ATTENTION ON NOVEL TRAITS: Needed or Novelty 46th Beef Improvement Federation Annual Meeting and Research Symposium
Co-hosted by the University of Nebraskaâ€“Lincoln Department of Animal Science, the US Meat Animal Research Center (USMARC), and the Nebraska Cattlemen. The University of Nebraskaâ€“Lincoln is an equal opportunity educator and employer.
TABLE OF CONTENTS Schedule of Events.........................................................................................................................................................................4-6 General Sessions Speaker Biographies ...................................................................................................................................8-11 Clay Mathis Dan Shike Rick Funston Kim Vonnahme Tom Field Donnell Brown J.D. Radakovich
Lorna Marshall Donagh Berry Holly Neibergs Raluca Mateescu Susan Duckett Galen Erickson Harvey Freetly
2014 BIF Commercial Producer of the Year Award Nominees..........................................................................................12-13 BIF Commercial Producer of the Year Past Award Recipients................................................................................................ 14 2014 BIF Seedstock Producer of the Year Award Nominees..............................................................................................15-17 BIF Seedstock Producer of the Year Award Past Recipients.................................................................................................... 18 Pioneer Award Past Recipients................................................................................................................................................19-21 Continuing Service Award Past Recipients........................................................................................................................... 22-23 Ambassador Award Past Recipients............................................................................................................................................ 24 BIF Travel Felloweship Recipients.................................................................................................................................................25 Frank Baker Memorial Scholarship........................................................................................................................................ 26-27 Frank Baker Memorial Scholarship Award Recipient Heather Bradford........................................................................ 28-35 Frank Baker Memorial Scholarship Award Recipient Xi Zeng........................................................................................... 36-43 Frank Baker Memorial Scholarship Award Past Recipients.....................................................................................................44 Roy A. Wallace Memorial Fund....................................................................................................................................................45 Roy A. Wallace Memorial Fund Past Recipients........................................................................................................................46 Tours ................................................................................................................................................................................................. 47
Proceedings.............................................................................................................................................................................. 48-159 General Session I Papers......................................................................................................................................................... 48-97 Economic Considerations For The Cow Herd, Clay Mathis....................................................................................................48 Heifer Intake and Efficiency as Indicators of Cow Intake and Efficiency, Dan Shike..........................................................50 Beef Heifer Development and Lifetime Productivity, Rick Funston........................................................................................56 The Long-Lasting Impact of Nutrition: Developmental Programming, Kim Vonnahme.....................................................62 General Session II Papers...................................................................................................................................................... 68-115 Selection for Novel Traits: An International Genomics Perspective, Donagh Berry............................................................68 Economic Benefits of Using Genetic Selection to Reduce the Prevalence of Bovine Respiratory Disease Complex in Beef Feedlot Cattle, Holly Neibergs............................................................................................................................................82 It Is Possible to Genetically Change the Nutrient Profile of Beef, Raluca Mateescu.......................................................... 87 Changes in Dietary Regime Impact Fatty Acid Profile of Beef, Susan Duckett...................................................................93 Improving Feed Efficiency in the Feedlot: Opportunities and Challenges, Galen Erickson.............................................101 Relationship Between Selection for Feed Efficiency and Methane Production, Harvy Freetly........................................ 112 Technical Committes............................................................................................................................................................ 116-159 Healthfulness of Beef: A Genome-Wide Association Study Using Crossbred Cattle, Cashley Ahlberg and Lauren Schiermiester.................................................................................................................................................................... 116 Breeding for Reduced Environmental Footprint in Beef Cattle, Donagh Berry.................................................................126 Across-Breed EPD Tables for the Year 2014 Adjusted to Breed Differences for Birth Year of 2012, Larry Kuehn......134 Mean EPDs Reported by Different Breeds, Larry Kuehn.......................................................................................................155 Sponsors.................................................................................................................................................................................160-165
SCHEDULE OF EVENTS
ATTENTION ON NOVEL TRAITS:
June 18 12:00-7:00 Registration 1:00-4:00 Board Meeting 5:00-6:00 Welcome Reception 6:00-9:00 USMARC Symposium: 50 Years of Service to the Beef Industry History of the Germplasm Evaluation Project: Past: Larry Cundiff Present: Mark Thallman The Genomics Era: Steve Kappes The USMARC / UNL partnership: Ronnie Green Panel Discussion Seedstock: Bill Rishel Genomics: Dave Nichols Commercial: Burke Teichert Feedlot: Chuck Folken
June 19 6:00 a.m. - Registration 6:00 p.m. 8:00-12:30 General Session I: Focus on the Cowherd 8:00 Welcome -- Lt. Gov. Heidemann, Ronnie Green, Steve Whitmire 8:30 Economic Considerations for Profitable Cowherds Clay Mathis, King Ranch Institute 9:15 Heifer Intake and Feed Efficiency as Indicators of Cow Intake and Efficiency Dan Shike, University of Illinois 10:00 Break 10:30 Decreasing Costs through Improved Heifer Development Strategies Rick Funston, University of Nebraska– Lincoln
Needed or Novelty 11:00 The Long Lasting Impact of Nutrition: Developmental Programming Kim Vonnahme, North Dakota State University 11:30 Merging Genetics and Management for Improved Profitability— Tom Field, Moderator Panelists: Donnell Brown, JD Radakovich, Lorna Marshall 12:30-2:30 Awards Luncheon Commercial Producer of the Year Award Pioneer Award Continuing Service Award 2:30-5:30 Technical Breakout Sessions Advancements in Cowherd Efficiency and Adaptability 2:30-3:15 Mike MacNeil, Delta G, — The Challenge: Evaluating Cow Lifetime Productivity and Efficiency 3:15-4:00 Mike Davis, The Ohio State University — Measuring Cow Efficiency and Productiv- ity: What Do We Know from the Research? 4:00-4:45 Larry Kuehn, USDA MARC — Cow Efficiency/Productivity: The MARC Perspective 4:45-5:30 Scott Speidel, CSU — National Cattle Evaluation: Approaches to Cow Productivity and Efficiency Advancements in Emerging Technology 2:30-2:45 Ronnie Green, University of Nebraska– Lincoln — Following the Yellow Brick Road of Beef Cattle Genomics – 25 Years of Perspective 2:45-3:15 Daniel Pomp — The Evolution of Commercial DNA Analysis in the Cattle Industry 3:15-3:45 Michael Bishop, Illumina — Accelerating Agrigenomics: The Business of Cattle Genetics 3:45-4:15 Matt Spangler, University of Nebraka– Lincoln — Using Genomics to Pick High-hanging Fruit: Integrated Projects Update
4:15-4:45 Scott Fahrenkrug, Recombinetics — Molecular Breeding to Accelerate Livestock Improvement 4:45-5:15 Harvey Blackburn, National Center for Genetic Resources Preservation —Design and Function of a Genomics Database for the Cattle Industry Advancements in Selection Decisions* Advancements in Producer Applications* Brief statement on BIF’s roll in standardization and what we hope to accomplish in session 2:45-3:30 Bruce Golden, California Polytechnic State University —ERTs for the ‘New Beef Industry’ Dan Moser, Kansas State University — Don't Blame the Bull: Rethinking Contemporary Groups Starting at or Before Conception 4:15-5:00 Producer Panel: EPD Wish List and Farm-level Data Collection Challenges 5:00-5:30 Breed Association Panel: Performance Data Wish List Now and in the Future, Response to Producer Panel * Committee sessions will be joined 6:00-10:00 p.m. Dinner at Lincoln Station
June 20 6:00 a.m.- Registration 6:00 p.m. 8:00-12:30 General Session II: Focus on the Feedlot 8:00 Selection for Novel Traits: An International Genomics Perspective Donagh Berry, Animal & Grassland Research and Innovation Centre, Teagasc, Moorepark, Ireland 8:30 Economic Benefits of using Genetic Selection to Reduce the Prevalence of Bovine Respiratory Disease Complex in Beef Feedlot Cattle Holly Neibergs, Washington State University
9:00 It is Possible to Genetically Change the Nutrient Profile of Beef Raluca Mateescu, University of Florida 9:30 Changes in Dietary Regime Impact Fatty Acid Profile of Beef Susan Duckett, Clemson University 10:00 Break 10:30 Improving Feed Efficiency in the Feed- lot: Opportunities and Challenges Galen Erickson, University of Nebraska–Lincoln 11:00 Relationship Between Selection for Feed Efficiency and Methane Production Harvey Freetly, US Meat Animal Research Center 11:30 Wrap up 11:45 Annual meeting, regional caucuses, election of directors 12:30-2:30 Awards Luncheon Seedstock Producer of the Year Award Frank Baker Award Roy Wallace Scholarship Award Ambassador Award Presidents Address Elections 2:30-5:30 Technical Breakout Sessions Advancements in Live Animal, Carcass and End Product 2:30-3:00 Ken Odde, Kansas State University — Diminishing Beef Cow Research Herds … Their Role in the Past and Their Role in the Future 3:00-3:30 Tommy Wheeler, Meat Animal Research Center, Clay Center, NE — Meat Quality Research at MARC 3:30-4:15 John Gonzalez, Kansas State Universi- ty — Measuring and Quantifying the Role of Collagen Crosslinks in Beef Tenderness 4:15-4:45 Lauren Schiermiester & Cashley Ahlberg, University of Nebraska– Lincoln — Healthfulness of Beef: A Genome Wide Association Study Using Crossbred Cattle.
SCHEDULE OF EVENTS 4:45-5:30 Donagh Berry, Animal & Grassland Research and Innovation Centre, Teagasc, Moorepark, Ireland — Breeding for Reduced Environmental Footprint Advancements in Genetic Prediction 2:30-3:00 Dorian Garrick, Iowa State University — Opportunities and Challenges for a New Approach to Genomic Prediction 3:00-3:45 Breed Association Representatives — Updates on Implementation of Genom- ic-enhanced National Cattle Evaluation 3:45-4:00 Bruce Golden, California Polytechnic State University — Eliminating the approximation bias in NCE accuracy computations with high-performance Gibbs Sampling 4:00-4:30 Mark Thallman, US Meat Animal Research Center — Things that Annoy Me About National Cattle Evaluation 5:30-8:00 Board Meeting
June 21 Post Conference Tour 6:45 a.m. Depart hotel Circle Five Feedlot US Meat Animal Research Center GeneSeek 5:00 p.m. Arrive back to hotel
GENERAL SESSION SPEAKERS CLAY MATHIS was named Director and Endowed Chair of the King Ranch® Institute for Ranch Management in July, 2010. As Director, Mathis leads faculty and staff appointed to the King Ranch® Institute for Ranch Management and oversees teaching and outreach efforts of the Institute. He maintains and develops curriculum for the M.S. in Ranch Management degree program, which includes more than 42 hours of business and animal production coursework and intensive project work tackling issues on large partnering ranches across the United States. Mathis works closely with the KRIRM Management Council to identify topics and speakers for the entire suite of KRIRM lectureships and the annual Holt Cat® Symposium on Excellence in Ranch Management.
of Illinois. Shike is an Assistant Professor in Animal Sciences at the University of Illinois and is responsible for teaching and research in beef cattle nutrition and management in addition to serving as the coordinator for the Livestock and Meats judging teams.
Mathis is a native of New Braunfels, TX. He received a B.S. in Animal Science and M.S. in the Physiology of Reproduction from Texas A&M University. In 1998, he earned a Ph.D. from Kansas State University in Ruminant Nutrition where his research focused on supplementing grazing cattle. From 1998 to 2010, Dr. Mathis worked as a Professor and Extension Livestock Specialist at New Mexico State University.
RICK FUNSTON is a professor and Reproductive Physiologist at the University of Nebraska–Lincoln. He received his B.S. from North Dakota State University, M.S. from Montana State University his Ph.D. from the University of Wyoming, and completed a Post Doc at Colorado State University in Reproduction/Biotechnology. He divides his time between extension and research. His research on lighter heifer development is receiving national attention/adoption; research on fetal programming effects on postnatal calf performance including carcass characteristics and reproduction has received national and international recognition; and he is a team member of nationally recognized beef systems research. In the extension capacity, he provides leadership and subject matter expertise for educational programs in cow-calf production management for the West Central District and statewide expertise in beef reproductive management programs
Outside of his professional activities, he and his family have fed cattle in growing, finishing, and cull-cow feeding enterprises. Mathis and his wife, Rhonda, have three children: Morgan, Miles, and Amy Kaye.
DAN SHIKE lives in Sadorus, Illinois with his wife, Jennifer and 3 children; Olivia, Hunter, and Harper. Shike grew up on a diversified grain and livestock operation in western Illinois. Shike’s family owns and operates Shike Cattle Company and he is actively involved with his father and brother in the management and marketing of the cattle. Shike received his A.S. degree from Black Hawk College – East Campus, his B.S. from Kansas State University, and his M.S. and Ph.D. from the University 8
Shike’s research is focused on identifying nutritional strategies and management practices that improve efficiency, reproduction and profitability in beef cow/calf production. Specifically, his work focuses on understanding the relationship of intake, measures of efficiency, and methane production in the developing heifer and measures of intake and efficiency in the mature cow. He also evaluates how nutrition and management of the cow during gestation and lactation not only impacts the reproductive performance of the cow but also what impacts this may have on the developing fetus and early postnatal life.
received her B.S. degree in Animal Science from Iowa State University, M.S. at Oklahoma State University, and Ph.D. from the University of Wyoming. Kim moved to North Dakota State University for a post-doctorate program in 2003 and accepted an assistant professor position in the Department of Animal Sciences in 2004. She was promoted to Associate Professor in 2010. Kim served as the Co-Director of the Center for Nutrition and Pregnancy from 2009 until April 2012. Vonnahme’s research programs focuses on the impacts of maternal nutrition on fetal and placental development in sheep and cattle. More specifically, Kim is interested in how the maternal nutrition impacts uteroplacental blood flow, development of the placenta, and nutrient transfer. She has generated over $1.8 million in research grants, has 100 peer review publications, > 210 abstracts, 3 book chapters, and 1 patent. Kim is married to Michael Kangas and they have 2 children, Katie and Joe.
TOM FIELD is a passionate advocate for education, agriculture, free enterprise, engaged citizenship, and the potential of young people. He serves the people of Nebraska as the Director of the Engler Agribusiness Entrepreneurship Program and holder of the Engler Chair in Entrepreneurship at the University of Nebraska– Lincoln. He is also a noted agricultural author with works including his column “Out of the Box” and featured commentator of “The Entrepreneurial Minute” on the Angus Report on RFD-TV. He is the author of two agricultural text books which have been adopted in both domestic and international markets. A frequent speaker at agricultural events in the U.S. and abroad, he has consulted with a number of agricultural enterprises and organizations, and has served on numerous boards related to education, agriculture, and athletics. He is the co-owner of Field Land and Cattle Company, LLC in Colorado. He and his wife Laura watch over a brood that includes one year old twins, a set of twins in college, and one starting his career in Teach for America.
DONNELL BROWN and his
wife Kelli are the fifth generation to own and manage the R.A. Brown Ranch in Throckmorton, TX, a family business since 1895. They raise registered Angus, Red Angus and SimAngus cattle and sell 600 bulls each October. The ranch’s #1 goal is to improve the profitability and sustainability of their commercial customers. The R.A. Brown Ranch has been honored with the NCBA Cattle Business of the Century Award as well as being named BIF Seedstock Producer of the Year. The strength of their program is shown by the high percentage of repeat commercial bull customers, as well as by having more than 25 bulls in major AI studs. Their trademark is in their unique bull development system that tests their bulls for performance, efficiency and carcass superiority while developing them for longevity with the use of high forage diets on the rocky hills of west Texas. Donnell is a graduate of Texas Tech University. Prior to that, he served as President of the Texas FFA & as the National FFA President. He has served in a Strategic Planning capacity for four different breed associations as well as the National Cattlemen’s Beef Association. Donnell’s wife Kelli served as the National FFA President in 1988 and President of the Red Angus Association of America in 2009 & 2010.
J.D. RADAKOVICH was raised on a purebred and composite seedstock cattle operation in Iowa. Radakovich earned a Bachelor’s degree in Animal Science from Colorado State University and then worked for nine months on ranch stations throughout eastern Australia. Following that, Radakovich spent ten years in northern Nevada working on ranches before completing the King Ranch Institute for Ranch Management Masters Degree program. Currently, J.D. is the Manager of the Hoodoo Ranch in Cody, Wyoming. 9
GENERAL SESSION SPEAKERS LORNA MARSHALL grew up on a small registered Simmental and alfalfa hay operation near Wichita, Kansas. Following graduation from Kansas State University and Colorado State University, Marshall served as Director of Performance Programs and Youth Activities for the American Gelbvieh Association (1993-1995), Manager of Beef Sire Acquisition for ABS Global, DeForest, WI (1995-2011), and is currently the U.S. Beef Marketing Manager for Genex Cooperative, Shawano, WI where she works with Genex’s Large Herd Beef Initiative. Lorna and her husband, Troy, are first generation ranchers with a 300 head Angus and SimAngus seedstock operation that holds an annual bull sale each March. Both Troy and Lorna have been active in various beef industry organizations and associations. The Marshall’s ranch near Burlington, CO along with their children (and work force), Wyatt, Justis, and Wynn.
DONAGH BERRY is a part-
time beef and sheep farmer but also a principal investigator in quantitative genetics at the semi-state research center, Teagasc, Moorepark in Ireland. He is responsible for the research on genetics in dairy and beef cattle and has recently become involved in sheep and plant genetics. His main interests are in the derivation of breeding goals, genetic and genomic evaluations, statistics, decision support tools and breeding programs.
HOLLY NEIBERGS is an Associate Professor at Washington State University in the Department of Animal Sciences where she conducts research investigating the genetic basis of complex traits in cattle such as disease (bovine paratuberculosis, bovine viral diarrhea-persistent infection, and bovine respiratory disease), feed efficiency, and reproduction. She received a B.S. and M.S. (reproduction) in Animal Science at Washington State University and a Ph.D. at Texas A&M University in genetics. She completed a post-doctoral fellowship at the National Animal Disease Center in Ames, Iowa prior to her tenure at the University of Louisville College of Medicine where she studied the genetic basis of hereditary cancers and directed the Norton Hereditary Cancer Institute which offered genetic testing and counseling to families with histories of cancer. Neibergs returned to livestock genetics in 2007. RALUCA MATEESCU recently joined the faculty in the Department of Animal Science at University of Florida, after serving on the Animal Science faculty at Oklahoma State University for 7 years. As an Associate Professor of Genetics at Oklahoma State University, she developed a research program focused on applying the most recent genomic technologies to improving animal production efficiency and enhancing animal products for improved human health. She has also dedicated much of her time to incorporating the latest genomic discoveries in teaching, at both undergraduate and graduate level, to ensure that the student population is well prepared to become participants in the genetic revolution and informed users or consumers of biotechnology. She received a B.S. degree in Molecular Biology and Genetics from Bucharest University, Romania and received her M.S and Ph.D. in Animal Breeding and Genetics from Cornell University.
SUSAN DUCKETT is currently a Professor in the Department of Animal and Veterinary Sciences at Clemson University where she holds The Ernest L. Corley, Jr. Trustees Endowed Chair. She received her B.S. degree in Animal Science from Iowa State University, and M.S. and Ph.D. degrees in Animal Science from Oklahoma State University. She held faculty positions at the University of Idaho and University of Georgia prior to her appointment at Clemson University. Duckett’s research integrates ruminant nutrition and meat science to alter lipid metabolism, fatty acid composition and palatability of animal products.
HARVEY FREETLY is the Research Leader for the Nutrition and Environmental Management Research Unit at the USDA, Agricultural Research Service, U.S. Meat Animal Research Center in Clay Center, Nebraska. In 1990, he joined USDA after receiving his Ph.D. in Nutrition from the University of California – Davis. His research has focused on nutrient management during heifer development, pregnancy, and lactation. He is currently conducting research to determine the role of nutrition on developmental programming of heifers, and the microbiome of the gastro-intestinal tract of cattle that differ in feed efficiency.
GALEN ERICKSON is the Nebraska Cattle Industry Professor of Animal Science in the Department of Animal Science at the University of Nebraska–Lincoln, as well as Beef Feedlot Extension Specialist. He received his Ph.D. in 2001 from the University of Nebraska. Research and extension activities focus on utilization of byproducts for growing and finishing beef cattle, utilization of alternatives to grain for finishing cattle, the interaction between nutrition, management, and environmental issues including air quality and nutrient management, and growth promoters that include use of implants, feed additives, and beta-agonists. Along with graduate students, we have published approximately 275 Nebraska Beef Report articles and over 90 scientific journal articles over the past 13 years.
CB Farms Family Partnership Owners and Managers: Berry, Carla and Brandon Bortz Preston, Kansas A week after Berry and Carla Bortz graduated from Kansas State University in 1982, they got married and began farming in eastern Pratt County near Preston, Kansas. They started with two irrigated circles, two dryland quarters and about 300 acres of grass, which they used to background calves. Their family began in 1986 with the birth of their son Brandon, followed by their daughter, Amber, in 1987 and son, Darnell, in 1991. As the kids grew, so did the farm. In 2001, they started their cowherd and reduced the number of cattle purchased per year. Brandon and his wife, Cari, returned to the farm in 2012. Today, they farm 19 irrigated circles, 2,500 acres of dryland and 2,000 acres of native grass. They have 550 spring-calving cows, of which 150 are registered Black Angus. They also operate a 1,500 head feed yard. They grow corn, wheat, soybeans, milo, sunflowers, cotton, alfalfa, bermuda and feed. They finish all their calves at home along with some calves they purchase from their bull customers. The calves are marketed through U.S. Premium Beef (USPB). They believe that if the beef industry is going to survive and be something besides a niche in the protein market, operating costs must be reduced in all sectors of the industry. Their mission is to deliver a desirable product to the consumer from which they can derive an acceptable standard of living. To do this, they are going to control or participate in as many practices as they can from solar interception to product delivery on the plate. The Kansas Livestock Association is proud to nominate CB Farms Family Partnership.
Hansen Family Ranches Owners: Circle Ranches – Ed and Marilyn Hansen Lone Pine Livestock – Carl and Debbie Hansen Quarter Circle Lazy H Ranch – Chris and Janeth Hansen D Dart Ranch – Cheri and Scott Dent Managers: Hansen Family Livermore, Colorado The Hansen Family, from Livermore Colorado, is a fifth-generation family ranch originally purchased in 1940 by Sam and Castor Hansen; father and grandfather of Ed Hansen and grandfather and great-grandfather of Carl, Cheri, and Chris. The ranch headquarters have been located in Livermore, CO since 1940. In 1994, the Hansen family acquired a summer ranch in Grover, CO, replacing summer forest permits. The Grover Ranch consists of approximately 15,000 acres and provides reliable pasture and more flexible management. Currently, the herd consists of 250 head of Angus cattle. Three years ago, due to extreme drought, the family was forced to reduce one third of the herd. They raise grass hay, between 700-800 tons, to use for winter feed and sell for supplemental income. They also raise replacement heifers. The calving season runs between January 15th and March 15th. What makes the Hansen’s unique is that they implement a 100% AI breeding program. They raise their own clean-up bulls and purchase high quality bulls which they collect semen from and then use for inseminating the herd. This allows them to spread bull costs over more cows, and consequently, pay a higher dollar for quality sires. The cow herd spends from June 1st through October 15th at the Grover ranch where calves are sold and shipped. The cows spend the balance of the year at the home ranch in Livermore for calving and breeding. There are three generations involved in showing livestock at various expositions throughout the state. Hansen Family Ranches is proudly nominated by the Colorado Cattlemen’s Association and Colorado State University.
James Kean Owner/Manager: James Kean Louisa, Virginia James Kean runs a 300 head fall-calving cow/calf beef operation in Louisa, Virginia. James has a reputation for having high quality beef cattle with his calves always bringing a premium in the local state graded sales or Central Virginia Cattlemen Association (CVCA) special tele-auction sales. James has always paid special attention to maintaining high quality genetics in his herd through the use of AI with his heifers, as well as using top quality bulls many times from the Beef Cattle Improvement Association (BCIA) sales on the cows. The cow herd consists primarily of Angus and black baldy cows. James also raises small grain and corn row crops on his operations, which is used primarily for feed resources in his cattle operation. James is a leader for the local farming community serving on several boards of directors. James has been an active member of the Louisa Farm Bureau Board of Directors for over 25 years serving as President for several years. He has been a director on the Thomas Jefferson Soil and Water Conservation District for the past eleven years. James served on the Orange Madison Coop Board of Directors for 20 years. He has been a member of the CVCA since it started in the late 1980â€™s and served on the board of directors since January of 2011. James is also a 4-H volunteer and has helped with the Louisa Agriculture Fair for the past 15 years. James is married to Dr. Kate Hussman who recently retired from being a large animal veterinarian in Louisa County. James has two sons Brian and John. The Virginia Beef Cattle Improvement Association is proud to nominate James Kean.
XA Cattle Company Owners: Bill, Marie and Levi Farr Manager: Bill Farr Moorefield, Nebraska Bill, Marie and Levi Farr have lived at their current location in Lincoln County Nebraska for the past 7 years. Before that, they lived between Stockville, and Farnam, but, ran their cows there in the summer time. Currently, their operation consists of around 700 BalancerÂŽ cows and 150 registered Hereford cows; they also farm 2,000 acres of a combination of dry and irrigated corn, soybeans, and wheat. Their heifers start calving in February and the cows the last week of March. In November they gather their cattle out of the hills and begin weaning the calves. The calves are backgrounded at their headquarters in their feedlot and then sold the end of February or the first week of March, depending on weather and markets. The cows run on winter pasture until they are worked. They are then moved to stocks until calving time. The replacement heifers are grown and developed there at the ranch and developed to gain 1.5 pounds per day. XA Cattle Company is proudly nominated by the American Gelbvieh Association.
PAST AWARD RECIPIENTS
COMMERCIAL PRODUCER OF THE YEAR
Darnall Ranch, Inc. Maddux Cattle Company Quinn Cow Company Downey Ranch JHL Ranch Kniebel Farms and Cattle Company Broseco Ranch Pitchfork Ranch Prather Ranch Olsen Ranches, Inc. Tailgate Ranch Griffith Seedstock Maxey Farms Bill & Claudia Tucker Mossy Creek Farm Giles Family Mike & Priscilla Kasten Randy & Judy Mills Merlin & Bonnie Anderson Virgil & Mary Jo Huseman Joe & Susan Thielen Fran & Beth Dobitz Jon Ferguson Kopp Family Dave & Sandy Umbarger Mike & Diana Hopper Jerry Adamson Gary Johnson Rodney G. Oliphant Charles Fariss Glenn Harvey
Nebraska Nebraska Nebraska Kansas Nebraska Kansas
2013 2012 2011 2010 2009 2008
Colorado Illinois California Nebraska Kansas Kansas Virginia Virginia Virginia Kansas Missouri Kansas Kansas
2007 2006 2005 2004 2003 2002 2001 2000 1999 1999 1998 1998 1997
Bob & Sharon Beck Al Smith Sam Hands Henry Gardiner Jess Kilgore Bert Hawkins Mose Tucker Mary & Stephen Garst Ron Baker Gene Gates Lloyd Nygard Pat Wilson Chan Cooper
Oregon Virginia Kansas Kansas Montana Oregon Alabama Iowa Oregon Kansas North Dakota Florida Montana
1984 1983 1982 1981 1980 1979 1978 1977 1976 1975 1974 1973 1972
Kansas South Dakota Kansas Oregon Oregon
1995 1994 1993 1992 1991
Oregon Nebraska Kansas Kansas Virginia Oregon
1990 1989 1988 1987 1986 1985
SEEDSTOCK PRODUCER Marshall Cattle Company Owners/Managers: Troy and Lorna Marshall Burlington, Colorado
Marshall Cattle Company is a first generation Angus and SimAngus seedstock operation that includes 270 registered females located in Burlington, Colorado. Their mission statement is “to provide revolutionary genetic solutions that provide value and maximize profits for our customers. We are dedicated to ensuring a thriving beef industry for the next generation while conducting ourselves in a manner that reflects our faith in God.” They purchased their ranch 13 years ago and recently hosted their 10th annual production sale in eastern Colorado. They have both a spring and fall breeding program to spread out labor and the use of high quality clean-up herd sires. Their program relies heavily on AI and ET to multiply the impact of their best genetics and high accuracy, balanced trait sires that excel for the economically-relevant traits of beef production. Marshall Cattle Company is the home of “SuperMamas”; to them, that means females that are designed to thrive in the harsh eastern Colorado environment under commercial conditions. Their females must be moderate framed, easy fleshing, structurally sound, good dispositioned, nice uddered, and reproductively efficient with appropriate levels of milk. Recently, they have collected feed efficiency and PAP data in addition to the more traditional traits. The labor force on the ranch consists of Troy and Lorna Marshall and their three children, along with a spring intern. Because they are a first generation operation, they know they would not be where they are today without the help and support of many neighbors and friends. Each year at the beginning of their bull sale, they present the “Above and Beyond” award to someone who has truly gone above and beyond the call of duty to make their success possible – friends, neighbors, extension agents, veterinarians, customers, cooperators, and this year their auctioneer! They know their future success depends on customer satisfaction, honesty and integrity. Repeat customers, long-standing relationships and genetic value are what they strive to produce. Marshall Cattle Company is proudly nominated by the Colorado Cattlemen’s Association and Colorado State University.
Schuler Red Angus Owner: The Darrell Schuler Family Manager: Butch Schuler Bridgeport, Nebraska Schuler Red Angus is located in the panhandle of western Nebraska on Lawrence Fork Creek. Darrell and Mary Lou began raising commercial Herefords there in 1959. By the 1970’s, they were using Red Angus bulls and discovered that the crossbred calves were superior to the ranch’s traditional straight-bred cattle. A registered Red Angus herd was started in 1976 to develop genetics for the ranch’s commercial herd and provide seedstock for neighboring ranches. The seedstock herd expanded in the 1980s and continued to improve through the use of artificial insemination, performance-testing and a data-based breeding program utilizing EPDs. Customer input and feedback from meat-packers regarding the ranch’s finished commercial cattle encouraged the Schuler’s to begin collecting carcass data in 1991. They later developed structured carcass tests utilizing their own and customer cattle. Today, over 25% of Red Angus’ high-accuracy carcass trait sires have been proven by Schuler Red Angus. A composite seedstock herd called “Schuler Reds” was started in 1992 which utilizes Red Angus, Simmental and Gelbvieh genetics giving Schuler Red Angus customers the opportunity to add heterosis and breed complementarity via a simple crossbreeding system. The current ranching operation encompasses 17,000 acres including 2,000 acres of private pasture leases and 1,250 acres of irrigated farm ground. Butch and Susan Schuler with their children Stephanie and David manage the operation of approximately 1,000 head of spring calving females. The Schuler’s hosted their 32nd production sale this spring selling 150 registered Red Angus and Schuler Red composite bulls. The Nebraska Cattlemen and the Red Angus Association of America are proud to nominate Schuler Red Angus.
SEEDSTOCK PRODUCER Shelton Angus Farm Owners/Managers: W.H. “Buddy” Shelton Gretna, Virginia
Shelton Angus Farm is a family operated registered Angus seedstock operation in Gretna, Virginia. The farms are located in Pittsylvania County which is historically one of the largest tobacco producing regions in the southeast. The registered herd was established in 1963 by Walter H. and Ruby Shelton. Management of the cattle became the responsibility of W.H. “Buddy” Shelton Jr. in 1988 after he returned to home post-graduation from Virginia Tech. One hundred twenty brood cows are maintained on an all fescue grazing system. The herd is exclusively fall calving, which is typical in south-central Virginia. The herd has been on a 100% A.I. breeding program since 1988. Bulls are developed collaborative with other seedstock breeders in the region, and historically marketed through the Virginia BCIA program, until five years ago when Shelton Angus initiated their own annual fall bull sale. Genetic improvement in the herd centers on functionality and adaptability to the region’s fescue environment, along with economically relevant traits to their feeder cattle-producing customers. Embryo transfer and genomics are key technologies which have assisted in the advancement of the herd. Additionally, Shelton Angus focuses on customer service by providing group backgrounding and marketing opportunities to their clients, as well as facilitating retained ownership and collection of carcass data which benefits both their customers’ and their own breeding programs. Buddy Shelton has been an active leader in agriculture, including serving as President of Virginia Angus, President of the Pittsylvania County Cattleman’s Association, along with being engaged in Farm Bureau, 4-H and youth groups, and his local church. Shelton Angus Farm is proudly nominated by the Virginia Beef Cattle Improvement Association.
Wedel Red Angus Owners/Managers: Frank and Susan Wedel Leoti, Kansas Wedel Red Angus is located in the short-grass country known as the High Plains of western Kansas. Frank and Susan Wedel own the seedstock operation, which is located 15 miles northwest of Leoti, KS. Their introduction to Red Angus began in 1989 when they purchased their first Red Angus bulls to help solve calving-ease problems in their commercial cowherd. They purchased their first Red Angus heifers in 1990 and began selling bulls in 1993. The first few years they sold 30 to 40 bulls per year private treaty. In 2001, they held their first production sale. At that time, they determined that to be a viable seedstock supplier they needed to expand their business. They sold their commercial cows and focused on the seedstock business. This year they will sell more than 140 bulls and make 500 matings. Their cowherd includes Red Angus as well as Sim/Red Angus and Char/Red Angus hybrids. The mature cows graze year-round northeast of Wallace, KS, using an intensive rotational grazing program. The young heifers spend their first two years at Leoti until their first calf is weaned, then join the mature cows. Each year, they purchase steer and heifer calves from their bull customers. The steers are finished at commercial feed yards. The heifers are developed for replacements. Those that don’t make the cut are finished and carcass and performance data are collected and shared with producers. They sell 150 to 160 heifers in their production sale each year and about that many are bred and sold in the fall. Their motto is “Knowing our customers is our highest priority because their success becomes our success.” Wedel Ranch is proudly nominated by the Kansas Livestock Association.
Wells Farm Owner/Manager: Mike Wells Selma, Alabama Wells Farm, owned by Dr. and Mrs. Mike Wells, has been producing registered cattle in Dallas County, Alabama for 23 years on farmland purchased by Mike’s grandparents in 1942. The original commercial cattle were mostly Angus based bred to Hereford bulls. The value of the Simmental breed was quickly seen when introduced in the late 1970’s. Today, the Wells Farm cattle herd consists of approximately 90 breeding females, with 60% purebred Simmental and 40% SimAngus. Calves are born from late August until October to produce superior yearling bulls for commercial cattlemen. EPD’s, carcass ultrasound and performance data are all heavily relied on to select herd sires, whether AI or clean up. All Purpose and Terminal Indices and carcass EPD’s are of particular importance in the selection process. Relationships with contemporary Simmental breeders have been built over the years, whose opinions are also valued in sire selection. As a full time veterinarian and the sole employee of Wells Farm, easy keeping cattle with good dispositions are a priority. Wells Farm has participated in seven different Alabama BCIA bull evaluations and marketing opportunities, but currently market almost exclusively by private treaty, with a select few going to the Wiregrass BCIA Forage Based Bull Evaluation each year. A favorite part of being in the Simmental business is being able to meet commercial cattlemen from all over the southeast and let them select their own bull in a relaxed environment. Many of their customers prefer to purchase bulls this way, and Wells Farm has been fortunate to sell a high percentage of bulls to repeat customers each year. Future goals of Wells Farm are to continue to replace older females with top replacement heifers to accelerate genetic improvement and to continue to strive to produce the best Simmental and Sim-Angus bulls. The Alabama Beef Cattle Improvement Association is proud to nominate Wells Farm.
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SEEDSTOCK PRODUCER OF THE YEAR
Bradley 3 Ranch V8 Ranch Mushrush Red Angus Sandhill Farms Harrell Hereford Ranch Champion Hill TC Ranch Pelton Simmental Red Angus Sauk Valley Angus Rishel Angus Camp Cooley Ranch Moser Ranch Circle A Ranch Sydenstricker Angus Farms Fink Beef Genetics Morven Farms Knoll Crest Farms Flying H Genetics Wehrmann Angus Ranch Bob & Gloria Thomas Frank Felton Tom & Carolyn Perrier Richard Janssen R.A. “Rob” Brown
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Keith Bertrand Ignacy Misztal Glenn Selk
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Oklahoma Oklahoma Kansas
2003 2003 2003
Mike Tess Mike MacNeil Jerry Lipsey
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H.H. “Hop” Dickenson Martin & Mary Jorgensen L. Dale Van Vleck
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Richard McClung John and Bettie Rotert Daryl Strohbehn Glen Klippenstein
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2010 2010 2010 2010
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2001 2001 2001
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2000 2000 2000
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1999 1999 1999
John Crouch Bob Dickinson Douglas MacKenzie Fraser
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1998 1998 1998
Larry V. Cundiff Henry Gardiner Jim Leachman
Nebraska Kansas Montana
1997 1997 1997
A.L. “Ike” Eller Glynn Debter
James S. Brinks Robert E. Taylor
Bruce Golden Bruce Orvis Roy McPhee (posthumously)
California California California
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Jack and Gini Chase Jack Cooper Dale Davis Les Holden Don Kress
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2005 2005 2005 2005 2005
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Tom Chrystal Robert C. DeBaca Roy A. Wallace
Iowa Iowa Ohio
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Mick Crandell Mel Kirkiede
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Bill Graham Max Hammond Thomas J. Marlowe
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1984 1984 1984
Jim Elings W. Dean Frischknecht Ben Kettle Jim Sanders Carroll O. Schoonover
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Gordon Dickerson Mr. & Mrs. Percy Powers
F.R. “Ferry” Carpenter Otha Grimes Milton England L.A. Maddox, Jr. Charles Pratt Clyde Reed
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1981 1981 1981 1981 1981 1981
Richard T. “Scotty” Clark Bryon L. Southwell
Robert Koch Mr. & Mrs. Carl Roubicek Joseph J. Urick
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James D. Bennett M.K. “Curly” Cook O’Dell G. Daniel Hayes Gregory Dixon Hubbard James W. “Pete” Patterson Richard Willham
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Roy Beeby Will Butts John W. Massey
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Jay L. Lush
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Name Ben Eggers
Year Sydenstricker Genetics Select Sires American Simmental Association University of Missouri American Hereford Association
Tom Field Stephen Hammack Brian McCulloh Larry Olson
Nebraska Texas Wisconsin South Carolina
2012 2012 2012 2012
Tommy Brown Mark Enns Joe Paschal Marty Ropp Bob Weaber
Alabama Colorado Texas Montana Missouri
2011 2011 2011 2011 2011
Bill Bowman Twig Marston David Patterson Mike Tess
Missouri Nebraska Missouri Montana
2010 2010 2010 2010
Darrh Bullock Dave Daley Renee Lloyd Mark Thallman
Kentucky California Iowa Nebraska
2009 2009 2009 2009
Doug Fee Dale Kelly Duncan Porteous
Canada Canada Canada
2008 2008 2008
Craig Huffhines Sally Northcutt
Brian House Lauren Hyde Jerry Taylor Jack Ward
2013 2013 2013 2013
Name Lisa Kriese-Anderson Dave Notter
State Alabama Virginia
Year 2006 2006
Jerry Lipsey Michael MacNeil Terry O’Neill Robert Williams
Montana Montana Montana Missouri
2005 2005 2005 2005
Chris Christensen Robert “Bob” Hough Steven M. Kappes Richard McClung
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2004 2004 2004 2004
Sherry Doubet Ronnie Green Connee Quinn Ronnie Silcox
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2003 2003 2003 2003
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Mississippi Kansas Virginia
2002 2002 2002
William Altenburg Kent Andersen Don Boggs
Colorado Colorado South Dakota
2001 2001 2001
Ron Bolze Jed Dillard
Bruce Golden John Hough Gary Johnson Norman Vincil
Colorado Georgia Kansas Virginia
1999 1999 1999 1999
Keith Bertrand Richard Gilbert Burke Healey
Georgia Texas Oklahoma
1998 1998 1998
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Texas North Dakota
Name Gene Rouse
Name James Bennett M.K. Cook Craig Ludwig
State Virginia Georgia Missouri
Year 1984 1984 1984
Doug L. Hixon Harlan D. Ritchie
Paul Bennett Pat Goggins Brian Pogue
Virginia Montana Canada
1995 1995 1995
Bruce E. Cunningham Loren Jackson Marvin D. Nichols Steve Radakovich Doyle Wilson
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1994 1994 1994 1994 1994
Glenn Butts Jim Gosey
C.K. Allen William Durfey
Robert McGuire Charles McPeake Henry W. Webster
Alabama Georgia South Carolina
1993 1993 1993
James S. Brinks Martin Jorgensen Paul D. Miller
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1978 1978 1978
Jack Chase Leonard Wulf
Lloyd Schmitt Don Vaniman
A.L. Eller, Jr. Ray Meyer
Virginia South Dakota
Larry V. Cundiff Dixon D. Hubbard J. David Nichols
Nebraska Virginia Iowa
1975 1975 1975
Bill Borror Jim Gibb Daryl Strohbehn
California Missouri Iowa
1987 1987 1987
Frank H. Baker D.D. Bennett Richard Willham
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1974 1974 1974
Larry Benyshek Ken W. Ellis Earl Peterson
Georgia California Montana
1986 1986 1986
F. R. Carpenter Robert DeBaca E.J. Warwick
Colorado Iowa Maryland
1973 1973 1973
Jim Glenn Dick Spader Roy Wallace
Iowa Missouri Ohio
1985 1985 1985
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Oklahoma Cowman Magazine
Burt Rutherford Jay Carlson Larry Atzenweiler and Andy Atzenweiler Kelli Toldeo Gren Winslow and Larry Thomas Angie Denton Belinda Ary Steve Suther Kindra Gordon Troy Marshall Joe Roybal Greg Hendersen Wes Ishmael Shauna Rose Hermel Keith Evans Bill Miller Ed Bible Nita Effertz Hayes Walker III J.T. “Johnny” Jenkins Dick Crow Robert C. DeBaca Forrest Bassford Fred Knop Chester Peterson Warren Kester
BEEF Magazine BEEF Magazine Missouri Beef Cattlemen
Texas Kansas Missouri
2012 2011 2010
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Hereford World Cattle Today Certified Angus Beef LLC Freelance Writer Seedstock Digest BEEF Magazine Drovers Clear Point Communications Angus Journal & BEEF Magazine American Angus Association Beef Today Hereford World Beef Today America’s Beef Cattleman Livestock Breeder Journal Western Livestock Journal The Ideal Beef Memo Western Livestock Journal Drovers Journal Simmental Shield BEEF Magazine
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2007 2006 2005 2004 2003 2002 2001 2000 1999 1998 1997 1996 1995 1994 1993 1991 1990 1989 1988 1987 1986
Name Kristi Allwardt Ryan Boldt Heather Bradford Miranda Culbertson Erika Downey Kara Marley Jamie Parham Kelli Retallick Jason Warner Xi Zeng
University Oklahoma State University Colorado State University Kansas State University Colorado State University Texas A & M University South Dakota State University South Dakota State University Kansas State University University of Nebraskaâ€“Lincoln Colorado State University
FRANK H. BAKER MEMORIAL SCHOLARSHIP
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 2ó 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 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.
FRANK H. BAKER Born: May 2, 1923, Stroud, Oklahoma Died: February 15, 1993, Little Rock, Arkansas
Frank H. Baker Photograph of portrait in Saddle and Sirloin Club Gallery – EvereƩ Raymond Kinstler, ArƟst
FRANK BAKER MEMORIAL SCHOLARSHIP
THE EFFECT OF QUANTITY AND BREED COMPOSITION OF GENOTYPES FOR GENOMIC PREDICTION IN PUREBRED OR CROSSBRED CATTLE Heather Bradford1 Kansas State University, Manhattan
Introduction The implementation of genomics enabled producers to more accurately select young animals for breeding resulting in a decrease in generation interval. Beef cattle typically have long generation intervals compared with species like poultry and swine, and genomic selection can increase response to selection. Genomic selection should have the most benefit for traits that are hard to measure, measured late in life, sex-limited, and measured after harvest (Hayes and Goddard, 2010). Traits like female fertility are sex-limited and difficult to measure while being economically relevant to producers. Selection for female fertility would benefit greatly from the inclusion of genomic data to increase accuracy. There are many economically relevant traits that beef producers could better select for by using genomics. Review of Literature Linkage A quantitative trait loci (QTL) is a gene that affects a quantitative trait. Phenotype results from the total of the effects of all QTL including dominance and any gene interactions, environment, and the interaction of genetics and environment. A single nucleotide polymorphism (SNP) is a single base difference in a DNA sequence that may or may not be located within a gene. Linkage disequilibrium (LD) results when a SNP allele and QTL allele are linked and inherited together more often than expected (Hayes and Goddard, 2010). Meusissen et al. (2001) first proposed genomic selection using all SNP markers simultaneously. This method relies on dense SNP panels with 28
the intent that all QTL affecting a trait are in LD with at least 1 marker (Hayes and Goddard, 2010). Because of the LD between SNP and QTL, the association between SNP and QTL affecting a trait of interest can be used to create genomic predictions. These genomic predictions are the sum of the effect of each SNP on the trait of interest. Across-breed LD is much more restricted than within-breed LD due to differential selection since the divergence of individual breeds (Hayes and Goddard, 2010). Because of the difference in LD across breeds, genomic predictions historically needed to be breed-specific. The accuracy of genomic predictions was largely the result of LD, and the loss of LD resulted in less accuracy in subsequent generations (Habier et al., 2007). Thus, SNP effects have to be periodically re-estimated because of the erosion of LD. A population of genotyped animals with phenotypes or very accurate breeding values is typically used for estimating SNP effects. This group is referred to as a training or reference population while a separate group of animals with genotypes and phenotypes or breeding values is the validation population. The SNP effects estimated from the training population are used to predict genetic merit in the validation population. The accuracy of the genomic prediction, referred to as a genomic breeding value (GBV), direct genomic value (DGV), or molecular breeding value (MBV), can then be assessed in the validation population, because the validation population was independent of the training population that was used to develop the predictions. Genetic correlations between the genomic prediction and phenotypic trait data can be estimated with a two trait animal model using REML (Kachman, 2008). The square of this genetic correlation is the
percent of the additive genetic variance that was explained by the genomic test (Thallman et al., 2009). Genomic results are then incorporated into traditional genetic evaluations resulting in genomic-enhanced expected progeny differences (GE-EPD). Number of Genotyped Animals As more animals are genotyped, researchers can better estimate SNP effects resulting in more accurate genomic predictions. Simulations with various training population sizes and relationships to the validation population showed accuracy increases when the size of the training population increases, even if those animals are many generations removed from the validation population (Saatchi et al., 2010). In a study of 9 breeds for feed efficiency and carcass traits, breeds with larger training populations had greater accuracies than average (Bolormaa et al., 2013). The number of animals in training had a greater impact on lowly heritable traits, and the relationship to the training population became less important for those traits (Saatchi et al., 2010). As beef cattle training populations increase, the greatest impact on accuracy should be for lowly heritable traits. As the adoption of genomic technology has increased, there has been the opportunity to evaluate the realized increase in accuracy resulting from an increase in the size of the reference population. Direct genomic values for Uruguayan Herefords were more accurate when using predictions for American (n = 1,081) instead of Uruguayan Herefords (n = 395; Saatchi et al, 2013). The difference in accuracy likely resulted from the larger training population for the American Hereford prediction and not the relationship to the population used in training. There was a linear increase in accuracy exceeding 0.10 as the size of the reference population increased from 500 to 2,500 head of crossbred sheep (Daetwyler et al., 2012b). However, the increase in accuracy would not be expected to continue to increase linearly as the size of the reference population continues to increase. Predictions from small reference populations with fewer than 1,000 individuals become considerably more accurate as more animals are included in the reference population.
