J. Dairy Sci. 87:3125–3131
`© American Dairy Science Association, 2004.
`
`Invited Review: Selection on Net Merit to Improve Lifetime Profit
`
`P. M. VanRaden
`Animal Improvement Programs Laboratory
`Agricultural Research Service, USDA, Beltsville, MD 20705-2350
`
`ABSTRACT
`
`Genetic selection has made dairy cows more profit-
`able producers of milk. Genetic evaluations began with
`2 traits measured on a few cows but now include many
`traits measured on millions of cows. Selection indexes
`from USDA included yield traits beginning in 1971,
`productive life and somatic cell score beginning in 1994,
`conformation traits in 2000, and cow fertility and calv-
`ing ease in 2003. This latest revision of net merit should
`result in 2% more progress, worth $5 million/yr nation-
`ally, with improved cow health and fitness, but slightly
`less progress for yield. Fertility and longevity evalua-
`tions have similar reliability because cows can have
`several fertility records, each with lower heritability,
`compared with one longevity record with higher herita-
`bility. Lifetime profit can be estimated more accurately
`if less heritable traits are evaluated and included in-
`stead of ignored. Milk volume has a positive value for
`fluid use, but a negative value for cheese production.
`Thus, multiple selection indexes are needed for differ-
`ent markets and production systems. Breeding pro-
`grams should estimate future rather than current costs
`and prices. Many other nations have derived selection
`indexes similar to US net merit.
`(Key words: selection index, net merit, genetic
`progress)
`Abbreviation key: CM$ = cheese merit, DCE =
`daughter calving ease, DPR = daughter pregnancy
`rate, FM$ = fluid merit, NM$ = net merit, PL = produc-
`tive life, SCE = service sire calving ease.
`
`INTRODUCTION
`
`Dairy cattle improve because breeders choose the
`best bulls and best cows to be parents of the next gener-
`ation. Definitions of what is best and methods of selec-
`tion have become more scientific over time. Breeders
`have to plan ahead because genetic choices today will
`improve profit only in future generations. A review of
`
`Received February 6, 2004.
`Accepted April 29, 2004.
`E-mail: paul@aipl.arsusda.gov.
`
`past selection may be of use in determining new selec-
`tion goals.
`This report presents a history of net merit and other
`animal breeding terms, a discussion of the traits in-
`cluded and the economic values assigned, an interna-
`tional comparison of selection indexes, and some future
`directions in selection of dairy cattle.
`
`HISTORY
`
`For thousands of years, breeders have tried to decide
`which animal traits are most important. A few ancient
`breeders profited from selection simply by assuming
`that animal health traits were inherited. For example,
`Jacob “grew exceedingly rich” by breeding from stronger
`rather than feebler animals (Genesis, ∼1500BC). Selec-
`tion changed domestic animals, but not always in the
`right direction. For example, some breeders consumed
`or killed their healthiest animals and kept less fit ani-
`mals in their breeding populations (Malachi, ∼475BC).
`
`Goals
`
`During the last century, genetic principles became
`known and genetic progress became a goal of most
`breeders. For selection to be profitable, the market
`should offer rewards for animals with superior traits.
`Lush (1960) discussed the potential to improve a trait
`such as protein percentage before it had a price in the
`market: “One would like to select today in accordance
`with the economic values which will prevail 10 to 20
`yr from now. To do that with complete success would
`require prophetic ability of a high order; still it must
`be done as best we can. The breeder’s main task in this
`respect is to decide which price and other economic
`variations are only temporary and which are long-
`time trends.”
`Specialized breeds selected for different traits can
`make more profit than a single breed selected for many
`traits. For example, dairy breeds serve a different pur-
`pose than beef breeds even though all cows can produce
`both milk and beef. Miller (1977) compared dairy selec-
`tion in North America to dual-purpose selection in Eu-
`rope and concluded: “In the future, particularly if inter-
`national selection goals become more uniform, research
`
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`Exhibit 1009
`Select Sires, et al. v. ABS Global
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`3126
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`VANRADEN
`
`will be needed to determine what can be gained by
`introducing semen of bulls from other countries into
`US improvement programs.” Dairy selection programs
`are now global, but different breeders might still benefit
`from selecting different breeds or different animals
`within breeds depending on local prices, environments,
`and purposes.
