intake (RFI) as a method of calculating feed efficiency was first proposed by Koch et al. (1963). It is calculated by finding the difference between actual and predicted feed consumption. The prediction for feed intake should include some body weight measurement and a production trait of choice. Body weight should be adjusted to account for maintenance energy costs and the production trait should be adjusted so production energy costs are accounted for as well (Koch et al., 1963). This partitions feed intake into two portions. The first portion consisting of both maintenance and production energy costs and the remaining portion commonly referred to as residual. By estimating how much the animal deviates from its predicted intake one can estimate its efficiency (RFI). Koch et al. (1963) calculated efficiency using three different regression methods. He first calculated a FCR where gain was adjusted for weight. This method was the most common method used among producers at the time. A second method calculated feed intake (F) adjusting for gain (G) and weight (BW). The third method calculated gain adjusting for feed intake (F) and weight (BW). It was concluded that the best method to calculate efficiency was to calculate feed intake adjusting for weight and gain and was later given the name of “RFI”.

(Koch et al., 1963) It was correlated with the adjusted FCR by over .9 and was considered to be more representative of the efficiency of the animal (Koch et al., 1963).

Residual feed intake is calculated such that it is phenotypically independent of the

trait used as the response variable and the animal body weight. It is believed by some (Korver, 1988) that it can quantify and represent the variation that can be found in basic metabolic processes which determine an animal’s efficiency. Kennedy et al. (1993) showed that although RFI may be phenotypically independent of the traits that are adjusted it isn’t necessarily genetically independent. He proposes that one must adjust on a genotypic level for RFI to truly be independent. Residual Feed Intake – Nutrition prediction of energy requirements method (RFI(NRC)) Fan et al. (1995) developed another method to calculate RFI. It was proposed that by using a three step procedure that partitioned energy intake into energy for maintenance and various production functions based on requirements that residual feed consumption and feed efficiency can be calculated. The first step in this procedure used prediction equations from the NRC (1984). Fan et al. (1995) was able to calculate net energy required for maintenance (NERm) in Mcal of NE/day by using the live weight (W, kg) of the animal.

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Net energy required for medium sized bulls for live weight gain (NERg) in Mcal of NE/day was calculated using both live weight (W, kg) and live weight gain (LWG, kg/day).

(Fan et al., 1995) The second step simply calculated net energy values for maintenance (NEm) and growth (NEg) using average metabolizable energy values (AME) for each diet. Daily ME requirements for maintenance (MERm) and growth (MERg) in Mcal of ME/day were then calculated.

By summing daily ME requirements for both maintenance and growth the total ME requirements (MER) were calculated.

In the third step feed efficiency measurements were calculated. Gross feed efficiency (FE) was measured as a ratio of ADG to metabolizable energy intake (MEI, Mcal of ME/day).

Net feed efficiency (NFE) was measured as a ratio of ADG:MEIg where MEIg was metabolizable energy intake for growth per day, and calculated as the proportion of ME requirements for growth in MER.

Residual feed consumption (RFC) for bulls per day in Mcal of ME/day was calculated as MER subtracted from MEI.

This three-step approach takes into account breed, sex, weight gain, milk yield, and days pregnant. This method of calculating feed efficiency has two advantages. Net energy can be calculated independent of the diet fed and feed requirements can be estimated separately for maintenance and the production trait of interest (Fan et al., 1995). One main disadvantage is that it is not necessarily independent of production traits.

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Cow/calf Efficiency

Cow/calf efficiency measures the total feed intake of the cow and her progeny over an entire production cycle. The cycle generally starts after the weaning of one calf to weaning of the next. Total amount of feed consumed is then compared to calf weight weaned and is given as a ratio of total weight of weaned calf over total weight of feed consumed (Jenkins and Ferrell, 1994; Shuey et al., 1993).

This is a reasonable method of calculating whole herd efficiency when accounting for

both biological and economic efficiency of the cow herd. Some of its drawbacks include its exclusion of the terminal generation, herd replacements, and cull sales. Gregory (1972) on the other hand argues that these groups of animals only account for a very small portion of the total amount of feed used in production therefore making this method of calculating feed efficiency a viable option. Genetic Variation in feed efficiency There is plenty of evidence showing that when feed efficiency is provided as gross efficiency or residual feed intake that variation exists.

Heritabilities have been calculated for feed efficiency measurements in growing cattle (Table 1). Heritabilities for gross efficiency range from 0.16 - 0.46 (Bishop et al., 1991; Bishop et al., 1992; Fan et al., 1995; Gengler et al., 1995; Jensen et al., 1991; Mrode et al., 1990) while heritabilities for RFI range from .14 - .44 (Fan et al., 1995; Jensen et al., 1991; Koch et al., 1963; Korver et al., 1991).

As mentioned above feed conversion ratio (FCR) is highly correlated with growth

rate and maturity patterns. Therefore, it is possible that the variation that can be estimated in FCR could be largely attributed to growth and maturity. Residual feed intake heritabilities mentioned above have all been calculated using phenotypic regression and not genetic regression which Kennedy et al. (1993) suggests is more accurate. On the other hand genetic correlations between phenotypic RFI and production traits are reported to be quite low (Korver et al., 1991). If this truly is the case then phenotypic and genetic RFI is practically the same providing sufficient evidence that heritability estimates using RFI calculated from phenotypic regression is accurate. There is evidence, however, that RFI and gain have a genetic correlation (Jensen et al., 1991). This allows for some doubt on whether there is true variation in RFI or if that variation can be attributed to gain instead. Fan et al. (1995) has displayed that both phenotypic and genetic correlations of RFI with production traits are influenced by the way RFI is calculated which depends on how the predicted feed intake is calculated. Overall, the majority of researchers concluded that there is some type of phenotypic and genetic variation for feed efficiency in growing cattle. Variation within beef populations

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related to feed efficiency allows for the opportunity for selection for feed efficiency within growing cattle. Physiological basis for variation in feed efficiency When variation in feed efficiency is understood on a physiological basis, correlated response to selection can be predicted much more accurately. Traits that are highly correlated with feed efficiency may be identified allowing for an easier method in identifying efficient animals within a herd. This could lead to physiological markers to be utilized in a strategic manner for the benefit of increasing the profitability of the herd. One potential source of variation is hormone concentrations that are important to growth, like testosterone and thyroid hormones thyroxine (T4) and triiodothyronine (T3). Testosterone stimulates muscle and skeletal growth by binding to receptors on muscles cells stimulating them to grow (Lawrence and Fowler, 1998). Thyroid hormone receptors found in the nuclei of cells stimulate oxidative metabolism and anabolic function of cells in all tissues by regulating oxygen consumption, mineral balance and synthesis and metabolism of proteins, carbohydrates and lipids (Lawrence and Fowler, 1998). If these hormones are deficient severe retardation of growth will occur (Lawrence and Fowler, 1998). Concentrations of these hormones are variable and may affect feed efficiency (Archer et al., 1999).

Gray et al. (2008) did a study using 325 registered Angus bulls over a period of 5

years representing 40 different sires had individual feed intake collected using the Calan gate system. Bulls were fed a corn silage based diet that was formulated for bulls to gain three lbs daily. Bulls were on test for 84 days excluding the two week acclimation period. Blood samples were collected at the end of the test which was approximately when bulls were one year of age (Gray et al., 2008). Genetic Variance components were estimated using GIBBS2F90 software (Misztal et al., 2002). For each analysis a single chain consisting of 150,000 iterations was employed, with a burn in period of 30,000 iterations. Convergence was assessed visually from the trace plot. Inferences on the variables were obtained as mean of the respective posterior distributions. It was found that T4 hormone concentrations were highly heritable (0.68) with a genetic correlation with DMI of 0.82.

Conclusions

The best way to determine efficiency of the herd is to measure daily feed intake of each individual animal over the full course of its existence within the herd. This would obviously be an economic challenge therefore requiring some alternative method..

Measuring feed intake at strategic periods within the animals’ life seems to be the

best option for now. Once individual feed intake has been collected the next step is to determine which method should be used in calculating feed efficiency.

Feed conversion ratio or its inverse gross efficiency is the most common method in

describing feed efficiency. Using a ratio will cause major problems when used in a linear

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selection index, especially when gain or feed intake are in the index as well (van der Werf, 2004).

Residual feed intake is the most common measurement for feed efficiency used for

selection and breeding programs in the recent literature. Some of its attractive components include its phenotypic independence of production traits and weight. Most selection experiments are based on RFI measurements taken during the post-weaning period. Using mice as a model, selecting animals after determining RFI during a post-weaning period is sufficient evidence that they will remain efficient as mature animals (Archer et al., 1998). This selection method has increased efficiency in the feedlot by almost 4% (Richardson et al., 2001).

Kennedy et al. (1993) on the other hand disagrees with the use of residual feed intake

as selection criterion. A selection index which includes feed intake, production traits of interest and weight was suggested to provide the same amount of information that RFI can provide. Furthermore, Kennedy et al. (1993) provides evidence that RFI in fact is a selection index manipulated into a different form. The most correct procedure for optimal selection may be to consider production traits and feed intake alone rather than RFI values (van der Werf, 2004). To increase accuracy of genetic improvement other efficiency traits can be included in the index such as body composition (van der Werf, 2004).

Although RFI provides no additional information for selection that a selection index

can’t provide it can shed some light on genetic variation of feed efficiency (van der Werf, 2004). Residual feed intake clearly has a heritability, variance, and covariance with production traits. By understanding the covariance of RFI with other efficiency traits, such as heat production, activity, and body composition, production efficiency can be defined better (van der Werf, 2004). Essentially selecting on RFI is another method of multivariate selection.

One method of selection that does not include collecting individual feed intake is

using physiological markers. Using physiological markers alongside direct measurements is considered the best method because of its potential of increased accuracy (Woolliams and Smith, 1988).

One area of research that would benefit the increasing of feed efficiency in beef cattle

within the herd is the thyroid hormones and their relationship with DMI in growing and mature cattle. It has been shown in a small study that there is a strong genetic relationship between T4 and DMI (Gray et al., 2008). This physiological measurement could be taken advantage of and used as another predictive measurement of DMI without the necessity to collect individual feed intake in a bull test setting. Including this in a selection index would inevitably increase the accuracy of selection which would in turn increase the response to selection.

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Implications

As feed costs continue to increase feed efficiency will continue to be a trait of interest for cattle producers. It has been shown that there definitely is variation in feed efficiency among beef cattle within a production system. The source of this variation in feed efficiency has become somewhat clearer but is not totally understood. Although the source of this variation is still incomplete selection programs have been successful at increasing the efficiency of the growing animal within the slaughter generation.

An understanding of biological relationships between growing and mature animals pertaining to feed efficiency has yet to be established. Understanding biological markers such as T4 needs to continue to be studied in other populations. This could be the key to decrease testing costs and still identify superior animals for selection in order to continue producing the best beef product possible. Literature Cited

Archer, J. A., P. F. Arthur, R. M. Herd, P. F. Parnell, and W. S. Pitchford. 1997. Optimum

postweaning test for measurement of growth rate, feed intake, and feed efficiency in british breed cattle. J. Anim. Sci. 75: 2024-2032.

Archer, J. A., W. S. Pitchford, T. E. Hughes, and P. F. Parnell. 1998. Genetic and phenotypic

relationships between food intake, growth, efficiency and body composition of mice post weaning and at maturity. Anim. Sci. 67: 171-182.

Archer, J. A., E. C. Richardson, R. M. Herd, and P. F. Arthur. 1999. Potential for selection to

improve efficiency of feed use in beef cattle: A review. Aust. J. Agric. Res. 50: 147-161.

Arthur, P. F., J. A. Archer, D. J. Johnston, R. M. Herd, E. C. Richardson, and P. F. Parnell.

2001. Genetic and phenotypic variance and covariance components for feed intake, feed efficiency, and other postweaning traits in angus cattle. J. Anim. Sci. 79: 2805-2811.

Bishop, M. D., M. E. Davis, W. R. Harvey, G. R. Wilson, and B. D. Vanstavern. 1991.

Divergent selection for postweaning feed conversion in angus beef-cattle .2. Genetic and phenotypic correlations and realized heritability estimate. J. Anim. Sci. 69: 4360-4367.

Bishop, S. C., J. S. Broadbent, R. M. Kay, I. Rigby, and A. V. Fisher. 1992. The performance

of hereford x friesian offspring of bulls selected for lean growth-rate and lean food conversion efficiency. Anim. Prod. 54: 23-30.

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Brown, A. H., Jr., J. J. Chewning, Z. B. Johnson, W. C. Loe, and C. J. Brown. 1991. Effects of 84-, 112- and 140-day postweaning feedlot performance tests for beef bulls. J. Anim. Sci. 69: 451-461.

Dickerson, G. E. 1978. Animal size and efficiency - basic concepts. Anim. Prod. 27: 367-

379. Fan, L. Q., D. R. C. Bailey, and N. H. Shannon. 1995. Genetic parameter-estimation of

postweaning gain, feed-intake, and feed-efficiency for hereford and angus bulls fed 2 different diets. J. Anim. Sci. 73: 365-372.

Ferrell, C. L., and T. G. Jenkins. 1984. Energy-utilization by mature, nonpregnant,

nonlactating cows of different types. J. Anim. Sci. 58: 234-243. Ferrell, C. L., and T. G. Jenkins. 1985. Cow type and the nutritional environment - nutritional

aspects. J. Anim. Sci. 61: 725-741. Fitzhugh, H. A. 1978. Animal size and efficiency, with special reference to the breeding

female. Anim. Prod. 27: 393-401. Gengler, N., C. Seutin, F. Boonen, and L. D. Vanvleck. 1995. Estimation of genetic-

parameters for growth, feed consumption, and conformation traits for double-muscled belgian blue bulls performance-tested in belgium. J. Anim. Sci. 73: 3269-3273.

Gray, K. A., G. B. Huntington, M. H. Poore, C. S. Whisnant, and J. P. Cassady. 2008.

Relationships among measures of feed utilization, adg, and ultrasonic measures. J Anim Sci 86 E-Suppl. 3: 53.

Gregory, K. E. 1972. Beef-cattle type for maximum efficiency putting it all together. J.

Anim. Sci. 34: 881-&. Jenkins, T. G., and C. L. Ferrell. 1994. Productivity through weaning of 9 breeds of cattle

under varying feed availabilities .1. Initial evaluation. J. Anim. Sci. 72: 2787-2797. Jensen, J., I. L. Mao, B. B. Andersen, and P. Madsen. 1991. Genetic-parameters of growth,

feed-intake, feed conversion and carcass composition of dual-purpose bulls in performance testing. J. Anim. Sci. 69: 931-939.

Kennedy, B. W., J. H. J. Vanderwerf, and T. H. E. Meuwissen. 1993. Genetic and statistical

properties of residual feed-intake. J. Anim. Sci. 71: 3239-3250. Klosterman, E. W. 1972. Beef-cattle size for maximum efficiency. J. Anim. Sci. 34: 875-880. Koch, R. M., K. E. Gregory, D. Chambers, and L. A. Swiger. 1963. Efficiency of feed use in

beef cattle. J. Anim. Sci. 22: 486-494.

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Koong, L. J., and C. L. Ferrell. 1990. Effects of short-term nutritional manipulation on organ size and fasting heat-production. Eur. J. Clin. Nutr. 44: 73-77.

Korver, S. 1988. Genetic-aspects of feed-intake and feed-efficiency in dairy-cattle - a review.

Livest. Prod. Sci. 20: 1-13. Korver, S., E. A. M. Vaneekelen, H. Vos, G. J. Nieuwhof, and J. A. M. Vanarendonk. 1991.

Genetic-parameters for feed-intake and feed-efficiency in growing dairy heifers. Livest. Prod. Sci. 29: 49-59.

Lawrence, T. L. J., and V. R. Fowler. 1998. Growth of farm animals. CAB International,

New York. Liu, M. F., and M. Makarechian. 1993a. Factors influencing growth performance of beef

bulls in a test station. J. Anim Sci. 71: 1123-1127. Liu, M. F., and M. Makarechian. 1993b. Optimum test period and associations between

standard 140-day test period and shorter test periods for growth-rate in station tested beef bulls. Journal of Animal Breeding and Genetics-Zeitschrift Fur Tierzuchtung Und Zuchtungsbiologie 110: 312-317.

Macoon, B., L. E. Sollenberger, J. E. Moore, C. R. Staples, J. H. Fike, and K. M. Portier.

2003. Comparison of three techniques for estimating the forage intake of lactating dairy cows on pasture. J. Anim. Sci. 81: 2357-2366.

Maynard, L. A., and J. K. Loosli. 1969. Animal nutriton. 6 ed. McGraw-Hill Book Co., New

York. Misztal, I., S. Tsuruta, T. Strabel, B. Auvray, T. Druet, and D. H. Lee. 2002. Blupf90 and

related programs (bgf90). In: Proc. 7th World Genet. Appl. Livest. Prod., Montpellier, France.CD-ROM communication p 28:07.

Montano-Bermudez, M., M. K. Nielsen, and G. H. Deutscher. 1990. Energy-requirements for

maintenance of crossbred beef-cattle with different genetic potential for milk. J. Anim. Sci. 68: 2279-2288.

Mrode, R. A., C. Smith, and R. Thompson. 1990. Selection for rate and efficiency of lean

gain in hereford cattle .2. Evaluation of correlated responses. Anim. Prod. 51: 35-46. Nkrumah, J. D., J. A. Basarab, M. A. Price, E. K. Okine, A. Ammoura, S. Guercio, C.

Hansen, C. Li, B. Benkel, B. Murdoch, and S. S. Moore. 2004. Different measures of energetic efficiency and their phenotypic relationships with growth, feed intake, and ultrasound and carcass merit in hybrid cattle. J. Anim. Sci. 82: 2451-2459.

NRC. 1996. Nutrient requirements of beef cattle. 7th rev. ed. Natl. Acad. Press, Washington

D.C.

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Richardson, E. C., R. M. Herd, V. H. Oddy, J. M. Thomspon, J. A. Archer, and P. F. Arthur.

2001. Body composition and implications for heat production of angus steer progeny of parents selected for and against residual feed intake. Aust. J. Exp. Agric. 41: 1065-1072.

Salmon, R. K., D. R. C. Bailey, R. Weingardt, and R. T. Berg. 1990. Growth efficiency in

mice selected for increased body-weight. Can. J. Anim. Sci. 70: 371-381. Shuey, S. A., C. P. Birkelo, and D. M. Marshall. 1993. The relationship of the maintenance

energy requirement to heifer production efficiency. J. Anim. Sci. 71: 2253-2259. Swiger, L. A., and L. N. Hazel. 1961. Optimum length of feeding period in selecting for gain

of beef cattle. J. Anim Sci. 20: 189-194. Taylor, S. C. S., H. G. Turner, and G. B. Young. 1981. Genetic-control of equilibrium

maintenance efficiency in cattle. Anim. Prod. 33: 179-194. van der Werf, J. H. J. 2004. Is it useful to define residual feed intake as a trait in animal

breeding programs? Aust. J. Exp. Agric. 44: 405-409. Veerkamp, R. F., and G. C. Emmans. 1995. Sources of genetic-variation in energetic

efficiency of dairy-cows. Livest. Prod. Sci. 44: 87-97. Wang, Z., J. D. Nkrumah, C. Li, J. A. Basarab, L. A. Goonewardene, E. K. Okine, D. H.

Crews, and S. S. Moore. 2006. Test duration for growth, feed intake, and feed efficiency in beef cattle using the growsafe system. J. Anim. Sci. 84: 2289-2298.

Woolliams, J. A., and C. Smith. 1988. The value of indicator traits in the genetic-

improvement of dairy-cattle. Anim. Prod. 46: 333-345.

134

Roy A. Wallace1944-2008

TheRoyA.WallaceBIFMemorialFundwasestablishedtohonorthelifeandcareerofRoyWallace.RoydevotedhislifetobeefcattleimprovementandhelovedwhatBIFstandsfor–anorganizationthatbringstogetherpurebredandcommercialcattlebreeders,academiaandbreedassociations,allcommittedtoimprovingbeefcattle.ThisscholarshipwasestablishedtoencourageyoungmenandwomeninterestedinbeefcattleimprovementtopursuethoseinterestsasRoydid--withdedicationandpassion.

‘AlwaysaBuckeye’,WallacewasborninOhioandcalledOhiohomehisentirelife.HegraduatedfromTheOhioStateUniversityinJune1967andwenttoworkforCentralOhioBreedingAssociationasbeeffieldrepresentative,andin1969,hejoinedSelectSires,Inc.asbeefsireanalyst.Hewaslaterpromotedtovice-president,beefprograms.DuringhistenureatSelectSires,Wallaceacquiredmorethan600beefbullsfrom19breedstobemarketedthroughouttheworld.

