View of The key elements for genetic response in Finnish dairy cattle breeding

The key elements for genetic response in Finnish dairy ^cattle breeding

Jarmo Juga and Ulla Voutilainen

The Finnish AnimalBreeding Association,POBox40,FIN-01301 Vantaa, Finland, e-mail:jarmo.juga@mloy.fi

This paper reviewssomekeyelements of Finnish animalbreedingresearch contributingtotheFinn- ish dairycattlebreeding programme anddiscusses thepossibilities andproblems in collectingdata for geneticevaluation,prediction ofbreeding values both within andacrosscountries,estimation of the economic value ofimportant traits,and selection ofbulls andcows.Economic valuesare^calcu- lated forfertility, udder health andproduction traits whenonegenetic standard deviation unit (gen.

sd.) ischanged ineach traitindependently and the financial returns from selection response inthe Finnish dairycattlebreeding programmeareestimated.

The followingcomponents were used to calculate the economic value of mastitis treatments:

1) cost ofmastitisincluding discardedmilk and treatment costs,2) reduction in milkprice due to higher somatic cell count,3) replacement costsand 4) lower production level of the herd due to involuntary cullingofcowsbecause of udderproblems. A high somatic cell count lowers thepriceof milk andeventually leads toinvoluntary culling. For treatments forfertilitydisorders thefollowing costs_wereincluded: 1) treatment costs2) higherreplacement costs and3)decreasedmilkproduction in_{the herd.}Days open included the followingcosts: 1) extra insemination, 2)reduced annualmilk yieldand3)fewercalvesborn.

Animalbreeding wasfound to be avery cost effective investment,yieldingreturns ofFIM 876.9 percow fromoneround of selection when the gene flow wasfollowed forover25 yearsintheFinn- ishdairy cattlebreedingprogramme.

Key words:breeding goal, breeding programme, dairy cattle, financial returns,multiple trait selec- tion

ntroduction

The aim of the national breeding programme is to improve the cost-effectiveness of milk pro- duction by genetic progress in economically

important traits. The Finnish dairy cattle breed- ing programme has been successful in improv- ing production traits and simultaneously achiev- ing a favourable, orat leastnot unfavourable,

genetic change in many functional traits, e.g udderhealth,milkability and conformation(Kor-

©Agricultural and Food ScienceinFinland ManuscriptreceivedFebruary 1998

Vol. 7(1998):207-217.

honen and Juga 1996). The only trait that has clearly declined is female fertility, in which the number of days open has increased.

A successful breeding programme hastocon- sider many aspects, namely, definition of the breeding goal, recordingschemes,prediction of breeding values(BY), selection of animals and mating strategies. We here review the key results of Finnish animal breeding research that have contributedtoFinlands dairy cattle breeding pro- gramme and discuss the possibilities and prob- lems in collecting data for genetic evaluation, prediction of breeding values both within and across countries, and selection of parental ani- mals. We also estimate the economic value of traits affecting the profitability of dairy produc- tion and attempttoestimate the financialreturns yielded by _oneround of selection of bulls and

cows.

Recording performance

The base foraccurate genetic evaluation is reli- able dataon the performance and pedigrees of animals,andonthe environmental effects influ- encing their performance. All traits witha clear impact on the economics of dairy production, animal welfareorenvironmental load needtobe recorded usingcommontermsandmeasuresand they should be recorded when there is sufficient variation among them. Many continuously re- corded traits suchasmilk productionor growth of the animal have convenientscales,e.g.kg.For

traits which donothave aconvenient scale and for which a scoring _system is used, e.g. linear type scoring and calving ^ease, standard scores and recording proceduresarerequired. It is im- portant that all countries should _use the _same standards in recording, which is why the Inter- national Committee of Animal Recording (ICAR) has such an important role in setting standards.

To be able to compare the results fromre- cording and genetic evaluation within andacross

countriesweneedsomestandardisation. For data tobe usefultoagenetic evaluation process, they must meet certain requirements. The main re- quirementsare(Tier and Graser 1994):

- informationmustbe availableonhow thean- imal has been treated in comparison withcon- temporaryherdmates;

- the data forming the basis of selectionarein- cluded in multiple trait analysis;

- dataarecontrasted with manyothers,i.e. data are onlarge groups with descendants from_a variety ofparents;

- the animals and their relatives canbe relia- bly identified throughout their lives ^- both in the field and in the datasystem; and

- information is availableonsystematiceffects, such as age, whenobserved, the age of the dam, the breed and sexof the animal.

