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Development of a breeding objective for
Estonian Holstein cattle
Elli Pärna, Heli Kiiman, Mirjam Vallas, Haldja Viinalass, Olev Saveli
Institute of Veterinary Medicine and Animal Sciences, Estonian University of Life Sciences, Kreutzwaldi 1,
51014 Tartu, Estonia, e-mail: elli.parna@emu.ee
Kalev Pärna
University of Tartu, J.Liivi 2, 50409 Tartu, Estonia
Economic weights for milk carrier (water plus lactose), fat and protein yields, calving interval, age at
first service, interval between the first service and conception of heifers and length of productive life of
Estonian Holsteins were estimated under assumed milk production quota and for non-quota conditions. A
bio-economic model of an integrated production system of a closed herd was used. Economic values of
milk carrier yield and length of productive life differed between quota and non-quota conditions, but there
were only minor differences between those marketing systems in economic values for functional traits. The
standardised economic values of the most important traits varied in magnitude between18 to 81% of the
economic value for milk yield. Discounting had a substantial impact on the economic value of length of
productive life. When defining the breeding objective for Estonian Holstein, the interval between the first
service and conception of heifers, and the length of productive life should be included in the breeding goal
along with the traits with the highest economic value, milk, fat and protein yield. In the optimum breeding
objective, relative weights of production vs. functional traits were 79 and 21%, respectively.
Key-words: economic weights, production traits, functional traits
© Agricultural and Food Science
Manuscript received December 2005
Introduction
In order to establish a total merit index using Hazel
(1943), the relative economic weights of each trait
contributing to the aggregate genotype (breeding
objective) must be known. Hazel (1943) defined
the economic value of a trait as the improvement
in profitability resulting from one unit of genetic
improvement in that trait, genetic merit of all others
remaining constant. Index selection is the optimal
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method to improve complex breeding objectives
including several traits (Hazel 1943). Aspects and
methods for the derivation of economic values
are reviewed by Groen et al. (1997) and Goddard
(1998).
In index selection, the breeding objective (also
known as total merit or the aggregate genotype) is
defined by a linear function of economically im-
portant traits. According to Thaller (1998) traits
included in the breeding objective must meet at
least the following conditions:
(i) They must be economically important.
(ii) They must be heritable.
(iii) They must have genetic variance.
Recordability of a trait is not required as long as
a correlated trait or traits exist that can be used as
indicators of genetic merit for the traits in the index.
Traits included in the selection index should meet
the following basic conditions:
(i) They should be easily measurable and re-
cordable.
(ii) They must be heritable with sufficient ge-
netic variance.
(iii) They can be identical with the traits in the
breeding goal or must be genetically corre-
lated to one or more traits in the breeding
goal.
In many countries, the breeding goal for dairy
cattle has been revised to include not only high
product yield but also longevity and functional
traits that reduce the cost of production. Among
such functional traits many countries include at
least one measure of daughter fertility, because
poor reproductive performance has a significant
economic impact at the farm level (Van Doormaal
et al. 2004). Selection has dramatically increased
milk production per cow, and disease resistance
has deteriorated concurrently (Weigel et al. 2004).
Incidence rates for many diseases also seem to have
increased during times of negative energy balance
(Collard et al. 2000). To avoid such consequences,
breeding organisations have changed their breed-
ing goal, paying less attention to production and
increasing the emphasis on functional traits.
After Estonia’s accession to the EU in 2004, the
impact of milk yield quotas on economic weights
had to be considered. According to Groen et al.
(1997), under quota conditions and decreasing milk
prices, functional traits, which increase efficiency
not by higher product output but by reduced input
costs, might have greater impact on farm profit
and should therefore, be included in breeding pro-
grammes. There also are non-economic reasons
for including functional traits in a breeding pro-
gramme, for example ethical considerations, con-
sumer concern, the need to simplify management
and improved farmer satisfaction/reduction of frus-
tration, which are becoming increasingly important
(Dempfle 1992, Groen et al. 1997, Olesen et al.
