Project report:
Sustainable Breeding in the
Nordic Red Dairy Breeds
Bærekraftig utnyttelse av den røde
nordiske mjølkekupopulasjonen som
genetisk ressurs
Project report:
Sustainable Breeding in the Nordic Red Dairy Breeds
Bærekraftig utnyttelse av den røde
nordiske mjølkekupopulasjonen som
genetisk ressurs
The project group has had the following memebers:
Torstein Steine (Geno)
Gunnar Klemetsdal (University of Life Sciences, Norway)
Lars-Olof Bårström (Svensk Avel)
Hans Eström (Nordic Gene-bank FarmAnimals)
Erling Strandberg (Sveriges Lantbruksuniversitet)
Jaana Kiljunen (Finnish Animal Breeding Association)
Jarmo Juga (Finnish Animal Breeding Association)
Morten Kargo (Danish Institute of Agricultural Sciences)
Lisbeth Holm (Dansire), and
AstridKarlsen (Geno)
2
1.0 Introduction .................................................................................................................................. 5
2.0 Assesing total profit of alternative levels of co-operation between Nordic cattle populations
.............................................................................................................................................................. 6
2.1 Introduction ................................................................................................................................ 6
2.2 Aim ............................................................................................................................................ 6
2.3 Literature review of critical points around comparisons of single and multiple objective
breeding schemes ............................................................................................................................. 6
2.4 Simulation study of three strategies of co-operation. ................................................................ 7
2.4.1 Material and methods .......................................................................................................... 7
2.4.2 Results ............................................................................................................................... 15
2.4.3 What is the exchange of red cattle bulls between Finland, Sweden and Norway today? . 22
2.4.4 Correlations between the breeding goals in the three countries ....................................... 22
2.4.5 Final conclusions .............................................................................................................. 22
2.5 References ................................................................................................................................ 23
3.0 Genotype by environment interaction ..................................................................................... 24
3.1 Introduction .............................................................................................................................. 24
3.2 Methods to study genotype by environment interaction .......................................................... 24
3.3 Does GxE exist? ....................................................................................................................... 25
3.4 Does GxE exist in Nordic countries? ....................................................................................... 26
3.5 Consequences for selection ...................................................................................................... 27
3.6 References ................................................................................................................................ 28
4.0 Breeding objectives and optimum use of sires in the Nordic red populations ..................... 30
4.1 Introduction .............................................................................................................................. 30
4.2 Methods.................................................................................................................................... 30
4.3 Results and discussion ............................................................................................................. 30
5.0 Use of EVA in Dairy Cattle Breeding Schemes ....................................................................... 35
5.1 Summary .................................................................................................................................. 35
5.2 Inbreeding – Cause and Consequences .................................................................................... 35
5.2.1 Balance between Genetic Gain and Relationship ............................................................. 36
5.2.2 Programmes to Handle Genetic Gain vs. Inbreeding........................................................ 37
5.2.3 ”Economic” Weighting of Genetic Gain and Relationship .............................................. 38
5.2.4 Implementation ................................................................................................................. 38
5.2.5 The Use of EVA for Red Dane ......................................................................................... 39
5.2.6 The use of EVA for Danish Holstein ................................................................................ 40
5.3 References ................................................................................................................................ 42
6.0 New techniques in dairy cattle breeding schemes ................................................................... 43
6.1 Use of Marker Assisted Selection in Nordic Breeding Schemes ............................................. 43
6.2 Use of sexed semen in the Nordic Red Breeds ........................................................................ 45
6.3 References ................................................................................................................................ 46
7.0 Nucleus breeding in Practice – a Nordic approach................................................................. 48
7.1 Introduction .............................................................................................................................. 48
7.2 The accuracy in the selection of bull dams .............................................................................. 48
7.3 Simulation of nucleus breeding schemes ................................................................................. 51
7.4 Nucleus breeding in practice .................................................................................................... 52
7.5 Recommendations and conclusion ........................................................................................... 54
7.6 References ................................................................................................................................ 54
8.0 Imports from other populations to the Scandinavian Red Dairy Breeds. ............................ 56
8.1 Introduction .............................................................................................................................. 56
8.2 What are we particular looking for for the Scandinavian Red breeds? ................................... 56
3
8.3 Where do we find possible sources for importations to the Scandinavian red breeds? ........... 57
8.4 Conclusions .............................................................................................................................. 58
9.0 Competitive advantages in the Nordic red breeds. ................................................................. 59
9.1 The Nordic Red Breeds ............................................................................................................ 59
9.2 Conclusions .............................................................................................................................. 61
10.0 Prospects for cooperation between Baltic and Nordic countries in dairy cattle breeding 62
10.1 Background ............................................................................................................................ 62
10.2 Milk recording in Baltic countries ......................................................................................... 62
10.3 Genetic evaluation ................................................................................................................. 63
10.4 Breeding programs in Baltic countries .................................................................................. 63
10.5 The possibilities for cooperation ............................................................................................ 63
10.6 Possibilities in marketing ....................................................................................................... 65
10.7 Contact people in Baltics ....................................................................................................... 65
10.8 References .............................................................................................................................. 65
11.0 Discussion/summing up ........................................................................................................... 67
Appendix 1. The use of EVA-programme in Finnish Ayrshire and Finncattle breeding
schemes.............................................................................................................................................. 69
4
1.0 Introduction
The breeding associations for the Nordic red cattle breeds Swedish Red (SRB), Norwegian Red
(NRF) and Finnish Ayrshire (FA) have a long history of exchanging semen of the best elite sires. In
later years also Danish Red (RDM) has been a part of this exchange system. Due to the exchange of
semen, genetic ties exist between the four populations, and it is obvious that the main genetic pool
for any of the red Nordic cattle populations is the red populations in the neighbouring Nordic
countries.
One of the reasons for the exchange of semen between the Nordic red populations is the similarity
in breeding schemes in these countries compared to the breeding schemes for other dairy
populations. The breeding programs for the Nordic red dairy breeds have focused on functional
traits such as health traits, fertility, stillbirths, and calving ease as well as production. This is in
contrary to the situation in the main dairy breed in the western world today, Holstein, where the
breeding program has focused entirely on production and conformation until recently. It is well
known that functional traits and production have unfavorable genetic correlations. Therefore
selection for production will result in deterioration of functional traits. This has resulted in a
competitive advantage for the Nordic red breeds on the international market, since these breeds are
able to offer genetic material that is strongly selected for functional traits as well as production.
Although semen has been exchanged between the Nordic red breeds during the last decades, the
degree of exchange of semen has been decided upon by each individual breeding association. There
has not been done any work to estimate the maximum, minimum or recommended use of sires
across countries to obtain the optimum genetic gain.
Another very important topic in this regard is inbreeding. Extensive use of a limited amount of elite
sires in dairy populations have increased the rate of inbreeding rapidly. One way to reduce the
amount of inbreeding is through import of semen from sires that are relatively non-related to the
population they are imported to. A major effect of the exchange of semen between the Nordic red
breeds, is that it has kept the inbreeding at a relatively low level in these populations.
In the last decade we have seen a reduction in the number of cows in the Nordic countries, while the
cost of running breeding programs are constant. It has become increasingly important to manage the
breeding schemes within the Nordic countries as efficiently and innovative as possible. Fundings
were provided by The Norwegian Ministry of Agriculture and Nordic Genbank Farm Animals in
2003 to start a project focusing on the future challenges in managing the Nordic Red dairy cattle
populations to maximize genetic gain while focusing on problems regarding inbreeding and
competitive advantages for the Nordic Red Breds.
Some of the problems raised by the group are the following:
 How much exchange of semen should there be between the Nordic red breeds to obtain
desired genetic gain with a low rate of inbreeding?
 What would happen if the breeding goals (total merit index) becomes more similar or more
dissimilar between the Nordic red breeds?
 Are the Nordic red breeds able to serve as a genetic pool for each other to avoid increased
inbreeding?
 May other breeds serve as a genetic pool to avoid increased inbreeding?