Wallacebecame involvedwithBIF in theearlydays. HeservedasamemberandboardmemberofBIFandwas theonlyperson tohaveattendedeveryBIFconventionovera40yearspan,from1967through2007.Heservedasamemberandchairmanofthereproductioncommitteeandamemberofthesireevaluationcommittee.HewasawardedwiththeBIFContinuingServiceAwardandtheBIFPioneerAward,andco-authoredtheBIF25-yearhistory,Ideas into Action.

WallacewasapastpresidentandboardmemberoftheBuckeyeBeefImprovementAssociation,theNationalCattlemen’sAssociationandtheOhioCattlemen’sAssociation.Inaddition,heservedonthetechnicalcommitteefortheAmericanAngusAssociationandon theperformancecommittee for theAmericanSimmentalAssociation.HewasalsoachairmanandmemberofthebeefdevelopmentcommitteeoftheNationalAssociationofAnimalBreeders(NAAB)andamemberoftheBeefReproductionLeadershipTeam.

Alwaysastrongproponentofperformancetesting,Wallaceservedinanadvisorycapacitytoseveralbreedassociationsintheareaofsireevaluation.Astrongsupporterofnationalsiresummaries,hewasanearlyadopterofstructuredsireevaluations.Heestablishedthelongest-runningyoungsireprogeny-testingprogramintheA.I.industrytoday–aprogramthathasidentifiedsuperiorgeneticsinseveralbreeds.HisearlyacceptanceandpromotionofEPDsasatooltomakegeneticprogresshelpedmakeEPDswhattheyaretodayinthebeefcow-calfindustry.

WallacewasinvolvedintheearlyselectionandimportationofseveralEuropeanbreedsinthe1970s;thesebreedshavemadeasignificantcontributiontoU.S.beefcattleimprovement.Hisselectionofbullswithgeneticsforlighterbirthweightsallowedbreederstouseprovencalving-easebullsonvirginheifers,reducingcalvingproblemswithoutsacrific-ingperformance.Herecognizedthevalueofbothcarcassandultrasoundevaluationsearlyon,andidentifiedseveralindustry-leadingbullswhichhavebeenveryinfluentialinimprovingthecarcasscompositionofbeefcattle.

Throughouthiscareer,Wallacewasdedicatedtofindingbetterwaystogetbeefcowsbredartificiallytogeneti-callysuperiorbulls.Someofhisearlyworkinvolvedfeedingprogesteronetobeefcattle,atechniquewhichevolvedintotheverysuccessfulMGAprogramsthatarewidelyusedtoday.Throughcollaborativeresearchwithreproductiveresearchersatseveralmajoruniversities,WallacehelpedtodevelopseveraleffectiveA.I.synchronizationprograms,includingSelectSynchandCIDR-Select.

RoyWallacewasavisionary,athinker,ateacher,amentor,acattlemanandafriend.Helovedgoodcattle,butmoreimportantly,helovedthepeoplethathehadtheopportunitytoworkwith.Heleftabigfootprintonthebeefindustry.

135

Paige Johnson AlexanderPh.D. Candidate

Texas Tech University

Rich ChappleM.S. Candidate

University of Missouri

David Daniel, Jr.M.S. Candidate

Auburn University

Jared DeckerPh.D. Candidate

University of Missouri

Allison EcholsM.S. Candidate

Virginia Polytechnic Institute & State University

Jose Alberto Barron Lopez

Ph.D. CandidateUniversity of Nebraska-

Lincoln

Tera Loyd BlackM.S. Candidate

University of Florida

Landon MarksM.S. Candidate

Mississippi State University

Allen (AJ) Munger M. Agribusiness Candidate

Kansas State University

Jordan PaulsrudB.S. Candidate

Iowa State University

Megan RolfPh.D. Candidate

University of Missouri

Tasia TaxisM.S. Candidate

University of Missouri

2010 Beef Improvement Federation ConferenceStudent Travel Fellowship Recipients

The 2010 BIF student travel fellowships are supported by the Agriculture and Food Research Initiative award number 2010-65205-20467 from the USDA National Institute for Food and Agriculture Animal Genome, Genetics, and Breeding Program. The $10,000.00 conference grant was awarded to Dr. Bob Weaber at the University of Missouri to support the BIF conference and travel fellowships.