2000). Inclusion of functional traits in breeding
programmes is expected to have a major impact
on selection response of such traits and cause only
moderate reduction in selection response for pro-
duction (Fewson and Niebel 1986).
Economic values of milk, fat and protein pro-
duction were first calculated for the Estonian cattle
population in 1997 (Pärna and Saveli 1997, Pärna
and Saveli 1998), and some functional traits were
added in 2002 (Pärna and Saveli 2002). Annual ge-
netic responses in milk, fat and protein yield were
estimated to be 57.4 kg, 1.98 kg and 1.67 kg, re-
spectively (Pärna and Meier 2001). Re-evaluating
economic values is needed because economic con-
ditions have since changed. Dairy farmers in Esto-
nia are facing structural changes and changes in the
market for which they produce. If economic values
are uncertain, selection response will be lower than
when economic values are known without error
(Smith 1983).
This investigation describes the derivation of
economic values for milk production and selected
functional traits for the Estonian Holstein (EHF)
population, and quantifies their relative importance
in the aggregate genotype. Absolute and relative
economic value of traits will be estimated with a
herd model, under an assumed quota on milk pro-
duction with defined fat and protein content and
separately under non-quota conditions. The fol-
lowing traits are considered: milk yield, fat yield,
protein yield, calving interval, age at first service,
interval between the first service and conception of
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heifers and length of productive life. Discounted
economic values for these traits also were calcu-
lated to investigate time delay effects.
Materials and methods
Performance of Estonian cattle
populations
In Estonia, 87.9% of cows are enrolled in an official
milk recording programme (Results of Animal Re-
cording in Estonia 2005, Figure 1). Distribution by
breeds is in favour of EHF (Figure 2). Since 1995,
average milk yield in Estonia has risen about 2400 kg
(39%, Figure 3), and population size has decreased
by 22%. Between 1998 and 2005, the number of
inseminations per pregnancy increased from 1.7 to
2.1 for cows and from 1.4 to 1.6 for heifers (Table
1, Results of Animal Recording in Estonia 1999,
2005, 2006). Calving interval for EHF increased
from 407 days in 1998 to 421 days in 2004 (Table
2). Furthermore, 23.5% of cows leaving the herd
were culled for fertility problems, 24.6% for udder
diseases and 10.7% for metabolic diseases, while
only 4.2% of cows were culled due to low produc-
tivity. This decline in non-yield traits increases
the importance of their consideration in aggregate
genotype and index selection so as to avoid further
deterioration and/or correct the deterioration that
has already occurred.
Currently, estimated breeding values (EBV) for
production, conformation and udder health traits
for bulls and cows in Estonia are computed by
the Animal Recording Centre four times per year
(Pentjärv and Uba 2004). Breeding value estima-
tion is carried out separately for the EHF and the
Estonian Red breed (ER), using the best linear un-
biased predictor (BLUP) test day animal model for
production and udder health traits and the BLUP
animal model for conformation traits. The EBV for
each production trait – milk (kg), fat (kg) and pro-
tein (kg) – is an average breeding value of the first,
second and third lactations, adjusted by the aver-
0
50 000
100 000
150 000
200 000
250 000
300 000
350 000
400 000
450 000
1914 1940 1944 1970 1990 2000 2004
Number of cows
0
10
20
30
40
50
60
70
80
90
100
Cows in milk
re cording (%)
Cows total Cows in milk recording %
Figure 1. Number of cows in milk recording.
Figure 2. Number of cows in milk recording by breeds.
20 000
40 000
60 000
80 000
100 000
120 000
140 000
1965 1990 1995 2000 2004
Numbe r of cows
in milk re cording
(ER, EHF)
400
500
600
700
800
900
1000
Numbe r of cows
in milk re cording
(EN)
Estonian
Red (ER)
Estonian
Holstein (EHF)
Estonian
Native (EN)
2000
2500
3000
3500
4000
4500
5000
5500
6000
6500
1964 1990 1995 2000 2004
Milk production (k g)
Estonian Red (ER) Estonian Holstein (EHF)
Estonian Native (EN) Breeds average
Figure 3. Annual milk yield per cow by breeds.