5
2.0 Assesing total profit of alternative levels of co-operation between Nordic cattle
populations
Anna K. Sonesson, AKVAFORSK
2.1 Introduction
Although the Nordic countries breeding for red dairy cattle have three different breeding goals and
different population and production structures, co-operation between the countries could be one
alternative to increase the competitiveness of the Nordic red cattle populations. In this study, we
will compare the effects of alternative levels of co-operation between the Nordic red cattle
populations on profit.
This report contains the following parts:
1. Literature review of critical points around comparisons of single and multiple objective
breeding schemes.
2. Simulation study of three strategies of co-operation.
3. What is the exchange of red cattle bulls between Finland, Sweden and Norway today?
4. What is the correlation between breeding goals in Finland, Sweden and Norway today?
5. Final conclusions
2.2 Aim
The main aim of this study is to compare three different selection strategies on the total profit of
Nordic red cattle after 25 years of selection using computer simulation.
Strategy 1. Selection within each of the three countries for each of its breeding goals.
Strategy 2. Selection over the three countries for each of its breeding goals.
Strategy 3. Selection over the three countries for one overall breeding goal.
A small literature review of previous studies on comparison of across-country breeding schemes is
included in this report. An estimate of the current exchange of bulls between Norway, Sweden and
Finland will also be given. The three correlation coefficients of the breeding goals in Norway,
Sweden and Finland are included as well.
2.3 Literature review of critical points around comparisons of single and multiple objective
breeding schemes
Critical points when comparing schemes
There are very few studies on comparison of single versus multiple objective/line selection. Banos
and Smith (1991) and Smith and Banos (1991) have looked at schemes for multiple objective
selection in a single line. This should not be mixed up with multiple line selection for multiple
objectives that have been compared to single line selection for single objective (e.g. Ollivier et al.,
1990; Smith, 1986). One problem is to compare strategies at the same base, i.e. genetic response,
costs of selection and inbreeding.
1. With single line selection, line mean response is the same as population mean response. For
multiple line selection, response is the mean of several lines. When selecting for multiple
objectives, animals are selected over lines. The need to allocate animals to the lines where
they perform best was emphasised by Banos and Smith (1991). Howarth (2001) suggested
to use the mean of the animals allocated to each of the lines. Thereby, it is possible to
compare selection response for single and multiple objective schemes. Multiple line Closed
(MLC), multiple lines open (MLO) and single line with an average objective (SLA) were
6
compared by Howarth (2001) with and without allocation of candidates and with different
correlations between selection objectives. Allocation had effect only at low correlations
between objectives, such that the two lines were divergent. Net allocated response of MLO
was highest for all schemes.
2. In these comparative studies, the total number of individuals over multiple lines (ML) equals
the number of individuals in the corresponding single line (SL). However, in the ML
approach, selection intensities can be higher, which results in higher rates of inbreeding. In
general, genetic gain for different schemes should be compared at the same rate of
inbreeding, such that a comparison at different rate of inbreeding is not appropriate.
Which are the critical variables for comparison of multiple objectives/lines?
In general, the correlation between the objectives has been the variable to compare multiple line
selection for multiple objectives at.
 Correlation of 0.8 between a trait measured in different environments before it was
‘biologically or agriculturally’ important (Robertson, 1959).
 Correlation of >0.75 between a nucleus and a particular breeder to make participation in a
group breeding program useful (Del Bosque Gonzalez and Kinghorn, 1990).
 Correlation of 0.9, when the number of progeny tested bulls equals the total number of bulls
over lines. When the number of progeny tested bulls was 5 % of the total number, the
correlation was 0.7. (Goddard, 1992).
 Correlation of 0.7-0.8 (Meuwissen, 1998).
These correlations require that objectives be composed of the same traits. Therefore, the correlation
of selection indices that are composed of different traits may be more important (Howarth, 2001).
However, also the means and variances of two populations are important variables when comparing
single and multiple breeding objectives (Banos and Smith, 1991 and Smith and Banos, 1991).
Howarth (2001) optimised size and proportional weight given to one or two breeding objectives. In
general, the line with the highest mean or variance increases in size, whereas the other lines
decrease in size. This convergence was faster when the correlation between breeding goals was
high.
Summary
 Not only the correlation between breeding objectives is an important variable to compare
single and multiple objective schemes for, but also genetic means and variances are
important variables.
 Because the selection ‘drifts’ towards a population with high genetic variance, it is
important to reduce the loss of genetic variation in the population, i.e. to control the rate of
inbreeding.
2.4 Simulation study of three strategies of co-operation.
2.4.1 Material and methods
Genetic values
Multitrait genetic values were simulated according to the infinitesimal model (Bulmer, 1985).
Genotypes, gi, of the unrelated base animals were sampled from the distribution N(0, G), where the
genetic covariance matrix G was standardised such that all genetic variances were equal to 1.0. Record yi
was calculated as yi=gi+ei, where ei is the environmental effect, which was sampled from N(0, E), where
7
E denotes the matrix of environmental covariances. Later generations were obtained by simulating
offspring genotypes from gi= +0.5gs+0.5gd+mi, where, s and d denote sire and dam of offspring i and
mi=Mendelian sampling component, which was sampled from N(0, 0.5(1-Fsd)G), where Fsd is the
average inbreeding coefficients of the sire and the dam.
Selection strategies
Three different selection strategies were compared for the total profit of Nordic red cattle after 25
years of selection.
Strategy 1. Selection within each of the three countries for each of its breeding goals.
Strategy 2. Selection over the three countries for each of its breeding goals.
Strategy 3. Selection over the three countries for one overall breeding goal.
Differences in costs between the strategies were ignored.
The effect of these three strategies on the profit within each country and on the total profit across
countries will be reported.
Population structure and size
Each of the three countries Norway (N), Sweden (S) and Finland (F) had two tiers:
1. Nucleus
2. Commercial
The number of candidates of males (mal) and females (fem) and the number of selected sires and
dams in the tiers are given below for each country.
In the figures, arrows indicate where the male (left part of the figure) and females (right part of the
figure) candidates come from for each of path of selection (sires in the nucleus, dams in the nucleus,
sires in the commercial tier, dams in the commercial tier). Sires are selected among males from the
nucleus, because these are assumed to be much superior to the males in the commercial tier. These
same sires are also used in the commercial tier, such that the males in the commercial tier are
ignored as candidates of selection. However, dams of the nucleus are selected among females from
both the nucleus and the commercial tier, because the best females of the commercial tier are
assumed to be equally good as the females of the nucleus since a more intense selected is made
among the females of the commercial tier. Dams of the commercial tier are selected only among the
females of the commercial tier.
8
Norway:
In the nucleus, 125 bulls are progeny tested on 250 daughters each year. From these, the best sires
will be selected to produce 125 male and 125 female offspring. We assumed that females lived
three years, such that there are 375 (=3*125) female candidates from the nucleus each year.
Due to computational limitations, the number of female selection candidates in the real commercial
tier was corrected for the selection intensity for the bulldam selection step, which is the limiting
selection step, due to the potential large use of each sire. In the real Norwegian population, 12000
potential bulldams are selected out of 270 000 dams in the commercial tier, corresponding to 4.0%
selected dams. We know that 125 progeny tested bulls are needed and assume that they originate
from the 4.0% selected dams. Therefore, 3125 (=125/4%) dams will be necessary each generation,
i.e. approximately 1000/yr for three years. In order to get 1000 female offspring/yr, we need to
select 2000 commercial dams, which are mated to the 25 best bulls from the nucleus. The scheme is
summarised below.
NUCLEUS
125 mal /yr
No. sires by OC
125 mal/yr
375 fem/ year
125 dams/yr
x
offspring
125 fem/yr
COMMERCIAL
25 sires/yr
1000 mal/yr
x
3125 fem/yr
2000 dams/yr
offspring 1000 fem/yr
9
Sweden:
The correction for the selection intensity for the bulldam selection step was done as follows for
Sweden. In the real Swedish population, 3700 potential bulldams are selected out of 160 000 dams
in the commercial tier, corresponding to 2.5% selected dams. We know that 105 progeny tested
bulls (for 140 daughters) are needed and assume that they originate from the 2.5% selected dams.