Accuracies continue to improve as the reference population grows from 1,000 to 1,000â€™s of individuals. When combining 3 Nordic Red populations with individual reference populations of 1,562 animals or fewer, reliabilities increased by a magnitude of 3 to 8% on average with a total reference population of 3,735 animals (BrĂ¸ndum et al., 2011). Increasing the training population from 1,300 to 5,250 animals while using the same methodology resulted in predictions that on average explained 18% more genetic variation and increased accuracy by 0.40 (Boddhireddy et al., 2014). Reliabilities were 5 to 32% greater when using a combined Chinese and Nordic Holstein population of 7,387 instead of only 2,171 Chinese Holsteins (Zhou et al., 2013). Predictions were more accurate even when combining populations of the same breed from different countries. There has been a consistent increase in accuracy as more animals were added to small to moderate sized reference populations. Research in dairy cattle has evaluated the impact of larger training populations when there are many genotyped animals. Reliabilities increased 10% on average when combining European Holstein populations to create reference populations with more than 9,000 bulls compared with individual country reference populations with 3,000 to 4,000 bulls (Lund et al., 2011). Improvements can still be made when reference populations contain several thousand head. Adding 3,593 foreign bulls to the U.S. Holstein evaluation with over 10,000 genotypes increased reliability by 2 to 3% (VanRaden et al., 2012). As the number of genotypes increases, the improvement in accuracy from a larger reference population isnâ€™t as substantial and would be expected to continue to decline. Because of the accuracy increase that results from larger training populations, combining genotyped populations to develop genomic predictions is of interest. The main focus in the beef industry has been genotyping purebred populations to develop predictions that are then used within that breed. Due to the cost of genomic testing and the number of proven animals needed for training, combining these populations could improve prediction accuracy. However, a very diverse training population could result in less accurate predictions because the training population is now less related to the individual breeds that are being 29
FRANK BAKER MEMORIAL SCHOLARSHIP predicted. Many simulations have been performed in addition to research in beef cattle and other species to evaluate the impact of the relationship between the training and validation populations. Relationship between Training and Validation The relationship between the reference animals and the populations that the genomic predictions will be used in affects accuracy. Daetwyler et al. (2012a) demonstrated that a large proportion of the accuracy of predictions results from the strong relationship between the reference and validation populations. When the training population consisted of generations that were more similar to the validation population, prediction accuracy was greater than if distant generations were used (Saatchi et al., 2010; Pszczola et al 2012). The animals in the more recent generations tend to be more related to the young animals in which the genomic tests are being used. The importance of the relationship of the training and validation populations likely resulted from recombination that took place between generations and reduced the LD between the markers and QTL (Saatchi et al., 2010). As LD erodes, accuracy decreases when a different SNP is associated with the QTL than in the reference population. When animals had a greater average squared relationship to the reference population, those animals had greater reliabilities (Pszczola et al., 2012). If an animal has relatives in the reference population, more confidence can be placed on the resulting genomic predictions because relatives with similar LD between SNP and QTL were used to estimate SNP effects. Because of the importance of the relationship between the reference and validation populations on accuracy, there has been much research on the breed specificity of genomic predictions. Within-breed Prediction In the beef industry, much emphasis has been placed on developing predictions for use within-breed with the results incorporated into national cattle evaluations. A simulation study by Kizilkaya et al. (2010) found slightly greater correlations between true and estimated breeding values when training and validating in purebreds compared with training and validating in a multibreed population. Estimates for 30
genetic correlations between GBV and the respective traits were 0.14 to 0.81 in Angus (Saatchi et al, 2011; Northcutt, 2013; Boddhireddy et al., 2014), 0.18 to 0.52 in Hereford (Saatchi et al., 2013), 0.39 to 0.76 in Limousin (Saatchi et al., 2012), and 0.29 to 0.65 in Simmental (Saatchi et al., 2012). Generally, there was sufficient LD between SNP and genes for traits included in national cattle evaluations to achieve strong genetic correlations. Because of these results, several beef breed associations currently publish GE-EPD. Connectedness within-breed can also affect the accuracy of predictions for animals that are distantly related to the training population. Genomic predictions developed for American Herefords were less accurate when used in Argentinian, Canadian, or Uruguayan Herefords, possibly resulting from lesser relationships to the training population or genetic by environment interactions (Saatchi et al., 2013). When comparing reliabilities for Red dairy cattle from 3 European countries, the within country predictions were always more reliable than if predictions were developed in 1 country and used in the others (BrĂ¸ndum et al., 2011). Because the reference populations were of similar size, the loss in accuracy again resulted from the lack of connectedness between countries. Further analysis revealed that the correlation of LD phase between countries ranged from 0.46 to 0.86 (BrĂ¸ndum et al., 2011). This correlation suggests there has been some divergence in the Red breed in these countries which impacts the ability to develop across-country genomic predictions. When using the American Hereford prediction in Argentinian Herefords, animals with American Herefords in their pedigree had on average greater correlations between DEBV and GBV than those without American genetics (Saatchi et al., 2013). Similarly, DGV accuracies were less when using Angus predictions in an Angus herd that was closed for many generations and was less related to the training population (Saatchi et al., 2011). These studies demonstrated the importance of the training population being a representative sample of a breed to obtain accurate estimates of genomic merit across the population. Across-breed Prediction It would be convenient if predictions could be developed in 1 breed and used for other breeds, but
this approach has produced very poor accuracies. Simulations trained in one breed and predicted in another resulted in significantly less accuracy than training in the breed of interest (Toosi et al., 2010). When Angus trained predictions were used in other breeds, simulations accounted for less than one-third of the genetic variation that was achieved in Angus (Kizilkaya et al., 2010). These simulations did not demonstrate favorable results for using breed-specific predictions across breeds. When breed-specific predictions for Angus, Hereford, or Limousin were used across breeds, in most cases the genetic correlation was not significant and in a few instances was slightly negative despite a moderate, positive genetic correlation when validating within-breed (Kachman et al., 2013). On average, the genetic correlation from using predictions developed in Herefords on Angus sires was not different from 0 while predictions developed specifically for Angus had the greatest accuracies (0.24 to 0.61; Weber et al., 2012b). Similar results were observed in Holstein and Jersey dairy cattle (Hayes et al., 2009). Using 50,000 SNP has not been sufficient density to use predictions across breeds. Breeds have different LD which erodes the accuracy of predictions developed in one breed when the prediction was used in another breed. The use of genomic predictions across breeds has not been feasible due to the limited prediction accuracy.
purebred Simmental. Only calving ease maternal and weaning weight maternal were not more accurate with the multi-breed training population (Saatchi and Garrick, 2013). Stayability was unchanged because there was no information for this trait in the other breeds. With the additional breeds, the size of the reference population more than doubled compared with only Simmental animals (Saatchi and Garrick, 2013). These studies suggest a benefit from combining single breed reference populations. Not only is the size of the reference population greater, but the reference population can capture more of the variation within the breed of interest.
Multi-breed for Purebred Prediction
Predictions that were developed without including the breed of interest were less accurate than if that breed had been included in training. The accuracy of multi-breed prediction in Australian sheep was always less if the breed for prediction was excluded from the training population (Daetwyler et al., 2012a). However, if the breed to be predicted was included in the reference population, multi-breed predictions were no more accurate than single-breed predictions (Pryce et al., 2011). If the within-breed predictions are based on a large enough reference population, the potential benefit from multi-breed predictions might be very minimal. If multi-breed predictions someday achieved equivalent or greater accuracies than within-breed predictions, all breeds of interest would need to be included in the training population.
Another approach would be to combine data for many purebreds to develop predictions that were then used for individual breeds. Combining populations resulted in greater accuracy, especially for lowly heritable traits, than training on each population individually (de Roos et al., 2009). Predictions were more accurate when trained in a multi-breed population instead of a purebred population and validated in the same purebred (Bolormaa et al., 2013). This possibly results from capturing more of the variants within the breed of interest. Genetic correlations averaged 0.47 (0.10 to 0.73) when trained on only Simmentals and 0.55 (0.18 to 0.91) when trained on Simmental, Angus, Red Angus, Gelbvieh, Brangus, Hereford, and Charolais (Saatchi and Garrick, 2013). This is of interest because animals registered with the American Simmental Association do not have to be
There was very little difference in the accuracy of GBV when using a Holstein or Holstein and Jersey reference population to validate in Holsteins, but there was an increase in accuracy when using the combined instead of the Jersey reference population to validate in Jerseys (Hayes et al., 2009). This could result from the very small number of Jersey bulls with genotypes. The addition of more genotypes, despite the breed, allowed for more accurate genomic predictions. Using a Holstein, Jersey, and Brown Swiss reference population, resulted in an increase in accuracy for some traits in Jersey and Brown Swiss above that of the single-breed prediction (Olson et al., 2012). Again, there was no benefit for Holsteins to use multi-breed predictions but some benefit for smaller breeds with fewer genotypes. Incorporating 2 breeds in the reference population to predict a third breed increased prediction 31
FRANK BAKER MEMORIAL SCHOLARSHIP accuracy compared with using 1 of the breeds to predict a different breed (Pryce et al., 2011). Again, the improvement in accuracy could result from the larger reference population used to predict marker effects in a different breed. Given a reference population of sufficient size, there has been no consistent benefit to using a multi-breed population to develop predictions for use in purebreds. Yet, while training populations of sufficient size are being collected, multi-breed predictions could help improve accuracy until enough animals were genotyped to produce reliable predictions. Crossbred for Purebred Prediction Another scenario is collecting data on crossbred animals for use in purebreds although this situation is unlikely in the beef industry with a lack of pedigree and performance recording in crossbred cattle. Simulations demonstrated, as the number of breeds represented in the crossbred population increased, the accuracy of predicting one of the purebreds decreased (Toosi et al., 2010). This decrease in accuracy could result from a decrease in the prevalence of haplotypes from the breed of interest in the training population as population size was held constant. That breed would then have a lesser contribution to the estimation of marker effects. Using the crossbred U.S. Meat Animal Research Center Germplasm Evaluation Program (GPE) population for training and validating in a purebred population resulted in MBV accuracies generally ranging from 0.20 to 0.40 with less accurate predictions in Charolais for most traits, likely a result of limited Charolais influence in the training population (Weber et al., 2012a). Validation in the 2,000 Bull Project animals, consisting of influential bulls representing 16 beef breeds, resulted in genetic correlations ranging from 0.19 to 0.37, which were similar to validation in purebreds (Weber et al., 2012a). Greater accuracies are being achieved in the beef industry by using within-breed predictions (Saatchi et al., 2011; Saatchi et al., 2012; Northcutt, 2013; Saatchi et al., 2013; Boddhireddy et al., 2014). An analysis of crossbred sheep of primarily Merino decent resulted in greater accuracies for Merinos than for terminal breeds (Daetwyler et al., 2010). Thus, the breed makeup for the crossbred population was important, and the breed of interest needed to be well represented in the crossbred genetics. There are many challenges associated 32
with using crossbred genotypes in the beef industry, mainly the lack of complete pedigree and performance recording outside of research herds. The use of crossbred predictions for many breeds appears less feasible. Another approach to using crossbred genotypes has been to model breed-specific SNP effects. Modelling with breed-specific compared with across-breed SNP effects resulted in similar prediction accuracies for a variety of simulation scenarios (Ibรกn z-Escriche et al., 2009). As marker density increased up to 2,000 markers on one chromosome, there was less value in using breed-specific SNP effects (Ibรกn z-Escriche et al., 2009). The use of breed-specific SNP effects required large breed differences to justify the additional effects in the model, and this model had an advantage when large training populations were used (Ibรกn z-Escriche et al., 2009). Developing reference populations of sufficient size to justify the use of breed-specific SNP effects will be challenging in the beef industry. Very few of the 2,500 SNP with the largest effect were common to the GPE and 2,000 Bulls populations (Weber et al., 2012a). These results suggest a potential need for breed-specific effects to better account for both differences in LD and the magnitude of the SNP effect across breeds. Crossbred Prediction Although genetic evaluation of crossbred beef cattle is not common, a cheap genomic test for economically relevant traits would be a valuable genetic selection tool for commercial producers. In addition, many beef breed associations include hybrid animals in their genetic evaluations, and breed-specific predictions might not be as accurate in those composite animals. Genomic predictions based on 3,000 SNP for feed efficiency in Angus-Brahman crosses had accuracies ranging from -0.13 to 0.36 (Elzo et al., 2012). The small number of SNP could have contributed to the limited accuracy that was achieved from those genomic predictions. The accuracy of crossbred predictions was numerically less in most cases than within-breed predictions; however, those estimates had large standard errors (Mujibi et al., 2011). Larger reference populations incorporating a broader sample of the possible breed crosses might improve accuracy as more of the population of interest would be used to
develop predictions. Purebred or Multi-breed for Crossbred Prediction Because most phenotypes in the beef industry are collected on purebreds, creating predictions based on the purebred data for use in selecting crossbreds could be beneficial. Training on Angus, Angus and Red Angus, or Hereford resulted in weak MBV accuracy (0.01 to 0.43) for growth and carcass traits when validating in the crossbred GPE population (Weber et al., 2012a). MBV accuracy tended to be less than that achieved with a multi-breed training population consisting of sires in the 2,000 Bull Project (Weber et al., 2012a). Using a multi-breed instead of purebred training population should produce better predictions for crossbred animals because breeds differ in the LD between SNP and QTL. Training on 2,000 Bull Project and validating on GPE yielded moderate genetic correlations (0.13 to 0.42) with little or no improvement from including breed effects in the DEBV for the 2,000 Bull Project (Weber et al., 2012a). Training on a multi-breed population instead of a purebred population increased accuracy more for composite breeds than purebreds (Bolormaa et al., 2013). Genotypes and phenotypes on purebreds can be useful to develop predictions for crossbreds. The American Angus Association and Zoetis currently market a genomic test for commercial Angus-influence cattle. This test provides predictions for a couple economically relevant traits, and commercial producers are using this test to add value to feeder cattle and to select replacement females. If the beef industry were to move toward larger scale crossbred genetic evaluation, establishing genomic predictions from existing purebred databases appears to be the most feasible method. Including many breeds in the reference population would help make these predictions more relevant for a wider array of commercial producers. Conclusions and Implications to Genetic Improvement of Beef Cattle As the use of genomic testing in the beef industry grows, reference populations of greater size are being established by individual breed associations. Breed-specific genomic predictions are becoming
more accurate as a result of the increase in genomic testing. Research on using genomic predictions developed in 1 breed for use in another has not been favorable. Yet, pooling genotypes from multiple breeds to develop predictions for a purebred was promising for increasing accuracy past that achieved with only the purebred genotypes. The use of multi-breed predictions could be of interest to smaller breeds with fewer genotyped animals and breeds that register percentage animals. Smaller breeds could benefit from the larger reference population that could be assembled from combining genotypes. Breeds with hybrid animals could benefit from the inclusion of the LD from other breeds to develop more accurate predictions. There is potential benefit from genetic evaluation at the commercial level. Because there is no infrastructure for performance recording in commercial cattle, genomic testing is a more feasible option to identify the genetically superior crossbred cattle. Preliminary research has demonstrated the feasibility of developing accurate genomic predictions from purebred or multi-breed populations to use in crossbred individuals. As genomic predictions become more refined in the seedstock industry, there is the potential to develop cheaper genomic tests for economically relevant traits in crossbred cattle. Literature Cited Boddhireddy, P., M. J. Kelly, S. Northcutt, K. C. Prayaga, J. Rumph, and S. DeNise. 2014. Genomic predictions in Angus cattle: Comparisons of sample size, response variables, and clustering methods for cross-validation. J. Anim. Sci. 92:485-497. Bolormaa, S., J. E. Pryce, K. Kemper, K. Savin, B. J. Hayes, W. Barendse, Y. Zhang, C. M. Reich, B. A. Mason, R. J. Bunch, B. E. Harrison, A. Reverter, R. M. Herd, B. Tier, H.-U. Graser, and M. E. Goddard. 2013. Accuracy of prediction of genomic breeding values for residual feed intake and carcass and meat quality traits in Bos taurus, Bos indicus, and composite beef cattle. J. Anim. Sci. 91:3088-3104.
FRANK BAKER MEMORIAL SCHOLARSHIP Brøndum, R. F., E. Rius-Vilarrasa, I. Strandén, G. Su, B. Guldbrandtsen, W. F. Fikse, and M. S. Lund. 2011. Reliabilities of gneomic predictions using combined reference data of the Nordic Red dairy cattle populations. J. Dairy Sci. 94:4700-4707. Daetwyler, H. D., J. M. Hickey, J. M. Henshall, S. Dominik, B. Gredler, J. H. J. van der Werf, and B. J. Hayes. 2010. Accuracy of estimated genomic breeding values for wool and meat traits in a multi-breed sheep population. Anim. Prod. Sci. 50:1004-1010.
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Saatchi, M., J. Ward, and D. J. Garrick. 2013. Accuracies of direct genomic breeding values in Hereford beef cattle using national or international training populations. J. Anim. Sci. 91:1538-1551.
Pryce, J. E., B. Gredler, S. Bolormaa, P. J. Bowman, C. Egger-Danner, C. Fuerst, R. Emmerling, J. SÓ§lkner, M. E. Goddard, and B. J. Hayes. 2011. Short communication: Genomic selection using a multi-breed, across-country reference population. J. Dairy Sci. 94:2625-2630.
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Saatchi, M., S. R. Miraei-Ashtiani, A. Nejati-Javaremi, M. Moradi-Shahrebabak, and H. Mehrebani-Yeghaneh. 2010. The impact of information quantity and strength of relationship between training set and validation set on accuracy of genomic estimated breeding values. Afr. J. Biotechnol. 9:438-442. Saatchi, M., M. C. McClure, S. D. McKay, M. M. Rolf, J. W. Kim, J. E. Decker, T. M. Taxis, R. H. Chapple, H. R. Ramey, S. L. Northcutt, S. Bauck, B. Woodward, J. C. M. Dekkers, R. L. Fernando, R. D. Schnabel, D. J. Garrick, and J. F. Taylor. 2011. Accuracies of genomic breeding values in American Angus beef cattle using K-means clustering for cross-validation. Genet. Sel. Evol. 43:40. Saatchi, M., R. D. Schnabel, M. M. Rolf, J. F. Taylor, and D. J. Garrick. 2012. Accuracy of direct genomic breeding values for nationally evaluated traits in US Limousin and Simmental beef cattle. Genet. Sel. Evol. 44:38. Saatchi, M., and D. J. Garrick. 2013. Improving genomic prediction in Simmental beef cattle using a multi-breed reference population. In: Proc. West. Sec. Am. Soc. Anim. Sci., Bozeman, MT. p. 174177.
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FRANK BAKER MEMORIAL SCHOLARSHIP
HIGH ALTITUDE DISEASE AND GENETICS OF BEEF CATTLE AT HIGH ELEVATION REGIONS Xi Zeng1 1 Colorado State University and the herd average weaning weight in 2009 (529.8± 72.4lbs; Neary, 2013). Native cattle at high altitude may be more resistant to HAD than low attitude cattle In high altitude states such as Colorado, Wyoming, due to artificial selection (Will et al., 1975). About 10% New Mexico, and Utah, bovine pulmonary hyperten- to 40% of cattle develop HAD when they were moved sion (BPH) is observed and commonly referred to as from low altitude to high altitude (Grover et al., 1963, “brisket disease” or “high altitude disease (HAD)” Will et al., 1970). (Holt and Callen, 2007). The disease was first studied 2.2 Physiology of HAD by Glover and Newsome (1915) in cattle for the sole purpose of advising Colorado and New Mexico stockBased on clinical and physiologic principles, three man to protect their herds. The cardinal sign of HAD is major high-altitude diseases were identified (West, swelling of the brisket due to fluid accumulation in the 2004): 1. Acute mountain sickness. The mechanisms thoracic cavity. It is believed that in response to alveo- are not fully understood, but brain swelling may be lar hypoxia, the pulmonary artery constricts resulting in a phenotype. 2. High-altitude pulmonary edema. The hypertension, right heart ventricular hypertrophy, vas- mechanism is probably uneven hypoxic pulmonary cular remodeling, and death from congestive heart fail- vasoconstriction that exposes some capillaries to a ure (Holt and Callan, 2007). Pulmonary arterial pres- high pressure, damaging their walls and leading to a sure (PAP) is a measure indicative of hypertension and high-permeability form of edema. 3. High-altitude has been reported to be moderately heritable in cattle cerebral edema. It closely related to acute mountain (0.34 to 0.46; Enns et al., 1992; Shirley et al., 2007). sickness and that it is the extreme end of the spectrum. Therefore, PAP has been widely used as an indicator These are specific description of different types of High trait of BPH/HAD in recent studies. Therefore, various Altitude Disease. What can be used as a general refstudies involving PAP have been applied to describe erence to identify HAD? The hypoxia from the high reasons for HAD, including genetics, since 1914. elevation regions is the major cause of HAD/BHP. Alexander and Jenson (1959, 1963) found that, hypoxia at 2 Literature review high elevation causes pulmonary vasoconstriction, increased pulmonary arterial pressure (PAP), right ventri2.1 Economics cle stress, congestive right heart failure, and hydrothoWhy HAD is important and worth to be studied in rax in the chest cavity and brisket. Additionally, Holt cattle at high elevation? There is a high economic rele- and Callen (2007) indicated that HAD is characterized vance to HAD, with an incidence of 3% to 5% typically by the presence of ventral edema in the brisket region in native cattle (Holt and Callen, 2007). However, it is secondary to increased vascular hydrostatic pressure a major cause of calf morbidity for beef cattle ranch- (intravascular hypertension) and the loss of fluid into es and feedyards above 1500 m (Hecht et al., 1962; the extra vascular space. Jensen et al., 1976). A producer losing 20% of his 600 calves equates to $78,864 of lost potential income be- 2.3 Relationship between PAP and HAD tween summer turnout and weaning based on the marAs an indicator of HAD, PAP scores were used to ket price ($1.24/lb. live weight, November 7th 2011) 1 Introduction
assist selection of cattle to reduce HAD in recent decades in high altitude regions. Holt and Callen (2007) indicate that: 1. The measured PAP of less than 41 mmHg at an elevation greater than 1500 m (5000 ft) are likely to maintain an acceptable PAP at high altitude and serve as good breeding stock; 2. Animals with measured PAP larger than 41 mmHg and less than 49mmHg at high altitude should be used with caution at high elevations; 3. Cattle with PAP larger than 49mmHg at any altitude are at risk for developing HAD and should not be maintained or used in breeding programs at high altitude. Therefore, these recommendations serve as phenotypic selection tools. Also, the information indicates that higher PAP measures imply higher risk for HAD. 2.4 Measurement of PAP
been shown to be moderately to highly heritable and repeatable in cattle (Schimmel, 1981; Enns et al., 1992; Shirley et al., 2007). The heritability and repeatability of PAP were first estimated in a dissertation work of Schimmel (1981). The PAP values in this study were collected from weaning calves and mature cow raised at the San Juan Basin Research Center, Hesperus, Colorado (elevation at 2,316m). He reported heritabilities of PAP as 0.77 ± 0.21, 0.60 ± 0.24, 0.40 ± 0.13 and 0.13 to 0.23 for bull, heifer, calves and cows. Enns (1992) reported a heritability estimate as 0.46 ± 0.16 of weaning measured PAP, which were from Angus cattle from western Colorado. The most recent published heritability of PAP was 0.34 ± 0.05 reported by Shirley (2007). In addition, an ongoing study estimated heritability for PAP measured in yearling Angus cattle was 0.21±0.04, 0.37±0.08, 0.19±0.14 and 0.23±0.03 for bulls, heifers, steers and compiled data (Cockrum, unpublished data). Similar to many other traits, the estimated heritability was varied among studies, which may account for the genetic by environmental effect of age of PAP and sex management. However, all of the studies showed a moderate to high heritability for PAP measure (0.23 to 0.77). Furthermore, the study from Cockrum, which will be published in August 2014 at 10th World Congress on Genetics Applied to Livestock production (WCGALP), was similar to results from Schimmel (1981), in that the heritability of PAP of bulls was higher than that of heifers. This fact may result from the high intensity artificial selection of bulls. The repeatability reports were limited in previous studies, the reason for which may be that the PAP score is usually measured once (i.e. yearling). However we can expect a moderate repeatability of PAP, based on the repeatability as 0.25 to 0.16 on cows reported by Schimmel (1981).
The procedure used to measure PAP has been used for more than 30 years. However, PAP measures can be influenced by any unprofessional action in the process. Therefore, the PAP score can only be taken by one licensed veterinarian in one herd in order for selection to be more effective. With the right equipment and facilities, a professional veterinarian can take PAP score for a large number of animals daily, which makes PAP a measurable and affordable trait for selection. The PAP test is a right heart catheterization procedure, which requires jugular venipuncture, catheter insertion and passing flexible catheter tubing through a large bore needle inserted into the jugular vein. The catheter is passed down the jugular vein, through the right atrium, into the right ventricle, and then into the pulmonary artery. Once the catheter is inside the pulmonary artery, an average blood pressure (average of systolic and diastolic values) is recorded from the heart monitor, which is attached to the catheter via a transducer (Ahola et al., 2007). 2.5.2 Genetic Correlation 2.5 Genetic Parameters for PAP 2.5.1 Heritability and Repeatability In order to reveal the genetics influences within HAD, heritability, repeatability and genetic correlation related to PAP have been estimated in many studies. Heritability is the proportion of phenotypic variation that is explained by additive genetic variation. Table 1 summarized the heritability of PAP reported in previous literature. Pulmonary arterial pressure (PAP) has
Veit and Farrell (1978) suggested that larger body size and metabolic demands would place stress on the bovine pulmonary system; thus pre-disposing cattle to respiratory disease and pulmonary hypertension. This viewpoint was supported by the estimated genetic correlation from Shirley et al. (2007), who reported the genetic correlation between PAP and birth weight (BW) or weaning weight (WW) to be moderate (i.e. 0.49 to 0.51). However, the genetic correlation between PAP and post weaning growth traits of yearling weigh (YW) and post weaning gain (PWG) were reported to be 0.22 37
FRANK BAKER MEMORIAL SCHOLARSHIP ± 0.04 and 0.04 ± 0.12, respectively, which is interpreted as weak, yet positive genetic correlation (Zeng et al., unpublished data). However, Schimmel (1981) reported a genetic correlation of between bull PAP and YW. Based on these results, the genetic parameters of PAP appeared to be varying among studies. The varied estimates of genetic correlations may be explained by genetic difference observed among PAP collected at different age (weaning versus yearling) or different sex (bulls versus heifers; Cockrum et al., unpublished data). The genetic correlation between PAP of weaning and yearling (0.56 ± 0.24), or between yearling PAP of bulls and heifers (0.67 ± 0.15) was not high, which suggested that PAP measurements at weaning and yearling, or in heifers and bulls were potentially different traits (Cockrum et al., unpublished data). These results implied a genetic difference between BPH/HAD of bulls and heifers. However, we must consider these results as potentially confound with growth management. Both of these trait measures (i.e. bulls and heifers) were based on the data from John E. Rouse Ranch of Colorado State University Beef Improvement Center (CSUBIC). In the production system, bulls were developed within a grain-supplemented performance test, whereas heifers and steers were grazed. Therefore, these may be a genetic by environmental interaction via two source of information: 1) sex; 2) diet environment. Similarly, the environmental effect on phenotype of PAP had been reported in earlier literatures, which suggested that age, gender, temperature and diet influenced PAP phenotype (Rhodes, 2005; Holt and Callen., 2007). 2.5.3 Model Used for Genetic Evaluation Both univariate and multivariate animal models were used in previous PAP studies. The fixed effects included in the models included PAP date, sex, age of dam, management contemporary group, and age of PAP as covariate (Shirley et al., 2007). The random effects in these models were animals. In previous studies, the major software used to execute these mixed animal models with continuous response variable was ASReml (Gilmour, 2009). However, PAP scores are not normally distributed which violate our assumption in evaluation of these models. The problem may be solved by transforming the PAP data to categorical data, and then execute a threshold animal model for genetic evaluation. We hypothesize the later is reasonable as we are 38
most interested in the extreme value of PAP. 2.5.4 Expected Progeny Differences (EPD) for PAP The expected progeny differences (EPD) for PAP were first estimated with data from the Tybar Ranch, Carbondale, CO. Since the first use of a PAP EPD for selection of resistance to HAD at the Tybar Ranch in 1992, the EPD for PAP was continuously used in cattle breeding in Colorado (Enns, 2011). Also, the PAP EPD has been used in the selection program in John E. Rouse Ranch of Colorado State University Beef Improvement Center (CSU-BIC) since 2006. Figure 1 presents the genetic trend of PAP EPD from both Tybar Ranch and CSU-BIC. The genetic trend in PAP score has been consistently downward (favorable) since the use of a PAP EPD in Tybar Ranch beginning in 1992. The downward (favorable) genetic trend has also been seen at CSU-BIC since the use of a PAP EPD in 2006. Producer reports collected in veterinary health studies suggest that, in some cases, low PAP cows should significantly reduce the incidence of HAD within their calf crop. However, report from other producers indicated that the selection on low PAP has no influence on reducing the mortality of pre-weaned beef calves (Neary, 2013). Therefore, more studies should be executed to ensure that genetic selection on low PAP would reduce the chance of cattle to HAD/BPH. Although, PAP has been widely recognized as an indicator to study HAD/BPH, there are limitations in this trait and its interpretation. First, cattle need adaption period for at least 30 days before PAP scored measurement when they move from low altitude to high altitude area (Holt and Callen, 2007). Second, the measure of PAP needs to be completed by a skilled veterinarian. 2.6 Genomic wide Association Study (GWAS) With the advance of molecular genetics techniques, high-density marker maps and tools are available and large number of animals can be genotyped with a reasonable investment. This fact allows genome wide association study (GWAS), which utilizes high-density single-nucleotide polymorphisms (SNP). The GWAS is an approach to revel common genetic variants in different individuals to assess if any variant is associated with a trait. In the beef industry, GWAS can be used in genomic selection using estimate genomic estimate
breeding value (GEBV), whose accuracy is much higher than traditional EBV. Also, GWAS has been widely used in identifying significant SNP, biological pathways and networks underlying complex traits. Therefore it is beneficial to conduct GWAS on PAP and use GEBV or marker assisted selection (MAS) to conduct selection of cattle at both low and high altitude for resistant to HAD. However, there are few published GWAS studies on PAP or HAD on cattle, except for the work from Newman et al. (2011) and unpublished work from Colorado State University to be presented in August 2014 at 10th World Congress on Genetics Applied to Livestock production (WCGALP), Vancouver, BC, Canada 2.6.1 Response Variable in GWAS Information resources used in GWAS can be alternative sources of information including single or repeated measures of individual phenotypic performance, information on progeny, estimated breeding value (EBV) from genetic evaluations, or a pooled mixture of more than one of these information sources (Garrick et al., 2009). The SNP/marker effects were come from the training data and would be used to fit the test data to estimate the GEBV. To guarantee the accuracy of GEBV prediction, the ideal data for training would be true genetic merit data observed on unrelated animals in the absence of selection (Garrick et al., 2009). Also, as indicated previously, the PAP scores are not normally distributed, which violate the assumption of statistical methods used in GWAS. Therefore, a deregressed estimated breeding value (DEBV) may be the best response variable used in future GWAS on PAP. 2.6.2 Method Used in GWAS Even though, published GWAS of PAP or HAD on cattle are forthcoming, the statistical methods used in GWAS for different traits are generally the same. In order to improve the accuracy of GWAS, many statistical methods have been applied during the past 20 years. Actually, these methods are different kinds of model selection methods. The most widely used methods include BLUP, BayesA, BayesB, BayeCπ, Bayesian LASSO, GBLUP, machine learning etc. Hayes and Goddard (2010) concluded that the highest accuracies of GWAS were achieved when the prior distribution of SNP effects matches the true distribution. The method assuming many SNP effects of zero and a small proportion of SNPs with moderate to large effects yield higher
accuracy GEBV. The genome based BLUP, BayesA and BayesB were first introduced, compared and discussed in the paper of Meuwissen et al. (2001). The BLUP method assumed a normal distribution of SNP effects, which suggested a very large number of QTL with small effects. The BayesA assumed at distribution of SNP effects, which is based on a large number of QTL with small effects and a small proportion with moderate to large effects. In BayesA, the variance of each SNP effect was assumed unequal and under an inverted chisquare distribution with scale parameter S and v degree of freedom, whereas it is assumed that the error variance was under a inverted chi-square with scale parameters 2. BayesB is a method assuming mixture distribution of zero effects and t distribution of effects for SNP, which suggest a large number of genome regions with zero effect and a small proportion of QTL with moderate effects. The variance distribution assumption for QTL loci and error term are the same as BayesA. Habier et al. (2011) developed BayesC and BayesCπ. Both assumed that there is πproportion of loci have 0 effect and (1-π) proportion of loci have moderate to large effect with common variance across these loci. The π is a fixed value in BayesC while in BayesCπ, π is sampled from a beta distribution based on data. The error variance is assumed under an inverted chi-square distribution with scale parameter 2 as other Bayes methods. These Bayesian methods can be executed using the GenSel software (Fernando and Garrick, 2008). Another method is Bayesian Lasso introduced by Yi and Xu (2008), which also assumed a very large proportion of SNP effect close to zero and small proportion with a moderate to large effect. In this method, the SNP effect is under a normal distribution and the variance of QTL is under an exponential distribution. The GBLUP is based on the restricted maximum likelihood (REML) concept. The SNP effects and variance can be estimated from mixed model developed by Henderson (1976) based on REML with treating the SNP as random effect and including a genomic relationship matrix. Using this methods, fixed effects can be estimated too. This GWAS method can be accomplished using many software packages including SVS (Golden Helix, Inc., Bozeman, MT), R, SAS (SAS In39
FRANK BAKER MEMORIAL SCHOLARSHIP stitute, Cary NC), ASReml (Gilmour et al., 2009), etc. and the myocardial signaling protein (FKBP1A). BeIn R, some GWAS packages written by other research- sides identification of significant genes, BPH related pathway results and gene networks were also explained ers can be used directly. in the study to help the understanding of the biological In addition to the previous methods, a machine signature of BPH. Qiu et al. (2012) found that gene learning method was developed by Long et al. (2007). families, which were related to sensory perception and This method can be used to classify suspect and healthy energy metabolism, as well as an enrichment of protein animals with high accuracy and identify disease relat- domains involved in sensing the extracellular environed SNPs. Specifically, a case-control experiment is ment and hypoxic, were express different between yak designed, then machine learning method was used to and cattle. This fact can be used to study the adaption select SNPs. Besides the naïve Bayes used in Long’s to high altitude in other animal species and humans. study, the machine learning method has many algo- In addition, a study (Wang et al., 2012) identified a rithms including support vector machine, decision tree, Hypoxia-inducible factor-2alpha (HIF-2α) encoding artificial neural machine, etc. gene, Endothelial PAS domain-containing protein 1 (EPAS-1), which is a key gene mutated in the Tibetan In recent years, a method named as multiple locus population adapted to living at high altitude. mixed model (MLMM) were used in GWAS studies. It is a method using a simple stepwise mixed-model reIn future, since cattle are considered a natural angression with forward inclusion and backward elimi- imal model to study HAD and higher density chip are nation of genotypic markers as fixed effect covariates available for genotyping, GWAS should be done on catwith a genomic relationship matrix (Segura, 2012). The tle. Since the heritable PAP score was widely treated as variance components are re-estimated between each an indicator of HAD. The GWAS study based on PAP forward and backward step. Currently, the MLMM is score can be used to identify the most significant SNP available in the SVS (Golden Helix, Inc., Bozeman, related to HAD, and then related genes can be studied. MT). The method discussed by Fortes et al. (2011) can be used to develop a gene network on PAP with the detect2.6.3 Results of GWAS ed SNPs. Furthermore, these genes can be used to conFew GWAS studies have been conducted for HAD duct a pathway study, which can help reveal the whole or PAP on cattle, but there are some genomic related picture of HAD and provide efficient treatment plan. studies on HAD for yaks and humans. Reviews of these 3 Conclusion and Implications to Genetic Improveeffects give us some genomic information on HAD ment of Beef Cattle across species. Also, these results can be compared to our future findings to help us explain our results. The Selection for resistance to HAD/BPH is important following is a review of potential candidate genes. for beef cattle, because HAD influences calf mortality at high altitudes (above 1500m). Pulmonary arterial In the study of Simonson et al. (2010), they reportpressure can be treated as indicator trait for selection of ed that gene Egl nine homolog 1 (EGLN1) and Pertolerance to high altitude, especially since it is physiooxisome proliferator-activated receptor alpha (PPARA) logically related to HAD/BPH and moderately heritawere associated with hypoxia response factor (HIF) ble. Genetic selection for low PAP by beef producers and expressed in high altitude adapted individuals, at high altitudes could potentially improve profitability which can be used to study the high altitude adaption by reducing the mortality rate. However, more genetpathway in humans. Newman et al. (2011) provided the ic evidence is needed to ensure that selection for low first molecular interrogation on BPH based on a case PAP could reduce the incidence of HAD. The GWAS of control GWAS and gene expression study. The study PAP score can be used to identify the most significant revealed six or more significant genes, among which SNPs or genes potentially related to HAD, and estimate three genes were candidates possibly involved in BPH GEBV to serve as a selection tool. Thus, genomic inforincluding NADH dehydrogenase (ubiquinone) flavomation can help the selection of cattle for resistance to protein 2 (NDUFV), myosin heavy chain 15 (MYH15) HAD at earlier ages. Besides the benefit of traditional 40
Table 1. Estimated heritability and repeatability for pulmonary arterial pressure (PAP) in previous literature Author Schimmel (1981)
Age of cattle Mature Cow
Figure 1. Genetic trend pulmonary artery pressure at the Tybar Ranch (Tybar) and the CSU John E. Rouse Beef Improvement Center (BIC) since selection with EPD began in 1992 in Tybar) and 2002 in BIC (Enns et al., 2011). genetic selection on PAP, GWAS of PAP will also help Alexander, A. F., & Jensen, R. 1959. Gross cardiac reveal the genomic architecture of HAD by studying changes in cattle with high mountain (brisket) genes, and increase the selection efficiency for resisdisease and in experimental cattle maintained tance to HAD. However, case/control data of HAD are at high altitudes. Amer. J. Vet. Res. 20:680-689. needed to help expose information of the complex high altitude disease. Thus, it is important and beneficial to Alexander, A. F., & Jensen, R. 1963. Pulmonary vascular pathology of high altitude-induced collaborate with breeders across mountains regions of pulmonary hypertension in cattle. Amer. J. Vet. the country to collect the HAD case in the future. Res. 24:1112-1122. 4 Literature Cited Ahola, J. K., Enns, R. M., & Holt, T. 2006. Examination of potential methods to predict pulmonary arterial pressure score in yearling beef cattle. J. Anim. Sci. 84:1259-1264.
Enns, R. M., Brinks, J. S., Bourdon, R. M., & Field, T. G. 1992. Heritability of pulmonary arterial pressure in Angus cattle. Proc. West. Sect. Am. Soc. Anim. Sci. (Vol. 43, pp. 111-112).
FRANK BAKER MEMORIAL SCHOLARSHIP Enns, R. M., Brigham, B. W., McAllister, C. M., & Speidel, S. E. 2011. Evidence of genetic variability in cattle health traits: Opportunities for improvement. Proc. Beef Improvement Federation http://www.beefimprovement.org/ proceedings.html. Fernando, R. L., & Garrick, D. J. 2008. GenSel-User manual for a portfolio of genomic selection related analyses. Animal Breeding and Genetics, Iowa State University, Ames. Fortes, M. R., Reverter, A., Nagaraj, S. H., Zhang, Y., Jonsson, N. N., Barris, W., & Hawken, R. J. 2011. A single nucleotide polymorphism-derived regulatory gene network underlying puberty in 2 tropical breeds of beef cattle. . Anim. Sci. 89:1669-1683. Garrick, D. J., Taylor, J. F., & Fernando, R. L. 2009. Deregressing estimated breeding values and weighting information for genomic regression analyses. Genet. Sel. Evol. 41: 44. Gilmour, A. R., Gogel, B. J., Cullis, B. R., & Thompson, R. 2009. ASReml user guide release 3.0. VSN International Ltd, Hemel Hempstead, UK. Glover, G. H., and Newman, I. E. 1915. Brisket Disease (Dropsy of high Altitude). Colorado Agriculture Experiment Station. 204 Preliminary Report, 3:24. Grover, R. F., Reeves, J. T., Will, D. H., & Blount, S. G. 1963. Pulmonary vasoconstriction in steers at high altitude. J. Appl. Physiol. 18:567-574. Habier, D., R. L. Fernando, K. Kizilkaya and D. J. Garrick. 2011. Extension of the Bayesian alphabet for genomic selection. BMC Bioinformatics 12:186. Hayes, B. and M. Goddard 2010. Genome-wide association and genomic selection in animal breeding. Genome 53:876-883. Henderson, C. 1976. A simple method for computing the inverse of a numerator relationship matrix used in prediction of breeding values. Biometrics : 69-83. 42
Hecht, H. H., Kuida, H., Lange, R. L., Thorne, J. L., & Brown, A. M. (1962). Brisket disease: II. Clinical features and hemodynamic observations in altitude-dependent right heart failure of cattle. Amer. J. Med. 32:171-183. Holt, T. N. and Callan, R. J. 2007. Pulmonary arterial pressure testing for high mountain disease in cattle. Vet. Clinics of N. Amer.: Food Anim. Practice. 23:575-596. Jensen, R., Pierson, R. E., Braddy, P. M., Saari, D. A., Benitez, A., Horton, D. P., ... & Will, D. H. (1976). Brisket disease in yearling feedlot cattle. J. Amer. Vet. Med. Assoc. 169:515-517. Long, N., D. Gianola, G. Rosa, K. Weigel and S. Avendano. 2007. Machine learning classification procedure for selecting SNPs in genomic selection: application to early mortality in broilers. J. Anim. Breed. Genet. 124:377-389. Meuwissen, T. H. E., B. Hayes and M. Goddard. 2001. Prediction of total genetic value using genome-wide dense marker maps. Genetics 157:1819-1829. Neary, J. M. 2013. Pre-weaned beef calf mortality on high altitude ranches in Colorado (Doctoral dissertation, Colorado State University). Newman, J. H., T. N. Holt, L. K. Hedges, B. Womack, S. S. Memon, E. D. Willers, L. Wheeler, J. A. Phillips III and R. Hamid. 2011. High-altitude pulmonary hypertension in cattle (brisket disease): Candidate genes and gene expression profiling of peripheral blood mononuclear cells. Pulmonary Circulation 1: 462. Qiu, Q., G. Zhang, T. Ma, W. Qian, J. Wang, Z. Ye, C. Cao, Q. Hu, J. Kim and D. M. Larkin. 2012. The yak genome and adaptation to life at high altitude. Nature Genetics. Rhodes, J. 2005. Comparative physiology of hypoxic pulmonary hypertension: historical clues from brisket disease. J. Appl. Phys. 98:1092-1100.
Segura, V., VilhjĂĄlmsson, B. J., Platt, A., Korte, A., Seren, Ăœ., Long, Q., & Nordborg, M. 2012. An efficient multi-locus mixed-model approach for genome-wide association studies in structured populations. Nature Genetics. 44:825-830. Schimmel, J. G. 1981. Genetic aspects of high mountain disease in beef cattle. PhD Diss. Colorado State Univ., Fort Collins Shirley, K. L., Beckman, D. W., & Garrick, D. J. 2008. Inheritance of pulmonary arterial pressure in Angus cattle and its correlation with growth. J. Anim. Sci. 86:815-819. Simonson, T. S., Y. Yang, C. D. Huff, H. Yun, G. Qin, D. J. Witherspoon, Z. Bai, F. R. Lorenzo, J. Xing and L. B. Jorde. 2010. Genetic evidence for high-altitude adaptation in Tibet. Science 329: 72-75. Veit, H. P., & Farrell, R. L. 1978. The anatomy and physiology of the bovine respiratory system relating to pulmonary disease. The Cornell Veterinarian. 68:555-581. Wang, J., Y. Zhang, C. Marian and H. W. Ressom. 2012. Identification of aberrant pathways and network activities from high-throughput data. Briefings in Bioinformatics 13:406-419. West, J. B. 2004. The physiologic basis of high-altitude diseases. Ann. Internal Med. 141:789-800. Will, D. H., & Alexander, A. F. 1970. High mountain (brisket) disease. Bovine Medicine and Surgery. WJ Gibbons, EJ Catcott, and JF Smithcors, ed. Am. Vet. Publ., Wheaton, IL, 412430. Will, D. H., Hicks, J. L., Card, C. S., & Alexander, A. F. 1975. Inherited susceptibility of cattle to high-altitude pulmonary hypertension. J. Appl. Phys. 38:491-494. Yi, N. and S. Xu. 2008. Bayesian LASSO for quantitative trait loci mapping. Genetics 179:10451055.
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Name Kelly W. Bruns William Herring D. H. “Denny” Crews, Jr. Dan Moser D. H. “Denny” Crews, Jr. Lowell S. Gould Rebecca K. Splan Patrick Doyle Shannon M. Schafer Janice M. Rumph Bruce C. Shanks Paul L. Charteris Katherine A. Donoghue Khathutshelo A. Nephawe Janice M. Rumph Katherine A. Donoghue Khathutshelo A. Nephawe Fernando F. Cardoso Charles Andrew McPeake Reynold Bergen Angel Rios-Utrera Matthew A. Cleveland David P. Kirschten Amy Kelley Jamie L. Williams Gabriela C. Márquez Betz Yuri Regis Montanholi Devori W. Beckman Kasey L. DeAtley Scott Speidel Lance Leachman Kent A. Gray Megain Rolf Brian Brigham Kristina Weber Jeremy Howard
University Michigan State University University of Georgia Louisiana State University University of Georgia Louisiana State University University of Nebraska–Lincoln University of Nebraska–Lincoln Colorado State University Cornell University University of Nebraska–Lincoln Montana State University Colorado State University University of Georgia University of Nebraska–Lincoln University of Nebraska–Lincoln University of Georgia University of Nebraska–Lincoln Michigan State University Michigan State University University of Guelph University of Nebraska–Lincoln Colorado State University Cornell University Montana State University Colorado State University Colorado State University University of Guelph Iowa State University New Mexico State University Colorado State University Virginia Polytechnic Institute North Carolina State University University of Missouri Colorado State University University of California-Davis University of Nebraska–Lincoln
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ROY A. WALLACE
The Roy A. Wallace BIF Memorial Fund was established to honor the life and career of Roy A. Wallace. Mr. Wallace worked for Select Sires for 40 years, serving as vice-president of beef programs and devoted his life to beef-cattle improvement. He became involved with BIF in its infancy and was the only person to attend each of the first 40 BIF conventions. He loved what BIF stood for - an organization that brings together purebred and commercial cattle breeders, academia, and breed associations, all committed to improving beef cattle. Wallace was honored with both the BIF Pioneer Award and the BIF Continuing Service Award and co-authored the BIF 25-year history, Ideas into Action. This scholarship was established to encourage young men and women interested in beef cattle improvement to pursue those interests as Mr. Wallace did, with dedication and passion. Proceeds from the Roy A. Wallace Beef Improvement Federation Memorial Fund will be used to award scholarships to graduate and undergraduate students currently enrolled as full-time students in pursuit of a degree related to the beef cattle industry. Criteria for selection will include demonstrated commitment and service to the beef cattle industry. Preference will be given to students who have demonstrated a passion for the areas of beef breeding, genetics, and reproduction. Additional consideration will include academic performance, personal character, and service to the beef cattle industry. Two scholarships will be offered in the amount of $1250 each. One will be awarded to a student currently enrolled as an undergraduate and one will be awarded to a student currently enrolled in a Master of Science or Doctoral program. (From BIF Website/www.beefimprovement.org).
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UNDERGRADUATES Name Sally Ruth Yon Cassandra Kniebel Natalie Laubner Tyler Schultz
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University Texas Tech University University of Kentucky University of Missouri Texas Tech University
Year 2010 2011 2012 2013
GRADUATES Name Paige Johnson Jessica Bussard Ky Pohler Loni Woolley
ROMAN L. HRUSKA U.S. MEAT ANIMAL RESEARCH CENTER Clay Center Nebraska
Roman L. Hruska U.S. Meat Animal Research Center (MARC) was penned into existence when Congress officially transferred a significant portion of the Naval Ammunition Depot property over to USDA on June 16, 1964.Â USMARC has had a storied history of research in support of the cattle, swine and sheep industries and today the Center is one of the largest research locations in USDAâ€™s Agricultural Research Service (ARS). The USMARC is celebrating its 50th anniversary in 2014. USMARC scientists have contributed to the annual BIF meeting since its beginning.
GeneSeek, coupled with its Igenity bioinformatics program, provides producers with the information they need to make the best breeding and management decisions early on, saving time and money. With genetic information, producers can more accurately predict key traits, such as marbling and ribeye area.
CIRCLE FIVE BEEF, INC. Henderson Nebraska Alan Janzen, owner
This feeding company started in 1972 with Alan, his father, uncle and three other area producers. The centralized location at Circle 5 Beef makes it easy to access both feed and packers in central Nebraska.
PROCEEDINGS GENERAL SESSION I: FOCUS ON THE COWHERD ECONOMIC CONSIDERATIONS FOR THE COW HERD C.P. Mathis1, C.T. Braden1, R.D. Rhoades1, and K.C. McCuistion1 1 ® King Ranch Institute for Ranch Management Texas A&M University-Kingsville Introduction Cow-calf producers are continually challenged to maintain the profitability of their operations despite the dynamic nature of weather patterns, cattle markets, and the cost of input commodities and services. Good managers make a multitude of small decisions to collectively keep costs low relative to the value of the weaned calves they produce. However, the real separation between “good” and “excellent” management is that the very best managers also understand and find leverage in the production system that have long-standing systematic benefit to the operation. Those producers with a clear view of the financial position of the ranch and the drivers of net income and return on assets will be best prepared to make the high leverage decisions with long-term benefit to the operation. This paper discusses the impact of key cow herd performance criteria on the net income of cowcalf enterprises, and is intended to help managers prioritize the areas in their unique operation that will likely yield the largest improvement in profitability if altered. Standardized Performance Analysis (SPA) benchmark information is used as a basis to estimate the impact of some management decisions on key cow herd performance criteria and net income. What is Driving Net Income? Benchmark data from the SPA database offers some historical insight into the key performance and financial measures affecting profit of cow-calf enterprises. It is also noteworthy that current SPA benchmark information only offers regional information from the southwest (TX, OK, and NM; Stan Bevers, personal communication). Table 1 is the Southwest SPA Key measures summary for 44 herds from 2008 to 2013. These herds ranged in size from 44 to 2,963 head and represent 17,196 cow years. Calf prices in 2013 and 2014 have reached exceptionally high 48
values, and these high prices are not reflected in the dataset. In fact, the average weaned calf price at 507 pounds was only $119/cwt; and is much lower than current prices. This does not discount the information for those interested in maximizing profit because drivers of profit remain the same regardless of the actual price of calves. Average net income during this period was below breakeven (-$65/cow exposed). It is discouraging that operations in the benchmark dataset were not profitable on average, but upon closer evaluation there are still a portion of the operations that were profitable. In fact, some cow-calf enterprises were highly profitable (figure 1). Production systems can vary greatly; however, those herds in the top net income quartile (average profit = $159/ cow exposed) generated not only greater gross income from calf sales relative to the other three-fourths of the 44 herds (figure 2), but also had the lowest production costs. The bottom line is that highly profitable herds typically return more income and have lower costs. Producers interested in being among the top net income quartile are encouraged to continuously ask themselves: 1) What are the most profitable herds doing that makes them different? 2) How can I improve profit the most in my operation? A Closer Look at Revenue The two sources of revenue for cow-calf operations are calf sales and cull cow and bull sales, with calf sales being the most important. Calf income is a function of quantity (number sold), quality (genetics and condition), and marketing. Table 2 shows calf weaning measures and revenues by net income quartile to provide insight into some of the differences that exist among profitable and unprofitable operations. Weaning percentage does not show an upward linear trend parallel to rising net income. This does not mean that weaning percentage is unimportant, but emphasizes that top net income quartile operations have a balance between cost and performance that maximize net income. The top quartile does not have the highest weaning percentage, but these operations have weaned the largest calves by 58 pounds over the second highest profit quartile. The advantage in weaning weight
primarily results from calves in the top quartile being approximately 20 days older at weaning (data not shown). Although not quantifiable from SPA data, it is likely that calves from the top quartile operations also have an additional advantage in genetics for growth and/or end product value. The overall average weaning rate and weight were 83.8 percent and 507 pounds, respectively (table 2). Using these values as a foundation, and assuming that 507-pound calves are worth $119/cwt (average SPA price from 2008-2012), the value of a single percentage unit change in weaning rate is about $6/ cow exposed (calculation: 507 lbs * 1% * $119/cwt = $6.03). If a more current 507-pound calf price of $200/cwt is assumed, a single unit increase in weaning percentage raises profit by more than $10/cow exposed. Therefore, any management change that cost less than $10/cow exposed to implement and increases weaning rate by one percentage unit or more will increase net income.
reducing purchase price of breeding stock, increasing salvage values, or increasing longevity of cows and bulls. Reducing equipment depreciation may be accomplished by sharing, renting, leasing, or contracting equipment. However, each of these options has some tradeoffs in convenience and control. Unlike livestock depreciation, which is a direct cost, the expense of equipment, buildings, and fences depreciation is an indirect or overhead cost. While capital purchases and improvements may have the potential to improve efficiency and production, the increase of the associated depreciation expense may offset the gain in efficiency from the improvement. The most profitable operations generally find ways to reduce this depreciation burden as much as possible.