136

Billy L. Easley . . . . . . . Kentucky . . . . .1972

Dale H. Davis . . . . . . . . Montana . . . . . .1972

Elliot Humphrey . . . . . . Arizona . . . . . . .1972

Harold A. Demorest . . . . Ohio . . . . . . . .1972

James D. Bennett . . . . . . Virginia . . . . . .1972

Jerry Moore . . . . . . . . . Ohio . . . . . . . .1972

John Crowe . . . . . . . . . California . . . . .1972

Marshall A. Mohler . . . . . Indiana . . . . . . .1972

Albert West III . . . . . . . Texas . . . . . . . .1973

C. Scott Holden . . . . . . . Montana . . . . . .1973

Carlton Corbin . . . . . . . Oklahoma . . . . .1973

Clyde Barks . . . . . . . . . North Dakota . . .1973

Heathman Herefords . . . . Washington . . . . .1973

James D. Hemmingsen . . . Iowa . . . . . . . .1973

Messersmith Herefords . . . Nebraska . . . . . .1973

Mrs. R. W. Jones, Jr.. . . . . Georgia . . . . . . .1973

Raymond Meyer . . . . . . South Dakota . . .1973

Robert Miller . . . . . . . . Minnesota . . . . .1973

William F. Borrow . . . . . California . . . . .1973

Bert Crame . . . . . . . . . California . . . . .1974

Bert Sackman . . . . . . . . North Dakota . . .1974

Dover Sindelar . . . . . . . Montana . . . . . .1974

Burwell M. Bates . . . . . . Oklahoma . . . . .1974

Charles Descheemacher . . Montana . . . . . .1974

J. David Nichols . . . . . . Iowa . . . . . . . .1974

Jorgensen Brothers . . . . . South Dakota . . .1974

Marvin Bohmont . . . . . . Nebraska . . . . . .1974

Maurice Mitchell . . . . . . Minnesota . . . . .1974

Wilfred Dugan . . . . . . . Missouri . . . . . .1974

Dale Engler . . . . . . . . . Kansas . . . . . . .1975

Frank Kubik, Jr. . . . . . . . North Dakota . . .1975

George Chiga . . . . . . . . Oklahoma . . . . .1975

Glenn Burrows . . . . . . . New Mexico . . . .1975

Howard Collins . . . . . . . Missouri . . . . . .1975

Jack Cooper . . . . . . . . . Montana . . . . . .1975

Joseph P. Dittmer . . . . . . Iowa . . . . . . . .1975

Leslie J. Holden . . . . . . Montana . . . . . .1975

Licking Angus Ranch . . . . Nebraska . . . . . .1975

Louis Chestnut . . . . . . . Washington . . . . .1975

Robert Arbuthnot . . . . . . Kansas . . . . . . .1975

Robert D. Keefer . . . . . . Montana . . . . . .1975

Walter S. Markham . . . . . California . . . . .1975

Ancel Armstrong . . . . . . Virginia . . . . . .1976

Gerhard Mittnes . . . . . . Kansas . . . . . . .1976

Healey Brothers . . . . . . Oklahoma . . . . .1976

Jackie Davis . . . . . . . . California . . . . .1976

Jay Pearson . . . . . . . . . Idaho . . . . . . . .1976

L. Dale Porter . . . . . . . . Iowa . . . . . . . .1976

Lowellyn Tewksbury . . . . North Dakota . . .1976

M.D. Shepherd . . . . . . . North Dakota . . .1976

Robert Sallstrom . . . . . . Minnesota . . . . .1976

Sam Friend . . . . . . . . . Missouri . . . . . .1976

Stan Lund . . . . . . . . . . Montana . . . . . .1976

Bill Wolfe . . . . . . . . . . Oregon . . . . . . .1977

Bob Sitz . . . . . . . . . . . Montana . . . . . .1977

Clair Percel . . . . . . . . . Kansas . . . . . . .1977

BIF Seedstock Producer Honor Roll of Excellence

137

Floyd Hawkins . . . . . . . Missouri . . . . . .1977

Frank Ramackers, Jr. . . . . Nebraska . . . . . .1977

Glen Burrows . . . . . . . . New Mexico . . . .1977

Henry and Jeanette Chitty . New Mexico . . . .1977

Hubert R. Freise . . . . . . North Dakota . . .1977

James Volz . . . . . . . . . Minnesota . . . . .1977

Lloyd DeBruycker . . . . . North Dakota . . .1977

Loren Schlipf . . . . . . . . Illinois . . . . . . .1977

Marshall A. Mohler . . . . . Indiana . . . . . . .1977

Robert Brown . . . . . . . . Texas . . . . . . . .1977

Tom and Mary Shaw . . . . Idaho . . . . . . . .1977

Tom Dashiell . . . . . . . . Washington . . . . .1977

Wayne Eshelman . . . . . . Washington . . . . .1977

Harold Anderson . . . . . . South Dakota . . .1977

WillIiam Borror . . . . . . . California . . . . .1977

A.L. Frau . . . . . . . . . . . . . . . . . . . . .1978

Bill Wolfe . . . . . . . . . . Oregon . . . . . . .1978

Bill Womack, Jr. . . . . . . . Alabama . . . . . .1978

Buddy Cobb . . . . . . . . Montana . . . . . .1978

Frank Harpster . . . . . . . Missouri . . . . . .1978

George Becker . . . . . . . North Dakota . . .1978

Healey Brothers . . . . . . Oklahoma . . . . .1978

Jack Delaney . . . . . . . . Minnesota . . . . .1978

James D. Bennett . . . . . . Virginia . . . . . .1978

Larry Berg . . . . . . . . . Iowa . . . . . . . .1978

Roy Hunst . . . . . . . . . . Pennsylvania . . . .1978

Bill Wolfe . . . . . . . . . . Oregon . . . . . . .1979

Del Krumweid . . . . . . . North Dakota . . .1979

Floyd Metter . . . . . . . . Missouri . . . . . .1979

Frank & Jim Wilson . . . . South Dakota . . .1979

Glenn & David Gibb . . . . Illinois . . . . . . .1979

Jack Ragsdale . . . . . . . . Kentucky . . . . .1979

Jim Wolf . . . . . . . . . . Nebraska . . . . . .1979

Leo Schuster Family . . . . Minnesota . . . . .1979

Peg Allen . . . . . . . . . . Montana . . . . . .1979

Rex & Joann James . . . . . Iowa . . . . . . . .1979

Bill Wolfe . . . . . . . . . . Oregon . . . . . . .1980

Blythe Gardner . . . . . . . Utah . . . . . . . .1980

Bob Laflin . . . . . . . . . . Kansas . . . . . . .1980

Charlie Richards . . . . . . Iowa . . . . . . . .1980

Donald Barton . . . . . . . Utah . . . . . . . .1980

Floyd Dominy . . . . . . . Virginia . . . . . .1980

Frank Felton . . . . . . . . Missouri . . . . . .1980

Frank Hay . . . . . . . . . . California . . . . . .1980

James Bryany . . . . . . . . Minnesota . . . . .1980

John Masters . . . . . . . . Kentucky . . . . .1980

Mark Keffeler . . . . . . . . South Dakota . . .1980

Paul Mydland . . . . . . . . Montana . . . . . .1980

Richard McLaughlin . . . . Illinois . . . . . . .1980

Richard Tokach . . . . . . . North Dakota . . .1980

Roy and Don Udelhoven . . Wisconsin . . . . .1980

Bob & Gloria Thomas . . . Oregon . . . . . . .1981

Bob Dickinson . . . . . . . Kansas . . . . . . .1981

Clarence Burch . . . . . . . Oklahoma . . . . .1981

Clayton Canning . . . . . . California . . . . . .1981

Dwight Houff . . . . . . . . Virginia . . . . . .1981

BIF Seedstock Producer Honor Roll of Excellence

138

G.W. Cronwell . . . . . . . Iowa . . . . . . . .1981

Harold Thompson . . . . . Washington . . . . .1981

Herman Schaefer . . . . . . Illinois . . . . . . .1981

J. Morgan Donelson . . . . Missouri . . . . . .1981

Jack Ragsdale . . . . . . . . Kentucky . . . . .1981

James Leachman . . . . . . Montana . . . . . .1981

Lynn Frey . . . . . . . . . . North Dakota . . .1981

Myron Autfathr . . . . . . . Minnesota . . . . .1981

Roy Beeby . . . . . . . . . Oklahoma . . . . .1981

Russ Denowh . . . . . . . . Montana . . . . . .1981

Bob Thomas . . . . . . . . Oregon . . . . . . .1982

Clare Geddes . . . . . . . . California . . . . . .1982

David A. Breiner . . . . . . Kansas . . . . . . .1982

Frankie Flint . . . . . . . . New Mexico . . . .1982

Garold Parks . . . . . . . . Iowa . . . . . . . .1982

Gary & Gerald Carlson . . . North Dakota . . . .1982

Harlin Hecht . . . . . . . . Minnesota . . . . .1982

Howard Krog . . . . . . . . Minnesota . . . . .1982

Joseph S. Bray . . . . . . . Kentucky . . . . .1982

Larry Leonhardt . . . . . . Montana . . . . . .1982

Orville Stangl . . . . . . . . South Dakota . . .1982

W.B. Williams . . . . . . . Illinois . . . . . . .1982

William Kottwitz . . . . . . Missouri . . . . . .1982

Alex Stauffer . . . . . . . . Wisconsin . . . . .1983

Bill Borror . . . . . . . . . . California . . . . . .1983

C. Ancel Armstrong . . . . Kansas . . . . . . .1983

Charles E. Boyd . . . . . . Kentucky . . . . .1983

D. John & Lebert Schultz . . Missouri . . . . . .1983

E.A. Keithley . . . . . . . . Missouri . . . . . .1983

Frank Myatt . . . . . . . . . Iowa . . . . . . . .1983

Harvey Lemmon . . . . . . Georgia . . . . . .1983

J. Earl Kindig . . . . . . . . Missouri . . . . . .1983

Jake Larson . . . . . . . . . North Dakota . . .1983

John Bruner . . . . . . . . . South Dakota . . .1983

Leness Hall . . . . . . . . . Washington . . . . .1983

Ric Hoyt . . . . . . . . . . Oregon . . . . . . .1983

Robert H. Schafer . . . . . . Minnesota . . . . .1983

Russ Pepper . . . . . . . . . Montana . . . . . .1983

Stanley Nesemeier . . . . . Illinois . . . . . . .1983

A. Harvey Lemmon . . . . Georgia . . . . . . .1984

Charles W. Druin . . . . . . Kentucky . . . . .1984

Clair K. Parcel . . . . . . . Kansas . . . . . . .1984

Donn & Sylvia Mitchell . . Canada . . . . . . .1984

Earl Kindig . . . . . . . . . Virginia . . . . . .1984

Floyd Richard . . . . . . . . North Dakota . . .1984

Fred H. Johnson . . . . . . Ohio . . . . . . . .1984

Glen Klippenstein . . . . . Missouri . . . . . .1984

Jack Farmer . . . . . . . . . California . . . . . .1984

Jerry Chappel . . . . . . . . Virginia . . . . . .1984

Joe C. Powell . . . . . . . . North Carolina . . .1984

John B. Green . . . . . . . . Louisiana . . . . .1984

Lawrence Meyer . . . . . . Illinois . . . . . . .1984

Lee Nichols . . . . . . . . . Iowa . . . . . . . .1984

Phillip A. Abrahamson . . . Minnesota . . . . .1984

Ric Hoyt . . . . . . . . . . Oregon . . . . . . .1984

Robert L. Sitz . . . . . . . . Montana . . . . . .1984

BIF Seedstock Producer Honor Roll of Excellence

139

Ron Beiber . . . . . . . . . South Dakota . . .1984

Arnold Wienk . . . . . . . . South Dakota . . .1985

Bernard F. Pedretti . . . . . Wisconsin . . . . .1985

David McGehee . . . . . . Kentucky . . . . .1985

Don W. Schoene . . . . . . Missouri . . . . . .1985

Earl Schafer . . . . . . . . . Minnesota . . . . .1985

Everett & Ron Batho . . . . Canada . . . . . . .1985

Fred Killam . . . . . . . . . Illinois . . . . . . .1985

George B. Halternan . . . . West Virginia . . . .1985

Glenn L. Brinkman . . . . . Texas . . . . . . . .1985

Gordon Booth . . . . . . . . Wyoming . . . . . .1985

J. Newill Miller . . . . . . . Virginia . . . . . .1985

Marvin Knowles . . . . . . California . . . . .1985

R.C. Price . . . . . . . . . . Alabama . . . . . .1985

Tom Perrier . . . . . . . . . Kansas . . . . . . .1985

A. Lloyd Grau . . . . . . . New Mexico . . . .1986

Clarence Vandyke . . . . . Montana . . . . . .1986

Clifford & Bruce Betzold . . Illinois . . . . . . .1986

Delton W. Hubert . . . . . . Kansas . . . . . . .1986

Dick & Ellie Larson . . . . Wisconsin . . . . .1986

Evin & Verne Dunn . . . . . Canada . . . . . . .1986

Gerald Hoffman . . . . . . South Dakota . . .1986

Glenn L. Brinkman . . . . . Texas . . . . . . . .1986

Henry & Jeanette Chitty . . Florida . . . . . . .1986

J.H. Steward/P.C. Morrissey . Pennsylvania . . . .1986

Jack & Gina Chase . . . . . Wyoming . . . . . .1986

John H. Wood . . . . . . . . South Carolina . . .1986

Lawrence H. Graham . . . . Kentucky . . . . .1986

Leonard Lodden . . . . . . North Dakota . . .1986

Leonard Wulf . . . . . . . . Minnesota . . . . .1986

Matthew Warren Hall . . . . Alabama . . . . . .1986

Ralph McDanolds . . . . . Virginia . . . . . .1986

Richard J. Putnam . . . . . North Carolina . . .1986

Roy D. McPhee . . . . . . . California . . . . .1986

W.D. Morris/James Pipkin . Missouri . . . . . .1986

Charles & Wynder Smith . . Georgia . . . . . . .1987

Clayton Canning . . . . . . Canada . . . . . . .1987

Eldon & Richard Wiese . . Minnesota . . . . .1987

Forrest Byergo . . . . . . . Missouri . . . . . .1987

Gary Klein . . . . . . . . . North Dakota . . .1987

Harold E. Pate . . . . . . . Illinois . . . . . . .1987

Henry Gardiner . . . . . . . Kansas . . . . . . .1987

Ivan & Frank Rincker . . . Illinois . . . . . . .1987

James Bush . . . . . . . . . South Dakota . . .1987

Larry D. Leonhardt . . . . . Wyoming . . . . . .1987

Lyall Edgerton . . . . . . . Canada . . . . . . .1987

R.J. Steward/P.C. Morrisey . Minnesota . . . . .1987

Tommy Brandenberger . . . Texas . . . . . . . .1987

Bill Bennett . . . . . . . . . Washington . . . . .1988

Darold Bauman . . . . . . . Wyoming . . . . . .1988

David and Carol Guilford . Canada . . . . . . .1988

David Luhman . . . . . . . Minnesota . . . . .1988

Don and Dian Guilford . . . Canada . . . . . . .1988

Donn & Sylvia Mitchell . . Canada . . . . . . .1988

Douglas D. Bennett . . . . . Texas . . . . . . . .1988

George Schlickau . . . . . . Kansas . . . . . . .1988

BIF Seedstock Producer Honor Roll of Excellence

140

Gino Pedretti . . . . . . . . California . . . . .1988

Glenn Debter . . . . . . . . Alabama . . . . . .1988

Hansell Pile . . . . . . . . . Kentucky . . . . .1988

Jay P. Book . . . . . . . . . Illinois . . . . . . .1988

Kans Ulrich . . . . . . . . . Canada . . . . . . .1988

Kenneth Gillig . . . . . . . Missouri . . . . . .1988

Leonard Lorenzen . . . . . Oregon . . . . . . .1988

Robert E. Walton . . . . . . Washington . . . . .1988

Scott Burtner . . . . . . . . Virginia . . . . . .1988

WillIowam Glanz . . . . . . Wyoming . . . . . .1988

Bob R. Whitmire . . . . . . Georgia . . . . . . .1989

Donald Fawcett . . . . . . . South Dakota . . . .1989

Ed Albaugh . . . . . . . . . California . . . . . .1989

Glynn Debter . . . . . . . . Alabama . . . . . .1989

Harry Airey . . . . . . . . . Canada . . . . . . .1989

Jack & Nancy Baker . . . . Missouri . . . . . .1989

Jerry Allen Burner . . . . . Virginia . . . . . . .1989

Kenneth D. Lowe . . . . . . Kentucky . . . . . .1989

Leonard A. Lorenzen . . . . Oregon . . . . . . .1989

Lester H. Schafer . . . . . . Minnesota . . . . .1989

Lynn Pelton . . . . . . . . . Kansas . . . . . . .1989

Orrin Hart . . . . . . . . . . Canada . . . . . . .1989

Ron Bowman . . . . . . . . North Dakota . . .1989

Sherm & Charlie Ewing . . Canada . . . . . . .1989

Tom Mercer . . . . . . . . . Wyoming . . . . . .1989

Bob Thomas Family . . . . Oregon . . . . . . .1990

Boyd Broyles . . . . . . . . Kentucky . . . . .1990

Charles & Rudy Simpson . . Canada . . . . . . .1990

Doug Fraser . . . . . . . . . Canada . . . . . . .1990

Douglas & Molly Hoff . . . South Dakota . . .1990

Dr. Burleigh Anderson . . . Pennsylvania . . . .1990

Gerhard Gueggenberger . . California . . . . .1990

John & Chris Oltman . . . . Wisconsin . . . . .1990

John Ragsdale . . . . . . . Kentucky . . . . .1990

Larry Erahart . . . . . . . . Wyoming . . . . . .1990

Otto & Otis Rincker . . . . Illinois . . . . . . .1990

Paul E. Keffaber . . . . . . Indiana . . . . . . .1990

Richard Janssen . . . . . . . Kansas . . . . . . .1990

Steven Forrester . . . . . . Michigan . . . . . .1990

T.D. & Roger Steele . . . . Virginia . . . . . .1990

Ann Upchurch . . . . . . . Alabama . . . . . .1991

Dave & Carol Guilford . . . Canada . . . . . . .1991

Jack & Gina Chase . . . . . Wyoming . . . . . .1991

Jack Cowley . . . . . . . . California . . . . .1991

James Burnes & Sons . . . . Wisconsin . . . . .1991

James R. O’Neill . . . . . . Iowa . . . . . . . .1991

Jim Taylor . . . . . . . . . Kansas . . . . . . .1991

John Bruner . . . . . . . . . South Dakota . . .1991

Larry Wakefield . . . . . . . Minnesota . . . . .1991

N. Wehrmann/R. McClung . Virginia . . . . . .1991

R.A. Brown . . . . . . . . . Texas . . . . . . . .1991

R.M. Felts & Son Farm . . . Tennessee . . . . .1991

Ralph Bridges . . . . . . . . Georgia . . . . . . .1991

Richard & Sharon Beitelspacher

. . . . . . . . . . . . . . South Dakota . . .1991

Rob & Gloria Thomas . . . Oregon . . . . . . .1991

BIF Seedstock Producer Honor Roll of Excellence

141

Steve & Bill Florschcuetz . Illinois . . . . . . .1991

Summitcrest Farms . . . . . Ohio . . . . . . . .1991

Tom Sonderup . . . . . . . Nebraska . . . . . .1991

A.W. Compton, Jr. . . . . . . Alabama . . . . . .1992

Bill Rea . . . . . . . . . . . Pennsylvania . . . .1992

Bob Buchanan Family . . . Oregon . . . . . . .1992

Calvin & Gary Sandmeier . South Dakota . . .1992

Dennis, David & Danny Geffert

. . . . . . . . . . . . . . Wisconsin . . . . .1992

Dick Montague . . . . . . . California . . . . .1992

Eugene B. Hook . . . . . . Minnesota . . . . .1992

Francis & Karol Bormann . Iowa . . . . . . . .1992

Glenn Brinkman . . . . . . Texas . . . . . . . .1992

Harold Dickson . . . . . . . Missouri . . . . . .1992

Leonard Wulf & Sons . . . Minnesota . . . . .1992

Robert Elliot & Sons . . . . Tennessee . . . . .1992

Tom & Ruth Clark . . . . . Virginia . . . . . .1992

Tom Drake . . . . . . . . . Oklahoma . . . . .1992

Bob Zarn . . . . . . . . . . Minnesota . . . . .1993

Clarence, Elaine & Adam Dean

. . . . . . . . . . . . . . South Carolina . . .1993

Collin Sander . . . . . . . . South Dakota . . .1993

D. Eldridge & Y. Aycock . . Oklahoma . . . . .1993

Harrell Watts . . . . . . . . Alabama . . . . . .1993

J. David Nichols . . . . . . . Iowa . . . . . . . .1993

J. Newbill Miller . . . . . . Virginia . . . . . .1993

Joseph Freund . . . . . . . Colorado . . . . . .1993

Lynn Pelton . . . . . . . . . Kansas . . . . . . .1993

Miles P. “Buck” Pangburn . Iowa . . . . . . . .1993

Norman Bruce . . . . . . . Illinois . . . . . . .1993

R.A. Brown . . . . . . . . . Texas . . . . . . . .1993

R.B. Jarrell . . . . . . . . . Tennessee . . . . .1993

Rueben Leroy & Bob Littau . South Dakota . . . .1993

Ted Seely . . . . . . . . . . Wyoming . . . . . .1993

Wes & Fran Cook . . . . . . North Carolina . . .1993

Bobby F. Hayes . . . . . . . Alabama . . . . . .1994

Bruce Orvis . . . . . . . . . California . . . . . .1994

Buell Jackson . . . . . . . . Iowa . . . . . . . .1994

Calvin & Gary Sandmeier . South Dakota . . .1994

Dave Taylor & Gary Parker . Wyoming . . . . . .1994

Jere Caldwell . . . . . . . . Kentucky . . . . .1994

John Blankers . . . . . . . . Minnesota . . . . .1994

John Pfeiffer Family . . . . Oklahoma . . . . .1994

Ken & Bonnie Bieber . . . . South Dakota . . .1994

Mary Howe di’Zerega . . . Virginia . . . . . .1994

Richard Janssen . . . . . . . Kansas . . . . . . .1994

Ron & Wayne Hanson . . . . Canada . . . . . . .1994

Bobby Aldridge . . . . . . . North Carolina . . .1995

Chris & John Christensen . South Dakota . . .1995

Donald J. Hargrave . . . . . Canada . . . . . . .1995

Gene Bedwell . . . . . . . . Iowa . . . . . . . .1995

Gordon & Mary Ann Booth . Wyoming . . . . . .1995

Howard & JoAnne Hillman . South Dakota . . .1995

John Robbins . . . . . . . . Montana . . . . . .1995

Billy Mack & Tom Maples . Alabama . . . . . .1995

Mary Howe de’Zerega . . . Virginia . . . . . .1995

BIF Seedstock Producer Honor Roll of Excellence

142

Maurice Grogan . . . . . . Minnesota . . . . .1995

Thomas Simmons . . . . . . Virginia . . . . . .1995

Tom Perrier . . . . . . . . . Kansas . . . . . . .1995

Ward Burroughs . . . . . . . California . . . . . .1995

C. Knight & B. Jacobs . . . Oklahoma . . . . .1996

C.W. Pratt . . . . . . . . . . Virginia . . . . . .1996

Cam Spike & Sally Forbes . Wyoming . . . . . .1996

Chris and John Christensen . South Dakota . . .1996

D. Borgen and B. McCulloh Wisconsin . . . . .1996

Frank Felton . . . . . . . . Missouri . . . . . .1996

Frank Schiefelbein . . . . . Minnesota . . . . .1996

Galen & Lori Fink . . . . . Kansas . . . . . . .1996

Gerald & Lois Neher . . . . Illinois . . . . . . .1996

Ingrid & Willy Volk . . . . North Carolina . . .1996

Mose & Dave Hebbert . . . Nebraska . . . . . .1996

Robert C. Miller . . . . . . Minnesota . . . . .1996

WillIowam A. Womack, Jr. . Alabama . . . . . .1996

Alan Albers . . . . . . . . . Kansas . . . . . . .1997

Blaine & Pauline Canning . California . . . . . .1997

Bob & Gloria Thomas . . . . Oregon . . . . . . .1997

Darel Spader . . . . . . . . South Dakota . . .1997

E. David Pease . . . . . . . California . . . . . .1997

Gregg & Diane Butman . . Minnesota . . . . .1997

Harold Pate . . . . . . . . . Alabama . . . . . .1997

James I. Smith . . . . . . . North Carolina . . .1997

Jim & JoAnn Enos . . . . . Illinois . . . . . . .1997

Juan Reyes . . . . . . . . . Wyoming . . . . . .1997

Nicholas Wehrmann . . . . Virginia . . . . . .1997

Richard McClung . . . . . . Virginia . . . . . .1997

Abilgail & Mark Nelson . . California . . . . .1998

Adrian Weaver & Family . . Colorado . . . . . .1998

Airey Family . . . . . . . . Canada . . . . . . .1998

Dallis & Tammy Basel . . . South Dakota . . .1998

Dave & Cindy Judd . . . . . Kansas . . . . . . .1998

Dick & Bonnie Helms . . . Nebraska . . . . . .1998

Duane L. Kruse Family . . . Illinois . . . . . . .1998

Earl & Neadra McKarns . . Ohio . . . . . . . .1998

James D. Benett Family . . Virginia . . . . . .1998

Tom Shaw . . . . . . . . . Idaho . . . . . . . .1998

Wilbur & Melva Stewart . . Canada . . . . . . .1998

Duane Schieffer . . . . . . . Montana . . . . . .1999

John Kluge . . . . . . . . . Virginia . . . . . . .1999

Kelly & Lori Darr . . . . . Wyoming . . . . . .1999

Kent Kline . . . . . . . . . South Dakota . . .1999

Kramer Farms . . . . . . . Illinois . . . . . . .1999

Lynn & Gary Pelton . . . . Kansas . . . . . . .1999

Noller & Frank Charolais . . Iowa . . . . . . . .1999

Rausch Herefords . . . . . . South Dakota . . .1999

Steve Munger . . . . . . . . South Dakota . . .1999

Terry O’Neill . . . . . . . . Montana . . . . . .1999

Tony Walden . . . . . . . . Alabama . . . . . .1999

Alan & Deb Vedvei . . . . . South Dakota . . .2000

Banks & Margo Hernon . . Alabama . . . . . .2000

Blane & Cindy Nagel . . . . South Dakota . . .2000

Galen. Lori and Megan Finkk Kansas . . . . . . .2000

Harlin & Susan Hecht . . . Minnesota . . . . .2000

BIF Seedstock Producer Honor Roll of Excellence

143

Jim & Janet Listen . . . . . Wyoming . . . . . .2000

John & Betty Botert . . . . Missouri . . . . . .2000

John C. Curtin . . . . . . . Illinois . . . . . . .2000

Kent Kline & Steve Munger South Dakota . . .2000

Larry & Jean Croissant . . . Colorado . . . . . .2000

Mike & T.K. McDowell . . Virginia . . . . . .2000

Ralph Blalock, Sr., Blalock, Jr. and David Blalock

. . . . . . . . . . . . . . North Carolina . . .2000

Vaughn Meyer & Family . . South Dakota . . .2000

Blane & Cindy Nagel . . . . South Dakota . . .2001

Bob & Nedra Funk . . . . . Oklahoma . . . . .2001

Dale, Don & Mike Spencer . Nebraska . . . . . .2001

Don & Priscilla Nielsen . . Colorado . . . . . .2001

Eddie L. Sydenstricker . . . Missouri . . . . . .2001

George W. Lemm . . . . . . Virginia . . . . . .2001

Ken Stielow & Family . . . Kansas . . . . . . .2001

Kevin, Jessica and Dakota Emily Moore

. . . . . . . . . . . . . . Texas . . . . . . . .2001

Marvin & Katheryn Robertson

. . . . . . . . . . . . . . Virginia . . . . . .2001

MCallen Ranch . . . . . . . Texas . . . . . . . .2001

Steve Hillman & Family . . Illinois . . . . . . .2001

Tom Lovell . . . . . . . . . Alabama . . . . . .2001

DeBruycker Charolais . . . Montana . . . . . .2002

Ellis Farms . . . . . . . . . Illinois . . . . . . .2002

Holly Hill Farm . . . . . . . Virginia . . . . . .2002

Isa Cattle Co., Inc. . . . . . . Texas . . . . . . . .2002

Lyons Ranch . . . . . . . . Kansas . . . . . . .2002

Noller and Frank Charolais . Iowa . . . . . . . .2002

Rishel Angus . . . . . . . . Nebraska . . . . . .2002

Running Creek Ranch . . . Colorado . . . . . .2002

Shamrock Angus . . . . . . Wyoming . . . . . .2002

Stewart Angus . . . . . . . Indiana . . . . . . .2002

Triple “M” Farm . . . . . . Alabama . . . . . .2002

Bedwell Charolais . . . . . Iowa . . . . . . . .2003

Boyd Farm . . . . . . . . . Alabama . . . . . .2003

Camp Cooley Ranch . . . . Texas . . . . . . . .2003

Hilltop Ranch . . . . . . . . Texas . . . . . . . .2003

Moser Ranch . . . . . . . . Kansas . . . . . . .2003

Mystic Hill Farms . . . . . Virginia . . . . . .2003

Pingetzer’s Six Iron Ranch . Wyoming . . . . . .2003

San Isabel Ranch . . . . . . Colorado . . . . . .2003

Shamrock Vale Farms . . . Ohio . . . . . . . .2003

Adams Angus Farm . . . . . Alabama . . . . . .2004

Byland Polled Shorthorns . Ohio . . . . . . . .2004

Camp Cooley Ranch . . . . Texas . . . . . . . .2004

Eaton Charolais . . . . . . . Montana . . . . . .2004

Flat Branch Cattle Company Illinois . . . . . . .2004

Judd Ranch, Inc. . . . . . . . Kansas . . . . . . .2004

Rausch Herefords . . . . . . South Dakota . . .2004

Reynolds Ranch . . . . . . Colorado . . . . . .2004

Silveira Brothers Angus and Diversified Farming

. . . . . . . . . . . . . . California . . . . . .2004

Symens Brothers Limousin . South Dakota . . .2004

Touchstone Angus . . . . . Wyoming . . . . . .2004

Triple U Ranch . . . . . . . Iowa . . . . . . . .2004

BIF Seedstock Producer Honor Roll of Excellence

144

Altenburg Super Baldy . . . Colorado . . . . . .2005

Bar S Ranch . . . . . . . . Kansas . . . . . . .2005

Ellis Farms . . . . . . . . . Illinois . . . . . . .2005

Ingram Cattle Company . . Mississippi . . . . .2005

Moore Farms . . . . . . . . Alabama . . . . . .2005

Morrison Stock Farm . . . . Ohio . . . . . . . .2005

Pangburn Stock Farm . . . . Iowa . . . . . . . .2005

Rishel Angus . . . . . . . . Nebraska . . . . . .2005

Rogers Bar HR . . . . . . . Mississippi . . . . .2005

Soldiers’ Hill Angus Farm . Virginia . . . . . .2005

Sunnyhill Angus Farm . . . Illinois . . . . . . .2005

Waukaru Farms, Inc. . . . . Indiana . . . . . . .2005

Benoit Angus Ranch . . . . Kansas . . . . . . .2006

Champion Hill . . . . . . . Ohio . . . . . . . .2006

EE Ranches, Inc. . . . . . . Mississippi . . . . .2006

Earhart Farms . . . . . . . . Wyoming . . . . . .2006

Figure 4 Cattle Company / Volk Ranch LLLP

. . . . . . . . . . . . . . Colorado . . . . . .2006

Lawler Farm . . . . . . . . Alabama . . . . . .2006

Powder Creek Simmentals . Georgia . . . . . .2006

Quaker Hill Farm LLC . . . Virginia . . . . . .2006

Sauk Valley Angus . . . . . Illinois . . . . . . .2006

Thomas Charolais, Inc. . . . Texas . . . . . . . .2006

Vorthmann Limousin . . . . Iowa . . . . . . . .2006

Waukaru Farms, Inc. . . . . Indiana . . . . . . .2006

Pelton Simmental . . . . . . Kansas . . . . . . .2007

5L Red Angus . . . . . . . . Montana . . . . . .2007

Bridle Bit Simmentals . . . . Colorado . . . . . .2007

Echo Ridge Farm . . . . . . Virginia . . . . . . .2007

Heartland Cattle Company . Iowa . . . . . . . .2007

Lindskov-Thiel Ranch . . . South Dakota . . . .2007

Star Lake Cattle Ranch . . . Oklahoma . . . . .2007

TC Ranch . . . . . . . . . . Nebraska . . . . . .2007

Tinney Farms . . . . . . . . Alabama . . . . . .2007

Tomlinson Farms . . . . . . Illinois . . . . . . .2007

Andras Stock Farm . . . . . Illinois . . . . . . .2008

Croissant Red Angus . . . . Colorado . . . . . .2008

Harms Plainview Ranch . . . Kansas . . . . . . .2008

Little Mountain Farm . . . . Alabama . . . . . .2008

C. H. Morris & Sons . . . . Virginia . . . . . . .2008

Nolin Red Angus . . . . . . Iowa . . . . . . . .2008

Schott Limousin Ranch . . . South Dakota . . . .2008

TC Ranch . . . . . . . . . . Nebraska . . . . . .2008

Thomas Ranch . . . . . . . South Dakota . . . .2008

Calyx Star Ranch . . . . . . Mississippi . . . . .2009

Champion Hill . . . . . . . . Ohio . . . . . . . .2009

Gibbs Farms . . . . . . . . . Alabama . . . . . .2009

Harrell Hereford Ranch . . . Oregon . . . . . . .2009

Musgrave Angus . . . . . . Illinois . . . . . . .2009

Oak Meadow Farm Simmentals

. . . . . . . . . . . . . . Minnesota . . . . .2009

Oak Ridge Angus . . . . . . California . . . . . .2009

Quaker Hill Farm . . . . . . Virginia . . . . . . .2009

Skarda Farms . . . . . . . . Iowa . . . . . . . .2009

Stucky Ranch . . . . . . . . Kansas . . . . . . .2009

BIF Seedstock Producer Honor Roll of Excellence

145

John Crowe California . . . . .1972

Mrs. R. W. Jones, Jr. Georgia . . . . . . .1973

Carlton Corbin Oklahoma . . . . .1974

Jack Cooper Montana . . . . . .1975

Leslie J. Holden Montana . . . . . .1975

Jorgenson Brothers South Dakota . . .1976

Glenn Burrows New Mexico . . . .1977

James D. Bennett Virginia . . . . . .1978

Jim Wolf Nebraska . . . . . .1979

Bill Wolfe Oregon . . . . . . .1980

Bob Dickinson Kansas . . . . . . .1981

A.F. “Frankie” Flint New Mexico . . . .1982

Bill Borror California . . . . . .1983

Lee Nichols Iowa . . . . . . . .1984

Ric Hoyt Oregon . . . . . . .1985

Leonard Lodoen North Dakota . . . .1986

Henry Gardiner Kansas . . . . . . .1987

W.T. “Bill” Bennett Washington . . . .1988

Glynn Debter Alabama . . . . . .1989

Douglas & Molly Hoff South Dakota . . .1990

Summitcrest Farms Ohio . . . . . . . .1991

Leonard Wulf & Sons Minnesota . . . . .1992

J. David Nichols Iowa . . . . . . . .1993

R.A. “Rob” Brown Texas . . . . . . . .1993

Richard Janssen Kansas . . . . . . .1994

Tom & Carolyn Perrier Kansas . . . . . . .1995

Frank Felton Missouri . . . . . .1996

Bob & Gloria Thomas Oregon . . . . . . .1997

Wehrmann Angus Ranch Virginia . . . . . .1997

Flying H Genetics Nebraska . . . . . .1998

Knoll Crest Farms Virginia . . . . . .1998

Morven Farms Virginia . . . . . .1999

Fink Beef Genetics . . . . . Kansas . . . . . . .2000

Sydenstricker Angus Farms Missouri . . . . . .2001

Circle A Ranch . . . . . . . Missouri . . . . . .2002

Moser Ranch . . . . . . . . Kansas . . . . . . .2003

Camp Cooley Ranch . . . . Texas . . . . . . . .2004

Rishel Angus . . . . . . . . Nebraska . . . . . .2005

Sauk Valley Angus . . . . . Illinois . . . . . . .2006

Pelton Simmental Red Angus

. . . . . . . . . . . . . . Kansas . . . . . . .2007

TC Ranch . . . . . . . . . . Nebraska . . . . . .2008

Champion Hill . . . . . . . . Ohio . . . . . . . .2009

Harrell Hereford Ranch . . . Oregon . . . . . . .2009

BIF Seedstock Producer of the Year

146

2010 SEEDSTOCK PRODUCER AWARD NOMINEES

Circle Ranch

Owners: Tim and Jill Curran and FamilyIone, California

CircleRanchisownedandoperatedbyTimandJillCurranandfamilyandislocatedinJacksonValleynearIone,California.TheCurransareafifthgenerationAmadorcountyranchingfamilythatdatesbackto1856.CircleRanchhasbeeninthecommercialcattlebusinesssince1975,butin1992purchasedagroupofSimmentalheifersfromNicholsFarmsinIowatoproducebullsfortheircommercialcowherd.Atthistime,CircleRanchwasinapartnershipwithJimandRebeccaKirkinMontereyCounty,California.ThepartnershiphadasetofAngusbasedcommercialcowsthattheyfeltneededsomecontinentalinfluence.TheylikedwhattheSimmentalbreedhadtoofferbutlikemanybreedsatthetimehadtendedtobelargerframed.TheNicholsheifersthattheyselectedaccomplishedwhattheywerelookingfortoproducebullsfortheircommercialAngusbasedcowherd.Theyretainedtheirmoderateframe,reapedthebenefitsofhybridvigor,increasedtheirredmeatyield,andendedupwithasetof½bloodfemalesthatweretheperfectcommercialfemalefortheirenvironment.Withinafewyearstheywereproducingmorebullsthantheycoulduseandstartedtosellafew.Theyfeltthattheidealbeefcowwasablendof25%to50%SimmentalandthebalanceAngus.Itsoonbecameobviousthattohelptheircus-tomersmaintainthispercentagefemalethelogicalsolutionwastoproducecompositebulls.TodayCircleRanchmarkets90bullsannuallywiththemajorityofthoseaSimAnguscompositeof50%Simmentaland50%Angus.