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age breeding value of the cows born in a defined
base year (currently, 1995). Milk production in-
dex (SPAV) is expressed as relative breeding value
(RBV) with mean of 100 and standard deviation
of 12 points for base animals, combining breed-
ing values for milk, fat and protein yield weighted
by relative economic values of 0:1:4 for EHF and
0:1:6 for ER (Pentjärv and Uba 2004).
The information source for breeding value es-
timation of udder health traits is somatic cell count
(SCC) in one millilitre of milk, transformed into
the somatic cell score (SCS) using the internation-
ally accepted formula SCS = log2 (SCC/ 100000)
+ 3 (Pentjärv and Uba 2004).
Udder health index (SSAV) is calculated as the
sum of EBVs of the first, second and third lacta-
tions with index weights 0.26, 0.37 and 0.37, re-
spectively, and is expressed as RBV (Pentjärv and
Uba 2004). For genetic evaluation of conforma-
tion traits, data from first lactation cows are used
to compute RBVs for 16 linear traits for EHF and
14 linear traits for ER as well as for three general
traits. Conformation index (SVAV is expressed
as RBV, combining relative breeding values for
type, udder and feet by relative economic weights
of 0.3:0.5:0.2 for ER and 0.3:0.4:0.3 for EHF
(Pentjärv and Uba 2004).
Description of the method
A bio-economic model characterizing the integrated
production system of a closed dairy herd was used
(Wolfová et al. 2001) for the derivation of trait
economic values. The total discounted profit for
the herd was calculated as the difference between
all revenues and all costs that occurred during the
whole life of animals born in the herd in one year,
discounted to the birth year of those animals:
StFUT SZZ
00 =
)(0
kk Ck
k
Rkk qCqRNZ −= ∑
Ω∈
with Ω={BCa, CCa, FBu, BHei, CHei, CCo1,
CCo2+}
where
0
TZ - total discounted profit in the population
of the given breed (closed herd)
SStFU - number of standard female units (StFU
= one cow place occupied during an en-
tire year)
Z0 - discounted profit per StFU
Nk - average number of animals in category k
per StFU
Rk,Ck - average revenues and costs, respectively,
per animal of category k
qRk, qCk - discounting coefficients for revenues
and costs, respectively, in category k
Non-return rate 90 days, % No of inseminations per female
1998 1999 2004 2005 1998 1999 2004 2005
Cows 53.0 54.0 51.7 52.2 1.7 1.9 2.1 2.1
Heifers 68.1 68.8 69.4 68.2 1.4 1.5 1.6 1.6
Total 56.3 57.1 56.0 56.1 1.6 1.8 1.9 2.0
Table 1. Artificial insemination and non-return rate of Estonian Holstein cattle (per pregnant cow and
heifer).
1998 1999 2000 2001 2002 2003 2004 2005
Estonian Holstein 407 407 410 410 411 413 421 420
Estonian Red 401 405 404 402 404 404 410 405
Estonian Native 394 410 408 418 416 400 413 405
Table 2. Distribution of cows by calving interval (days).
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The discounting coefficients for the revenues were
calculated by the following formula:
kR
k
t
R uq
∆−
+= )1(
where
kR
t∆ - average time interval between the birth
of animals of category k and collection time of
revenues
u - discount rate (expressed as a fraction).
The discounting coefficients for costs were calcu-
lated in the same way and with the same discount
rate.
The categories (k) of animals were the following:
BCa - male and female breeding calves during
the rearing period from birth to 6 months
of age
CCa - calves culled prior to 6 months of age
(only calves not suitable for breeding)
FBu - fattening bulls, from 6 months of age to
slaughter
BHei - breeding heifers (used for replacement of
the cow herd) from the age of 6 months to
first calving
CHei - heifers culled before calving (not suitable
for breeding or not pregnant)
CCo1 - cows culled during the first lactation
CCo2+ - cows culled in the second and later lac-
tations.
