Dairy Cattle: Breeding and Genetics
`
`H. Duane Norman
`Suzanne M. Hubbard
`Paul M. VanRaden
`Animal Improvement Programs Laboratory, Agricultural Research Service, U.S. Department of Agriculture (USDA-ARS),
`Beltsville, Maryland, U.S.A.
`
`Abstract
`Five primary factors affect breeding genetically improved dairy cattle: 1) identification; 2) pedigree;
`3) performance recording; 4) artificial insemination; and 5) genetic evaluation systems (traditional and
`genomic). Genetic progress can be measured as increased efficiency (higher performance with fewer ani-
`mals). Knowledge of differences in genetic merit of dairy populations resulted in a global marketplace
`for germplasm and live animals, which led to calculation of international genetic evaluations. Selection
`indexes in which genetically evaluated traits are combined according to economic value are used by nearly
`all countries that calculate genetic evaluations.
`
`IntroductIon
`
`For thousands of years, the dairy cow has been a valuable
`producer of food for humans and animals. Animal breed-
`ing began when owners decided that mating the best with
`the best was a winning strategy; however, choosing which
`animals are best requires considerable insight. As genetic
`principles were discovered, animal breeding transformed
`into a science rather than an art. Early cattle gave only a
`few liters of milk per day; some herds now average 40 L/
`cow/day, and a few individual cows have averaged over
`80 L/day for an entire year. Although much has been
`learned about how to feed and manage dairy cows to obtain
`larger quantities of milk, current yield efficiency would not
`have been achieved unless concurrent progress had been
`made in concentrating those genes that are favorable for
`sustained, high milk production.
`
`genet
`
`Ic Improvement
`
`Five factors are primarily responsible for the exceptional
`genetic improvement achieved by dairy cattle: 1) perma-
`nent unique identification (ID); 2) parentage recording;
`3) recording of milk yield and other traits of economic im-
`portance; 4) artificial insemination (AI); and 5) accurate
`genetic evaluation systems. Ironically, failure of any one
`factor effectively neutralizes most genetic improvement.
`
`Identification
`
`character ID number: 3-letter country code, 3-letter breed
`code, 1-letter gender code, and 12-digit animal number.
`Global ID has come at a price; larger ID numbers contrib-
`ute to more data entry errors. Electronic ID tags and read-
`ers are becoming more common for managing feeding,
`milking, breeding, and health care of individual cows, with
`the data transferred to an on-farm computer, especially for
`large herds. In some countries, unique ID for each animal
`is mandatory.
`
`parentage (pedigree)
`
`Genetic improvement was slow before breeders began to
`summarize and use performance information from bulls’
`daughters. Proper recording of sire ID was required for this
`advance and has been used throughout the last century in
`selection decisions. Proper recording of dam ID was en-
`couraged during that period, but its benefit to selection de-
`cisions was less during early years. As genetic principles
`became better understood, accurate estimates of dams’
`genetic merit became extremely important. Cows of high
`genetic merit were designated as elite and usually were
`mated to top sires to provide young bulls for progeny-test
`programs of AI organizations. In countries that require
`unique ID for each animal, the sire, dam, and birth date
`sometimes are known for nearly 100% of animals. Genetic
`evaluation systems today use sophisticated statistical mod-
`els that can include performance information from many
`or all known pedigree relationships.
`
`performance recording
`
`Systems for dairy cattle ID have evolved from being unique
`Little genetic improvement can be achieved without ob-
`to the farm to being unique internationally. Although five
`jective measurement of traits targeted for improvement.
`characters or digits are sufficient to be unique within a
`Countries vary considerably in percentage of cows that are
`herd, today’s international dairy industry requires a 19-
`Encyclopedia of Animal Science, Second Edition DOI: 10.1081/E-EAS2-120045687
`Copyright © 2011 by Taylor & Francis. All rights reserved.
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`Cytokine –
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`DairyCattle:BreedingandGenetics 
`
`in milk-recording programs. In the United States, almost
`50% of dairy cows are enrolled in a dairy records manage-
`ment program, which supplies performance records to the
`national database, and parentage of only about two-thirds
`of those cows is known.