Today,CircleRanchmaintainsacowherdcontaining350mothercowsofwhich275areregisteredSi-mAngusorAngus.ThecowherdisnearlyallfallcalverswiththeheiferscalvinginmidJulyandthecowsstart-ingAugust1st.ThecowsaresummeredonirrigatedpastureonbothownedandleasedlandinJacksonValleyandnearSloughhouse.ThecowsarewinteredinthefoothillsalsonearIoneandSloughhouse.

In2007,CircleRanchpartneredwithBruinRanch(Auburn,CA)tocreatetheBeefSolutionsBullSale.ThedecisiontoenterintoapartnershipwithBruinRanchwaseasybecausetheyraiseoutstandingAnguscattleandsharemanyofthesamebreedingphilosophiesthatCircleRanchdoes.ThebullsaleisheldinSeptemberattheirhomeranchinIone.In2009,BeefSolutionsmarketed144bullsincluding70bullsfromCircleRanch.

TheCaliforniaBeefCattleImprovementAssociationisproudtonominateCircleRanch.

Edgewood Angus

Owners: Pete, Connie and Peter HendersonWest Point, Virginia

EdgewoodAngusconsistsofa200-cowregisteredAngusherdwhichhasbeendevelopedsincetheearly1980sfromacommercialherd.PeteandhiswifeConnie,alongwiththeirson,Peter,andinconjunctionwiththeirdaughtersandPeter’swifehavemanagedtomakeEdgewoodafamilyaffair. In2000, theoperationex-panded from75 to roughly450acresandmoved theprimaryoperation fromWilliamsburg toKingWilliam,Virginia.Sincethattime,theyhaveallworkedveryhardonimprovingpastures,fencing,andcattlemanagementinfrastructure.

147

EdgewoodAngushasbeenconsigningbullstotheBCIAteststationsforover14years.DuringthattimetheyhavedevelopedastrongreputationforqualitygeneticsandhavehadseveralbullstoptheBCIAtestsandsales.Consistent,predictablegeneticshasbeenthefocuswhichhasbeenaccomplishedthroughtheuseofprovensires.CustomerserviceisahighpriorityforEdgewoodAngus,andtheyworkdiligentlytoassesstheneedsoftheircommercialbullbuyerstodesigngeneticsthatwilldothejobforthem.

EdgewoodhasbeenawardedtheBartenslagerAwardandPremierAngusBreederAwardontwooccasionsfromBCIAin2007and2009.InDecember,Edgewoodhostedtheirsecondannualon-farmperformancetestedOpenHousebullsale.Selectfemalesareofferedthroughconsignmentsales.

PeteisthepastpresidentofBCIAandiscurrentlythechairoftheBCIACulpeperTestandSaleCommit-tee.HeisalsoveryactivewithVirginiaAngusandotherbeefandAgentities.

TheVirginiaBeefCattleImprovementAssociationisproudtonominateEdgewoodAngus.

McBee Cattle Company

Owners: Ron and Teri McBeeFayette, Missouri

McBeeCattleCompany,ownedandoperatedbyRonandTeriMcBee,hasbeenlocatedinnorthcentralMissourisince1979.Theranchpresentlyconsistsof1650acresofrollingfescuebasedpastureinan80paddockintensivegrazingsystemthatishometo250registeredBraunvieh/AngusHybridfemalesand200commercialfemalesofmostlyBraunviehinfluencedgenetics.Utilizingbothspringandfallcalvingseasons,anextensiveAIprogram,andseveralhomeraisedsiresareusedtoadvanceboththepurebredandhybridherds.

Beginningin1999,thebestBraunviehcowshavebeenmatedtosomeofthetopperformanceAngusbullstobeginthedevelopmentoftheirfirstgenerationF1’s,calledMcBeefBuilderHybrids.MatingtheirtopF1bullstotheirF1females,theyarenowproducing2ndand3rdgenerationF1hybrids.IncludingbullsfromcustomerswhohavepurchasedMcBeegeneticsinacooperatorprogram,100to120bullsaredevelopedannuallywithahighfiber,lowstarchcommodityrationandperformancemeasuredforgrowthandcarcasstraits.Afterarigidcullingprogram,only60%areofferedforbreeding,inanannualSELECTIONDAYsaleattheranch.

CustomerservicehasalwaysbeenpartoftheMcBeeprogram.Since1993,bullcustomers’calveshavebeengroupedandmarketedthroughtheMcBeeCalfRoundupsheldeveryspringandfall.CustomerscanseeadditionalpremiumsthroughtheMcBeeGateCutHeiferSaleandthenewMcBeeGeneticAdvantageProgram.

TheBraunviehAssociationofAmericaisproudtonominateMcBeeCattleCompany.

Rincker Simmentals

Owners: Curt, Pam, Cari and Brent RinckerShelbyville, Illinois

RinckerSimmentalsofShelbyville,IllinoisinShelbyCountyhavebeeninthepurebredbeefseedstockbusinesssince1972.ThisiswhenCurtbeganraisingSimmentalcattlewithhisfather,Leland.Priortothis,theLelandRinckerfamilywasintheAngusbusiness.Lelandwasrecentlyrecognizedasthe2008IllinoisSimmen-

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talPioneerBreederoftheyear.TheRinckerSimmentalherdcomprises80Simmentalbroodcowsthatproduceseedstockforthepurebredandcommercialherds.BullsandheifersaresoldthroughtheIllinoisPerformanceTestedBullSale,Mid-AmericanShowcaseSimmentalSale,andthefarm’sIlliniEliteSale.Bullsarealsocon-signedinanEasternMissourisaleatBowlingGreen.Anotherwaytomarkettheirgeneticsisthroughthefarm’swebsiteatwww.rincker.com.

AnaggressiveEmbryoTransferProgramhasbeenusedforseveralyearstomultiplytheprogenyofout-standingfemalesintheherd.ThisbreedingprogramhasresultedinnumeroussuccessesattheIllinoisStateFairOpenandJuniorShows.Theherdispredominantlyawinterandspringcalvingherdalthoughrecentinteresthasledtolimitedfallcalvingtoenhancetheinterestinbothbreedingbullsandfemales.

PerformanceTesting,EPDsandDollarValueIndexingarestressedatRinckerSimmentalswiththesetraitscombinedintoaphenotypicpackagethatpossessesstructuralsoundness,desirableconformationandbalanceforthebeefcattleindustry.RinckerSimmentalshavecontinuallyconsignedseveralofthetopindexingandsellingbullsintheIllinoisPerformanceTestedBullSale.

OneofthemajorcomponentsthathaveallowedtheRinckerSimmentaloperationtobesuccessfulistheirpastureandgrazingprogram.Theprimarilycattlefarmcomprises265acreswith105acresdedicatedtotallytograzing,50acresforhayproductionandgrazing,andtheremainderforcashgrainproductionandwintergrazing.

RinckerSimmentals is proudlynominatedby theUniversity of IllinoisExtension and the IllinoisBeefAssociation.

Sandhill Farms

Owners: Kevin and Vera SchultzHaviland, Kansas

SandhillFarmsisafamilyoperationlocatedinsouthcentralKansasnearHaviland.Originallyhome-steadedin1869,eachgenerationoftheSchultzfamily,whichcurrentlyisraisingitssixth,hasmadeitslivingontheresourcesprovidedbytheland,principallyinEdwardsCounty.Today,asinthepast,theoperationiscom-prisedofbothfarmingandranchingenterprises.

RoySchultzoriginallypurchasedregisteredpolledHerefordbullsforuseonthecommercialcowherdinthemid1940s.Hisson,Ron,continuedthisprogram.Sincethattime,allfemalesretainedforreplacementsinthecommercialherdhavebeenraisedontheranch.Inthe1970s,aspartofa4-Hproject,grandsonKevinboughtandstartedtheregisteredHerefordherd.Theherdtodayconsistsof300broodcows,withabouttwo-thirdsbeingregisteredandone-thirdbeingpurebredcommercial.

Thespring-onlybreedingprogramusesartificial insemination(AI),embryo transfer (ET)andcleanupbulls.AIisutilizedonallcommercialandregisteredyearlingheifers,andtopperformingregisteredcows.Thecommercialandbottom-endregisteredcowsareusedasrecipientsintheETprogram.SandhillFarmshastestedmorebullsintheAmericanHerefordAssociation(AHA)sireteststhananyotherbreederinthelast10years.Af-terbeingtestedandproven,thetopbullsareusedinthebreedingprogram.Outsidebullsthatarehighlyaccurateandprovenfrommultipleherdsarealsoused.

Ultrasoundhasbeenusedintheprogramfor thepast17years. Atfirst, littleornoimprovementwasmadeinthisarea,astheywereaddressingothermorelimitingfactorsincustomeracceptance.However,astheyworkedtowardtheirgoalofmakingthecattlebetter,theyfoundoutliersthatcouldprovideboththetypeofcattletheyknewworkedandthebackingofprovenEPDs.Today,theirsalebulls’averageEPDsrankinthetop1%of

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thebreedintwoofthefourindexes,andtop10%intheremainingindexes.

Inthecowherd,turninggenerationsisagoal.Intheirspringsale,theysoldallthe5-year-oldcowswithheifercalves.Priortocalving,thepregnanciesaresexed.Thisallowsthemtocontinuekeepingalltheyearlingfemales,andmaintainstabilityinthecowherd.Nexttosellingbulls,theirfavoritethingtodoiscullthecowherd.Thisenablesthemtoimprovetheoverallqualityoftheircattle.Theybelievewhenbuyingcattlefromaseedstockproducer,youarebuyingthebenefitsanddisciplineoftheircullingprogram.

SandhillFarmsisproudlynominatedbytheKansasLivestockAssociation.

Schuler Red Angus

Owners: Schuler-Olsen Ranches, Inc. and the Darrell Schuler FamilyBridgeport, Nebraska

LocatedinthepanhandleofwesternNebraska,Schuler-OlsenRancheswasstartedbyDarrellandMaryLouSchulerin1959withcommercialHerefordcattle.Acrossbreedingprogramwasimplementedintheearly1970’sandafterwitnessingthebenefitsofheterosisandbreedcomplementarityfirst-hand,aregisteredRedAngusherdwasstartedin1976todevelopseedstockforuseontheranch’scommercialcattleandtoselltoneighboringoperations.

Theseedstockherdexpandedinthe1980’sandwasimprovedthroughartificialinsemination,utilizationofEPDsandacompleteperformancetestingprogram.Recognizingtheneedforidentifiablecarcasstraits,in1991SchulerRedAngusbeganfinishingitscommercialprogenyandcollectingcarcassdatawiththeassistanceofUNLBeefCattleSpecialist,Dr.IvanRush.ThisprogramexpandedtoincludestructuredcarcasstestingoncustomercattlesiredbySchulerRedAngusbulls.Over25%oftheRedAngusbreed’shighaccuracycarcasstraitsireshavebeenprovenbySchulerRedAngus.Acompositecowherdwasstartedin1992whichincludedRedAngus,Hereford,Gelbvieh,andSimmentalgenetics.

Thecurrentranchingoperationencompasses17,000acresincluding2,000acresofprivatepastureleasesand1250acresofirrigatedfarmground.ButchandSusanSchulerandtheirchildrenStephanieandDavidman-agetheoperationtodaywithapproximately1000headofspringcalvingfemales.TheSchuler’shostedtheir28thproductionsalethisspringselling150RedAngusandRedAnguscompositebullsand40headofregisteredRedAngusheifers.

SchulerRedAngusisproudlynominatedbytheNebraskaCattlemen.

Spring Creeks Cattle Company

Owners: Bob and Rhonda Mitchell and sons (Matt, Bart and Scott)Wauzeka, Wisconsin

NeartheWisconsinRiverinsouthwesternWisconsin,SpringCreeksCattleCompanymanagemorethan600registeredLimousin,Lim-Flex®,andAngusbreedingstock. Over35years,theirenterprisehasgrowntoinclude3,000acresofownedandleasedpastureandcropland.Whilefarmingisthenormaroundthere,theytakearanchingapproachtotheiroperationandfarmtosupportthecattle,allowingthemtooptimizetheirnaturalresourcesresponsibly.

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Theyfocusonproducingsound,practicalcattlethatcanbeproductiveinanyenvironment.TheyhavebuilttheircowherdwithprovenherdbullsandeliteAIsires.Theirbreedingprogramcentersaround“breedin’thefeedin’kind”andproducingseedstockthathaveapositiveeffectintoday’sbeefindustry.Theysellbulls,maturecows,bredfemalesandopenheifersoffthefarm.

Theyraisecalveswithoutantibioticsorhormonesandpromotetheirnaturallylean,meatycarcasses.AportionoftheircalfcropgoestoLaura’sLeanBeefandStraussFreeRaised®vealeachyear.TheywerehappytohelpintroduceStraussBrandstotheLimousinbreedandforgeitssourcingagreementwiththeNorthAmericanLimousinFoundation,providinganewmarketingopportunitytoallLimousin-influencedcalves.

TheSpringCreeksCattleCompanyareproudspokespeoplefortheindustry,includingBartandAmy’sappearanceon“TheOprahWinfreyShow”in2008toexplainhowtheyraiseandmanagefree-raisedvealcalves.Theirfamilyisinvolvedinavarietyofindustryorganizations,includingBob’srecentserviceontheNALFBoardofDirectorsandhistermsasitsvicepresidentandtreasurer.

TheNorthAmericanLimousinFoundationisproudtonominateSpringCreeksCattleCompany.

Windy Hill Angus Farm

Owner: Jack TateBoaz, Alabama

WindyHillAngusFarmhasbeenraisingregisteredAnguscattlefor32yearsinBoaznearSardisCity,Alabama.Thecowherdhasrangedfrom30to90cows,andatpresentconsistsof65cowsand10openheif-ers. ThebreedingherdconsistsofprimarilyafallcalvingseasonfromSeptembertoJanuarytocapitalizeonbullevaluationsandproductionsalemarketingwithasmallspringherdcalvinginMarch,aswell.Allbreedingfemalesarebredusingestroussynchronizationandartificialinseminationwithacleanupbullfora75-90%AIsiredcalfcropwith10%ofthecalfcropproducedfromtheembryotransferprogram.

Allproductionweights,carcassultrasound,andaveragedailygainsfromanon-farm84daytestarecol-lected. Allperformance informationhasbeenmaintainedbyutilizing theAngusHerdImprovementRecordsforthepast30years.Foryears,bullcalvesandtheirbloodlineswereevaluatedbyparticipatingintheAlabamaBCIANorthAlabamaBullEvaluationandmorerecentlyalsointheUniversityofFloridaBullTest.Bullsarealsomarketedbyprivatetreaty,intheNortheastAlabamaPerformanceBreedersBullSale,andinAlabamaBCIAsponsoredsales.In2009,WindyHillAngusheldtheirfirstproductionsalewithguestconsignorsonthefirstSaturdayinMayandheldtheirsecondsaleonMay1,2010.

JackTateisanactiveleaderinhisstateandnationalbreedassociations.Heisapast-presidentanddirec-toroftheAlabamaAngusAssociationandhasservedasadelegatetotheAmericanAngusAssociationnationalmeetinginLouisville,Kentuckyforthepast20years.

WindyHillAngusFarmisproudlynominatedbytheAlabamaBeefCattleImprovementAssociation.

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Chan Cooper Montana 1972

Alfred B Cobb, Jr Montana 1972

Lyle Eivens Iowa 1972

Broadbent Brothers Kentucky 1972

Jess Kilgote Montana 1972

Clifford Ouse Minnesota 1973

Pat Wilson Florida 1973

John Glaus South Dakota 1973

Sig Peterson North Dakota 1973

Max Kiner Washington 1973

Donald Schott Montana 1973

Stephen Garst Iowa 1973

J K Sexton California 1973

Elmer Maddox Oklahoma 1973

Marshall McGregor Missouri 1974

Dave Matti Montana 1974

Lloyd DeBruycker Montana 1974

Gene Rambo California 1974

Jim Wolf Nebraska 1974

Henry Gardiner Kansas 1974

Johnson Brothers South Dakota 1974

John Blankers Minnesota 1975

Paul Burdett Montana 1975

Oscar Burroughs California 1975

John R Dahl North Dakota 1975

Eugene Duckworth Missouri 1975

Gene Gates Kansas 1975

V A Hills Kansas 1975

Robert D Keefer Montana 1975

Kenneth E Leistritz Nebraska 1975

Ron Baker Oregon 1976

Dick Boyle Idaho 1976

James Hackworth Missouri 1976

John Hilgendorf Minnesota 1976

Kahau Ranch Hawaii 1976

Milton Mallery California 1976

Robert Rawson Iowa 1976

William A Stegner North Dakota 1976

U S Range Exp Stat Montana 1976

Maynard Crees Kansas 1977

Ray Franz Montana 1977

Forrest H Ireland South Dakota 1977

John A Jameson Illinois 1977

Leo Knoblauch Minnesota 1977

Jack Pierce Idaho 1977

Mary & Stephen Garst Iowa 1977

Todd Osteross North Dakota 1978

Charles M Jarecki Montana 1978

Jimmy G McDonnal North Carolina 1978

Victor Arnaud Missouri 1978

Ron & Malcom McGregor Iowa 1978

Otto Uhrig Nebraska 1978

Arnold Wyffels Minnesota 1978

Bert Hawkins Oregon 1978

Mose Tucker Alabama 1978

Dean Haddock Kansas 1978

Myron Hoeckle North Dakota 1979

Harold & Wesley Arnold South Dakota 1979

BIF Commercial Producer Honor Roll of Excellence

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Ralph Neill Iowa 1979

Morris Kuschel Minnesota 1979

Bert Hawkins Oregon 1979

Dick Coon Washington 1979

Jerry Northcutt Missouri 1979

Steve McDonnell Montana 1979

Doug Vandermyde Illinois 1979

Norman, Denton & Calvin Thompson South Dakota 1979

Jess Kilgore Montana 1980

Robert & Lloyd Simon Illinois 1980

Lee Eaton Montana 1980

Leo & Eddie Grubl South Dakota 1980

Roger Winn, Jr Virginia 1980

Gordon McLean North Dakota 1980

Ed Disterhaupt Minnesota 1980

Thad Snow Canada 1980

Oren & Jerry Raburn Oregon 1980

Bill Lee Kansas 1980

Paul Moyer Missouri 1980

G W Campbell Illinois 1981

J J Feldmann Iowa 1981

Henry Gardiner Kansas 1981

Dan L Weppler Montana 1981

Harvey P Wehri North Dakota 1981

Dannie O’Connell South Dakota 1981

Wesley & Harold Arnold South Dakota 1981

Jim Russell & Rick Turner Missouri 1981

Oren & Jerry Raburn Oregon 1981

Orin Lamport South Dakota 1981

Leonard Wulf Minnesota 1981

Wm H Romersberter Illinois 1982

Milton Krueger Missouri 1982

Carl Odegard Montana 1982

Marvin & Donald Stoker Iowa 1982

Sam Hands Kansas 1982

Larry Campbel Kentucky 1982

Earl Schmidt Minnesota 1982

Raymond Josephson North Dakota 1982

Clarence Reutter South Dakota 1982

Leonard Bergen Canada 1982

Kent Brunner Kansas 1983

Tom Chrystal Iowa 1983

John Freltag Wisconsin 1983

Eddie Hamilton Kentucky 1983

Bill Jones Montana 1983

Harry & Rick Kline Illinois 1983

Charlie Kopp Oregon 1983

Duwayne Olson South Dakota 1983

Ralph Pederson South Dakota 1983

Ernest & Helen Schaller Missouri 1983

Al Smith Virginia 1983

John Spencer California 1983

Bud Wishard Minnesota 1983

Bob & Sharon Beck Oregon 1984

Leonard Fawcett South Dakota 1984

Fred & Lee Kummerfeld Wyoming 1984

Norman Coyner & Sons Virginia 1984

Franklyn Esser Missouri 1984

BIF Commercial Producer Honor Roll of Excellence

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Edgar Lewis Montana 1984

Boyd Mahrt California 1984

Neil Moffat Canada 1984

William H Moss, Jr Georgia 1984

Dennis P Solvie Minnesota 1984

Robert P Stewart Kansas 1984

Charlie Stokes North Carolina 1984

Milton Wendland Alabama 1984

Bob & Sheri Schmidt Minnesota 1985

Delmer & Joyce Nelson Illinois 1985

Harley Brockel South Dakota 1985

Kent Brunner Kansas 1985

Glenn Havery Oregon 1985

John Maino California 1985

Ernie Reeves Virginia 1985

John R Rouse Wyoming 1985

George & Thelma Boucher Canada 1985

Kenneth Bentz Oregon 1986

Gary Johnson Kansas 1986

Ralph G Lovelady Alabama 1986

Ramon H Oliver Kentucky 1986

Kay Richarson Florida 1986

Mr & Mrs Clyde Watts North Carolina 1986

David & Bev Lischka Canada 1986

Dennis & Nancy Daly Wyoming 1986

Carl & Fran Dobitz South Dakota 1986

Charles Fariss Virginia 1986

David Forster California 1986

Danny Geersen South Dakota 1986

Oscar Bradford Alabama 1987

R J Mawer Canada 1987

Rodney G Oliphant Kansas 1987

David Reed Oregon 1987

Jerry Adamson Nebraska 1987

Gene Adams Georgia 1987

Hugh & Pauline Maize South Dakota 1987

P T McIntire & Sons Virginia 1987

Frank Disterhaupt Minnesota 1987

Mac, Don and Joe Griffith Georgia 1988

Jerry Adamson Nebraska 1988

Ken, Wayne & Bruce Gardiner Canada 1988

C L Cook Missouri 1988

C J and D A McGee Illinois 1988

William E White Kentucky 1988

Frederick M Mallory California 1988

Stevenson Family Oregon 1988

Gary Johnson Kansas 1988

John McDaniel Alabama 1988

William Stegner North Dakota 1988

Lee Eaton Montana 1988

Larry D Cundall Wyoming 1988

Dick & Phyllis Henze Minnesota 1988

Jerry Adamson Nebraska 1989

J W Aylor Virginia 1989

Jerry Bailey North Dakota 1989

James G Guyton Wyoming 1989

Kent Koostra Kentucky 1989

Ralph G Lovelady Alabama 1989

BIF Commercial Producer Honor Roll of Excellence

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Thomas McAvory, Jr Georgia 1989