Putting the Performance and Financial Pieces Together A cow calf enterprise is a complex biological system where inputs and outputs are interconnected. Managers interested in maximizing profit are encouraged to focus on optimizing weaning rate and weaning A Closer Look at Expenses weight, as well as feed, labor, and depreciation exTotal cost before non-calf revenue adjustpenses. However, there is no silver bullet or prescripment averaged $608/cow exposed (table 1), but when tion that is most effective at accomplishing the perfect evaluated by net income quartile, the quartile averbalance because of the vast differences in resources age ranged $451 to $700. The top quartile producers and goals from one ranching operation to the next. simply wean and market more pounds of calf/cow The key is to evaluate potential changes based on unit exposed at a much lower cost than the less profitable cost of production. This measure will merge inputs operations. Figure 3 shows that over half of the exand outputs into a single value. In reality, only a small penses to a cow-calf enterprise can be categorized as portion of cow-calf enterprises have an accounting and depreciation, labor, or feed. In most cow-calf enterprises these three expense categories offer opportunity performance measurement system in place to accuratefor high leverage change to the production system that ly calculate unit cost of production. Implementation of a managerial accounting system should be the inican yield significant financial improvement. Other expenses like repairs and maintenance, fertilizer, fuel, tial step to improving profit because a clear picture of leases, and veterinary services are important when tak- the current financial status of the operation is needed to make the best business decisions for the future. en together, but independently are generally not high It will take many small decisions across all leverage expenses. facets of the business to keep cost low, yet still achieve Feed and labor expenses are typically well understood, but depreciation is an expense often more performance goals. However, in most systems there difficult to grasp. The result is a considerable amount are a few high-leverage interventions that can make a big impact. These changes will not be the same on all of unaccounted expense in livestock, equipment, and infrastructure depreciation. Managers should be aware operations, but all managers should seek to find these of the effect depreciation of livestock, equipment, and areas in the operation that if changed could yield drainfrastructure has on the long term equity of an opera- matic improvement. Table 3 lists examples of changtion. The ways to decrease livestock depreciation are: es that may have a significant long-standing benefit 49
to an operation. These interventions are included as examples only, and are not intended to be generalized recommendations for all operations. Notice that labor, depreciation, and pounds weaned are all affected in almost every intervention. A number of other examples could also be included, especially those that affect genetic makeup of the cowherd, which is always a long-standing change. Cash Flow. Without minimizing the importance of previously discussed financial principles, operating capital is essential. A yearly financial plan with projected monthly cash flows adjusted according to operational plans is invaluable in preventing un-expected asset liquidation out of necessity. Not being able to service short-term liabilities can lead to the liquidation of revenue producing assets, resulting in long-term reduced profit potential. While the value of cows liquidated today is capitalized on, the value of future production is lost. The importance of not liquidating assets in order to operate cannot be over emphasized. Conclusions The most profitable cow-calf operations are efficient, generally weaning the most pounds of calf per cow exposed with the lowest breakeven. Most importantly, these operations yield the greatest return on assets. Success in the cattle industry does not happen on accident. Decision makers at the most profitable operations have built production and marketing systems that, most importantly minimize labor, feed, and depreciation expenses relative to weaned calf value. Producers interested in improving the profitability of their cow-calf operation are encouraged to utilize a managerial accounting system that maintains a clear picture of the operation financials and allows measurement of unit cost of production. Furthermore, managers should seek practical, high leverage alterations to the production system with a keen focus on optimizing weaning rate and weaning weight, as well as feed, labor, and depreciation expenses.
HEIFER INTAKE AND EFFICIENCY AS INDICATORS OF COW INTAKE AND EFFICIENCY Daniel W. Shike1, Chris J. Cassady1, J. W. Adcock1, and Keela M. Retallick2 Univeristy of Illinois at Urbana-Champaign California Polytechnic State University, San Luis Obispo 1
Introduction Feed costs account for over 60% of the total costs associated with maintaining a beef cow and are the largest detriment to profitability for beef producers (Miller et al., 2001). Approximately 60 â€“ 70% of energy for beef production is required by the cow herd. Of the energy needed for the cow herd, approximately 70% goes to maintenance energy (Ferrell and Jenkins, 1982). Thus, nearly 50% of all energy required by the beef industry is used simply to maintain the cow herd. Although variation in maintenance energy appears to exist, maintenance requirements of cattle have shown little no change over the past 100 years (Johnson et al., 2003). Limited work has been done evaluating the relationship between heifer intake and performance during the postweaning growing period and cow performance and reproduction traits. The objective of this study was to determine the relationship between residual feed intake (RFI), residual body weight gain (RG), and intake in heifers during the postweaning period and subsequent cow performance and reproduction as 2-year-old lactating and dry cows. Materials and Methods Postweaning heifer evaluation A postweaning intake and performance evaluation was conducted on Angus and Simmental x Angus heifers (n=511) over a 5-yr period at the Beef Field Research Laboratory in Urbana, IL. Heifers were developed on a diet consisting of approximately 70% corn silage, 25% corn co-products, and 5% supplement each year. Heifer intake and performance were monitored for a minimum of 70 d each year; according to BIF standards. Individual intakes were recorded using the GrowSafeÂŽ automated feeding system. For years 1, 2, and 3, cattle were weighed on 2 consecutive days at the beginning and end of the test period, and ADG was calculated by dividing total BW gain by the number of days on test. Individual animal mid-
test metabolic weight (MWW) was determined by the average of the beginning and end weights of the test period. For years 4 and 5, cattle were weighed on 2 consecutive days at the beginning and end of the test and biweekly throughout. Heifer ADG was calculated by regressing each individual weight over all time points of the test. Individual MWW was determined by taking the mid-date test weight via the regression equation. Individual animal 12th rib fat thickness (BF) was recorded via ultrasound on years 4 and 5. Heifer RFI and RG were determined for each individual animal. For all years, animals were separated into contemporary groups, based on breed type and source of origin. For years 1, 2, and 3, RFI was assumed to represent the residuals from a multiple regression model regressing DMI on ADG and MWW, using pen as a random effect, and RG was assumed to represent the residuals from a multiple regression model regressing ADG on DMI and MWW, using pen as a random effect. For years 4 and 5, RFI was assumed to represent the residuals from a multiple regression model regressing DMI on ADG, MWW, and BF using pen as a random effect, and RG was assumed to represent the residuals from a multiple regression model regressing ADG on DMI, MWW, and BF using pen as a random effect. Heifers were classified as low, medium, or high RFI, RG, or intake. Classification groups were established by calculation of the mean and SD of the heifers for RFI, RG, and intake. Heifers that were less than 0.5 SD below the mean were classified as “Low,” heifers that were ± 0.5 SD of the mean were classified as “Med,” and heifers that were more than 0.5 SD above the mean were classified as “High.” Heifers with structural soundness problems or very poor performance were culled annually prior to the breeding season. Heifers (n=366) kept as replacements were synchronized and AI. Heifers were exposed to clean-up bulls for 60 d following AI. Reproductive data were collected for first service AI conception and overall pregnancy rates. Calving data was recorded to determine age of cow (days) at first calving and calf birth weight. 2-year-old cow evaluation Each year, cows were placed in the barns at the Beef Field Research Laboratory in Urbana, IL for two 14 d evaluation phases (60 d (lactating) and 240 d (dry) postpartum) where they were fed a common forage based diet (~60% TDN). During these evaluation periods, measurements were taken to characterize each
individual cow relative to several production traits. At 60 d postpartum, twenty-four hour milk production estimates were determined using a 12-hr weigh-suckle-weigh technique (Beal et al., 1990). Individual intake was measured during each evaluation period by using the GrowSafe® automated feeding system. At the conclusion of each evaluation period, weights were taken on two consecutive days, hip height recorded, BCS scored (1-9 scale) by a trained technician, and cows were ultrasound for BF. Calves were weaned at approximately 6 mo of age. Weaning weights were recorded and submitted to the American Angus Association and American Simmental Association. An adjusted weaning weight was then calculated by the associations. As a measurement of cow efficiency during the lactating period, a cow RFI value was calculated for each cow. Cow RFI was assumed to represent the residuals from a multiple regression model regressing DMI on metabolic weight (MW), BF, and 24-hour milk production. Statistical Analysis The MIXED procedure of SAS was used to test the effect of heifer intake and efficiency classification on cow production traits. The model used included the fixed effect of RFI, RG, or intake classification group (high, medium, and low.) The GLIMMIX procedure of SAS was used to test the effect of heifer intake and efficiency classification on reproductive traits (binomial data). The model used included the fixed effect of RFI, RG, or intake classification group (high, medium, and low). Mean values were considered to be significantly different when P < 0.05 and considered a tendency when P > 0.05 and < 0.10. Results and Discussion Heifers were classified into Low, Med, or High RFI groups, and the effects of the RFI classification on female reproductive and performance traits are presented in table 1. There were no differences in percentage of females kept as replacements, first AI conception rate, overall pregnancy rate, or age at calving between the RFI classifications. The heifer RFI classification did not affect calf birth weight or weaning weight. Heifer RFI classification did not affect cow BW, hip height, BF or milk production at 60 d postpartum, but there was a trend (P = 0.08) for cows from the Med RFI group to have decreased BCS at 60 d postpartum compared to cows from the High RFI group. Cows classified as Med or High RFI had greater (P < 0.01) DMI than cows in the Low RFI group 51
Table 1. Effects of RFI classification on female reproductive and performance traits Heifer RFI Category Item Low Med High SEM P-value Reproductive traits Retained as replacement, % 69 76 71 0.36 First AI conception rate, % 45 50 42 0.50 Overall pregnancy rate, % 86 83 85 0.80 Cow age at first calf, d 736 734 741 3 0.16 1 Calf performance Calf birth weight, lb 73 73 75 1 0.51 Calf weaning weight, lb 598 586 618 12 0.12 2 2-year-old cows (lactating) Cow BW, lb 1270 1257 1272 14 0.68 Cow hip height, in 52.6 52.8 52.9 0.2 0.54 xy x y Cow BCS 5.7 5.6 5.7 0.1 0.08 Cow BF, in 0.25 0.24 0.25 0.01 0.91 24 h milk production, lb 18 17 18 1 0.70 a b b Cow DMI, lb 32.4 35.9 36.9 1.1 <0.01 a b b Cow RFI, lb -1.67 0.56 1.09 0.65 <0.01 3 2-year-old cows (dry) Cow BW, lb 1378 1368 1384 14 0.67 Cow hip Height, in 53.5 53.5 53.5 0.2 0.99 Cow BCS 5.8 5.8 5.9 0.1 0.81 Cow BF, in 0.27 0.27 0.28 0.01 0.55 Cow DMI, lb 29.0x 30.9xy 33.4y 1.3 0.06 a,b Row means that do not have a common superscript differ, P < 0.05 x,y Row means that do not have a common superscript tend to differ, P > 0.05 and < 0.10 1 Progeny of 2-year-old cows 2 2-year-old cow traits measured at 60 d postpartum 3 2-year -old cow traits measured at 240 d postpartum at 60 d postpartum. Cows classified as Med and High RFI heifers had greater Cow RFI than cows that were classified as Low RFI heifers; heifers that ate less than predicted during the postweaning evaluation also ate less than predicted as 2-year-old lactating cows. There were no differences in cow BW, hip height, BCS, or BF at 240 d postpartum among heifer RFI classification groups; however, there was a trend (P = 0.06) for cows from the High RFI group to have increased DMI compared to cows from the Low RFI group. There has been limited work done evaluating the effects of efficiency during the postweaning period on cow performance and reproduction. Shaffer et al. (2011) reported that High RFI heifers tended (P 52
= 0.06) to reach puberty at a younger age than Med or Low RFI but this did not result in any differences among RFI classifications for conception rate or pregnancy. Crowley et al. (2011) reported a negative genetic correlation between RFI in growing males and cow age at first calving but did not find any correlations with fertility or calving difficulty. Crowley et al. (2011) also found a negative genetic correlation between growing male RFI and cow BW but reported no correlation between RFI and maternal weaning weight. Black et al. (2013) found that heifers classified as Med or High RFI had greater DMI as cows than heifers classified as Low RFI.
Heifers were also classified into Low, Med, or High RG groups, and the effects of the RG classification on female reproductive and performance traits are shown in table 2. There were no differences in percentage of females kept as replacements, first AI conception rate, overall pregnancy rate, or age at calving between the RG classifications. The RG classification also did not affect calf birth weight or weaning weight. Heifer RG classification did not affect cow BW, BCS, BF, milk production, DMI, or cow RFI at 60 d postpartum, but there was a trend (P = 0.06) for cows from the High RG group to have increased hip heights compared to the cows from the Low RG group. There were no differences in cow BW, hip height, BCS, BF, or DMI at 240 d postpartum among heifer RG classification groups. Crowley et al. (2011) found that RG in growing males was genetically correlated to age at first calving. Crowley et al. (2011) also reported that growing male RG was genetically correlated to cow BW and maternal weaning weight (0.67 and 0.57, respectively). Heifers were also classified into Low, Med, or High intake groups, and the effects of the intake classification on female reproductive and performance traits are shown in table 3. There were a greater (P < 0.01) percentage of heifers retained as replacements from the groups classified as Med or High Intake heifers compared to the heifers classified as Low Intake. Heifers were culled prior to breeding for either structural soundness problems or very poor performance. We speculate that that the difference in percentage of heifers retained as replacements is likely a reflection of some of the low intake heifers being the smaller, poorer gaining heifers. There were no differences in first AI conception rate or overall pregnancy rate; however, heifers classified as Low Intake were younger (P = 0.04) at calving then the heifers classified as High Intake. Cows that were classified as High Intake heifers had calves with greater (P < 0.01) birth weights than cows that were classified as Low or Med Intake heifers. However, there were no differences in calf weaning weights among cows from different heifer intake classification groups. Cows from the Med and High Intake groups had greater (P = 0.02) BW at 60 d postpartum than cows from the Low Intake group. Cows from the High Intake group had increased (P < 0.01) hip height than cows from the Low Intake heifer group, and cows from the Med Intake
heifer group were intermediate. Heifer intake classification did not affect milk production, BCS, or BF at 60 d postpartum. Cows from the High Intake group had increased (P < 0.01) DMI compared to cows from the Low Intake group, and cows from the Med Intake group were intermediate. Cows from the High Intake group also had greater (P = 0.04) cow RFI than the cows from the Low Intake group. Results at 240 d postpartum were very similar to results at 60 d postpartum. Cows from the Med and High Intake groups again had greater (P < 0.01) BW at 60 d postpartum than cows from the Low Intake group. Cows from the High Intake group also again had increased (P < 0.01) hip height than cows from the Low Intake group, and cows from the Med Intake group were intermediate. Heifer intake classification did not affect BCS or BF at 240 d postpartum either. Similar to 60 d postpartum, cows from the High Intake heifer group had increased (P = 0.02) DMI compared to cows from the Low Intake group, and cows from the Med Intake group were intermediate. Crowley et al. (2011) reported a negative correlation between growing male concentrate intake and cow age at first calving. Crowley et al. (2011) also found a positive correlation between growing male concentrate intake and calving difficulty, cow BW, and maternal weaning weight. Conclusions Results from this study suggest that heifers that are more efficient based off of RFI will consume less DMI as cows with no differences in cow or calf performance or reproduction. There were no differences detected between RG and cow performance or reproductive traits. Heifers that have greater DMI calve at an older age, have larger BW and greater hip height as 2-year-old cows, and have increased DMI as cows. Further evaluation of the relationship of heifer intake and efficiency measures on cow production traits after 2 years of age is needed. Literature Cited Beal, W. E., D. R. Notter, and R. M. Akers. 1990. Techniques for estimation of milk yield in beef cows and relationships of milk yield to calf weight gain and postpartum reproduction. J. Anim. Sci. 68:937-943.
Table 2. Effects of RG classification on female reproductive and performance traits Heifer RG Category Item Low Med High SEM P-value Reproductive traits Retained as 71 71 74 0.80 replacement, % First AI con46 44 47 0.91 ception rate, % Overall preg86 83 85 0.72 nancy rate, % Cow age at 71 71 74 0.80 first calf, d Calf performance1 Calf birth 73 74 74 1 0.84 weight, lb Calf weaning 596 600 601 12 0.94 weight, lb 2-year-old cows (lactating)2 Cow BW, lb 1261 1256 1280 14 0.42 Cow hip 52.5x 52.7xy 53.0y 0.2 0.06 height, in Cow BCS 5.7 5.6 5.6 0.1 0.91 Cow BF, in 0.26 0.24 0.24 0.01 0.45 24 h milk 18 17 18 1 0.47 production, lb Cow DMI, lb 35.1 35.0 35.2 1.1 0.99 Cow RFI, lb 0.92 -0.30 -0.54 0.63 0.21 3 2-year-old cows (dry) Cow BW, lb 1372 1359 1398 14 0.12 Cow hip 53.3 53.4 53.7 0.2 0.14 height, in Cow BCS 5.9 5.8 5.8 0.1 0.83 Cow BF, in 0.28 0.27 0.27 0.01 0.67 Cow DMI, lb 29.6 31.7 31.9 1.3 0.36 x,y Row means that do not have a common superscript tend to differ, P > 0.05 and < 0.10 1 Progeny of 2-year-old cows 2 2-year-old cow traits measured at 60 d postpartum 3 2-year -old cow traits measured at 240 d postpartum
Table 3. Effects of intake classification on female reproductive and performance traits Heifer Intake Category Item Low Med High SEM P-value Reproductive traits 80b 76b <0.01 Retained as replacement, % 57a First AI conception rate, % 51 44 45 0.62 Overall pregnancy Rate, % 84 84 86 0.87 a ab b Cow age at first calf, d 731 738 741 3.1 0.04 1 Calf performance Calf birth weight, lb 71a 73a 77b 1.4 <0.01 Calf weaning weight, lb 605 590 607 13.8 0.47 2 2-year-old cows (lactating) Cow BW, lb 1225a 1273b 1285b 16.2 0.02 a b c Cow hip height, in 52.1 52.8 53.2 0.2 <0.01 Cow BCS 5.6 5.7 5.7 0.1 0.75 Cow BF, in 0.24 0.26 0.24 0.01 0.25 24 h milk production, lb 18 18 17 0.8 0.73 a b c Cow DMI, lb 30.2 35.4 38.4 1.2 <0.01 Cow RFI, lb -1.24a -0.20ab 1.19b 0.74 0.04 3 2-year-old cows (dry) Cow BW, lb 1305a 1377b 1409b 18.6 <0.01 a b c Cow hip height, in 52.9 53.5 53.9 0.2 <0.01 Cow BCS 5.7 5.8 5.9 0.1 0.24 Cow BF, in 0.27 0.27 0.29 0.01 0.44 a ab b Cow DMI, lb 27.3 30.7 33.1 1.5 0.02 a,b,c Row means that do not have a common superscript differ, P < 0.05 1 Progeny of 2-year-old cows 2 2-year-old cow traits measured at 60 d postpartum 3 2-year -old cow traits measured at 240 d postpartum Black, T. E., K. M. Bischoff, V. R. G. Mercadante, G. H. L. Marquezini, N. DiLorenzo, C. C. Chase, Jr., S. W. Coleman, T. D. Maddock and G. C. Lamb. 2013. Relationships among performance, residual feed intake, and temperament assessed in growing beef heifers and subsequently as 3-yearold, lactating beef cows. J. Anim. Sci. 91:22542263. Crowley, J. J., R. D. Evans, N. McHugh, D. A. Kenny, M. McGee, D. H. Crews, Jr., and D. P. Berry. 2011. Genetic Relationships between feed efficiency in growing males and beef cow performance. J. Anim. Sci. 89:3372-3381.
Ferrell, C. L., and T. G. Jenkins. 1982. Efficiency of cows of different size and milk production potential. Pages 12–24 in USDA, ARS, Germplasm Evaluation Program Progress Report No. 10.MARC, Clay Center, NE. Johnson, D. E., C. L. Ferrell, and T. G. Jenkins. 2003. The history of energetic efficiency research: Where have we been and where are we going? J. Anim. Sci. 81:E27–E38 Miller, A. J., D. B. Faulkner, R. K. Knipe, D. R. Strohbehn, D. F. Parrett, and L. L. Berger. 2001. Critical control points for profitability in the cowcalf enterprise. Prof. Anim. Sci. 17:295-302. Shaffer, K. S., P. Turk, W. R. Wagner, and E. E. D. Felton. 2011. Residual feed intake, body composition, and fertility in yearling beef heifers. J. Anim. Sci. 2011:1028-1034. 55
BEEF HEIFER DEVELOPMENT AND LIFETIME PRODUCTIVITY1
Heifer Development System and Pubertal Status Association among BW, puberty, and heifer pregnancy rate appears to have changed over time R. L. Endecott1, R. N. Funston2, J. T. Mulliniks3, and A. (Funston et al., 2012). Earlier research demonstratJ. Roberts4 1 Department of Animal and Range Sciences, Montana ed limiting post-weaning growth negatively affected age of puberty and pregnancy rates, whereas more State University, Bozeman 59717 2University of Nebraska West Central Research and Extension Center, recent studies demonstrate less of a negative impact of delayed puberty on pregnancy rate. Funston et al. North Platte 69101 (2012) hypothesized that changes over time may have 3 Department of Animal and Range Sciences, New resulted from: Mexico State University, Las Cruces 88003 4US1) the shift from calving heifers at 3 yr of age DA-ARS, Fort Keogh Livestock and Range Research to calving at 2 yr of age and subsequent Laboratory, Miles City, MT 59301 selection pressure for decreased age at puberty; Introduction 2) genetic changes in age of puberty The heifer development paradigm is adapting resulting from selection for bull scrotal to less traditionally inexpensive feed available and circumference; and changes in cattle genetics over the last 40 years, making it critical to understand how management practices 3) perhaps a change in fertility of pubertal affect lifetime production efficiency. Increased feed estrus compared with subsequent estrous costs have negatively impacted heifer development cycles. protocols that rely heavily on harvested feeds. Much Other factors may also contribute to the change of the research leading to the paradigm of developing over time. Establishment and use of EPDs in selectheifers to a target BW of 60 to 65% mature BW at breeding was conducted during the late 1960s through ing for growth, milk, and carcass characteristics have contributed to changes in reproductive performance the 1980s. However, trait selection based on EPDs due to genetic associations with these and other traits has created substantial genetic change in the last 40 (American Angus Association, 2012; American Hereyears. This impact of genetic change on heifer develford Association, 2014; American International Chaopment has not been widely considered. Research in rolais Association, 2014). For example, genetic trend the last decade has compared traditional, more intenfor increased mature weight would be expected to sive systems with systems using less feed and relying correspond with an increase in BW at puberty. Results on compensatory gain. These studies provide evidence that developing heifers to a lighter target BW at summarized in Figure 1 illustrate that BW at time of puberty has increased over time. Although information breeding, that is, 50 to 57% of mature BW compared with 60 to 65% BW, reduced development costs while concerning mature size is not provided in most studies not impairing reproductive performance (Funston and represented in Figure 1, the progression from a mature size of 1,100 lb in the initial studies to 1,300 lb in the Deutscher, 2004; Roberts et al., 2009; Funston and Larson, 2011; Mulliniks et al., 2012). However, much most recent studies may be reasonable. Even though heifer development research is limited in its consider- different management and feeding practices were implemented within and among studies summarized in ation of long-term applications. Longevity has a relFigure 1, the data indicate a majority of heifers would atively low heritability; thus, heifer development and achieve puberty at or below 60% mature BW, assumother management strategies have a greater potential ing mature BW of 1,100; 1,200; or 1,300 lb for heifto impact cow retention in the breeding herd. While ers used in the 3 time periods. Data in Figure 1 also limited information exists about the impacts of heifer indicate average age of puberty was prior to 430 d of development strategies on cow longevity, data from age, which would correspond to the start of breeding non-ruminant and non-livestock species implies that in order to begin calving at 2 yr of age. Furthermore, it limiting caloric intake during juvenile development is expected that selection and management processes can increase lifespan (Speakman and Hambly, 2007). implemented over time have contributed to a greater proportion of heifers achieving puberty at lower target 1 Adapted from Endecott et al. (2013). BW. 56
Figure 1. Body weight (BW) and age of heifers at puberty in studies over the last 5 decades where heifers were developed on 2 or more levels of growth during the post weaning period. Data from 1960 to 1971 are depicted with black diamonds (Wiltbank et al, 1966 and 1969; Short & Bellows 1971). Data from 1972 to 1987 are noted with black squares (Ferrell, 1982; Greer et al., 1983; Byerly et al., 1987). Data from 1990-2009 are shown as black triangles (Hall et al., 1995; Lynch et al., 1997; Freetly et al., 1997; Ciccioli et al., 2005; Roberts et al., 2009). The data indicate that BW at puberty has increased over the time periods that different studies were conducted. Horizontal lines represent BW representing 60, 55, and 51% of 1,300 lb mature BW; 60 and 55% of 1,200 lb BW; and 65 and 60% of 1,100 lb BW. The black vertical line at 430 d of age represents the age to start breeding in order to calve at 2 yr of age. Not all heifers achieved puberty in the time frame encompassed by some of the studies depicted. However, the data indicate genetic potential of heifers under different management strategies to achieve puberty at or below 60% of a mature BW predicted to be representative of cows for each time period. Fertility of the pubertal estrus is another component of the heifer development paradigm that needs to be reevaluated. Industry recommendation that heifers be developed so they experience puberty prior to start of breeding is derived from results of Byerly et al. (1987) who observed 21% lower pregnancy rate in heifers bred on their first estrus compared with heifers bred on their third estrus. However, mean age and BW of heifers at the time of breeding were confounded by estrus status classification. Mean age at breeding for heifers bred at first estrus was 322 d, whereas heifers bred on third estrus averaged 375 d old. Furthermore, age of breeding accounted for increased pregnancy in heifers classified to be bred at first estrus, but not in
heifers assigned to be bred on the third estrus. Thus, the implications of data from the first estrus group bred at an average age of less than 11 months for the industry where the majority of heifers would traditionally be bred 13 to 15 mo of age is questionable. Recently, research reported 6% lower pregnancy rates in heifers that were not pubertal at the start of the breeding season compared with heifers that were pubertal (Roberts et al., 2013; Vraspir et al., 2013). Although these results are not a direct assessment of first estrus fertility, the results indicate the magnitude of infertility is not near the extent indicated in the original study by Byerly et al. (1987). 57
Nutrition Following the Start of Breeding and Through Subsequent Calvings Establishing impact of heifer development protocols on longevity is complex, requiring consideration for nutritional factors following the start of breeding through subsequent calvings. Resulting maintenance requirements and behavior traits associated with development protocols must be considered. Most longer-term heifer development studies manage replacement heifers as a group on breeding pastures after development. Heifers developed under conditions of dormant or scarce forage, low precipitation, undulating terrain, and large pastures, or those that are restricted gain, pen-developed often exhibit compensatory gain during summer grazing (Olson et al., 1992; Roberts et al., 2009; Funston and Larson, 2011; Mulliniks et al., 2012). Examples of this include comparisons of heifers developed in a drylot at 1.52 lb/d ADG from initiation of the study to breeding with heifers developed at 0.57 lb/d on a low-quality pasture with protein supplementation (Mulliniks et al., 2012). Development treatments resulted in 77-lb difference in weight at start of breeding. However, the pasture-developed heifers had greater gain (1.83 lb/d) from start of breeding to pregnancy diagnosis than drylot heifers (1.34 lb/d). Range-developed heifers compensated for their minimal pre-breeding ADG and gained more weight during the breeding season than feedlot-developed heifers, due to lower maintenance requirements and the ability to respond to a seasonal improvement in forage quality (Marston et al., 1995; Ciccioli et al., 2005). Pasture developed heifers tended to have greater pregnancy rates than heifers developed in a drylot (91 vs. 84%). Other research (Funston and Larson, 2011; Larson et al., 2011) compared heifers grazing on corn residue or winter range as an alternative to drylot feeding. Heifers grazing corn residue gained 0.5 lb/d more than heifers developed on winter grass or a drylot. Heifers grazing winter grass or corn residue were supplemented with the equivalent of 0.31 lb/d of protein and gained between 0.42 and 0.93 lb/d during winter grazing. Once placed on higher quality spring pasture, the heifers gained 1.19 to 1.61 lb/d during the breeding season. Heifers grazing corn residue weighed less prior to breeding than heifers developed in the drylot, had achieved 56% of their mature BW, had similar pregnancy rates at the end of the breeding season, and achieved similar BW prior to calving with a similar percentage (> 60%) calving in the first 21 d of the 58
calving season and calf birth date. Decreased winter gain in the low input development systems resulted in greater gain during the breeding season, which may explain similar overall pregnancy rates. If nutrition following start of breeding is inadequate, poor reproductive performance may result. White et al. (2001) found restricting nutrients to 40% of maintenance prevented ovulation in 70% of heifers with no change in BCS. Perry et al. (2009) reported decreased pregnancy success for heifers moved from feedlot to summer grazing post-AI. Post-insemination nutrition may affect embryonic survival through a variety of mechanisms. Nutritionally-mediated changes to the uterine environment can occur by changing components of uterine secretions or by influencing the circulating concentrations of progesterone that regulate the uterine environment (Foxcroft, 1997). Arias et al. (2012) determined yearling heifers that gained BW had greater AI pregnancy rate (77%) than heifers that maintained (56%) or lost (61%) BW during the first 21-d period post-AI. Therefore, nutritional plane postAI may be as or more important than pre-breeding nutritional plan in yearling heifers. Collectively, the studies discussed above provide evidence that developing heifers to lighter weights at start of breeding reduces maintenance requirements providing them with greater opportunity to be in positive nutrient balance in conditions when forage quality is marginally sufficient around the time of breeding. Differences in size and corresponding maintenance requirements may persist over time to result in greater retention in subsequent years. Pregnancy rates through the 4th calf remained similar between high- and low-gain heifers developed in Nebraska, where nutrition following the development period was considered adequate (Funston and Deutscher, 2004). In contrast results from New Mexico, where nutrition may have been limiting. Mulliniks et al. (2012) reported 68% retention in the breeding herd through 5 yr of age for range-developed heifers fed a high-RUP (rumen undegradable protein) supplement compared with 41% retention for range-raised counterparts fed a lower-RUP cottonseed meal-based supplement, and 42% retention for heifers developed in a feedlot. This relationship tended to be significant as early as 2 and 3 yr of age, respectively. These data indicate not only where a heifer is developed (i.e., low-input vs. feedlot), but also what she is fed when developed (i.e., high-RUP vs. lower-RUP supplement) may influence
her longevity in the cow herd. Nutrition through subsequent calvings may interact with heifer development protocol to influence cow longevity. In the Nebraska and New Mexico studies discussed above, heifers were managed in common after the respective heifer development treatments. In contrast to the Nebraska and New Mexico data sets, a study in Montana evaluated cows provided different levels of feed inputs during post-weaning development and subsequent winter supplementation over a 10-year period. Each year following weaning, heifers were developed in dry lots on a corn silage-based diet. Heifers were fed to appetite (control) or restricted to fed 20% less than controls at similar weight. In subsequent winters, control females were provided supplemental feed expected to be adequate for production on winter range, whereas restricted heifers were fed level of supplemental feed expected to be marginal for range conditions. Heifers used in this study were produced by dams that had received either marginal or adequate levels of winter supplemental feed, thereby creating 4 classifications: restricted heifers from dams provided marginal levels of winter supplemental feed; restricted heifers from dams provided adequate levels of winter supplemental feed; control heifers from dams provided marginal levels of winter supplemental feed; control heifers from dams provided adequate levels of winter supplemental feed. All females were required to wean a calf each year of production to remain in the herd. Retention at year 1 (heifer pregnancy) and at start of the 2nd breeding season were influenced by the interaction of heifer and dam nutritional treatments; being greater for restricted heifers from dams on marginal level of supplement than restricted heifers from adequately supplemented dams. Retention from 2 to 3 years of age was less for restricted animals than controls. No differences in loss were observed between 3 and 4 years of age, but control animals incurred greater loss between year 4 and 5 resulting in similar percent retention among the different classification groups at 5 years of age. Collectively, rebreeding results from New Mexico and Nebraska would indicate that lower-input heifer development where all heifers are managed together after the post-weaning period did not impair rebreeding, but continued subsequent restriction in the form of marginal winter supplementation, as experienced by the Montana heifers, resulted in lower retention rates in 2 to 3-year-old cows. Restricted heifers that failed to rebreed in the Montana
study were lighter prior to calving (871 vs 888 lb) and prior to start of breeding (818 vs 842 lb) as 2-yr-olds compared with pregnant heifers from both development groups and non-pregnant heifers developed on ad libitum feed. This primary difference between lower-input heifer development programs emphasizes the importance of managing extensively developed heifers for continued growth after lower inputs during post-weaning development. The data also indicate that the way the dams are fed may program the heifer fetuses to respond differently to low input development later in life. Heifer development protocols may influence resulting behavior traits associated with the environment in which the heifer was developed. Range-developed heifers may retain better grazing skills and be more productive during the subsequent summer (Olson et al., 1992; Perry et al., 2009). In a recent study at 2 locations in Nebraska (Summers et al., 2013), heifers were either developed on winter range vs. corn residue or drylot vs. corn residue. Pregnancy rate based on heifer development system was similar; however, heifers developed on corn residue exhibited greater ADG when placed on corn residue as a pregnant heifer compared with either winter range or drylot developed heifers (Summers et al., 2013), supporting the hypothesis of a learned behavior for grazing corn residue. However, drylot-developed heifers that graze dormant forage during the winter prior to development in a pen may not exhibit a change in grazing skills upon returning to a grazing environment. Mulliniks et al. (2012) reported similar ADG in drylot-developed heifers between the drylot phase (1.52 lb/d) and grazing phase (1.34 lb/d). Data from other species indicates the environment experienced during development can have lifetime impacts. Adequate heifer growth and development to ensure minimal calving difficulty can be important for longevity (Rogers et al., 2004) however, providing additional supplemental feed during post-weaning development to accomplish this may be less efficient than later in development. Similar calving difficulty has been observed between low- and high-gain heifers developed in confinement (Funston and Deutscher, 2004), between heifers developed with low-inputs on corn residue and winter range and feedlot-developed heifers (Funston and Larson, 2011), and between low-input developed heifers grazing either winter range or corn residue (Larson et al., 2011). Within 59
study, all heifers were exposed to a low-birthweight EPD bull battery in the same breeding pastures. Calving date for first calf heifers may impact cow longevity and productivity. Calving late in yr 1 increases the proportion of cows that either calve later next year or do not conceive (Burris and Priode, 1958). Research has indicated heifers having their first calf earlier in the calving season remained in the herd longer compared with heifers that calved later in the calving season (Rogers et al., 2004; Cushman et al., 2013). Therefore, heifers calving earlier in the calving season have greater potential for longevity and lifetime productivity. However, the above-mentioned studies do not demonstrate that heifer development affected date of calving or longevity. Economic Analysis of Heifer Development Systems Mulliniks et al. (2012) evaluated enterprise budgets for the 3 New Mexico heifer development treatments. Assumptions included comparing 100 heifers in each treatment, and all heifers would be sold in the fall of their yearling year, regardless of pregnancy status. Gross returns were greatest for the RUP-supplemented range heifers and least for heifers developed in the feedlot; feed costs were greatest for feedlot-developed heifers. Compared with feedlot-developed heifers, net returns were $99.71 and $87.18 greater per heifer developed for the high-RUP and cottonseed meal-supplemented heifers grazing dormant native range, respectively. The increase in net returns for range-raised heifers was due to greater pregnancy rates and decreased development costs. A similar approach was used to evaluate the heifer development protocols in the Montana data set. Gross returns were greater for control heifers, but restricted heifers had lower feed costs. This resulted in an increase of $37.24 in net returns per developed heifer for the restricted group. Research from the University of Nebraska reports similar savings in development costs, where developing heifers on dormant winter forage resulted in a $45 savings per pregnant heifer compared with drylot development (Funston and Larson, 2011), and a similar development cost comparing 2 extensive development systems, winter range vs. corn residue (Larson et al., 2011). Studies from New Mexico, Montana, and Nebraska illustrate that restricting gain during post-weaning development by limiting DMI or developing heifers on dormant winter forage result60
ed in increased economic advantages compared with developing heifers at greater rates of ADG to achieve a greater target BW. Summary and Conclusions Developing heifers to lighter target BW may be advantageous in maintaining positive energy balance or adapting to negative energy balance through the breeding season in many range settings. Likewise, heifers developed under a range setting may be better adapted to maintain desired metabolic status during breeding than heifers reared in a pen or developed at a high rate of gain. Implications of heifer development system on cow longevity must be considered when evaluating economics of a heifer enterprise; however, studies evaluating the effects of heifer development systems on cow longevity are extremely limited. Literature Cited American Angus Association. 2012. Angus genetic trend by birth year. Available at :. (Accessed April 25, 2014). American Hereford Association. 2014. Hereford genetic trend by birth year. Available at: http://hereford.org/userfiles/F12_Trend.pdf. (Accessed April 25, 2014). American International Charolais Association. 2014. Charolais genetic trend by birth year. Available at: http://www.charolaisusa.com/pdf/2012/09.07/ GeneticTrendGraphically.pdf. (Accessed April 25, 2014). Arias, R. P., P. J. Gunn, R. P. Lemanager, and S. L. Lake. 2012. Effects of post-AI nutrition on growth performance and fertility of yearling beef heifers. Proc. West. Sec. Amer. Soc. Anim. Sci. 63:117-121. Burris, M. J., and B. M. Priode. 1958. Effect of calving date on subsequent calving performance. J. Anim. Sci. 17:527-533. Byerly, D. J., R. B. Staigmiller, J. G. Berardinelli, and R. E. Short. 1987. Pregnancy rates of beef heifers bred either on pubertal or third estrus. J. Anim. Sci. 65:645-650. Christie, M. R., M. L. Marine, R. A. French, and M. S. Blouin. 2012. Genetic adaptation to captivity can occur in a single generation. Proc. Natl. Acad. Sci. 109:238-242.
Ciccioli, N. H., S. L. Charles-Edwards, C. Floyd, R. P. Wettemann, H. T. Purvis, K. S. Lusby, G. W. Horn, and D. L. Lalman. 2005. Incidence of puberty in beef heifers fed high- or low-starch diets for different periods before breeding. J. Anim. Sci. 83:26532662. Cushman, R. A., L. K. Kill, R. N. Funston, E. M. Mousel, and G.A. Perry. 2013. Heifer calving date positively influences calf weaning weights through six parturitions. J. Anim. Sci. 91:4486-4491. Endecott, R. L., R. N. Funston, J. T. Mulliniks and A. J. Roberts. 2013. Implications of beef heifer development systems and lifetime productivity. J. Anim. Sci. 91:1329-1335. Ferrell, C. L. 1982. Effects of postweaning rate of gain on onset of puberty and productive performance of heifers of different breeds. J. Anim. Sci. 55:12721283. Foxcroft, G. R. 1997. Mechanisms mediating nutritional effects on embryonic survival in pigs. J. Reprod. Fert. Suppl. 52:47-61. Freetly, H. C. and L. V. Cundiff. 1997. Postweaning growth and reproduction characteristics of heifers sired by bulls of seven breeds and raised on different levels of nutrition. J. Anim. Sci. 75:2841-2851. Funston, R. N. and G. H. Deutscher. 2004. Comparison of target breeding weight and breeding date for replacement beef heifers and effects on subsequent reproduction and calf performance. J. Anim. Sci. 82:3094-3099. Funston, R. N. and D. M. Larson. 2011. Heifer development systems: Dry-lot feeding compared with grazing dormant winter forage. J. Anim. Sci. 89:1595-1602.
Larson, D. M., A. S. Cupp, and R. N. Funston. 2011. Heifer development systems: A comparison of grazing winter range or corn residue. J. Anim. Sci. 89:2365:2372. Lynch, J. M., G. C. Lamb, B. L. Miller, R. T. Brandt, Jr., R. C. Cochran, and J. E. Minton. 1997. Influence of timing of gain on growth and reproductive performance of beef replacement heifers. J. Anim. Sci. 87:3043-3052. Marston, T. T., K. S. Lusby, and R. P. Wettemann. 1995. Effects of postweaning diet on age and weight at puberty and milk production of heifers. J. Anim. Sci. 73:63-68. Mulliniks, J. T., D. E. Hawkins, K. K. Kane, S. H. Cox, L. A. Torell, E. J. Scholljegerdes, and M. K. Petersen. 2012. Metabolizable protein supply while grazing dormant winter forage during heifer development alters pregnancy and subsequent inherd retention rate. J. Anim. Sci. 91:1409-1416. Olson, K. C., J. R. Jaeger, and J. R. Brethour. 1992. Growth and reproductive performance of heifers overwintered in range or drylot environments. J. Prod. Agri. 5:72-76. Perry, G., J. Walker, C. Wright, and K. Olson. 2009. Impact of method of heifer development and postAI management on reproductive efficiency. Proc. Range Beef Cow Symp. XXI, pp 35-42. Roberts, A. J., T. W. Geary, E. E. Grings, R. C. Waterman, and M. D. MacNeil. 2009. Reproductive performance of heifers offered ad libitum or restricted access to feed for a one hundred forty-day period after weaning. J. Anim. Sci. 87:3043-3052.
Funston, R. N., J. L. Martin, D. M. Larson, and A. J. Roberts. 2012. Nutritional aspects of developing replacement heifers. J. Anim. Sci. 90:1166-1171.
Roberts, A. J., J. Ketchum, R. N. Funston, and T. W. Geary. 2013. Impact of number of estrous cycles exhibited prior to start of breeding on reproductive performance in beef heifers. Proc. West. Sec. Amer. Soc. Anim. Sci. 64:254-257.
Greer, R. C., R. W. Whitman, R. B. Staigmiller, and D. C. Anderson. 1983. Estimating the impact of management decisions on the occurrence of puberty in beef heifers. J. Anim. Sci. 56:30-39.
Rogers, P. L., C. T. Gaskins, K. A. Johnson, and M. D. MacNeil. 2004. Evaluating longevity of composite beef females using survival analysis techniques. J. Anim. Sci. 82:860-866.
Hall, J. B., R. B. Staigmiller, R. B. Bellows, R. E. Short, W. M. Moseley, and S. E. Bellows. 1995. Body composition and metabolic profiles associated with puberty in beef heifers. J. Anim. Sci. 73:3409-3420.
Short, R. E. and R. A. Bellows. 1971. Relationships among weight gains, age at puberty and reproductive performance in heifers. J. Anim. Sci. 32:127131. 61
THE LONG-LASTING IMPACT OF NUTRITION: DEVELOPMENTAL PROGRAMMING
Speakman, J. R. and C. Hambly. 2007. Starving for life: What animal studies can and cannot tell us about the use of caloric restriction to prolong human lifespan. J. Nutr. 137:1078-1086. Summers, A.F., S. P. Weber, H. A. Lardner, and R. N. Funston. 2014. Effect of beef heifer development system on average daily gain, reproduction, and adaptation to corn residue during first pregnancy. J. Anim. Sci. jas.2013-7225; published ahead of print March 25, 2014, doi:10.2527/jas.2013-7225. R. A. Vraspir, A. F. Summers, A. J. Roberts, and R. N. Funston. 2013. Effect of pubertal status and number of estrous cycles prior to the breeding season on pregnancy rate in beef heifers. Proc. West. Sec. Amer. Soc. Anim. Sci. 64:116-120. White, F. J., L. N. Floyd, C. A. Lents, N. H. Ciccioli, L. J. Spicer, and R. P. Wettemann. 2001. Acutely restricting nutrition causes anovulation and alters endocrine function in beef heifers. Oklahoma State University Anim. Sci. Res. Report. Oklahoma Ag. Expt. Sta. Pub. P986. Wiltbank, J. N., K. E. Gregory, L. A. Swiger, J. E. Ingalls, J. A. Rothlisberger, and R. M. Koch. 1966. Effects of heterosis on age and weight at puberty in beef heifers. J. Anim. Sci. 25:744-751. Wiltbank, J. N., C. W. Kasson, and J. E. Ingalls. 1969. Puberty in crossbred and straightbred beef heifers. J. Anim. Sci. 29:602-605.
Kimberly A. Vonnahme1 Department of Animal Sciences, North Dakota State University
Introduction Livestock producers are interested in utilizing nutrients in the most efficient way to optimize growth. Often, one tends to focus on the growth that an animal achieves after birth, however, the majority of mammalian livestock (i.e. swine, sheep, and cattle) spend 3540% of their life (i.e. from conception to consumption) within the uterus, being nourished solely by the placenta. The maternal system can be influenced by many different extrinsic factors, including nutritional status, which ultimately can program nutrient partitioning and ultimately growth, development and function of the major fetal organ systems (Wallace, 1948; Wallace et al., 1999; Godfrey and Barker, 2000; Wu et al., 2006). The trajectory of prenatal growth is sensitive to direct and indirect effects of maternal environment, particularly during early stages of embryonic life (Robinson et al., 1995), the time when placental growth is exponential. Moreover, pre-term delivery and fetal growth restriction are associated with greater risk of neonatal mortality and morbidity in livestock and humans. Offspring born at an above average weight have an increased chance of survival compared with those born at a below average weight in all domestic livestock species, including the cow, ewe, and sow. Just as growth-restricted human infants are at risk of immediate postnatal complications and diseases later in life (Godfrey and Barker, 2000), there is increasing evidence that production characteristics in our domestic livestock may also be impacted by maternal diet (Wu et al., 2006). Some of the complications reported in livestock include increased neonatal morbidities and mortalities (Hammer et al., 2011), intestinal and respiratory dysfunctions, slow postnatal growth, increased fat deposition, differing muscle fiber diameters and reduced meat quality (reviewed in Wu et al., 2006). The objective of this proceedings paper is to highlight some of our laboratoryâ€™s investigations on how maternal environment can impact fetal and placental development, impacts on uterine and/or umbilical blood flow in cattle and sheep, and potential timing of intervention, or potential therapeutics, which may increase uteroplacental blood flow.