According to van Arendonk et al. (1998), 15% of the errorsin pedigree registration in _a nucleus scheme_{cause a}substantial reduction in genetic progress. Such a highrate oferrors is not acceptable in well organised nucleus schemes,the_rate ofpedigree errorsbefore DNA verification ofpossible bull dams in Finland be- ing, for instance only 1to2%.However,theex- ample illustrates well the importance ofthe qual- ity of the data.

All the Nordiccountries, Finland included, havealong history of also recording secondary traits suchasfertility,health, temperament,milk- ability, stillbirths, calving ease and conforma- tion. A new set of datato be used in perform- ance evaluation is the data originating from slaughter houses. Such data will be available through thenew identificationsystemrequired by theEU, according to which all animals have tobe uniquely identifiedand traced back^toherds of origin, and the data mustbe stored in a cen- tral database. The slaughter informationcan eas- ily be linked torecording information via the unique identity number and used in genetic eval- uation of slaughter weight and carcass classifi- cation and fat scores.

Research into the utilisation of slaughter in- formation in genetic evaluation of pure bred

dairy cattle and the beef sires used in crossbreed- ing with dairy cows is currently under way in Finland. Preliminary results from the first data setof the performance of different breeds in crossbreeding are already available, and some (co)variance components have been estimated.

The goal is to have BLUP (Best Linear Unbi- ased Prediction) indices for the slaughter weight and carcass and fat scores before long for all breeds in Finland and toinclude them in theto- tal merit index of dairy bulls (Liinamo and van Arendonk 1998).

ICAR will be facingnewchallenges in stand- ardisation in the near future, since membersre- quire moreflexibility in recording schemes due toadvances in the electronic devices connected tomilking machines and feeding robots and due to the increasing interest in self-recording on farms. Improvements in electronic measuring generate huge amounts of daily data that will replace specifictest days in milk recording and enable the dailyonfarm recording ofnewtraits such asfeed efficiency and somatic cellcount.

Prediction of breeding values

Genetic evaluation is of fundamental value in animal breeding, since the selection of animals is mostaccurately carried outby using the pre- dicted breeding valuesorexpected progeny dif- ferences(EPD) of traits in the breeding goal. Due toits importantrole,the methodology has been studied intensively during the recent decades with very good results. Good predictions depend upon high-quality ^data, appropriate models and good estimates of (co)variances. The predictions should be made using all available qualified data and allow valid comparison of animals across space and time(Tier and Graser 1994).Collect- ing the data is a huge and a very costly task.

Hence, it is important that the recording should be carriedoutefficiently, concentratingon eco- nomically valuable traits and utilising, whenev-

er possible, information coming from other sources, too.Due tothesecosts the number of traits evaluated in^mostcountries is usually very limited; the emphasis is on milk production traits,and functional traits suchasfertility, calv- ing difficulties and health,tendtobe neglected.

The cost is not the only reason for failing to record secondary traits, however; it is very of- tenduetothe lack ofanationally uniform infra- structure, competition between local companies and organisations, poor logistics and historical load.

Production systems vary considerably be- tween countries and continents. The models used in genetic evaluation should therefore be opti- mised in sucha mannerthat the information col- lectedaccountsfor heterogeneousvariance,het- erosis and genotype-by-environment interaction.

This meansthat a model which is good in one countryisnotnecessarily so in another.

Good-quality data have been used intensive- ly ^tostudy evaluation methods in Finland. Dur- ing the last 20 years much research has been carried out onBLUPmethods, whichare now the mostwidely used procedures for predicting breeding values in livestock. The thrust ofre- search has been _onestimating the (co)variance components of milk production traits ^(Män- tysaari andVanVleck 1989, Juga 1992, Pösö and Mäntysaari 1996

a,

1992). The latterwas taken into routine use in Finlandas oneof the first countries in theworld, in 1990.After implementation of theAM in prac- tice the statistical model has been studiedcare- fully toreduce the bias originating from prefer- entialtreatmentof bull dams(Uimariand Män- tysaari 1995, Lidauer and Mäntysaari 1996).