Therefore, 4500 (=105/2.5%) dams will be necessary each generation, i.e. approximately 1500/yr
for three years. In order to get 1500 female offspring/yr, we need to select 3000 commercial dams,
which are mated to the 25 best bulls from the nucleus.
NUCLEUS
105 mal /yr
No. sires by OC
105 mal/yr
315 fem/ year
105 dams/yr
x
offspring
105 fem/yr
COMMERCIAL
25 sires/yr
1500 mal/yr
x
4500 fem/yr
3000 dams/yr
offspring 1500 fem/yr
10
Finland:
The correction for the selection intensity for the bulldam selection step was done as follows for
Finland. In the real Finnish population, 3300 potential bulldams are selected out of 210 000 dams in
the commercial tier, corresponding to 1.6% selected dams. We know that 120 progeny tested bulls
(with 200 daughters) are needed and assume that they originate from the 1.6% selected dams.
Therefore, 7635 (=120/1.6%) dams will be necessary each generation, i.e. approximately 2550/yr
for three years. In order to get 2550 female offspring/yr, we need to select 5100 commercial dams,
which are mated to the 25 best bulls from the nucleus.
NUCLEUS
120 mal /yr
No. sires by OC
120 mal/yr
360 fem/ year
120 dams/yr
x
offspring
120 fem/yr
COMMERCIAL
25 sires/yr
2550 mal/yr
x
7635 fem/yr
5100 dams/yr
offspring 2550 fem/yr
11
Traits to select for
We assumed that each country selected for two traits: kg protein (kgP) and mastitis (mast). These traits
were seen as different traits in each country, such that 2 traits x 3 countries = 6 traits in total were
considered. We assumed that both the nucleus and the commercial tier had the same (co)variances.
Genetic (G) and error (E) (co)variances are given in Figure 1. The within-country variances were given
by the breeding organisation of the countries. The correlation between countries was considered to be 0.9
for kgP and 0.7 for mast. The correlation between kgP in one country and mast in another country was
0.7 * (average correlation over the two countries). In the simulation, genetic variances were standardised
to 1, such that all genetic gains are expressed in genetic standard deviation units.
Figure 1. Genetic (a) and error (b) (co)variances between traits, kg Protein (kgP) and mastitis (mast), in
Norway (N), Sweden (S) and Finland (F).
(a)
N kgP
N mast
S kgP
S mast
F kgP
F mast
 264
.359 .005

 294 .363
.171 .0025

 187 .298

.129 .001
405
.291
232
.156





.0025


.177 165

.001 .180 .0008
(b)
N kgP
N mast
S kgP
S mast
F kgP
F mast
 674

 .687 .152



902




 .960 .0845




350


 .824 .056

The two traits, kgP and mast, were recorded on females at age two in both the nucleus and commercial
tiers. 125 males from Norway, 105 males from Sweden and 120 males from Finland were progeny tested
for kgP and mast each year. This data became available at age 5, such that progeny from these sires were
born when they were 6 years old.
considered as the maximum acceptable rate of inbreeding in ongoing breeding programs (e.g. Goddard,
1992).
Results are based on averages over 50 replicated schemes and the average of the last 10 years of
selection is given.
Economic weights
The estimated breeding values of the candidates for selection were calculated using a multitrait across
country animal model. EBVs of the two traits were weighted according to the relative economic weight
(v) of the trait (kgP and mast) in each country as EBV= vkgP * EBVkgP + vmast * EBVmast for each
individual for Selection strategies 1 and 2.
12
For strategies 1 and 2, the economic weights were (in genetic standard deviations):
Norway
kgP: 0.5= vkgP(N)
mast: 0.5 = vmast(N)
(1)
(2)
Sweden
kgP: 0.7 = vkgP(S)
mast: 0.3 = vmast(S)
(3)
(4)
Finland
kgP: 0.7 = vkgP(F)
mast: 0.3 = vmast(F)
(5)
(6)
For strategy 3, where selection was over the three countries for one breeding goal, the economical
weights were also weighted by the population sizes of each country. The across-country economical
weight for kgP and mast are
Norway
kgP: 0.5*270000/640000 = 0.210 = v’kgP(N)
mast: 0.5*270000/640000 = 0.210 = v’mast(N)
(7)
(8)
Sweden
kgP: 0.7*160000/640000 = 0.175 = v’kgP(S)
mast: 0.3*160000/640000 = 0.075 = v’mast(S)
(9)
(10)
Finland
kgP: 0.7*210000/640000 = 0.230 = v’kgP(F)
mast: 0.3*210000/640000 = 0.098 = v’mast(F)
(11)
(12)
For strategy 3, EBV= v’kgP(N) * EBVkgP(N) + v’mast(N) * EBVmast(N) + v’kgP(S) * EBVkgP(S) + v’mast(S) *
EBVmast(S) + v’kgP(F) * EBVkgP(F) + v’mast(F) * EBVmast(F). Hence, this total breeding goal will for a large
part be selecting for the Norwegian breeding goal. This is because Norway has the largest population
size and economic values are expressed per genetic standard deviation.
The within-country profit was calculated as the total sum of the resulting genetic gain times the
economic weights (1-6) for the two traits of each country.
The across-country profit was calculated as the sum of the resulting genetic gain times the scaled
economic weights (7-12) for the two traits of each country.
Selection and mating strategies
Dams of both the nucleus and commercial tiers were selected using truncation selection. Within the
nucleus tier, sires were selected using optimum contribution selection, where the contributions were
adapted to the already selected dams.
We assumed that one selection round corresponds to one year.
Truncation selection
For the paths that used truncation selection, i.e. selection of nucleus dams, selection of commercial sires
and dams, the number of individuals with the highest EBVs were selected each year.
Optimum contribution selection
Optimum contribution selection for overlapping generations was applied for the selection of nucleus
bulls as proposed by Meuwissen & Sonesson (1998). This method maximises the genetic level of next
generation of animals under three restrictions. The first restriction is on the contribution per sex, i.e. each
sex must contribute 50% of the genes to the next generation. The second restriction is on the increase of
13
the average relationship of the selected parents, i.e. on the rate of inbreeding of the progeny. The third
restriction is that the females are selected as defined by the truncation selection. For populations with
overlapping generations, there are animals of different age-classes at year t from which parents are
selected and the different age-classes get different weights, which equal their long-term contributions
and which are derived from the gene-flow theory of Hill (1974). The weights indicate how much ageclasses have to contribute in the future. The algorithm iterates on these weights such that the contribution
of each individual selection candidate and the contributions of each age-class, are optimised. An addition
to the method of Meuwissen and Sonesson (1998) in this study is that these weights were also optimised
over tiers by calculating the longterm contributions of tier*age-class. For strategies 2 and 3, since
candidates came from all countries and each country had two tiers, there were six tiers in total to be
considered. The output from the selection method is a vector of genetic contributions for each selection
candidate. These genetic contributions are translated to the number of progeny a certain individual
should get. Hence, the number of selected sires in the nucleus is not fixed, but optimised with this
algorithm.
Mating
Random mating was applied. A progeny got a sire or dam assigned by random sampling with sampling
probabilities following the optimal contributions of the selection candidates. This vector of genetic
contributions was translated to the number of matings each candidate should get by multiplying it by the
desired number of male and female progeny of the scheme.
In the nucleus tier, a mating pair always got two progeny, one female and one male. In the commercial
tier, the male progeny was neglected, because we assumed that all sires came from the nucleus tier. Only
female progeny were therefore accounted for in the commercial tier.
14
2.4.2 Results
Profit within country
Norway:
In the nucleus tier,
3, where all countries select for the same overall breeding goal than for strategy 1, where each
country selects for its own breeding goal (Table 1, Figure 2). This resulted in a total profit of 0.163
for strategy 3 and 0.153 for strategy 1, i.e. an increase in total profit of 6.5%. This is probably
because the overall breeding goal of strategy 3 consists for a large part (42%) of the original
Norwegian breeding goal, due to the large size of the Norwegian population, and then it becomes
attractive to select from the Swedish (18%) and Finnish (22%) nuclea and Finnish commercial tier
(18%) (Table 2).