Placental Development and Uteroplacental Blood Flow The placenta plays a major role in the regulation of fetal growth. In ruminants, the fetal placenta attaches to discrete sites on the uterine wall called caruncles. These caruncles are aglandular sites which appear as knobs along the uterine luminal surface of non-pregnant animals, and are arranged in two dorsal and two ventral rows throughout the length of the uterine horns (Ford, 1999). The placental membranes attach at these sites via chorionic villi in areas termed cotyledons. The caruncular-cotyledonary unit is called a placentome and is the primary functional area of physiological exchanges between mother and fetus. Placental nutrient transport efficiency is directly related to uteroplacental blood flow (Reynolds and Redmer, 1995). All of the respiratory gases, nutrients, and wastes that are exchanged between the maternal and fetal systems are transported via the uterus-placenta (Reynolds and Redmer, 1995, 2001). Thus, it is not surprising that fetal growth restriction in a number of experimental paradigms is highly correlated with reduced uteroplacental growth and development (Reynolds and Redmer, 1995, 2001). Establishment of functional fetal and uteroplacental circulations is one of the earliest events during embryonic/placental development (Patten, 1964; Ramsey, 1982). It has been shown that the large increase in transplacental exchange, which supports the exponential increase in fetal growth during the last half of gestation, depends primarily on the dramatic growth of the uteroplacental vascular beds during the first half of pregnancy (Meschia, 1983; Reynolds and Redmer, 1995). Therefore, an understanding of factors that impact uteroplacental blood flow will directly impact placental function and thus fetal growth. Adequate uteroplacental blood flow is critical for normal fetal growth, and therefore, not surprisingly, experimental conditions designed to investigate fetal growth retardation and placental insufficiency, be it over-nutrition, nutrient restriction, hyperthermia, or high altitude, commonly share reduced uterine and umbilical blood flows (for review see Reynolds et al., 2006). Therefore, modifying uterine blood flow and nutrient transfer capacity in the placenta allows for increased delivery of oxygen and nutrients to the exponentially growing fetus. Fowden et al. (2006) reviewed key factors affecting placental nutrient transfer capacity, which were size, nutrient transporter abundance, nutrient synthesis and metabolism, and hormone synthesis and metabolism. Discovery of novel therapeutic agents that improve placental function
would decrease the incidence of morbidity and mortality as well as suboptimal offspring growth performance in livestock species. Therapeutic agents targeting placental blood flow increased fetal growth in compromised pregnancies (Reynolds et al., 2006). There is an ever-increasing wealth of data that are demonstrating how realimentation, or other therapeutic agents, may be used to rescue at-risk pregnancies. In our laboratory, we have investigated the role that realimentation, protein supplementation, and melatonin supplementation has on uteroplacental blood flow and/or vascular reactivity of the placental arteries. In order to perform the former, we have employed the use of Doppler ultrasonography. Other methods of determining blood flow are effective, but require surgery and increased numbers of animals to determine blood flow at different time points during pregnancy because of the growth of the uterine vasculature as gestation advances. Uterine and umbilical artery cardiac cycle waveforms were plotted in Doppler mode by velocity (cm/s; y-axis) and time (s; x-axis). Fetal or maternal heart rate (beats/ min), pulsatility index (PI), resistance index (RI), and blood flow (BF) were calculated using preset functions on the ultrasound instrument. Abbreviations for the various instrument-generated functions are as follows: peak systolic velocity (PSV), end diastolic velocity (EDV), mean velocity (MnV), and cross sectional area of the vessel (CSA). Equations are as followed: PI = [PSV (cm/s) – EDV (cm/s)] / MnV (cm/s); RI = [PSV (cm/s) – EDV (cm/s)] / PSV (cm/s); blood flow (BF, mL/min) = MnV (cm/s) × cross sectional area of the vessel (cm2) × 60 s. By continuously monitoring the same animal, which has not undergone surgical manipulation, we feel that we can effectively determine how different interventions may regulate uteroplacental blood flow. Our current animal models are outlined below. Nutrient Restriction In normal pregnancies, resistance of the uteroplacental arteries have been documented to decrease as gestation advances. Our laboratory has reported that when pregnant ewe lambs are nutrient-restricted, lamb birth weight is reduced compared to control fed ewes (Swanson et al., 2008; Meyer et al., 2010). While placental weights are not different, we have demonstrated that when ewes are restricted, there is ~33% decrease in endothelial nitric oxide synthase mRNA expression on d 130 of gestation in the maternal portion of the placenta compared to control-fed animals (Lekatz et al., 2010a). We hypothesized that this reduction in birth weight was due to a greater placental vascular 63
resistance, and decreased uteroplacental blood flow in restricted ewes compared to control ewes. In order to evaluate the effects of maternal nutrient restriction on the umbilical hemodynamics, we have a model of global restriction that begins on day 50 of gestation until term (~145 days). Restricted ewes had increased (P = 0.01) PI and RI compared to control ewes (Lekatz et al., 2010a; Lemley et al., 2012). Moreover, we have demonstrated that umbilical blood flow is reduced when a nutrient restriction is applied (Lemley et al., 2012).
flow, which was decreased in restricted ewes from day 80 through 110 of gestation compared to adequately fed ewes. Moreover, at day 110 of gestation, restricted ewes had a 23% decrease in umbilical artery blood flow compared to adequately fed ewes (Lemley et al., 2012). While we are continuing our investigations into the impacts of melatonin supplementation in atrisk pregnancies, we feel that melatonin treatment may be useful in negating the consequences of intrauterine growth restriction that occur due to specific abnormalities in umbilical blood flow.
Therapeutic supplements thought to target placental blood flow and nutrient delivery to the fetus have been shown to increase fetal growth in animal models of intrauterine growth restriction (Vosatka et al., 1998; Richter et al., 2009; Satterfield et al., 2010); however, few studies have addressed uteroplacental hemodynamics in models of improved fetal growth. For instance, melatonin supplementation was shown to negate the decreased birth weight in nutrient- restricted rats (Richter et al., 2009), which was attributed to increased placental antioxidant enzyme expression in nutrient-restricted rats supplemented with melatonin. Our hypothesis was that dietary melatonin treatment during a compromised pregnancy would improve fetal growth and placental nutrient transfer capacity by increasing uterine and umbilical blood flow. The uteroplacental hemodynamics and fetal growth were determined in ewes that received a dietary supplementation with or without melatonin (5 mg) in adequately fed (100% of NRC recommendations) or nutrient-restricted (60% of control) ewes. Dietary treatments were initiated on d 50 of gestation and umbilical blood flow, as well as fetal growth (measured by abdominal and biparietal distances) were determined every 10 d from d 50 to d 110 of gestation. By d 110 of gestation, fetuses from restricted ewes had a 9% reduction (P = 0.01) in abdominal diameter compared to fetuses from adequately nourished ewes, whereas fetuses from melatonin supplemented ewes tended to have (P = 0.08) a 9% increase in biparietal diameter (Lemley et al., 2012).
In cattle, we have recently demonstrated that nutrient restriction from early to mid-pregnancy (i.e. day 30-140) does not alter uterine blood flow (Camacho et al., 2014). However, upon realimentation, the uterine artery blood flow increases in those cows that were previously restricted, but only to the horn in which the calf is housed (Camacho et al., 2014). Interestingly, it appears that realimentation alters the growth trajectory of the bovine placenta (Vonnahme et la., 2007), something that has not been investigated in the ewe. Recent data in our laboratory demonstrates that the placental artery reactivity to vasoactive agents in vitro are more responsive to vasodilators (Reyaz and Vonnahme, unpublished data), and there is an increase in capillary numbers (Mordhorst and Vonnahme, unpublished data), perhaps to allow for more nutrient uptake. The ability of the uterus-placenta to compensate upon realimentation is quite intriguing and we are continuing our studies to determine which portions of the placenta (i.e. maternal or fetal) may contribute to compensatory prenatal growth of the fetus.
We did observe a significant melatonin treatment by day interaction (P < 0.001) for umbilical artery blood flow which was increased in melatonin supplemented ewes from day 60 through 110 of gestation compared to control (no melatonin supplementation). Moreover, at day 110 of gestation melatonin supplemented ewes had a 20% increase in umbilical artery blood flow compared to control ewes. In addition, a significant nutritional plane by day interaction (P < 0.0001) was observed for umbilical artery blood 64
Protein Supplementation While the literature is now booming with increasing evidence of how nutrient restriction impairs several physiological parameters, few concentrate on enhancing postnatal growth in livestock species. In a recent series of papers in cattle, cows gestated on range (where crude protein of forage is < 6%) that were protein supplemented during late gestation had calves similar in birth weight, but had calves with increased weaning weight compared to protein unsupplemented cows (Stalker et al., 2006, Martin et al., 2007; Larson et al., 2009). It is valuable to note that the protein supplementation enhanced growth after birth. Furthermore, the pregnancy rates in heifer calves born from protein supplemented cows were enhanced compared to control cows (93 vs 80%; Martin et al., 2007). It was our hypothesis that the increased fertility and growth rate of the calves from supplemented dams may be due to enhanced uterine blood flow and/or
placental nutrient transfer. Ongoing studies in our laboratory are investigating how protein supplementation during late gestation can impact uterine blood flow. For the past 2 years we have investigated how protein supplementation (in the form of DDGS) can impact uterine blood flow. When we use DDGS with a low quality forage source, uterine blood flow is reduced compared to control cows (Mordhorst and Vonnahme, unpublished observations). Just recently, we demonstrated that when DDGS is given with a corn-stalk forage base, we increase uterine blood flow (Kennedy and Vonnahme, unpublished observations). We are investigating how specific nutrients differed between these 2 studies in order to tease apart the mechanism that may be impacting how protein influences uterine blood flow in the beef cow. In order to more fully understand the impacts of maternal protein on uteroplacental blood flow and placental vascular development, we also have used an ovine model where the diets are isocaloric, with differing levels of protein in the diet. Singleton fetuses from ewes consuming the high protein diet are heavier on d 130 of gestation compared to fetuses from ewes consuming the low protein diet, with no differences in placental weight apparent (Camacho et al., 2010). When uterine blood flow was obtained from a single time point (d 130 of gestation), ewes consuming the high protein diet had a decrease in uterine blood flow compared to the low group, with the control being intermediate (Camacho et al., 2010). This is similar to our first year protein supplementation work with beef cattle. Moreover, when investigating the ability of the fetal placental arteries to vasodilate to increasing concentrations of bradykinin, placental arteries from high protein ewes had a decreased responsiveness compared to control and low protein ewes (Lekatz et al., 2010b). Understanding if additional calories (i.e. cow study), or a greater proportion of total calories coming from protein (i.e. sheep study), needs to be elucidated, and further work is underway in our laboratory. Summary and Conclusions We hope to improve approaches to management of livestock during pregnancy which may impact not only that damâ€™s reproductive success, but her offspringâ€™s growth potential and performance later in life. Future applications of this research may be used to develop therapeutics for at-risk pregnancies in our domestic livestock. If these therapeutics can be used on-farm, producers would have the ability to increase animal health while also reducing costs of animal production. While each species is unique in its placental development and vascularity, comparative studies may
ultimately assist researchers in understanding how the maternal environmental impacts placental, and thus fetal, development. Literature Cited Camacho, L. E., Lekatz, L. A., VanEnom, M. L., Schauer, C. S., Maddock Carlin, K. R., and Vonnahme, K. A. 2010. Effects of maternal metabolizable protein supplementation in late gestation on uterine and umbilical blood flows in sheep. J. Anim. Sci. 88: E-Suppl. 2: 106. Camacho, L.E., Lemley, C.O., Prezotto, L.D., Bauer, M. L., Freetly, H. C., Swanson, K.C. and Vonnahme, K.A. 2014. Effects of maternal nutrient restriction followed by realimentation during midgestation on uterine blood flow in beef cows. Theriogenology. 81:1248-1256. Ford, S.P. 1999. Cotyledonary placenta. Encyclopedia of Reproduction. 1:730-738. Fowden, A. L., Giussani D. A., and Forhead, A. J. 2006. Intrauterine programming of physiology systems: causes and consequences. Physiology 21:29-37. Godfrey, K.M. and Barker, D. J. 2000. Fetal nutrition and adult disease. Am. J. Clin. Nutr. 71:1344S-1352S. Hammer, C. J., Thorson, J. F., Meyer, A. M., Redmer, D. A., Luther, J. S., Neville, T. L., Reed, J. J., Reynolds, L. P., Caton, J. S., and Vonnahme, K. A. 2011. Effects of maternal selenium supply and plane of nutrition during gestation on passive transfer of immunity and health in neonatal lambs. J. Anim. Sci. 89:3690-3698. Larson, D.M. Martin, J.L., Adams, D.C., and Funston, R.N. 2009. Winter grazing system and supplementation during late gestation influence performance of beef cows and steer progeny. J. Anim. Sci. 87:1147-1155.
Lekatz, L.A., Caton, J.S., Taylor, J.B, Reynolds, L.P., Redmer, D.A., and Vonnahme, K.A. 2010a. Maternal selenium supplementation and timing of nutrient restriction in pregnant sheep: Impacts on maternal endocrine status and placental characteristics. J. Anim. Sci. 88:955-971. Lekatz, L. A., Van Emon, M. L., Shukla, P. K., Oâ€™Rourke, S. T. , Schauer, C. S., Carlin K. M., and Vonnahme, K. A. 2010b. Influence of metabolizable protein supplementation during late gestation on vasoreactivity of maternal and fetal placental arteries in sheep. J. Anim. Sci. 88:E-Suppl. 2:869-870. Lemley, C.O., Meyer, A. M., Camacho, L. E. , Neville, T. L., Newman, D. J., Caton, J. S. ,and Vonnahme, K. A. 2012. Melatonin supplementation alters uteroplacental hemodynamics and fetal development in an ovine model of intrauterine growth restriction (IUGR). Amer. J. Physiol. 302:R454-467. Martin, J.L., Vonnahme, K.A. , Adams, D.C. , Lardy, G.P. , and Funston, R.N. 2007. Effects of dam nutrition on growth and reproductive performance of heifer calves. J. Anim. Sci. 85:841-847. Meschia, G. 1983. Circulation to female reproductive organs. In: Handbook of Physiology 3:241-267. Meyer, A.M., Reed, J.J., Neville, T.L. , Taylor, J.B., Hammer, C.J., Reynolds, L.P., Redmer, D.A., Vonnahme, K.A., and Caton, J.S. 2010. Effects of nutritional plane and selenium supply during gestation on ewe and neonatal offspring performance, body composition, and serum selenium. J. Anim. Sci. 88:1786-1800. Patten, B.M. 1964. Foundations of Embryology (2nd Ed) McGraw-Hill, New York. Ramsey, E.M. 1982. The Placenta, Human and Animal. Praeger, New York.
Reynolds, L.P., and Redmer, D.A. 1995. Utero-placental vascular development and placental function. J. Anim. Sci. 73:1839-1851. Reynolds, L.P., and Redmer, D.A. 2001. Angiogenesis in the placenta. Biol. Reprod. 64:1033 1040. Reynolds, L.P., Caton, J.S., Redmer, D.A., Grazul-Bilska, A.T., Vonnahme, K.A., Borowicz, P.P., Luther, J.S., Wallace, J.M., Wu, G., and Spencer, T.E. 2006. Evidence for altered placental blood flow and vascularity in compromised pregnancies. J. Physiol. 572:51-58. Richter H.G., Hansell J.A., Raut S., and Giussani, D.A. 2009. Melatonin improves placental efficiency and birth weight and increases the placental expression of antioxidant enzymes in undernourished pregnancy. J. Pineal Res. 46:357364. Robinson, J., Chidzanja, S., Kind, K., Lok, F., Owens, P., and Owen J. 1995. Placental control of fetal growth. Reprod. Fertil. Dev. 7:333â€“344. Satterfield ,M.C., Bazer, F.W., Spencer, T.E., and Wu, G. 2010. Sildenafil citrate treatment enhances amino acid availability in the conceptus and fetal growth in an ovine model of intrauterine growth restriction. J. Nutr. 140,251-258. Stalker, L.A., Adams, D.C. , Klopfenstein, T.J., Feuz, D.M., and Funston, R.N. 2006. Effects of pre- and postpartum nutrition on reproduction in spring calving cows and calf feedlot performance. J. Anim. Sci. 84:2582-2589. Swanson, T.J., Hammer, C.J., Luther, J.S., Carlson, D.B., Taylor, J.B., Redmer, D.A., Neville, T.L., Reed, J.J., Reynolds, L.P., Caton, J.S., and Vonnahme, K.A. 2008. Effects of plane of nutrition and selenium supplementation on colostrum quality and mammary development in pregnant ewe lambs. J. Anim. Sci. 86:2415-2423.
Vonnahme, K.A., Zhu, M. J., Borowicz, P. P., Geary, T.W., Hess, B. W., Reynolds, L. P., J. S. Caton, Means, W. J. and Ford, S. P. 2007. Effect of early gestational undernutrition on angiogenic factor expression and vascularity in the bovine placentome. J. Anim. Sci. 85:2464-2472. Vosatka R.J., Hassoun, P.M., and Harvey-Wilkes, K.B. 1998. Dietary L-arginine prevents fetal growth restriction in rats. Am. J. Obstet. Gynecol. 178:242-246. Wallace, L.R. 1948. The growth of lambs before and after birth in relation to the level of nutrition. J. Agric. Sci.,Cambridge. 38:243-300 and 38:367-398. Wallace, J.M, Bourke, D.A., and Aitken, R.P. 1999. Nutrition and fetal growth: paradoxical effects in the over-nourished adolescent sheep. J. Reprod. Fertil. Suppl. 54:385-399. Wu, G., Bazer, F.W., Wallace, J.M., and Spencer, T.E. 2006. Board invited review. Intrauterine growth retardation: implications for the animal sciences. J. Anim. Sci. 84:2316-2337.
PROCEEDINGS GENERAL SESSION 2: FOCUS ON THE FEEDLOT Selection for Novel Traits: An international GENOMICS perspective 1
Donagh P. Berry1 Animal & Grassland Research and Innovation Centre Teagasc, Moorepark, Ireland
Introduction Genomic selection is being heralded as the “..most promising application of molecular genetics in livestock production since work began almost 20 years ago” (Sellner et al., 2007). The objective of genomic selection is to increase the accuracy of identifying genetically elite (and inferior) animals at a younger age but also at a lower cost per animal. Genetic gain may be defined as (Rendel and Robertson, 1950): ∆G = i ∙ r ∙ σ L where ∆G is annual genetic gain; i is the intensity of selection; r is the accuracy with which you know the genetic merit of each animal, σ is the genetic standard deviation (i.e., the square root of the genetic variance or simply just a measure of the genetic differences among animals), and L is the generation interval. Genomic selection attempts to alter i, r and L. It may also influence the detected genetic variation. Genomic selection, however, does not necessarily improve all three components simultaneously as it may reduce the accuracy of selection (i.e., r) compared to traditional methods but reduce the generation interval (i.e., L) proportionally more thereby increasing annual genetic gain. Because the cost of “testing” a young bull with genomic selection is approximately 0.3% (i.e., 0.003) the cost of progeny testing the selection intensity can be increased considerably thus advancing genetic gain. Genomic selection (and genomics in general) is particularly advantageous for traits that are: • Sex linked (e.g. milk yield and female fertility) • Take a long time to measure (e.g., cow longevity) • Exhibit low heritability (e.g., female fertility) • Difficult and/or expensive to measure (i.e., novel traits like feed intake complex, meat quality) 68
Genomic selection (GS) has been successfully implemented into national dairy cattle genetic evaluations in many countries since 2009 (Spelman et al., 2013). Retrospective analysis (McParland et al., 2014) signifies that GS is up to 29% more accurate at predicting an animal’s true genetic merit (based on progeny performance) compared to just parental average. However, the breeding structures of dairy and beef are quite different and this has implications for the successful implementation of genomic selection in beef but also the justification for international cooperation, especially for novel traits. The objective of this article is to discuss the potential for international collaboration in genomics in beef cattle; although examples will be given for novel traits the relevance of the discussion is applicable to all traits although the marginal benefit is greatest for novel traits where the population of phenotyped and genotyped animals may be smaller (discussed later). Differences between Dairy and Beef Breeding Structures and Implications for Genomic Selection Many differences exist between dairy and beef breeding structures so therefore the approaches applied todate in dairy cattle may not be directly applied in beef, although there are obvious similarities. Breed. One breed (i.e., Holstein-Friesian) predominates the dairy cattle populations in most developed countries making it relatively easy and inexpensive to develop large informative reference populations for the generation of accurate genomic predictions. It is now well known that the stronger the genomic relationship between the reference population of genotyped and phenotyped animals with the candidate animals, the greater will be, on average, the accuracy of genomic predictions (Habier et al., 2007; Pszczola et al., 2012). Accurate across-breed genomic predictions have to-date been elusive (Karoui et al, 2012; Berry 2012) in cattle. Figure 1 shows a genome wide association study for direct calving difficulty in Irish Holstein and Charolais animals. The scoring system for calving difficulty is the same across both breeds and the genetic evaluations are across breeds. A genomic region with a large association (2.49% of genetic variation) with calving difficulty was detected on chromosome 18 in the Holstein-Friesian population and, although these SNPs were also segre-
gating in the Charolais population, no association was detected in this region of the genome. Similarly a genomic region associated with calving difficulty in Charolais (3.13% of the genetic variation) was detected on Chromsome 2 but not in Holstein-Friesians despite the SNPs segregating in both populations. Moreover, the sign of the allelic effects for 50% of SNP differed when estimated in either the Holstein-Friesian population or the Charolais population. This is likely due to differences in linkage phase between breeds and background polygenic effects and is undoubtedly a contributor to the sometimes observed negative correlations between genomically predicted EPDs and progeny-based EPDs when the population being tested is not adequately represented in the genomic reference population. This difference between dairy and beef and the current inability for genomic algorithms and genomic information to be useful for acrossbreed genomic evaluation implies that each breed has to generate (and therefore incur the cost) of generating its own reference population. The same is true for novel traits implying a large cost for each country to implement and genomic selection program.
Effective Population Size. The effective population size globally of Holstein-Friesians is likely to be somewhere between 40 and 100 (McParland et al., 2007; Saatchi et al., 2011). The global effective population size of beef breeds is likely to be larger (McParland et al., 2007; Saatchi et al., 2011) given the vast differences in breeding policies implemented in the different populations. The accuracy of genomic predictions is a function of the size of the reference population, the heritability of the trait under investigation, and the effective population size of the population (Daetwyler et al., 2008). Larger effective population sizes require larger reference populations to achieve the equivalent accuracy of genomic predictions compared to populations with smaller effective population sizes. The number of independent genomic segments is likely to vary with effective population size. The number of independent loci (Me) in a 30 Morgan genome can be derived deterministically for a range of different effective population sizes as (Goddard, 2009): M e=
2Ne L Log10 (4Ne L)
where Ne is the effective population size and L is the length of the genome in Morgans. The number of animals (N) required to achieve a given accuracy (i.e., square root of the reliability) can then be derived as (Calus et al., 2012): N=
s Figure 1. Manhattan plots of the single nucleotide polymorphisms associated with direct calving difficulty in 770 Holstein-Friesian (Top figure) and 927 Charolais (bottom figure) (Purfield et al., 2014).
r 2 Me h2 (q2 - r2)
Where q2 is the proportion of genetic variance captured by the SNPs (here assumed to be 0.8) and h2 is the heritability of the traits (here assumed to be 0.20). Figure 2 illustrates the number of animals that need to be phenotyped and genotyped to achieve a given accuracy for different effective population sizes. The larger the effective population size the larger the dataset of phenotyped and genotyped animals that is required to achieve an equivalent accuracy of genomic predictions compared with populations with smaller effective population sizes.
Therefore, a large population of phenotyped and genotyped animals will be required to achieve an acceptable accuracy of genomic predictions.
Figure 2. Number of animals that need to be both genotyped and phenotyped to achieve different levels of accuracy (i.e., square root of the reliability) when the effective population size is 50 (solid line), 100 (long-dashed line), 200 (shorter dashed line) and 300 (smallest dashed line) Greater Usage of AI in Dairy. In general, there is a greater usage of AI in dairy cattle than in beef. The accuracy of an animalâ€™s EPD from traditional genetic evaluations increases with increasing quantity of progeny records; therefore the accuracy of progeny tested bull can be very high. Because the heritability statistic measures the strength of the resemblance between the phenotypic value of an animal and its true genetic merit, the effective heritability of high accuracy EPDs is close to unity. Figure 3 illustrates the number of genotyped and phenotyped animals required to achieve different accuracy levels of genomic predictions. Clearly to achieve the same accuracy of genomic predictions, less genotyped and phenotyped animals are required for higher heritability traits (or animals with higher accuracy EPDs). Dairy cattle genomic breeding programs firstly focused on the genotyping of thousands of AI progeny tested bulls because of their greater accuracy and thus greater effective heritability. Using this approach for a trait with a heritability of 0.20, 7903 genotyped animals with own performance records would be equivalent to 1756 bulls (i.e., less than one quarter) with an EPD accuracy (i.e., square root of reliability) of 0.95. Hence, all else being equal, the implementation of genomic selection in beef where less high reliability sires exist will be considerably more expensive than in dairy. Collaboration can help reduce this cost. Because novel traits do not generally tend to be measured on large populations of animals, the generation of high accuracy EPDs for a large population of sires is generally not achievable. 70
Less Phenotypes and Parentage Recording in Beef. Accurate recording of detailed phenotypes on large populations of commercial animals is generally the norm in most dairy cow populations. Furthermore, parentage of most dairy females is known facilitating accurate EPDs of their pedigree. Although phenotypic recording exists in many beef populations it is, however, lacking (for some traits at least) in some populations. As alluded to previously, genotypes from animals with high accuracy EPDs can be more informative than genotypes of animals with lower accuracy EPDs. Onestep genomic procedures will not alleviate this issue as animals with non-recorded pedigree will still have to be genotyped to allocate the animal to its pedigree. Ultra-low cost genomic tools for parentage assignment may aid in allocating animals to parents and thus increase the accuracy of traditional genetic evaluations for some animals. Lack of pedigree information and phenotypes is generally not of concern for animals with novel phenotypes since if the resources are being expended in generating the phenotypes then the pedigree is usually also recorded. Lack of Participation in International Genetic Evaluations. Many genomic evaluations in dairy cattle, including Ireland, operate a two-step procedure where
Figure 3. Number of phenotyped and genotyped animals that are required to achieve an accuracy of genomic prediction of 0.4 to 0.8 (length of dashes decrease and the accuracy increases); calculations are based on the assumption of 1000 independent genomic regions and the genomic markers explaining 80% of the total genetic variance.
derivatives of EPDs are used as input phenotypes for the development of the genomic prediction equations. Many countries, especially those with a small breeding program, exploit MACE evaluations generated by INTERBULL. Therefore, phenotypic information is available on bulls even if they do not have any daughter performance records in that country. The level of participation of beef breeds in international genetic evaluations is less although initiatives such as BreedPlan and INTERBEEF as well as pan-American are underway. If participating in international genetic evaluations the extent of genotype-by-environment interactions should be quantified and the appropriate approach taken thereafter in the genetic evaluation. To estimate precise genetic correlation between populations, good genetic connectedness is needed (Berry et al., 2014b). This is particularly true for novel traits which tend to be mueasured in research herds. Type of International Genomic Cooperation Initiatives in Genomics Several alternative strategies of international cooperation in genomics exist and some of these are briefly discussed. It should be noted that there is widespread international collaboration among dairy populations both in the sharing of genotypes and phenotypes. This is despite the points previously raised to that genomic selection in dairy is arguably considerably easier (i.e., less expensive) than in beef. Sharing of Information on What Animals Have Been Genotyped. One of the easiest and least controversial approaches to achieving useful international collabo-
ration in the selection of novel traits is the sharing of information on what animals have been genotyped and on what genotyping panel. An example of the current international list compiled (currently only participated in by Ireland, the UK, France and Australia) is in Table 1; Australian information was deleted to fit in the page as were several other columns with bull aliases. Moreover, whether DNA is available or if the bull is of particular importance in a country for genotyping can be noted. The complete list can be obtained from the author (firstname.lastname@example.org). Furthermore, requests to join the list can be directed to the author. What is immediately obvious from Table 1 is that already some bulls have been genotyped more than once representing a squandering of funds. Figure 4 outlines the number of dairy bulls that were genotyped more than once up to the year 2010 across 10 different countries. Almost 700 genotypes were genotyped more than once; each genotype at the time cost approximately â‚Ź160 implying a squandering of over â‚Ź110,000. Advantages: Ability to identify animals that have already been genotyped and thus engage with sharing of genotypes to avoid duplication of genotyping; no competitive advantage is gained by genotyping the same animal twice Disadvantages: I cannot think of any disadvantage other than for some unknown reason not wanting others to know what animals have been genotyped in a given population. Information on what dairy animals are genotyped is generally freely available. Which dairy animals are genotyped and on what genotype platform in the US is freely available at https://www.cdcb.us/eval. htm
Table 1. A small section of the international list of beef bulls genotyped ANIMAL_NAME
types benefit the exporting country more. The greater the genomic relationships between the reference population and the candidate population the greater, on average, will be the accuracy of the genomic predictions (Habier et al., 2007; Pszczola et al., 2012). Therefore, if the back-pedigree of animals being exported into a country exists within that country’s reference population (assuming they also have phenotypic measures such as EPDs from pedigree or other descendants) then the accuracy of genomic predictions for those candidate animals will be greater. Hence the sharing of genotypes benefits both countries; the exporting country receives more accurate genomic proofs on their candidate bulls while the importing country gains access to potentially genetically elite individuals.
Figure 4. Presentation by Donagh Berry at the INTERBULL business meeting in 2010 on the number of animals that had been genotyped more than once across ten participating countries. The cost per animal genotype at that time was €160
Figure 5. Number of bull genotypes included in the Irish genomic selection reference population for each year which were genotyped by Irish funding (black bars) or obtained through bilateral sharing (diagonal bars) 72
Figure 6. Mean animal allele concordance rate for Illumina Bovine50 Beadchip genotypes across different paternal half-sib progeny group sizes. Errors bars represent the, within animal, lowest and greatest mean concordance rate. Also represented (diamonds) is the mean animal allele concordance rate for a subset of the data across different parental half-sib progeny groups sizes when the paternal grand sire’s genotype is also available (Berry et al., 2014a). Sharing of genotypes is also advantageous even if the animal has no phenotype in the country. This is because the genotype of a non-genotyped animal (but potentially with a phenotype) can be imputed from its progeny genotypes. Figure 6 shows that parental alleles can be imputed with, on average, ≥96% accuracy if genotypes on ≥5 progeny are available (Berry et al., 2014a). Sharing of different genotype platforms (i.e., different densities or different commercial providers) can also relatively easily be facilitated through imputation across genotyping panels (Druet et al., 2010; Berry et al., 2014c). Furthermore, access to genotypes of animals even with no phenotypes can be used to improve the accuracy of imputation of their descendants or pedigree genotyped on lower density panels. The precedence already exists in the sharing of genomic information. Sharing of genotypes in dairy is occurring among many populations. The 1000 bulls’ genome project has collated to-date sequence data from over 1000 dairy and beef bulls. Furthermore, several thousand SNP and microsatellite genotypes were collated for the development of algorithms to convert SNP data to microsatellite data for parentage testing (McClure et al., 2013). This approach has benefited the en-
tire global cattle industry by eliminating the necessity to SNP genotype animals already genotyped back-pedigree for microsatellite. The introduction of this tool in Ireland in March 2013 has already saved the beef industry €200,000 by not having to re-genotype back-pedigree. Ireland is moving to parentage testing using just SNP data in both dairy and beef cattle. The advantage of this approach is that the SNPs, if undertaken as part of a larger panel, can also be used for genomic selection. Also, in Ireland the custom genotyping panel includes almost 2,000 research SNPs which can be used to validate in an independent commercial population. This can be particularly useful to elucidate, at no cost, if any SNPs strongly associated with novel traits are also antagonistically correlated with other performance traits in commercial populations. Sharing of genotypes of young bulls can be of particular benefit if the genotypes are run through each country’s genomic prediction equations and the genetically elite animals identified and subsequently imported. Such an approach benefits the exporter (sells the germplasm) and importer (access to genetically elite germplasm). However as previously alluded to, this approach is best achieved if the genotypes and phenotypes of the back-pedigree of these candidate animals are already in the importing country’s genomic reference population. At the very least the genotypes of each animal for the SNP parentage panel should be available without restriction. This panel cannot be used in genomic selection but is extremely useful in parentage testing. An example of a document that could be used in the bilateral sharing of genotypes is given in Appendix I. Advantages: The reference population size can be increased dramatically; in the case of the Irish dairy genomic selection breeding program, the size of the reference population was increased 300% (Figure 5). This will increase the accuracy of genomic evaluations (Figure 3). The marginal benefit of additional genotypes is greater when the reference population is smaller (Figure 3) as is usually the case for novel traits. For the (larger) exporting country the accuracy of genomic predictions on their candidate animals in the importing country is, on average, expected to be greater. The approach of sharing of genotypes should not be construed as an approach to facilitate the generation of genomic breeding values for bodies that decided not to invest in genomic selection; it involves bilateral sharing so there
must therefore be (equal) investment. Genotypes can also be used to achieve more accurate imputation from lower density panels. Disadvantages: Populations with a very large reference population may have little to gain from sharing of genotypes if they already have most of the other available genotypes in their reference population, Furthermore, the marginal benefit of additional genotypes in a reference population diminishes as the size of the actual reference population increases (Figure 3). There is still however a marginal benefit of additional genotypes on phenotyped animals even with many thousands of animals in the reference population. There is sometimes a perception that genotype sharing should not be undertaken because it was expensive to generate the population; however it is usually exactly the same expense for the other country assuming equal numbers of genotypes are shared. Sharing is a less expensive way to achieving a large reference population. Sharing of Phenotypes. Many dairy cow population share phenotypic information through the international genetic evaluation at INTERBULL. Some beef populations also share phenotypic data via INTERBEEF, Breedplan and Pan-American initiatives. Advantages: access to “phenotypes” on a large population of animals which increases the accuracy of genomic prediction with the marginal benefit being greatest when the reference population is relatively small as is usually the case for novel traits. The sharing of phenotypes and genotypes can also be used as an independent population to evaluate the precision and robustness of developed genomic selection algorithms. This is particularly important for novel traits where the population size is small. Disadvantage There is background intellectual property associated with the generation of phenotypes, especially for novel traits and acquiring such phenotypes are usually costly. There is therefore reluctance among some to provide these data free of charge to others. To overcome this however the approach described previously on equal exchange of genotypes could be imposed for phenotypes. This however is only sensible if undertaking univariate (within country) genetic/genomic evaluations and excluding phenotypes from a multi-population evaluation would not be recommended. Again to overcome this, a price per unit phenotype could be generated; this could be relatively easily achieved using selection index theory. Then a value on each population’s 73
correlations with other populations could be generated. The consortium may purchase these phenotypes or may pay an annual licensing fee to have access to these data for use in the multi-population evaluation. The price paid per population will differ based on the information content of the phenotypes (i.e., coheritability between populations) but also on how much that population is also contributing to the database of phenotypes. Sharing of Genomic Keys. Collection of novel traits is generally a costly exercise and therefore the number of traits collected is usually limited. For example a population may deeply phenotype for one health trait but not for others. Sharing of genomic keys among populations that have phenotyped for a different suite of novel traits could provide potentially useful information on the likely correlated responses in other (not measured) novel traits. The validity of genomic keys from other populations could be relatively easily tested by phenotyping a smaller number of animals and relating their phenotypic values to those predicted from the shared genomic keys. Furthermore, visibility on the genomic keys from other population could help inform genomic prediction algorithms in that population for the same phenotype and therefore place greater emphasis on genomic regions detected as significant from more than one populations. Combined genomic keys can also provide a greater in depth knowledge of the underlying biological pathways governing differences in performance facilitating more powerful biological pathway analysis. Advantages: Could remove the necessity to phenotype for all novel traits possible but could also be useful in elucidating the genetic merit of a population for example for diseases that currently do not exist in that population. Disadvantages: There may be discontent in the sharing of genomic keys that required considerable resources to generate. Agreements can be put in place a priori on either selling the genomic keys or direct sharing (with some financial remuneration if differences exist between populations in the size of the reference population of the cost of generating the phenotypes). Pan International Bull List to Increase Connectedness. Genetic connectedness is fundamental for the estimation of precise genetic correlations among populations (Berry et al., 2014b). There may be an advantage of generating a small list of bulls (varying every 74
year with some crossreference bulls across years) that should be used in different populations to improve connectedness. Connectedness algorithms could be used to identify populations that could benefit most from such an initiative; such algorithms are commonly used to improve connectedness between flocks in sheep (Fouilloux et al., 2008). Such an initiative is particularly important for novel traits which tend to be recorded mainly in research herds; thus a pan-global list of bulls for recommended use in research herds could be generated. Only a few progeny need to be generated thereby having a likely minimal impact on the objectives of the research projects. Advantages: More precise estimates of genetic correlations among populations necessary for inclusion in multi-trait genomic evaluations across populations to increase the accuracy of genomic predictions Disadvantages: Could be difficult to reach a consensus on such a list of bulls given the likely different breeding objectives in different populations and may reduce the statistical power of the experiment. Validation or Fine-mapping of Putative Causal Variants. Genomic selection requires the continual regeneration of genomic predictions including more recent generations of phenotyped animals in the prediction process. Moreover, for novel traits generally measured in research herds, the genomic relationships between these animals and the candidate population may be low; the weaker the genomic relationship between the animals in the reference population and the candidate population the lower will be the accuracy of genomic predictions (Habier et al., 2007; Pszczola et al., 2012). The reason the accuracy of genomic predictions is expected to decline over generations is due to recombination during meiosis because the SNPs exploited in genomic predictions are very unlikely to be the causal mutation and thus the linkage disequilibrium between the genotyped SNP and the true causal mutation can break down during meiosis. Hence, many research projects are engaged with attempted to locate the actual causal mutation thereby avoiding the necessity to continually re-estimate the genomic prediction equations. To facilitate the discovery of causal mutations, a very large population of animals is required to ensure adequate statistical power. Therefore, few, if any, animals exist to validate the discoveries or fine-map the genomic regions further. Different populations tend to have different linkage phases so therefore using alternative
populations could be extremely beneficial in fine-mapping further and eventually identifying the causal mutation if segregating in the validation population. Advantage: Detection of the causal mutation or mutations in very strong linkage disequilibrium with the causal mutation should increase the accuracy of genomic predictions (especially across breeds) and the
predication accuracy will be less subjected to erosion over generations. Disadvantage: Many think they will make millions out of patenting of causal mutations. A good example of how this does not always materalise is the K232A polymorphism in DGAT1 which has a very large effect
Table 2. Number of lactations (N) as well as the mean, genetic standard deviation, heritability (h2) and repeatability (t) of dry matter intake in all countries (i.e., all countries) or each individual country.
Country Cows All Canada Denmark Germany Iowa Ireland Netherlands UK Wisconsin Australia Heifers Australia New Zealand
Mean σg (kg DM/day) (kg DM/day)
t 0.66 (0.01) 0.46 (0.06) 0.62 (0.04) 0.84 (0.05)
10,641 411 668 1141 398 1677 2956 2840 447 103
19.7 22.2 22.1 20.2 23.5 16.7 21.4 17.4 24.9 15.6
1.13 1.01 1.48 0.64 1.48 0.88 1.15 1.07 0.90
0.27 (0.02) 0.11 (0.11) 0.46 (0.12) 0.16 (0.06) 0.58 (0.12) 0.29 (0.07) 0.38 (0.04) 0.30 (0.06) 0.19 (0.13)
0.39 (0.08 0.25 (0.07)
0.64 (0.02) 0.54 (0.03) 0.72 (0.02)
Table 3. Genetic correlations (below diagonal; standard errors in parenthesis) between dry matter intake measured in groups of countries1 as well as the number of sires common (above diagonal; sires plus maternal grandsires in common in parenthesis) between the groups of countries. Region North-America EU high-input EU low-input Grazing North-America 39 (72) 4 (10) 6 (8) EU high-input 0.76 (0.21) 125 (144) 23 (28) EU low-input 0.79 (0.38) 0.84 (0.14) 4 (4) Grazing 0.14 (0.43) 0.33 (0.20) 0.57 (0.43) 1 North-America = Iowa + Wisconsin + Canada; EU-high input = Netherlands+Germany+Denmark+high input feeding treatment in the UK; EU-low input= low input feeding treatment in the UK; Grazing = Ireland + Australia;
on milk production traits in dairy cattle (Berry et al., 2010). Royalties must be paid if this extremely large genomic effect is to be used in a breeding program but to my knowledge few, if any bodies actually exploit this mutation in their breeding program. There is a growing consensus that discovered causal mutations should be published in the scientific literature. An alternative it to retain the mutation as a trade secret within the company that made the discovery. The downside of this approach is that others may detect the mutation in the near future and publish it. Case Study of International Genetic and Genomic Evaluations in Dairy Cows for a Novel Trait– Dry Matter Intake Despite the large contribution (~60%) of feed to the variable costs of production in dairy cattle systems (Ho et al., 2005; Shalloo et al., 2004), feed intake is currently not explicitly included in the breeding goal of any dairy cattle population. This omission is principally due to an absence of sufficient feed intake information to estimate breeding values of individual animals. Collation of international data on feed intake and associated information from research herds and nucleus breeding herds is one approach to increase the quantity of feed intake data available for estimation of breeding values. This was the motivation of the global Dry Matter Intake initiative (gDMI) participated in by 9 countries. A total of 224,174 feed intake test-day records from 10,068 parity one to five records from 6,957 Holstein-Friesian cows, as well as records from 1,784 growing Holstein-Friesian heifers were collated from 9 countries in the US, Europe and Austral-Asia. Animal and back-pedigree genotypes were also pooled (Pryce et al., 2014) with the aim of undertaking an international genomic evaluation for feed intake which is still being researched (de Haas et al., 2014). Genetic parameter estimates for the different populations are in Tables 3 and 4, respectively (Berry et al., 2014b). Of less specific interest here are the actual results, but the point being made is that nine countries understood that the only way to achieve accurate genomic predictions for this novel trait was to pool their respective datasets. The same approach can be easily applied to other novel phenotypes or to different breeds. For example three countries pooled information on milk quality to derive more accurate rapid predictors of these novel traits from infrared spectroscopy in milk (Soyeurt et 76
al., 2011); could a similar approach be adopted in the prediction of meat quality using rapid measures? The number of countries participating in prediction of milk quality initiative has since more than doubled. This is because the benefit of collaborating far outweighs the benefits of not. Four Simple Steps Required for International Collaboration in Genotype Sharing to Happen! 1. Decide whether or not you want accurate genomic predictions for your breed or population – if so then international collaboration is the best approach to achieve this 2. Email Donagh.email@example.com if you are willing to participate in a publically available excel spreadsheet on what animals are genotyped in your population and on what genotyping platform 3. You can also let it be known whether or not you are willing to share genotypes, either bilaterally or multilaterally. 4. Exchange genotypes Literature Cited Berry, DP. 2012. Across-breed genomic evaluations in cattle. European Association of Animal Production. Bratislava, Slovakia, 27-31 August 2012. P 127. Berry, D.P., McParland, S., Kearney, J.F., Sargolzaei, M., and Mullen, M.P. 2014a. Imputation of un-genotyped parental genotypes in dairy and beef cattle from progeny genotypes. Animal (in press). Berry, D.P., Coffey, M.P., Pryce, J.E., de Haas, Y., Løvendahl, P., Krattenmacher, N., Crowley, J.J., Wang, Z., Spurlock, D., Weigel, K., Macdonald, K, and Veerkamp, R.F. 2014b. International genetic evaluations for feed intake in dairy cattle through the collation of data from multiple sources. J. Dairy Sci. (in press). Berry, D.P., Howard, D., O’Boyle, P., Waters, S., Kearney, J.F. and McCabe, M. 2010. Associations between the K232A polymorphism in the diacylglycerol-O-transferace 1 (DGAT1) gene and performance in Irish Holstein-Friesian dairy cattle. Irish J. of Agric. & Food Res. 49:1-9.
Berry, D.P., McClure M.C., and Mullen, M.P. 2014c. Within and across-breed imputation of high density genotypes in dairy and beef cattle from medium and low density genotypes. J. Anim. Breed. Gen. (in press). Calus, M.P.L., Berry, D.P., Banos, G., de Haas, Y. and Veerkamp, R.F. 2013. Genomic selection: the option for new robustness traits?. In: Advances in Animal Biosciences, Cambridge University, 4 : 618-625. Daetwyler H.D., Villanueva B. and Woolliams J.A. 2008. Accuracy of predicting the genetic risk of disease using a genome-wide approach. PLoS One 3, e3395. de Haas Y., Pryce, J.E., Berry, D.P., and Veerkamp, R.F. 2014. Genetic and genomic solutions to improve feed efficiency and reduce environmental impact of dairy cattle. Proc. World Cong. on Gen. Appl. to Livest. Prod. Vancouver. August 2014. Druet, T., Schrooten, C., and de Roos A.P. 2010. Imputation of genotypes from different single nucleotide polymorphism panels in dairy cattle. J. Dairy Sci. 93:5443-5454. Fouilloux, M.N., Clément, V., and Laloë, D., 2008. Measuring connectedness among herds in mixed linear models: From theory to practice in largesized genetic evaluations. Genet. Sel. Evol. 40:145159. Goddard, M 2009. Genomic selection: prediction of accuracy and maximization of long term response. Genetica 136: 245–257.
Saatchi M., McClure M.C., McKay S.D., Rolf M.M., Kim J., Decker J.E., Taxis T.S., Chapple R.H., Ramey H.R., Northcutt S.L., Bauck S., Woodward B., Dekkers J.C.M., Fernando R.L., Schnabel R.D., Garrick D.J. and Taylor J.F. 2011. Accuracies of genomic breeding values in American Angus beef cattle using K-means clustering for cross-validation. Genet. Sel. Evol. 43:40. McClure, M.C., Sonstegard, T.S., Wiggans, G.R., Van Eenennaam, A.L., Weber, K.L., Penedo, C.,T., Berry, D.P., Flynn, J., Garcia, J., Carmo, A.S., Regitano, L.C.A., Albuquerque, M., Silva, M.V.G.B., Machado, M.A., Coffey, M., Moore, K., Boscher, M.Y., Gene 2013. Imputation of microsatellite alleles from dense SNP genotypes for parentage verification across multiple Bos taurus and Bos indicus breeds. Frontiers in Genet. 4 : 1-11. McParland, S., Berry, D.P. and Kearney, J.F. 2014. Retrospective analysis of the accuracy of genomic selection in Irish dairy cattle. Proc. Irish Agric. Res. Forum. March 10-11, 2014. Tullamore, Ireland. McParland, S., Kearney, J.F., Rath, M. and Berry, D.P. 2007. Inbreeding trends and pedigree analysis of Irish dairy and beef cattle populations. J. Anim. Sci. 85:322-331. Prendiville R and McHugh, N. 2014. Comparative live weight, body condition score at breeds, onset of puberty and age at first calving for heifers of high and low maternal Index. Proceedings of the Irish Agricultural Research Forum. March 10-11, 2014. Tullamore, Ireland. Pp117.
Ho, C., Nesseler R, Doyle P, Malcolm B. 2005. Future dairy farming systems in irrigation regions. Austral. Farm Bus. and Manage. J. 2: 59–68.
Pryce, J.E., Johnston J, Hayes B.J., Sahana G, Weigel K.A., McParland S., Spurlock D., Krattenmacher N., Spelman R.J., Wall E., Calus M.P.L. 2014. Imputation of genotypes from low density (50,000 markers) to high density (700,000 markers) of cows from research herds in Europe, North America, and Australasia using 2 reference populations. J. Dairy Sci. 97: 1799-1811
Karoui S., Carabaño M.J., Díaz C. and Legarra A. 2012. Joint genomic evaluation of French dairy cattle breeds using multiple-trait models. Genet. Sel. Evol. 44:39.
Pszczola M., Strabel T., Mulder H.A. and Calus M.P.L. 2012. Reliability of direct genomic values for animals with different relationships within and to the reference population. J. Dairy Sci. 95: 389–400.
Habier D., Fernando R. and Dekkers J. 2007. The impact of genetic relationship information on genome-assisted breeding values. Genetics 177: 2389–2397.
Purfield D.C., Bradley, D.G, Evans R.D., Kearney, J.F. and Berry, DP. 2014. Genome-wide association study for calving performance using high density genotypes in dairy and beef cattle. BMC genomics. (submitted). Rendel J., and Robertson A. 1950. Estimation of genetic gain in milk yield by selection in a closed herd of dairy cattle. J. Genetics. 50:1-8. Sellner, E. M., J.W. Kim, M.C. McClure, K.H. Taylor, R.D. Schnabel and J.F. Taylor. 2007. Applications of genomic information in livestock. J. Anim. Sci. 85 : 3148-3158. Shalloo L., Dillon P., Rath M. and Wallace, M. 2004. Description and validation of the Moorepark Dairy Systems Model (MDSM). J. Dairy Sci. 87:19451959. Soyeurt, H., Dehareng, F., Gengler, N., McParland, S., Wall, E., Berry, D.P. and Coffey, M. 2011. Mid-infrared prediction of bovine milk fatty acids across multiple breeds, production systems, and countries. J. Dairy Sci. 94: 1657-1667. Spelman, R., Hayes, B.J. and Berry, D.P. 2013. Use of molecular technologies for the advancement of animal breeding: Genomic selection in dairy cattle populations in Australia, Ireland and New Zealand. Anim. Prod. Sci. 53: 869-875.
Agreement between BODY1 NAME BODY1 ADDRESS BODY1 ADDRESS BODY1 ADDRESS
hereinafter referred to as “XXXXXX”
and BODY2 NAME BODY2 ADDRESS BODY2 ADDRESS BODY2 ADDRESS
hereinafter referred to as “YYYYYY”
Agreement dated this [insert date] and continuing until this [insert date + three years] or until terminated under guidelines in Article 5.
1 Purpose of the agreement XXXXXX and YYYYYY agree to collaborate in the area of genomic evaluation and selection namely through: • • •
exchanging information about methods used for genomic evaluation and selection in cattle, exchange of animals genotypes to avoid multiple genotyping of the same animals, and exchange of genotypes of animals in general.
2 Exchange of information about methods used for genomic evaluation and selection (a)
XXXXXX and YYYYYY agree to exchange the following information solely for the purposes referred to herein (the Purpose): i. their respective methods used for genomic evaluation and selection in cattle; ii. animal genotypes and the identity of animals proposed to be genotyped; iii. the estimation of effects at the single nucleotide polymorphisms (SNP) and the conclusions derived for the corresponding breeding program; and iv. such other information as is referred to herein (the Confidential Information)
The Confidential Information exchange will take place on a regular basis at the conferences and other forums as agreed between the parties.
For the avoidance of doubt any exchange of Confidential Information specifically excludes a license to a software package that one party may use to implement genomic evaluation or selection.
Nothing in this agreement shall be construed as assigning or otherwise transferring any proprietary rights including Intellectual property rights in a party’s Confidential Information to the other party.
3 Confidentiality (a)
All Confidential Information given by a party to the other party under the terms of this agreement is valuable information of the disclosing party and the receiving party undertakes to keep the Confidential Information secret and to protect and preserve the confidential nature and secrecy of the Confidential Information.
A receiving party: i. must not disclose Confidential Information of the disclosing parties to any person except as permitted under this Agreement; ii. must not permit unauthorised persons to have access to the disclosing party’s Confidential Information; iii. must not make or assist or permit any person including its officers and employees, agents or advisors (Representatives) to make any unauthorised use, disclosure or reproduction of the disclosing Party’s Confidential Information iv. must take reasonable steps to enforce the confidentiality obligations imposed or required to be imposed by this Agreement and must co-operate with the disclosing party in any action that it may take to protect the confidentiality of the Confidential Information disclosed under this Agreement.
A receiving party must only use the disclosing party’s Confidential Information for the Purpose and must only disclose Confidential Information to its Representatives for the conduct of the Purpose and then only on a need to know basis.
Each party must ensure that its Representatives do not do or omit to do anything which if done or omitted to be done by the receiving party would be a breach of the receiving party’s obligations under this agreement.
4 Avoid multiple genotyping of the same animals XXXXXX and YYYYYY will use the same genotyping technology and platform, namely the same SNP-chip. Currently, the Illumina Bovine SNP 50™ BeadChip and the Illumina Bovine SNP HD™ BeadChip will be used. XXXXXX and YYYYYY will exchange the identity of the animals they have or plan to genotype.