More recently, the emphasis has beenon test- day models using random regressions topredict breeding values for production traits of dairy animals(Kettunen etal. 1997), that will permit better modelling ofcontemporarygroups across test days and prediction of breeding values for lactation _curves.Another advantage of test-day Vol.7(1998): 207-217.

models is thatthe solutions ofherd,test-day and animal effects can be utilised in management reports and follow-ups of feeding practices.

Other traits have notbeen neglected, either.

The geneticparameters of fertility traits (Män- tysaari and Van Vleck 1989,Pösö and Mäntysaari

1996^b, Hyppänen and Juga 1997),health traits (Pösöand Mäntysaari 1996

a,

1997)have all been analysed and used in national evaluation procedures. Predicting the breeding values of functional traits forms the base forso called ”Nordic profile” (Juga 1998), since selec- tion for total merit requires information onall economically important traits.

Properties of the animal model predictors

The prediction method currently ^most widely used is the BLUP method with the AM (Hender- son 1984).Ituses all available information(on performance, pedigree, relatedtraits, systemat- ic (fixed) effects) simultaneously for providing an accurateand unbiased prediction of an ani- mal’s BV. Every phenotypic observation on an animal is determined by environmental and ge- netic effects. It is usually assumed that pheno- typic observations and genetic and residual ef- fects follow amultivariate normaldistribution,

implying that traitsare determined by infinitely many additive genes of infinitesimal effect at unlinkedloci, what is known as the infinit- esimal model (e.g. Bulmer ^1980), although Strandén and Gianola(1997)foundsomeadvan- tagesin using t-distributed residualsoverGaus- sian distributed residuals. It is also assumed that genetic and residual variances areknown or, at least,that their proportionality isknown,and that there is no correlation between the genetic and residual effects (Henderson 1984).

The genetic effects include additive genetic effects, dominance and epistasis. Since the ad- ditive genetic value is afunction of the genes transmitted from parents ^toprogeny, it is the main_component for selection and therefore the maincomponent of interest. Use ofmore com- plicated genetic models has also been studied, for instance by including the dominance effect in the model (Smithand Mäki-Tanila 1990, Ui- mari and Kennedy 1990, Uimari and Mäki-Ta- nila 1992). Including dominance effects in the model increases the computational problems, which is why theyareusually ignored.

Use of AM predictors increases the correla- tion between the breeding values estimated, which is anunfavourableproperty of the model since it leads to increasing annual rates of in- breeding. Research has thereforeput more ef- fort into optimising the breeding programmesto find _an appropriate balance between expected genetic gain and expected decline in fitness (Meuwissen and Woolliams 1994).

Full use of the AM requires that sound ge- neticparameters should be available,especially when direct-maternal and/or multi-trait models requiring the genetic correlations(R ⁾ between effects and/or traitstohave been estimated reli- ably are used. Choosingan optimal model is important for forming groups ofcontemporar- ies, which is a special problem in Finland with its small herdsizes,studying genotype-by envi- ronment interactions, and building genetic groups when selectionpaths are differentiated (Ménissier 1994). Improving the models and methods is important, since the accuracy of pre- dicted breeding values contributes directly to genetic response (e.g. Falconer and Mackay 1996).Such improvement is feasible dueto the increasing power and decreasing costs ofcom- puters and tothe _greaterefficiency of_comput- ing algorithms. Efforts ^to improve the predic- tion methods require some investment, but the increased genetic response affects the whole population and thus makes the investment very profitable.

210

International genetic evaluation

In many European countries dairy cattle breed- ing has reliedonthe import ofsuperior sires from other populations, mainly Holstein breed from the USA. The difficulty is to identify the for- eign sires with thegreatestpotential for making agenetic contributionto alocal population. Not only do methods of evaluation differ between countries, but results _are expressed in widely differing ways, differentbasesareused and there are genotype-by-environment interactions (Wickham etal. 1996).Furthermore, breeding objectives differ between countries due todif- ferent production systems.