Overall, Norway had the lowest profit (Tables 1, 3, and 5), probably because there is a high relative
economic weight on mastitis, which is a lowly heritable trait and more difficult to improve than kg
P, even though the Norwegian cow population is the largest of the three countries.
Strategies 2 and 3 mainly had a large advantage for
strategy 1 (Figure 2). This is because initial selection response is larger in the large unrelated
population under strategies 2 and 3, whereas it becomes similar to response under strategy 1 as
relationships between populations build up. This effect is not important when calculating profit,
15
Table 1. Number of male (Malcand) and female (Femcand) candidates and selected sires (Malsel)
and dams (Femsel
using strategies 1-3 *
Strategy Malcand Femcand Malsel Femsel MalL FemL
1
2
3
379
1058
1060
3525
16972
16957
17.8
4.5
9.3
125
125
125
6.62
7.18
7.26
2.78
2.96
3.17
F
0.001
0.001
0.001
G kgP G mast Profit
(sd)
(sd)
0.172
0.193
0.207
0.133
0.120
0.119
0.153
0.156
0.163
*
Strategy 1. Selection within each of the three countries for each of its breeding goals
Strategy 2. Selection over the three countries for each of its breeding goals
Strategy 3. Selection over the three countries for one overall breeding goal
Table 2. Use of genes from the nucleus (Nucl) or commercial (Com) tiers of Norway (N), Sweden
(S) and Finland (F) in the nucleus of Norway using strategies 1-3*
Strategy
%NuclN
%ComN
%NuclS
%ComS
%NuclF
%ComF
1
2
3
0.72
0.52
0.30
0.28
0.14
0.05
x
0.08
0.18
x
0.05
0.08
x
0.10
0.22
x
0.11
0.18
*
Strategy 1. Selection within each of the three countries for each of its breeding goals
Strategy 2. Selection over the three countries for each of its breeding goals
Strategy 3. Selection over the three countries for one overall breeding goal
16
Genetic response (sd)
6
5
1 kgP
4
1 mast
2 kgP
3
2 mast
3 kgP
2
3 mast
1
25
23
21
19
17
15
13
11
9
7
5
3
1
0
Year
Figure 2. Genetic response in the nucleus of Norway for kg protein (kg P) and mastitis (mast) using
strategies 1-3
Sweden
In the Swedish nucleus, the effect of the three strategies was very small (Table 3). For strategy 1,
profit was 0.170, for strategy 2, profit was 0.168 and for strategy 3, profit was 0.172. Sweden has
the smallest scheme, and will therefore gain from collaborating with especially Finland (Table 4)
that has a similar breeding goal.
The contribution from the nuclea of the different countries is approximately the same for all three
countries for strategy 3 (Tables 2, 4 and 6). Hence, the same animals are probably, to a large extent,
selected in all countries, because they select for the same breeding goal.
17
Table 3. Number of male (Malcand) and female (Femcand) candidates and selected sires (Malsel)
and dams (Femsel), male (MalL) and female (FemL) generation interval, rate of inbreeding (
using strategies 1-3 *
Strategy Malcand Femcand Malsel Femsel MalL FemL
1
2
3
317
1058
1060
5046
16972
16957
14.5
4.7
7.8
105
105
105
6.4
7.2
7.3
3.0
3.1
3.2
F
0.001
0.001
0.001
G kgP G mast Profit
(sd)
(sd)
0.200
0.200
0.204
0.100
0.092
0.096
0.170
0.168
0.172
*
Strategy 1. Selection within each of the three countries for each of its breeding goals
Strategy 2. Selection over the three countries for each of its breeding goals
Strategy 3. Selection over the three countries for one overall breeding goal
Table 4. Use of genes from the nucleus (Nucl) and commercial (Com) tiers of Norway (N), Sweden
(S) and Finland (F) in the nucleus of Sweden using strategies 1-3*
Strategy
%NuclN
%ComN
%NuclS
%ComS
%NuclF
%ComF
1
2
3
x
0.08
0.28
x
0.02
0.05
0.69
0.44
0.18
0.31
0.18
0.08
x
0.17
0.24
x
0.11
0.18
*
Strategy 1. Selection within each of the three countries for each of its breeding goals
Strategy 2. Selection over the three countries for each of its breeding goals
Strategy 3. Selection over the three countries for one overall breeding goal
18
Genetic response (sd)
6
5
1 kgP
4
1 mast
2 kgP
3
2 mast
3 kgP
2
3 mast
1
25
23
21
19
17
15
13
11
9
7
5
3
1
0
Year
Figure 3. Genetic response in the nucleus of Sweden for kg protein (kg P) and mastitis (mast) using
strategies 1-3.
Finland:
The Finnish nucleus had a large increase in profit for strategy 1 (0.180) compared to strategy 2
(0.167) and strategy 3 (0.164) (Table 5). Hence, Finland will loose most in the co-operation with
Norway and Sweden. This is probably because they have a high relative economic weight on kgP
(0.7), which is highly heritable and because the number of cows is larger (210 000) than Sweden
(160 000) that has the same relative economic weights on the two traits. Another reason for the low
profit of strategies 2 and 3 for Finland is that bulls are selected more intensely than in strategy 1
(number of selected sires is reduced and the number of candidates is increased), which might have
reduced the genetic variance in the next generation more than when Finland selected on its own in
strategy 1.
Table 5. Number of male (Malcand) and female (Femcand) candidates and selected sires (Malsel)
and dams (Femsel), male (MalL) and female (FemL) generation interval, rate of inbreeding ( F),
genetic gain ( G) for kg protein (kg P) and mastitis (mast) and total profit in the nucleus of Finland
using strategies 1-3 *
Strategy Malcand Femcand Malsel Femsel MalL FemL
1
2
3
365
1058
1060
8377
16972
16957
15.8
6.1
9.8
120
120
120
6.4
7.2
7.7
3.1
3.2
3.2
F
0.001
0.001
0.001
G kgP G mast Profit
(sd)
(sd)
0.205
0.194
0.188
0.121
0.106
0.108
0.180
0.167
0.164
*
19
Strategy 1. Selection within each of the three countries for each of its breeding goals
Strategy 2. Selection over the three countries for each of its breeding goals
Strategy 3. Selection over the three countries for one overall breeding goal
Table 6. Use of genes from the nucleus (Nucl) or commercial (Com) tiers of Norway (N), Sweden
(S) and Finland (F) in the nucleus of Finland using strategies 1-3*
Strategy
%NuclN
%ComN
%NuclS
%ComS
%NuclF
%ComF
1
2
3
x
0.13
0.29
x
0.02
0.05
x
0.11
0.17
x
0.04
0.08
0.64
0.40
0.24
0.36
0.31
0.18
*
Strategy 1. Selection within each of the three countries for each of its breeding goals
Strategy 2. Selection over the three countries for each of its breeding goals
Strategy 3. Selection over the three countries for one overall breeding goal
Genetic response (sd)
6
5
1 kgP
4
1 mast
2 kgP
3
2 mast
3 kgP
2
3 mast
1
25
23
21
19
17
15
13
11
9
7
5
3
1
0
Year
Figure 4. Genetic response in the nucleus of Finland for kg protein (kg P) and mastitis (mast) using
strategies 1-3
20
Profit over countries
The total profit over countries was very similar for the three strategies (Table 7).
The genetic gain was about the same for all traits within a country, except for Norway that had
higher genetic gain for kg P for strategies 2 and 3 than for strategy 1.
Table 7. Profit for kg protein (kg P) and mastitis (mast) for Norway (N), Sweden (S) and Finland
(F) using strategies 1-3 *
Strategy
1
2
3
kgP N
mast N
kgP S
Profit
mast S
kgP F
mast F
Total profit
0.036
0.041
0.043
0.028
0.025
0.025
0.035
0.035
0.036
0.007
0.007
0.007
0.047
0.045
0.043
0.012
0.010
0.011
0.166
0.163
0.165
*
Strategy 1. Selection within each of the three countries for each of its breeding goals
Strategy 2. Selection over the three countries for each of its breeding goals
Strategy 3. Selection over the three countries for one overall breeding goal
21
2.4.3 What is the exchange of red cattle bulls between Finland, Sweden and Norway today?
In order to get an overview of the exchange of bulls of today, the three breeding organisations gave
a data-file on the use of foreign bulls. We required that sires should have their first daughter group
of at least 20 daughters born 1990 or later and with EBV for kgP in both countries.