`Breeders can measure incomes and expenses and se-
`lect for profit or just look at their cows and select the
`most stylish. Swett and Graves (1930) concluded that
`“the whole system of dairy cattle judging has been built
`on the superficial observations of breeders and cattle
`fanciers and upon a great many theories and supposi-
`tions which have been passed down through several
`generations of breeders and instructors until they have
`become rather generally accepted as facts, even though
`there is little if any tangible evidence to support many
`of them. The show ring as now conducted is more of a
`sporting than an educational event, and as such, it
`undoubtedly creates false impressions as to the relative
`importance of beauty and performance.” Cow shows and
`judging contests still have these same problems, but
`linear conformation traits now allow breeders to select
`for particular traits that do affect profit.
`Many traits that affect dairy cattle profit can be in-
`cluded in selection indexes. For example, the national
`index of Sweden included 12 traits as early as 1975
`(Philipsson et al., 1994; Philipsson and Lindhe´, 2003).
`More traits provide more information about profit, but
`too many could confuse breeders and distract attention
`away from those with highest value. “A key priority in
`research and education should be to identify those traits
`that really affect cost of producing milk and concentrate
`selection on them” (McDaniel, 1976). Reasons to include
`or exclude particular traits were reviewed by Pearson
`(1986).
`Trait values often are assigned by committee and
`consensus rather than by strict economic or mathemati-
`cal models. Recently, Solkner and Fuerst (2002) com-
`pared index methods across countries and “found it very
`difficult, though, to find details on the rationale for
`choosing traits included in the index and methodology
`used for derivation of the index weights.” Some diffi-
`culty may be caused by economic goals being debated
`informally in local languages and not translated into
`published scientific documents. According to Freeman
`(1984), “determining selection goals is one of the most
`difficult,
`if not the most difficult, task of animal
`breeders.”
`
`Terms
`
`Net merit defines a goal for selection and lets breeders
`measure progress toward the goal. “The idea of a yard-
`
`Journal of Dairy Science Vol. 87, No. 10, 2004
`
`stick or selection index for measuring the net merit of
`breeding animals is probably almost as old as the art
`of animal breeding itself” (Hazel, 1943). Lush (1948)
`further defined net merit as the sum of the effects of
`all genes for all traits important to the breeder: “Instead
`of genes, he sees individual plants or animals, each of
`which differs from the others in many respects. When
`deciding whether to select or reject a plant or animal
`for breeding, he adds together what he thinks are its
`advantages and disadvantages for his purposes. This
`is selection for a complex character, net merit, effected
`by many genes.”
`Reliability measures the agreement of estimated
`merit with true merit (Goodale, 1928). In Goodale’s
`(1928) booklet on herd sire selection, a section titled
`“The reliability of the index figures” presented the basic
`idea of progeny testing: “The breeding worth of bulls
`in terms of milk production could be measured very
`accurately if every bull were mated to cows of the same
`quality, for then, whatever differences existed in pro-
`duction of the daughters would come from differences
`in the breeding worth of the bulls. The daughters’ milk
`production would itself be the measure of the bull’s
`value.”
`Transmitting ability is the average value of genes
`transmitted to progeny (Yapp, 1925) and was further
`explained in 1926 in USDA’s first attempt at a national
`sire summary: “The pedigree of any individual is only
`an indication of what the transmitting ability of that
`individual, for milk and butterfat production, may be.
`Until such time as we have pedigrees in which the
`sires have a sufficient number of tested daughters from
`tested dams, so that their breeding performance can be
`analyzed, as has been done with these 23 sires, predic-
`tions can not be made with certainty as to the transmit-
`ting ability of any untried individual” (Graves, 1926).
`That first report included data from 198 daughter-dam
`pairs and 23 sires, whereas US genetic evaluations now
`include data from >20 million cows and >100,000 sires.
`
`USDA Economic Indexes
`
`In 1971, USDA introduced its first economic index,
`called Predicted Difference Dollars, which estimated
`gross income per lactation using milk and fat yield (Nor-
`man and Dickinson, 1971). In 1977 and 1984, similar
`economic index formulas based on milk-fat-protein
`price and cheese yield price, respectively, were intro-
`duced (Norman et al., 1979; Norman, 1986).