Due tothe increasing international trade in genetic material and joint breeding programmes including progeny testing of bulls across coun- tries by major artificial insemination companies it has become important tobe able tocompare breeding animals between countries. To do this two approaches have recently been used: 1)the calculation of international indices from na- tional evaluation results using Schaeffer’s (1985,1994) methods for multiplecountry com- parison as is done with dairy bulls by INTER- BULL(Banos etal. 1994);2) the calculation of across countryevaluations simultaneouslystart- ing with data such asthose in the North Ameri- canCattle Evaluation (NACE) for Herefordcat- tle in USA and Canada (Bertrand ^etal. 1997).

The problems in optimising the statistical mod- el increase, however, when one goes over to multiple countryevaluation. This is duetodif- ferences in production systemsand to poor ge- netic links between countries. With weakgenet- ic links between ^two populations the genetic correlation will be underestimated (Sigurdsson and Banos 1995);if_nolinks exist between the populations, across-country comparison is _not possible.

Theacross-countrycomparison of dairy bulls is carried out by INTERBULL using the multi- ple-country comparison method (MACE) de- scribed by Schaeffer (1994), which allows for less than unity genetic correlations between

countries. Currently 20 countries send in evalu- ation dataonproduction traits for Holstein-Frie- sian, eight countries for Ayrshire, ten countries for Brown Swiss, four countries for Guernsey, six countries for Jersey and six countries for Sim- mental breed (Interbulletin 1997). More than 50 000 Holstein-Friesianbulls, 11 000 Simmen- tal bulls and fewer than ¹⁰000 bulls per breed from other breeds _acrosscountries_{get an} inter- nationalevaluation,which is published for each participating_{country on}theirownbase and scale.

The breeding organisations in eachcountrysub- scribingtothe INTERBULL service_arerespon- sible for publishing theresults; noother bodyor country is allowedtodoso.

Future research priorities in INTERBULL will reflect the greater number of traits being considered for international evaluations. Priori- ty will be giventoresearch seeking tosolve the practical problems associated with making ac- curate international comparisons of dairy ani- mals (Wickhametal. 1996).

Integrated breeding value calculations based on raw data from more thanone country are a topic of study for production and functional traits,atleast within Nordic countries. The inte- grated breeding value estimationcanbe seen as afinal goal in across-countrygeneticevaluation, since genetic correlationscanbe estimated from original data and he evaluation is carriedoutwith the same method in both countries. Problems may be caused, however, by the very large data sets, differences in trait definitions and record- ing precision, difficulties in identifying animals between countries, and poorly linkeddata, pre- venting genetic_{parameters to}be estimated be- tweencountries and traits.

Defining ^the breeding goal

Breeders arefaced with the question of combin- ing informationon different traits of interest before animals areranked. Constructing _atotal merit index from BVs provided byanAM using Voi 7^(1998):207-217.

the classical selection index theory(Hazel 1943) is notrivial task. It requires information on the economic value of individual traits in the breed- ing goal andonthe covariance structurewithin the goal traits and between the goal traits and information traits. It is very often difficultto quantify the_trueeconomic value ofa trait, asit is^toestimate positive definite covariance matri- cesbetween many different traits simultaneous- ly, particularly with the limitations imposed by computer resources. Groen^etal. (1997) gavea detailed review of the methods used in defining the breeding goal, calculating the economic weights and constructing the total merit index.

The breeding goal canbe writtenas H_kl=Vg’ ^WhereaK,= ^C,V

and ^Hk| is theaggregate genotype ofanani- mal,k is the time for comparison, 1 is the selec- tion path,a_k| is_a m*l vectorof discountedeco- nomic values of mgenotype traits, g is a m*l

vectorof genetic superiority ofmgenotype traits, c:isam*m diagonal matrix with cumulative dis- counted expressions of mgenotype traits, and

v,_k is a m*l ^vector with economic values of m genotype traits.