Bill Salton Iowa 1989

Lauren & Mel Schuman California 1989

Jim Tesher North Dakota 1989

Joe Thielen Kansas 1989

Eugene & Ylene Williams Missouri 1989

Phillip, Patty & Greg Bartz Missouri 1990

John C Chrisman Wyoming 1990

Les Herbst Kentucky 1990

Jon C Ferguson Kansas 1990

Mike & Dianna Hooper Oregon 1990

James & Joan McKinlay Canada 1990

Gilbert Meyer South Dakota 1990

DuWayne Olson South Dakota 1990

Raymond R Peugh Illinois 1990

Lewis T Pratt Virginia 1990

Ken and Wendy Sweetland Canada 1990

Swen R Swenson Cattle Texas 1990

Robert A Nixon & Sons Virginia 1991

Murray A Greaves Canada 1991

James Hauff North Dakota 1991

J R Anderson Wisconsin 1991

Ed and Rich Blair South Dakota 1991

Reuben & Connee Quinn South Dakota 1991

Dave & Sandy Umbarger Oregon 1991

James A Theeck Texas 1991

Ken Stielow Kansas 1991

John E Hanson, Jr California 1991

Charles & Clyde Henderson Missouri 1991

Russ Green Wyoming 1991

Bollman Farms Illinois 1991

Craig Utesch Iowa 1991

Mark Barenthsen North Dakota 1991

Rary Boyd Alabama 1992

Charles Daniel Missouri 1992

Jed Dillard Florida 1992

John & Ingrid Fairhead Nebraska 1992

Dale J Fischer Iowa 1992

E Allen Grimes Family North Dakota 1992

Kopp Family Oregon 1992

Harold, Barbara & Jeff Marshall Pennsylvania 1992

Clinton E Martin & Sons Virginia 1992

Loyd and Pat Mitchell Canada 1992

William Van Tassel Canada 1992

James A Theeck Texas 1992

Aquilla M Ward West Virginia 1992

Albert Wiggins Kansas 1992

Ron Wiltshire Canada 1992

Andy Bailey Wyoming 1993

Leroy Beiterspacher South Dakota 1993

Glenn Valbaugh Wyoming 1993

Oscho Deal North Carolina 1993

Jed Dillard Florida 1993

Art Farley Illinois 1993

Jon Ferguson Kansas 1993

Walter Hunsuker California 1993

Nola & Steve Kielboeker Missouri 1993

BIF Commercial Producer Honor Roll of Excellence

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Jim Maier South Dakota 1993

Bill & Jim Martin West Virginia 1993

Ian & Adam McKillop Canada 1993

George & Robert Pingetzer Wyoming 1993

Timothy D Sufphin Virginia 1993

James A Theeck Texas 1993

Gene Thiry Canada 1993

Fran & Beth Dobitz South Dakota 1994

Bruce Hall South Dakota 1994

Lamar Ivey Alabama 1994

Gordon Mau Iowa 1994

Randy Mills Kansas 1994

W W Oliver Virginia 1994

Clint Reed Wyoming 1994

Stan Sears California 1994

Walter Carlee Alabama 1995

Nicholas Lee Carter Kentucky 1995

Charles C Clark, Jr Virginia 1995

Greg & Mary Gunningham Wyoming 1995

Robert & Cindy Hine South Dakota 1995

Walter Jr & Evidean Major Kentucky 1995

Delhert Ohnemus Iowa 1995

Henry Stone California 1995

Joe Thielen Kansas 1995

Jack Turnell Wyoming 1995

Tom Woodard Texas 1995

Jerry and Linda Bailey North Dakota 1996

Kory M Bierle South Dakota 1996

Mavis Dummermuth Iowa 1996

Terry Stuard Forst Oklahoma 1996

Don W Freeman Alabama 1996

Lois & Frank Herbst Wyoming 1996

Mr & Mrs George A Horkan, Jr Virginia 1996

David Howard Illinois 1996

Virgil & Mary Jo Huseman Kansas 1996

Q S Leonard North Carolina 1996

Ken & Rosemary Mitchell Canada 1996

James Sr , Jerry, & James Petlik South Dakota 1996

Ken Risler Wisconsin 1996

Merlin Anderson Kansas 1997

Joe C Bailey North Carolina 1997

William R “Bill” Brockett Virginia 1997

Howard McAdams, Sr & Howard McAdams, Jr North Carolina 1997

Rob Orchard Wyoming 1997

David Petty Iowa 1997

Rosemary Rounds and Marc & Pam Scarborough South Dakota 1997

Morey and Pat Van Hoecke Minnesota 1997

Randy and Judy Mills Kansas 1998

Mike and Priscille Kasten Missouri 1998

Amana Farms, Inc Iowa 1998

Terry and Dianne Crisp Canada 1998

Jim and Carol Faulstich South Dakota 1998

James Gordon Fitzhugh Wyoming 1998

John B Mitchell Virginia 1998

Holzapfel Family California 1998

Mike Kitley Illinois 1998

BIF Commercial Producer Honor Roll of Excellence

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Wallace & Donald Schilke North Dakota 1998

Doug & Ann Deane and Patricia R Spearman

Colorado 1998

Glenn Baumann North Dakota 1999

Bill Boston Illinois 1999

C-J-R- Christensen Ranches Wyoming 1999

Ken Fear, Jr Wyoming 1999

Giles Family Kansas 1999

Burt Guerrieri Colorado 1999

Karlen Family South Dakota 1999

Deseret Ranches of Alberta Canada 1999

Nick and Mary Klintworth North Dakota 1999

MW Hereford Ranch Nebraska 1999

Mossy Creek Farm Virginia 1999

Iris, Bill, & Linda Lipscomb Alabama 1999

Amana Farms, Inc Iowa 2999

Tony Boothe Alabama 2000

Glenn Clabaugh Wyoming 2000

Connie, John & Terri Griffith Kansas 2000

Frank B Labato Colorado 2000

Roger & Sharon Lamont and Doug & Shawn Lamont South Dakota 2000

Bill and Claudia Tucker Virginia 2000

Wayne and Chip Unsicker Illinois 2000

Billy H Bolding Alabama 2001

Mike and Tom Endress Illinois 2001

Henry and Hank Maxey Virginia 2001

Paul McKee Kansas 2001

3-R Ranch Colorado 2002

Agri-Services Division, Oklahoma Department of Corrections Oklahoma 2002

Alpine Farms Virginia 2002

Amana Farms Iowa 2002

Griffin Seedstock Kansas 2002

Indian Knoll Cattle Co Illinois 2002

Miles Land and Livestock Wyoming 2002

Shovel Dot Ranch Nebraska 2002

Torbert Farms Alabama 2002

White Farms Iowa 2002

Voyles Farms Indiana 2002

Clear Creek Cattle Company Wyoming 2003

Crider Salers North Dakota 2003

Mike Goldwasser Virginia 2003

Patterson Ranch Colorado 2003

W S Roberts and Sons Indiana 2003

Shriver Farms Ohio 2003

Stroud Farms Alabama 2003

Tailgate Ranch Company Kansas 2003

Burkhalter Cattle Alabama 2004

Doler Farm Mississippi 2004

LU Ranch Wyoming 2004

Namminga Angus South Dakota 2004

Nellwood Farms Georgia 2004

Olsen Ranches, Inc Nebraska 2004

Prather Ranch (Ralphs Ranches Inc ) California 2004

Blair Porteus and Sons Ohio 2004

Rx Ranch Missouri 2004

Schuette Farms Illinois 2004

BIF Commercial Producer Honor Roll of Excellence

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Valdez Ranches Colorado 2004

Wickstrum Farms, Inc Kansas 2004

CK Ranch Kansas 2005

Diamond V Ranch North Dakota 2005

Dover Ranch Montana 2005

Gaines Farm Alabama 2005

Hillwinds Farm Virginia 2005

Krupps Farm Illinois 2005

Jack and Ila Mae Larson Colorado 2005

Mule Creek Ranch Kansas 2005

Paxton Ranch Nebraska 2005

Pontious Farms Ohio 2005

Prather Ranch California 2005

Shovel Dot Ranch Nebraska 2005

Wintergreen Farm Iowa 2005

Duck Farm Inc Virginia 2006

Hunt Hill Cattle Co Mississippi 2006

McDorman Farms Ohio 2006

Pitchfork Ranch Illinois 2006

Rock Creek Ranch Kansas 2006

Sutherland Ranches Colorado 2006

Van Waarhuizen, Inc Iowa 2006

Broseco Ranch Texas 2007

4Z Farms Kansas 2007

CK Ranch Kansas 2007

Barry and Larry Dowell Families Illinois 2007

Eagle Rock Ranch Colorado 2007

Eatinger Cattle Company, Inc Nebraska 2007

JHL Ranch Nebraska 2007

Lacey Livestock California 2007

Lerwick Brothers LLC Wyoming 2007

MG Farms Mississippi 2007

Stuart Land & Cattle Company Virginia 2007

CL Ranches Ltd Canada 2008

Eatinger Cattle Company, Inc Nebraska 2008

Frank Farms Colorado 2008

Genereux Ranch Montana 2008

Jack Giltner Iowa 2008

Hollow Hill Farm Virginia 2008

JL Cattle Company Colorado 2008

Kniebel Farms & Cattle Company Kansas 2008

Otley Brothers Inc Oregon 2008

Toland’s River Oak Ranch Illinois 2008

Tom Bengard Ranches California 2008

Win Parmer Ranch Alabama 2008

Anderson Land and Cattle Kansas 2009

Tom Bengard Ranches California 2009

Joe Davis Cattle Farm South Carolina 2009

Freedom Hills Ranch Illinois 2009

JHL Ranch Nebraska 2009

Gale Rippey Farms Virginia 2009

Slusher Valley Farms Virginia 2009

Stephens Farm Alabama 2009

BIF Commercial Producer Honor Roll of Excellence

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Chan Cooper Montana 1972

Pat Wilson Florida 1973

Lloyd Nygard North Dakota 1974

Gene Gates Kansas 1975

Ron Baker Oregon 1976

Mary & Stephen Garst Iowa 1977

Mose Tucker Alabama 1978

Bert Hawkins Oregon 1979

Jess Kilgore Montana 1980

Henry Gardiner Kansas 1981

Sam Hands Kansas 1982

Al Smith Virginia 1983

Bob & Sharon Beck Oregon 1984

Glenn Harvey Oregon 1985

Charles Fariss Virginia 1986

Rodney G Oliphant Kansas 1987

Gary Johnson Kansas 1988

Jerry Adamson Nebraska 1989

Mike & Diana Hopper Oregon 1990

Dave & Sandy Umbarger Oregon 1991

BIF Commercial Producer of the Year

Kopp Family Oregon 1992

Jon Ferguson Kansas 1993

Fran & Beth Dobitz South Dakota 1994

Joe & Susan Thielen Kansas 1995

Virgil & Mary Jo Huseman Kansas 1996

Merlin & Bonnie Anderson Kansas 1997

Mike & Priscilla Kasten Missouri 1998

Randy & Judy Mills Kansas 1998

Giles Family Kansas 1999

Mossy Creek Farm Virginia 1999

Bill & Claudia Tucker Virginia 2000

Maxey Farms Virginia 2001

Griffith Seedstock Kansas 2002

Tailgate Ranch Kansas 2003

Olsen Ranches, Inc Nebraska 2004

Prather Ranch California 2005

Pitchfork Ranch Illinois 2006

Broseco Ranch Colorado 2007

Kniebel Farms and Cattle Company Kansas 2008

JHL Ranch Nebraska 2009

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2010 COMMERCIAL PRODUCER AWARD NOMINEES

Stan and Lisa Buzzard

Beecher City, Illinois

In 1996, Stan and Lisa Buzzard of Beecher City, Illinois, purchased 27 bred heifers from the Elite Heifer Program at Paris, Kentucky, which was the start of their commercial cow-calf business. The Buzzards now run an 800 acre grain and commercial beef cattle operation consisting of 120 cow-calf pairs and 45 replacement heifers. The brood cows are an Angus/Charolais cross and are smoky colored and are bred to high performance, superior carcass Angus bulls to produce approximately 25% smoky calves and 75% black calves.

In the spring the cow-calf pairs are divided into groups and are placed into multiple pastures. Their herd calving occurs during the months of February through April. The herd has a tight calving period with 85% of their calves born within the first 30 days of calving. With multiple pastures, the Buzzards are able to analyze the performance of each individual sire. Calves are early weaned the first week in August at 165 days of age, while maintaining a 96 percent calf crop at weaning with a 526 pound weaning weight. The steers are finished in the feedlot and the heifers are enrolled in the Illinois Heifer Development Program. The Buzzards have consigned heifers in the spring calving sale since 2003 receiving premium prices with repeat buyers.

Stan began farming in 1973, while at the same time working and co-owning a concrete construction com-pany. In 2000, he sold his shares of the company to devote his time to the farm.

Stan and Lisa Buzzard are proudly nominated by the University of Illinois Extension and the Illinois Beef Association.

Downey Ranch

Owners: Members of the Joseph L. Downey FamilyManagers: Joe Carpenter and Barb Downey

Wamego, Kansas

Downey Ranch, Inc. (DRI) is located in the Flint Hills, just southeast of Manhattan, KS. Formed in 1986 by Joe Downey, the ranch encompasses more than 6,300 acres of mostly forages and a herd of 550 cows. The ranch calves 425 spring cows, which includes 140 registered Angus and 285 commercial Angus, F-1 baldies and Red Angus x Angus cows. In addition, there are 125 fall-calving commercial cows with the same breed make-up.

From day one, DRI has focused on the efficient production of high-quality beef utilizing a multitude of practices to make sure no animal “ever has a bad day.” Low-stress handling of all cattle; integrated disease management, including vaccinations and providing an environment to prevent disease; fence-line weaning; early weaning; research- and feedback-based management; individual end-point management of feedlot cattle, etc. are tools used by DRI to accomplish this goal.

Currently, almost all calves (except seedstock) are marketed through U.S. Premium Beef on an age- and source-verified quality grid. Commercial bred females and registered bulls are marketed through an annual pro-duction sale. DRI assists female and bull customers in optimizing return on their calves using those tools that have proven successful.

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With a good handle on production, DRI now is focused on intense financial analysis, having added a fam-ily member to the team for that purpose. With all these tools, DRI hopes to ensure a well-run business, capable of supporting family members and employees, that exists long into the future.

The Kansas Livestock Association is proud to nominate Downey Ranch.

G.W. Jones and Sons Farms

Owners: Raymond B. Jones, Elizabeth Jones Lowe, and Carolyn Jones BlueHuntsville, Alabama

In 1804, the Jones ancestors moved to Madison County, Alabama from Lincoln County, Tennessee, for the purpose of farming. For the last 205 years, the Jones family has continued to farm in Madison, currently operating 2,000 acres for the cattle operation, which began in 1939, and has now been surrounded by the City of Huntsville. The farming enterprises have also expanded over the years and include farming operations in Jackson, Marshall, and Limestone counties.

From the 1940’s through most of the 1980’s, Horned Hereford cattle primarily made up the cow herd. Today, half Red Angus half red Gelbvieh, or Balancer, herd sires produce a calf crop consisting of approximately half Red Angus, half red Gelbvieh. The cow herd consists of approximately 450 cows with a fall calving season of 63 days beginning on October 1st. All performance data is collected, maintained, and evaluated using the Red Wing Cow/Calf software. Calves are marketed at the end of May or June. The steers are sold at an average of 650 lbs in truck load lots directly to a feedlot. A select group of heifers are retained as replacements, with the balance being sold to local cattlemen.

G.W. Jones & Sons Farm has received favorable feedback from their customers on the feedlot and carcass performance of their calves. Their reputation of performance has resulted in repeat customers for the past several years. Future plans are to continue to improve using all the tools available to the 21st century cattlemen. Available tools to improve genetics, EPD’s, record keeping, and forage production will be utilized as the family continues their farming legacy in this wonderful country called America.

The Alabama Beef Cattle Improvement Association is proud to nominate G. W. Jones and Sons Farms.

M&B Limousin

Owners: Mike & Betsy CravensLee’s Summit, Missouri

The Cravens have been in business for over twenty years. Their operation is comprised of 1050 acres. They maintain both spring and fall calving seasons on their 280 head operation.

M&B Limousin started as a small commercial herd with rented Limousin bulls. Impressed with the calv-ing ease, performance, and sturdiness of Limousin, they have expanded their commercial herd of crossbred cows in addition to maintaining 110 head of registered Limousin cows. The Cravens believe in sound science and read-ily use expected progeny differences and genomics data in selection and breeding decisions. They have devel-oped strong relationships with Strauss Veal and Laura’s Lean Beef, two branded programs which Cravens market a large percentage of calves through. They also appreciate economic advantages of age and source verification and keeping calves natural.

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The Cravens have operated single handedly, while both working full-time jobs. Retirement has allowed full operational focus, improving their cowherd and maintaining industry ties. True stewards of the land, the Cra-vens are continually working on pasture improvement and preservation of the land. As well, they have continued to upgrade facilities and working equipment in an effort to improve operational efficiency. Mike and Betsy are always looking for ways to increase reproductive efficiency and performance of their cattle. They have fine tuned their nutrition program and honed their selection criteria.

Mike attributes much success to a formal background in Animal Science, judging at the University of Mis-souri Livestock, and 40 year ownership of “The Old Mill, Grain and Seed.”

M&B Limousin is proudly nominated by the North American Limousin Foundation.

Duane Martin Livestock

Owners: Duane Martin FamilyIone, California

Duane Martin Livestock is a diversified ranching operation covering seven states of grazing feeders, stockers and cow/calf. These states include California, Oregon, Montana, Nevada, Idaho, Wyoming and Colo-rado. Duane has been in the ranching business for forty-six years, building from the ground up. He started out driving a ready mix cement truck. He earned enough to buy his first cows at age 24. Ever since then, every dollar he’s made has been related to the cattle industry. He is involved in order buying, cattle feeding, grazing stockers and cow/calf.

The calving season is both spring and fall, which is determined by the climate and location of the ranches. The cow/calf operation includes over 8,000 mother cows. Duane Martin Livestock owns a feedlot in Wiggins, Colo-rado, Magnum Feedyards, and feeds many of his cattle there along with other feedlots in the heartland.