`In 1994, productive life (PL) and somatic cell score
`(SCS) were combined with yield traits into a net merit
`index (NM$) using economic values that were obtained
`as averages of independent literature estimates (Van-
`Raden and Wiggans, 1995). The correlated response in
`
`Exhibit 1009
`Select Sires, et al. v. ABS Global
`
`

`

`REVIEW: SELECTION ON NET MERIT TO IMPROVE LIFETIME PROFIT
`
`3127
`
`feed intake was subtracted, focusing attention on net
`income per lactation instead of gross income as mea-
`sured by the earlier milk pricing formulas. These con-
`tinued to be published along with NM$ until being re-
`placed in 1999 by cheese merit (CM$) and fluid merit
`(FM$) indexes that included PL and SCS. The only
`national indexes that included health traits before 1994
`were in Scandinavia (Philipsson et al., 1994; Leitch,
`1994).
`In August 2000, NM$, CM$, and FM$ were revised
`to include linear conformation composites (Holstein As-
`sociation USA, 2000) using a lifetime profit function
`(VanRaden, 2000) developed by scientists in multistate
`project S-284, “Genetic Enhancement of Health and
`Survival for Dairy Cattle”. From 1994 to 2000, the type
`traits had affected NM$ only as early predictors of PL
`rather than by direct selection. Selection indexes of
`breed associations included final score for type as early
`as 1976 (VanRaden, 2002). Several of the breed associa-
`tion indexes were revised recently to include individual
`conformation and health traits. The USDA indexes and
`predictions of PL include udder composite, feet and leg
`composite, and body size composite instead of using all
`17 type traits. The units of NM$, CM$, and FM$
`changed in 2000 from per-lactation to lifetime profit,
`and the standard deviations of these indexes became 3
`times greater because an average of 3 lactations was
`assumed.
`In August 2003, service sire calving ease (SCE),
`daughter calving ease (DCE), and daughter pregnancy
`rate (DPR) were included in NM$ calculations. Evalua-
`tions of SCE for US Holstein bulls had been available
`since 1978 (Berger, 1994), whereas evaluations of DCE
`(Van Tassell et al., 2003) and DPR (VanRaden et al.,
`2003) were introduced only recently. The fertility trait,
`DPR, is calculated from days open and measures ability
`of the daughter to cycle, express heat, conceive, and
`retain the pregnancy. Economic values for all traits in
`NM$ were reestimated, and breed-specific composites
`were used instead of using those defined by the Holstein
`Association USA (2000) for all breeds.
`
`METHODS
`Prices of inputs and outputs change across time, and
`past trends may not predict future prices. Incomes and
`expenses associated with each trait in NM$ were re-
`ported by VanRaden and Seykora (2003). All of the
`details will not be repeated here, but some of the more
`controversial traits and assumptions deserve expla-
`nation.
`
`Values of Traits
`Cow size is an example of a highly debated trait. Most
`breeders were taught from youth in judging contests
`
`that bigger heifers and cows were better. They observed
`that larger animals brought higher sales prices, but
`often forgot that more feed was required to grow larger
`replacements and to maintain heavier mature weights.
`Management practices that increase heifer growth rate
`probably do increase profit because well-grown heifers
`can calve and begin producing milk sooner. Bigger cows
`may give more milk, but larger cow size decreases profit
`if the selection index already has production traits in-
`cluded. The reason is that higher feed costs for large
`cows exceed their higher beef income, whereas any in-
`crease in yield is already accounted for by the yield PTA.
`Some economic traits are not easy to measure di-
`rectly, and correlated traits can be used as substitutes.
`But if reliable PTA are available for economic traits
`such as protein yield, little or no information is added
`by considering correlated indicator traits. Cow shows
`and judging contests may do more harm than good be-
`cause they ignore incomes and expenses that can be
`measured and focus only on visual traits. Some breeders
`prefer to own cows with a pleasing appearance, but the
`goal of the NM$ index is to accurately predict the cow’s
`profit on a commercial dairy.
`Health and fitness traits received less attention in
`the past because accurate genetic evaluations were not
`available for less heritable traits. Selection for only
`yield and type traits is risky because some selection is
`needed to maintain health and fertility. Without selec-
`tion pressure, fertility declined steadily because of unfa-
`vorable correlations with yield traits. This decline
`might have been reduced or avoided with index selec-
`tion, which is ideal even when traits with lower reliabil-
`ity are included. The heritability of DPR is lower than
`that of PL, but the reliabilities of DPR and PL are
`similar (Norman et al., 2003) because cows can have
`multiple fertility records but just one longevity record,
`and fertility records arrive before reports of culling.