5 Exchange of animals genotypes (a)
XXXXXX is granted the right to obtain genotypic information of genotyped sires from YYYYYY and YYYYYY are granted the right to obtain genotypic information of genotyped sires from XXXXXX. Each party shall exchange approximately equal numbers of genotyped sires to the other. Each party, XXXXXX and YYYYYY, will retain ownership of the genotyping information they provided.
The genotyping information XXXXXX obtains from YYYYYY may be used by XXXXXX for genetic evaluation in the COUNTRY base and scale and selection purposes only. All results and products originating from genotyping information obtained from YYYYYY belong to XXXXXX.
The genotyping information YYYYYY obtains from XXXXXX may be used by YYYYYY for genetic evaluation in the COUNTRY base and scale and selection purposes only. All results and products originating from genotyping information obtained from XXXXXX belong to YYYYYY.
XXXXXX and YYYYYY may extract the genotype for parentage testing SNPs and provide these to third parties for the purpose of validating parentage of animals in their respective cattle populations.
6. Termination (a)
XXXXXX and YYYYYY have the right to terminate this agreement by giving 3 months written notice to the other party.
Termination of this agreement will be without prejudice to any other rights and remedies of the parties arising out of any default which occurs before the termination and will be without prejudice to any claim for money payable at the time of termination in respect of work done, genotyping information exchanges or liabilities incurred before the termination.
Upon termination or expiration of this agreement, or the request of the disclosing party, the receiving party will deliver to the disclosing party (or with the disclosing partyâ€™s prior consent, destroy or erase); i. all material forms of the other partyâ€™s Confidential Information (including biological or other samples) in its possession or the possession of any of its Representatives; and ii. a statutory declaration made by an authorised officer of the party declaring that the provisions of this article have been complied with.
Return of material forms of Confidential Information does not release a party or its Representatives from the confidentiality obligations set out in this agreement
The obligations of confidentiality contained herein survive termination or expiration this agreement.
7 Dispute Resolution In the event of any dispute between the parties in relation to the terms and conditions of this agreement, the parties will first seek to resolve such dispute by promptly giving notice of the dispute to the other party and in good faith endeavour to resolve such dispute. If the dispute remains unresolved for 20 days, the parties will first seek a resolution through the use of mediation. If the dispute still remains unresolved, a resolution through the use of arbitration shall be tried and only as a last resort, resolution is pursued through courts. Nothing in this Agreement will be interpreted as preventing a party from seeking urgent interlocutory relief through the courts to protect its interest in the Confidential Information disclosed to the other party.
8 Governing Law This Agreement shall be governed by the laws of COUNTRY.
Signed for XXXXXXX:
Signed for YYYYYYYY:
ECONOMIC BENEFITS OF USING GENETIC SLEECTION TO REDUCE THE PREVALENCE OF BOVINE RESPIRATORY DISEASE COMPLEX IN BEEF FEEDLOT CATTLE H.L. Neibergs1, J.S. Neibergs1, AJ Wojtowicz1, J.F. Taylor2, C.M. Seabury3, J. E. Womack3 Washington State University, Pullman
University of Missouri, Columbia
Texas A&M University, College Station
Abstract The prevalence of bovine respiratory disease complex (BRDC) has remained unchanged for decades despite efforts to suppress the disease through prevention programs aimed at vaccination and metaphylaxis. An additional approach that focuses on host response to infection by the pathogens responsible for BRDC, through the selection of animals that are less susceptible to the disease, has been undertaken as part of the USDA funded â€œIntegrated Program for Reducing BRDC in Beef and Dairy Cattleâ€? with Dr. James Womack at Texas A&M University as the Project Director. This study, now beginning its fourth year, has found that estimates of heritability for susceptibility to BRDC were greater than 17% for a binary case-control definition of BRDC and greater than 29% for a semi-quantitative (clinical score) definition of BRDC in a commercial feedlot in Colorado. The higher heritability estimate for the more precise definition of BRDC was anticipated as heritability improves as accuracy of a measured phenotype (in this case the BRDC phenotype) improves. The estimated annual rate of genetic gain due to selection on these phenotypes was estimated at 1.2% (case-control) and 2.1% (clinical scores). The economic cost of $204.10 per BRDC feedlot steer was determined through the loss of carcass quality, death and treatment costs. When this value was combined with the 16.2% national prevalence of BRDC in the feedlot and the estimated reduced prevalence of BRDC (rate of genetic gain made by selecting to increase the proportion of cattle that are more resistant to disease), the feedlot industry could gain between $8 and $16 million per 82
year through the implementation of selection for cattle that are less susceptible to BRDC. Introduction Bovine respiratory disease complex is the result of of viral and bacterial pathogens and is the leading cause of illness and death in feedlot cattle (USDA 2001, Gagea et al. 2006, Snowder et al 2006). The prevalence of bovine respiratory disease complex (BRDC) detected in feedlot cattle varies by year with a 15 year range of from 5% to 44%, and also by season, with higher prevalence rates in the fall and winter (Snowder et al 2005, Miles 2009). The average prevalence rate of BRDC was 16.2%, with virtually all feedlots (96.9%) reporting one or more cases between July 1, 2010 and June 30, 2011 (USDA 2011). Recent reports have indicated that greater than 60% of all cattle in the feedlot have lung lesions resulting from BRDC and many of these animals were undetected as suffering from an illness (Schneider et al. 2009). Of animals that showed noticeable signs of illness, BRDC was the most common cause (67% to 82%) of illness detected in feedlot cattle (Edwards 1996, USDA 2011). An estimated 1.4% of all feedlot cattle die from BRDC prior to harvest. The high prevalence of BRDC in feedlot cattle has not fallen in spite of best management practices and vaccination programs (Gagea et al. 2006, Miles 2009). According to the USDA (2011), the majority of feedlots over 1,000 head used one respiratory vaccine to combat disease. Unfortunately, only about 25% of cattle were vaccinated for both the viral and bacterial pathogens associated with BRDC. Specifically, 96.6% of feedlots vaccinated for bovine viral diarrhea virus (BVDV), 93.7% vaccinated for infectious bovine rhinotracheitis virus (IBR), 85.1% vaccinated for parainfluenza 3 virus (PI3) and 89.5% of feedlots vaccinated for bovine respiratory syncytial virus (BRSV) (USDA 2011). Approximately 66% of feedlots used vaccines that incorporated BRDC bacterial pathogens Hemophilus somnus and Mannheimia haemolytica (previously named Pasteurella haemolytica) and 21.8% vaccinated against Mycoplasma bovis. One strategy used to prevent or minimize an outbreak of BRDC is to treat cattle with an injectable antibiotic (metaphylaxis) for BRDC pathogens. Factors that heightened concerns of BRDC and caused feedlots to consider metaphylaxis included: cattle
with a poor appearance on arrival (88.4% of feedlots would consider metaphylaxis), the presence of one or more animals from the same source affected with BRDC (83.8%), the presence of BRDC affected cattle in the same pen (70.5%), or if cattle came from a sale barn (88.3%) (USDA 2011). In all, 59.3% of feedlots used metaphylaxis treatment for some cattle in the feedlot. Unfortunately this strategy, as well as vaccination, have collectively failed to reduce the prevalence of BRDC and further suggests that other approaches, such as focusing on the host response to pathogen challenges, may be helpful in reducing the prevalence. The use of new approaches, such as genomic selection, is supported by studies providing evidence that genetic factors are important in BRDC prevalence rates. Although the environment and stress play a major role in BRDC infection rates, there is increasing evidence that susceptibility to BRDC is at least partially under direct genetic control. Differences in BRDC susceptibility have been found between cattle breeds and sire lines, and heritability estimates in the low to moderate range (0.04 to 0.21) have been reported for BRDC susceptibility in beef and dairy cattle (Lyons et al 1991, Muggli-Cockett et al. 1992, Snowder et al 2005, Heringstad et al. 2008, Schneider et al 2009, Neibergs et al 2013, Seabury et al 2014). This suggests that selecting for BRDC resistant cattle could have a substantial impact on BRDC prevalence (Snowder et al 2009). A limited number of quantitative trait loci related to bovine health, including resistance to BRDC, have been reported (Casas and Snowder 2008, Settles et al 2009, Zanella et al 2011). New tools are now available to investigate the role of genetics in diseases such as BRDC that were not available just a few years ago. These resources have now been harnessed to identify the bovine genomic regions associated with BRDC susceptibility so that breeding less susceptible breeding stock can be identified and utilized (Neibergs et al 2013, Seabury et al 2014). The identification of individual genetic differences in cattle that predispose them to enhanced susceptibility to BRDC serves as the basis for selecting cattle that are less likely to become ill as breeding stock. The development of genomic breeding values for sires that are less susceptible to BRDC is underway as part the ongoing USDA-funded multi-institutional research project “Integrated Program for Reducing BRDC in Beef and Dairy Cattle” (www.brdcomplex.org).
To adopt selection as a means of reducing BRDC in feedlot cattle, it must be feasible and profitable for the feedlot industry. The average cost per treatment for cattle with BRDC in feedlots over 1,000 head was reported as $23.60 by the USDA (2011) but the average number of treatments given per affected animal was not provided. However, for U.S. cattle weighing less than 700 pounds, over 18% did not respond to their first treatment, 4% died, 14.9% were retreated and 2.3% were considered chronic and were subsequently shipped to slaughter prior to reaching a normal slaughter weight. For cattle weighing over 700 pounds, just over 13% did not respond to their first treatment, 3.6% died, 12.4% were retreated and 1.9% were considered chronic (USDA 2011). For light cattle weighing less than 700 pounds treated a second time, 63.1% of cattle responded to treatment, 13.3% died and 12% were treated a third time. For cattle heavier than 700 pounds that were retreated, 69.5% responded, 13.2% died and 17.1% were treated a third time (USDA 2011). Although the exact cost of BRDC to the beef industry is unknown, it has been estimated to be responsible for losses of over $800 million annually and represents the single most economically important disease of cattle (Chirase and Green 2001, Snowder et al. 2006a, Gagea et al 2006). The aims of this study were to estimate the heritability of BRDC susceptibility in Bos taurus feedlot cattle at a commercial facility that did not treat cattle with meatphylaxis, estimate the rate of genetic change that would result from selection for cattle that were less susceptible to BRDC and determine the economic gain of selecting cattle for reduced BRDC susceptibility in the feedlot based on the estimated rate of genetic change. Materials and Methods Nine hundred ninety-five Bos taurus beef cattle were evaluated using the BRDC diagnostic criteria of McGuirk (2008) and determined to be either affected with BRDC (n=497) or to be unaffected (n=498). Animals’ health statuses were defined by clinical signs of fever, cough, nasal discharge, and either ocular discharge or the ear position or head tilt scores (McGuirk 2008). For each clinical sign, a numerical value of 0 to 3 was assigned based on the severity of the clinical signs. Values for ocular discharge and ear position/head tilt were compared and the largest of these values was summed with all of the other clinical score values to reach a cumulative score. Animals with summed cumulative scores ≥5 were deemed 83
BRDC affected and animals with summed cumulative scores <5 were deemed unaffected. The mean clinical score for cases was 8.04 ± 1.23 and the mean score for controls was 2.06 ± 0.037. All cases and controls were housed together in the same pens until harvest. Weights of animals at diagnosis, finished weights, days until harvest, treatment costs and estimated feed costs were provided for study animals. Treatment costs were based on a one-time injectable antibiotic treatment for BRDC as cattle were not retreated per the policy of the feedlot facility. The steers were marketed as a pen when they reached a finished weight. Six lots were shipped throughout the study period and were followed to processing where the carcasses were evaluated for yield and quality grade. Hot carcass weight was provided for all study animals. Heritability estimates for BRDC susceptibility were obtained by GenABEL/GRAMMAR (GenABEL.org) from relationship matrixes obtained from genotypes of each animal derived from the Illumina BovineHD assay that contains 778,000 single nucleotide polymorphisms (SNPs). All cattle were steers and consisted of 908 Angus, 18 Charolais, 25 Hereford, and 44 Red Angus. To account for potential breed differences in susceptibility to BRDC, animal breed was fit as a fixed effect in the model used to obtain heritability estimates. Data were filtered for quality at both the animal and SNP level, such that animals with a genotyping success rate of less than 90% (n=63 animals), or SNPs that failed to genotype greater than 95% of the time or that had minor allele frequencies less than 1%, were removed. In addition, animals with ambiguous genetic gender identification (n=3) were removed leaving a total of 932 males and 678,895 SNPs for the analyses. Two different phenotypes for BRDC were used to estimate heritabilities. The first phenotype was a binary case-control phenotype where cases had McGuirk health scores ≥5 and controls had scores <5 and will be referred to as the ‘case-control’ phenotype. The second BRDC phenotype used numerical values of the McGuirk system (that ranged from 0 to 12) as a semi-quantitative phenotype and will be referred to as the ‘clinical score’ phenotype. The heritability estimate for the case-control phenotype was 17.7% and was 29.2% for the clinical score phenotypes. These estimates were similar to those estimated in previous studies and as estimated by investigators of the BRDC-CAP for dairy calves (Lyons et al. 1991, Neibergs et al 2013, Seabury et al 2014). 84
To estimate the rate of genetic change, the equation described by Falconer (1989) was used:
where ΔBV/t is the rate of genetic change per year, which represents the reduction in BRDC prevalence in feedlot steers, i is the standardized selection intensity, r is the accuracy of selection, σa is the additive genetic standard deviation of the trait of interest, and L is the generation interval in years. The following parameters were assumed to estimate the model. In a typical beef cow-calf operation, the annual cow culling rate is between 13% and 20%, so for this example, we used a cow culling average of 15%. If sexed-semen was not used and half of the calves were heifers, then 30% of the heifers would need to be retained to maintain a constant herd size. This corresponds to a standardized selection intensity coefficient of 1.16. The accuracy of selection was assumed to equal the square root of the heritability for BRDC (42% for case-control and 54% for clinical scores) that would be realized from phenotypic selection. This would form a conservative estimate for the accuracy of prediction of molecular breeding values for susceptibility to BRDC. The genetic standard deviation for BRDC prevalence was based on the following assumptions and calculations: BRDC prevalence in beef steers will vary between operations, seasonally and annually. The USDA (2011) average prevalence of BRDC for feedlot cattle of 16.2% (with a standard error of 1.4) was used as the BRDC prevalence rate. The binomial phenotypic variance of BRDC susceptibility with a prevalence rate of (p) can be calculated as p(1-p) and, assuming that heritability is constant (independent of p), the additive genetic variance for a prevalence p is VA = h2p(1-p). Thus, for a heritability of 17.7% (case-control) or 29.2% (clinical score), the additive standard deviations for the prevalence rate of 16.2% are σa = 0.1563 and 0.1984, respectively. The generation interval (L) for beef cows was estimated at 6 years. Biologically, the shortest possible generation interval is the sum of age at sexual maturity and gestation length, or approximately 2 years of age.
Results and Discussion The rates of genetic change for the case-control phenotype was 1.28% with a BRDC prevalence rate of 16.2% The rates of genetic change for the clinical score phenotype was even higher at 2.07% for BRDC as defined by clinical scores (Table 1). Direct costs attributable to BRDC include declines in carcass quality, death losses, treatment and labor costs, and prevention costs. In this study, losses due to carcass quality and death, and costs for treatment were used to estimate direct costs. Prevention costs (vaccination and best management practices implemented at the feedlot) were identical between BRDC cases and controls and so were not estimated for this study. Labor costs for treating BRDC cases were not provided by the feedlot and so were not included in the direct costs. Table 2 presents the quality grades and death loss data for the cattle affected and unaffected with BRDC. The BRDC cases had a lower number of choice animals compared with healthy animals (P=0.005), but a similar number of select carcasses between cases and controls (P>0.05). The drop in carcass value shown in Table 2 reflects that the loss in quality grade of BRDC affected animals was not due to a simple slip of quality grade from choice to select, but a more extreme loss of carcass value to that of condemned ($0 value), railers (carcasses with quality issues that result in a standard value) or animals that died prior to harvest ($0 value). The average loss in value of BRDC cases compared to controls was $162.78 per head in 2013. The average treatment cost of the single BRDC treatment of an injectable antibiotic was $41.32 per head. Because cases and controls were co-mingled, fed and harvested together, there was no difference (P>0.05) between cases and controls on rate of gain, hot carcass weight or yield grade. When treatment costs were combined with the losses due to carcass quality, the estimated total direct cost of each BRDC case in the feedlot was $204.10. In 2013, 9,131,500 heifers and 16,003,400 steers were harvested from U.S. feedlots that contained 1,000 or more head (http://quickstats.nass.usda. gov/results/135554B0-FDB3-34F2-A5F9-8ADBFEBAC18D) for a total of 25,134,900 animals. With the most current national estimate of BRDC prevalence in feedlots of 16.2% (USDA 2011), 4,071,854 feedlot
Table 1. Factors Affecting the Rate of Genetic Change in Reducing BRDC Susceptibility cattle were estimated to be affected with BRDC in 2013. A conservative estimate of the cost of BRDC to the feedlots (based on a single treatment cost, and loss of carcass value) was determined to be $204.10 per animal or $830,658,210 in total losses to the feedlot industry. With the current estimates of the rate of genetic gain (1-2%, see Table 1) that could be achieved through selection for cattle that were less susceptible to BRDC, the feedlot industry could realize gains between $8,306,582 to $16,613,164 per year based on 2013 costs and market prices, by selecting for cattle that are less susceptible to BRDC.
Table 2.Carcass Quality of Bovine Respiratory Disease Complex Cases and Controls Conclusions Genomic selection for health traits, such as BRDC, offers new approaches to reduce the prevalence of economically important diseases. New technologies allow the identification of cattle that are less susceptible to BRDC and the opportunity to select less susceptible breeding stock so that the next generation of feedlot cattle will be less likely to be affected with BRDC. The use of molecular breeding values in sires and elite dams has become common for cattle genotyped through commercial companies and/or breed associations. As part of the aims for the ongoing â€œIntegrated Program for Reducing BRDC in Beef and Dairy Cattleâ€? the genomic regions that are predictive of cattle that are less resistant to BRDC will become publicly available. These SNPs will then be 85
freely available to be placed on commercial genotyping platforms to benefit the beef and dairy industries. Molecular or genomic breeding values for susceptibility to BRDC can be computed for genotyped cattle so that selection decisions based on BRDC susceptibility may be made across the industry. The use of genomic selection offers significant opportunities to reduce BRDC prevalence and gain increased profitability in the beef feedlot industry. Acknowledgement This work was supported by the USDA-NIFA grant no. 2011-68004-30367. Literature Cited Casas E., G.D. Snowder. 2008. A putative quantitative trait locus on chromosome 20 associated with bovine pathogenic disease prevalence. J Anim. Sci. 86:2455-2460. Chirase, N.K., L.W. Grene. 2001. Dietary zinc and manganese sources administered from the fetal stage onwards affect immune response of transit stressed and virus infected offspring steer calves. Anim. Feed Sci. Tech. 93:217-228. Edwards, A.J. 1996. Respiratory diseases of feedlot cattle in the central USA. Bovine Pract. 30:5-7. Falconer, D. S. (1989). Introduction to Quantitative Genetics. 3rd ed. Longman Scientific and Technical, New York, NY. Gagea, M.I., K.G. Bateman, T. van Dreumel, B.J. McEwen, S. Carman, M. Archambault, R.A. Shanahan, J.L. Caswell. 2006. Diseases and pathogens associated with mortality in Ontario beef feedlots. J. Vet. Diagn. Invest. 18:18-28. Heringstad, B., Y.M. Chang, D. Gianola, O. Steras. 2008. Short communication: Genetic analysis of respiratory disease in Norwegian Red calves. J. Dairy Sci. 91:367-370. Lyons, D. T., A.E. Freeman, A.L. Kuck. 1991. Genetics of health traits in Holstein cattle. J. Dairy Sci. 74(3): 1092-1100. Lyons, D.T., A.E. Freeman, A.L. Kuck. 1991. Genetics of health traits in Holstein cattle. J. Dairy Sci. 74(3): 1092 1100. McGuirk, S.M. 2008. Disease management of dairy calves and heifers. Vet. Clin. NA: Food Anim. Pract. 24:139-153. 86
Miles, D.G. 2009. Overview of the North American beef cattle industry and the prevalence of bovine respiratory disease (BRD). Anim. Health Res. Rev. 10:101-103. Muggli-Cockett N.E., L.V. Cundiff, K.E. Gregory. 1992. Genetic analysis of bovine respiratory disease in beef calves during the first year of life. J. Anim. Sci. 70:2013-2019. Neibergs, H.L., C.M. Seabury, J.F. Taylor, Z. Wang, E. Scraggs, R.D. Schnabel, J. Decker, A. Wojtowicz, J.H. Davis, T.W. Lehenbauer, A.L. Van Eenennaam, S.S. Aly, P.C. Blanchard, B.M. Crossley. 2013. Identification of loci associated with Bovine Respiratory Disease in Holstein calves. 2013. Plant & Animal Genome XXI, San Diego, California. Schneider, M.J., R.G. Tait, W.D. Busby, J.M. Reecy. 2009. An evaluation of bovine respiratory disease complex in feedlot cattle: Impact on performance and carcass traits using treatment records and lung lesion scores. J. Anim Sci. 87: 1831-1827. Seabury, C.M., J.F. Taylor, H.L. Neibergs, BRD Consortium. 2014. GWAS for differential manifestation of clinical signs and symptoms related to bovine respiratory disease complex in Holstein calves. 2014. Plant & Animal Genome XXII, San Diego, California. Settles, M., R. Zanella, S.D. McKay, R.D. Schnabel, J.F. Taylor, T. Fyock, R.H. Whitlock, Y Schukken, JS Van Kessel, J Karns, E Hovingh, JM Smith, HL Neibergs. 2009. A whole genome association analysis identifies loci associated with Mycobacterium avium subsp. paratuberculosis infection status in US Holstein cattle. Anim. Genet .40:655-662. Snowder G.D., L.D. Van Vleck, L.V. Cundiff, G.L. Bennett. 2005. Influence of breed, heterozygosity, and disease prevalence on estimates of variance components of respiratory disease in preweaned beef calves. J. Anim. Sci. 83:1247. Snowder G.D., L.D. Van Vleck, L.V. Cundiff, G.L. Bennett. 2006a. Bovine respiratory disease in feedlot cattle: Environmental, genetic and economic factors. J. Anim. Sci. 84:1999-2008.
Snowder, G.D., L.D. Van Vleck, L.V. Cundiff, G.L. Bennett, M. Koohmaraie, M.E. Dikeman. 2006b. Bovine respiratory disease in feedlot cattle: Phenotypic, environmental, and genetic correlations with growth, carcass, and longissimus muscle palatability traits. J. Anim. Sci. 85:1886-1892. USDA. 2001. Treatment of respiratory disease in U.S. Feedlots. USDA-APHIS-VS, CEAH. Fort Collins, CO #N347-1001. USDA, 2011. Feedlot 2011 Part IV: Health and health management on U.S. feedlots with a capacity of 1,000 or more head. USDA-APHIS-VS-CEHNAHMS. Fort Collins, CO #638.0913 Zanella, R., E.G. Casas, G.D. Snowder, H.L. Neibergs. 2011. Fine mapping of loci on BTA2 and BTA26 associated with bovine viral diarrhea persistent infection and linked with bovine respiratory disease in cattle. Front. Livest.Genom. 2:82.
IT IS POSSIBLE TO GENETICALLY CHANGE THE NUTRIENT PROFILE OF BEEF Raluca Mateescu1 University of Florida
Introduction For the last 25 years health professionals have encouraged people to reduce their intake of red meat as a means of reducing saturated fat intake with the goal of decreasing serum cholesterol level and, hence, the risk of atherosclerosis and cardiovascular disease (CVD) (Mensink, 2011). This recommendation is based on the perception that red meat is the major contributor to both total fat and saturated fat in the Western diet and that animal fat is a high risk factor for these diseases. Although this perception was seldom questioned, it is recently coming under increasing scrutiny and recent studies show that reducing intake of meat may not reduce the risk of CVD (McNeill and Van Elswyk, 2012). In this context, reducing the intake of red meat would only result in reducing the intake of a food with the highest nutritional value per unit of energy (nutritional density) as well as many bioactive components with important health promoting properties. Modern consumers are increasingly aware of the relationship between diet and health, and this awareness is responsible for the trend toward consumption of food perceived to be safe, nutritious and promoting good health and wellbeing. Meat provides valuable amounts of high quality protein containing several essential amino acids, fatty acids, vitamins (E and B complex, being major sources of B12) and minerals (USDA/HHS Dietary guidelines Americans, 2010). Equally important, meat is also a source of many bioactive components with health promoting properties such as conjugated linoleic acid, minerals of high bioavailability such as iron, zinc and selenium, peptides (carnitine, creatine, creatinine, carnosine and anserine), choline, etc. Therefore, the beef industry is in a good position to respond to the demands of health-conscious consumers. To capitalize on this trend, the industry needs to focus its research and promotion efforts toward nutritional and health benefits of meat consumption. A strategy designed to ensure that meat plays the role it deserves as a major component of a healthy diet 87
should include research designed to document the relationship between meat consumption and specific health benefits, to develop the genetic or management tools needed to increase the components with positive and reduce those with negative health consequences, and to develop consumer education programs to promote nutritional and health benefits of meat consumption. The industry should also emulate the fruit and vegetable blueprint in pursuing scientific evidence on positive aspects of meat consumption on human health. Why is the Nutrient Profile of Beef Important? While the prevalence of obesity is rapidly increasing (Flegal et al., 2012) and has reached a 33.8% high among US adults (Shields et al., 2011), many Americans are not meeting the recommended daily intake for many nutrients (USDA-ARS, 2011), i.e., they are “overfed and undernourished”. Among all diet components, meat has the unique status of providing per unit of energy high amount of high quality protein along with many nutritive factors and other components important for human health. Given its high nutrient density, red meat can, and should, play a critical role in meeting the nutritional needs of the consumers. Beef is already an important food group in the diet of many consumers and improvement of its healthfulness will be an efficient way to provide health benefits to a large proportion of the population, without dramatically changing dietary habits or affecting food quality, convenience and costs. Animals and Sample Collection. A total of 2,285 Angus sired bulls (n = 540), steers (n = 1,311), and heifers (n = 434) were used in this study. All cattle were finished on concentrate diets in Iowa (n = 1,085), California (n = 360), Colorado (n = 388), or Texas (n = 452). Animals were harvested at commercial facilities when they reached typical US market endpoints with an average age of 457 ± 46 days. Production characteristics including detailed sample collection and preparation of these cattle were reported previously (Garmyn et al., 2011). Briefly, external fat and connective tissue were removed from 1.27-cm steaks for nutrient and other bioactive compounds composition and 2.54-cm steaks were removed for Warner-Bratzler shear force (WBSF) and sensory analysis. All steaks were vacuum packaged, aged for 14 d from the harvest date at 2°C and frozen 88
at -20°C. Steaks were cooked and subjected to WBSF and sensory analysis at Oklahoma State University Food and Agricultural Products Center. Nutrient and bioactive compounds composition analysis was conducted at Iowa State University. Mineral and Peptide Content of Angus Beef Dietary minerals are essential components of human diets, and most dietitians recommend that these minerals be supplied from foods in which they occur naturally. Meat provides valuable amounts of important minerals but limited information is available regarding their content and natural variation in beef, the extent to which that variation is the result of genetic differences or if it is associated with meat palatability traits. The objectives of our study were to quantify the genetic and environmental components of observed variation in the concentrations of several minerals and peptides in LM of Angus beef cattle, to estimate genetic parameters and their associations with a wide portfolio of beef palatability traits. The concentrations for several minerals and peptides are shown in Table 1. Iron and Zinc Iron deficiency is one of the most common and widespread nutritional disorder in the world affecting both developing and industrialized nations (WHO, 2006). In the U.S. and Europe the iron deficiency is greater particularly in pregnant women and infants living in lower socioeconomic groups (Agostoni et al., 2008). A recent study from Australia (Samman, 2007) indicates that ~30% of young women had mean daily iron intakes of less than 70% of the recommended daily intake and among young female athletes was even higher at 51%. The intake of iron was inversely correlated with the amount of red meat consumed on the day of the survey (Baghurst, 1999). The picture is similar in the US with iron deficiency anemia being identified by the Centers for Disease Control & Prevention as the most common nutritional deficiency. The iron concentration in our data set was 14.44 µg/g muscle, representing on average 1.44 mg iron per 3.5 oz serving of beef. The current recommended daily allowance varies depending on gender and age from 8 to 18 mg per day. In this context, a 3.5 oz serving of beef would provide between 8 and 18% of the recom-
Table 1. Simple statistics for calcium, copper, iron, magnesium, manganese, phosphorus, potassium, sodium and zinc concentrations (Âľg/g muscle), and carnitine, creatine, creatinine, carnosine and anserine concentrations (mg/g muscle) of steaks from Angus cattle. Trait N1 Mean SD2 CV3 Calcium 2,260 38.71 19.79 .51 Copper 1,980 0.78 0.85 1.09 Iron 2,259 14.44 3.03 0.21 Magnesium 2,274 254.54 43.06 0.17 Manganese
Sodium 2,273 489.44 92.92 0.19 Zinc
Number of cattle Standard deviation 3 Coefficient of variation 1 2
mended daily allowance. The amount of iron absorbed compared with the amount ingested is typically low, and the source of iron is an important factor determining the efficiency of absorption (Kapsokefalou and Miller, 1993; Andrews, 2005; West and Oates, 2008; Han, 2011). Of particular importance are the results reported by Etcheverry and co-workers (2006) which indicate that, in adolescents, non-heme iron and zinc absorption from a beef meal is significantly greater than that from a meal providing soy protein. The obvious and probably most effective dietary strategy to improve iron status in population groups exhibiting iron deficiency (especially infants, growing children and young women) is to increase intake of absorbable iron by increasing consumption of meat and the concentration of iron in meat. Both strategies represent opportunities for the beef industry by developing programs focusing on the benefits of meat consumption targeting segment of the population at risk of iron deficiency and implementing genetic programs
to increase iron content. Zinc is an essential nutrient involved in a number of metabolic processes, including protein and nucleic acid synthesis, insulin and other enzymes, growth and immunity, therefore, critically important for good health. The World Health Organization (WHO) considers zinc deficiency to be a major contributor to the burden of disease in developing countries, especially in those with a high mortality rate. Based on WHO estimates, it appears that 25% of the populations of South and South-East Asia and Latin America are at risk of inadequate zinc intake, compared with 10% of the population of Western Europe and North America. Similar to iron, zinc in animal products is more readily absorbed than from plant foods. Beef is the major source of zinc in the diet. Relatively high heritability for iron (Table 2) and moderate heritability for zinc along with their positive genetic correlation (0.49) indicate that a selection pro 89
Table 2. Genetic (σ2a) and residual (σ2e) variance and heritability (h2) estimates with SE for calcium, copper, iron, magnesium, manganese, phosphorus, potassium, sodium and zinc concentrations (µg/g muscle) and carnitine, creatine, creatinine, carnosine and anserine concentrations in LM from Angus cattle obtained by single-trait REML analysis. Trait1 σ2a σ2e h2 ± SE Calcium
0.000 ± 0.03
0.000 ± 0.04
0.544 ± 0.09
0.065 ± 0.04
0.009 ± 0.03
0.036 ± 0.03
0.037 ± 0.03
0.187 ± 0.06
0.091 ± 0.04
0.015 ± 0.03
gram with emphasis on increasing the beef content for these two minerals is feasible and permanent and cumulative genetic improvement should be successful. The associations of iron and zinc concentrations with several palatability traits (tenderness, juiciness and flavor) were all low indicating that increasing the iron and zinc content, no negative consequences on palatability traits are expected (Mateescu et al., 2013a). Genome-wide Association Study for Iron Concentration Given the difficulty of collecting records for these traits in selection candidates, implementation of a selection program would require identification of genetic markers associated with iron and zinc content to be used in marker-assisted selection programs. Toward this end, a genome-wide association study using the Bovine SNP50 Infinium II BeadChip was conducted to identify chromosomal regions associated with concentrations of iron in LM of Angus beef cattle, to estimate genomic breeding values for iron concentration and assess their accuracy, and to determine how other economically important traits might be affected by genomic selection to improve iron concentration (Mateescu et al., 2013b).
Seven regions on six chromosomes (1, 2, 7, 10, 15 and 28) were identified to have major effect on iron content of LM in Angus cattle. Many of these chromosomal regions contain, or are in close proximity to, genes associated with iron homeostasis or iron metabolism, providing strong candidate genes for further investigation as well as confirming the validity of the genome-wide association results. The proportion of phenotypic variance of iron concentration in muscle explained by SNP genotypes (genomic heritability) was 0.37 and the accuracy of genomic breeding value (GEBV) was 0.59. This level of accuracy indicates that selection based on genomic merit for iron concentration would be as efficient as selection based on individual phenotype for a trait with heritability of 0.35. We estimated that in a selection program to improve iron concentration based on GEBV, and for each unit (µg/g of meat) improvement in iron GEBV, 0.73 units (µg/g of meat) improvement in the actual iron concentration is expected. To assure long-term sustainability of the industry, a beef cattle improvement program should consider traits that influence production efficiency, traits that influence quality of the eating experience, traits that influence animal health and well-being, and traits that would provide health benefits to humans con-
suming the product. Increasing the concentration of iron and zinc in beef muscle through selection should benefit the beef cattle industry as well as consumers by producing meat that is healthier for humans to eat and, therefore, encouraging consumption. In addition, increasing iron concentration in muscle would contribute to improved functionality of beef (defined as retention of red color at days 3-4 of retail display) and improved beef flavor. Vitamin E and iron content in muscle are the most important factors determining the functionality of meat, with redness being positively related to both vitamin E and heme iron content in lamb meat (Ponnampalam et al., 2012). Increasing iron content in muscle is expected to also improve color stability (shelf life) of beef at retail display. A significant genetic and phenotypic correlation was reported recently (Garmyn et al., 2011; Mateescu et al., 2013a) between beef flavor and iron concentration, indicating an increase in iron concentration would contribute toward an improved beef flavor. Other Nutrients Meat also contains many other compounds with human health importance. Among these components, some of which are not generally recognized as nutrients, we evaluated carnitine, creatine, creatinine, carnosine and anserine. There is growing evidence regarding the positive effect these meat bioactive compounds play in human health and wellbeing. They are powerful antioxidants and play important roles in muscle metabolism and other metabolic functions. The focus in this paper will be on the role meat, with its quality protein and bioactive compounds, can play in preventing and reversing muscle wasting diseases such as sarcopenia. Sarcopenia refers to the gradual loss of muscle mass and strength at a rate of about 1% per year that accompany the aging process. Sarcopenia leads to reduced mobility and weakness, increased risk of diabetes and weight gain, poor quality of life and morbidity. The underlying mechanism is unknown but age-associated changes in diet and exercises are primary suspects. The â€˜National Health & Nutrition Examination Surveyâ€™ estimated that about 25% of adults over age 50 have low levels of B12 vitamin, strongly suggesting inadequate amount of animal products in their diet. What is known is that physical exercise, particularly weightlifting, and adequate
synthetic response occurring at rest and following resistance exercise in middle-aged men following the ingestion of 4 oz (113 g) of beef protein or an equivalent amount of soy-based protein marketed and sold as a bona-fide replacement for beef (Phillips, 2012). The results show that meat, with its quality protein and bioactive compounds, is better than plant protein at promotion of myofibrillar (the contractile protein of skeletal muscle) protein synthesis at rest and also following resistance exercise. Based on these results men over 50 should include lean beef in their diet to prevent or delay the onset of muscle loss. The inclusion of a serving of beef would also provide substantial amounts of iron, zinc, vitamin B-12 as well as carnatine, creatine, creatinine, carnosine, anserine and other nutrients and bioactive compounds that are missing or present in small amounts and with low bioavailability in plant-based proteins. Our study found creatine, carnosine and anserine to be moderately heritable (Table 2) whereas almost no genetic variation was observed in carnitine and creatinine. The additive genetic variation for some of these traits is large enough to be exploited in selection or management if changing the concentration of these compounds is contemplated but at this point in time such a program may not be necessary. The natural concentrations of these components in beef seem adequate to enhance the protein quality and prevent the onset of sarcopenia in the segment of the population at risk. Few associations between these compounds and WBSF or meat quality assessed by sensory panels were detected, and these associations were favorable, suggesting that palatability would not be compromised if the nutritional profile of beef would be improved by altering the concentration of these compounds. Conclusions Following recent trends, consumers are likely to continue to pay increased attention to the effect of diet on health. Red meat is a very nutritious food and contains numerous compounds with positive health effects but, unfortunately, the average consumer is not familiar with these benefits. An online poll conducted for American Meat Institute by Harris Poll revealed that most consumers do not fully recognize the unique nutritional benefits that beef has to offer. For example, only 12% of consumers correctly identi91
fied animal products like beef and poultry as the only natural source of vitamin B12. In the same poll, 20% of the consumers said cruciferous vegetables such as broccoli and cauliflower while 13% said citrus fruit were the natural source of vitamin B12, when in fact neither of these types of foods contains vitamin B12. The beef industry needs to increase its efforts to document and promote the nutritional and health benefits of beef in order to capitalize on the consumer trend. The most convincing way to demonstrate the positive effects of meat consumption on health is via well designed human intervention studies and the beef industry should take a proactive role and increase its efforts to promote/support studies addressing the most prevalent chronic diseases, in which dietary intervention using red meat would reduce risks or improve quality of life. In this paper two important human health issues, iron deficiency and sarcopenia, were discussed. In both cases, increasing consumption of red meat to meet the recommended daily intake would mitigate the health problem given that the segments of the population affected (young women and elderly people), have a relatively low red meat consumption. This represents a golden opportunity to improve human health and increase red meat consumption. Literature Cited Andrews, N. C. 2005. Understanding heme transport. N. England J. Med. 353:2508-2509. Baghurst, K. 1999. Red meat consumption in Australia: intakes, contributions to nutrient intake and associated dietary patterns. Euro. J. Cancer Prevention 8:185-191. Etcheverry, P., K. M. Hawthorne, L. K. Liang, S. A. Abrams, and I. J. Griffin. 2006. Effect of beef and soy proteins on the absorption of non-heme iron and inorganic zinc in children. J. Amer. Coll. Nutr. 25:34-40. Flegal, K. M., M. D. Carroll, B. K. Kit, and C. L. Ogden. 2012. Prevalence of Obesity and Trends in the Distribution of Body Mass Index Among US Adults, 1999-2010. J. Amer. Med. Assoc. 307:491497.
Garmyn, A. J. et al. 2011. Estimation of relationships between mineral concentration and fatty acid composition of longissimus muscle and beef palatability traits. J. Anim. Sci. 89:2849-2858. Han, O. 2011. Molecular mechanism of intestinal iron absorption. Metallomics: Integrated Biometal Sci. 3:103-109. Kapsokefalou, M., and D. D. Miller. 1993. Lean beef and beef fat interact to enhance nonheme iron absorption in rats. J. Nutr. 123:1429-1434. Mateescu, R. G. et al. 2013a. Genetic parameters for concentrations of minerals in longissimus muscle and their associations with palatability traits in Angus cattle. J. Anim. Sci. 91:1067-1075. Mateescu, R. G. et al. 2013b. Genome-wide association study of concentrations of iron and other minerals in longissimus muscle of Angus cattle. J. Anim. Sci. 91:3593-3600. McNeill, S., and M. E. Van Elswyk. 2012. Red meat in global nutrition. Meat Sci. 92:166-173. Mensink, R. P. 2011. Dietary Fatty acids and cardiovascular health - an ongoing controversy. Ann. Nutri. Metabol. 58:66-67. Phillips, S. M. 2012. Nutrient-rich meat proteins in offsetting age-related muscle loss. Meat Sci.92:174178. Ponnampalam, E. N., K. L. Butler, M. B. McDonagh, J. L. Jacobs, and D. L. Hopkins. 2012. Relationship between muscle antioxidant status, forms of iron, polyunsaturated fatty acids and functionality (retail colour) of meat in lambs. Meat Sci. 90:297-303. Samman, S. 2007. Red Meat and Iron. Nutri. Dietetics 64:126. West, A. R., and P. S. Oates. 2008. Mechanisms of heme iron absorption: current questions and controversies. World J. .Gastroenterol. 14:4101-4110.
CHANGES IN DIETARY REGIME IMPACT FATTY ACID PROFILE OF BEEF Susan K. Duckett1 Clemson University
Introduction Heart disease remains the leading cause of death in the US (CDC, 2011). In addition, over one-third of the US population is considered obese (CDC, 2012).
% of total fatty acids
Fig. 1 Fatty acid composition of beef. 45 40 35 30 25 20 15 10 5 0 C16:0
Fatty acid (no. of carbons:no of double bonds)
Obesity is a worldwide epidemic that increases the risk for developing insulin resistance and several chronic diseases such as diabetes, heart disease, stroke and non-alcoholic fatty liver disease. A dietary factor that contributes both to heart disease and obesity is dietary fat consumption. Of particular interest are the intake of saturated fatty acids (SFA), trans fatty acids (TFA), and total fat in the human diet. Concerns about dietary saturated fat content are related to consumption of diets high in specific SFA raise serum low-density lipoprotein (LDL) or bad cholesterol concentrations. The hypercholesterolemic or cholesterol-elevating SFA are: palmitic (C16:0) acid, myrisitic (C14:0) acid, and lauric (C12:0) acid (Mattson and Grundy, 1985; Grundy, 1986; Bonanome and Grundy, 1988; Mensink and Katan, 1989 & 1990; Denke and Grundy, 1992; Zock et al., 1994). In contrast, stearic (C18:0) acid, another SFA, does not raise serum cholesterol and is considered to be neutral (Bonanome and Grundy, 1988; Keys et al., 1965; Hegsted et al., 1965; Grande et al., 1970; Kris-Etherton et al., 1993). Estimates are that these three hypercholesterolemic fatty acids make up about two-thirds of the saturated fatty acids in the American diet and that dietary intake should be reduced to less than 7% of total energy (Grundy, 1997). The predominant fatty acids in beef
longissimus muscle (LM) are: oleic (C18:1; 40%) acid, palmitic (C16:0; 27%) acid, and stearic (C18:0; 15%) acid (Fig. 1, Duckett et al., 1993). Consumption of beef does provide hypercholesterolemic fatty acids in the diet, namely palmitic acid, and therefore efforts to reduce its amount would be perceived as beneficial for human health. Trans fatty acids are receiving attention lately and are even being banned from the menu in some U.S. cities. Trans fatty acids are produced during the hydrogenation of unsaturated vegetable oils (40-60% of total fatty acids as trans) and are found in margarines or processed products that list partially hydrogenated vegetable oil in the ingredient list. This process of hydrogenation increases shelf life of the oil by reducing polyunsaturated fatty acid levels. In this process, many short chain trans fatty acids are produced (trans bonds in 6-16 position) and consumption of these artificial trans fatty acids increases bad (LDL) cholesterol and decreases good (HDL) cholesterol. Results from the Nursesâ€™ Health Study found that women who consumed 4 teaspoons of margarine containing artificial trans fat had a 50% greater risk of heart disease than women who ate margarine only rarely (Willet et al., 1993). Mensink and Katan (1990) compared the effects of a trans or saturated fatty acid rich diet in humans and demonstrated that trans fats have a more negative effect on serum cholesterol levels than saturated fats. Clifton et al. (2004) reported high correlations (r = 0.66) between dietary trans fat intake from margarine and level of trans fat in adipose tissue, and that the level of trans fat in adipose tissue was associated with increased risk of coronary artery disease. One strategy to limit SFA intake is to replace these fatty acids with dietary unsaturated fatty acids on an isocaloric basis. Certain unsaturated fatty acids are considered to be hypocholesterolemic or LDL-cholesterol lowering. These fatty acids include: monounsaturated fatty acids (MUFA) and polyunsaturated fatty acids (PUFA). Mattson and Grundy (1985) showed that MUFA were as effective as PUFA in lowering LDL-cholesterol. Mensink and Katan (1987) compared high fat diets containing MUFA versus low fat diets. They found that high fat diets containing MUFA were as effective as low-fat diets in lowering LDL-cholesterol. Consumption of diets rich in monounsaturated fatty acids increases good, high-density lipoproteins (HDL) and lowers bad, LDL-cholester 93
ol levels (Mensink and Katan, 1989; Wardlaw and Snook, 1990). Canola and olive oils contain predominately MUFA at levels of 58% and 72% of total fatty acids, respectively. Grundy (1997) recommend intakes of oleic acid, the predominant MUFA, at 16% of total energy. Polyunsaturated fatty acids (PUFA) are subdivided into two categories, omega-6 and omega-3, based on location of the double bonds in the fatty acid chain. Omega-6 fatty acids are common in grains and vegetable oils. Omega-3 fatty acids are common in plant lipids and fish oils. Diets containing omega-6 or omega-3 fatty acids lower blood total and LDL-cholesterol; however, omega-6 PUFA also tend to lower HDL-cholesterol (Mensink and Katan, 1989). Consumption of diets high in omega-3 fatty acids is associated with reduced risk of heart disease, stroke and cancer (Kris-Etherton et al., 2002). Currently, Americans consume greater amounts of omega-6 PUFA than omega-3 PUFA, which has dramatically altered the omega-6 to omega-3 ratio in the human diet. Health professionals recommend that we consume a diet with a more balanced ratio (< 4:1) of omega-6 to omega-3 PUFA. The World Health Organization recommends a daily intake of 1.1 g/d of omega-3 fatty acids with approximately 0.8 g/d of linolenic acid and 0.3 g/d of EPA and DHA. McAfee et al. (2011) reported that consumption of grass-fed red meat products increases plasma and platelet n-3 PUFA status, which indicates that lower n-6:n-3 ratios typically observed in forage-finished beef can potentially impact human health. The predominant fatty acids (70% or greater) in forages are PUFA; however, the predominant fatty acids in beef LM are MUFA and SFA due to the extensive biohydrogenation of PUFA to SFA by ruminal microbes (Duckett et al., 2002; Sackmann et al., 2003), and conversion of SFA to MUFA via adipose tissue desaturases (Duckett et al., 2009). Intermediates of ruminal biohydrogentation, trans-11 vaccenic acid (TVA) and conjugated linoleic acid (CLA), can be found in beef LM. However, the majority of CLA, cis9 trans-11 isomer, in beef comes from desaturation of TVA to CLA in adipose tissues (Pavan and Duckett, 2007). Conjugated linoleic acid, cis-9 trans-11 isomer, has been shown to possess anticarcinogenic properties (Ha et al., 1987) that could be beneficial to human health. Recommendations are that we should consume about 300 mg of CLA, cis-9 trans-11 isomer, 94
per day (Ip et al., 1994). Dietary intake of specific fatty acids is important to human health. Health professionals recommend consuming a diet low in saturated and trans fatty acids. Limiting intake of animal fats is also typically recommended as a way to reduce total fat and saturated fat intake (AHA, 2014). Diet composition provided to the finishing animal can alter fatty acid composition of the LM to enhance healthfulness of beef products. This paper will review current research examining how finishing system alters fatty acid composition. Grain vs. Grass Finishing Systems Fatty acid composition as a percentage of total fatty acids of beef muscle from concentrate-finished versus pasture-finished beef is shown in Table 1. The results are from two experiments that evaluated finishing (final 150 d prior to slaughter) of Angus-cross steers (n = 326) on a high concentrate diet (82% concentrate:18% corn silage) versus pasture (mixed pastures consisting of bluegrass, orchardgrass, endophyte-free tall fescue and white clover; Duckett et al., 2009 & 2013). Steers were fed to an equal animal age endpoint in order to minimize confounding of treatments by animal age or environmental effects. The percentage of omega-6 PUFA did not differ between concentrate- and pasture-finished beef. Monounsaturated fatty acid percentage was greater for concentrate than grass-finished. Omega-3 PUFA percentage was greater for grass- than concentrate-finished. This resulted in a lower, more desirable for human health, ratio of omega-6 to omega-3 fatty acids in grass-finished beef (1.54) compared to concentrate-finished beef (5.01). The percentage of CLA, cis-9 trans-11 isomer, was greater for grass- than concentrate-finished. Trans-11 vaccenic acid percentage was also greater for grass- than concentrate-finished beef. Fatty acid composition can be presented in two ways, 1) as a percentage of total fatty acids or 2) as the total amount per specific steak weight, estimated on a cooked basis (assume 25% cooking shrink). Table 1 showed the percentage of each fatty acid as part of the total fatty acid amounts. Table 2 shows the gravimetric content of total fatty acid types as amount per 18.7 oz. cooked serving. The American Heart Association recommends a 3-oz serving size of beef; however, most retail beef products available exceed this amount. Therefore, the estimates for fatty
Table 1. Fatty acid percentage of the longissimus muscle from steers finished on a high-concentrate diet or mixed pasture. CONCENPASTURE TRATE n 135 191 a Total lipid content (TL), % 5.39 2.48b Total fatty acid content (TFA), % 4.56a 2.25b Saturated fatty acids (SFA), % 43.23b 44.48a C14:0, Myristic acid, % 2.76a 2.46b C16:0, Palmitoleic acid, % 26.62a 24.92b C18:0, Stearic acid, % 13.83b 17.09a Monounsaturated fatty acids (MUFA), % 42.78a 35.13b C14:1, Myristoleic acid, % 0.61a 0.41b C16:1, Palmitoleic acid, % 3.49a 2.71b C18:1, Oleic acid, % 39.68a 32.02b Polyunsaturated fatty acids (PUFA), n-6, % 3.59 3.61 a C18:2, Linoleic acid, % 2.89 2.67b C20:4, Arachidonic acid, % 0.70b 0.94a Polyunsaturated fatty acids (PUFA), n-3, % 0.78b 2.47a C18:3, Linolenic acid, % 0.36b 1.13a C20:5, EPA, % 0.12b 0.50a C22:5, DPA, % 0.26b 0.76a C22:6, DHA, % 0.04b 0.08a Ratio of n-6 to n-3 fatty acids 5.01a 1.54b Trans-11 vaccenic acid (TVA), % 0.53b 3.37a Conjugated linoleic acid (CLA), cis-9 trans-11, % 0.35b 0.71a ab Means in the same row with uncommon superscripts differ (P < 0.05). acid content per ‘real-world’ serving is for 18.7-oz assuming that a 6.7-oz cooked hamburger (McDonalds Big Mac) and 12-oz ribeye steak (Longhorn’s Outlaw ribeye steak, 18 oz bone-in) were consumed per day. Based on these assumptions, beef from steers fed high- concentrate diet would provide about 47% more total fat and saturated fat content than beef from steers finished on pasture. Intake of MUFA would be 58% higher for beef from steers fed a high-concentrate diet than pasture. Intake of n-6 PUFA would be 48% greater and intake of n-3 PUFA would be 64% lower for beef from steers fed high-concentrates versus pasture. The actual amount of CLA provided from both beef sources would be similar and meet the recommended daily consumption levels. However, CLA can also be produced in
Forage Species for Finishing Angus-cross steers (n = 60) from the Clemson University beef herd were used in this 2-yr grazing study (Schmidt et al., 2013). Each winter, 30 steers grazed cereal rye/ryegrass and tall fescue pastures prior to being blocked by BW and assigned randomly to 1 of 5 forage-finishing treatments of alfalfa (Medicago sativa L.), bermudagrass (Cynodon dactylon), chicory (Cichorium intybus L.), cowpea (Vigna unguiculata L.), and pearl millet (Pennisetum glaucum (L. R Br.). Finishing forage treatments started when forage growth for each individual forage species was adequate for grazing. Steers were slaughtered when there was either insufficient forage mass for continued steer gain or when steer live weight exceeded 568 kg. The steak from the 12th rib was trimmed of all exter
Table 2. Fatty acid amount per â€˜real-worldâ€™ serving (18.7 oz; lunch = 6.7-oz hamburger cooked; dinner = 12-oz ribeye steak broiled) for beef finished on high-concentrate diet or mixed pasture.