`The first traits to be evaluated in most countries were
`milk and butterfat yield and percentage. Since the 1970s,
`accurate evaluation of protein yield and percentage, con-
`formation traits, calving traits (calving ease/dystocia, still-
`birth/calf survival, calf size/birth weight, and gestation
`length), longevity (herdlife, productive life, stayability,
`survival, and risk of involuntary culling), mastitis resis-
`tance (udder health/traits, somatic cell count/score, and
`clinical mastitis), female fertility (heifer and cow concep-
`tion rates, daughter pregnancy rate, nonreturn rate, number
`of inseminations, days open, calving interval, and other re-
`productive intervals), and workability (milking speed and
`temperament) have been initiated in many countries.[1]
`
`Artificial Insemination
`
`Because some dilution of semen can provide nearly as
`high a conception rate as the original collected sample, 100
`progeny or more can originate from a single ejaculate. In
`addition, semen can be frozen and kept for decades with-
`out any serious compromise to fertility. The ability to ex-
`tend and freeze semen while achieving satisfactory fertility
`facilitates progeny testing early in a bull’s life. A progeny
`test involves obtaining dozens of daughters of a bull and
`allowing those daughters to calve and be milked so that
`their performance can be summarized and a determination
`can be made on whether the bull is transmitting favora-
`ble traits to his offspring. After distribution of semen for
`a progeny test, most bulls traditionally were held in wait-
`ing until the outcome of the progeny test. Progeny testing
`many bulls provided an opportunity to select from among
`them, keep only the best, and use those few bulls to produce
`several thousand daughters and, in some cases, millions of
`granddaughters. Characteristics of U.S. progeny-test pro-
`grams were documented by Norman et al.[2] Percentage
`of dairy animals that result from AI in the United States
`is nearly 80%; that percentage varies considerably among
`countries.
`
`g enetic evaluation systems
`
`Traditional
`
`Accurate methods for evaluating genetic merit of bulls
`and cows for economically important traits are needed to
`identify those animals that are best suited to be parents of
`the next generation. The degree of system sophistication
`needed depends partially on effectiveness of the sampling
`program in randomizing bull daughters across herds that
`represent various management levels. If randomization is
`
`equitable for all bulls, less sophisticated procedures can
`be used. In the United States, methodology for national
`evaluations has progressed from daughter–dam compari-
`son (1936) to herdmate comparison (1960) to modified
`contemporary comparison (1974) and, finally, to an animal
`model (1989).[3] A recent development in genetic evalua-
`tion systems is the use of test-day models, which have been
`adopted by several countries. Because test-day models ac-
`count better for environmental effects and variations in
`testing schemes, they can provide more accurate estimates
`of genetic merit than do lactation models; however, test-
`day models are statistically more difficult and computa-
`tionally more intensive.[4] Once evaluations are released
`to the dairy industry, dairy farmers have an opportunity to
`select among the best bulls for their needs and purchase
`frozen semen marketed by AI organizations. Mating deci-
`sions for specific animals can be based on estimated ge-
`netic merit for individual traits or selection indexes that
`combine traits of economic interest.
`
`Genomic
`
`The most recent advance in evaluation methodology for
`dairy cattle is the combination of genomic information
`with traditional phenotypic and pedigree data to produce a
`genomically enhanced estimate of genetic merit. Advances
`in genomic technology in recent years allow genotypes for
`more than 40,000 single-nucleotide polymorphisms (SNP;
`an SNP is a DNA base pair) equally distributed across all
`30 chromosomes to be used as a third source of data for ge-
`netic evaluations of dairy cattle in addition to phenotypes
`and pedigrees. Genotypes must be matched to phenotypes
`to estimate SNP effects. Genomic predictions are computed
`using linear and nonlinear systems of equations.[5] Linear
`predictions assume that all markers contribute equally to
`genetic variation (no major genes are present). Nonlinear
`predictions assume that previous distributions of effects of
`marker or quantitative trait loci are not normal.
`Genomic data greatly increase the reliability of pre-
`dicted genetic merit when added to phenotypic data for
`large populations. Because genomic predictions are calcu-
`lated as soon as a DNA sample is available and provide
`an evaluation with accuracy equivalent to one based on
`records from early offspring, this technology is causing
`dramatic changes in the dairy industry that are expected to
`accelerate the rate of genetic improvement. Dairy breeding
`programs with rapid turnover of generations could result
`in >50% faster progress by using genomically enhanced
`evaluations.
`
`o ther factors
`
`Dairy farmers continue to make additional genetic im-
`provement by culling within the herd. Herd replacements
`often allow a turnover of about 30% of milking animals
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`DairyCattle:BreedingandGenetics
`
`Fig.  Mean milk yield, genetic merit (breeding value), and
`sire genetic merit of U.S. Holstein cows with national genetic
`evaluations by birth year.