The undiscounted profit (i.e., the average profit per
year in the entire balanced system) was calculated
by setting u=0 so that all coefficients q took the
value of 1.
The discounted economic weight of a given
trait i was defined as the partial derivative of the
total profit function for the closed herd with respect
to the given trait, when all other traits were as-
sumed to take their mean values:
where
xi - value of the trait i under consideration
x - vector of the values of all traits
(dimension of x = number of traits)
μ - vector of the means of all traits.
Detailed definitions of all evaluated traits and
complete description of the method and of indi-
Derived parameters
Average length for productive life, days 1640
Age at first calving, days 929
Slaughter age of cows culled in the first lac-
tation, days
1109
Live weight of heifers at first breeding, kg 424
Live weight of heifers at first calving, kg 579
Number of calves born per StFU 1 0.96
Number of culled calves per StFU 0.13
Number of calves surviving to the end of rear-
ing period per StFU
0.74
Number of fattened bulls per StFU 0.37
Number of heifers with first calving per StFU 0.29
Number of cows culled in 2nd and later lacta-
tions per StFU
0.22
Average milk production of cows culled in 1st
lactation, kg
4044
Average annual milk production of one cow, kg 5833
Revenues, EEK 2
from one culled calf 655
from one fattened bull 3155
from one culled heifer 2645
from one cow culled in 1st lactation 5249
from one cow culled in 2nd or later lactations 6061
from culled calves per StFU 85
from fattened bulls per StFU 1349
from culled heifers per StFU 195
from cows culled in 1st lactation per StFU 377
from annual milk production of cows 18337
Total costs, EEK 2
for culled calves per StFU 98
for breeding calves per StFU 1760
for heifers for culling per StFU 396
for calf for culling, EEK per day 12.6
for breeding calf 13.3
per heifer from first breeding to calving 5671
per heifer from ERPC to calving per StFU 3655
for fattened bulls per StFU 1266
for cows culled in 1st lactation per StFU 543
for cows culled in 2nd/later lactation per StFU 13451
of feed for gain per day per cow culled in 2nd
or later lactation
0.37
insemination costs per day per cow culled in
2nd or later lactation
1.46
additional feed costs per day for pregnancy in
cows
0.24
feed costs for milk of cows culled in 2nd/later
lactation per StFU
2598
variable labour costs for cows culled in 2nd/lat-
er lactation per StFU
2343
Total profit per StFU, EEK
1 StFU = Standard Female Unit = one cow
place occupied during an entire year
2 EEK = Estonian Krone
1245
Table 3. Economic analysis of milk and beef production
of the Estonian Holstein population.
{ } StFUiTi SxZa //0 μx=∂∂=
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vidual models used for the calculation of economic
weights can be found in Wolfová and Wolf (1996).
A computer program developed by those authors
was used for the calculations of economic values of
the various traits. Scenarios with and without milk
quota were investigated.
Farm revenues came from milk production and
from production of beef from bull calves and culled
cows. Costs were divided into variable costs per
cow, fixed costs per cow, and fixed costs per farm.
Production and economic data for the Estonian
Holstein population, as shown in Table 3, were
provided by the joint stock companies. Phenotypic
means in Table 4 were taken from the Results of
Animal Recording in Estonia (2000).
Definition of functional traits
To simplify derivation of economic values, defini-
tion of functional traits and categories of animals
are in accordance with the program (Wolfova and
Wolf 1996).
Calving interval is the interval in days between
successive parturitions. It is assumed that the inter-
val between calving and the ensuing first service
and that length of pregnancy are constant. Variation
in calving interval is therefore dependent upon var-
iation in the interval between the first service and
the service resulting in conception. Thus, calving
interval reflects the ability of the cows to conceive
and/or the ability of insemination to impregnate.
Age at first service (in days) is defined as the
average age of heifers at first insemination. It is
assumed that any change in the age at first service
will result in the same change in the age at first
calving.
Interval between the first and last service of
heifers characterises the ability of the heifers to
become pregnant and/or the ability of insemination
to result in conception. Any change in this interval
is expected to yield the same change in the age of
heifers at first calving.