Total fat content, g Saturated fatty acids, g Monounsaturated fatty acids, g Polyunsaturated fatty acids, omega-6, g Polyunsaturated fatty acids, omega-3, g Trans-11 vaccenic acid, g Conjugated linoleic acid, cis-9 trans-11, g nal fat and epimysial connective tissue for subsequent fatty acid analysis. Forage species utilized for finishing did not alter total lipid, fatty acid, saturated, monounsaturated or polyunsaturated fatty acid content of the LM. However, individual concentrations of certain fatty acids were altered. Most notably, trans-11 vaccenic (C18:1 trans-11; TVA) acid concentration in the LM was greater for BG than CH, CO and AL. Conjugated linoleic acid (CLA), cis-9 trans-11 isomer, concentration was greatest (P < 0.05) for BG and PM than AL, CH, and CO. Since the grasses (BG and PM) in this study had higher NDF content than did the legumes (AL, CO) or forbs (CH), this likely resulted in higher outflow of TVA at the duodenum, which corresponded to greater tissue deposition of TVA and CLA in these forage treatments. Steers grazing CH, the forage species with a greater linolenic acid percentage, produced LM with the greater linolenic acid concentrations compared to AL, BG, and PM. In contrast, linolenic acid levels in PM forage were similar to CH but linolenic acid levels in the LM of PM steers were less than CH and CO. For CO, forage linolenic levels were less than CH and PM but LM linolenic acid concentrations were greater for CO than AL, BG, or PM. Due to the process of biohydrogentation in the rumen and desaturation in the adipose tissues, differences in forage fatty acid levels are not directly translated to similar changes in LM fatty acid composition. The n-6 to n-3 ratio was greater for CH and PM than AL, BG and CO. For AL, BG, and CO. Anticarcinogenic fatty acids, TVA and CLA cis-9 trans-11 isomer, concentrations were greater in beef finished on grasses (BG and 96
CONCENTRATE 99 42.8 42.4 3.55 0.77 0.52 0.35
PASTURE 51 22.7 17.9 1.84 1.26 1.72 0.36
PM) compared to other forage species. The highest ratios of n-6 to n-3 fatty acids were produced in LM when steers grazed CH and PM. Corn Grain Supplementation on Grass or Legume Finishing Thirty-two Angus x Hereford steers were used (BW = 461 Âą 17.4 kg) to evaluate the effects of forage type (legumes [LG, alfalfa and soybeans] vs. grasses [GR, non-toxic tall fescue and sudangrass]) with or without daily corn supplementation (none [NONE] vs. 0.75 % BW/d of corn grain [CORN]) on animal performance and beef quality in a 2-yr study (Wright et al., 2014). The finishing period was 98 d in yr 1 and 105 d in yr 2. Fatty acid composition as a percent of the total fatty acids in the LM is presented in Table 4. All interactions between forage species and corn supplementations were non-significant (P > 0.05). Finishing on grasses increased stearic acid (C18:0) and trans-11 vaccenic acid concentrations compared to legumes. Grazing legumes increased linolenic acid and total n-3 fatty acid concentrations in the LM compared to grazing grasses. The concentration of other fatty acids in the LM was not altered by forage type. Corn supplementation increased myristic (C14:0) and palmitic (C16:0) acid concentrations but did not alter total saturated fatty acid percentage. Oleic (C18:1 cis-9) and palmitoleic (C16:1 cis-9) acid concentrations tended to be increased with corn grain supplementation. As a result, the total monounsaturated fatty acid (MUFA) percentage in the LM was greater with corn supplementation. Linolenic (C18:3) acid concentration was reduced with corn grain supplementation; however, other individual and
Table 3. Fatty acid percentage of the longissimus muscle from steers finished on a five different forage species. Forage Speciesa AL BG CH CO PM n TFA, % 2.35 2.82 2.18 2.38 2.16 SFA, % 43.59 43.12 43.42 44.46 41.54 C14:0, % 2.77 2.39 2.65 2.42 2.32 C16:0, % 26.63 25.42 25.84 26.19 24.54 d bc bcd b C18:0, % 14.16 15.31 14.92 15.54 14.68cd MUFA, % 39.67 39.20 37.46 38.08 39.50 C14:1, % 0.65 0.51 0.58 0.46 0.54 C16:1, % 3.28 3.11 3.07 3.10 3.36 C18:1, % 35.74 35.58 33.81 34.53 35.60 PUFA, n-6, % 4.14 3.60 5.37 4.22 4.41 c c b c C18:2, % 2.93 2.60 4.12 3.13 3.09c C20:4, % 1.22 1.00 1.24 1.09 1.33 PUFA, n-3, % 2.19 1.91 2.52 2.38 1.96 c c b b C18:3, % 1.03 0.90 1.46 1.32 0.86c C20:5, % 0.73 0.63 0.66 0.65 0.68 C22:5, DPA, % 0.73 0.63 0.66 0.65 0.68 C22:6, DHA, % 0.06 0.06 0.05 0.05 0.06 c c b c Ratio of n-6:n-3 1.88 1.90 2.11 1.80 2.26b TVA, % 2.01d 3.03b 2.35cd 2.40cd 2.84bc CLA, cis-9 trans-11, % 0.38c 0.52b 0.40c 0.40c 0.55b a Forage species: AL = alfalfa, BG = bermudagrass, CH = chicory, CO = cowpea, and PM = pearl millet. bcd Means in the same row with uncommon superscripts differ (P < 0.05). Table 4. Fatty acid percentage of the longissimus muscle from steers finished legume or grass pastures with or without corn grain supplementation (0.75% BW/d). Forage Corn Gain a Type Supplementationa GR LG 0 0.75% n 16 16 16 16 c b c TFA, % 3.42 5.02 4.04 4.40b SFA, % 44.89 44.18 44.16 44.91 c MUFA, % 39.69 40.53 39.34 40.89b PUFA, n-6, % 3.58 3.87 3.88 3.57 PUFA, n-3, % 0.99c 1.25b 1.20 1.04 Ratio of n-6:n-3 3.68 3.29 3.28 3.69 b c TVA, % 2.62 2.07 2.61 2.08 d CLA, cis-9 trans-11, % 0.54 0.50 0.56 0.49e a Forage type = GR: grass (tall fescue + sorghum-sudan); LG: legumes (alfalfa + soybean) bc Means in the same row with uncommon superscripts differ (P < 0.05). de Means in the same row with uncommon superscripts differ (P < 0.10). 97
Table 5. Fatty acid percentage of the longissimus muscle from steers fed high concentrate diets or grazed pastures during phase 1 (30-d post weaning for 11 d) or phase 3 (final d to slaughter). Phase 1a HC HC PA PA a HC PA HC PA Phase 3 n 10 10 9 10 TFA, % 3.63 3.60 3.41 3.32 SFA, % 44.47 44.73 46.26 45.12 MUFA, % + 44.64 42.59 43.62 42.72 PUFA, n-6, % * 3.20 3.28 2.84 2.48 PUFA, n-3, % *+ 0.98 1.57 1.57 1.82 b c d Ratio of n-6:n-3 # 3.28 2.18 1.83 1.36e TVA, % # 0.84c 1.49b 1.29b 1.43b CLA, cis-9 trans-11, % + 0.35 0.47 0.39 0.46 a Phase 1 (30-d postweaning for 111 d) or Phase 3 (final ~100 d before slaughter): HC = high concentrate diet; PA = pasture. *Denotes Phase 1 effect (P < 0.05). +Denotes Phase 3 effect (P < 0.05). #Denotes interaction between Phase 1 and Phase 3 effect (P < 0.05). bcde Means with uncommon superscripts differ (P < 0.05). total n-3 fatty acid concentration were not altered by supplementation. Conjugated linoleic acid, cis-9 trans-11 isomer, concentration tended to be lower for corn supplemented than non-supplemented. The ratio of n-6 to n-3 fatty acids did not differ between CS and NS. Timing of High Concentrate and Forage Finishing Research was conducted to determine the timing of exposure to a high concentrate diet on subsequent beef quality and composition (Volpi Lagreca et al., 2014). Steers (n = 40) were backgrounded for 30-d post weaning and then randomly assigned to high concentrate diet (HC) or pasture (PA) in Phase 1 (111 d). After the completion of Phase 1, all steers grazed high-quality pastures for 98 d (Phase 2). At the end of Phase 2, steers were randomly divided based on Phase 1 treatments into two treatments of HC or PA for Phase 3. Phase 3 started when steers were about 454 kg BW and finished when steers reached 568 kg BW (live weight endpoint). Total fatty acid content did not differ due to Phase 1 or Phase 3 treatments even though marbling scores did differ with Phase 1 and Phase 3 treatments. Total SFA percentage was not altered by Phase 1 or Phase 3 treatments. Exposure to HC in Phase 1 in98
creased the concentration of n-6 PUFA and decreased the concentrations of n-3 PUFA. Exposure to HC in Phase 3 increased MUFA, and decreased PUFA n-3 and CLA cis-9 trans-11 isomer concentrations. Interactions between Phase 1 and Phase 3 feeding treatments were significant for ration n-6 to n-3 fatty acids and trans-11 vaccenic acid. The ratio of n-6 to n-3 was higher in longissmus muscle of steers that spent more time on a high concentrate diet (HC-PA-HC > PA-PA-PA). In addition, late exposure to HC resulted in lower ratios than early exposure to HC (HC-PA-PA > PA-PA-HC). Timing of exposure to HC or PA diets can alter fatty acid composition of the longissimus muscle. However, all ratios of n-6 to n-3 fatty acids, regardless of the length of time exposed to HC, were below the recommended 4:1 level for human health. Summary Animal nutrition can alter LM fatty acid composition. Finishing on high concentrate diets increases total fatty acid content and MUFA concentrations. The enzyme, stearoyl-CoA desaturase (SCD-1), is responsible for the conversion of SFA to MUFA, and is very responsive to energy content of the diet. Research comparing gene expression in subcutaneous fat from high-concentrate finished versus pasture finished cattle found that SCD-1 was up-regulated by
46-fold and MUFA increased concentration by 68% in high concentrate finished (Duckett et al., 2009). Even supplementation of corn grain, at a level of 0.75% of body weight, can also increase MUFA percentages in the LM. Finishing on forages typically increases n-3 PUFA and lowers total and saturated fat content. Finishing on different forage species results in minor changes in fatty acid composition of LM. However, finishing on grasses will increase TVA and CLA concentrations; whereas, finishing on legumes will increase n-3 PUFA and total lipid content. Concentration of n-3 PUFA decreases with the number of days cattle are fed a high-concentrate diet. Forages contain predominately n-3 PUFA (58% C18:3); in contrast to corn grain which contains predominately n-6 PUFA (58% C18:2). Therefore, finishing programs that utilize high concentrate diets for longer time periods will have lower n-3 fatty acid concentration. Finishing programs that combine periods of pasture and high concentrate finishing will provide higher levels of n-3 fatty acids than just high concentrate alone. With certain finishing strategies, fatty acid composition of beef can be altered to enhance specific fatty acids that consumers find desirable. Literature Cited AHA. 2014. The American Heart Associationâ€™s Diet and Lifestyle Recommendations. Available at: ttp://www.heart.org/HEARTORG/ GettingHealthy/NutritionCenter/HealthyEating/The-American-Heart-Associations-Diet-and-Lifestyle-Recommendations_ UCM_305855_Article.jsp Bonanome, A. and S. M. Grundy. 1988. Effect of dietary stearic acid on plasma cholesterol and lipoprotein levels. N. Engl. J. Med. 318:1244-8. CDC. 2011. Leading Cause of Death, available at: http://www.cdc.gov/nchs/fastats/lcod.htm. CDC. 2012. Adult obesity facts, available at: http:// www.cdc.gov/obesity/data/adult.html. Clifton, P. M., J.B. Keogh, and M. Noakes. 2004. Trans fatty acids in adipose tissue and the food supply are associated with myocardial infarction. J. Nutr. 134:874-879. Denke, M. A. and S. M. Grundy. 1992. Comparison of effects of lauric acid and palmitic acid on plasma lipids and lipoproteins. Am. J. Clin. Nutr. 56:895-8.
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Mattson, F. H., and S. M. Grundy. 1985. Comparison of effects of dietary saturated, monounsaturated, and polyunsaturated fatty acids on plasma lipids and lipoproteins in man. J. Lipid Res. 26:194-202. Mensink, R. P. and M. B. Katan. 1989. Effect of a diet enriched with monounsaturated or polyunsaturated fatty acids on levels of low-density and high-density lipoprotein cholesterol in healthy women and men. N. Engl. J. Med. 321:436-41. Mensink, R. P. and M. B. Katan. 1990. Effect of dietary trans fatty acids on high-density and low-density lipoprotein cholesterol levels in healthy subjects. N. Engl. J. Med. 323:439-45. Pavan, E. and S. K. Duckett. 2013. Fatty acid composition and interrelationships among eight retail cuts of grass-fed beef. Meat Sci. 93:371-377. Pavan, E. and S. K. Duckett. 2007. Corn oil supplementation to steers grazing endophyte-free tall fescue. II. Effects on longissmus muscle and subcutaneous adipose fatty acid composition and stearoyl-CoA desaturase activity and expression. J. Anim. Sci. 85:1731-1740. Sackmann, J. R., S. K. Duckett, M. H. Gillis, C. E. Realini, A. H. Parks, and R. B. Eggelston. 2003. Effects of forage and sunflower oil levels on ruminal biohydrogenation of fatty acids and conjugated linoleic acid formation in beef steers fed finishing diets. J. Anim. Sci. 81:31743181. Turpeinen, A. M., M. Mutanen, I. Salimen, S. Basu, D. L. Palmquist, and J. M. Griinari. 2002. Bioconversion of vaccenic acid to conjugated linoleic acid in humans. Am. J. Clin. Nutr. 76:504-10. 100
Willet, W. C., M. J. Stampfer, J. E. Manson, G. A. Colditz, F. E. Speizer, B. A. Rosner, and C. H. Hennekens. 1993. Intake of trans fatty acid and risk of coronary heart disease among women. Lancet 341:581-585. Wright, A. M., C. Fernandez Rosso, M. C. Miller, E. Pavan, J. G. Andrae, and S. K. Duckett. 2014. Effect of forage type with or without corn supplementation on beef fatty acid composition and palatability. Meat Sci. (Submitted). Zock, P. L., J. H. M. de Vries, and M. B. Katan. 1994. Impact of myristic acid versus palmitic acid on serum lipid and lipoprotein levels in healthy women and men. Arterioscler. Thromb. 14:56775.
IMPROVING FEED EFFICIENCY IN THE FEEDLOT: OPPORTUNITIES AND CHALLENGES Galen E. Erickson1 1 University of Nebraska-Lincoln Introduction Feedlots focus heavily on feed efficiency or feed conversion and evaluate pens of cattle as a tool of how well management, nutrition, weather, and cattle purchasing decisions are performing. Feed efficiency would generally be gain divided by intakes (G:F), whereas conversions generally refer to intakes divided by gains. Conversion would be more typical for discussions with producers. Regardless of which is used, intakes should always be on a DM basis. Another concern is that feed efficiency is based on gain which requires measuring initial and final weights. Body weights are important currency to use when measuring efficiency; however, these weights can have errors that impact accuracy. Live cattle weights are dramatically impacted by gastrointestinal fill. Most yards will use receiving weight or pay weight for initial body weight and fill is likely less than once cattle arrive and consume hay and water. Lastly, use of live final body weights are less meaningful than carcass weights as final prices are based on hot carcass weights, even when selling live because packers are evaluating red meat yield and dressing percent when negotiating price. Therefore, when evaluating management or nutrition in feedlots, the impact on carcass weight is the ultimate outcome. We believe targeting gain and efficiency on a carcass basis is the direction for the beef industry. Unfortunately, carcass weights are still converted back to live weights to calculate gains and efficiency. OPPORTUNITIES Nutritional Methods Corn processing Corn grain has been a staple in feedlot diets due to abundance, low prices, and serving as the cheapest source of energy. Corn is the most common grain fed in the U.S.; however, other grains can be utilized in a similar manner such as grain sorghum, barley, or wheat. Corn contains about 2/3 or 70% starch which is readily digested once the kernel is broken. Whole kernels are quite resistant to digestion in the
rumen and intestinal tract of cattle unless broken due to mastication. To avoid passage of whole kernels and thus aid in starch digestion, corn grain is commonly processed. The three most common corn processing methods are dry-rolling, ensiled high-moisture, or steam-flaking. How corn is processed (and which grain source is fed) can have dramatic impacts on feed efficiency. Based on individual studies and reviews, in diets with 80 to 85% corn grain inclusion, feeding HMC is only 1 to 2% better than DRC. However, feeding SFC improves feed efficiency by 12 to 15% (Cooper et al, 2002; Owens et al., 1997). Byproducts Numerous summaries are available on the impact of feeding distillers grains and corn gluten feed to beef cattle. We have a summary available at our http://beef.unl.edu website. Historically, producers have been able to purchase distillers grains at 70 to 80% of corn price (DM basis). This price was considerably greater in 2013 and 2014 at 100 to 130% of corn price. The relatively strong prices on distillers grains are likely a reflection of strong demand for dry distillers (DDGS) for export and for use in non-ruminants. Our data suggest that wet distillers grains plus solubles (WDGS) has 143% the value of corn to the feedlot producer at 20% inclusion, and approximately 130% at 40% inclusion (Bremer et al., 2011; Table 1). When distillers grains are dried partially to make modified distillers grains (MDGS), the feeding value decreases to 117 to 124% of corn (at 20 to 40% inclusions). When distillers are completely dried to make DDGS, the value to the feeder is 112% of corn. The concept that WDGS results in better feed efficiency than MDGS which is better than DDGS has also been documented in individual studies (Nuttelman et al., 2011, 2013) Other byproducts such as wet corn gluten feed, distillers solubles or syrup, and Sweet Bran all have different impacts on feed efficiency of the cattle. Predicting impact of these byproducts should be based on performance data as most experiments compare the value to corn it is replacing.
Table 1. Meta-analysis of finishing steer performance when fed different dietary inclusions of corn wet distillers grains plus solubles (WDGS), modified distillers grains plus soluble (MDGS) or dried distillers grains plus soluble (DDGS) replacing dry rolled and high moisture corn. (Bremer et al., 2011) DGS Inclusion a:
0DGS 10DGS 20DGS 30DGS 40DGS
WDGS DMI, lb/day 23.0 23.3 23.3 23.0 22.4 0.01 < 0.01 ADG, lb 3.53 3.77 3.90 3.93 3.87 < 0.01 < 0.01 F:G 6.47 6.16 5.96 5.83 5.78 < 0.01 < 0.01 d Feeding value, % 150 143 136 130 MDGS DMI, lb/day 23.0 23.8 24.1 24.0 23.4 0.95 < 0.01 ADG, lb 3.53 3.77 3.90 3.92 3.83 < 0.01 < 0.01 F:G 6.47 6.29 6.17 6.10 6.07 < 0.01 0.05 d Feeding value, % 128 124 120 117 DDGS DMI, lb/day 23.0 24.0 24.6 24.9 24.9 < 0.01 0.03 ADG, lb 3.53 3.66 3.78 3.91 4.03 < 0.01 0.50 F:G 6.47 6.39 6.32 6.25 6.18 < 0.01 0.45 d Feeding value, % 112 112 112 112 Dietary treatment levels (DM basis) of distillers grains plus solubles (DGS), 0DGS = 0% DGS, 10DGS = 10% DGS, 20DGS = 20% DGS, 30DGS = 30% DGS, 40DGS = 40% DGS. b Estimation equation linear and quadratic term t-statistic for variable of interest response to DGS level. d Percent of corn feeding value, calculated from predicted F:G relative to 0WDGS F:G, divided by DGS inclusion. a
Distillers grains plus solubles are the most common byproduct used today and some discussion is warranted. Besides whether you are using dry or wet distillers, another major factor affecting how well distillers grains work for finishing cattle is related to how corn is processed. Unlike historical corn-based diets with 80 to 85% grain where feeding SFC works best, diets that contain distillers grains do not respond similarly (Table 2). Numerous studies have illustrated that feeding distillers grains appears to fit better with diets that contain DRC or HMC, and not as well with SFC (Corrigan et al, 2009; Vander Pol et al., 2008; Buttrey et al., 2012). Feeding SFC is better than DRC with diets containing distillers solubles (Titlow et al., 2013; Harris et al., 2014) and with Sweet Bran (Scott et al., 2003; Macken et al., 2006). The conclusion is that steam-flaking corn will normally improve feed efficiency (grain based diets, diets with distillers solubles, Sweet Bran, or corn gluten feed) but steam-flak-
ing does not improve efficiency as much when diets contain distillers grains plus solubles. It is unclear why this occurs, but is quite repeatable. Forage Concentration Forages fed in feedlot diets are often referred to as roughages. Forages are also routinely used for grain adaptation or the gradual (18 to 28 days) switch of cattle diets from a primarily forage-based diet to primarily a concentrate-based diet. While grain adaptation is very important, especially for teaching cattle to eat differently, the focus of this section is on the amount of forage in the final, high-concentrate finishing diet. Roughages are bulky ingredients with large shrink losses that feedlots would prefer to avoid. In general, as forage concentration is decreased in feedlot diets, feed efficiency improves. Cattle can be fed no roughage in feedlot diets. However, risk of ruminal acidosis increases and results in lower DMI,
Table 2. Effect of corn processing in diets containing increasing amounts of wet distillers grains plus solubles (Corrigan et al., 2009)1.
15.0 27.5 40.0
Dry-rolled corn DMI, lb/d 3 22.3 22.2 21.4 21.3 ADG, lb 2 3.64 3.77 3.87 3.92 G:F 2 0.163 0.170 0.181 0.185 High-moisture corn DMI, lb/d 3 20.1 21.0 20.2 20.0 ADG, lb 3 3.68 3.96 3.97 3.86 G:F 2 0.183 0.189 0.197 0.194 Steam-flaked corn DMI, lb/d 3 20.2 20.2 19.8 18.8 ADG, lb 3 3.67 3.74 3.60 3.44 G:F 0.182 0.186 0.182 0.183 For ADG: Effect of corn processing method, P < 0.01; effect of WDGS level, P = 0.01, and effect of corn processing method Ă— WDGS level, P < 0.01. For G:F: Effect of corn processing method, P < 0.01; effect of WDGS level, P < 0.01, and effect of corn processing method Ă— WDGS level, P < 0.01. 2 Linear effect of WDGS level within corn processing method (P < 0.05). 3 Quadratic effect of WDGS level within corn processing method (P < 0.05) 1
lower ADG, and equal or often times improved efficiency. Conventional inclusions of roughage would be approximately 4% neutral detergent fiber (NDF) from the roughage source. This equates to about 7 or 8% alfalfa hay, 5% crop residues like straw or stalks, and 10 to 12% corn silage. Exchanging these roughages on an equal NDF basis is the logical approach (Galyean and Defoor, 2003; Benton et al., 2007) to maintain DMI and ADG. There are a few examples where increasing forage will negatively impact feed efficiency, yet improve profitability. Two examples are with alkaline treated crop residues and feeding elevated dietary inclusions of corn silage. Across a series of six feedlot
studies (Table 3), feeding 20% alkaline treated cornstalks (treated with 5% calcium oxide) made the cattle 2.3% less efficient (greater F:G) yet is often profitable depending on corn and cornstalks prices. We have also evaluated feeding 15, 30, or 45% corn silage in diets with distillers grains (Table 4; Table 5). Feeding 45% silage instead of 15% decreased feed efficiency by 5 to 5.5% (Burken et al., 2013a; Burken et al., unpublished) yet increased profits (Burken et al., 2013b). Most times, increased feed efficiency means increased profits, but not always.
Table 3. Summary of F:G across experiments with 20% treated stalks (TRT) compared to a 5% stalks control (CON) or not treating (NONTRT). See literature cited for trial references.
Johnson calf Johnson yrlgs Shreck 3â€? Peterson 40% Cooper Average
CON 6.36a 6.42a 6.54 5.79 5.53
Treatments TRT NONTRT a 6.22 7.05b 6.85b 7.65c 6.55 7.72 5.88 5.83 -
CON vs TRT DIFF % DIFF -0.14 -2.2% 0.43 6.7% 0.01 0.2% 0.09 1.6% 0.30 5.4% 2.34%
CON vs NONTRT % DIFF 10.8% 19.2% 18.0% -
Table 4. Impact of feeding 15, 30, 45, or 55% dietary corn silage in diets with 40% distillers grains on feedlot performance (Burken et al., 2013a) Treatment P-value 15 30 45 55 Lin. Quad. 23.15 22.77 22.70 21.92 0.01 0.45 DMI, lb/day 3 4.04 3.92 3.76 3.53 <0.01 0.19 ADG, lb 0.175 0.172 0.166 0.161 <0.01 0.33 Feed:Gain 1 15:40= 15% Corn Silage, 40% MDGS; 30:40= 30% Corn Silage, 40% MDGS; 45:40= 45% Corn Silage, 40% MDGS; 55:40= 55% Corn Silage, 40% MDGS; 30:65= 30% Corn Silage, 65% MDGS; 45:0= 45% Corn Silage, 0% MDGS. Lin. = P-value for the linear response to corn silage inclusion, Quad.= P-value for the quadratic response to corn silage inclusion, 30 = t-test comparison of treatments 30:40 and 30:65, 45 = t-test comparison of treatments 45:40 and 45:0.
Calculated from hot carcass weight, adjusted to a common 63% dressing percentage.
Within a row, values lacking common superscripts differ (P < 0.10). abc
Marbling Score: 400 = Small00, 500 = Modest00. 4
Calculated from hot carcass weight, adjusted to a common 63% dressing percentage. 3
F-test= P-value for the overall F-test of all diets. Int. = P-value for the interaction of corn silage X MDGS. Silage = P-value for the main effect of corn silage inclusion. MDGS = P-value for the main effect of MDGS inclusion. 2
Table 5. Effect of feeding 15 or 45% corn silage with 20 or 40% MDGS inclusion on cattle performance and carcass characteristics (Burken et al., unpublished). Treatment1 P-value2 Control 15:20 15:40 45:20 45:40 F-test Int. Silage MDGS 27.2 26.1 26.4 26.9 26.7 0.13 0.41 0.07 0.86 DMI, lb/day 3 4.32 4.26 4.42 4.19 4.22 0.11 0.18 0.01 0.06 ADG, lb 3 bc ab a c c 0.159 0.163 0.167 0.156 0.158 <0.01 0.61 <0.01 0.07 Feed:Gain 879 874 885 866 869 0.18 0.41 0.01 0.12 HCW, lb 1 Control = 5% cornstalks, 40% MDGS; 15:20 = 15% Corn Silage, 20% MDGS; 15:40 = 15% Corn Silage, 40% MDGS; 45:20 = 45% Corn Silage, 20% MDGS; 45:40 = 45% Corn Silage, 40% MDGS
Use of technology (implants and beta agonists) For conventional beef production, numerous technologies are commonly used in the feedlot sector. The two main categories are feed additives and implants. Within both categories, there are many options. Feed additives are FDA approved and must follow legal guidelines established when they were approved, meaning no off-label use is allowed. While many are approved for growth promotion and improved feed efficiency, those label claims will be removed in the future if there is crossover to medically important additives used in human medicine. The first common additive is ionophores. The most common ionophore fed to finishing cattle is monensin (Rumensin, Elanco Animal Health). In a recent review, feeding monensin improved feed efficiency by 2.5 to 3.5% in recent studies (Duffield et al., 2010). The second most common feed additive is tylosin (Tylan, Elanco Animal Health). Tylosin is fed to decrease liver abscesses that result from feeding high-grain diets. Feeding tylosin increases carcass weight and thus gain likely due to trim losses. The most severe abscess category (A+) causes the biggest impact on performance. In a few large summaries of databases, cattle with A+ liver abscesses had 7 to 10 lb decreases when not adhered , 26 to 30 lb decreases if adhered to the carcass (Davis et al., 2007; Brown and Lawrence, 2010). The much greater decrease in carcass weight with adhered abscesses is due to greater trim at the packing plant presumably. While intake is unknown on these individual cattle with abscesses, clearly gain is decreased. For finishing heifers, feeding MGA (melengesterol acetate) is common to suppress estrus which improves gain and feed efficiency. Beta agonists are the other major feed additive fed to cattle at the end of the feeding period to increase carcass weights, gain, and improve feed efficiency. Two beta agonists are approved for use in the U.S.: ractopamine (tradename Optaflexx from Elanco Animal Health) and zilpaterol (tradename Zilmax from Merck Animal Health). Optaflexx was approved in 2003 to be fed at a rate of 8.2 to24.6 g/ton of diet DM and between 70 to 430 mg/animal daily for the last 28 to 42 d of the feeding period with no withdrawal time (FDA, NADA 141-221, 2003). Based on data, most feedlots will feed 200 to 300 mg/animal and target 28 days. Zilmax was approved in 2006 to be fed at a concentration of 7.56 g/ton of diet DM to provide 60 to 90 mg/animal daily for the last 20 to 105
40 d before harvest, with a three day withdrawal time (FDA NADA 141-258, 2006). Zilmax is not commercially available today. When it was fed, 20 days were targeted followed by a 3 day withdrawal. Because these products have dramatic increases in carcass weight and weight gain and are fed at the end of the feeding period when cattle normally have poorer feed efficiency, they dramatically improve the efficiency of the beef industry and also profitability. Feeding Optaflexx increases carcass weights by 13.4 to 20.3 lb depending when fed at 200 to 300 mg daily to steers (Pyatt et al., 2013) with a 10 to 15% improvement in feed efficiency during the final 28 days. Feeding Zilmax for the last 20 days increases live weights by 19 lb, but increases carcass weights by 33 lb primarily by shifting bodyweight from less internal fat to greater muscle mass (Elam et al., 2009). On a live basis, there is not a dramatic improvement in feed efficiency. However, if adjusted for carcass weight gain and increased yield of red meat, feeding Zilmax dramatically improves efficiency of the beef industry as well. The last major technology used by feedlots to improve feed efficiency is the use of implants. Steroid implants are approved to be placed in the middle third of the ear, just below the skin and slowly release hormone over a set period of days (usually 90 to 120 days but some last more 200 days). Implants can be classified into two major categories, estrogenic or combination implants and can further be classified based on strength or overall amount of steroid hormone. Combination implants provide both estrogen and trenbolone acetate (TBA) which is an analog of testosterone. There is no withdrawal on implants as the location used in the animal is discarded at slaughter although it is economically wise to use the last implant 90 to 120 days prior to slaughter to fully capture the value. Guiroy et al. (2002) summarized the impact of different implant strengths and concluded that final live body weight is increased by 40 to 100 lb depending on strength. More recently, stronger combinations and longer payout periods have likely lead to even greater increases in weights within approximately the same number of days. In general, implanting increases ADG for the entire feeding period by 10 to 15% and improves feed efficiency by 8 to 12%. Preston et al. (1990) concluded that implanted cattle require a few more days (7-10 depending on gender) to reach similar body composition or fatness. Implanting does 106
not depress quality grades of cattle if compared at equal fatness, but does with equal days fed in the feedlot. No other technology used today in feedlot cattle has as great of a return as use of implants. CHALLENGES Measuring feed efficiency in pen settings While we think about feed efficiency of individual cattle, we don’t measure individual feed efficiency in feedlots. Cattle are fed in pens. While gains are estimated (note estimated due to weighing conditions) for individuals, there is no sound, scientific method for accurately predicting individual feed intakes. Obtaining individual dry matter intake is critical but is generally limited to research settings or small-scale evaluations using Calan gates, GrowSafe, or other systems. Based on these data, we know dry matter intakes vary by 20% or more within groups of “like” cattle, gains vary by more than 30%, which leads to tremendous variation in feed efficiency (+/- 20%). If feed efficiency varies by 20% from the mean, then you cannot calculate intake based on a gain measurement of individuals very effectively. While variation is good for selection purposes, variation makes comparing individuals within pens extremely difficult to predict, even with sophisticated calculations. Age Cattle age when entering the feedlot has dramatic impacts on performance while in the feedlot phase. Numerous comparisons between feeding yearlings versus calf-feds are available. One important component of these comparisons is whether the cattle are genetically similar or not. Normal procedures in commercial production would be large framed, heavier weaned calves would be targeted to be fed as calf-feds whereas smaller framed, lighter weaned calves are traditionally “grown” into yearlings by backgrounding and/or grazing prior to entering the finishing phase. Griffin et al. (2007) compared performance and economics of feeding calf-feds or yearlings that were not similar in genetics at weaning. Calf-feds had fall receiving weights of 642 lb in mid November whereas the group “grown” into yearlings weighed 526 lb at that time. After backgrounding through the winter by grazing cornstalks and some drylotting, grazing pasture in the summer, the yearlings were 957 at feedlot entry the following fall. This
Table 6. Cattle background impact on feedlot performance for calf-feds, summer yearlings, and fall yearlings originating from the same pool of cattle as weaned calves (Adams et al., 2010)1.
Initial BW DMI, lb/d ADG, lb G:F Hot carcass weight, lb
576 789 928 a b 20.1 25.1 29.0c 3.59a 4.10b 4.28b 0.179a 0.164b 0.147c 774 856 919
was a 7 year comparison. Yearlings ate more feed per day, had greater daily gains, but were less efficient (F:G = 6.76) than calf-feds (F:G=5.63). Yearlings finished heavier with 50 heavier carcasses at about the same fat thickness. Using similar cattle (i.e., starting with the same “pool” of cattle each fall, Adams et al. (2010) fed those cattle as either calf-fed, summer (short) yearlings, or fall (i.e., long) yearlings and compared performance (Table 6). In their study, they imposed two treatments that included either sorting or not which had little impact on performance during finishing. Evaluating just finishing performance, yearlings eat more per day, gain more per day, but are less efficient than calf-feds (Table 4). Summer fed yearlings are intermediate. Meaning reasons exist for either feeding cattle as calf-feds or growing them into yearlings including forage resources available, optimizing finish weight (i.e., carcass weight), and economics. Even though yearlings are less efficient while in the feedlot, grazing or utilizing forage is unique to ruminant production is makes these systems very economical despite poorer feed efficiency while in just the feedlot phase. Feeding yearlings also increases saleable weight per weaned calf as they “grow” frame during the backgrounding phase. Table 6. Cattle background impact on feedlot performance for calf-feds, summer yearlings, and fall yearlings originating from the same pool of cattle as weaned calves (Adams et al., 2010)1. Bovine Respiratory Disease Bovine respiratory disease (BRD) is detrimental to the cattle industry and is perceived to have large economic impacts from treatment costs and lost performance. However, many studies that evaluate the impact of BRD on feedlot cattle performance are incorrect and lead to erroneous estimates of eco-
nomic impact. Numerous studies illustrate that BRD negatively impacts gains and these are based on how individuals within pens that were diagnosed with BRD (and presumably have BRD) gained compared to those not treated. Gardner et al. (1999) observed a 12% decrease in gain and 44 lb lighter carcasses for cattle treated more than once compared to not treated at all for BRD. Treating once didn’t have much impact. Ranch-to-rail data from New Mexico found a 14% decrease in daily gain and 15 lb lighter carcasses for cattle treated more than once (Waggoner et al., 2007). The study with the greatest number of cattle (about 21,000 head total) was by Reinhardt et al. (2009) using cattle in Iowa feedlots. They observed a 20% decrease in daily gain for steers and a 27% decrease for heifers treated more than once versus not at all. Treating once was intermediate in their study. Cattle treated more than once were also 15 lb lighter at slaughter. All of these data are on pen-fed cattle where intakes, and thus feed efficiency, are unknown. When economics are applied, most have assumed average pen intakes which means a 20% decrease in gain translates to cattle being 20% less efficient. Some data are available on the impact on feed efficiency with intakes measured. An excellent study was done at Oklahoma State where cattle were received and then after receiving, cattle were penned (grouped) based on whether they got treated 0, 1, 2, 3, or 3+ times during the first 60 days. If they were sick during receiving, gain decreased dramatically during the first 60 days. Interestingly, gains were very similar from day 60 to finish after being penned or grouped based on how many times they had gotten sick. Cattle consumed less feed after the receiving period if they had gotten sick so cattle were actually more efficient during finishing if they had gotten sick, and improved linearly as number of times treated increased. Clearly, 107
the majority of BRD occurs within a few weeks of receiving (Babcock et al., 2009). We evaluated data from our individual feeding facility at UNL (Calan gates), as well as individual feeding data from the University of Illinois using GrowSafe. In those two datasets, if cattle contracted BRD within the first 30 days (out of 120 or more total days), there was no impact on intakes, gains, or efficiency. If treated after the first 30 days on feed for BRD, cattle tended to eat less, gain less, but efficiency was the same as healthy cohorts. This suggests to us that cattle gain less and eat less when they are sick. After a receiving period (especially if only treated once or twice), gains come back some, but cattle still eat less which is interesting. Getting sick early in the feeding period or at receiving probably has little impact overall if treated and they recover (treated only once). These changes in intake and efficiency (or lack thereof) should be taken into account when applying economics to cattle that are affected by BRD. In all these studies, it is important to point out that data are based on visual observation, body temperature, and a diagnosis of BRD which may not always be 100% accurate. Weighing conditions Most people in the beef industry take for granted that when weights are collected, cattle weigh whatever the scale reads. While that is true, this weight may not be repeatable. The main factor affecting weights and particularly variation in weights is gut fill. How this impacts feed efficiency in feedlots is probably less of a concern, but can be a real concern when establishing a weight and price for sale of cattle (entering or leaving a feedlot) and also when calculating gains from initial and final weights. Length of time when gains are measured improves these estimates of gain. Watson et al. (2013) summarized the impact of different weighing conditions on gain estimates for growing cattle. Equalizing gut fill by limit feeding and multiple day weights improved accuracy in gain estimates for growing cattle especially when measured over short durations. The reason for being aware is that cattle that are severely shrunk when arriving at the feedlot due to transport distance and removal of feed and water for long periods of time will certainly â€œrefillâ€? when given access to feed and water. Using severely shrunk weights will inflate gains and make cattle appear more efficient. Likewise, using 108
weights on cattle once fill is replenished at the yards will deflate gains some and cattle will appear less efficient. True biological efficiency of cattle is not impacted by shrink as very little carcass weight is ever lost in normal situations of shrink due to transport and handling cattle at marketing. Carcass weight gain for efficiency As discussed earlier, feeding Zilmax dramatically increases carcass weight (33 lb) yet only increases live weight by 19 lb compared to controls within studies. As a result, dressing percentage (carcass weight divided by live weight) is dramatically increased (usually by about 1.5 percentage units). Genetics can dramatically influence the relative amount of carcass gain compared to live weight gain. One example of this are some recent data collected at UNL using Piedmontese and active, inactive, or heterogenous myostatin allele cattle. Table 7 shows two years of data feeding calf-fed steer calves with these three genotype variations and the impact on finishing performance (Moore et al., 2013). Live gains were decreased with the inactive myostatin genetic background but when carcass-adjusted, gains were not decreases and cattle were dramatically more efficient (in both scenarios). Cattle were much leaner but dressing percentage increased 4.25 percentage units. Similar results were observed with yearling heifers finished. The beef industry should begin evaluating efficiency on a carcass weight basis, including calculation of carcass gain (new measure of average daily gain) and feed efficiency from that gain calculation. More evidence for this is recent work illustrating the economic benefits of feeding cattle longer and larger when marketing on a carcass weight basis or grid basis because gain of carcass does not decrease at the end of the feeding period like live weight gain does (MacDonald et al., 2014). The biggest challenge is lack of accurate carcass weights at the beginning of the feeding period to use in calculating carcass gain. However, for feedlots selling cattle on a carcass weight basis, collection of live weights at the end of the feeding period when loaded for transport to the slaughter plant are meaningless as well. If not collected, an estimate has to be made for final live weight from carcass weight anyway to calculate closeouts.
Table 7. Live and carcass-adjusted BW performance, and carcass traits of calf-fed steers varying in allele copies of myostatin using Piedmontese. Myostatin1 P – Value2 ACTIVE HET INACTIVE Lin. Quad. Performance traits DMI, lb/d 18.9 17.1 15.0 < 0.01 0.69 Final BW, lb3 1132 1099 1015 < 0.01 0.27 ADG, lb/d 2.56 2.35 2.26 < 0.01 0.43 F:G 7.30 7.25 6.67 < 0.01 0.07 4 Carcass-adjusted BW ADG, lb/d 2.53 2.39 2.58 0.72 0.05 F:G 7.41 7.09 5.88 < 0.01 < 0.01 Carcass traits HCW, lb 712 699 684 0.18 0.93 Dress, % 63.0 63.7 67.3 < 0.01 < 0.01 5 Marbling 597 453 283 < 0.01 0.57 2 LM area, in 12.4 14.6 15.5 < 0.01 0.05 th 12 rib Fat, in 0.51 0.28 0.13 < 0.01 0.26 1 Myostatin: homozygous active (ACTIVE), heterozygous (HET), and homozygous inactive (INACTIVE) 2 P-value: Lin. = linear response to inactive myostatin and Quad. = quadratic response to inactive myostatin 3 Live BW collected on 2 consecutive d prior to shipment, shrunk 4 % 4 Carcass-adjusted BW calculated at 63 % dressing 5 Marbling score: 500 = SM, 400 = SL, 300 = TR, 200 = PD Comparisons at equal body composition Body composition influences overall feed efficiency due to the energetics of depositing fat or muscle. Some of the impact of age (calf-feds versus yearlings) is due to composition of growth. However, when cattle are not finished to the same endpoint in terms of carcass fatness, leaner animals are more efficient. Likewise, as cattle grow during finishing and deposit more fat, their efficiency (of live weight gain) decreases as well. The reason cattle efficiency is impacted by composition of gain is because it requires about the same amount of calories (energy) to deposit protein and fat in the carcass. However, muscle is about 75% water and 25% protein. As a result, muscle growth (not protein) is about 3 times greater in efficiency of growth which is logical so cattle deposit muscle first and retain energy as fat only when additional energy is consumed above that required to grow muscle. The point is that cattle sold “early” that are leaner may be more efficient than cattle fed later and at least some of the efficiency difference is due to composition of gain.
LITERATURE CITED Adams, D. R., T. J. Klopfenstein, G. E. Erickson, W. A. Griffin, M. K. Luebbe, M. A. Greenquist, and J. R. Benton. 2010. Effects of sorting steers by body weight into calf-fed, summer yearling, and fall yearling feeding systems. Prof. Anim. Sci. 26:587-594. Babcock, A. H., B. J. White, S. S. Dritz, D. U. Thomson, and D. G. Renter. 2009. Feedlot health and performance effects associated with the timing of respiratory disease treatment. J. Anim. Sci. 87:314-327. Benton, J. R., G. E. Erickson, T. J. Klopfenstein, K. J. Vander Pol, and M. A. Greenquist. 2007. Effects of roughage source and level with the inclusion of wet distillers grains on finishing cattle performance and economics. Neb. Beef Cattle Rep. MP 90:29-32. 109
Bremer, V. R., A. K. Watson, A. J. Liska, G. E. Erickson, K. G. Cassman, K. J. Hanford, and T. J. Klopfenstein. 2011. Effect of distillersâ€™ grains moisture and inclusion level in livestock diets on greenhouse gas emissions in the corn-ethanol-livestock life cycle. Prof. Anim. Sci. 27:449-455. Brown, T. R., and T. E. Lawrence. 2010. Association of liver abnormalities with carcass grading performance and value. J. Anim. Sci. 88:40374043. Burken, D. B., B. L. Nuttelman, T. J. Klopfenstein, and G. E. Erickson. 2013a. Feeding elevated levels of corn silage in finishing diets containing MDGS. Neb. Beef Cattle Rep. MP98:74-75. Burken, D. B., T. J. Klopfenstein, and G. E. Erickson. 2013b. Economics of feeding elevated levels of corn silage in finishing diets containing MDGS. Neb. Beef Cattle Rep. MP98:76-77. Buttrey, E. K., N. A. Cole, K. H. Jenkins, B. E. Meyer, F. T. McCollum, S. L. M. Preece, B. W. Auvermann, K. R. Heflin, and J. C. MacDonald. 2012, Effects of twenty percent corn wet distillers grains plus solubles in steam-flaked or dry-rolled corn-based finishing diets on heifer performance, carcass characteristics, and manure characteristics. J. Anim. Sci. 90:5086-5098. Cooper, R., B. Dicke, D. J. Jordon, T. Scott, C. Macken, and G. E. Erickson. 2014. Impact of feeding alkaline-treated corn stover at elevated amounts in commercial feedlot cattle. Neb. Beef Cattle Rep. MP99:69-71. Cooper, R. J., C. T. Milton, T. J. Klopfenstein, and D. J. Jordon. 2002a. Effect of corn processing on degradable intake protein requirement of finishing cattle. J. Anim. Sci. 80:242â€“247. Corrigan, M. E., G. E. Erickson, T. J. Klopfenstein, M. K. Luebbe, K. J. Vander Pol, N. F. Meyer, C. D. Buckner, S. J. Vanness, and K. J. Hanford. 2009. Effect of corn processing method and corn wet distillers grains plus solubles inclusion level in finishing steers. J. Anim. Sci. 87: 3351-3362. 110
Davis, M. S., W. C. Koers, K. J. Vander Pol, and O. A. Turgeon. 2007. Liver abscess score and carcass characteristics of feedlot cattle. J. Anim. Sci. 85(E Suppl):84 abstr. Duffield, T. F., J. K. Merriill, and R. N. Bagg. 2012. Meta-analysis of the effects of monensin in beef cattle on feed efficiency, body weight gain, and dry matter intake. J. Anim. Sci. 90:4583-4592. Elam, N. A., J. T. Vasconcelos, G. Hilton, D. L. VanOverbeke, T. E. Lawrence, T. H. Montgomery, N. T. Nichols, M. N. Streeter, J. P. Hutcheson, D. A. Yates, and M. L. Galyean. 2009. Effect of zilpaterol hydrochloride duration of feeding on performance and carcass characteristics of feedlot cattle. J. Anim. Sci. 87:2133-2141. FDA. 2003. Freedom of information summary. Original new drug application NADA 141-221. Ractopamine hydrochloride (OPTAFLEXX 45). Type A medicated article for cattle fed in confinement for slaughter. Found at: http://www. fda.gov/downloads/AnimalVeterinary/ Products/ ApprovedAnimalDrugProducts/FOIADrugSummaries/UCM236239.pdf. Accessed 11 February 2014. FDA. 2006. Freedom of information summary. Original new drug application NADA 141-258. Zilpaterol hydrochloride (ZILMAX). Type A medicated article for cattle fed in confinement for slaughter. Found at: http://www.fda.gov/ downloads/AnimalVeterinary/Products/ ApprovedAnimalDrugProducts/FOIADrugSummaries/ ucm051412.pdf. Accessed 11 February 2014 Galyean, M. L., and P. J. Defoor. 2003. Effects of roughage source and level on intake by feedlot cattle. J. Anim. Sci. 81 (Suppl 2):E8-E16. Gardner, B. A., H. G. Dolezal, L. K. Bryant, F. N. Owens, and R. A. Smith. 1999. Health of finishing steers: Effects on performance, carcass traits, and meat tendernsess. J. Anim. Sci. 77:3168-3175.
Griffin, W. A., T. J. Klopfenstein, G. E. Erickson. D. M. Feuz, J. C. MacDonald and D. J. Jordon. 2007. Comparison of performance and economics of a long-yearling and calf-fed system. Prof. Anim. Sci. 23:490-499.
Nuttelman, B. L., D. B. Burken, C. J. Schneider, G. E. Erickson, and T. J. Klopfenstein. 2013. Comparing wet and dry distillers grains plus solubles for yearling finishing cattle. Neb. Beef Cattle Rep. MP98:62-63.
Guiroy, P. J., L. O. Tedeschi, D. G. Gox, and J. P. Hutcheson. 2002. The effects of implant strategy on finished body weight of beef cattle. J. Anim. Sci. 80:1791-1800.
Nuttelman, B. L., W. A. Griffin, J. R. Benton, G. E. Erickson, and T. J. Klopfenstein. 2011. Comparing dry, wet, or modified distillers grains plus soluble on feedlot cattle performance. Neb. Beef Cattle Rep. MP94:50-52.