Finland had used 16 bulls from Sweden and 1 from Norway. Sweden had used 36 bulls from
Finland and 9 from Norway. Norway had used 15 bulls from Sweden and 4 from Finland.
2.4.4 Correlations between the breeding goals in the three countries
Correlation between breeding goals is one critical parameter to compare of single/multiple
objectives. When using the real values of the genetic (co)variances matrix (G) (Figure 1) and the
relative economic weights (1-6) the following correlations between the breeding goals were
calculated as
rT1T2 = v1´Gv2/ (v1´Gv1v2´Gv2)1/2 , where v1 contains the relative economic weights of country 1
and v2 contains the relative economic weights of country 2. The resulting correlations were:
Norway-Sweden:
0.798
Norway-Finland:
0.800
Sweden-Finland:
0.832
Hence, all correlations are around 0.8, which is the ‘breakeven point’ for when there is any
advantage of co-operating for two populations (see literature review). The highest correlation is
between Sweden and Finland, probably because they use the same relative economic weights for
kgP and mast.
2.4.5 Final conclusions
 The exchange of bulls between Norway, Sweden and Finland is small today. Especially the
exchange with Norway is small.When using the real values of the variances with the
artificial relative economic weights, the correlation between the breeding goals of Sweden
and Finland is highest (0.832), whereas the correlation between Norway and Sweden or
Finland was around 0.800. These correlations are at the ‘breakeven point’ for when there is
any advantage of co-operating for two populations, higher correlations indicating that the
breeding goals are the same and thus that the populations would gain in co-operating.
 There was only a small effect of the three different strategies on the profit of the schemes.
However, these results will be sensitive to some assumptions made in the study, e.g. the
number of selected sires, the relative economic weights, and the covariances used.
 At the start of a co-operation, a genetic lift can be expected, because the populations are still
unrelated and selection intensities can be high.
 When co-operating, a too high selection intensity might lead to reduced genetic variances in
the next generation, which in turn will reduce genetic gains.
22
2.5 References
Banos, G. and Smith, C. (1991) Selecting bulls across countries to maximise genetic improovemnt
in cattle. J. Anim. Breed. Genet. 108: 174-181
Bulmer M.G., The mathematical theory of quantitative genetics. Clarendon Press, Oxford, 1985.
Del Bosque Gonzalez, A.S. and Kinghorn, B.P. (1990) Implications of differing selection objectives
within open nucleus breeding schemes. In. Proc. Australian Association of Animal Breeding
and Genetics, Hamilton and Palmerston North, vol. 8, pp. 95-102
Goddard, M.E. (1992) Optimal effective size for the global population of black and white dairy
cattle. J. Dairy Sci. 75: 2902-2911
Hill, W.G. (1974) Prediction and evaluation of response to selection with overlapping generations.
Anim. Prod. 18: 117-139.
Howarth, J.M. (2001) Selection strategies to exploit diversity between economic breeding
objectives. PhD thesis, The University of New England, Australia
Meuwissen, T.H.E. and Sonesson, A.K. (1998) Maximizing the response of selection with a
predefined rate of inbreeding- overlapping generations. J. Anim. Sci. 76: 2575-2583
Meuwissen, T.H.E. (1998) Risk management and the definition of breeidng objectives. In. Proc. 6 th
WCGALP, vol XXV, pp. 347-354
Ollivier, L., Guéblez, R., Webb, A.J. and van der Steen, H.A.M. (1990) breeidng goals for
nationallya nd internationally operating pig breeder’s organisations. In. Proc 4th WCGALP,
vol. XV, pp. 383-394.
Robertson, A. (1959) The sampling variance of the genetic correlation coefficient. Biometrics 15:
469-485
Smith, C. (1986) Variety of breeding stocks for the production-marketing range and for flexibility
and uncertainty. In. Proc. 3rd WCGALP, Lincoln, vol X, pp. 14-20
Smith, C. and Banos, G. (1991) Selection within and across populations in livestock improvement.
J. Anim. Sci. 69: 2387-2394
23
3.0 Genotype by environment interaction
Erling Strandberg1 and Rebecka Kolmodin2, 1Dept. of Animal Breeding and Genetics, SLU, 75007
Uppsala, 2Swedish Red Cattle Breed Organisation, Örnsro, 532 94 Skara
3.1 Introduction
The ability to respond to changes in the environment is a vital characteristic of all organisms. This
ability is sometimes called phenotypic plasticity or environmental sensitivity. Differences in
environmental sensitivity between individuals result in genotype by environment interaction (GxE).
Strictly speaking GxE means that the difference between the phenotypic values of two genotypes is
not the same in two environments. If the difference changes sign between environments, there is reranking of genotypes. If the difference changes in size only, there is a scaling effect (Falconer and
Mackay, 1996; Kolmodin, 2003).
In Figure 1 these two situations are exemplified.
30
25
20
15
A
B
C
10
5
Phenotypic value
Phenotypic value
30
25
20
15
A
B
C
10
5
1
2
1
Environment
2
Environment
Figure 1. Example of GxE resulting in scaling (left) and re-ranking (right) for three genotypes.
3.2 Methods to study genotype by environment interaction
There are basically three different methods to describe the extent of GxE. For all methods,
observations on the same or related individuals in two or more different environments are needed to
study GxE. The common use of artificial insemination in dairy cattle makes it possible to compare
the performance of daughters of the same sires in different environments (de Jong, 1995).
1) Interaction term model.
In the first method the phenotypic value of an individual is simply described as the sum of the
genotypic value, the environmental value and the residual. The environmental value E could e.g. be
classification into herds, or herd production classes.
P = G + E + e [1]
When interaction between genotype and environment exists an interaction component, GxE, is
added to the equation:
P = G + E + GxE + e [2]
The phenotypic variance (  P2 ) of the observed phenotypes (P) can be derived from [2] as:
2
 P2   G2   E2   GE
  e2
[3]
24
assuming all covariances being zero.
2) Multiple trait model.
The second method used to describe GxE is based on phenotypic values in different environments
and genetic correlations (rg) between these. The phenotypic expression in the two environments is
seen as two separate traits and rg can be studied to see whether GxE exists. When rg between the
phenotypic values of the same genotype expressed in different environments is high, the phenotypic
expression is considered as the same trait in the different environments (Falconer and Mackay,
1996). In other words, if rg between the phenotypic expressions of the trait in two different
environments is equal or close to 1 there is no GxE (Robertson, 1959). When rg is low, the
phenotypic expressions in the different environments are not the same trait and this is an indication
of GxE (Falconer & Mackay, 1996). The genetic correlation (rg) can be estimated using a multiple
trait analysis based on grouping herds with similar production environments to clusters and treating
the observation from the different clusters as separate traits. GxE is indicated by low rg between
clusters (Falconer and Mackay, 1996).
3) Reaction norm model.
When the production environment can be described as a continuous variable, a third method called
the reaction norm model, is possible to use (de Jong, 1995). The phenotypic expression of a
genotype as a function of the environment is described by the reaction norm (Kolmodin, 2003). A
given difference of an environment can have a greater effect on one genotype than on another
(Falconer and Mackay, 1996). The reaction norm model has an advantage in describing the
environment on a continuous scale that the multiple trait model does not possess (Fikse et al.,
2003).