The aggregate genotypecannotbeobserved, since the genotypic values of the traits in aggre- gateBV are notmeasurable. The practical solu- tion is topredict the aggregate genotypewitha selection index method(Hazel 1943). The infor- mation does not necessarily needto be on the same traits included in the aggregate genotype (total merit); somecorrelated traits _can also be used. A selection indexcangenerally bepresent- edas^{(Groen et}al. 1996)

1.kl^.=b, ,’x,kl ^’ whereb,.kl⁼P'Ga..kl

and b_k|is an*l vectorwithregression coef- ficients of n index traits, x is a n*l vector with observations, P is an*n matrix with covarianc- esbetween index traits and G is a m*n matrix with covariances between m genotypetraits and n index traits.

No universally best method exists for deriv-

ing economic values; what is best will depend

on the traits and production circumstancescon- sidered and on what is possible in practice (Groen ^etal. 1997). Groen (1989) lists five cri- teriatobe considered when deriving economic weights:

1. Efficiency: biological versuseconomic defi- nition

2. Perspective; to maximise profit (=revenues- costs),_tominimise_costsortomaximise_rev- enues/costs

3. Planning term: strategicversus tactical 4. System level:animal, farm, sector orinter-

national

5. Method: positive approach(data evaluation) versusnormative approach (data simulation) All fiveaspectsprovide alternative strategies that can be justified. Therefore no universally acceptable economic valuesexist; these values needtobe derived in eachcountry.

The economic values for production, health and fertility traitswere calculatedatboth ani- mal and herd level using the information from Finnish milk recording and progeny testing of dairy bulls. Milk recording data from Finland in 1995_wereusedto estimate the average feeding

cost of maintenance and production. All prod- uct prices and production levels were from_au- tumn 1996, and genetic standard deviationswere from the national genetic evaluation of dairy cattle carriedoutin December 1996. Finland has a national quota for total milk (carrier⁺ fat⁺ protein) ortotal fat within the EU and, as a re- sult,farmquotas areused. Derivation ofeconom- ic values atherdlevel, i.e. rescaling (Smith et al. 1986),is therefore logical and hastobe used when index weights arecalculated for national use.At animal level therevenuesfrom increased output are maximised,but by rescaling wemin- imise the^costper unit ofoutput.This yields low- er economic values for production traits(Table

1)and hence a higher relative value for func- tional traits. Finland has^not exceeded thecoun- try quotasince joining theEU,whichmeansthat farm quotas have not been realised either. It therefore makes_sense^to_usethe animal level for herds that areready ^totakegreaterrisks. Hence

212

the breeding objectives will differ somewhat between herds. Breeding objectives will also dif- fer due _to the preferences of farmers and pro- duction circumstances. This has been made pos- sible, and actually encouraged ^to some extent, by the breeding planning services provided by extension organisation.However, inseminations by elitebulls,young bulls and beef bulls across herdsfollow, onaverage, the nationalrecommen- dations, whichare 50-55%, and 5-10%, respectively.

The following components were included in calculating the economic value of mastitistreat- ments: 1) cost of mastitis including discarded milk and treatment costs, ₂₎reduction in milk price due to the higher somatic cell ^count, 3) replacement costs and 4) the lower produc- tion level of the herd duetoinvoluntary culling of_cowsbecause of udder problems. A high so- matic cell count lowers the price of milk and eventually leads^toinvoluntary culling. Fortreat- mentsfor fertility disorders the following costs were included: 1) treatmentcosts, 2) higher re- placement ^cost and 3) decreased milk produc- tion in the herd. Days open included the follow- ing costs: 1) extrainsemination, 2) reduced an- nual milk yield and 3) lower number of calves born. The economic values for different traits per genetic standard deviation at animal and herd level are presented in Table 1. The economic values correspond quite well to the current in- dex weights used in the total merit index. The economic value of protein yield is approximate- ly twice the economic value of udder health or daughter fertility traits.

Genetic response in a large dairy cattle breeding scheme

The^most widely used selection scheme in dairy cattle breeding is basedonprogeny testing and selection ofbulls tobe usedin artificial insemi- nation (AI). The schemes in different countries

Table 1.The economic value ofa change inthe genetic standard deviation unit (10 indexpoints inrelative indices) indifferent traitsinFinnishAyrshire.