Harris, M. E., G. E. Erickson, K. H. Jenkins, and M. K. Luebbe. 2014. Evaluating corn condensed distillers solubles concentration in steam-flaked corn finishing diets on cattle performance and carcass characteristics. Neb. Beef Cattle Rep. MP99:86-87
Owens, F. N., D. S. Secrist, W. J. Hill, and D. R. Gill. 1997. The effect of grain source and grain processing on performance of feedlot cattle: A review. J. Anim. Sci. 75:868-879.
Johnson, J. M., D. B. Burken, W. A. Griffin, B. L. Nuttelman, G. E. Erickson, T. J. Klopfenstein, M. J. Cecava, and M. J. Rincker. 2013. Effect of feeding greater amounts of calcium oxide treated corn stover and Micro-Aid on performance and nutrient mass balance. Neb. Beef Cattle Rep. MP98:70-73. MacDonald, J. C., C. J. Schneider, K. M. Rolfe, S. D. Kachman, T. J. Klopfenstein, and G. E. Erickson. 2014. Optimal marketing date of steers depends on marketing strategy. Neb. Beef Cattle Rep. MP99:92-96 Macken, C. N., G. E. Erickson, T. J. Klopfenstein and R. A. Stock. 2006. Effects of corn processing method and protein concentration in finishing diets containing wet corn gluten feed on cattle performance. Prof. Anim. Sci. 22:14-22. Moore, S. K., C. J. Schneider, K. M. Rolfe, B. L. Nuttelman, D. B. Burken, W. A. Griffin, J. R. Benton, G. E. Erickson, and M. L. Spangler. 2013. Association of inactive myostatin in Piedmontese-influenced steers and heifers on performance and carcass traits at different endpoints. Neb. Beef Cattle Rep. MP98:53-55.
Peterson, S. J., B. L. Nuttelman, C. J. Schneider, D. B. Burken, J. C. MacDonald, and G. E. Erickson. 2014a. Optimum inclusion of alkaline-treated cornstalks and distillers grains fed to calf-fed steers. Neb. Beef Cattle Rep. MP99:72-74. Preston, R. L., S. J. Bartle, A. C. Brake, and R. E. Castlebury. 1990. Effect of anabolic growth implants on the time required to reach quality grade equivalent to nonimplanted cattle. Texas Tech Univ. Agric. Sci. Tech. Rep. No T-5-283. Pyatt, N. A., G. J. Vogel, J. W. Homm, R. L. Botts, and C. D. Bokenkroger. 2013. Effects of ractopamine hydrochloride on performance and carcass characteristics in finishing steers: 32-trial summary. J. Anim. Sci. 91(Suppl. 2):79 abstr. Reinhardt, C. D., W. D. Busby, and L. R. Corah. 2009. Relationship of various incoming cattle traits with feedlot performance and carcass traits. J. Anim. Sci. 87:3030-3042. Shreck, A. L., B. L. Nuttelman, W. A. Griffin, G. E. Erickson, T. J. Klopfenstein, and M. J. Cecava. 2012b. Reducing particle size enhances chemical treatment in finishing diets. Neb. Beef Cattle Rep. MP95:108-109.
Scott, T. L., C. T. Milton, G.E. Erickson, T. J. Klopfenstein, and R. A. Stock. 2003. Corn processing method in finishing diets containing wet corn gluten feed. J. Anim. Sci. 81:3182-3190. Titlow, A. H., A. L. Shreck, S. A. Furman, K. H. Jenkins, M. K. Luebbe, and G. E. Erickson. 2013. Replacing steam-flaked corn and dry-rolled corn with condensed distillers solubles in finishing diets. Neb. Beef Cattle Rep. MP98:51-52. Vander Pol, K. J., M. A. Greenquist, G. E. Erickson, T. J. Klopfenstein, and T. Robb. 2008. Effect of corn processing in finishing diets containing wet distillers grains on feedlot performance and carcass characteristics of finishing steers. Prof. Anim. Sci. 24:439-444. Waggoner, J. W., C. P. Mathis, C. A. Loest, J. E. Sawyer, F. T. McCollum and J. P. Banta. 2007. Case Study: Impact of morbidity in finishing beef steers on feedlot average daily gain, carcass characteristics, and carcass value. Prof. Anim. Sci. 23:174-178. Watson, A. K., B. L. Nuttelman, T. J. Klopfenstein, L. W. Lomas, and G. E. Erickson. 2013. Impacts of a limit feeding procedure on variation and accuracy of cattle weights. J. Anim. Sci. 91:5507-5517.
RELATIONSHIP BETWEEN SELECTION FOR FEED EFFICIENCY AND METHANE PRODUCTION Harvey Freetly1 1 USDA, ARS, U. S. Meat Animal Research Center, Clay Center, NE 68933* *USDA is an equal opportunity provider and employer. Where Does Methane Come From? Enteric methane is a product of fermentation in the gastro-intestinal tract of ruminants. A group of archaea bacteria collectively called “methanogens” are responsible for the synthesis of methane. Methanogens live in environments that are void of oxygen and are frequently involved with the fermentation of organic material. In addition to being found in the gastro-intestinal tract of animals, they are found in other sites where fermentation occurs such as bogs, marshes (marsh gas), landfills, waste water containment ponds, and feedlot surfaces. Methanogens typically use acetate or carbon dioxide and hydrogen as substrate to grow and produce methane as a byproduct. In ruminants, the majority of the methanogen species use carbon dioxide and hydrogen. In ruminants, the methanogens grow in the reticulum-rumen complex and in the cecum. Most of the methane that a ruminant produces is in the reticulum-rumen (87%), and is released into the environment through the mouth (Murray et al., 1976). Most of the methane produced in the cecum (89%) is absorbed in the blood and travels to the lungs where it is exhaled during respiration (Murray et al., 1976). About 3% of the methane is released from the rectum (Murray et al., 1976; Muñoz et al., 2012). Methanogens live in a symbiotic relationship with the other bacteria in the rumen; however, they make up a relative small proportion of the total rumen microbes (Krause and Russell, 1996; Mosoni et al., 2011). Methanogens help maintain a zero net hydrogen balance in the rumen by releasing hydrogen in the form of methane rather than other microbes producing longer chained volatile fatty acids such as propionate. The Problem Methane is a greenhouse gas. Depending on the size and level of feed intake, cattle will produce 10 to 16 kg of methane per year (Hristov et al., 2013).
Methane represents a lost opportunity to capture feed energy. If captured, this lost energy could potentially be used for maintenance, growth, and lactation. There is a lot of variation in the fraction of intake energy released as methane (Johnson and Johnson, 1995). This variation can partially be explained by the composition of the diet. About 3% of intake energy consumed by steers fed a high-corn diet is lost as methane energy (Archibeque et al., 2007). The percentage increases when cattle are eating a high-forage diet. Increasing the forage:concentrate ratio increased methane production (Reynolds et al., 1991; Sauvant and Giger-Reverdin, 2007). Methanogens are sensitive to low rumen pH and their prevalence decreases (Van Kessel and Russell, 1996). Pregnant beef cows eating a corn silage based diet will release 5 to 7% of their gross energy intake as methane (Freetly et al., 2008). A number of strategies have been used to reduce methane production including chemical inhibitor, ionophores, and manipulation of the rumen ecology. A potential approach for reducing methane production is to select for increased feed efficiency. Methane and Feed Efficiency The relationship between methane production and feed efficiency is dependent on how feed efficiency is defined. Selecting cattle for greater residual gain or greater gain:feed ratios may result in an increase in methane production. Residual gain is the difference in amount of body weight gain an animal achieves compared to what it is predicted to gain for a given feed intake. Cattle that more completely digest their feed will get more nutrients per unit of feed and produce more methane. In our studies in cattle not selected for feed efficiency, methane production increased with increased gain:feed ratios when they were fed a roughage diet, but there were no differences when they were fed a concentrate diet (Freetly and Brown-Brandl, 2013). The different response in the two experiments may have been due to the relative digestibility of the two diets. The concentrate diet was highly digestible and the variance in the rate of digestibility may have been lower than compared to cattle consuming the less digestible roughage diet. Goopy et al. (2014) found that methane production increased with increased rumen retention times. They also determined that sheep that produced more methane had greater rumen volume.
Residual feed intake (RFI) is the difference in amount of feed consumed by an animal from that predicted for its rate of body weight gain and size. Negative RFI are more efficient since they ate less feed than is predicted to be needed for a given rate of production. Residual feed intake has been used as a measure of feed efficiency and has been used in selection programs to improve feed efficiency. Selection on RFI decreases feed intake (Herd et al., 2002). Methane production increases with increased feed intake; however, the methane per unit of feed decreases (Blaxter and Clapperton, 1965). Hegarty et al. (2007) reported that cattle selected for low RFI have a reduced daily methane production, and Nkrumah et al. (2006) found that steers that ranked low for RFI had a reduced methane production. Zhou et al. (2009) determined the relative proportion of different species of methanogens differ between cattle classified as having less or greater RFI which may influence the potential to produce methane. The studies of Nkrumah et al. (2006) and Hegarty et al. (2007) differ when methane productions are expressed per unit of feed fed. Hegarty et al. (2007) found that cattle selected for a low RFI also had a reduced total feed intake, but they did not differ in the amount of methane produced per unit of feed. In our studies, we found RFI did not account for differences in methane production when we adjusted for feed intake (Freetly and Brown-Brandl, 2013). Nkrumah et al. (2006) found that steers with a low RFI produced less methane per unit fed than other steers. Collectively, these studies suggest that selection for low RFI does not inherently mean that methane production per unit of feed is decreased, but methane production is reduced by decreasing the amount of feed consumed. Other Factors to Consider Factors other than feed efficiency contribute to the methane footprint of cattle. Hristov et al. (2013) has reviewed several management strategies used to reduce methane production. These include the feeding of inhibitors, electron receptors, ionophores, plant bioactive compounds, enzymes, yeast products, and oils. Other approaches have included decreasing the rumen protozoa and manipulating the rumen archaea and bacteria ecology. One of the biggest factors that determine the lifetime methane production of calves is the number of days from birth to harvest. If we 113
assume a 160-day finish period and cattle consume 35 Mcal/day and 3% of the consumed energy is released as methane, then total methane release is 168 Mcal. Using the same assumptions on a 150-day finishing period, methane production is 158 Mcal. The 10day decrease on feed results in a 6% drop in methane production. Similarly, backgrounding programs that prolong the age at harvest will increase lifetime methane production. Management and selection programs that decrease the age at harvest will reduce lifetime methane production. The bulk of the annual methane production from cattle can be attributed to the cow herd. If we consider the measure of methane efficiency to be the amount of calf marketed per unit of methane produced in a cow’s lifetime, then factors that make a cow economically efficient are the same that makes her efficient with regard to methane production. Selecting and managing cattle for prolonged lifetime productivity, and pounds of calf marketed per unit of feed consumed will improve methane efficiency. Literature Cited Archibeque, S. L., H. C. Freetly, N. A. Cole, and C. L. Ferrell. 2007. The influence of oscillating dietary protein concentrations on finishing cattle. II. Nutrient retention and ammonia emissions. J. Anim. Sci. 85:1496-1503. Blaxter, K. L. and J. L. Clapperton. 1965. Prediction of the amount of methane produced by ruminants. Br. J. Nutr. 19:511-522. Freetly, H. C., J. A. Nienaber, and T. Brown-Brandl. 2008. Partitioning of energy in pregnant beef cows during nutritionally induced body weight fluctuation. J. Anim. Sci. 86:370-377. Freetly, H. C., and T. Brown-Brandl. 2013. Enteric methane production from beef cattle that vary in feed efficiency. J. Anim. Sci. 91:4826-4831. Goopy, J. P., A. Donaldson, R. Hegarty, P. E. Vercoe, F. Haynes, M. Barnett, and V. Hutton Oddy. 2014. Low-methane yield sheep have smaller rumens and shorter rumen retention time. Br. J. Nutr. 111:578-585. Hegarty, R. S., J. P. Goopy, R. M. Herd, and B. McCorkell. 2007. Cattle selected for lower residual feed intake have reduced daily methane production. J. Anim. Sci. 85:1479-1486. 114
Herd, R. M., P. F. Arthur, and R. S. Hegarty. 2002. Potential to reduce greenhouse gas emissions from beef production by selection to reduce residual feed intake. Communication 10–22 in Proc. 7th World Congr. Genet. Anim. Prod., Montpellier, France. Hristov, A. N., J. Oh, J. L. Firkins, J. Dijkstra, E. Kebreab, G. Waghorn, H. P. S. Makkar, A. T. Adesogan, W. Yang, C. Lee, P. J. Gerber, B. Henderson, and J. M. Tricarico. 2013. Special topics--Mitigation of methane and nitrous oxide emissions from animal operations: I. A review of enteric methane mitigation options. J. Anim. Sci. 91:5045-5069. Johnson, K. A., and D. E. Johnson. 1995. Methane emissions from cattle. J. Anim. Sci. 73:24832492. Krause, D. O. and J. B. Russell. 1996. Symposium: Ruminal microbiology. How many ruminal bacteria are there? J. Dairy Sci. 79:14671475. Mosoni, P., C. Martin, E. Forano, D. P. Morgavi. 2011. Long-term defaunation increases the abundance of cellulolytic ruminococci and methanogens but does not affect the bacterial and methanogen diversity in the rumen of sheep. J. Anim. Sci. 89:783-791. Muñoz, C., T. Yan, D. A. Wills, S. Murray, and A.W. Gordon. 2012. Comparison of sulfur hexafluoride tracer and respiration chamber techniques for estimating methane emissions and correction for rectum methane output from dairy cows. J. Dairy Sci. 95:3139-3148. Murray, R. A. A. M. Bryant , and R. A. Leng. 1976. Rates of production of methane in the rumen and large intestine of sheep. Br. J. Nutr. 36:114. Nkrumah, J. D., E. K. Okine, G. W. Mathison, K. Schmid, C. Li, J. A. Basarab, M. A. Price, Z. Wang, and S. S. Moore. 2006. Relationships of feedlot feed efficiency, performance, and feeding behavior with metabolic rate, methane production, and energy partitioning in beef cattle. J. Anim. Sci. 84:145-153.
Reynolds, C. K., H. F. Tyrrell, and P. J. Reynolds. 1991. Effects of dietary forage-to-concentrate ratio and intake on energy metabolism in growing beef heifers: Whole body energy and nitrogen balance and visceral heat production. J. Nutr. 121: 994-1003. Sauvant, D., and S. Giger-Reverdin. 2007. Empirical modeling meta-analysis of digestive interactions and CH4 production in ruminants, pp. 561-563 in Energy and Protein Metabolism and Nutrition. I. Ortigues-Marty, N. Miraux, and W. Brand-Williams, ed., Wageningen, The Netherlands. Van Kessel, J. A., and J. B. Russell. 1996. The effect of pH on ruminal methanogenesis. FEMS Microbiol. Ecol. 20:205-210. Zhou, M., E. Hernandez-Sanabria, and L. L. Guan. 2009. Characterization of variation in rumen methanogenic communities under different dietary and host feed efficiency conditions, as determined by PCR-denaturing gradient gel electrophoresis analysis. Appl. Environ. Micro. 75:6524-6533.
TECHNICAL COMMITTEES HEALTHFULNESS OF BEEF: A GENOME-WIDE ASSOCIATION STUDY USING CROSSBRED CATTLE C.M. Ahlberg1, L.N. Schiermiester1, M. L. Spangler1 1 Animal Science Department, University of Nebraska– Lincoln Introduction Consumers are becoming increasingly health-conscious and demand healthy and palatable meat, both of which are affected by lipid composition (Dunner et al., 2013). Red meat has relatively high levels of saturated fatty acids and beneficial oleic acid, and low concentrations of beneficial polyunsaturated fatty acids (Dunner et al., 2013). However, fats are not the only nutrients that affect the nutritional value of beef. Beef is an excellent source of iron required in the human diet, yet the consistency of iron content in beef products is highly variable (Duan et al., 2009). Considerable attention has been placed on improving the nutritional value of beef and the development of products that are beneficial to human health and disease prevention (Scollan et al., 2006). It has been illustrated that animal nutritional regime differences can alter the nutrient profile of beef (Realini et al., 2004) and that genetic factors can also play a role (De Smet et al., 2004; Mateescu et al., 2013a,b). Identification of genetic variants that would allow producers to select for optimum nutritional values with respect to fatty acids, minerals, and vitamins, without sacrificing performance or product quality, could ultimately increase value and consumer satisfaction of beef. Genetic selection aided by genomic predictors may serve as an important and highly applicable tool in improving the nutritional value of beef given the expensive and difficult nature of phenotypic data collection. The objectives of the current study were to determine the proportion of phenotypic variation explained by the Ilumina BovineSNP50KBead-Chip for cholesterol (CH), polyunsaturated fatty acids (PUFA), monounsaturated fatty acids (MUFA), protein, potassium, iron and sodium, to identify chromosomal regions that harbor major genetic variants underlying the variation of these traits. 116
Materials and Methods Experimental Design Crossbred steers and heifers of unknown pedigree and breed fractions (n= 236) with varying percentages of Angus, Simmental and Piedmontese were placed in a Calan gate facility at the Agricultural Research and Development Center (ARDC) feedlot facility near Mead, NE. Prior to arrival, animals were genotyped for the Piedmontese-derived myostatin mutation (C313Y) to determine their myostatin genotype (MG) as either homozygous normal (313C/313C, 0 copy, n=83), heterozygous (313C/313Y, 1-copy, n=96), or homozygous for inactive myostatin (313Y/313Y, 2-copy, n=57). Cattle were fed in four groups over a 2-yr period. Groups 1 and 3 consisted of calf-fed steers and groups 2 and 4 consisted of yearling heifers as described by Howard et al., (2013). Animals had ad libitum access to water and were fed a diet that met or exceeded National Research Council (NRC) (1996) requirements. The finishing ration for steers and heifers in year 1 included wet distillers grain with solubles, a 1:1 blend of high moisture and dry rolled corn, grass hay and supplement at 35, 52, 8, and 5 % of the diet on a dry matter basis. The finishing ration for steers and heifers in year 2 included modified distillers grain with solubles, sweet bran, a 1:1 blend of high moisture and dry rolled corn, grass hay and supplement at 20, 20, 48, 8, and 4 % of the diet on a dry matter basis. Animals were on an all-natural program and were not implanted or fed growth-promoting additives. Cattle were harvested as a group based on average body weight and external fat. Steaks were sampled from the M. Longissimus thoracis et lumborum (LTL) and the M. Semitendinosus (ST) three days post mortem. Steaks were cut to ½ inch thick and trimmed to 1/8 inch of subcutaneous fat. Steaks were shipped to Midwest Laboratories, Inc. (Omaha, NE) for further analysis. Lipid, and mineral analysis results were reported for a 113.40 gram serving size. Statistics for carcass traits are summarized in Table 1. Fatty acids (MUFA and PUFA) and CH were analyzed as both a percentage of total lipid content
and mg/100g of whole (wet) tissue. Omega 3, 6 and 9 fatty acids were reported as MUFA or PUFA. The interpretation of these two measurement scales is dramatically different, as a sample with relatively low PUFA content as measured in mg/100g of whole (wet) tissue would likely have low total lipid content and as a consequence would have relatively high PUFA content when measured as a percentage of total lipids. Potassium, iron and sodium were analyzed as ppm of whole tissue. Statistical Analysis Myostatin genotype has been shown to have an effect on fatty acid composition. Consequently, outliers, adjusted for group and MG, classified as being > 3 SD from the mean of the residual variance (zero), were removed from the analysis. Summary statistics for fatty acid and mineral traits after editing are detailed in Table 2. A genome wide association study (GWAS) using the BovineSNP50K Bead-Chip was conducted via the GenSel platform (Version 0.9.2.045; Fernando and Garrick, 2011). A Bayes C model was employed (Habier et al., 2011) with group (concatenation of year (i.e. feeding regime) and sex; 4 classes) fitted as a fixed effect. The proportion of markers having a null effect was set to 0.95. A chain length of 150,000 iterations was run with the first 50,000 discarded as burn-in. The genomic estimated breeding value (GEBV) was estimated by summing posterior mean marker effects by marker genotype across all SNP. Phenotypic correlations were estimated using multivariate analysis of variance (MANOVA) procedures with group fitted as a fixed effect. To estimate potential GEBV re-ranking, correlations between GEBV were estimated across traits within a cut (i.e. ST or LTL) and between cuts within each trait. Additionally, the cattle genome was separated into 1 Megabase (Mb) windows and SNP variance within a window was summed to give an estimate of the total SNP variance for each window (n=2,677). The percentage of top 5% (n=134) windows in common across traits and cuts were then compared with GEBV correlations among traits and between cuts. The top 0.5% 1-Mb windows (n=13) for each trait were extended by 1-Mb in both directions and a posi-
tional candidate gene approach was conducted using Bos taurus build UMD_3.1 assembly (Zimin et al., 2009). Due to the limited functional annotation of the Bos taurus genome, human orthologs of beef cattle positional candidate genes were obtained and used for functional characterization by using Ensembl Genes 69 database and the BioMart data mining tool (http:// www.ensembl.org/biomart/martview/dd0c118c99ed15210cc6e97131d873fb). Overrepresented gene ontology terms, and pathway analysis were identified using DAVID (http://david.abcc.ncifcrf.gov). Results Genomic Heritabilities The posterior mean (standard deviation; SD) genomic heritability estimates (proportion of phenotypic variation explained by the markers) are presented in Table 3. For both cuts, heritability estimates for protein and mineral traits ranged from 0.05 to 0.75. The posterior mean (SD) genomic heritability estimates for CH, PUFA and MUFA as a percentage of total lipid content for both cuts ranged from 0.40 to 0.70. When analyzed as mg/100g of total wet tissue, the posterior mean (SD) genomic heritability estimates for CH, PUFA and MUFA for both cuts ranged from 0.45 to 0.85. Mateescu et al. (2013a) estimated the heritability based on pedigree information and phenotypic data to be 0.48, 0.00, and 0.15 for LTL iron, potassium, and sodium, respectively. The proportions of phenotypic variation explained by the BovineSNP50 assay were 0.37, 0.03, and 0.09 for iron, potassium and sodium, respectively (Mateescu et al., 2013b). These results are in general agreement with the findings of the current study for the traits of iron and sodium. The vastly different estimates for potassium may be attributed to the admixed population or the small sample size, and the fact that this population was segregating the C313Y mutation. One SNP within one of the top 1Mb windows for potassium was in perfect LD with the myostatin mutation. Lower posterior mean estimates of genomic heritability for ST sodium is likely a function of the lower phenotypic variation of sodium content, which can be explained biological117
ly by the body highly regulating sodium levels (Hollenberg, 1980). For LTL and ST CH, LTL PUFA and ST MUFA posterior mean estimates of genomic heritability remained the same regardless of the scale of measurement (percentage of total lipids or mg/100g of whole (wet) tissue). The genomic heritability estimate for LTL MUFA was higher when measured on mg/100g of whole (wet) tissue than on a percentage of total lipids. ST PUFA genomic heritability was lower when measured on mg/100g whole (wet) tissue basis. The coefficients of variation for ST PUFA were 0.61 and 0.34 when measured as a percentage of total lips and mg/100g, respectively. This increase in variation could partially explain the increase in the proportion of variation explained by the markers. Although the ST had lower concentrations of PUFA as measured in mg/100g of wet tissue, it also had lower values for total lipids. Consequently when PUFA was adjusted for total lipid content, the mean PUFA as a percentage of total lipid content was actually higher than the LTL. The same general trend of the ST containing a higher proportion PUFA and MUFA as a percentage of total fatty acids was also reported by Sexton et al. (2012). Estimates of heritability for fatty acids are sparse in the literature. Pitchford et al. (2002) reported low to moderate estimates of heritability for fatty acid traits in beef cattle. However, Cameron (1990) reported high (0.53-0.71) heritability estimates for palmitic, stearic, oleic, and linoleic fatty acids. This is consistent with the estimate of 0.75 for the heritability of C18:1 in a population of Japanese black cattle (Uemoto et al., 2010), and supports a moderate to high level of genetic control of fatty acids within meat. Genomic Estimated Breeding Value and Phenotypic Correlations Correlations between GEBV follow the phenotypic correlation trends as reported by Ahlberg et al., (2014). Phenotypic correlations are presented in Tables 4 and 5. Among the protein and mineral, as well as the mineral and protein with lipids, correlations were low to moderate between and within the two cuts and were varied in the direction of the correlation when measured as a percentage of fat and as mg/100g of wet tissue. Phenotypic correlations among lipid traits were moderate to strong as 118
a percentage of fat and as mg/100g of wet tissue. The MUFA was negatively correlated with PUFA and CH within and across cuts when measured on a percentage of total fat. However, when measured as mg/100g of wet tissue, MUFA and PUFA were strong positively correlated and CH was moderate negatively correlated with MUFA and PUFA between and across cuts. Consequently, from a selection perspective, the phenotype used (percentage or mg/100g) would lead to the selection of different animals. This is primarily because increases in fat content dilute fatty acids found in membranes, notably CH and PUFA. Expression of results as mg/100g of wet tissue thus reflects overall increases in fat content. The interpretation of results relative to fatty acids is conditional on understanding the scale of the phenotypes (percentage of total fatty acids or mg/100g of wet tissue). When the gravimetric amount of PUFA, for instance, is low the amount of PUFA relative to total fatty acids (percentage of total fatty acids) can be high simply because the amount of total fatty acids was also very low. Similarly, when PUFA content is relatively high as a percentage of total fatty acids (i.e. when the amount of total fatty acids is also low) CH would also be expected to be relatively high when measured as a percentage of total fatty acids. The expectation that with the increase in adipose tissue that CH increases, PUFA decreases and MUFA increases on a percent fat basis is challenged in the case of cattle with the double muscling genotype. Raes et al. (2001) have shown that the double muscling genotype within the Belgian Blue breed has low proportions of MUFA and high proportions of PUFA in muscle lipid compared with normal genotype animals. This is due to the low concentration of total lipid in the muscle and a high ratio of phospholipid and total lipid. Phospholipids are high in PUFA content in order to perform the function as a constituent of cellular membranes (Wood et al., 2008). However, when PUFA content is high in mg/100g of whole (wet) tissue, total fatty acid content is also likely high leading to a reduction in the proportionate amount of CH. Significant correlations between GEBV suggest that selection for increased iron concentration in the ST would lead to increased levels of MUFA and decreased levels of both CH and PUFA as a percentage of total lipids. In both cuts, selection for
increased levels of potassium would have the opposite effects leading to increased PUFA and CH and decreased MUFA as a percentage of total lipids. On a total tissue basis, selection for increased potassium in both cuts would lead towards a correlated decrease in PUFA and MUFA and increase in CH. Selection for increased iron would lead to a correlated decrease in CH and an increase in MUFA and PUFA in the ST on a total wet tissue basis.
Sodium was lowly to moderately correlated with all traits measured, in agreement with Mateescu et al. (2013) who also reported low to moderate correlations between sodium and other mineral traits. However, correlations between GEBV between the different cuts for sodium was high despite the low proportion of variation explained by the markers. This strong GEBV correlation may be due to markers picking up breed/family relationships, which would give rise to a larger positive GEBV correlation. Candidate Gene Annotation Functional annotation analysis resulted in a common gene found among lipid traits was GULP1 (Engulfment adaptor PTB domain containing 1). GULP1 is an adaptor protein that binds and directs the trafficking of LRP1 (Low density lipoprotein receptor-related protein 1), which is involved in lipid homeostasis (He and Lin, 2010). ITGAV is associated with metabolic processes and negative regulation of lipid transport and storage (Kim et al., 2013). Some significant SNP from the top 0.5% 1-Mb windows that were on BTA2 for each trait were in high LD with the myostatin C313Y alleles. Consequently, these SNP may simply be an artifact of the importance of the myostatin mutation for some for the traits analyzed. Between all traits and cuts there was a wide range in the number of 1-Mb windows that were on BTA2, ranging from 1 to 9 windows. Traits with few top windows on BTA2 are likely not impacted as much by C313Y. Previous work by Aldai et al. (2005) showed significant differences between animals of the Asturiana de los Valles breed of cattle that were homozygous for the myostatin deletion and those that were homozygous normal for protein percentage. The authors also showed that homozygous
myostatin animals had lower proportions of MUFA and higher proportions of PUFA illustrating that this mutation has a measureable impact on these traits. This is supported by Wiener et al. (2009) who showed a significant effect of the myostatin mutation in South Devon cattle for both PUFA and MUFA concentrations. Outside of the myostatin mutation, Mateescu et al. (2013c) reported 16 SNP in a single Mb region (103-104 Mb) on BTA2 to explain 1.33% of the phenotypic variation of iron content, although the region reported by Mateescu et al. (2013) does not overlap with the regions reported in the current study. Conclusions In general, the mean estimates of the posterior heritability were moderate to high for fatty acids, suggesting that significant progress could be made through selection with the aid of genomics. The proportion of variation for mineral traits was more variable, although a moderate proportion of variation was explained by the markers for iron and potassium content. Differences did exist for fat traits depending on the scale of measurement (mg/100g or percentage of total lipid content), in terms of relationships between traits, chromosomal regions underlying genetic variation, and in some cases the proportion of variation explained by the markers. The choice between these two scales would impact the ranking of animals. Further investigation of fatty acid and mineral concentrations need to be conducted in other populations to fully understand the proportion of variation explained by markers and better predict candidate genes. Potential candidate genes, GULP1 and ITGAV located on BTA2 in close proximity to C313Y, were identified and involve regulation of lipids. Further analysis of expression of these genes will allow for better understanding of lipid transport and regulation in muscle and their subsequent role in determining meat quality of livestock.
Table 1. Summary statistics for carcass traits. 0 1 2 Trait n Minimum Maximum Mean a a copy copya copy HCW, kg. Group 1c
60 25 26 9 265.80 Group 2c c 58 20 22 16 268.52 Group 3 c Group 4 59 19 20 20 271.25 Back Fat, cm. Group 1 59 19 28 12 0.10 Group 2 60 25 26 9 0.10 Group 3 58 20 22 16 0.25 Group 4 59 19 20 20 0.25 b Marbling Score Group 1 59 19 28 12 100 Group 2 60 25 26 9 100 Group 3 58 20 22 16 250 Group 4 59 19 20 20 270 a Refers to the number of copies of the inactive Myostatin allele. b Marbling score units: 400 = Sm00, 500 = Modest00 c Group 1 refers to year 1 steers, group 2 refers to year 1 heifers, group 3 refers to year 2 steers and Group 4 refers to year 2 heifers.
385.55 400.98 434.00
319.85 332.19 346.24
24.96 26.84 34.19
1.40 2.03 2.29 3.05
0.73 0.84 0.86 1.02
0.37 0.41 0.55 0.68
470 860 880 730
294.92 373.00 533.79 426.78
100.75 118.40 166.97 114.75
Table 2. Summary statistics for nutrient traits. Trait Units LDa MUFA (% of fat) LTL MUFA (mg/100g) b ST MUFA (% of fat) ST MUFA (mg/100g) LTL PUFA (% of fat) LTL PUFA (mg/100g) ST PUFA (% of fat) ST PUFA (mg/100g) LTL Cholesterol (% of fat) LTL Cholesterol (mg/100g) ST Cholesterol (% of fat) ST Cholesterol (mg/100g) LTL Sodium (ppm) ST Sodium (ppm) LTL Potassium (ppm) ST Potassium (ppm) LTL Iron (ppm) ST Iron (ppm) LTL Protein (%) ST Protein (%) a M. Longissimus dorsi (LTL) b M. Semitendinosus (ST)
n 224 227 223 227 223 224 222 227 222 225 223 225 226 227 227 226 224 226 225 227
Mean 46.25 6087.70 45.11 2461.14 5.27 572.60 8.50 378.87 0.50 45.76 1.94 46.26 418.69 393.92 3015.18 3484.30 13.65 13.92 21.69 22.91
Minimum 33.2 270.97 26.6 37.24 2.66 149.86 1.14 36.24 0.14 33.00 0.22 32.00 336.50 317.40 2283.00 2867.00 8.99 7.50 17.34 18.58
Maximum 55.00 13849.38 56.70 10308.06 15.30 1197.99 25.60 735.02 2.84 59.00 17.10 58.00 491.20 478.60 3614.00 4087.00 19.56 25.50 27.44 26.17
Standard Deviation 4.31 3233.42 5.55 1977.84 2.21 180.07 5.20 132.31 0.45 4.48 2.48 4.73 32.14 29.02 268.71 227.99 2.06 2.62 1.86 1.33
Table 3. Genomic heritabilities Trait Units Heritability (SE) a LTL MUFA (% of fat) 0.40 (0.10) LTL MUFA (mg/100g) 0.85 (0.04) STb MUFA (% of fat) 0.60 (0.07) ST MUFA (mg/100g) 0.60 (0.10) LTL PUFA (% of fat) 0.70 (0.06) LTL PUFA (mg/100g) 0.70 (0.08) ST PUFA (% of fat) 0.65 (0.06) ST PUFA (mg/100g) 0.45 (0.04) LTL Cholesterol (% of fat) 0.50 (0.09) LTL Cholesterol (mg/100g) 0.50 (0.06) ST Cholesterol (% of fat) 0.45 (0.10) ST Cholesterol (mg/100g) 0.45 (0.11) LTL Sodium (ppm) 0.15 (0.08) ST Sodium (ppm) 0.05(0.05) LTL Potassium (ppm) 0.75 (0.08) ST Potassium (ppm) 0.65 (0.09) LTL Iron (ppm) 0.35 (0.13) ST Iron (ppm) 0.35 (0.09) LTL Protein (%) 0.70 (0.08) ST Protein (%) 0.75 (0.06) a M. Longissimus dorsi (LTL) b M. Semitendinosus (ST)
-0.32 (0.01) -
-0.09 (0.19) 0.30 (0.01) -
0.64 (0.01) -0.16 (0.02) 0.24 (0.01) -
0.35 (0.06) -0.31 (0.01) 0.02 (0.78) 0.24 (0.01) -
-0.53 (0.01) 0.32 (0.01) -0.09 (0.16) -0.48 (0.01) -0.59 (0.01) -
0.59 (0.01) -0.39 (0.01) -0.03 (0.70) 0.44 (0.01) 0.68 (0.01) -0.85 (0.01) -
0.59 (0.01) -0.34 (0.01) -0.05 (0.45) 0.39 (0.01) 0.50 (0.01) -0.61 (0.01) 0.68 (0.01) -
-0.05 (0.46) 0.35 (0.01) 0.02 (0.73) -0.06 (0.37) -0.12 (0.07) 0.20 (0.01) -0.21 (0.01) -0.08 (0.22) -
0.15 (0.03) -0.06 (0.36) 0.25 (0.01) 0.12 (0.07) 0.17 (0.01) -0.21 (0.01) 0.19 (0.01) 0.25 (0.01) 0.20 (0.01) -
M. Longissimus thoracis et lumborum (LTL) b M. Semitendinosus (ST) ST protein (STPR), ST iron (STI), ST sodium (STS), ST potassium (STPO), ST cholesterol (STCH), ST monounsaturated fatty acids (STMUFA), ST polyunsaturated fatty acids (STPUFA), LTL protein (LTLPR), LTL iron (LTLI), LTL sodium (LTLS), LTL potassium (LTLPO), LTL cholesterol (LTLCH), LTL monounsaturated fatty acids (LTLMUFA), and LTL polyunsaturated fatty acids (LTLPUFA) d STCH, STMUFA, STPUFA, LTLCH, LTLMUFA and LTLPUFA units as percent of total fat e Phenotypic correlations (P value). fStandard errors for correlations were 0.067.
Table 4. Phenotypic correlations with lipid traits measured as a percent of total fatabcdef.
0.49 (0.01) -0.30 (0.01) 0.02 (0.82) 0.46 (0.01) 0.29 (0.01) -0.52 (0.01) 0.47 (0.01) 0.64 (0.01) 0.18 (0.01) 0.50 (0.01) -
0.45 (0.01) -0.27 (0.01) 0.03 (0.83) 0.28 (0.01) 0.75 (0.01) -0.66 (0.01) 0.74 (0.01) 0.71 (0.01) -0.11 (0.11) 0.23 (0.01) 0.49 (0.01) -
-0.44 (0.01) -0.32 (0.01) -0.07 (0.27) -0.37 (0.01) -0.50 (0.01) 0.74 (0.01) -0.58 (0.01) -0.60 (0.01) 0.15 (0.02) -0.24 (0.01) -0.55 (0.01) -0.63 (0.01) -
0.48 (0.01) -0.36 (0.01) -0.008 (0.91) 0.28 (0.01) 0.56 (0.01) -0.65 (0.01) 0.71 (0.01) 0.70 (0.01) -0.20 (0.01) 0.20 (0.01) 0.51 (0.01) 0.82 (0.01) -0.71 (0.01) -
-0.09 (0.19) 0.20 (0.01) -
-0.32 (0.01) -
0.64 (0.01) -0.16 (0.02) 0.24 (0.01) -
0.40 (0.01) -0.33 (0.01) 0.02 (0.78) 0.29 (0.01) -
-0.48 (0.01) 0.22 (0.01) 0.05 (0.47) -0.36 (0.01) -0.27 (0.01) -
-0.43 (0.01) 0.19 (0.01) 0.02 (0.81) -0.29 (0.01) -0.21 (0.01) 0.82 (0.01) -
0.59 (0.01) -0.34 (0.01) -0.05 (0.45) 0.39 (0.01) 0.41 (0.01) -0.47 (0.01) -0.39 (0.01) -
-0.05 (0.46) 0.35 (0.01) 0.02 (0.73) -0.06 (0.37) -0.07 (0.29) 0.06 (0.35) 0.16 (0.02) -0.08 (0.22) -
0.14 (0.03) -0.06 (0.36) 0.25 (0.01) 0.12 (0.07) 0.15 (0.02) -0.15 (0.02) -0.11 (0.12) 0.25 (0.01) 0.20 (0.01) -
0.49 (0.01) -0.30 (0.01) 0.02 (0.82) 0.46 (0.01) 0.33 (0.01) -0.36 (0.01) -0.24 (0.01) 0.64 (0.01) 0.18 (0.01) 0.50 (0.01) -
0.31 (0.01) -0.29 (0.01) 0.01 (0.84) 0.28 (0.01) 0.34 (0.01) -0.24 (0.01) -0.19 (0.01) 0.46 (0.01) -0.03 (0.67) 0.23 (0.01) 0.39 (0.01) -
-0.65 (0.01) 0.40 (0.01) 0.04 (0.56) -0.46 (0.01) -0.45 (0.01) 0.53 (0.01) 0.42 (0.01) -0.82 (0.01) 0.09 (0.20) -0.24 (0.01) -0.70 (0.01) -0.45 (0.01) -
LTLMUFA -0.47 (0.01) 0.31 (0.01) 0.03 (0.63) -0.27 (0.01) -0.27 (0.01) 0.46 (0.01) 0.45 (0.01) -0.76 (0.01) 0.08 (0.22) -0.28 (0.01) -0.62 (0.01) -0.35 (0.01) 0.83 (0.01) -
M. Longissimus thoracis et lumborum (LTL) b M. Semitendinosus (ST) ST protein (STPR), ST iron (STI), ST sodium (STS), ST potassium (STPO), ST cholesterol (STCH), ST monounsaturated fatty acids (STMUFA), ST polyunsaturated fatty acids (STPUFA), LTL protein (LTLPR), LTL iron (LTLI), LTL sodium (LTLS), LTL potassium (LTLPO), LTL cholesterol (LTLCH), LTL monounsaturated fatty acids (LTLMUFA), and LTL polyunsaturated fatty acids (LTLPUFA) d STCH, STMUFA, STPUFA, LTLCH, LTLMUFA, LTLPUFA units as mg/100g of total wet tissue. e Phenotypic correlations (P value). fStandard errors for correlations were 0.067.
Table 5. Phenotypic correlations with lipid traits measured as mg/100g of total (wet) tissueabcdef.
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Raes, K., S. De Smet, & D. Demeyer, 2001. Effect of double-muscling in Belgian Blue young bulls on the intramuscular composition with emphasis on conjugated linoleic acid and polyunsaturated fatty acids. Animal Science 73: 253-260. Realini, C. E., S. K. Duckett, G. W. Brito, M. D. Rizza, D. De Mattos, 2004. Effect of pasture vs. concentrate feeding with or without antioxidants on carcass characteristics, fatty acid composition, and quality of Uruguayan beef. Meat Sci. 74: 567-577. Scollan, N. D., J. Hocquette, K. Nuernberg, D. Dannenberger, I. Richardson, & A. Moloney. 2006. Innovations in beef production systems that enhance the nutritional and health value of beef lipids and their relationship with meat quality. Meat Sci. 74: 17-33. Sexten, A. K., C. R. Krehbiel, J. W. Dillwith, R. D. Madden, C. P. McMurphy, D. L. Lalman,R. G. Mateescu. 2012. Effect of muscle type, sire breed, and time of weaning on fatty acid composition of finishing steers. J. Anim. Sci. 90: 616-625. Uemoto, Y., T. Abe, N. Tameoka, H. Hasebe, K. Inoue, H. Nakajima, N. Shoji, M. Kobayashi, and E. Kobayashi. 2011. Whole-genome association study for fatty acid composition of oleic acid in Japanese Black cattle. Animal Genetics, 42: 141–148. Wiener, P., J. A. Wooliams, A. Frank-Lawale, M. Ryan, R. I. Richardson, G. R. Nute, & J. D. Wood. 2009. The effects of a mutation in the myostatin gene on meat and carcass quality. Meat Sci. 83: 127-134. Wood, J.D., M. Enser, A.V. Fisher, G. R. Nute, P. R. Sheard, R. I. Richardson, S. I. Hughes, F. M. Whittington. 2008. Fat deposition, fatty acid composition and meat quality: A review. Meat Sci. 78: 343-358. Zimin, A. V., A. L. Delcher, L. Florea, D. R. Kelley, M. C. Schatz, D. Puiu, F. Hanrahan, G. Pertea, C. P. van Tassell, T. S. Sonstegard, G. Marcais, M. Roberts, P. Subramanian, J. A. Yorke, & S. L. Salzberg. 2009. A whole-genome assembly of the domestic cow, Bos taurus. Genome Biol. 10(4): R42. 126
BREEDING FOR REDUCED ENVIRONMENTAL FOOTPRINT IN BEEF CATTLE Donagh P. Berry1 11
Animal & Grassland Research and Innovation tre
Teagasc, Moorepark, Ireland Introduction There is considerable commentary in recent years on “sustainable intensification”. The world human population is expanding and becoming more affluent and thus the demand for animal-derived protein (i.e., milk and meat) is increasing. Global production of meat from livestock is expected to double over the fifty years from 1999/2001 (229 million tonnes) to 2050 (465 million tonnes; Steinfeld et al., 2006). This therefore implies that, all else being equal, the environmental footprint per unit meat produced will need to be halved if the total environmental footprint of livestock production is not to increase. Most commentary on livestock environmental footprint, however, tends to focus on greenhouse gas emissions. O’Mara (2011) stated that animal agriculture is responsible for 8.0 to 10.8% of global greenhouse gas emissions. If, however, complete lifecycle analysis (i.e., accounting for the production of inputs to animal agriculture as well as change in land use such as deforestation) is undertaken this figure can be up to 18%. Cattle are the largest contributors to global greenhouse gas emissions (O’Mara, 2011). Livestock production systems are, nonetheless, also implicated for pollution of freshwater supplies (e.g., nitrogen and phosphorus) as well as depleting water reserves. Livestock is implicated for 32-33% of Nitrogen and Phosphorus contamination of freshwater supplies (Steinfeld et al., 2006). Moreover, 64% of the world’s population is expected to reside within water-stressed areas by the year 2025 (Steinfeld et al., 2006). Livestock production accounts for 8% of the water used by the human population (Steinfeld et al., 2006). Thus water use efficiency in animal production, as well as pollutant potential of water supplies, is also a crucial characteristic of animals for environmental footprint. Much research is focusing particularly on differences
among individuals in greenhouse gas emissions. Here I discuss other, often easier and more holistic approaches to potentially reduce the environmental footprint of modern day cattle production systems while simultaneously improving profitability. This article should be viewed more for provoking discussion than a definitive solution to how best to reduce the environmental footprint of modern-day production systems. Animal breeding programs may be summarized graphically as in Figure 1.
EBI was criticized at the time for not accurately reflecting the prevailing market signals. In 2006/2007 however, the milk payment system in Ireland changed to be strongly reflective of the relative economic weights in the EBI; thus the EBI had been identifying the most suitable germplasm for this payment system for the previous 5-6 years. Irish beef breeding programs will soon include EPDs for carcass cuts with the anticipation that the carcass payment system will change in the near future to better reflect carcass quality. Moreover, research is also underway on the inclusion of meat quality traits in the Irish national beef index, again in anticipation of financial incentives for superior meat quality in the future; the precedence already exists through incentives for meat from Angus cattle. Therefore goals of breeding programs should be expanded to not only include profit but to do so in an “environmentally and socially responsible and sustainable manner”. Although difficult, cognizance must be given to the likely policy enforced in several years. Breeding objective
Figure 1. Schematic of an animal breeding program. Goal The goal of most cattle production systems in the developed world is profit. Profit is dictated by revenue and cost of production. Some traits however currently have no monetary value in most countries but are deemed to have “public good” attributes. Moreover, animal breeding strives to identify and select germplasm that will be most profitable in several years. Thus, although some attributes may have little (e.g., water) or no (e.g., greenhouse gases) current monetary value in most countries, the same may not be true in the future when the (grand-)progeny of the animals selected today will be producing. A good example is the evolution of the milk payment system in Ireland. The Irish national dairy cow breeding objective, the EBI, launched in 2001 penalized higher producing animals with lower milk composition. This was during a time when Irish producers were paid on a differential milk pricing system with no penalty for milk volume. The
The breeding objective lists traits and their respective relative weightings to best describe the goal of the breeding program. Such traits should include revenue generating traits (e.g., carcass yield and value) as well as cost of production traits (e.g., feed intake, health and survival and in the case of maternal breeding objectives reproduction and longevity). Ideally such objectives should also include direct environmental characteristics like daily (or lifetime) methane emissions, nitrogen (and other minerals) excretion, as well as water intake. It is vitally important at this stage that cognizance is not given to whether or not these traits can be (easily) measured. Such details will be resolved in the later steps of the breeding program. If the genetic variation present in a trait cannot be adequately captured then, if not deemed sufficiently important, it can be discarded in the iterative process of the breeding program (Figure 1). The relative weighting on each trait in the breeding goal can be derived using several approaches including economic values (i.e., from bioeconomic models or
profit functions), choice experiments or willingness to pay experiments (e.g., 1,000 minds) or desired gains approaches. The contribution however of increased animal performance to reduced environmental footprint of the entire production system must be recognized. Goddard et al. (2011) defined herd feed conversion efficiency (FCE) for a beef herd as:
Herd FCE =
WOff (wean âˆ’ loss) DMI Cow + wean â‹… DMI Off
where Woff is the slaughter weight of the offspring, wean is the weaning rate, loss is the cow loss rate, DMIcow is the total feed intake of the cow, and DMIoff is the total feed intake of the offspring. This clearly shows that factors other than feed intake or direct environmental measures such as fertility (i.e., weaning rate) and cow loss rate can also affect herd efficiency and thus environmental footprint. Of key importance here is that DMI reflects total DMI and not daily DMI. Average daily DMI is almost always used in the definition of feed efficiency traits like residual feed intake (RFI; Berry and Crowley, 2013). Berry and Crowley (2012) however clearly demonstrated that animals superior for RFI, although eating less per day, may require a longer period of time to reach a target weight and thus eat more during this finishing period compared to animals ranked on their proposed index trait which included growth rate. A similar conclusion was reached in poultry (Willems et al., 2013). Although the direct translation to reduced environmental footprint is not clear, it is logical to assume that animals with lower total feed intake are also likely to have a reduced environmental footprint. This is because feed intake and daily methane emissions are positively correlated (Fitzsimons et al., 2013) and there is an expectation therefore also that lower feed intake (achieved through genetic gain without compromising performance), on average, results in less water intake, as well as less feces and uterine produced.. The dual objective of reducing feed intake per-day and number of days on feed (i.e., growth rate) is what
is being achieved in holistic breeding objectives; this therefore is expected to reduce the environmental footprint of the growing cattle sector but cognizance must of course be taken of the cattle production system in its entirety (i.e., cow-calf production system). Table 1 describes the national breeding objectives for beef cattle in Ireland; the genetic evaluations are undertaken across breed and there is a single national breeding objective which operates across all breeds. The breeding objectives have a positive weight on carcass weight (i.e., reducing age at slaughter for a fixed carcass weight) and a negative economic weight on feed intake. The expected responses to selection based on the terminal index are in Figure 1. Gains in carcass weight (i.e. earlier age at slaughter for the same carcass weight) are expected despite an expected reduction in daily feed intake. This therefore is a double-whammy of reduced feed intake per day and reduced number of days of feeding. This is clearly exemplified by the mean performance of slaughtered animal divergent for the Irish terminal index (Table 2; Connelly et al., 2014); genetic merit for each animal was based on a genetic evaluation that did not include the animalâ€™s own performance record. The genetically elite animals were slaughtered 54 days younger despite their carcasses weighing 17% more than the lowest genetic merit group. Moreover, the EBVs for daily feed intake of the highest and lowest genetic merit group were -0.08 kg/ day and 0.48 kg/day, respectively. These characteristics combined suggest that not only do the genetically elite animals eat food (and therefore require less associated labour and capital costs) for 54 days less but they are also eating potentially more than half a kg less per day less than their genetically inferior counterparts. These characteristics are likely to result in a lower environmental footprint of these genetically elite animals; it is important to remember that this is being achieved without any direct inclusion of an environmental trait in the breeding goal. Further reductions in environmental footprint are no doubt possible with the direct inclusion of environmental trait in the breeding objective but such inclusions will likely come at a cost and it is currently not clear what marginal gains could actually be achieved by such endeavors. Improving cow fertility and longevity can reduce the environmental load of the entire beef production system as described in the equation above. Garnsworthy (2004) documented, using modelling, that if dairy cow
fertility in the UK national herd could be restored to 1995 level from 2003 levels then herd methane emissions could be reduced by 10 to 11% while ammonia emissions could be reduced by 9% under a milk quota environment; the respective reductions were 21 to 24% and 17% if ideal fertility levels were achieved. A reduction of 4 to 5% in herd methane emissions was expected in the UK if fertility levels were restored to 1995 levels from 2003 levels where no milk quota existed (Garnsworthy, 2004). These improvements were due primarily to a reduced number of non-producing replacement animals and to a lesser extent greater milk yield (i.e., in beef would result in greater calf growth rate) when fertility was improved. No cognizance was taken here of the impact of replacement rate on genetic gain.