`Longevity can be increased by selecting on PL evalua-
`tions or by selecting for the individual traits that con-
`tribute to PL (Rogers, 1994). Advantages of selecting
`for individual traits are that each trait is analyzed with
`its own heritability and the contributions of each trait
`to PL may change over time (Tsuruta et al., 2004).
`Thus, emphasis on particular contributing traits can
`be reassigned if conditions change. An advantage of
`selection on PL is that all reasons for culling are in-
`cluded.
`Cow fertility is associated with several costs not ac-
`counted for by PL. These include increased labor and
`supplies for heat detection, inseminations, pregnancy
`exams, increased units of semen needed per pregnancy,
`and yield losses because ideal lactation length cannot
`be achieved. Cost estimates included heat detection la-
`bor and supplies of $20 per lactation, which increase
`
`Journal of Dairy Science Vol. 87, No. 10, 2004
`
`Exhibit 1009
`Select Sires, et al. v. ABS Global
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`

`

`3128
`
`VANRADEN
`
`by 0.5% per day open; pregnancy exams, which cost $10
`and 0.012 more are required per additional day open;
`a semen price of $15 per unit and insemination labor
`of $5 per unit, both multiplied by a 0.025 unit increase
`per day open; and a reduced profit of $0.75 per day open
`from lactations longer or shorter than optimum. These
`per-lactation losses were converted to lifetime value by
`multiplying by 2.8 and converted from days open to
`DPR by multiplying by −4, which resulted in a DPR
`economic value of $17 per PTA unit. Further study of
`these assumptions is needed.
`Calving ease is jointly affected by the service sire and
`by the dam. For many years, breeders were advised to
`avoid difficult births by mating heifers to favorable SCE
`bulls and cows to less favorable bulls rather than select-
`ing for SCE (Rogers, 1994). However, selection and mat-
`ing programs together can reduce difficulty by more
`than assortative mating alone (Dekkers, 1994). Selec-
`tion for SCE in addition to DCE ensures that the se-
`lected group will include bulls that cause less difficulty
`when mated to heifers. Economic values for SCE and
`DCE were estimated from previous North American
`studies (Dekkers, 1994; Dematawewa and Berger,
`1997), and these values were within the range of esti-
`mates obtained in Europe (Groen et al., 1997).
`Milk volume can have a positive or negative value
`depending on fluid or manufacturing use (Weigel et al.,
`1997). Many milk drinkers prefer to buy low-fat milk,
`and US fluid processors have little incentive to main-
`tain protein or solids content, except in California
`where minimums are higher. In some fluid markets,
`producers can lose money by selecting for protein be-
`cause added feed costs exceed protein premiums.
`Cheese production requires protein and fat, but not
`water or lactose. These milk price differences may cause
`greater index reranking than many other trait values.
`Specialized breeds or lines of cows producing high- or
`low-component milk to match local markets may be
`more profitable than a randomly mating population try-
`ing to produce for all markets.
`
`Theory
`
`Selection index theory uses heritabilities, phenotypic
`correlations, and genetic relationships among traits to
`maximize accuracy. For breeders, however, selection
`is simpler if multitrait PTA are supplied that already
`account for these parameters. Often, PTA are supplied
`only for measured traits, such as milk production, and
`not for important unmeasured traits, such as feed con-
`sumption. The value of measured traits then includes
`the direct value of the measured trait and also the
`genetic regression of the unmeasured traits on the mea-
`sured traits (Rogers, 1994).
`
`Journal of Dairy Science Vol. 87, No. 10, 2004
`
`Economic relationships are often nonlinear, but lin-
`ear selection indexes provide accurate rankings and
`are easier to explain and use (Goddard, 1983). Profit
`functions that seem nonlinear across the phenotypic
`range are more nearly linear across the narrower range
`of PTA, especially for traits with low heritability. A
`correlation of 0.999 was obtained when linear and non-
`linear NM$ formulas were compared (VanRaden, 2000).