Trait Economic value per Economic value per genetics.d.,cowlevel genetics.d.,rescaling (FM) formilktraits (FIM)

73

Fat yield 127

Proteinyield 366

Carrier 253

Fertility treatments 71

Days open 80

Mastitis treatments 96 Somatic cell count 33 Growth rate 1 104

259 144 71 80 96 33 104 1from Liinamo and^vanArendonk 1998.

more orless follow the optimisationstrategy in- troduced by Skjervold (1963), withsomevaria- tion in current applications. Interest in using multiple ovulation and embryo transfer in nu- cleus breeding (MOET) has increased since the early 1980

s,

terJuga and Mäki-Tanila (1987) published their results showing that the original results obtained by Nicholas and Smith (1983)_were toooptimis- tic. New strategies concentrated mainly on al- ternative mating designs (e.g. Ruane and Thomp- son 1991,Strandén etal. ^1991,Woolliams 1989) or on maximising the genetic gain while _con- straining the increase in inbreedingrate (Meu- wissen 1997). No country has based its dairy cattle breeding solely on a MOET scheme,but many nucleusbreeding programmes have been setup^toaccomplish AI-breedingschemes,here in Finland too, where we have moved froma decentralised scheme (Mäntysaari et al. 1996) to anopen centralised nucleus scheme. Nucleus breeding schemesare expected to increase the genetic response in abreeding scheme by better controlled management of animal selection, a shorter generation interval and future prospects of including marker assisted selection (MAS)or other biotechnological methods in the pro- Vol. 7(1998): 207-217.

Table2.The discountedgeneticresponse indifferentpaths inthe Finnish dairycattlebreedingprogramme (adaptedfrom Korhonen andJuga 1996) and the financial returns ofgeneticresponse.

Bull sires Bull dams Cowsires Cowdams Total genetic Financial response returnsFIM

Discounted expression 0.283 0.407 0.808 0.907

Milkyield

(carrier) 97.8 265.6 303.3 -11.8 654.9 269.2

Proteinyield 3.8 8.6 11.6 0.5 24.5 663.8

Days open 0.9 1.2 0.7 -0,18 2.6 -56.1

Total 876.90

gramme. Nucleus breeding may, however, im- pair the effective population size and theaccu- racy of selection when G*E interaction would exist. Hence thestrategyof maximising the ge- netic response withapredefinedrateof inbreed- ing (Meuwissen 1997) would bealogical choice in effective but sustainable nucleus breeding.

A well-managed breeding programme gen- erates substantialreturns from each year’s ge- netic response. Thereturnsthereforeaccumulate overyears,making animal breeding averygood investment. According to Hill (1974) the dis- counted economic response from_asingle selec- tionroundat timetx_(t) canbe expressedas x(.)

=c^'w^’r_<.»

where c'is the discountfactor,w’ is thevec- tor of undiscountedreturns overselection paths and traits and r_(t) is the selection differential.

Using the realised selection differentials giv- enby Korhonen and Juga (1996) and following the respective discounted expressions witha3%

interestrate for a 25 year period for bull sire, cow sire,bull dam andcow dam paths (Table 2) and multiplying the discounted selection differ- entials by the rescaled economic values from Table 1, weachieved an economic response of FIM 876.9 percow. Negative weight for fatcon- tenthas been excluded from the total merit in- dex since 1996 and nonegative trend isexpect- ed,which is why the change in fat traitswasnot included in this calculation. A horizon of 25

yearsreflects slightly more than three genera-

tions with7.6 years of average generation inter- val (Korhonen and Juga 1996), which is long enoughtobe equaltothe asymptoticrate of_re- sponse in acontinuing programme(Hill 1974).

The long perspectivecauses someproblems,too, since the methoduses implicit assumptions of the sameeconomic values and similar selection within selection paths overthe years.

The response is accumulatedoversuccessive selection rounds, which is typical only of ani- mal breeding investments. Note that when the economic genetic response iscumulative, the costs are not, hence_aprofit could still be made even if the yearly costs were greater than the

returnsfrom the yearly gain.However, with rel- atively low discountrates, as today, when the real interest _rate is low, the profit horizon can have a major effect on the expected total gain (Weller 1994).Increasing the discountrate to5%

would have approximately the same effect as reducing the time horizonto20 years.