Table 1. Relative emphasis on traits in the Irish national beef maternal and terminal breeding objectives Many studies focus on methane intensity as a breeding goal trait. Methane intensity may be described as the total (daily) methane output per unit feed intake. Heritable genetic variation in methane intensity has been reported (Donoghue et al., 2013) but Berry (2012) cautioned strongly on the interpretation of such heritability estimates as it is unclear what proportion of the heritability originates from the numerator or denominator of the intensity equation. Berry (2012) used a dataset of 2,605 growing beef bulls, described in detail by Crowley et al. (2010), to justify his concerns.
Figure 1. Expected annual gain in genetic standard deviation units (assuming an annualized genetic gain of 0.15 standard deviation units) for direct calving difficulty (DCD), gestation length (GEST), perinatal mortality (MORT), docility, dry matter intake (DMI) carcass weight (Ccwt), carcass conformation (Cconf) and carcass fat (Cfat), assuming 100 progeny records for the calving traits and docility, 6 progeny records for feed intake and 85 progeny records for the three carcass traits. Daily methane emissions per animal were sampled from a normal probability distribution with a mean of 300 g/day and a standard deviation of 40 g/day. Methane intensity was defined as daily methane emissions divided by actual recorded daily feed intake available on those animals. As expected the heritability of the simulated daily methane emissions was zero; the heritability of feed intake was 0.49 (Crowley et al., 2010). The heritability of methane intensity was 0.19 (0.05). Berry (2012) proposed that to measure the potential of genetic selection to alter methane emissions without compromising performance, a statistical approach analogous to that used to define residual feed intake (Koch et al., 1963), should be used. This trait may be termed residual methane production (RMP) and could be defined as the residuals from a model regressing individual animal daily methane emission on energy sinks like growth rate and metabolic live-weight. Feed intake may also be included as covariate in the statistical model. If this approach was used in the example above, then the heritability of the residual methane trait was, as expected, zero. It is the genetic variation in this RMP trait that is of crucial importance as this depicts the scope for genetic improvement while still continuing to produce meat for the growing human demand. The heritability of this statistic merely de129
scribes how much one would have to invest to generate accurate genetic evaluations for this trait for individual animals. A similar approach should be undertaken for other environment traits like water use efficiency and nitrogen use efficiency. Selection criterion Traits and their respective weights in the selection criterion are chosen to maximize the correlation between the overall criterion and the overall breeding objective. Many are investing in high-tech facilities for the accurate measurement of feed intake (and efficiency) as well and environmental traits (and other non-environmental traits). To my knowledge a detailed peer-reviewed cost-benefit of such endeavors, taking cognizance of selection index theory, has not be undertaken. One must remember that current carcass trait genetic evaluations (if the country actually has one!) are not perfect. Most countries use imprecise approaches to predict actual carcass value through themeasurement of carcass conformation which does not directly take cognizance of individual meat cut yields, let alone meat quality. Therefore, why such an emphasis on attempting to generate extremely accurate measures for other traits? I am not saying it is incorrect, but at least the true cost-benefit should be elucidated and a discussion should be had. Such an exercise must take cognizance of the ability to predict some of these traits, with reasonable accuracy, using selection index theory. Therefore, of real importance is what “residual” variation in the trait of interest remains that is not already captured by other easy to record traits. Ber-
could explain 72% of the genetic variation in daily feed intake in growing cattle from live-weight, growth rate and ultrasound fat measures all of which are relatively easily measurable. The proportion of genetic variation increased to 90% when a subjective measure of muscularity was also included in the selection index; this is because RFI and muscularity are genetically correlated (Berry and Crowley, 2013). There is no denying that variation in RFI exists, my question is how much is there that cannot be captured through other means. It is very likely that a larger proportion of the variation total animal intake can be captured with easy to record traits since total feed intake will also be determined by days on feed. Moreover, animals not at the feed bunk are simply not eating. RFID is now routinely used in many feedlots, and if not is relatively inexpensive. Although differences in bite rate and bite size among animals exists (Chen et al., 2014), total time spent feeding (from using sensors at the feed face and transponders on animals) must explain additional genetic variation in RFI. (Robinson and Oddy, 2004; Chen et al., 2014). The difference between metabolizable energy intake and net energy intake is heat increment. Promising research from Guelph (Montanholi et al., 2009) suggests that measurement of animal heat produced from simple infra-red cameras can be used to predict RFI. Some will say that 70% prediction accuracy is too low; however the genetic correlation between carcass conformation and total meat yield (adjusted to a common carcass weight) is 0.55 (Pabiou et al., 2009) implying that the current carcass grading system in the EU explains only 30% of the genetic variation in carcass meat proportion! Would resources not be better spent on
Table 2. Association between terminal EBV (Index) and age at slaughter, carcass weight, conformation and fat score as well as price per kg and overall animal value; pooled standard error (SE) also included. Index Age (days) Carcass Conformation Fat Price per kg Value weight (kg) (scale 1 – 15) (scale 1-15) (€/kg) (€) Very High 726 371 8.68 (R+) 6.33 (3=) 3.85 1412 High 775 327 5.04 (O=) 6.40 (3=) 3.60 1174 Low 779 321 4.97 (O=) 6.44 (3=) 3.60 1153 Very Low 780 316 4.88 (O=) 6.33 (3=) 3.57 1123 SE 0.89 0.31 0.01 0.97 0.24 1.65 Table 2. Association between terminal EBV (Index) and age at slaughter, carcass weight, conformation and fat score as well as price per kg and overall animal value; pooled standard error (SE) also included. 130
improving this and selecting animals with more meat yield to feed the growing human demand? Breeding Scheme Design The breeding scheme design incorporates the genetic and genomic evaluations as well as the breeding scheme used to ensure long-term and sustainable genetic gain. There is an expectation among some that genomic selection will solve all issues in beef cattle breeding. On the contrary, genomic selection can actually exacerbate any issues that exist in a breeding program. For example, genomic selection is expected to increase genetic gain approximately 50% implying that the rate of genetic deterioration in a given (non-monitored) trait will also likely increase by 50%. Moreover, although genomics can be used to reduce the accumulation of inbreeding, in most instances in dairying, inbreeding is increasing as breeding companies battle to increase the rate of short term genetic gain. The greater use of young bulls may minimize the ability, or increase the difficulty, to purge out unfavourable characteristics. Furthermore, inaccurate, imprecise or non-pertinent genetic evaluations will not be solved with genomic selection. The input variables for genomic selection are either (a derivative) of EPDs from the genetic evaluation systems (i.e., two step) or the direct phenotypes themselves with the genetic relevant evaluation model (i.e., one-step). Therefore, implementation of genomic selection will be most optimal once the fundamentals of a successful animal breeding program are in place. There have been long discussions on how best to incorporate feed efficiency in a breeding program (Berry and Crowley, 2013) and to-date no consensus exists. Table 3 (Berry and Pryce, 2014) outlines the advantages of disadvantages of including a residual feed intake or dry matter intake itself in the breeding objective. The same discussions are likely to prevail for environmental footprint traits especially if residual-based traits are derived. In other words should residual methane production, total daily methane production, or methane intensity be included in the breeding objective or as a stand-alone trait. The disadvantages of selection on ratio traits (i.e., methane intensity) like feed conversion efficiency has been discussed at length (Berry and Crowley, 2013) suggesting that methane intensity (or any other environment trait like water intake per unit average daily gain or per unit feed intake) may be not
production, total water intake) in the breeding objective but the adjusted trait (either as EPDs or categorized as high, average, or low depending on the accuracy of the EPDs) as a stand-alone trait. By categorizing traits (or the stand alone trait as a monetary value like feed cost saved) issues with which sign is desirable (i.e., apparently negative RFI) is removed. One could simply change the sign but this will cause confusion if (international) scientists are discussing with producers since they will subconsciously say that genitive RFI is better. By categorizing, issue with fluctuation EPDs because of low reliability will be minimized. A similar categorization of traits is undertaken in Ireland for beef cattle where animals are grouped into 5 categories (termed stars in Ireland) where the top category (i.e., 5-star) are animals in the top 20% for genetic merit for that trait. Although knowledge if the animal resides in 1% percentile or the 19% is useful, getting producers to use and engage with animal breeding may actually be more beneficial. Dissemination Arguably the link in a successful animal breeding program that is most often ignored is dissemination. There is not much point having the best genetic evaluation system and breeding program in the world if nobody understands it or is willing to use the elite germplasm. Animal breeders find it difficult to understand why the best germplasm is not used; even if individual bull reliability is low, on average, if producers use the elite bulls the entire population will make gains. However, individual producers are more concerned with theperformance of their own animals and herd rather than the national population. It is still remarkable how many producers globally do not believe genetic evaluations. One has to question the investment in genomics to produce more accurate EPDs when the EPDs are sometimes not even used in the first place. Of course genomics will increase the accuracy of these genetic evaluations but resources must be put into explaining and demonstrating the impact of genetic differences on phenotypic performance. Dairy cattle breeders did an excellent job in convincing (mostly) non-geneticists that breeding can actually improve reproductive performance. This was achieved (eventually!) through demonstration, not structured demonstration, but because of widespread use of elite genetics in dairying the results across so many herds were impossible to ignore. Nonetheless, controlled experiments, although costly, 131
Table 3. Reasons in favor and against including DMI or RFI in a breeding goal
(Buckley et al., 2014) and beef (Prendiville et al., 2014). Controlled experiments on feed efficiency and the mean methane emissions per stratum also exist (Nkrumah et al., 2006). Structured demonstration herds or research herds can also be informative for breeding to reduce environmental footprint. Because the routine capture of data on most direct environmental traits for genetic evaluations can be expensive, it may not make economic sense to collect such information. Estimating the impact of current breeding strategies on genetic change in environmental traits can be achieved using selection index theory. However, procuring sufficient data to estimate precise genetic parameters can also be costly. Evaluating in a controlled environment, the de132
tailed environmental footprint of animals selected to be genetically divergent for a given selection strategy can be very useful in elucidating the impact of current breeding strategies on expected genetic trends in environmental footprint. Moreover the ideal reference population for accurate genomic evaluations should be genomically and phenotypically diverse (as well as related to the candidate population of animals). Animals divergent for the breeding strategy employed can therefore be very useful for the development of genomic predictions. This is especially true for difficult to measure traits such as direct environmental traits. Economic analysis Animal breeders (either academic or seedstock pro-
ducers) must not be afraid to discontinue certain paths if it is not economically advantageous or if more economic gain can be realized with a different strategy. Such economic analyses however must include long-term impact, discounted to current day equivalents. Economic analyses of breeding programs can be undertaken at the producer level, the breeding company level, or at the national/global level. Moreover, as previously alluded to, the economic cost of most environmental traits can be difficult to quantify unless there is some financial incentive (e.g., carbon trading) or penalty (e.g., nitrates directive) for same. Many of the benefits of reduced environmental footprint of cattle production systems will be realized at the national or even global level. Research in this area is on-going (Wall et al., 2010) Conclusions Environmental footprint of modern-day production systems will undoubtedly become more important in the near future as global food production increase and the ramifications of such are contemplated. Many approaches exist to possibly reduce the environmental footprint of animal production systems. Animal breeding has the advantage of being cumulative and permanent; the main disadvantage of the long generation interval in breeding is being ameliorated with the advent of genomic selection. Nonetheless, the alternative strategies in the animal breederâ€™s toolbox to achieve the objective of reduced environmental footprint of animal production without compromising animal performance must be thoroughly investigated taking cognizance of the cost of each strategy. Literature Cited Berry, D.P., and Crowley, J.J. 2012. Residual intake and gain; a new measure of efficiency in growing cattle. J. Anim. Sci. 90:109-115 Berry, D.P. and Crowley, J.J. 2013 Genetics of feed efficiency in dairy and beef cattle. J. Anim. Sci. 91:15941613 Berry, DP and Pryce J.E. 2014. Feed Efficiency in Growing and Mature Animals. Proc. World Cong. Gen. Appl. Livest. Prod. Buckley, F., McParland S. and Brennan A. 2014. The Next Generation Herd â€“Year 1 results. Moorepark Open Day Booklet. 9th April 2014. Moorepark, Ireland.
Campion, B., Keane M.G., Kenny D.A., and Berry D.P. 2009. Evaluation of estimated genetic merit for carcass weight in beef cattle: Live weights, feed intake, body measurements, skeletal and muscular scores, and carcass characteristics. Livest. Sci. 126: 87-99 Chen L, Mao F., Crews D.H., Jr., Vinsky M., and Li, C. 2014. Phenotypic and genetic relationships of feeding behavior with feed intake, growth performance, feed efficiency, and carcass merit traits in Angus and Charolais steers. J. Anim. Sci. 92:974-983; Clarke, A.M., Drennan M.J., McGee M., Kenny D.A., Evans R.D., and Berry D.P.. 2009. Intake, growth and carcass traits in male progeny of sires differing in genetic merit for beef production. Animal 3:791-801 Coleman, J., Berry D.P., Pierce K.M., Brennan A., and Horan, B. 2010. Dry matter intake and feed efficiency profiles of 3 genotypes of Holstein-Friesian within pasture-based systems of milk production. J. Dairy Sci. 93:4318-4331 Connolly, S.M., Cromie A.R., and Berry D.P. 2014. Genetic differences in beef terminal traits and Index is reflected in phenotypic performance difference in commercial beef herds. Proc. World Cong. Gen. Appl. Lives. Prod.. Vancouver. Crowley, J.J., McGee M., Kenny D.A., Crews D.H. Jr, Evans R.D., and Berry D.P. 2010. Phenotypic and genetic parameters for different measures of feed efficiency in different breeds of Irish performance tested beef bulls. J. Anim. Sci. 88:885-894 Donoghue K.A., Herd R.M., Bird S.H., Arthur P.F. and Hegarty R.G. 2013. Preliminary genetic parameters for methane production in Australian beef cattle. Proc. Assoc. Advancement Anim. Breed. Gen. 20:290293 Goddard, M.E., Bolormaa S., and Savin K. 2011. Selection for feed conversion efficiency in beef cattle. In; recent advances in Animal Nutrition 18, Australia. Ed. P. Cronje. University of New England. Australia Fitzsimons, C., Kenny D.A., Deighton M.H., Fahey A.G. and McGee M. 2013. Methane emissions, body composition, and rumen fermentation traits of beef heifers differing in residual feed intake. Journal of Animal Science. 91: 5789-5800 Koch, R.M., Swiger, L.A., Chambers, D. and Gregory, K.E. 1963. Efficiency of feed use in beef cattle. J. Anim. Sci. 22: 486-494 133
ACROSS-BREED EPD TABLES FOR THE YEAR 2014 ADJUSTED TO BREED DIFFERENCES FOR BIRTH YEAR OF 2012
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L. A. Kuehn1 and R. M. Thallman1
Roman L. Hruska U.S. Meat Animal Research Center, USDA-ARS, Clay Center, 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 2014 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 2012. 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 (born in 2012) of two different breeds 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 2014 update of estimates of sire breed means from data of the Germplasm Evaluation (GPE) project at USMARC adjusted to a year 2012 basis using EPD from the most recent national cattle evaluations. The 2012 basis year is chosen because yearling records for weight and carcass traits should have been accounted for in EPDs for progeny born in 2012 in the Spring 2014 EPD national genetic evaluations. Factors to adjust Spring 2014 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 5-7 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, 2014) national cattle evaluations. Serious errors can occur if the table adjustments are used with earlier or later EPD which may have been calculated with a different within-breed base. The following describes the changes that have occurred since the update released in 2013 (Kuehn and Thallman, 2013): New samplings of sires in the USMARC GPE program continued to increase progeny records for all of the breeds. The GPE program has entered a new phase in which more progeny are produced from breeds with higher numbers of registrations. Breeds with large increases in progeny numbers as a percentage of total progeny included South Devon and Tarentaise (especially for yearling weight) and Santa Gertrudis and Chiangus (especially for maternal milk). However, all of the breeds will continue to produce progeny in the project and sires continue to be sampled on a continuous basis for each of the 18 breeds in the across-breed EPD program. These additional progeny improve the accuracy of breed differences estimated at USMARC (column 3 in Tables 1-7) particularly for breeds with less data in previous GPE cycles (e.g., South Devon, Tarentaise, Santa Gertrudis, Chiangus). Materials and Methods All calculations were as outlined in the 2010 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), Kuehn et al. (2007-2011), and Kuehn and Thallman (2012, 2013). 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. 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 and different selection histories, 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 (255), 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 (265) 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, as suggested in the 2010 BIF Guidelines. 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 135
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. 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 coefficients 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. In addition, the regression coefficients by sire breed may reflect differences in genetic trends for different breeds. 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 136
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 2014 EPD for breed i for animals born in the base year (YY, which is two years before the update; e.g., YY = 2012 for the 2014 update), EPD(i)USMARC is the weighted (by total relationship of descendants with records at USMARC) average of 2014 EPD of bulls of breed i having descendants with records at USMARC, b is the pooled coefficient of regression of progeny performance at USMARC on EPD of sire (for 2014: 1.16, 0.84, 1.05, and 1.11 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 1.41 lb, 13.83 lb, 20.51 lb, -0.04 marbling score units (i.e. 4.00 = Sl00, 5.00 = Sm00), 0.26 in2, and 0.035 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 9.78 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 on a 2012 birth year basis. 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 traits (MAR, REA, FAT). Because of the accu-
racy 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 2012 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 2012) of two different breeds and out of dams of a third, unrelated breed. Thus, they represent half the difference expected between purebreds of the respective breeds. In each table, breed of sire differences were added to the raw mean of Angus-sired progeny born 2009 through 2013 at USMARC (Column 4) to make these differences more interpretable to producers on scales they are accustomed to. Figures 1-4 illustrate the relative genetic trends of most of the breeds involved (if they submitted trends) adjusted to a constant base using the adjustment factors in column 6 of Tables 1-7. These figures demonstrate the effect of selection over time on breed differences; breeders within each breed apply variable levels of selection toward each trait resulting in reranking of breeds for each trait over time. These figures and Column 5 of Tables 1-7 can be used to identify breeds with potential for complementarity in mating programs. 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 yearling weight for a Red Angus bull is +85.0 (which is above the birth year 2012 average of 83 for Red Angus) and for a Charolais bull is +37.0 (which is below the birth year 2012 average of 45.7 for Charolais). The across-breed adjustment factors in the last column of Table 3 are -29.9 for Red Angus and 40.9 for Charolais. Then the adjusted EPD for the Red Angus bull is 85.0 + (-29.9) = 55.1 and for the Charolais bull is 37.0 + (40.9) = 77.9. The expected yearling weight difference when both are mated to another breed of cow, e.g., Hereford, would be 55.1 â€“ 77.9 = -22.8 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 deviated. Birth Weight The range in estimated breed of sire differences for BWT (Table 1, column 5) ranged from 1.1 lb for Red Angus to 7.5 lb for Charolais and 10.9 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. Changes in breed of sire effects were generally small, less than 1.5 lb for all breeds relative to last yearâ€™s update (Kuehn and Thallman, 2013). Weaning Weight All of the 17 breed differences (Table 2, column 5) were within 6 lb of the values reported by Kuehn and Thallman. (2013). Changes in breed effects for all 18 breeds seem to be stabilizing since continuous sampling started in 2007. Yearling Weight Breed of sire effects for yearling weight were also similar to Kuehn and Thallman (2013) in general. South Devon and Tarentaise had the first yearling weight records recorded in the GPE program; their breed differences relative to Angus were smaller than estimated from previous sampling in the 1970s. Angus continued to have the greatest rate of genetic change for yearling weight, causing most breed of sire differences relative to Angus to decrease at least slightly. Maternal Milk Changes to the maternal milk breed of sire differences (Table 4, column 5) were generally small. All changes were less than 6 lb difference from those reported in 2013. However, the breed solution estimates (Table 4, column 3) are expected to change the most in future updates as GPE heifers from each of the 18 breeds being continuously sampled are developed and bred. Chiangus and Santa Gertrudis estimates and factors for maternal milk are presented here for the first time. No females from newly sampled South Devon or Tarentaise sires have weaned progeny as of yet. We would expect their solutions to change the most in future reports. Marbling, Ribeye Area, and Fat Thickness Most changes to breed of sire differences were minor for each of these carcass traits. South Devon was predicted to have less marbling relative to Angus in comparison to Kuehn and Thallman (2013), likely due to new progeny carcass records from sires sampled in 2011. Adjustment factors for Brahman are reported for the first time in this update. 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. The sires 137
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 0.57, 0.17, 0.44, and 0.15, respectively. Heritability estimates for MAR, REA, and FAT were 0.50, 0.48, and 0.43, respectively. 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 1.16 for BWT, 0.84 for WWT, and 1.05 for YWT were used to adjust breed of sire solutions to the base year of 2012. 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 18 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 at USMARC, especially steers and market heifers, 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 1.11 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 138
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 higher, though they have decreased from the previous year. While REA and FAT are both close to the theoretical estimate of 1.00, we continued to use the theoretical estimate of 1.00 to derive breed of sire differences and EPD adjustment factors. Pooled regression estimates for these two traits may be used in future updates. 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 were last reported in 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). ImplicationsÂ Bulls of different breeds can be compared on a common EPD scale by adding the appropriate acrossbreed 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.
Table 1. Breed of sire solutions from USMARC, mean breed and USMARC EPD used to adjust for genetic trend to the year 2012 base and factors to adjust within breed EPD to an Angus equivalent – BIRTH WEIGHT (lb) Ave. Base EPD Breed Soln BY 2012 BY 2012 Factor to Number Breed USMARC at USMARC Sire Breed Sire Breed adjust EPD AI Direct 2012 Bulls (vs Ang) Average Differencea To Angus Breed Sires Progeny (1) (2) (3) (4) (5) (6) Angus 152 1936 1.8 1.8 0.0 87.0 0.0 0.0 Hereford 149 2318 3.5 2.3 3.7 91.3 4.4 2.7 Red Angus 49 682 -1.2 -2.1 0.2 88.0 1.1 4.1 Shorthorn 55 508 2.2 1.5 6.7 93.5 6.6 6.2 South Devon 25 195 2.6 2.1 4.2 91.1 4.1 3.3 Beefmaster 53 465 0.3 0.9 6.3 91.8 4.9 6.4 Brahman 56 682 1.7 0.5 11.2 97.9 10.9 11.0 Brangus 53 477 0.8 0.8 3.9 90.3 3.4 4.4 Santa Gertrudis 21 276 0.2 0.6 6.6 92.3 5.4 7.0 Braunvieh 30 454 2.8 4.5 5.7 90.2 3.3 2.3 Charolais 107 1126 0.5 0.2 8.3 94.4 7.5 8.8 Chiangus 24 288 3.7 3.5 4.5 91.1 4.1 2.2 Gelbvieh 79 1038 0.8 2.1 4.2 89.3 2.4 3.4 Limousin 67 1104 1.7 1.0 3.4 90.6 3.7 3.8 Maine Anjou 48 506 1.7 2.7 6.7 91.8 4.8 4.9 Salers 50 459 1.6 2.4 3.2 88.9 2.0 2.2 Simmental 85 1107 2.2 3.4 5.7 90.8 3.8 3.4 Tarentaise 17 245 1.9 2.1 2.5 88.9 2.0 1.9 Calculations: (4) = (3) / b + [(1) – (2)] + (Recent Raw Angus Mean: 87.0 lb) with b = 1.16 (5) = (4) – (4, Angus) (6) = (5) – (5, Angus) – [(1) – (1, Angus)] a The breed difference estimates represent half the differences that would be expected between purebreds
of the two breeds. Table 2. Breed of sire solutions from USMARC, mean breed and USMARC EPD used to adjust for genetic trend to the year 2012 base and factors to adjust within breed EPD to an Angus equivalent – WEANING WEIGHT (lb) Ave. Base EPD Breed Soln BY 2012 BY 2012 Factor to Number Breed USMARC at USMARC Sire Breed Sire Breed adjust EPD AI Direct 2012 Bulls (vs Ang) Average Differencea To Angus Breed Sires Progeny (1) (2) (3) (4) (5) (6) Angus 152 1784 48.0 27.1 0.0 574.3 0.0 0.0 Hereford 147 2144 46.5 27.7 -3.1 568.6 -5.7 -4.2 Red Angus 49 656 54.0 48.0 -1.1 558.1 -16.1 -22.1 Shorthorn 55 478 15.2 15.0 -1.9 551.3 -22.9 9.9 South Devon 25 176 43.0 26.1 -5.2 564.1 -10.2 -5.2 Beefmaster 53 442 10.0 12.9 19.3 573.4 -0.8 37.2 Brahman 56 591 16.0 6.1 19.9 587.0 12.8 44.8 Brangus 53 456 24.3 21.7 8.3 565.9 -8.3 15.4 Santa Gertrudis 21 263 3.5 5.0 15.5 570.4 -3.9 40.6 Braunvieh 30 422 39.3 47.3 -2.8 542.1 -32.1 -23.4 Charolais 106 1022 25.6 14.5 21.2 589.8 15.5 37.9 Chiangus 24 256 38.4 40.7 -5.0 545.2 -29.1 -19.5 Gelbvieh 79 974 64.5 57.2 8.9 571.4 -2.9 -19.4 Limousin 66 1015 45.9 29.7 1.5 571.4 -2.9 -0.8 Maine Anjou 48 470 38.8 38.6 -6.3 546.1 -28.2 -19.0 Salers 50 436 41.0 33.8 1.3 562.1 -12.1 -5.1 Simmental 84 1009 64.2 57.2 19.9 584.1 9.8 -6.4 Tarentaise 17 237 16.0 -2.6 0.9 573.0 -1.3 30.7 Calculations: (4) = (3) / b + [(1) – (2)] + (Raw Angus Mean: 553.4 lb) with b = 0.84 (5) = (4) – (4, Angus) (6) = (5) – (5, Angus) – [(1) – (1, Angus)] a The breed difference estimates represent half the differences that would be expected between purebreds of the two breeds.
Table 3. Breed of sire solutions from USMARC, mean breed and USMARC EPD used to adjust for genetic trend to the year 2012 base and factors to adjust within breed EPD to an Angus equivalent – YEARLING WEIGHT (lb) Ave. Base EPD Breed Soln BY 2012 BY 2012 Factor to Number Breed USMARC at USMARC Sire Breed Sire Breed adjust EPD AI Direct 2012 Bulls (vs Ang) Average Differencea To Angus Breed Sires Progeny (1) (2) (3) (4) (5) (6) Angus 135 1598 86.0 48.3 0.0 1051.3 0.0 0.0 Hereford 140 1997 75.5 46.4 -26.7 1017.2 -34.1 -23.6 Red Angus 47 595 83.0 69.9 -8.7 1018.4 -32.9 -29.9 Shorthorn 52 429 24.9 23.7 3.3 1018.0 -33.3 27.8 South Devon 25 175 80.0 55.1 -18.4 1020.9 -30.4 -24.4 Beefmaster 49 337 14.0 19.1 4.3 1012.6 -38.7 33.3 Brahman 56 534 25.0 10.9 -28.7 1000.4 -50.9 10.1 Brangus 48 333 43.5 40.4 -2.9 1014.0 -37.3 5.2 Santa Gertrudis 21 237 5.2 9.5 5.0 1014.0 -37.3 43.5 Braunvieh 30 399 61.9 73.6 -23.5 979.6 -71.8 -47.7 Charolais 101 930 45.7 28.2 21.9 1052.0 0.6 40.9 Chiangus 24 222 70.7 71.1 -24.0 990.4 -61.0 -45.6 Gelbvieh 75 920 93.2 73.7 0.6 1033.6 -17.7 -24.9 Limousin 64 954 83.3 59.2 -29.1 1009.9 -41.4 -38.7 Maine Anjou 44 429 77.8 78.5 -11.8 1001.7 -49.7 -41.5 Salers 50 404 80.0 64.6 -8.7 1020.8 -30.6 -24.6 Simmental 78 891 93.2 83.0 22.2 1044.9 -6.4 -13.6 Tarentaise 17 234 28.6 1.1 -38.7 1004.2 -47.1 10.3 Calculations: (4) = (3) / b + [(1) – (2)] + (Raw Angus Mean: 1013.6 lb) with b = 1.05 (5) = (4) – (4, Angus) (6) = (5) – (5, Angus) – [(1) – (1, Angus)] a The breed difference estimates represent half the differences that would be expected between purebreds of the two breeds.
Table 4. Breed of maternal grandsire solutions from USMARC, mean breed and USMARC EPD used to adjust for genetic trend to the year 2012 base and factors to adjust within breed EPD to an Angus equivalent – MILK (lb) Ave. Base EPD Breed Soln BY 2012 BY 2012 Factor to Number Breed USMARC at USMARC Sire Breed Sire Breed adjust EPD AI Direct Direct 2012 Bulls (vs Ang) Average Differencea To Angus Breed Sires Gpr Progeny (1) (2) (3) (4) (5) (6) Angus 127 2915 659 24.0 14.1 0.0 563.3 0.0 0.0 Hereford 129 3593 841 18.8 10.0 -24.2 540.4 -22.9 -17.7 Red Angus 40 855 233 18.0 14.5 2.1 558.8 -4.5 1.5 Shorthorn 41 409 156 2.2 4.0 12.8 563.2 -0.1 21.7 South Devon 14 347 69 24.0 19.1 7.0 564.6 1.3 1.3 Beefmaster 34 336 101 2.0 -0.1 -8.7 547.7 -15.6 6.4 Brahman 53 807 241 6.0 6.9 18.6 569.2 5.9 23.9 Brangus 35 313 80 11.1 5.7 -7.0 552.5 -10.8 2.1 Santa Gertrudis 21 163 89 0.2 -2.2 -3.7 552.5 -10.8 13.0 Braunvieh 26 637 158 33.0 33.5 23.6 574.2 10.9 1.9 Charolais 87 1561 385 7.7 5.5 -2.1 553.7 -9.6 6.7 Chiangus 21 161 82 10.2 5.3 -8.7 550.4 -12.8 1.0 Gelbvieh 69 1509 359 28.0 30.6 21.9 570.5 7.2 3.2 Limousin 60 1709 394 22.6 19.0 -2.4 554.9 -8.4 -7.0 Maine Anjou 36 610 161 20.2 20.8 -0.6 552.3 -10.9 -7.1 Salers 45 504 172 19.0 19.9 10.4 561.9 -1.4 3.6 Simmental 65 1663 387 23.7 27.2 15.0 563.5 0.2 0.5 Tarentaise 6 341 78 0.6 5.3 18.1 565.0 1.7 25.1 Calculations: (4) = (3) / b + [(1) – (2)] + (Raw Angus Mean: 553.4 lb) with b = 1.11 (5) = (4) – (4, Angus) (6) = (5) – (5, Angus) – [(1) – (1, Angus)] a The breed difference estimates represent half the differences that would be expected between purebreds of the two breeds.
Table 5. Breed of sire solutions from USMARC, mean breed and USMARC EPD used to adjust for genetic trend to the year 2012 base and factors to adjust within breed EPD to an Angus equivalent – MARBLING (marbling score unitsa) Ave. Base EPD Breed Soln BY 2012 BY 2012 Factor to Number Breed USMARC at USMARC Sire Breed Sire Breed adjust EPD AI Direct 2012 Bulls (vs Ang) Average Differenceb To Angus Breed Sires Progeny (1) (2) (3) (4) (5) (6) Angus 118 714 0.50 0.19 0.00 6.10 0.00 0.00 Hereford 137 928 0.05 -0.01 -0.51 5.34 -0.76 -0.31 Red Angus 46 220 0.41 0.46 -0.07 5.67 -0.43 -0.34 Shorthorn 51 228 0.03 0.02 -0.36 5.44 -0.66 -0.19 South Devon 22 68 0.40 -0.07 -0.37 5.89 -0.21 -0.11 Brahman 54 222 0.00 -0.01 -1.04 4.76 -1.35 -0.85 Santa Gertrudis 21 113 -0.01 -0.02 -0.87 4.93 -1.17 -0.67 Charolais 46 239 0.02 -0.03 -0.65 5.20 -0.91 -0.43 Chiangus 24 107 0.22 0.20 -0.42 5.39 -0.71 -0.43 Gelbvieh 71 400 0.01 -0.24 -0.77 5.27 -0.84 -0.35 Limousin 59 383 -0.01 -0.07 -0.97 4.88 -1.22 -0.71 Maine Anjou 44 220 0.20 0.12 -0.78 5.09 -1.02 -0.72 Salers 46 193 0.20 -0.39 -0.67 5.71 -0.40 -0.10 Simmental 74 423 0.13 -0.03 -0.63 5.32 -0.78 -0.41 Calculations: (4) = (3) / b + [(1) – (2)] + (Raw Angus Mean: 5.79) with b = 1.00 (5) = (4) – (4, Angus) (6) = (5) – (5, Angus) – [(1) – (1, Angus)] a 4.00 = Sl00, 5.00 = Sm00 b The breed difference estimates represent half the differences that would be expected between purebreds of the two breeds.
Table 6. Breed of sire solutions from USMARC, mean breed and USMARC EPD used to adjust for genetic trend to the year 2012 base and factors to adjust within breed EPD to an Angus equivalent – RIBEYE AREA (in2) Ave. Base EPD Breed Soln BY 2012 BY 2012 Factor to Number Breed USMARC at USMARC Sire Breed Sire Breed adjust EPD AI Direct 2012 Bulls (vs Ang) Average Differencea To Angus Breed Sires Progeny (1) (2) (3) (4) (5) (6) Angus 118 715 0.48 0.08 0.00 13.19 0.00 0.00 Hereford 137 928 0.28 -0.04 -0.20 12.92 -0.28 -0.08 Red Angus 46 220 0.14 -0.13 -0.23 12.83 -0.36 -0.02 Shorthorn 51 228 -0.02 0.01 0.16 12.92 -0.27 0.23 South Devon 22 68 0.23 0.21 0.37 13.18 -0.02 0.23 Brahman 54 227 0.08 0.04 -0.12 12.72 -0.48 -0.08 Santa Gertrudis 21 114 0.05 0.01 -0.16 12.68 -0.52 -0.09 Charolais 46 240 0.21 0.07 1.03 13.97 0.77 1.04 Chiangus 24 108 0.08 0.04 0.42 13.25 0.06 0.46 Gelbvieh 71 402 0.42 0.33 0.92 13.81 0.61 0.67 Limousin 59 384 0.55 0.31 1.31 14.35 1.15 1.08 Maine Anjou 44 220 0.17 0.14 1.00 13.81 0.62 0.93 Salers 46 194 0.03 0.03 0.78 13.56 0.37 0.82 Simmental 74 424 0.76 0.52 0.91 13.93 0.74 0.46 Calculations: (4) = (3) / b + [(1) – (2)] + (Raw Angus Mean: 12.79 in2) with b = 1.00 (5) = (4) – (4, Angus) (6) = (5) – (5, Angus) – [(1) – (1, Angus)] a The breed difference estimates represent half the differences that would be expected between purebreds of the two breeds.
Table 7. Breed of sire solutions from USMARC, mean breed and USMARC EPD used to adjust for genetic trend to the year 2012 base and factors to adjust within breed EPD to an Angus equivalent – FAT THICKNESS (in) Ave. Base EPD Breed Soln BY 2012 BY 2012 Factor to Number Breed USMARC at USMARC Sire Breed Sire Breed adjust EPD AI Direct 2012 Bulls (vs Ang) Average Differencea To Angus Breed Sires Progeny (1) (2) (3) (4) (5) (6) Angus 118 715 0.010 0.001 0.000 0.639 0.000 0.000 Hereford 137 927 0.002 -0.004 -0.056 0.580 -0.059 -0.051 Red Angus 46 219 -0.003 -0.008 -0.037 0.598 -0.040 -0.027 Shorthorn 51 228 -0.009 -0.008 -0.144 0.485 -0.154 -0.135 South Devon 22 68 0.010 0.008 -0.128 0.503 -0.135 -0.135 Brahman 54 227 0.010 -0.003 -0.154 0.489 -0.150 -0.150 Santa Gertrudis 21 114 0.002 0.006 -0.098 0.527 -0.111 -0.103 Charolais 46 239 0.001 0.002 -0.213 0.416 -0.222 -0.213 Chiangus 24 107 0.011 0.011 -0.136 0.494 -0.144 -0.145 Gelbvieh 70 400 -0.050 -0.078 -0.211 0.447 -0.191 -0.131 Maine Anjou 44 220 -0.003 0.000 -0.225 0.401 -0.237 -0.224 Salers 46 194 0.000 -0.007 -0.215 0.422 -0.216 -0.206 Simmental 74 424 -0.060 -0.051 -0.201 0.420 -0.219 -0.149 Calculations: (4) = (3) / b + [(1) – (2)] + (Raw Angus Mean: 0. 630 in) with b = 1.00 (5) = (4) – (4, Angus) (6) = (5) – (5, Angus) – [(1) – (1, Angus)] a The breed difference estimates represent half the differences that would be expected between purebreds of the two breeds.
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
Weighted by relationship to phenotyped animals at USMARC for BWT, WWT, YWT, MAR, REA, and FAT and by relationship to daughters with phenotyped progeny MILK. a
Table 9. Estimates of variance components (lb2) for birth weight (BWT), weaning weight (WWT), yearling weight (YWT), and maternal weaning weight (MWWT) and for marbling (MAR; marbling score units2), ribeye area (REA; in4), and fat thickness (FAT; in2) from mixed model analyses
Analysis Direct Animal within breed (19 breeds) Maternal genetic within breed (19 breeds) Maternal permanent environment Residual
Carcass Direct MAR Animal within breed (13-14 breeds) 0.280 Residual 0.278 a Direct maternal covariance for weaning weight was -61.96 lb2
479.78 435.18 723.89 1256.00 REA 0.674 0.737
3560.76 4533.89 FAT 0.0105 0.0141
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 Pooled Sire breed Angus Hereford Red Angus Shorthorn South Devon Beefmaster Brahman Brangus Santa Gertrudis Braunvieh Charolais Chiangus Gelbvieh Limousin Maine Anjou Salers Simmental Tarentaise
BWT 1.16 ± 0.04
1.05 ± 0.09 1.18 ± 0.07 1.06 ± 0.14 0.66 ± 0.21 -0.31 ± 0.53 2.03 ± 0.33 1.91 ± 0.21 1.69 ± 0.23 3.63 ± 0.71 0.68 ± 0.26 1.13 ± 0.12 1.46 ± 0.30 1.11 ± 0.14 0.99 ± 0.11 1.44 ± 0.18 1.26 ± 0.23 1.10 ± 0.14 0.85 ± 0.59
WWT 0.84 ± 0.03
0.86 ± 0.07 0.72 ± 0.05 0.82 ± 0.14 0.58 ± 0.20 0.67 ± 0.31 1.00 ± 0.22 1.04 ± 0.18 0.94 ± 0.20 1.04 ± 0.23 0.59 ± 0.24 0.95 ± 0.11 0.17 ± 0.25 0.85 ± 0.11 1.01 ± 0.09 0.92 ± 0.19 0.80 ± 0.26 1.47 ± 0.13 1.06 ± 0.24
YWT 1.05 ± 0.04
1.23 ± 0.07 1.01 ± 0.06 0.60 ± 0.16 0.52 ± 0.26 0.50 ± 0.32 0.66 ± 0.34 1.35 ± 0.21 1.35 ± 0.28 1.10 ± 0.29 0.59 ± 0.38 0.85 ± 0.12 0.56 ± 0.29 1.13 ± 0.12 1.15 ± 0.12 0.76 ± 0.25 0.47 ± 0.25 1.33 ± 0.13 1.48 ± 0.34
MILK 1.11 ± 0.07
1.05 ± 0.15 1.05 ± 0.15 1.42 ± 0.27 1.16 ± 0.71 0.18 ± 1.57 3.31 ± 0.70 -0.05 ± 0.66 0.06 ± 0.56 0.26 ± 0.89 0.52 ± 0.54 1.16 ± 0.24 0.18 ± 0.44 0.82 ± 0.25 1.81 ± 0.25 2.02 ± 0.41 1.70 ± 0.40 0.89 ± 0.31 1.13 ± 0.93
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 Pooled Sire breed Angus Hereford Red Angus Shorthorn South Devon Brahman Santa Gertrudis Charolais Chiangus Gelbvieh Limousin Maine Anjou Salers Simmental
MAR 0.60 ± 0.04
0.90 ± 0.08 0.66 ± 0.15 0.76 ± 0.15 1.68 ± 0.30 -0.15 ± 0.23 2.57 ± 1.01 0.83 ± 0.62 1.29 ± 0.25 0.57 ± 0.22 1.21 ± 0.20 1.20 ± 0.37 0.77 ± 0.68 0.07 ± 0.07 0.84 ± 0.17
REA 0.83 ± 0.06
0.75 ± 0.13 0.62 ± 0.13 1.07 ± 0.20 1.66 ± 0.50 1.64 ± 2.25 1.22 ± 0.36 1.12 ± 0.44 1.07 ± 0.27 0.20 ± 0.43 1.30 ± 0.16 1.22 ± 0.17 -0.91 ± 0.48 1.63 ± 0.60 0.69 ± 0.15
FAT 0.94 ± 0.08
1.11 ± 0.15 0.97 ± 0.18 0.51 ± 0.40 1.87 ± 0.48 5.59 ± 2.65 1.50 ± 0.60 0.74 ± 0.46 1.45 ± 0.44 0.35 ± 0.45 1.70 ± 0.27
-1.19 ± 0.73 1.29 ± 0.59 0.11 ± 0.31
Figure 1. Relative genetic trends for birth weight (lb) of the seven most highly used beef breeds (1a) and all breeds that submitted 2014 trends (1b) adjusted for birth year 2012 using the 2014 across-breed EPD adjustment factors. 1a.
Figure 2. Relative genetic trends for weaning weight (lb) of the seven most highly used beef breeds (2a) and all breeds that submitted 2014 trends (2b) adjusted for birth year 2012 using the 2014 across-breed EPD adjustment factors. 2a.
Figure 3. Relative genetic trends for yearling weight (lb) of the seven most highly used beef breeds (3a) and all breeds that submitted 2014 trends (3b) adjusted for birth year 2012 using the 2014 across-breed EPD adjustment factors. 3a.
Figure 4. Relative genetic trends for maternal milk (lb) of the seven most highly used beef breeds (4a) and all breeds that submitted 2014 trends (4b) adjusted for birth year 2012 using the 2014 across-breed EPD adjustment factors. 4a.
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MEAN EPDs REPORTED BY DIFFERENT BREEDS Larry A. Kuehn1 and R. Mark Thallman1
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 40 years beginning with evaluations of growth traits. Since that time, EPDs have been added for several other production traits such as calving ease, stayability, 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 24 breeds are presented in this report. Mean EPDs for growth traits are shown in Table 1 (24 breeds), for other production traits in Table 2 (18 breeds), and for carcass and composition traits in Table 3 (20 breeds). Several breeds also have EPDs and indices 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 animalsâ€™ EPDs for their respective breeds (see Across-breed EPD Tables reported by Kuehn and Thallman 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).
Table 1. Birth year 2012 average EPDs from 2014 evaluations for growth traits Breed
Angus Hereford Murray Grey Red Angus Red Poll Shorthorn South Devon
Beefmaster Braford Brahman Brangus Red Brangus Santa Gertrudis Senepol Simbrah
Braunvieh Charolais Chianina Gelbvieh Limousin Maine-Anjou Salers Simmental Tarentaise
Birth Weight (lb) 1.8 3.5 3.8 -1.2 1.6 2.2 2.6 0.3 1.1 1.7 0.8 1.5 0.2 0.8 3.9
2.8 0.5 3.7 0.8 1.7 1.7 1.6 2.2 1.9
Weaning Weight (lb) 48 46.5 22 54 15 15.2 43
10 1.2 16 24.3 12.7 3.5 8.3 62.5
39.3 25.6 38.4 64.5 45.9 38.8 41 64.2 16
Yearling Weight (lb) 86 75.5 35 83 23 24.9 80
14 17 25 43.5 20.0 6.2 9.0 84.4
61.9 45.7 70.7 93.2 83.3 77.8 80 93.2 28.6
Maternal Milk (lb) 24 18.8 4 18 7 2.2 24
2 3 6 11.1 5.3 0.2 4.0 22.5
33.0 7.7 10.2 27.9 22.6 20.2 19 23.7 0.6
Total Maternal (lb) 42.1 15 45
9.8 45 9
52.7 20.5 29.3 60.2
Table 2. Birth year 2012 average EPDs from 2014 evaluations for other production traits
Angus Hereford Murray Grey Red Angus Shorthorn South Devon
Beefmaster Brangus Simbrah
Braunvieh Charolais Chianina Gelbvieh Limousin Maine Anjou Salers Simmental Tarentaise
Calving Ease Direct (%) 5 0.8 -0.6 4 -1.3 5.1 2.7
-0.2 3.0 5.5 9.7 9.0 9.2 0.3 9.3 -1.2
Calving Ease Maternal Scrotal Docility (%) Circ (cm) Score 8 1.1 -0.2 5 -1.4 7.1 6.7
-0.6 3.7 -2.2 6.8 4.5 3.5 0.4 10.6 0.6
0.77 0.8 0.2
0.2 0.55 0.66 0.46
Mature Weight (lb) 35 85 52
Heifer Pregnancy (%) 9.2
Stayability (%) 11
6 12 12.9 5.1 25.3
30 18 1.1 27
Retail Product (%)
Braunvieh 18.2 Charolais 15.1 Chianina 8.3 -0.08 Gelbvieh 25.9 -0.18 Limousin 25.1 -0.07 Maine-Anjou -0.5 0.33 Salers 22 0.0 Simmental 28.4 -0.31 a Derived using ultrasound measures and reported on an ultrasound scale (IMF% instead of marbling score)
Beefmaster Braford Brahman Brangus Santa Gertrudis Simbrah
Angus Hereford Murray Grey Red Angus Shorthorn South Devon
Carcass Wt (lb)
Table 3. Birth year 2012 average EPDs from 2014 evaluations for carcass and composition traits
-0.24a 0.02 0.22 0.01 -0.01 0.20 0.2 0.13
0.00a 0.01 0.00 0.02a -0.01 -0.09
0.50 0.05 0.0a 0.41 -0.03 0.4
0.58a 0.21 0.08 0.42 0.55 0.17 0.03 0.76
0.04a 0.06 0.08 0.31a 0.05 0.43
0.48 0.28 0.10a 0.14 -0.02 0.23
-0.003 0.00 -0.06
-0.43a 0.001 0.011 -0.05
0.00a 0.011 0.01 0.000a 0.002 -0.060
0.01 0.002 0.00a -0.003 -0.009 0.01
Carcass Ribeye Area Fat Thickness (in) (in2)
Rump fat (in)
Carcass Value ($) 17.24
Gestational length (d) -0.2
$ Feedlot 16.12
$ Calving Ease 18.20
600-d wt (lb) 51
Terminal Index ($) 68.3
All Purpose Index ($) 119.5
Mainstream Terminal Index ($) 44.5
Feedlot Merit ($) 31.61
Days to calving (d) -0.8
$ British Maternal Index 20.06
Terminal Index ($) 51.60
Feedlot Value ($) 27.89
Calving Ease Index ($)
Weaned Calf Value ($) 29.89
All Purpose Index ($) 69.10
Cow Energy Value ($) -3.78
Certified Hereford Beef Index ($)
Yearling Height (in) 0.5
Brahman Influence Index ($)
Mature Height (in) 0.4
Mature Cow Maintenance (Mcal/mo) 0
Baldy Maternal Index ($) 16.98
Residual Average Daily Gain (lb) 0.16
Table 4. Birth year 2012 average EPDs from 2014 evaluations for other traits unique to individual breeds Grid Value ($) 30.29
Beef Value ($) 69.76
GeneSeek ARS, Meat Animal Research Center IANR Merial Zoetis, Inc. Angus Journal Beef Magazine
Nebraska Cattlemen Foundation National Association of Animal Breeders (Accelerated Genetics, ABS Global Inc., Genex, ORIGEN and Select Sires)
National Program for Genetic Improvement of Feed Efficiency in Beef Cattle American Angus Association American Hereford Association Boehringer Ingelheim GrowSafe Systems Red Angus Association of America Illumina Progressive Cattleman Nebraska Cattlemen
SILVER Home Agency
American Gelbvich Association American Shorthorn Association American Simmental Association Capple Sales, Inc. Hunt Limousin Ranch Rishel Angus The CUP Lab The Home Agency Wagonhammer Ranches Livestock Gentec Allflex Trans Ova Genetics Nebraska Beef Council
P O Box 1555 North Platte, Nebraska 69103 308.534.5305
American Brahman Breeders Association A & B Cattle American Maine Anjou Association American-International Charolais Association Flying H Genetics Cross Diamond Cattle Company Gallagher Grace Mayer International Brangus Breeder Association Lincoln County Feedyard M&P Gelbvieh Moly MFG., Inc. Schuler Red Angus/Schuler-Olsen Ranches Inc. Affymetrix Agrigenomics Cow Sense Software Nebraska Corn Board
MOLY MFG., INC.
TG ET GA RE AT RR TE E U UR
American Akaushi Association Canaday Ranch Frenzen Angus & Polled Herefords