Length of productive life is defined as the aver-
age number of lactations per cow in the herd. Pro-
ductive life is understood as functional productive
life. That is, cows culled for low milk production
are not included in the calculation of the average
length of productive life. Such cows form a special
category of animals. For simplicity, it is assumed
that culling and selection based upon production
occur only in the first lactation. When calculating
the economic weight for length of productive life,
it is assumed that changes in its length result from
improvement in the health conditions of cows.
Under constant herd size, increased productive
life implies that fewer heifers are needed as re-
placements. This was accounted for in calculating
economic weights by assuming that as productive
life increased, more heifers could be culled on milk
yield during early first lactation. The resultant in-
Economic parameters Biological parameters
Price of milk carrier (EEK per kg) 1.75 305-day milk production in 1st lactation (kg) 5539
Price for 1% protein content Milk protein content (%) 3.24
in milk (EEK) 0.3 Milk fat content (%) 4.09
Price for 1% fat content Average number of lactations 4
in milk (EEK) 0.1 Maximum number of lactations 10
Price of one insemination (EEK) 300 Age of heifers at 1st service (days) 624
Discounting rate (% per year) 10 Length of pregnancy (days) 278
Calving interval (days) 410
Number of inseminations per pregnancy in cows 2.0
Interval between calving and 1st service in cows (days) 83.3
1 1 € = 15.65 EEK.
Table 4. Economic and biological parameters to derive economic values1.
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crease in selection gain was then taken into account
in the calculations.
Results and discussion
Costs, revenues, total profit and phenotypic pa-
rameters for EHF and economic and biological
parameters used to derive economic values are pre-
sented in Tables 3 and 4, respectively. The economic
weights for milk, fat yield, protein yield, calving
interval, age at first service, interval between the
first service and conception of heifers, and length
of productive life under the different economic
scenarios are shown in Table 5. The economic
weights for milk production traits and functional
traits were expressed per unit change of each trait
and also per standard female unit (one cow place
occupied during an entire year). Situations with and
without quota and with and without discounting
were considered. With no discounting, milk quota
influenced the economic weights of milk yield and
length of productive life of cows by decreasing both
values. There were, however, only minor differences
in economic values of interval between first service
and conception in heifers, calving interval, and age
at first breeding in quota vs. no quota scenarios.
Discounted economic values can be lower or
higher than economic values calculated without
discounting. If changes in the performance of the
given trait influence only revenues or only costs,
then the discounted economic values are lower. If,
however, a change in performance influences reve-
nues, costs and the discounting coefficient (through
changing the time interval (∆t) in the equation for
calculating the discounting coefficient), then the
discounted economic values will be higher. Dif-
ferences between discounted and non-discounted
economic values are especially large for length of
productive life of cows. The differences in eco-
nomic values increased along with the time interval
between birth of improved animals and impact of
improved traits on revenues or costs. Ignoring cu-
mulative discounted expressions will lead to bias
in assigning relative selection emphasis to traits,
resulting in lower than optimal genetic response
(Groen et al 1997). Extended productive life of a
cow increases profit at farm level by reducing the
annual cost of replacements per cow in the herd
and by increasing the average herd yield through
an increase in the proportion of cows in the higher
producing age classes. Extending average produc-
tive life of cows reduces the number of replacement
heifers to be reared, therefore allowing an increase
in the size of the milking herd for a given acre-
age. Increased productive life of cows might also
increase profit due to changes in voluntary culling
performed by the farmer (Groen et al. 1997).
Trait Unit Economic value (in EEK per unit of given trait and per StFU)
without milk quota with milk quota
u = 0 u = 0.10 u = 0 u = 0.10
Milk carrier yield kg 1.2 0.7 0.9 0.8
Fat yield kg –4.8 –2.9 –4.8 –2.9
Protein yield kg 25.0 15.2 25.0 15.2
Age at first service days –0.5 –2.7 –0.6 –2.7
Interval between 1st service and conception
in heifers
days –7.1 –7.3 –7.1 –7.2
Calving interval days 0.2 –1.8 –0.1 –1.6
Length of productive life lactations 254.9 51.7 210.0 75.9
StFU = Standard Female Unit = one cow place occupied during an entire year
u = discounting rate per year
Table 5. Economic values for milk and functional traits for the Estonian Holstein population.