`Source: Animal Improvement Programs Laboratory, Agricultural
`Research Service, U.S. Department of Agriculture: Beltsville,
`MD; http://aipl.arsusda.gov (accessed April 2009).
`
`few cases, to the point of extinction. As selection meth-
`ods intensified, concern about level of inbreeding has in-
`creased, and interest in crossbreeding has grown somewhat
`to alleviate this concern and capture the known benefits of
`heterosis.
`
`InternAtIonAl evAluAtIons
`
`Increasing global trade in semen, embryos, and livestock
`resulted in a need for accurate comparisons of animal
`performance both within and across countries. However,
`such comparisons are made difficult by different genetic
`evaluation methods, breeding objectives, and management
`environments. In 1983, the International Bull Evaluation
`Service (Interbull) was established as a nonprofit organi-
`zation for promoting development and standardization of
`international genetic evaluations of cattle.[9] Currently, In-
`terbull provides evaluations for bulls from 27 countries for
`production, 22 countries for conformation, 23 countries
`for udder health, 18 countries for longevity, 14 countries
`for calving, 19 countries for female fertility, and 11 coun-
`tries for workability.[1]
`
`selectIon Indexes
`
`Nearly all dairy countries that calculate genetic evalua-
`tions for different traits produce an overall economic index
`in which traits are combined according to economic value.
`Past decisions on whether to allow animals to be parents
`have been made based on independent examination of each
`trait. Today’s indexes for countries (Table 1) differ in the
`traits included and values assigned to each.[10]
`
`per year. Some culling decisions are under the manager’s
`voluntary control, but others may be driven by fitness traits
`that limit the animal’s ability to remain profitable and stay
`in the herd. A cow must be capable of timely pregnancies
`so that a new lactation can begin with high yield, and she
`must remain free of chronic diseases and conditions such as
`mastitis and lameness so that lactation can be maintained.
`Supplemental breeding techniques also can help to in-
`crease genetic gains. Embryo transfer has increased the
`number of offspring possible from individual cows and
`helped to assure that potential bull dams will produce a
`son. Nucleus herds allow direct comparison of elite fe-
`males, but they have had limited use as an alternative to
`traditional AI progeny testing. Cloning technologies (em-
`bryo splitting, nuclear transfer, and adult cloning) also can
`produce some genetic gains, but their commercial use has
`been limited because of cost.[6] Use of sexed semen to pro-
`duce offspring of a desired gender has increased, but it re-
`duced conception rates, and higher production costs limit
`widespread use. Producing more females allows farmers to
`increase within-herd genetic gains.
`
`genet
`
`Ic progress
`
`Practical success of genetic improvement procedures is
`evident in most dairy populations around the world. As
`cow numbers decreased, yield per cow increased (Fig. 1),
`in part because of improved genetic capacity for efficient
`dairy production, as indicated by similar trends in the ge-
`netic merit of dairy bulls and cows (Fig. 2).
`Because of increased efficiency achieved through ge-
`netic programs, competition for sales of genetic material
`has increased. Higher productivity of North American
`breeds, particularly Holstein, in the 1980s[7] has led to U.S.
`semen exports of $100 million per year.[8] As a result, the
`international dairy population is much more related, and
`population sizes of many local breeds were reduced, in a
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`Fig.
`Numbers of U.S. cows and mean milk yield by year.
`Source: National Agricultural Statistics Service, U.S. Department
`of Agriculture: Washington, DC; http://www.nass.usda.gov
`(accessed April 2009).
`
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`DairyCattle:BreedingandGenetics 
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`Relative emphasis of traits in Holstein selection indexes around the world.