3.3 Does GxE exist?
For dairy cattle, GxE has been studied for various environmental factors. Between herd production
levels, feeding regimes or management systems within a country, or a group of neighbouring
countries, there is seldom reranking of genotypes (Cromie, 1999; Kolmodin et al., 2002; Boettcher
et al., 2003; Calus and Veerkamp, 2003). Between countries or regions that differ considerably, e.g.
in climate or management system, reranking of genotypes is more common. For example, the
genetic correlation between the same milk production trait evaluated in any country in Western
Europe, the USA, or Canada (the northern hemisphere group) is high (0.85-0.9), while the
correlation between the northern hemisphere group and New Zealand and Australia is lower (0.750.84)(Emanuelson et al., 1999), indicating that reranking occurs. Low genetic correlations have also
been estimated between milk yield evaluated in Mexico and the US (0.63) (Cienfuegos-Rivas et al.,
1999), milk yield in the UK and Kenya (0.49) (Ojango and Pollott, 2002), and longevity in Canada
and New Zealand (-0.07—0.21) (Mwansa and Peterson, 1998). It was also found that there was
GxE between environments with different heat-humidity indexes, with a lowest correlation of
around 0.6 between low and high stress environments (Ravagnolo and Misztal, 2000).
The phenotypic and genetic variances between animals are often smaller in low than in high yield
environments, i.e. there is a scaling effect of GxE. As genetic progress is a function of the genetic
variance (Falconer and Mackay, 1996), the expected response to selection is smaller in low than in
high yield environments. For example, the expected response in Kenya, a low yield environment, to
selection based on UK breeding values is only 44 % of the expected response in the UK, a high
yield environment (Ojango and Pollott, 2002). Thus, investments in high merit semen from bulls
evaluated in high yield environments may not pay off for farmers in low yield environments.
25
3.4 Does GxE exist in Nordic countries?
In the following we will summarize results on GxE in Nordic dairy cattle. Some results are based on
estimated genetic correlations between countries as currently used by Interbull (www.interbull.org,
November, 2004), and some results are based on research done on Nordic or Swedish dairy cattle.
Results for Nordic Red cattle are given, unless otherwise stated.
Milk production traits
The genetic correlations between countries for protein production are 0.86-0.92 (Interbull) with
similar values for amount of milk and fat as well. Kolmodin et al. (2002), using a reaction norm
approach on Nordic Red cattle, showed no GxE for protein yield between average and high protein
yielding herds but a rank correlation of 0.93 between low and average yielding herds.They also
found a rank correlation of down to 0.9 between average and very large herds, but not between
herds in the normal range of size.
Udder health
Genetic correlations used by Interbull for somatic cell count (SCC) were around 0.9-0.95 between
the three countries (for Norway, no information was available as SCC is not used for genetic
evaluation). For clinical mastitis, the values were somewhat lower (0.84-0.91). Correlations with
Norway were even lower, 0.68-0.75.
Jansson (2004) found GxE for SCC using both a multiple-trait approach and a reaction norm
approach, between herds with low and high average SCC levels. In the multiple-trait approach, the
genetic correlation was 0.8-0.84 between low and high quartile SCC herds. There was also an
indication of GxE for mastitis between low and high SCC herds (0.89, n.s.).
Fertility
Svendsen et al. (2001) estimated genetic correlations between countries for non-return rate (number
of inseminations for Sweden) of 0.76-0.95, the highest between Denmark and Finland and the
lowest between Finland and Sweden. Kolmodin et al. (2002) found no GxE for calving to last
insemination (CLI) between herds with average and long CLI but some indication for herds with
short average CLI.
Calving performance
For the direct (sire of calf) effect of calving ease, genetic correlations were above 0.88 among all
countries except Norway, for which correlations with other countries ranged from 0.38-0.76. A
similar situation existed for stillbirths, where correlations were 0.36-0.57 when Norway was
involved but higher (0.75-0.93) among other countries (Svendsen et al., 2001).
For the maternal effect (maternal grandsire effect) of calving ease correlations were above 0.75
among all countries, including Norway. For stillbirths correlations were generally between 0.65 and
0.8, but with a high exception of 0.95 between Denmark and Finland and a low value of 0.44
between Sweden and Norway (Svendsen et al., 2001).
Longevity
Petersson et al. (2005) found a genetic correlation for longevity between herds with short or long
average longevityof around 0.74. They also found an indication of GxE for longevity between herds
with average or low 305-day protein yield (correlation about 0.77) but less so between herds with
26
average and high yielding herds (0.9). For Holstein the genetic correlation between Denmark and
Finland for direct longevity (i.e. not using indicator traits of longevity but only length of productive
life by itself) 0.9.
Summary
For production traits there seems to be little or no GxE both between average levels in the Nordic
countries as indicated by the genetic correlation between countries, and between various herd
environments with respect to production level. Therefore, using the same breeding value for all
environments would not be expected to give too wrong results.
For SCC the correlations also indicate little GxE between the three countries where this information
was available. For clinical mastitis the correlations were slightly lower, perhaps owing to the lower
accuracy in estimating breeding values for this trait. For these traits there may, however, be withincountry GxE that is not visible on the across-country scale.
For fertility traits, there is currently work on harmonization of trait definitions which may help in
increasing the correlation among countries somewhat.
For calving performance traits, correlations were not very strong, except possibly for direct calving
ease among Denmark, Finland, and Sweden. For the maternal effects it is not quite clear whether
the breeding values are the maternal grandsire solution unadjusted or adjusted for direct genetic
effects.
For longevity rather little is known across countries but there seems to be some GxE within the
Swedish population with respect to herd average longevity and production.
The genetic correlations may be underestimated owing to weak genetic connectedness between
countries. This may be especially so for correlations involving Norway. Other reasons for
underestimation can be that different definitions, edits, and genetic evaluation methods are used in
the different countries. The on-going work in the joint Nordic genetic evaluation (NAV) regarding
harmonization can be expected to decrease these effects in the future. However, the current
assumption in NAV of unity genetic correlation between countries will cover up any true GxE than
might exist.
3.5 Consequences for selection
The simplest consequence of GxE is that we select the wrong animals and that we miss out in
performance in those environments not similar to that which the genetic evaluation assumes. How
much one would lose is a complex question, considering that one would have to compare two (or
more) breeding programs, with corresponding decrease in population size, incresed inbreeding and
risk in outcome, with one breeding program applied to a larger population. Some of these issues are
addressed in the study by Sonesson in this publication.
One question that often arises is how much reranking occurs with a given genetic correlation. In
Figure 2 some examples are given where a certain correlation between true and predicted breeding
values is assumed (or between two breeding values predicted from two methods or environments).
We rank on “true” breeding value and select the top fraction. Then we rank on predicted breeding
value and select the top fraction. The graph shows how large a proportion of those in the top
fraction for predicted breeding values are also in the top fraction for the true breeding values. If this
27
proportion is 100% then we would select exactly the same individuals in the top fraction on both
criteria.
Figure 2 shows that if the genetic correlation is very high, we more or less correctly select the top
fraction, however, even with a correlation of 0.95 only about 80% of the top 10% are correctly
selected. With a correlation of 0.9 this proportion decreases to below 70%. With larger top fraction
the correct proportion increases (and obviously goes to unity for all correlation as the top fraction
goes to 100%).
Prop. correctly among top fraction .
100%
90%
80%
70%
Correlation
60%
0.99
0.95
0.9
0.8
0.7
50%
40%
10
20
30
40
50
Top fraction, %
Figure 2. Proportion correctly ranked on predicted breeding value for various correlations between
true and predicted breeding values for various top fractions. In total 100 bulls available, 1000
replicates.
3.6 References
Boettcher, P., J. Fatehi, and M. Schutz. 2003. Genotype x environment interactions in conventional
versus pasture-based dairies in Canada. J. Dairy Sci. 86:383-389.
Calus, M. and R. F. Veerkamp. 2003. Estimation of environmental sensitivity of genetic merit for
milk production traits using a random regression model. J. Dairy Sci. 86:3756-3764.
Cienfuegos-Rivas, E. G., P. A. Oltenacu, R. W. Blake, S. Schwager, H. Castillo-Juarez, and F.
Ruiz. 1999. Interaction between milk yield of Holstein cows in Mexico and the United
States. J. Dairy Sci. 82:2218-2223.