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Milk quota is an important factor determining
the economic value of milk yield. Usually quota
on milk yield is adjusted for fat content and the
system size is fixed output, meaning that with a
change in genetic merit for milk yield, the farmer
needs to either reduce the number of cows (Groen
et al. 1997) or buy additional quota (Veerkamp et
al. 2002). The general approach when deriving eco-
nomic values for milk production traits in a quota
situation has been to reduce the number of cows
at the farm (Gibson 1989, Groen 1989, Vargas et
al 2002). With milk quota, the economic values
for milk production traits (carrier, fat and protein)
are decreased as shown by Groen (1989), Gibson
(1989) and Veerkamp et al. (2002). Veerkamp et al
(2002) derived separate economic values for situ-
ations in which milk quota was managed through
a reduction in the number of cows (fixed output)
or by purchasing additional quota (fixed number of
cows). The economic value for fat production was
negative in the situation with quota as fixed output
whereas the economic value was positive in the
situation with purchase of quota. Consequently, the
economic value for fat was highly dependent on the
cost of purchasing or leasing quota.
To more easily compare the relative economic
importance of diverse traits, economic weights
often are expressed in terms of each trait’s ge-
netic standard deviation. In Table 6, the relative
economic weights per genetic standard deviation
and additionally the relative economic weights in
relation to the most important trait, milk yield, and
relative emphasis of milk and functional traits in
the aggregate genotype are given. Genetic standard
deviations for milk production traits were taken
from Pärna and Saveli (1998) and for functional
traits from Miesenberger et al. (1998) and Wolfová
et al. (2001). The standardised economic weights
of fat yield represented 18% and of protein yield
81% of the economic weight for milk yield (Ta-
ble 6). Economic weights for the interval between
the first service and conception of heifers and for
length of productive life represented 22 and 28% of
the economic weight of milk yield, respectively.
Because cost components depend on other
traits in the model and the level of the production
system considered, it can only give an idea of costs
associated with the derivation of economic values
for different traits (Nielsen 2004).
Dairy cattle breeders have developed increas-
ingly accurate indexes to select for profit. Accord-
ing to VanRaden (2002) selection indexes from 11
countries (Germany, France, New Zealand, Neth-
erlands, Canada, Great Britain, Australia, Italy,
Denmark, Sweden and Spain) emphasise protein
yield over fat yield and nearly all select against
milk volume or for increased concentration. Most
countries now select for longevity, health, and
conformation traits. The derivation of a selection
index involves decisions regarding which traits are
economically important, calculation of marginal
economic gains resulting from improvement for
Trait Unit Genetic
standard
deviation
Economic value
(in EEK) per
genetic standard
deviation
relative to
milk yield
Relative emphasis
(%) in the aggregate
genotype
Milk carrier yield kg 365 328.5 1.00 40
Fat yield kg 12.6 –60.5 –0.18 –7
Protein yield kg 10.6 265 0.81 32
Interval between 1st service
and conception in heifers
days 10 –71.0 –0.22 –9
Calving interval days 10 –1.0 0 0
Length of productive life days 180 92.2 0.28 12
Table 6. Relative economic values of traits in the Estonian Holstein population, assuming a milk marketing quota and
a discount rate of 0.10.
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those traits, decisions about traits to be recorded,
calculation of phenotypic and genetic parameters
related to the complete set of traits, and derivation
of index weights based on all this information. Al-
though the method was developed more than 60
years ago, it is still considered superior to all other
approaches of multiple trait selection (Sölkner and
Fuerst 2002).