`The lifetime profit function is nonlinear because per-
`lactation incomes and expenses are multiplied by the
`number of lactations. The official, linear NM$ formula
`is the derivative of the nonlinear profit function evalu-
`ated at the mean for each trait. The NM$ measures
`expected profit for an average daughter, but may under-
`estimate total future profit because genes contributed
`to grand-progeny and more remote descendants are ig-
`nored. Calculation of return on investment or dis-
`counting future profits to present value would be useful
`for an investment analysis but might have little affect
`on the relative values of traits or genetic rankings.
`Genetic progress is proportional to accuracy, inten-
`sity, and genetic standard deviation, and is inversely
`proportional to generation interval. Real progress is
`also proportional to directional loss, or the loss from
`selecting in a less than optimal direction. Directional
`loss equals the correlation of the estimated economic
`function with the true economic function (Smith, 1983).
`Intelligent breeders may debate whether particular
`traits, such as body size, milk volume, or dairy form,
`should receive 2 or 3 times as much emphasis, or be
`ignored, or even selected in the opposite direction.
`Choosing the correct direction of selection is more es-
`sential for real progress than improving accuracy, in-
`tensity, or generation interval.
`
`RESULTS AND DISCUSSION
`
`A history of the main changes in USDA indexes and
`the percentages of relative emphasis on each trait are
`provided in Table 1. The enhanced NM$ index imple-
`mented in 2003 was correlated by 0.98 with the previous
`NM$ formula from 2000 for recent progeny-tested bulls.
`The expected 2% increase in genetic progress is worth
`$5 million/yr nationally based on a $250 million value of
`current progress. However, some of the extra progress
`results from revised relative values for existing traits
`rather than just the addition of cow fertility and calving
`ease. Because NM$ now includes more traits that di-
`rectly affect profit, accuracy of selection has increased.
`Correlations of individual trait PTA with the 2000
`and 2003 versions of the NM$ index are provided in
`Table 2. The revised 2003 index had higher correlations
`with DPR, PL, SCE, and DCE, but lower correlations
`with milk and protein. Table 2 also provides a compari-
`
`Exhibit 1009
`Select Sires, et al. v. ABS Global
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`

`REVIEW: SELECTION ON NET MERIT TO IMPROVE LIFETIME PROFIT
`
`3129
`
`Table 1. History of the main changes in traits and relative economic
`weights (%) in USDA selection indexes.
`
`USDA economic indexes (and years introduced)
`
`PD$1 MFP$2 CY$3 NM$4 NM$
`(1971)
`(1976)
`(1984)
`(1994)
`(2000)
`−2
`45
`53
`—
`—
`
`27
`46
`27
`—
`—
`
`6
`25
`43
`20
`−6
`
`5
`21
`36
`14
`−9
`
`—
`
`—
`
`—
`—
`
`—
`
`—
`
`—
`
`—
`
`—
`—
`
`—
`
`—
`
`—
`
`—
`
`—
`—
`
`—
`
`—
`
`7
`
`4
`−4
`—
`
`—
`
`—
`
`Traits included
`
`52
`Milk
`48
`Fat
`—
`Protein
`Productive life —
`Somatic
`—
`cell score
`Udder
`composite
`Feet/leg
`composite
`Size composite —
`Daughter
`—
`pregnancy rate
`Service
`sire calving
`difficulty
`Daughter
`calving
`difficulty
`
`—
`
`—
`
`—
`
`—
`
`NM$
`(2003)
`
`0
`22
`33
`11
`−9
`
`7
`
`4
`−3
`7
`−2
`
`−2
`
`1Predicted difference dollars.
`2Milk-fat-protein dollars.
`3Cheese yield dollars.
`4Net merit dollars.
`
`son of actual genetic progress in the last decade with
`expected progress in the next decade if breeders select
`on NM$. Genetic progress for NM$ should increase
`slightly because domestic and foreign sampling pro-
`grams will test more bulls more accurately than in pre-
`vious decades. Expected progress for each trait was
`calculated from the correlations with NM$ multiplied
`by the standard deviation of PTA multiplied by an ex-
`pected NM$ gain of 3.4 standard deviations over the
`decade. The standard deviation of true transmitting
`ability for NM$ was estimated to be $191.