Cromie, A. 1999. Genotype by environment interaction for milk production traits in Holstein
Friesian dairy cattle in Ireland. PhD-thesis, Queens University of Belfast, Ireland,
de Jong, G. 1995. Phenotypic plasticity as a product of selection in a variable environment. Am.
Nat. 145:493-512.
Emanuelson, U., G. Banos, and J. Philipsson. 1999. Interbull Centre Report. Interbull Bulletin 22:16.
28
Falconer, D. S. and T. F. C. Mackay. 1996. Introduction to quantitative genetics, 4th ed. Longman
Group, Essex.
Fikse, W. F., R. Rekaya, and K. Weigel. 2003. Assessment of environmental descriptors for
studying genotype by environment interaction. Livestock Prod. Sci. 82:223-231.
Jansson, K. 2004. Genotype by environment interaction for udder health in Swedish Holstein cows.
MSc-thesis, Dept of Animal Breeding and Genetics, SLU,
Kolmodin, R. 2003. Reaction norms for the study of genotype by environment interaction in animal
breeding. PhD-thesis, Agraria 437, Swedish University of Agricultural Sciences, Uppsala.
Kolmodin, R., E. Strandberg, P. Madsen, J. Jensen, and H. Jorjani. 2002. Genotype by environment
interaction in Nordic dairy cattle studied by use of reaction norms. Acta Agric. Scand.
Section A (Animals) 52:11-24.
Mwansa, P. and R. Peterson. 1998. Estimates of GxE effects for longevity among daughters of
Canadian and New Zealand sires in Canadian and New Zealand dairy herds. Interbull
Bulletin 17:110-114.
Ojango, J. and G. Pollott. 2002. The relationship between Holstein bull breeding values for milk
yield derived in both the UK and Kenya. Livestock Prod. Sci. 74:1-12.
Petersson, K.-J., R. Kolmodin, and E. Strandberg. 2005. Genotype by Environment Interaction for
Productive Life in Swedish Red and White Dairy Cattle. Acta Agric. Scand. Section A
(Animals) submitted.
Ravagnolo, O. and I. Misztal. 2000. Genetic component of heat stress in dairy cattle, parameter
estimation. J. Dairy Sci. 83:2126-2130.
Robertson, A. 1959. The sampling variance of the genetic correlation coefficient. Biometrics
15:469-485.
Svendsen, M., T. Mark, U. Nielsen, J. Pösö, and M. Gundel. 2001. Genetic relationships among
functional traits in the Nordic Ayrshire populations. Proceeding of the 2001 Interbull
meeting, August 30-31, Budapest, Hungary. Interbull Bulletin 27:60-63.
29
4.0 Breeding objectives and optimum use of sires in the Nordic red populations
Erling Sehested, GENO
4.1 Introduction
Genetic gain in traits of interest is a function of breeding objectives, genetic parameters and design
of breeding program. In general, genetic parameters can be regarded as constants, and thereby is not
a variable component in the process of optimising the breeding plan. Definition of the breeding
objective in terms of aggregate genotype (economic values of the traits) has traditionally been given
much attention. Several approaches have been proposed. Examples are using partial derivatives of
profit functions, desired gains and multiple regression analysis of observed profit from herd data.
When the breeding objective is defined, the design of the breeding plan should be optimised with
respect to this objective. The term to optimise is maximum gain in aggregate genotype with
minimum costs. The most important aspects of a breeding plan are number of test bulls per year,
fraction of cows inseminated with test bulls, number of elite bulls, number of bull sires, selection
intensity of bull dams and fraction of bull dams being heifers. Given the number of cows in the
population, progeny group size is a function of number of test bulls per year and fraction of cows
inseminated with test bulls. The purpose of this study was to investigate effects of these two
variables on genetic gain in different sets of aggregate genotype.
4.2 Methods
Breeding goals and characteristics of the breeding populations of the 4 Nordic red populations were
approximately as shown in tables 1 and 2 around 2000. These assumptions together with assumed
genetic parameters as shown in table 3 were used in a deterministic simulation to compute expected
gain in the aggregate genotype. Selection intensities were corrected for deviations from normal
distribution due to selection in finite (small) sample sizes. Eventually expected genetic changes with
varying levels of number of test bulls per year and fraction of cows inseminated with test bulls were
computed according to:
T 
iSS SS  iSD SD  i DS DS  i DD DD
LSS  LSD  LDS  LDD
where: i

SS
SD
DS
DD
L
= selection intensity
= Standard deviation on breeding values
= Bull sire
= Cow sire
= Bull dam
= Cow dam
= Generation interval
Selection and culling effects of cow-dams were ignored (iDD=0)
4.3 Results and discussion
Results for the four populations are shown in figures 1-4. For all populations the distance from
current to optimal combination of the two variables is rather large. In general all populations should
test more bulls per year and inseminate a larger proportion of the cows with test bulls. Using more
test bulls can be expensive. Substantial increase of the proportion of cows inseminated with test
bulls will probably not be accepted by the farmers. Therefore it is important to investigate the net
effect of changing. For NRF, SRB and RDM this effect is only marginal, from .8 to 1.7% increase
in genetic gain. Hence for these breeds a change does not seem to be worthwhile. For FAY,
30
however, moving from current to optimal will give 6.2% increase in genetic gain. On the other
hand, increasing number of test bulls per year from 150 to 415 is far from realistic.
The optimum combination differs between the populations, NRF and FAY being somewhat extreme
while SRB and RDM are intermediate. The breeding goal in NRF is characterized by heavy weights
on low heritable traits such as disease resistance and fertility. The optimum design for such
breeding goals is to have large progeny groups in order to achieve high accuracies for these traits. In
FAY highly heritable traits such as milk production and conformation dominates. Optimum for
FAY is therefore a situation with many test bulls and small progeny groups. This gives high
selection intensity and acceptable accuracies.
Table 1. Alternative breeding goals
Trait
Kg protein
Protein %
Mastitis
Other diseases
Udder
Body/leg conf
Beef
Milking ease
Temperament
Non-return
Calving ease
Stillbirth
NRF
21
0
21
3
11
6
12
0
4
14
4
4
SRB
31
0
16
5
16
6
7
0
0
12
3,5
3,5
RDM
32
0
13
0
15
6
6
9
2
10
7
0
FAY
41,7
12,5
12,5
0
12,5
0
0
0
0
20,8
0
0
Table 2. Characteristics of breeding population
NRF
No cows
290 000
% Test bulls
40
No test bulls
125
No elite bulls
10
No bull sires
5
Bull dam selection, % 5
% Heifers as bull dams 34
Progeny group sizes
150-300
SRB
193 000
30
110
5
5
5
34
50-130
RDM
59 000
40
70
5
5
5
34
50-100
FAY
208 000
45
150
15
5
5
34
50-200
Table 3. Assumed genetic parameters
Heritability
Protein
Mastitis
Prot. Mast Udder Beef Milk.
speed
,20 ,03 ,10
,15 ,20
-,31 ,17
,16 ,05
,09
-,09 -,01
Legs Temp Fert Calv.
ease
,10 ,10
,05 ,05
-,12 ,14
-,10 -,03
,11 ,05
,01 -,01
Stillb Other
dis.