Depending on the number of functional traits
included in a breeding scheme, the relative impor-
tance of production versus functional traits varies
from 70:30 to 30:70 (VanRaden 2002), sometimes
even more. For the EHF population, the relative
weighting for traits representing production vs.
traits representing functionality was 79:21. In all
of the scenarios examined, selection response in
financial terms will come largely from production
traits, because genetic parameters favour selection
response for fat and protein yields (high heritabil-
ity, high positive genetic correlation). Model cal-
culations by Sölkner and Fuerst (2002) have shown
that without inclusion of functional traits in an in-
dex, most of them will deteriorate whereas small
positive responses may be expected under selection
when they are included in an index.
Conclusions
Although the imposition of a milk quota influenced
relative economic values of milk carrier yield and
length of productive life of EHF, there were only
minor differences in the economic values of func-
tional traits between quota and non-quota scenarios.
In our investigation, the most important trait after
milk volume was protein yield, reaching 81% of
the standardised economic value of milk. Among
functional traits that were investigated, length of
productive life of cows was most important (28%
of the value for milk volume). Discounting had the
greatest impact on the economic value of length
of productive life. When defining the breeding
objective for EHF, the interval between the first
service and conception of heifers, and the length of
productive life should be included in the breeding
goal along with the traits with the highest economic
value, milk, fat and protein yield. Relative weight-
ings for production vs. functional traits in aggregate
genotype were 79 and 21%, respectively.
Acknowledgements. Studies were carried out with the
financial support from Estonian Science Foundation
(Grant 5772) and the targeted financing of research project
No.1080045s07 and 0422102s02.
References
Collard, B.L., Boettcher, P.J., Dekkers, J.C.M., Petitclerc,
D. & Schaeffer, L.R. 2000. Relationships between en-
ergy balance and health traits of dairy cattle in early
lactation. Journal of Dairy Science 83: 2683–2690.
Dempfle, L. 1992. Berücksichtigung von Fruchtbarkeit
und Eutergesundheit in der Rinderzüchtung. Züch-
tungskunde 64: 447– 457.
Fewson, D. & Niebel, E. 1986. Berücksichtigung indirek-
ter Merkmale in Zuchtplänen für Zweinutzungsrinder.
Züchtungskunde 58: 4–20.
Gibson, J.P. 1989. Economic weights and index selection
of milk production traits when multiple production quo-
tas apply. Animal Production 49: 171–181.
Goddard, M.E. 1998. Consensus and debate in the def-
inition of breeding objectives. Journal of Dairy Sci-
ence 81: 6–18.
Groen, A.F. 1989. Economic values in cattle breeding.
II. Influences of production circumstances in situa-
tions with output limitations. Livestock Production Sci-
ence 22: 17–30.
Groen, A.F., Steine, T., Colleau, J.J., Pedersen, J., Přibyl,
J. & Reinisch, N. 1997. Economic values in dairy cat-
tle breeding with special reference to functional traits.
Livestock Production Science 1–21.
Hazel, L.N. 1943. The genetic basis for constructing se-
lection indexes. Genetics 28: 476– 490.
Miesenberger, J., Sölkner, J. & Essl, A. 1998. Economic
weights for fertility and reproduction traits relative to
other traits and effects of including functional traits into
a total merit index. Interbull Bulletin 18: 78–84.
Nielsen, H.M. 2004. Economic values for production and
functional traits in dairy cattle breeding goals derived
by stochastic simulation. Ph.D. thesis. The Royal Ve-
terinary and Agricultural University, Denmark. Danish
Institute of Agricultural Sciences, Research Centre Fou-
lum, Denmark. 156 p.
Olesen, I., Groen, A.F. & Gjerde, B. 2000. Definition of
animal breeding goals for sustainable production sy-
stems. Journal of Animal Science 78: 570–582.
Pentjärv, A. & Uba, M. 2004. 95 years of animal record-
ing. In: Viinalass, H. (ed) Animal breeding in Estonia.
Tartu, OÜ Repro Express. p. 14–16.