`
`Actual genetic progress reported in Table 2 for breed-
`ing values equals twice the progress in transmitting
`abilities. An exception is that SCE trend is only for
`transmitting ability because calving ease trend is af-
`fected jointly by the sire trend plus twice the maternal
`grandsire trend. Much progress for yield traits was
`achieved during the 1990s, but actual trends for SCS,
`DPR, SCE, and size were not in the desired direction.
`Reasons are that SCS, PL, DPR, and DCE were not
`even evaluated in 1990, and that many breeders did
`not quickly adopt or emphasize the new traits when
`evaluations became available.
`
`International Selection Goals
`
`Selection indexes for the 13 largest national Holstein
`populations evaluated by Interbull are compared in Ta-
`ble 3. For consistency, selection for SCS and calving
`traits is represented by positive values even if some
`national scales are defined with lower numbers desir-
`able. Selection on yield traits is 50 to 70% of total em-
`phasis in most countries. Most countries select either
`against milk volume or for concentration. Four coun-
`tries select for larger cows and 3 select for smaller cows.
`Because some breeders prefer show cows and some pre-
`fer efficient cows, selection also differs within countries.
`Published indexes are useful for ranking and promoting
`top animals even though individual breeders may em-
`phasize different traits and have their own goals.
`National indexes are updated quite frequently and
`have become more similar over time. Already, 6 of the
`countries (United States, Germany, New Zealand,
`United Kingdom, Australia, and Denmark) revised
`their indexes since a similar survey 2 yr ago (VanRaden,
`2002), and another country (Japan) is included. Hol-
`stein International (Wesseldijk, 2004) recently com-
`pared national indexes and trends across time. These
`
`Table 2. Correlations of individual traits with indexes and expected trends and actual trends in breeding
`values.
`
`PTA trait
`
`Correlation of PTA with index
`
`Expected genetic
`trend/decade
`
`NM$
`(2000)
`
`NM$
`(2003)
`
`CM$
`(2003)
`
`FM$
`(2003)
`
`NM$
`(2003)
`
`Actual genetic
`trend
`1990–2000
`
`Protein (kg)
`Fat (kg)
`Milk (kg)
`Productive life (mo)
`Somatic cell score
`Udder composite
`Feet/leg composite
`Size composite
`Daughter pregnancy rate (%)
`Service sire calving difficulty (%)
`Daughter calving difficulty (%)
`
`0.81
`0.68
`0.68
`0.51
`−0.35
`0.19
`0.17
`−0.10
`0.00
`−0.13
`−0.11
`
`0.74
`0.67
`0.58
`0.58
`−0.38
`0.22
`0.16
`−0.10
`0.15
`−0.23
`−0.21
`
`0.74
`0.67
`0.49
`0.56
`−0.37
`0.21
`0.16
`−0.10
`0.17
`−0.23
`−0.20
`
`0.71
`0.64
`0.72
`0.58
`−0.39
`0.22
`0.16
`−0.09
`0.12
`−0.22
`−0.22
`
`35
`44
`1082
`4.8
`−0.44
`1.4
`1.0
`−0.6
`1.0
`−1.3
`−1.6
`
`33
`32
`1092
`1.5
`0.04
`1.5
`1.3
`0.8
`−1.0
`0.7
`−1.0
`
`Journal of Dairy Science Vol. 87, No. 10, 2004
`
`Exhibit 1009
`Select Sires, et al. v. ABS Global
`
`

`

`3130
`
`VANRADEN
`
`index formulas are updated and genetic evaluation
`methods are documented on national evaluation center
`Web sites and by Interbull (2004).
`Breeders can use published national indexes such as
`NM$ to increase lifetime profit, but also may focus on
`particular traits that have higher value in their own
`market and production environment. With free trade
`between nations and efficient transportation, local in-
`put and output prices eventually should approach
`global prices. Breeders should also consider other infor-
`mation that is not yet included in national indexes,
`such as genetic recessives, expected future inbreeding,
`and bull fertility. Foreign bulls may have evaluations
`for other traits, such as cow fertility, that are not yet
`exchanged internationally.
`The need for international evaluations increases as
`national breeding programs converge and breeders
`within each nation select for more traits. Statistical
`methods can accurately convert and combine informa-
`tion across countries, but common trait definitions
`would increase correlations among national evalua-
`tions, and common scales of expression would simplify
`international marketing. Multiple genetic rankings
`would still be required, even with uniform definitions,
`scales, and economic values, because management and
`climate differences cause true genotype × environment
`interaction for important traits (Zwald et al., 2003).