Concluding remarks

Genetic evaluation is apowerful tool for the se- lection of animals. The animal model BLUP is now a method of choice for calculating breed- ing values with high accuracy. Increased com- puting power permits the use of complicated multiple trait models including direct and ma-

214

ternal effects in dairycowevaluation.The meth- od requires the estimation of reliable genetic parameters, which is achallenging task. Anew erain dairy cattle genetic evaluation is theacross countrycomparison, which givesmore accura- cyto the import and export of superior ^semen, embryos and live animals and tojoint breeding programmes between countries.

The importance of functional traits such as udder health and daughter fertility is clearly dem- onstrated in economic values. Thecosts includ- ed in the calculationwere direct^costs; animal welfareorother ethicalaspects werenotempha- sised. Including these factors would inevitably increase the economic value of such traits. Hence more emphasis should be placed globally on evaluating traits other than production onesand on calculating a total merit index giving the maximum response in the total economic value of milk and meat production. Total merit indi- ces, which put the breeding goal into effect in

eachcountry, shouldreally reflect the localeco- nomic and environmental circumstancesto al- low variations for selection practices between countries. This would maintain the genetic vari- ation in the global breeding population and en- able alternative geneticstobe imported from other subpopulations when the population size of a countryis limited.

Animal breeding wasfoundtobeaverycost- effectiveinvestment,resulting inreturnsof FIM 876.9 percowfrom each round of selection when the gene flowwasfollowed forover25 years in Finnish dairy cattle breeding programme. And yet the predicted genetic response in this pro- gramme isonly moderate, leaving much room for improvement in allpartsof the programme.

Future advances in predictionmethods, nucleus schemes and international co-operation will doubtless improve thereturns for the benefit of dairy farmers.

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SELOSTUS

Perinnöllinen edistyminen suomalaisessa lypsykarjan jalostusohjelmassa

JarmoJugaja Ulla Voutilainen

Kotieläinjalostuskeskus^-FABA

Tämän kirjallisuuskatsauksen tavoitteena oli selvit- tääsuomalaisen jalostustutkimuksen tärkeimpien tu-

losten vaikutusta suomalaiseenlypsykarjan jalostus- ohjelmaan jatarkastella tiedon keruuseen, maiden^si-

säiseen sekä maiden väliseenjalostusarvojenlasken- taan,taloudellistenpainokertoimien laskentaan ja eläinten valintaan liittyviämahdollisuuksiaja ongel- mia. Lisäksi tavoitteena oli arvioida uudelleen suo- malaisen lypsykarjan hedelmällisyyden, utaretervey- den jatuotanto-ominaisuuksien taloudelliset arvot ja perinnöllisen muutoksen taloudellinen merkityssuo- malaisessalypsykarjan jalostuksessa.

Utarehoitojen taloudellista arvoa määritettäessä otettiin huomioon 1)utaretulehduksen hinta mukaan

lukien pois heitetty maito ja hoitokulut, 2) maidon

hinnanaleneminen korkean solupitoisuuden vuoksi, 3)lisääntyvätlehmien uudistuskulutja⁴⁾karjan alen-

tunutmaitotuotos, mikäjohtuusuuremmastaensikoi-

den osuudesta. Korkea soluluku ilmannäkyvää^uta- retulehdusta alentaa maidosta maksettavaa hintaa ja johtaa lopulta eläimenpoistoon. Hedelmällisyyshoi- tojen kustannuksiksi sisällytettiin ¹⁾ hoitokulut, 2) lisääntyneet uudistuskustannuksetja 3) karjan alentunut tuotos.Tyhjäkaudenkustannuksiksi lasket- tiin 1)ylimääräiset siemennyskulut, 2) lehmän alen- tunutvuosituotos ja3) alentunut syntyvien vasikoi- denlukumäärä.

Kotieläinjalostusinvestoinnilla onerittäin hyvä kustannus/hyöty -suhde,sillä yhden valintakierrok- sentaloudelliseksi arvoksi saatiin876,9 mk lehmää kohden seuraamalla valittujen eläinten geenivirtaa seuraavien25vuoden ajalta jadiskonttaamalla talou- dellinenarvonykyhetkeen.

Vol. 7(1998): 207-217.