Pärna, E. & Meier, A. 2001. Eesti holsteini geneetilise
A G R I C U L T U R A L A N D F O O D S C I E N C E
Pärna E. et. al. Development of a breeding objective for EHF
220
A G R I C U L T U R A L A N D F O O D S C I E N C E
Vol. 16 (2007): 212-221
221
selektsiooniedu majanduslik väärtus. Agraarteadus
12, 2: 100–110.
Pärna, E.& Saveli, O. 1997. Economic Value of Milk Com-
ponents in Cattle Breeding. In: Proceedings of the 3rd
Baltic Animal Breeding Conference. Riga. p. 15 –18.
Pärna, E. & Saveli, O. 1998. Selection on the major com-
ponents of milk to maximise profit in dairy herds. In:
Proceedings of the 6th World Congress on Genetics
Applied to Livestock Production. Armidale, Australia.
25: 399–402.
Pärna, E. & Saveli, O. 2002. Economic weights for pro-
duction and functional traits of Estonian Holstein pop-
ulation. In: Proceedings of the 7th World Congress on
Genetics Applied to Livestock Production. Montpelli-
er, France, 29: 323–326.
Results of Animal Recording in Estonia 1998. 1999.
Põllumajanduse Registrite ja Informatsiooni Keskus.
Pentjärv A. (ed). ELMATAR. 52 p.
Results of Animal Recording in Estonia 2000. 2001.
Jõudluskontrolli Keskus. Pentjärv A., Kruus M. & Aer
R. (eds). ELMATAR. 52 p.
Results of Animal Recording in Estonia 2004. 2005.
Jõudluskontrolli keskus. Pentjärv A., Kruus M. & Aer
R. (eds). ELMATAR. 52 p.
Results of Animal Recording in Estonia 2005. 2006. Jõud-
luskontrolli keskus. Pentjärv A., Kruus M. & Tisler R.
(eds). ELMATAR. 52 p.
Smith, C. 1983. Effects of changes in economic weights
on the efficiency of index selection. Journal of Animal
Science 56: 1057–1064.
Sölkner, J. & Fuerst, C. 2002. Breeding for functional
traits in high yielding dairy cows. In: Proceedings of the
7th World Congress on Genetics Applied to Livestock
Production. Montpellier, France, 29: 107–114.
Thaller, G. 1998. Genetics and breeding for fertility. In-
terbull Bulletin 18: 55 – 61.
Van Doormaal, B.J., Kistemaker, G., Fatehi, J., Miglior,
F., Jamrozik, J. & Schaeffer, L.R. 2004. Genetic eval-
uation of female fertility in Canadian dairy breeds. In-
terbull Bulletin 32: 86–89.
VanRaden, P.M. 2002. Selection of dairy cattle for life-
time profit. In: Proceedings of the 7th World Congress
on Genetics Applied to Livestock Production. Montpel-
lier, France, 29: 127–130.
Vargas, B., Groen, A.F., Herrero, M. & van Arendonk
J.A.M. 2002. Economic values for production and func-
tional traits in Holstein cattle of Costa Rica. Livestock
Production Science 75: 1010–116.
Veerkamp, R.F., Dillon, P., Kelly, E., Cromie, A.R. &
Groen, A.F. 2002. Dairy cattle breeding objectives
combining yield, survival and calving interval for pas-
ture-based systems in Ireland under different milk
quota scenarios. Livestock Production Science 40:
123–137.
Weigel, K.A., Zwald, N.R., Chang, Y.M., Welper, R.D.
& Clay, J.S. 2004. Potential for genetic evaluation of
health and fertility traits using data collected in on-
farm herd management software programs. Interbull
Bulletin 32: 90–93.
Wolfová, M. & Wolf, J. 1996. PC-program for estimating
economic weights in cattle. User’s manual for the pro-
gram EW. Version 1.1.
Wolfová, M., Přibyl, J. & Wolf, J. 2001. Economic weights
for production and functional traits of Czech dairy
cattle breeds. Czech Journal of Animal Science 46:
421– 432.
Development of a breeding objective forEstonian Holstein cattle
Introduction
Materials and methods
Results and discussion
Conclusions
References