Duane continues the philosophy passed on by his father, Frank Martin, which is to “never sell a thin ani-mal” and “water (availability on a ranch) is half the feed.” This commitment to animal welfare reaches further, with continual improvement of facilities, professional development for staff, and the ranch policy to not sell sick or injured animals, but rather to heal them or euthanize.

The links between cow-calf, stocker, and feedlot provide much of the advantages of vertical integration, without being rigid since the strength of the business has been to take advantage of marketing opportunities and knowing when to buy or sell cattle into the various markets.

Everything is centered around cattle, not with money from other businesses. There has been no outside inheritance or capital. The Martins believe that theirs is an example of what beef production should be – a busi-ness that can profitably stand alone, and that is their goal. They say there is never enough time in the day to do it as wonderfully as they want.

Duane Martin Livestock is proudly nominated by the California Beef Cattle Improvement Association.

162

Optimal Beef, LLC

Owner: Alan GraybealBlacksburg, Virginia

Optimal Beef is located in the Blue Ridge Mountains of southwest Virginia. The cattle operation has been in existence since 1950 when Alan’s father purchased land and began his beef cattle production system. Alan joined the cattle enterprise fulltime in 1993, at which point they purchased additional land and significantly ex-panded their cow herd. Currently they maintain a herd of 400 Angus/Simmental crossbred cows. Cows are calved in February/March and in September/October. Generally all cows are bred AI, and then herd bulls are introduced for 64 days. All calves are weaned and backgrounded at the farm. Replacement heifers are selected from their calf crop and developed to enter the cow herd. All remaining calves are fed for retained ownership through Circle Five Feedyard in Henderson, Nebraska and enter an age and source verified program.

Their cow herd is maintained on 800 acres of owned land. Pasture and hay management are critical to the success of their operation. Rotational grazing is used on all their land and they strive to maintain a mixture of grasses and legumes in their pastures. Some paddocks are also used for haying, and in late summer some are stockpiled for winter grazing. Their goal is to feed hay for only 75 days during the winter months.

Optimal Beef is proudly nominated by the Virginia Beef Cattle Improvement Association.

163

Jay L. Lush . . . . . . . . . Iowa . . . . . . . .1973

Reuben Albaugh . . . . . . . California . . . . .1974

Charles E. Bell, Jr. . . . . . . USDA . . . . . . .1974

John H. Knox . . . . . . . . New Mexico . . . .1974

Paul Pattengale . . . . . . . Colorado . . . . . .1974

Fred Wilson . . . . . . . . . Montana . . . . . .1974

Ray Woodward . . . . . . . ABS . . . . . . . .1974

Glenn Butts . . . . . . . . . PRT . . . . . . . .1975

Keith Gregory . . . . . . . MARC . . . . . . .1975

Braford Knapp, Jr. . . . . . . USDA . . . . . . .1975

Forrest Bassford Western Livestock Journal . . . . . . . . .1976

Doyle Chambers . . . . . . Louisiana . . . . .1976

Mrs. Waldo Emerson Forbes Wyoming . . . . . .1976

C. Curtis Mast . . . . . . . Virginia . . . . . .1976

Ralph Bogart . . . . . . . . Oregon . . . . . . .1977

Henry Holsman . . . . . . . South Dakota . . .1977

Marvin Koger . . . . . . . . Florida . . . . . . .1977

John Lasley . . . . . . . . . Florida . . . . . . .1977

W. L. McCormick . . . . . Georgia . . . . . .1977

Paul Orcutt . . . . . . . . . Montana . . . . . .1977

J.P. Smith Performance Registry Int’l . . . . . . . . .1977

H.H. Stonaker . . . . . . . . Colorado . . . . . .1977

James B. Lingle . . . . . . . Wye Plantation . .1978

R. Henry Mathiessen . . . . Virginia . . . . . .1978

Bob Priode . . . . . . . . . Virginia . . . . . .1978

Robert Koch . . . . . . . . MARC . . . . . . .1979

Mr. & Mrs. Carl Roubicek . Arizona . . . . . .1979

Joseph J. Urick . . . . . . . USDA . . . . . . .1979

Richard T. “Scotty” Clark . USDA . . . . . . .1980

Bryon L. Southwell . . . . . Georgia . . . . . .1980

F.R. “Ferry” Carpenter . . . Colorado . . . . . .1981

Otha Grimes . . . . . . . . Oklahoma . . . . .1981

Milton England . . . . . . . Texas . . . . . . . .1981

L.A. Moddox . . . . . . . . Texas . . . . . . . .1981

Charles Pratt . . . . . . . . Oklahoma . . . . .1981

Clyde Reed . . . . . . . . . Oklahoma . . . . .1981

Gordon Dickerson . . . . . Nebraska . . . . . .1982

Mr. & Mrs. Percy Powers . Texas . . . . . . . .1982

Jim Elings . . . . . . . . . . California . . . . .1983

W. Dean Frischknecht . . . Oregon . . . . . . .1983

Ben Kettle . . . . . . . . . Colorado . . . . . .1983

Jim Sanders . . . . . . . . . Nevada . . . . . . .1983

Carroll O. Schoonover . . . Wyoming . . . . . .1983

Bill Graham . . . . . . . . . Georgia . . . . . .1984

Max Hammond . . . . . . . Florida . . . . . . .1984

Thomas J. Marlowe . . . . . Virginia . . . . . .1984

Mick Crandell . . . . . . . South Dakota . . .1985

Mel Kirkiede . . . . . . . . North Dakota . . .1985

Charles R. Henderson . . . New York . . . . .1986

Everett J. Warwick . . . . . USDA . . . . . . .1986

Glenn Burrows . . . . . . . New Mexico . . . .1987

Carlton Corbin . . . . . . . Oklahoma . . . . .1987

Murray Corbin . . . . . . . Oklahoma . . . . .1987

BIF Pioneer Award Recipients

164

Max Deets . . . . . . . . . Kansas . . . . . . .1987

Christian A. Dinkle . . . . . South Dakota . . .1988

George F. & Mattie Ellis . . New Mexico . . . .1988

A.F. “Frankie” Flint . . . . New Mexico . . . .1988

Roy Beeby . . . . . . . . . Oklahoma . . . . .1989

Will Butts . . . . . . . . . . Tennessee . . . . .1989

John W. Massey . . . . . . Missouri . . . . . .1989

Donn & Sylvia Mitchell . . Canada . . . . . . .1990

Hoon Song . . . . . . . . . Canada . . . . . . .1990

Jim Wilton . . . . . . . . . Canada . . . . . . .1990

Bill Long . . . . . . . . . . Texas . . . . . . . .1991

Bill Turner . . . . . . . . . Texas . . . . . . . .1991

Frank Baker . . . . . . . . . Arkansas . . . . . .1992

Ron Baker . . . . . . . . . Oregon . . . . . . .1992

Bill Borror . . . . . . . . . California . . . . .1992

Walter Rowden . . . . . . . Arkansas . . . . . .1992

James D. Bennett . . . . . . Virginia . . . . . .1993

M.K. “Curly” Cook . . . . . Georgia . . . . . .1993

O’Dell G. Daniel . . . . . . Georgia . . . . . .1993

Hayes Gregory . . . . . . . North Carolina . . .1993

Dixon Hubbard . . . . . . . USDA . . . . . . .1993

James W. “Pete” Patterson . North Dakota . . .1993

Richard Willham . . . . . . Iowa . . . . . . . .1993

Tom Chrystal . . . . . . . . Iowa . . . . . . . .1994

Robert C. DeBaca . . . . . Iowa . . . . . . . .1994

Roy A. Wallace . . . . . . . Ohio . . . . . . . .1994

James S. Brinks . . . . . . . Colorado . . . . . .1995

Robert E. Taylor . . . . . . . Colorado . . . . . .1995

A.L. “Ike” Eller . . . . . . . Virginia . . . . . .1996

Glynn Debter . . . . . . . . Alabama . . . . . .1996

Larry V. Cundiff . . . . . . Nebraska . . . . . .1997

Henry Gardiner . . . . . . . Kansas . . . . . . .1997

Jim Leachman . . . . . . . Montana . . . . . .1997

John Crouch . . . . . . . . Missouri . . . . . .1998

Bob Dickinson . . . . . . . Kansas . . . . . . .1998

Douglas MacKenzie Fraser . Alberta . . . . . . .1998

Joseph Graham . . . . . . . Virginia . . . . . . .1999

John Pollak . . . . . . . . . New York . . . . .1999

Richard Quaas . . . . . . . New York . . . . .1999

J. David Nichols . . . . . . Iowa . . . . . . . .2000

Harlan Ritchie . . . . . . . Michigan . . . . . .2000

Robert R. Schalles . . . . . Kansas . . . . . . .2000

Larry Benyshek . . . . . . . Georgia . . . . . .2001

Minnie Lou Bradley . . . . Texas . . . . . . . .2001

Tom Cartwright . . . . . . . Texas . . . . . . . .2001

H.H. “Hop” Dickenson . . . Kansas . . . . . . .2002

Martin & Mary Jorgensen . South Dakota . . .2002

L. Dale Van Vleck . . . . . Nebraska . . . . . .2002

George Chiga . . . . . . . . Oklahoma . . . . .2003

Burke Healey . . . . . . . . Oklahoma . . . . .2003

Keith Zoellner . . . . . . . Kansas . . . . . . .2003

Frank Felton . . . . . . . . Missouri . . . . . .2004

Tom Jenkins . . . . . . . . Nebraska . . . . . .2004

Joe Minyard . . . . . . . . South Dakota . . .2004

BIF Pioneer Award Recipients

165

Jack and Gini Chase . . . . Wyoming . . . . . .2005

Jack Cooper . . . . . . . . . Montana . . . . . .2005

Dale Davis . . . . . . . . . Montana . . . . . .2005

Les Holden . . . . . . . . . Montana . . . . . .2005

Don Kress . . . . . . . . . . Montana . . . . . .2005

John Brethour . . . . . . . . Kansas . . . . . . .2006

Harlan & Dorotheann Rogers . . . . . . . . . . . . . Mississippi . . . . .2006

Dave Pingrey . . . . . . . . Mississippi . . . . .2006

Rob Brown . . . . . . . . . Texas . . . . . . . .2007

BIF Pioneer Award Recipients

David and Emma Danciger . Colorado . . . . . .2007

Jim Gosey . . . . . . . . . . Nebraska . . . . . .2007

Donald Vaniman . . . . . . . Montana . . . . . .2008

Louis Latimer . . . . . . . . Canada . . . . . . .2008

Harry Haney . . . . . . . . . Canada . . . . . . .2008

Bob Church . . . . . . . . . Canada . . . . . . .2008

Bruce Golden . . . . . . . . California . . . . . .2009

Bruce Orvis . . . . . . . . . California . . . . . .2009

Roy McPhee (posthumously) . . . . . . . . . . . . . California . . . . . .2009

166

Clarence Burch . . . . . . . Oklahoma . . . . 1972

F. R. Carpenter . . . . . . . Colorado . . . . . 1973

Robert DeBaca . . . . . . . Iowa . . . . . . . 1973

E.J. Warwick . . . . . . . . Washington, DC . 1973

Frank H. Baker . . . . . . . Oklahoma . . . . 1974

D.D. Bennett . . . . . . . . Oregon . . . . . . 1974

Richard Willham . . . . . . Iowa . . . . . . . 1974

Larry V. Cundiff . . . . . . Nebraska . . . . . 1975

Dixon D. Hubbard . . . . . Washington, DC . 1975

J. David Nichols . . . . . . Iowa . . . . . . . 1975

A.L. Eller, Jr. . . . . . . . . Virginia . . . . . . 1976

Ray Meyer . . . . . . . . . South Dakota . . 1976

Lloyd Schmitt . . . . . . . . Montana . . . . . 1977

Don Vaniman . . . . . . . . Montana . . . . . 1977

James S. Brinks . . . . . . . Colorado . . . . . 1978

Martin Jorgensen . . . . . . South Dakota . . 1978

Paul D. Miller . . . . . . . . Wisconsin . . . . 1978

C.K. Allen . . . . . . . . . Missouri . . . . . 1979

William Durfey . . . . . . . NAAB . . . . . . 1979

Glenn Butts . . . . . . . . . PRI . . . . . . . . 1980

Jim Gosey . . . . . . . . . . Nebraska . . . . . 1980

Mark Keffeler . . . . . . . . South Dakota . . 1981

J.D. Mankin . . . . . . . . . Idaho . . . . . . . 1982

Art Linton . . . . . . . . . . Montana . . . . . 1983

James Bennett . . . . . . . Virginia . . . . . 1984

M.K. Cook . . . . . . . . . Georgia . . . . . 1984

Craig Ludwig . . . . . . . . Missouri . . . . . 1984

Jim Glenn . . . . . . . . . . IBIA . . . . . . . 1985

Dick Spader . . . . . . . . . Missouri . . . . . 1985

Roy Wallace . . . . . . . . Ohio . . . . . . . 1985

Larry Benyshek . . . . . . . Georgia . . . . . 1986

Ken W. Ellis . . . . . . . . California . . . . 1986

Earl Peterson . . . . . . . . Montana . . . . . 1986

Bill Borror . . . . . . . . . California . . . . 1987

Jim Gibb . . . . . . . . . . Missouri . . . . . 1987

Daryl Strohbehn . . . . . . Iowa . . . . . . . 1987

Bruce Howard . . . . . . . Canada . . . . . . 1988

Roger McCraw . . . . . . . North Carolina . . 1989

Robert Dickinson . . . . . . Kansas . . . . . . 1990

John Crouch . . . . . . . . Missouri . . . . . 1991

Jack Chase . . . . . . . . . Wyoming . . . . . 1992

Leonard Wulf . . . . . . . . Minnesota . . . . 1992

Robert McGuire . . . . . . Alabama . . . . . 1993

Charles McPeake . . . . . . Georgia . . . . . 1993

Henry W. Webster . . . . . South Carolina . . 1993

Bruce E. Cunningham . . . Montana . . . . . 1994

Loren Jackson . . . . . . . Texas . . . . . . . 1994

Marvin D. Nichols . . . . . Iowa . . . . . . . 1994

Steve Radakovich . . . . . . Iowa . . . . . . . 1994

Doyle Wilson . . . . . . . . Iowa . . . . . . . 1994

Paul Bennett . . . . . . . . Virginia . . . . . 1995

Pat Goggins . . . . . . . . . Montana . . . . . 1995

Brian Pogue . . . . . . . . . Canada . . . . . . 1995

Doug L. Hixon . . . . . . . Wyoming . . . . . 1996

Harlan D. Ritchie . . . . . . Michigan . . . . . 1996

Glenn Brinkman . . . . . . Texas . . . . . . . 1997

BIF Continuing Service Award Recipients

167

Russell Danielson . . . . . . North Dakota . . 1997

Gene Rouse . . . . . . . . . Iowa . . . . . . . 1997

Keith Bertrand . . . . . . . Georgia . . . . . 1998

Richard Gilbert . . . . . . . Texas . . . . . . . 1998

Burke Healey . . . . . . . . Oklahoma . . . . 1998

Bruce Golden . . . . . . . . Colorado . . . . . 1999

John Hough . . . . . . . . . Georgia . . . . . 1999

Gary Johnson . . . . . . . . Kansas . . . . . . 1999

Norman Vincil . . . . . . . Virginia . . . . . 1999

Ron Bolze . . . . . . . . . . Kansas . . . . . . 2000

Jed Dillard . . . . . . . . . Florida . . . . . . 2000

William Altenburg . . . . . Colorado . . . . . 2001

Kent Andersen . . . . . . . Colorado . . . . . 2001

Don Boggs . . . . . . . . . South Dakota . . 2001

S.R. Evans . . . . . . . . . Mississippi . . . . 2002

Galen Fink . . . . . . . . . Kansas . . . . . . 2002

Bill Hohenboken . . . . . . Virginia . . . . . 2002

Sherry Doubet . . . . . . . Colorado . . . . . 2003

Ronnie Green . . . . . . . . Virginia . . . . . 2003

Connee Quinn . . . . . . . Nebraska . . . . . 2003

Ronnie Silcox . . . . . . . . Georgia . . . . . 2003

Chris Christensen . . . . . . South Dakota . . 2004

Robert “Bob” Hough . . . . Texas . . . . . . . 2004

Steven M. Kappes . . . . . Nebraska . . . . . 2004

Richard McClung . . . . . . Virginia . . . . . 2004

Jerry Lipsey . . . . . . . . . Montana . . . . . 2005

Micheal MacNeil . . . . . . Montana . . . . . 2005

Terry O’Neill . . . . . . . . Montana . . . . . 2005

Robert Williams . . . . . . Missouri . . . . . 2005

Jimmy Holliman. . . . . . . Alabama . . . . . 2006

Lisa Kriese-Anderson . . . Alabama . . . . . 2006

Dave Notter . . . . . . . . . Ohio . . . . . . . 2006

Craig Huffhines . . . . . . . Missouri . . . . . 2007

Sally Northcutt . . . . . . . Missouri . . . . . 2007

Dale Kelly . . . . . . . . . . Canada . . . . . . 2008

Doug Fee . . . . . . . . . . Canada . . . . . . 2008

Duncan Porteous . . . . . . Canada . . . . . . 2008

Mark Thallman . . . . . . . Nebraska . . . . . 2009

Renee Lloyd . . . . . . . . . Iowa . . . . . . . 2009

Dave Daley . . . . . . . . . California . . . . . 2009

Darrh Bullock . . . . . . . . Kentucky . . . . . 2009

BIF Continuing Service Award Recipients

168

BIF Ambassador Award Recipients

Warren Kester . . . . . . . . . . . . . BEEF Magazine . . . . . . . . . . . . . . Minnesota . . . . . . .1986

Chester Peterson . . . . . . . . . . . . Simmental Shield . . . . . . . . . . . . . Kansas . . . . . . . . .1987

Fred Knop . . . . . . . . . . . . . . . Drovers Journal . . . . . . . . . . . . . . Kansas . . . . . . . . .1988

Forrest Bassford . . . . . . . . . . . . Western Livestock Journal . . . . . . . . . Colorado . . . . . . .1989

Robert C. DeBaca . . . . . . . . . . . The Ideal Beef Memo . . . . . . . . . . . Iowa . . . . . . . . . .1990

Dick Crow . . . . . . . . . . . . . . . Western Livestock Journal . . . . . . . . . Colorado . . . . . . .1991

J.T. “Johnny” Jenkins . . . . . . . . . . Livestock Breeder Journal . . . . . . . . . Georgia . . . . . . . .1993

Hayes Walker III . . . . . . . . . . . . America’s Beef Cattleman . . . . . . . . Kansas . . . . . . . . .1994

Nita Effertz . . . . . . . . . . . . . . . Beef Today . . . . . . . . . . . . . . . . . Idaho . . . . . . . . .1995

Ed Bible . . . . . . . . . . . . . . . . Hereford World . . . . . . . . . . . . . . Missouri . . . . . . . .1996

Bill Miller . . . . . . . . . . . . . . . . Beef Today . . . . . . . . . . . . . . . . . Kansas . . . . . . . . .1997

Keith Evans . . . . . . . . . . . . . . . American Angus Association . . . . . . . Missouri . . . . . . . .1998

Shauna Rose Hermel . . . . . . . . . . Angus Journal & BEEF Magazine . . . . . Missouri . . . . . . . .1999

Wes Ishmael . . . . . . . . . . . . . . Clear Point Communications . . . . . . . Texas . . . . . . . . .2000

Greg Hendersen . . . . . . . . . . . . Drovers . . . . . . . . . . . . . . . . . . . Kansas . . . . . . . . .2001

Joe Roybal . . . . . . . . . . . . . . . BEEF Magazine . . . . . . . . . . . . . . Minnesota . . . . . . .2002

Troy Marshall . . . . . . . . . . . . . . Seedstock Digest . . . . . . . . . . . . . . Missouri . . . . . . . .2003

Kindra Gordon . . . . . . . . . . . . . Freelance Writer . . . . . . . . . . . . . . South Dakota . . . . .2004

Steve Suther . . . . . . . . . . . . . . Certified Angus Beef LLC . . . . . . . . . Kansas . . . . . . . . .2005

Belinda Ary . . . . . . . . . . . . . . . Cattle Today . . . . . . . . . . . . . . . . Alabama . . . . . . . .2006

Angie Denton . . . . . . . . . . . . . . Hereford World . . . . . . . . . . . . . . Missouri . . . . . . . .2007

Gren Winslow and Larry Thomas . . . . Canadian Cattleman Magazine . . . . . . Canada . . . . . . . . .2008

Kelli Toldeo . . . . . . . . . . . . . . . Cornerpost Publications . . . . . . . . . . California . . . . . . .2009

169

2009 Frank H. Baker Memorial Scholarship Awarded to Speidel

SACRAMENTO, CALIF. (April 30, 2009) – Scott Speidel, research associate in breeding and genetics at Colo-rado State University, Fort Collins, Colo., is a recipient of the 2009 Frank H. Baker Memorial Scholarship Award, presented during the 41st Beef Improvement Federation (BIF) Research Symposium and Annual Meeting, April 30 - May 3, 2009, in Sacramento, Calif.