`
`4
`
`−3
`
`7
`9
`
`
`
`74
`
`11
`
`22
`33
`
`3
`
`4
`
`17
`
`−18
`18
`41
`
`12
`
`
`
`63
`
`10
`12
`
`3
`
`12
`
`9
`
`6
`
`−4
`21
`
`4
`
`9
`
`10
`16
`
`3
`
`3
`
`12
`12
`32
`
`3
`
`(NM$)
`
`(PLI)
`
`(TMI)
`
`(ICO)
`
`−18
`
`10
`
`8
`
`−17
`13
`34
`
`(BW)
`
`10
`
`3
`
`11
`
`7
`
`12
`
`−14
`35
`
`8
`
`(DPS)
`
`21
`
`4
`
`20
`55
`
`Country(Index)
`
`CONCLUSIONS
`
`Dairy cattle breeders in the United States improved
`their cows and their selection programs gradually over
`many decades, but goals and indexes have changed
`more rapidly in the last decade. Today, reducing ex-
`pense is nearly as important as increasing income.
`Breeders need rankings for overall merit more now than
`in the past because of the large number of available
`traits and animals to consider. The USDA’s net merit
`index was revised in August 2003 to include new genetic
`evaluations for calving ease and cow fertility. Economic
`values for other traits also were reviewed and revised.
`Dairy cattle breeders in many countries now select
`for yield, conformation, longevity, fertility, and health
`traits. Selection indexes recently have become more
`uniform across countries. Many income and expense
`traits are combined into accurate measures of lifetime
`profit, and international bull evaluations are now avail-
`able for many of these traits. Further research and
`education on health and fertility economic values may
`be more important than further refinement of evalua-
`tion methods.
`Many animal breeding terms derived more than 50
`yr ago are still used today because quantitative genetic
`theory continues to provide accurate evaluations of ge-
`
`4
`
`13
`
`6
`
`10
`
`3
`2
`8
`
`12
`42
`
`1
`5
`
`6
`4
`
`2
`2
`1
`
`4
`
`25
`
`4
`1
`
`36
`
`9
`
`8
`1
`
`2
`
`1
`
`13
`13
`
`13
`
`2
`2
`
`10
`35
`
`2
`
`5
`2
`6
`6
`
`14
`
`2
`9
`5
`9
`
`8
`
`−3
`10
`21
`
`<1
`
`4
`
`11
`17
`
`8
`
`3
`
`14
`43
`
`4
`3
`
`−4
`
`9
`
`8
`5
`
`−19
`12
`36
`
`(NTP)
`JapanNetherlandsNewZealandSpainSwedenUnitedKingdomUnitedStates
`
`(PFT)
`Italy
`
`(RZG)
`
`(ISU)
`
`(APR)
`AustraliaCanadaDenmarkFranceGermany
`
`(S-I)
`
`(LPI)
`
`Milkingspeed
`Temperament
`Growth(meat)
`Calvingtraits
`Finalscore
`Rump
`Dairycharacter
`Size
`(mobility)
`Feetandlegs
`Udderconformation
`Otherdiseases
`Fertility
`(somaticcellscore)
`Udderhealth
`Longevity
`Fat(%)
`Protein(%)
`Milk
`Fat
`Protein
`
`Trait
`
`Table3.RelativeemphasisontraitsinnationalselectionindexesforHolsteinpopulations.
`
`Journal of Dairy Science Vol. 87, No. 10, 2004
`
`Exhibit 1009
`Select Sires, et al. v. ABS Global
`
`

`

`REVIEW: SELECTION ON NET MERIT TO IMPROVE LIFETIME PROFIT
`
`3131
`
`netic merit. Genetic improvement for important traits
`is expected to continue for many more generations.
`Changes in commodity prices, production costs, man-
`agement systems, and genetic parameters may require
`new definitions of lifetime profit in the future.
`
`ACKNOWLEDGMENTS
`
`T. Seykora and many other members of multistate
`project S-1008, “Genetic selection and crossbreeding to
`enhance reproduction and survival of dairy cattle,” con-
`tributed to the 2003 revision of the net merit index.
`R. Pearson, G. Rogers, and 2 referees suggested many
`improvements to the manuscript.
`
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