`Traitemphasis,%
`Longevity Udderhealth
`8
`7
`7
`3
`7
`7
`12
`12
`20
`7
`10
`
`27
`25
`12
`15
`35
`
`Fertility Other healthtraits
`5
`10
`10
`2
`12
`12
`10
`
`3
`
`Table
`
`Protein
`Country(index)
`37
`Australia (APR)
`31
`Canada (LPI)
`34
`Czech Republic (SIH)
`37
`France (ISU)
`36
`Germany (RZG)
`40
`Hungary (HGI)
`25
`Ireland (EBI)
`42
`Israel (PD07)
`45
`Italy (PFT)
`55
`Japan (NTP)
`22
`Netherlands (NVI)
`40
`New Zealand (BW)
`20
`Scandinavia (NTM)
`26
`South Africa (BVI)
`35
`Spain (ICO)
`39
`Switzerland (ISEL)
`22
`United Kingdom (PLI)
`16
`United States (NM)
`26
`United States (TPI)
`Source: Adapted from Schneider.[10]
`
`Fat Milk Conformation
`14
`−18
`20
`15
`13
`9
`15
`5
`15
`14
`20
`5
`12
`5
`26
`12
`14
`12
`19
`16
`
`−12
`
`−6
`−14
`−5
`
`12
`
`−11
`
`11
`8
`8
`
`20
`6
`4
`
`3
`7
`21
`22
`14
`
`13
`10
`
`6
`7
`14
`3
`3
`10
`6
`10
`5
`
`23
`16
`
`19
`8
`13
`
`6
`18
`11
`10
`
`24
`7
`
`−13
`26
`
`4
`5
`3
`
`23
`25
`22
`
`13
`45
`35
`24
`6
`17
`26
`
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`conclusIon
`
`Animal ID that includes pedigree information, routine
`recording of performance traits, widespread use of AI,
`and development of state-of-the-art statistical models and
`evaluation systems have led to increasing genetic gains in
`traits of economic importance for dairy cattle during the
`past 100 years. The resulting improvement in production
`efficiency allows dairy products to be produced with fewer
`cattle, thereby reducing adverse environmental impacts
`and conserving natural resources. Increased genetic merit
`of dairy populations has resulted in a global marketplace
`for germplasm and live animals. Recent incorporation of
`genomic information promises faster progress during the
`next 100 years.
`
`references
`
`2.
`
`3.
`
`4.
`
`5.
`
`6.
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`7.
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`8.
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`9.
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`1.
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`International Bull Evaluation Service. Genetic Evaluations;
`2009, http://www-interbull.slu.se/eval/framesida-genev.htm
`(accessed June 2009).
`
`10.
`
`Norman, H.D.; Powell, R.L.; Wright, J.R.; Sattler, C.G.
`Timeliness and effectiveness of progeny testing through ar-
`tificial insemination. J. Dairy Sci.  00 , 86 (4), 1513–1525.
`VanRaden, P.M. History of USDA Dairy Evaluations;
`2003, http://aipl.arsusda.gov/aipl/history/hist_eval.htm (ac-
`cessed June 2009).
`Wiggans, G.R. Issues in defining a genetic evaluation
`model. Interbull Bull.  00,26, 8–12.
`VanRaden, P.M. Efficient methods to compute genomic
`predictions. J. Dairy Sci.  008,91 (11), 4414–4423.
`Norman, H.D.; Lawlor, T.J.; Wright, J.R.; Powell, R.L. Per-
`formance of Holstein clones in the United States. J. Dairy
`Sci.  00,87 (3), 729–738.
`Jasiorowski, H.A.; Stolzman, M.; Reklewski, Z. The Inter-
`national Friesian Strain Comparison Trial, A World Per-
`spective; Food and Agriculture Organization of the United
`Nations: Rome, Italy, 1988.
`National Association of Animal Breeders. Semen Sales Re-
`port for 2007–2008; 2009, http://www.naab-css.org/sales/
`table29.html (accessed June 2009).
`International Bull Evaluation Service. Interbull Summary; 2007,
`http://www-interbull.slu.se/summary/framesida-summary.htm
`(accessed June 2009).
`Schneider, S. 2009 World index underlies worldwide har-
`monization. Holstein Int.  009,16 (6), 26–31.
`
`12004567 265 Manila Typesetting Company 04/23/2010 03:14PM
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`Downloaded By: [Hubbard, Suzanne] At: 14:00 21 January 2011
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`Exhibit 1020
`Select Sires, et al. v. ABS Global
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`

`

`Publisher Taylor & Francis
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`41 Mortimer Street, London W1T 3JH, UK
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`Encyclopedia of Animal Science, Second Edition
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`Dairy Cattle: Breeding and Genetics
`H. Duane Normana; Suzanne M. Hubbarda; Paul M. VanRadena
`a Animal Improvement Programs Laboratory, Agricultural Research Service, U.S. Department of
`Agriculture (USDA-ARS), Beltsville, Maryland, U.S.A.
`
`Online publication date: 19 November 2010
`
`To cite this Chapter Norman, H. Duane , Hubbard, Suzanne M. and VanRaden, Paul M.(2010) 'Dairy Cattle: Breeding and
`Genetics', Encyclopedia of Animal Science, Second Edition, 1: 1, 262 — 265
`
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