Speidel accepted the award from Robert Williams, Ph.D., director of breed improvement and foreign marketing for the American-International Charolais Association, Kansas City, Mo.

The late Frank H. Baker played a key leadership role in helping establish the BIF in 1968.

Each year since 1994, two deserving graduate students have been recognized for his or her winning essays.

A California native, Speidel holds a bachelor’s degree in Animal Science from California State University, Fres-no; a master’s degree from the University of Arizona, Tucson, Ariz., and plans to complete his doctorage this fall at Colorado State University.

An abstract from his award-winning essay follows.

“Genetic Analysis of Longitudinal Data in Beef Cattle”

Currently, many different data types are collected by beef cattle breed associations for the purpose of genetic evaluation. These data points are all biological characteristics of individual animals that can be measured multiple times over an animal’s lifetime. Some traits can only be measured once on an individual animal, whereas others, such as the body weight of an animal as it grows, can be measured a multitude of times. Data such as growth has been often referred to as “longitudinal” or “infinite-dimensional” since it is theoretically possible to observe the trait an infinite number of times over the life span of a given individual.

The analysis of such data is not without its challenges, and as a result, many different methods have been or are beginning to be implemented in the genetic analysis of beef cattle data each an improvement over its predecessor. These methods of analysis range from the classic repeated measures to the more contemporary suite of random regressions that use covariance functions or even splines as their basis function.

Each of the approaches has both strengths and weaknesses when it comes to the analysis of longitudinal data. Therefore, the objective of this essay is to summarize past and current genetic evaluation technology for analyzing this type of data and to review some emerging technolo-gies beginning to be implemented in current national cattle evaluation schemes along with their potential im-plications to the beef industry.

The California Beef Cattle Improvement Association (CBCIA) and the California Cattlemen’s Association (CCA) hosted the 41st BIF Research Symposium and An-nual Meeting. For more information, visit www.bifcon-ference.com or www.calcattlemen.org/bif2009.html.

Photo by Cornerpost Publications, publisher of the California Cattleman. Caption: American-International Charolais Associa-tion Director of Breed Improvement and Foreign Marketing Rob-ert Williams, Ph.D., presented Colorado State University graduate student Scott Speidel (left), Fort Collins, Colo., with a Frank H. Baker Memorial Scholarship Award during the Beef Improvement Federation 41st Research Symposium and Annual Meeting, April 30 – May 3, 2009, in Sacramento, Calif.

170

Leachman Recipient of 2009 Frank H. Baker Memorial Scholarship Award

SACRAMENTO, CALIF. (April 30, 2009) – Lance D. Leachman, Christiansburg, Va., is a recipient of the 2009 Frank H. Baker Memorial Scholarship Award.

The award was presented by Robert Williams, Ph.D., director of breed improvement and foreign marketing, American-International Charolais Association, Kansas City, Mo., during the 41st Beef Improvement Federation (BIF) Research Symposium and Annual Meeting, April 30- May 3, 2009, in Sacramento, Calif.

The late Frank H. Baker played a key leadership role in helping establish the BIF in 1968.

Each year since 1994, two deserving graduate students have been recognized for his or her winning essays during the BIF annual meeting.

Leachman was born in Maidstone, Sask., Canada. He holds a bachelor of science degree in Animal Sciences and Industry with a business option from Kansas State University, Manhattan, Kan., and a master’s degree in Animal and Poultry Science – Breeding Genetics from Virginia Polytechnic State University (Virginia Tech), Blackburg, Va. Currently, Leachman is a graduate student at Virginia Tech.

An abstract from his award-winning essay follows.

“Combined Selection for the Beef Cattle Industry”

Beef cattle production entails a small sector of purebred seedstock producers supplying bulls to the much larger commercial sector. Crossbreeding plays a vital role in increasing the productivity and profitability of many com-mercial producers through breed complimentarity and heterosis.

In commercial herds, bull selection should be geared toward producing crossbreds that are optimal for the pro-duction system, thereby raising the question, “Are we better served in utilizing purebred information alone, or a combination of purebred and crossbred information, in genetic evaluation of potential sires?”

Combined Crossbred Purebred Selection (CCPS) allows the combination of vast amounts of performance data potentially available on crossbreds with that on purebred cattle in a selection index or BLUP evaluation. The genetic correlation between purebred and crossbred performance indicates the extent to which genetic progress achieved in purebreds will translate to crossbred offspring. Genetic correlations of less than 0.7 suggest that cross-bred data can aid in genetic improvement, due to the weak relationship between additive gene effects of purebreds and crossbreds. The purebred heritability and crossbred heritability are also useful for determining potential selec-tion accuracy and potential rates of progress with CCPS.

Combined Crossbred Purebred Selection has been used in the swine and poultry industries; however, the increased requirements for pedigree and performance recording have limited its acceptance in beef cattle. Still, if genetic gains were sufficiently accelerated with CCPS, the potential use of molecular genetic tolls to verify parentage in multiple sire pastures may provide incentive to collect phenotypes on crossbred cattle. Adoption of CCPS needs to be evaluated as cost-effective and applied initially to intensively managed commercial operations.

The California Beef Cattle Improvement Association (CBCIA) and the California Cattlemen’s Association (CCA) hosted the 41st BIF Research Symposium and Annual Meeting. For more information, visit www.bifconference.com or www.calcattlemen.org/bif2009.html.

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Beef Improvement Federation Names 2009 Seedstock Producers of the Year

SACRAMENTO, CALIF. (May 1, 2009) – The Beef Improvement Federation (BIF) recognized Champion Hill, Inc., of Bidwell, Ohio, and Harrell Hereford Ranch of Baker City, Ore., as the 2009 Seedstock Producers of the Year during its 41st Research Symposium and Annual Meeting, April 30 – May 3, 2009, in Sacramento, Calif.

Traditionally, a single breeder is honored, but this year, the committee selected two producers deserving of the nation’s top seedstock award, sponsored by BEEF magazine, Minneapolis, Minn.

CHAMPION HILL, INC.

Paul Hill and Marshall Reynolds of Champion Hill, Inc., run 220 breeding-age registered Angus females and 630 mostly half-blood Angus females, used as recipients, on 4,000 acres of owned and leased land in southeastern Ohio. Each year, the operation sells 300 females in two production sales and 200 bulls through a genetic part-nership.

Reynolds owned the land, and in 1993, formed Champi-on Hill, Inc., naming Hill as its president. Their philoso-phy has always been to breed the kind of cattle that will not only perform in the showring, but will also make a positive contribution to the beef cattle industry. The team at Champion Hill has selected females from the top cow families in the Angus breed to use as foundation donor cows in order to consistently produce the quality of cattle that their customers have come to expect.

“Paul Hill is one of the best promoters of seedstock in the Angus breed. He should be complimented on what he has done to advance the breed,” says Darrell Silveira of Silveira Bros., Firebaugh, Calif. He adds, “Paul is very deserving of this honor. He has come a long way since the first time we met in 1975, when our reputations were so great, that we were both stalled near the wash rack during the National Western Stock Show in Denver, Colo.”

“We are honored to have been chosen as the Beef Improvement Federation’s 2009 Seedstock Producer of the Year,” said Paul Hill. “Lynn and I accept this award on behalf of our partner, Marshall Reynolds, and the entire staff at Champion Hill. We would also like to thank our genetic partners Kelly and Martie Jo Schaff of Schaff Angus Valley, Saint Anthony, N.D.” Hill adds, “We are indebted to the Beef Improvement Federation for providing the guidance to the breed associations in order for them to provide the tools necessary to advance our herd.”

The Ohio Cattlemen’s Association nominated Champion Hill. For more information, visit www.championhillan-gus.com.

HARRELL HEREFORD RANCH

In 1870, ancestors of the Harrell family traveled the Oregon Trail, settling near Baker City, Ore. Three generations later in 1970, Bob and Edna Harrell established the Harrell Hereford Ranch along the foothills in eastern Oregon’s Baker Valley.

Champion Hill, Inc., Bidwell, Ohio, was named as one of the Beef Improvement Federation’s (BIF) 2009 Seedstock Producers of the Year during its 41st Research Symposium and Annual Meeting, April 30 – May 3, 2009, in Sacramento, Calif. Pictured receiving the award from BIF Outgoing President Tommy Brown (far left), Clanton, Ala., and BEEF magazine Senior Editor Burt Rutherford (far right) are Angus producers Lynn and Paul Hill of Champion Hill, Inc.

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The ranch is family owned-and-operated with Bob Harrell Jr. and his wife Becky and their daughter Lexie, shar-ing the duties with his mother Edna Harrell, and his sister and brother-in-law Beth and Wannie Mackenzie, who are also involved as partners in the Harrell-Mackenzie Quarter Horse operation.

“To be nominated by the American Hereford Association for this award was a great honor in itself, but to actually be recognized at this level is more than our family could have ever imagined,” says Bob Harrell Jr. “To be voted one of the top seedstock producers in this country by the most prestigious body of performance-minded producers in the world is a feeling that will probably never be matched again in our lifetime,” he adds.

The cattle ranch originated with 100 head of Hereford cows purchased from TT Herefords, Connell, Wash., and 80 acres of land. Today, the operation has grown to six ranches, consisting of 300 registered Hereford cows, 400 black baldy commercial cows, an 800-head feedlot for backgrounding cattle and 45 Quarter Horse broodmares. The cattle run on 8,000 acres of high-desert, native range and 3,000 irrigated tillable acres on which alfalfa and meadow hay, corn silage, earlage and small grains are raised.

The Harrell herd has been performance testing since its inception in 1970, and for nearly four decades the goal has been to produce performance cattle that work under a variety of management systems and branded beef pro-grams.

The Harrell’s celebrated their 30th anniversary sale in 2009, offering more than 100 bulls, 30 heifers and 20 Quarter Horses to buyers from 11 states and Canada. The successful event was driven by the program’s ad-herence to the economics of the American beef industry and their role as a seedstock leader. Through their breed-ing and customer programs, the Harrells have emerged as a true genetics and marketing partner for their com-mercial customers.

“Harrell Herefords is one of the most progressive seed-stock producers in the United States. They are the epit-ome of a family operation and understand what their commercial customers expect out of their program be-cause they are also in the commercial cattle business,” says Matt Macfarlane of Matt Macfarlane Marketing,

Sheridan, Calif. “The Hereford genetics offered by the Harrell family are truly balanced and are backed by one of the great cow herds of any breed in the country,” he adds.

To find out more about the Harrell Hereford Ranch, visit www.harrellherefordranch.com.

Highlights from the 41st Research Symposium and Annual Meeting, hosted by the California Beef Cattle Im-provement Association (CBCIA) and the California Cattlemen’s Association (CCA), in conjunction with the Beef Improvement Federation (BIF), can be found online at www.bifconference.com or www.calcattlemen.org/bif2009.html.

Photos by Cornerpost Publications, publisher of the California Cattleman.

The Beef Improvement Federation (BIF) named Harrell Her-eford Ranch, Baker City, Ore., as one of its 2009 Seedstock Pro-ducers of the Year, in Sacramento, Calif., during its 41st Research Symposium and Annual Meeting, April 30 – May 3, 2009. Bob Harrell Jr., his wife Becky and mother Edna Harrell are pictured receiving the award from BIF Outgoing President Tommy Brown (far left), Clanton, Ala., and BEEF magazine Senior Editor Burt Rutherford (far right).

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Nebraska Operation Named Beef Improvement Federation Commercial Producer of the Year

SACRAMENTO, CALIF. (April 30, 2009) – The Beef Improvement Federation (BIF) named the JHL Ranch, Ashby, Neb., as its 2009 Commercial Producer of the Year. The family has run cattle in the southwest corner in the Nebraska Sandhills since 1885. The JHL brand is reputed to be one of the oldest used in Nebraska having been legally registered in the state in 1920.

Ranch owners Art and Merry Brownlee, along with their son Ethan, accepted the award from BEEF magazine Senior Editor Burt Rutherford during the 41st BIF Research Symposium and Annual Meeting, April 30 – May 3, 2009, in Sacramento, Calif. The ranch was nominated by the Nebraska Cattlemen and the Braunvieh Association of America, both based in Lincoln, Neb.

Harlan Doeschot of Golden Link Braunvieh, Firth, Neb., said, “There is not a more deserving recipient of the commercial producer of the year award than the JHL Ranch. Art and Merry have tested their cattle and built a tremendous herd based on the data they have gathered.”

The Brownlees took the reins of the operation in 1995 and spent the past 14 years working toward their goal to ap-ply research and analysis principles to ranching. The ranch runs between 1,300 and 1,400 Angus- and Braunvieh-cross cows, utilizing 80 paddocks in an intensive, managed rotational grazing system on approximately 30,000 acres.

The complete tracking and analysis of two end products – replacement females and carcass merit, have driven the spring-calving operation. These actions have been made possible by the computer-based use of Deoxyribonucleic acid (DNA), ultrasound and linear measurements, as well as Expected Progeny Difference (EPD) technology.

The majority of the cows are bred through artificial insemination (AI) and calves are weaned at 150 days of age. The calves are backgrounded and supplemented on grass at the ranch and then custom fed with ownership re-tained to the rail. The ranch has marketed a USDA Source and Age Verified product since 1995.

After years of tracking, verifying and incorporating the progeny in the commercial herd, the JHL Ranch pur-chased an existing Braunvieh herd in 2009, launching its seedstock division.

“We are honored and blessed to be named as this year’s commercial producer of the year,” said Art. For the past decade, we have had the guidance and counsel of past recipients and individuals here today, as well as the Beef Improvement Federation guidelines to develop our program and take it to the next level. We are a testament that change is possible and change can be profitable,” he added.

For more information on the JHL Ranch, visit www.jhl-beef.com.

The 41st BIF Research Symposium and Annual Meeting is hosted by the California Beef Cattle Improvement As-sociation (CBCIA) and the California Cattlemen’s As-sociation (CCA). For more information, visit www.bif-conference.com or www.calcattlemen.org/bif2009.html.

Photo by Cornerpost Publications. Caption: Beef Improvement Fed-eration (BIF) Outgoing President Tommy Brown (far left), Clanton, Ala., and BEEF magazine Senior Editor Burt Rutherford (far right), Amarillo, Texas, present the 2009 BIF Commercial Producer of the

Year Award to Art and Merry Brown and their son Ethan of the JHL Ranch, Ashby, Neb. The award was sponsored by BEEF magazine, Minneapolis, Minn.

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Beef Improvement Federation Presents 2009 Pioneer Award to Lifelong Hereford Breeder

SACRAMENTO, CALIF. (May 2, 2009) – The Beef Improvement Federation (BIF) presents the Pioneer Award each year to deserving individuals who have made contributions to the genetic improvement of the beef industry.

This year, Bruce Orvis of Orvis Cattle Company, Farmington, Calif., was honored with the 2009 Pioneer Award during the BIF’s 41st Research Symposium and Annual Meeting, April 30 – May 3, 2009, in Sacramento, Calif.

A lifelong Hereford breeder, Orvis currently runs 300 head of registered cows and heifers at his ranch located in the foothills of the Sierra Nevada Mountains. He markets 50 to 60 bulls annually to producers throughout Cali-fornia, Nevada, Oregon and Mexico. The operation also runs 300 to 500 feeders on grass each year. He served as a member of the American Hereford Association Board of Directors from 2001 through 2004.

Orvis Cattle Company has been performance testing cattle since 1952. The ranch began ultrasound testing in 1987. The operation is one of the longest running herds in the state to be certified free of Johne’s disease. Over the years, Orvis Cattle Company bulls have been recognized at shows and sales across the state, including the Grand National Stock Show, San Francisco, and the Red Bluff Bull Sale, Red Bluff, Calif.

During college, Orvis joined the family herd established in 1918 by his father and grandfather, C.B Orvis & Son, and later W.S. Orvis & Sons. He was a standout football player and graduated from the College of the Pacific, Stockton, Calif., with a bachelor’s degree in business economics in 1950.

A founding member of the California Beef Cattle Improvement Association (CBCIA), Orvis served on its board of directors from 1959 through 1985. He served as president of the organization from 1961 through 1962. Orvis was named the CBCIA Seedstock Producer of the Year in 1993 and in 2000. In 1997, he received the CBCIA Horizon Award for his dedication to the California beef cattle industry.

Since 1970, Orvis has been an avid supporter of the Western Nugget National Hereford Show and Sale, held each winter in Reno, Nev. In 1995, he was appointed to the Western States Hereford Committee, which oversees the event. In addition, the Orvis family is an avid supporter of Hereford youth programs.

Bruce and his wife Roma have four children and twelve grandchildren. In 1996, the ranch was preserved for fu-ture generations when it was placed in a conservation easement with the California Rangeland Trust, an orga-nization governed by ranchers working to conserve the open space, natural habitat and stewardship of California ranches. For highlights from the BIF’s 41st Research Symposium and Annual Meeting, April 30 – May 3, 2009, in Sac-ramento, Calif., visit www.bifconference.com or www.calcattlemen.org/bif2009.html.

Photo by Cornerpost Publications, publisher of the California Cattleman. Caption: Bruce Orvis (second from right), Farmington, Calif., was recognized by the Beef Improvement Federation (BIF) with the 2009 Pioneer Award during its 41st Research Symposium and Annual Meeting, April 30 – May 3, 2009, in Sacramento, Calif. Pictured with Orvis are sons Mike Orvis (far left) of Livermore, Calif., and Bruce Orvis III of Arnold, Calif. BIF Outgoing President Tommy Brown (far right), Clanton, Ala., made the presentation.

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Golden Honored with Beef Improvement Federation Pioneer Award

SACRAMENTO, CALIF., (May 2, 2009) – Bruce Golden, Ph.D., Dairy Science department head and professor at California Polytechnic State University (Cal Poly), San Luis Obispo, Calif., was honored as a recipient of the Beef Improvement Federation (BIF) Pioneer Award during the organization’s 41st Research Symposium and An-nual Meeting, April 30 – May 3, 2009, in Sacramento, Calif.

A California native, Golden received his bachelor’s and master’s degrees in Animal Science from Washington State University, Pullman, Wash. In 1989, he received a doctorate in Animal Breeding and Genetics from Colo-rado State University (CSU), Fort Collins, Colo.

Golden was a faculty member at CSU for 19 years before becoming the chief operating officer of OptiBrand®, a company he founded in 1998 with two colleagues from CSU that worked with him to develop a secure, biometric and humane method to identify and trace livestock.

Past students consider him an enthusiastic teacher and over the years, he taught several undergraduate and gradu-ate classes at CSU and Cal Poly. Throughout his career, he has worked with a large number of graduate students who have gone on to careers in academics, industry and government.

During his time on the CSU faculty, Golden established the Center for Genetic Evaluation of Livestock (CGEL), which today remains one of the premier national cattle evaluation research and development groups in the world. He produced one of the first multiple trait national evaluations using the animal model for Red Angus in 1986. Since its inception, the CGEL has produced population-level genetic evaluations for dozens of beef breed associa-tions and producer groups in North America, South America, New Zealand and Ireland.

One of Golden’s strengths is in computing; he is responsible for the original creation of the genetic evaluation software known as the Animal Breeder’s Tool Kit (ABTK). The ABTK is a suite of computing tools for conduct-ing large-scale genetic analyses. After several updates and additions, the ABTK is still the core software used by the CGEL, as well as genetics researchers around the world.

His most significant programming contributions, include efficient algorithms for computation of the inverse re-lationship matrix from large pedigrees; approximation of the inverse coefficient matrix used to obtain prediction error variances and accuracy values; and biometric methods for animal identification. Golden was responsible for establishing the first Linux-based Beowulf cluster computing platform used for large-scale genetic prediction work in livestock at CSU.

During his research at CSU, he focused on trait development and Expected Progeny Differences (EPDs) for novel traits, such as heifer pregnancy, stayability and maintenance energy. In order to foster discussion about animal genetics in an open forum, he founded the Animal Genetics Discussion Group (AGDG), an online network of animal breeders from throughout the world.

Golden is a longtime participant in and supporter of the BIF. He was honored by the BIF with a Continu-ing Service Award in 1999. He has served on the BIF Genetic Prediction Committee and wrote significant portions of the National Cattle Evaluation (NCE) methods chapter in the BIF Guidelines for Uniform Beef Improvement Programs.

His presentation on the next generation of EPDs under the economically relevant trait framework, during the 2000 BIF annual meetings in Wichita, Kan., remains a key reference and discussion point today. In 2008, he gave an invited paper on the history of national cattle evaluation development in the United States at the Federation of Animal Science Societies (FASS) annual meeting.

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In addition to his pioneering work in genetic evaluation and improvement of beef cattle, Golden is an avid fly fisherman, guitarist, dog enthusiast and chef. He and his wife Mary live in San Luis Obispo, Calif.

For highlights from the BIF’s 41st Research Symposium and Annual Meeting, April 30 – May 3, 2009, in Sac-ramento, Calif., visit www.bifconference.com or www.calcattlemen.org/bif2009.html.

Photo by Cornerpost Publications, publisher of the California Cattleman. Caption: Bruce Golden (left), Ph.D., San Luis Obispo, Calif., was honored by the Beef Improvement Federation (BIF) with the 2009 Pioneer Award during the organization’s 41st Research Symposium and Annual Meeting, April 30 – May 3, 2009, in Sac-ramento, Calif. Presenting the award is BIF Outgoing President Tommy Brown, Clanton, Ala.

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McPhee Posthumously Honored with 2009 Pioneer Award by the Beef Improvement Federation

SACRAMENTO, CALIF. (May 2, 2009) – The Beef Improvement Federation (BIF) posthumously honored Roy McPhee with the 2009 Pioneer Award during the organization’s 41st Research Symposium and Annual Meeting, April 30 – May 3, 2009, in Sacramento, Calif.

The McPhee family of Lodi, Calif., believes that persistent selection for practical profit-making traits will lead to efficiency and success. The late Roy McPhee, together with his wife Nellie, and children Mike, Mary and Rita and their families, have worked together since 1970 to develop one of the nation’s largest Red Angus herds west of the Rocky Mountains.

In 2005, the Red Angus Association of America recognized McPhee Red Angus as one of the 40 most influential herds in the breed. The family owned-and-operated business markets cattle through its annual production sale at the ranch in late September, selling 100 bulls and 40 females.

McPhee Red Angus bulls have topped the toughest yearling bull tests in the nation, including the California Poly-technic State University (Cal Poly) Bull Test, San Luis Obispo, Calif.; the Midland Bull Test, Columbus, Mont.; and the Snyder Livestock “Bulls for the 21st Century” Test, Yerington, Nev.

McPhee first became involved in the registered cattle business in 1943 with the purchase of an Angus bull. After spending several years in the commercial cattle and feedlot feeding business, he decided to breed purebred Red Angus cattle because they were the only breed at that time to require performance information as a prerequisite to registering cattle. A former agricultural banker, McPhee had seen many registered programs startup, sputter and go out-of-business as a result of lack of commitment and a vision for longevity.

McPhee was named the California Beef Cattle Improve-ment Association (CBCIA) Seedstock Producer of the Year in 1986. In 1993, he was honored as the San Joa-quin-Stanislaus County Cattlemen’s Association Cattle-man of the Year. He was awarded CBCIA’s Horizon Award in 1996 for his contributions to the advancement of performance cattle in the state. The CBCIA was privi-leged to have had his leadership and guidance over the years.For highlights from the Beef Improvement Federation’s 41st Research Symposium and Annual Meeting, April 30 – May 3, 2009, in Sacramento, Calif., visit www.bif-conference.com or www.calcattlemen.org/bif2009.html.

Photo by Cornerpost Publications, publisher of the California Cattleman. Caption: The late Roy McPhee was posthumously honored by the Beef Improvement Federation (BIF) with the 2009 Pioneer Award presented to his family during the organization’s 41st Re-search Symposium and Annual Meeting, April 30 – May 3, 2009, in Sacramento, Calif. Pictured receiving the award from BIF Outgo-ing President Tommy Brown, Clanton, Ala., are (L to R): Rita McPhee, Lodi; Nellie McPhee, Lodi; and Mary Miller, Linden.

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Bullock Receives 2009 Beef Improvement Federation Continuing Service Award

SACRAMENTO, CALIF. (May 1, 2009) – The Beef Improvement Federation (BIF) honored Darrh Bullock, Ph.D., Lexington, Ky., with a Continuing Service Award, presented during its 41st Research Symposium and An-nual Meeting, April 30 – May 3, 2009, in Sacramento, Calif.

Bullock was raised on a large commercial cow-calf operation and watermelon family farm near Williston, Fla. He earned a bachelor’s degree in Animal and Dairy Science from Auburn University, Auburn, Ala., in 1984. He worked at the Auburn University Lower Coastal Plain Research Station, Camden, Ala., as the herdsman for the beef cattle breeding project from 1984 through 1986.

He returned to the Auburn campus and earned a master’s degree in Animal Breeding and Genetics in 1988. He earned a doctorate in Beef Cattle Breeding and Genetics from the University of Georgia, Athens, Ga., in 1992.

Bullock was appointed the University of Kentucky Cooperative Extension Service assistant professor in 1992. He earned the rank of associate Extension professor in 1997 and beef cattle Extension specialist in beef breeding and genetics in 2004.

His primary responsibility is coordinating the state’s beef breeding and genetic management educational ac-tivities. He also serves as a core member of the University of Kentucky Beef Integrated Resource Management (IRM) Committee and as the Extension Beef Group coordinator for all beef-related research, teaching and Exten-sion activities in the Department of Animal Sciences at the University of Kentucky, Lexington, Ky. He also serves as the overall Extension coordinator for the department.

Bullock continues to be very active in both national and international beef breeding organizations. He recently stepped down as the eastern regional secretary and chair of the Multi-trait Selection Committee for the Beef Im-provement Federation (BIF) and currently represents the National Beef Cattle Evaluation Consortium (NBCEC) on the BIF board. He also serves the BIF as the U.S. representative to the International Committee for Animal Recording (ICAR) Beef Recording Working Group, based in Rome, Italy.

He is a member of the board of directors and coordinator of educational programs for the NBCEC and he has also been active in the American Society of Animal Science (ASAS), serving in leadership positions for the southern section and southern region Extension Beef Group.

Bullock is a member of the National Cattlemen’s Beef Association, as well as the Kentucky Cattlemen’s Associa-tion and the Kentucky Beef Improvement Association. Darrh and his wife Helene have two children, Lukas and Hanna.

Highlights from the BIF’s 41st Research Symposium and Annual Meeting, hosted by the California Beef Cattle Improvement Association (CBCIA) and the California Cattlemen’s Association (CCA), can be found online at www.bifconference.com or www.calcattlemen.org/bif2009.html.

Photo by Cornerpost Publications, publisher of the California Cattleman. Caption: Darrh Bullock, Ph.D. (left), Lexington, Ky., was honored by the Beef Improvement Federation with a Continu-ing Service Award presented during its 41st Research Symposium and Annual Meeting, April 30 – May 3, 2009, in Sacramento, Calif. Presenting the award is BIF Outgoing President Tommy Brown, Clanton, Ala.

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Daley Honored with 2009 Beef Improvement Federation Continuing Service Award

SACRAMENTO, CALIF. (May 1, 2009) – The Beef Improvement Federation (BIF) honored David A. Daley, Ph.D., with a Continuing Service Award on May 1, during the organization’s 41st Research Symposium and An-nual Meeting in Sacramento, Calif., hosted by the California Beef Cattle Improvement Association (CBCIA) and the California Cattlemen’s Association (CCA). The award recognizes individuals who have made major contribu-tions to the BIF and/or the beef industry.

Together with his wife, Cindy Daley, Ph.D., and their three children, Daley owns and operates a commercial cow-calf and seedstock operation, based in Oroville, Calif., where his family has been ranching for five generations.

In the early 1990s, Daley founded and coordinated an international group of progressive cattlemen and academics focused on the use of composite and hybrid seedstock in the beef industry. For nearly a decade, the national meet-ings for the Composite Cattle Breeders’ International Alliance became a think-tank for progressive leaders in the industry to evaluate and compare non-traditional approaches to cattle breeding.

More recently, he has been coordinating research projects with Harris Ranch Beef Company, Coalinga, Calif., and Lacey Livestock, Independence, Calif., on the utilization of DNA fingerprinting in beef production and evaluating the implication of crossbreeding in vertically coordinated beef systems.

Daley has also been actively involved in the potential application and implementation of the National Animal Identification System (NAIS), including hosting a nationally recognized animal identification academy.

He presently serves as the president of the Butte County Cattlemen’s Association, a technical advisor to the CB-CIA and is active on state and national issues that affect the beef industry. His present involvement focuses on the implication of animal welfare issues to the beef industry, serving as vice chair of a statewide task force, as well as on an advisory committee for the University of California, Davis, on the same topic.

In addition, he currently serves as the Associate Dean, College of Agriculture, and the Director of the Agricultural Teaching and Research Center at California State University, Chico, where he manages a diversified farming op-eration and supervises the beef program. In that capacity, he has worked to develop and mentor student members of the California Young Cattlemen’s Association, an organization designed to develop future leaders in the beef industry.

For highlights from the BIF’s 41st Research Symposium and Annual Meeting, April 30 – May 3, 2009, in Sacra-mento, Calif., visit www.bifconference.com or www.calcattlemen.org/bif2009.html.

Photo by Cornerpost Publications, publisher of the California Cattleman. Caption: Beef Improvement Federation (BIF) Outgoing President Tommy Brown, Clanton, Ala., presents David A. Daley, Ph.D. (left), Oroville, Calif., with a BIF Continuing Service Award during the organization’s 41st Research Symposium and Annual Meeting, April 30 – May 3, 2009, in Sacramento, Calif.

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Lloyd Honored with 2009 Beef Improvement Federation Continuing Service Award

The Beef Improvement Federation (BIF) honored Renee Lloyd, Johnston, Iowa, with a Continuing Service Award during the organization’s 41st Research Symposium and Annual Meeting, April 30 – May 3, 2009, in Sacramento, Calif.

Lloyd is an account executive with McCormick Company, a full-service sales communication firm, located near Des Moines, Iowa. She works with agricultural clients on their marketing campaigns and communications initia-tives.

Prior to joining McCormick Company, Lloyd served for 10 years as the director of production education with the National Cattlemen’s Beef Association (NCBA), Centennial, Colo. While at the NCBA, she led producer education initiatives, including the Cattlemen’s College®, the Cattle Learning Center and the Integrated Resource Management (IRM) programs.

During her tenure with NCBA, she staffed many producer-leader working groups, councils and committees and assisted with the Beef Quality Assurance (BQA) efforts. She also was a field producer for NCBA’s weekly televi-sion program, Cattlemen to Cattlemen. She was also a tireless representative to the board of directors for the BIF. She was an important voice for the BIF and genetic improvement on the national beef cattle scene.

Lloyd was instrumental in the planning and implementation of many symposia and other producer education ini-tiatives as a part of the BIF team. She was a champion of finding resources to assure that the BIF proceedings were first-rate. A valued member of the annual meeting planning and implementation team for the 39th BIF Research Symposium and Annual Meeting in 2007, she never faltered in her responsibilities.

Before joining NCBA, she was employed as an area agricultural economist for the Oklahoma Cooperative Ex-tension Service in Ada, Okla., and Enid, Okla. During her 10-year career with the Extension service, she worked one-on-one with farmers and ranchers and developed educational programs in areas like cattle and grain market outlook, computer record keeping, financial planning and ag policy. She also assisted with the 4-H and junior livestock activities at the county and district levels.

Lloyd grew up in west central Illinois on a livestock and grain operation. She studied agricultural economics and earned a bachelor’s degree from Oklahoma State University, Stillwater, Okla., and a master’s degree from Vir-ginia Polytechnic Institute and State University, Blacksburg, Va. As a fourth generation beef producer, she still is involved with the family cattle operation in Illinois and enjoys spending time with her nieces and nephews on the family farm.

Highlights from the BIF’s 41st Research Symposium and Annual Meeting, hosted by the California Beef Cattle Improvement Association (CBCIA) and the California Cattlemen’s Association (CCA), can be found online at www.bifconference.com or www.calcattlemen.org/bif2009.html.

Photo by Cornerpost Publications, publisher of the California Cat-tleman. Caption: Renee Lloyd (left), Johnston, Iowa, was honored by the Beef Improvement Federation with a Continuing Service Award presented during its 41st Research Symposium and Annual Meeting, April 30 – May 3, 2009, in Sacramento, Calif. Presenting the award is BIF Outgoing President Tommy Brown, Clanton, Ala.

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Thallman Receives 2009 BIF Continuing Service Award

SACRAMENTO, CALIF. (May 1, 2009) – The Beef Improvement Federation (BIF) honored Mark R. Thallman, Ph.D., Blue Hill, Neb., with a Continuing Service Award during its 41st Research Symposium and Annual Meeting in Sacramento, Calif., April 30 – May 3, 2009.

After working for nearly a decade for major beef cattle seedstock producers, Thallman joined the U.S. Meat Animal Research Center (USMARC), Clay Center, Neb., operated by the U.S. Department of Agriculture’s Ag-ricultural Research Service (USDA-ARS) as a research asso ciate in October of 1996. He went on to become a permanent staff member in June of 1998.

Thallman is recognized internationally as a leading scientist in the areas of beef cattle breeding and statistical ge-netics. His career is devoted to the application of technology to acceler ate the genetic improvement of beef cattle. He has co-authored 23 peer-reviewed articles, one peer-reviewed book chapter, 24 conference pro ceedings and 12 technical reports, manuals and theses. He is the first author of 25 of these 60 publica tions, has given 43 invited presentations and is frequently consulted by the beef industry on a variety of topics.

Genetic evaluation has been a common theme throughout Thallman’s career. He is the USDA-ARS representa-tive to the BIF Board of Directors. He served on the Emerging Technologies subcommittee of the BIF to write guidelines for use of DNA testing in beef cattle improvement from 2004 to 2007. He also served on the Ad-Hoc Committee in 2007 to 2008 to revise the BIF guidelines, which is the most highly respected source that breed as-sociations and other organizations rely on when setting policy related to genetic improvement. He also currently serves as the chairman of the BIF Genetic Prediction Committee.

The ability to develop innovative solutions to challenging problems is one of his greatest attributes. Thallman has influenced the nature of DNA tests offered commercially, the ways in which the results of these tests are reported and the ways and extent to which producers utilize these tests. He identified selective reporting of DNA test results as a problem and proposed a solution that has been implemented by the major DNA testing companies.

Thallman developed a software package, GenoProb, in 2002, which is useful for detecting errors in marker data and pedigrees, as well as to compute probabilities useful for quantitative trait loci (QTL) detection. GenoProb has many worldwide users, including researchers, DNA testing companies, breed associations and breeding companies.

In addition to his pioneering work in genetic evaluation and improvement of beef cattle, Thallman enjoys riding and caring for his horses. He and his wife, Cheryl, have two daughters, Caroline and Allie.

Highlights from the 41st BIF Research Symposium and Annual Meeting, hosted by the California Beef Cattle Improvement Association (CBCIA) and the California Cattlemen’s Association (CCA), in conjunction with the BIF, can be found online at www.bifconference.com or www.calcattlemen.org/bif2009.html

Photo by Cornerpost Publications, publisher of the California Cattleman. Caption: Beef Improvement Federation (BIF) Outgoing Presi-dent Tommy Brown, Clanton, Ala., presents Mark R. Thallman, Ph.D. (left) Blue Hill, Neb., with a BIF Continuing Service Award during the organization’s 41st Research Symposium and Annual Meeting, April 30 – May 3, 2009, in Sacramento, Calif.

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Beef Improvement Federation Names 2009 Ambassador Award Recipient

SACRAMENTO, CALIF. (MAY 2, 2009) – Each year, the Beef Improvement Federation (BIF) recognizes an individual of the press corps that had made a major contribution to beef improvement or the BIF.

Kelli Meged Toledo, Visalia, Calif., received the 2009 Ambassador Award during the organization’s 41st Research Symposium and Annual Meeting, April 30 – May 2, 2009, in Sacramento, Calif.

Toledo has two decades of professional marketing and design experience. She received a bachelor’s degree in Business – Marketing from Montana State University (MSU), Bozeman, Mont., where she worked as the man-aging editor of the MSU newspaper. She went on serve as an intern at several publications before moving to California. For nine years, she was employed at an advertising agency in Visalia, Calif., handling marketing plan development, advertising design and print and electronic media relations.

In 1998, she founded Trailhead Designs, a full-service, marketing and design firm, where she handles everything from advertising development and video production to Web site design and marketing campaigns for agribusi-nesses.

That same year, Toledo was named co-publisher of the California Cattleman, the official monthly publication of the California Cattlemen’s Association (CCA), Sacramento, Calif. She oversees the editorial, as well as handles the design, development, production and accounting for the publication. Her partner Matt Macfarlane, Sheridan, Calif., handles the advertising sales, ring services and customer service for the magazine.

A Montana native, Toledo purchased her first group of registered Angus cattle with a 4-H loan more than three decades ago. She continued to build her herd and show cattle throughout her youth. She served as a board member and communications director for the National Junior Angus Association. In 1990, she married John Toledo and the couple went on to form Toledo Ranches, a diversified farming and Angus cow-calf operation.

Over the years, Toledo has devoted countless hours serving as a volunteer dedicated to promoting agriculture. She was member of the California Angus Association (CAA) board of directors for six years and served an additional two years as the CAA secretary. For the past decade, she has been the editor, also responsible design and layout, of the California Angus News.

She was a Kings County 4-H beef leader for 10 years. She served for 15 years as the co-chair of the American Angus Auxiliary Publicity Committee, where one of her duties was to produce the organization’s annual report. For six years, she was the newsletter editor for the Tulare-Kings Chapter of the California Women for Agriculture (CWA) and served as the CWA Kings Area director for two years.

She has served for 10 years on the CCA Allied Industry Council and worked with the California Beef Cattle Im-provement Association (CBCIA) to promote the organization’s annual tours, 50th anniversary celebration and the 41st BIF Research Symposium and Annual Meeting.

For highlights from the BIF’s 41st Research Symposium and Annual Meeting, April 30 – May 3, 2009, in Sacra-mento, Calif., visit www.bifconference.com.

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Thank you to the 2010 BIF sponsors for their continued support and valuable contributions.

PATRONIgenity Pfizer Animal GeneticsMU ExtensionUSDA-NIFA

PLATINUMAngus Productions, Inc.BEEF MagazineLand O’Lakes Purina FeedsMissouri Beef Industry CouncilOsborn and BarrSydenstricker GeneticsThe Beef Checkoff Program

GOLDAmerican Angus AssociationAmerican Hereford Association/

Certified Hereford BeefBoehringer Ingelheim Vetmedica, Inc.California Beef Cattle Improvement AssociationCanadian Beef Breeds CouncilGrowSafe Systems, Ltd.Missouri Show-Me-Select Replacement Heifer Program

SILVERABS Global, Inc.Accelerated GeneticsAllflex USA, Inc.American Chianina AssociationAmerican Gelbvieh AssociationAmerican International Charolais AssociationAmerican Simmental AssociationBeefmaster Breeders UnitedBioZymeCertified Angus BeefCircle A Angus Ranch/Circle A FeedersCRI Genex Coooperative, Inc. Destron FearingInternational Brangus Breeders AssociationJoplin Regional StockyardsMFA, Inc.Midwest MicrosystemsMissouri Beef CattlemanMissouri Beef Cattle Improvement AssociationORIgenRed Angus Association of AmericaSelect SiresTransOva Genetics

BRONZE

American Maine Anjou AssociationFCS FinancialFrank-Hazelrigg Cattle Co. LLC Indiana Beef Evaluation ProgramMissouri Angus Association

Green Springs Bull TestMissouri Grape and Wine ProgramProfessional Beef GeneticsMcBee Cattle CompanyLaBoube Farms / Bear Valley Farms, Inc.American Wagyu AssociationMO Department of Agriculture

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Beef Improvement Federation Research Symposium and Annual Meeting Pre- and Post-Conference Tours

June 28, 2010 Pre-Conference Tour, 7:00 am to 5:00 pm

Tour Stops

Sydenstricker Genetics . . . . . . . . . . . . . . . . . . . www.sydgen.com

Circle A Feeders . . . . . . . . . . . . . . . . . . . . . . . www.circlearanch.com/feeders.html

McBee Cattle Co. . . . . . . . . . . . . . . . . . . . . . . www.mcbeecattlecompany.com

Warm Springs Ranch . . . . . . . . . . . . . . . . . . . . www.warmspringsranch.com“Home of the World Famous Budweiser Clydesdales”

University of Missouri, Christopher S. Bond Life Sciences Center, “From tissue sample to diagnostic DNA test: The Process of Genomic Discovery,” Dr. Jerry Taylor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . http://animalsciences.missouri.edu/animalgenomics/

July 1, 2010 Post-Conference Tour, 7:00 am to 6:00 pm

Post Tour Stops

University of Missouri Beef Research Teaching Farm and Feed Intake System

LaBoube Farms and Guesthouse and Cattle Operations Managing Partner: Bear Valley Farms, Inc. . . . . . . . . . . . . . . . . . . www.laboubefarms.com

Stone Hill Winery — Tour/Lunch . . . . . . . . . . . . . . www.stonehillwinery.com

LongView Animal Nutrition Center, Purina Mills . . . . . www.purinamills.com/BetterAnimals.aspx

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~ Silver Sponsors ~

~ Bronze Sponsors ~

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