,05 ,02
,11 -,25
,02 ,27
P%
,30
,24
,19
31
Udder
Beef
Milk. Speed
Legs
Temperament
Fertility
Calving Ease
Stillbirth
Other diseases
-,05 ,25
,01
,15 ,26
,37 ,03
-,19 ,25
,06
,12
-,21
-,02
-,13
,08
-,07
,02
-,27
,14
-,06
-,03
-,07
-,12
-,06
-,13
,00
,01
,61
,11
,17
-,20
,36
-,18
-,04
-,03
,00
,01
,01
-,08
,00
,01
,01
-,11
-,07
,19
NRF
80
75
70
65
55
50
45
% test bulls
60
40
35
30
25
90
20
100 110 120 130 140 150 160 170 180 190 200 210 220 230 240 250
100
No test bulls
100,8
Figure 1.Optimal design, NRF
32
SRB
80
75
70
65
55
50
45
% test bulls
60
40
35
30
25
70
80
20
90 100 110 120 130 140 150 160 170 180 190 200 210 220 230 240 250
100
No test bulls
101,4
Figure 2. Optimal design, SRB
RDM
80
75
70
65
55
50
45
% test bulls
60
40
35
30
25
50
60
70
100
80
20
90 100 110 120 130 140 150 160 170 180 190 200 210 220 230 240 250
101,7
No test bulls
Figur 3. Optimal design, RDM
33
FAY
80
75
70
65
55
50
45
% test bulls
60
40
35
30
25
100
No test bulls
420
400
380
360
340
320
300
280
260
240
220
200
180
160
140
120
100
20
106,2
Figure 4. Optimal design, FAY
34
5.0 Use of EVA in Dairy Cattle Breeding Schemes
Morten Kargo Sørensen, Anders Christian Sørensen, Peer Berg, Danish Institute of Agricultural
Sciences, and Søren Borchersen, A. I. Centre Dansire
5.1 Summary
EVA is a programme, which among the potential parents of next group of offspring, optimises the
number of offspring per breeding animal, after which the programme will suggest a mating scheme
for the selected animals. The average breeding value is calculated for the selected matings as well
as the average relationship between the matings. The information used is pedigrees going back as
far as the pedigree can be traced and estimated breeding values of the parents. The selection of
animals depends on how much weight is being attached to avoid future inbreeding. As future
inbreeding is depending on the relationship among animals in the next generation EVA analyses the
relationship between the selected animals as well as the relationship between the selected animals
and the animals used in previous years. Therefore, it is important that the animals previously used in
the breeding scheme are included in the analysis i.e. the present young bulls, waiting bulls as well
as the planned young bulls (contracts) must be included in the analysis.
EVA differs from the computer inseminating schemes in that they only consider the inbreeding of
the calf in relation to the insemination of the actual cow or heifer, while EVA considers the total
population.
5.2 Inbreeding – Cause and Consequences
Because of inbreeding depression, inbreeding weakens genetic improvement in general and genetic
improvement of health, fertility and the productivity of the dairy cows in particular. However, in the
long term the largest problem of inbreeding is the expected reduction of future genetic gain because
of reduced genetic variation.
The breeding program including insemination, breeding value estimation and selection of the best
animals for breeding has been so successful that we are now facing a problem of large rates of
inbreeding if nothing is being done – and that is so even though the breeds are numerous in animals,
but they are small in effective number of breeding animals.
The increase of inbreeding of the production animals reduces the productivity, health and fertility
of the animals. Research results from United States shows that a 1% increase of inbreeding within
Holstein Friesian resulted in a loss of 37 kg milk, 1.2 kg fat, 1.2 kg protein per lactation, an
increasing age at first lactation of 0.4 days and a prolonged calving interval of 0.3 days as well as 6
days shorter productive life (Smith et al., 1998). Danish results show an increased incidence of
mastitis with inbreeding (Sørensen et al. 2005a). The conclusion of all research indicates that
inbreeding reduces the economic results in a herd.
At breed level inbreeding is expected to reduce the genetic variation and thus the genetic gain in
the long term. Calculations indicate that genetic gain per time unit in an efficient breeding scheme
is reduced by about 20% over a period of 20 years only because of the increased inbreeding, if
inbreeding has not been taken into account (Sørensen,1999). Therefore, inbreeding has now gained
more attention in the breeding program of the dairy breeds than previously.
35
For the time being the degree of inbreeding is not alarming. With the existing pedigree information
in the Danish cattle databases the degree of inbreeding in the potential Danish Holstein and Jersey
breeding animals is calculated (Sørensen et al., 2005b) to be in the interval of 3-4%, which is a
little lower than for the corresponding breeds in the United States, while it is about 1% for RDM.
The rate of the inbreeding is, however, alarming - >1% generation. That is why Dansire is giving
this problem full attention.
The increase of inbreeding can be kept under control by assuring that the future animals are not too
closely related. The control of relationship between members of the breeds in the long term
demands a consideration of the relationship between the young bulls produced for testing and those
used in the previous years. The EVA Programme is capable of weighing up the genetic gain and
future increase of inbreeding. It has now been taken into use in the dairy cattle association, Dansire.
In the long term it will likely mean that more sires of sons and bull dams are used.
5.2.1 Balance between Genetic Gain and Relationship
B
A
Inbre e ding / Re lationship
Figure 1 Schematic connection between
average relationship and genetic gain
A: Only focus on genetic gain
B: Focus on both genetic gain and the
average relationship in next generation.
B
A
Total genetic gain
Genetic gain
If the focus is only on genetic gain when selecting sires of sons and bull dams it will lead to a
major genetic gain in the next generation and also an increased relationship between the selected
young bulls. A closer relationship will result in a higher degree of inbreeding in future generations.
If only a smaller increase of degree of relationship can be accepted among the selected bulls the
genetic gain will be reduced in the next generation. Figure 1 shows a schematic relation between
the relationship and the maximum genetic gain in the next generation.
Numbe r of ge ne ration
Figure 2 The genetic gain of the two
different selection strategies in Figure 1
36
In the short term a little of the genetic gain is lost when considering the relationship in the
next generation. e.g. by choosing strategy B in stead of strategy A. By choosing strategy B
more sires of sons will be used leading to a reduced increase in average relationships,
whereby a higher degree of genetic variation is maintained. In the long term it will pay off to
choose strategy B rather than strategy A, as can be seen from Figure 2. Add to this that a
population following strategy B will suffer from a smaller degree of inbreeding depression
than the population following strategy A. The time where the two lines are intersecting is
dependent on where on the curve in Figure 1 B is placed – the longer to the left B has been
placed the later the curves will intersect. That means that the more weight is being attached to
the relationship, the longer time will pass to obtain the genetic gain, which is lost in the short
term.
5.2.2 Programmes to Handle Genetic Gain vs. Inbreeding
The above problems are not unique for cattle breeding – but can be found for all species. For
species with shorter generation intervals the inbreeding increase will even find place at a more
rapid pace. Therefore, there has been a major focus on developing programmes adressing this
issue. That is also the case in Denmark where Peer Berg has developed the computer
programme EVA (EVolutionary Algorithms). Also a Dutch developed programme (GenCont)
for handling these problems developed by Theo Meuwissen, now Norway, is available.
EVA is based on the method described by Grundy et al. (2000), where GenCont is based on
the method described by Meuwissen & Sonesson (1998). EVA is different because of its
ability to limit the inbreeding increase both per year and per generation. In a comparison of
the methods (Sonesson et al. 2000) small deviations were found among the methods, and it
was concluded that the method developed by Meuwissen and Sonesson cannot be further
developed to limit the inbreeding increase per generation.
There are differences in principles of the calculation methods used in the optimisation. In
GenCont an iterative method has been used, which can give negative genetic contributions,
and therefore it is necessary that restrictions are made iteratively on animals getting negative
contributions. In return the method is deterministic, which means that it is quick and presents
the same result every time. EVA uses a calculation method, which ensures the genetic
contributions to be positive, but as the method is stochastic the result might vary a little
depending on convergence of the algorithm. However, this has not turned out to be a problem
in practice. In addition EVA suggests a mating list given the optimum genetic contributions
based on minimising the degree of inbreeding in the next generation. Both methods are very
computer intensive, both in terms of memory and CPU time. There has not been made a direct
practical comparison, but it is expected that EVA will be the best to handle large data sets as it
is not based on inverting matrices as is the case with GenCont.
During the summer EVA has been adapted to a user-friendly Windows based version of the
programme.
37
5.2.3 ”Economic” Weighting of Genetic Gain and Relationship
EVA is capable of maximising the genetic gain with a control of the inbreeding increase
simultaneously. As mentioned above the programme is a further development of methods
developed by John Woolliams, Robin Thompson and Brian Grundy, all Edinburgh, where it
was shown that both genetic gain and degree of inbreeding can be described as functions of
genetic contributions. Optimising genetic contribution for each sire and dam thus allows for a
simultaneous consideration of genetic gain and inbreeding. The programme put weight on the
average relationship among the selected combinations and the previously used animals in the
breeding scheme and to the average breeding value of the selected animals. In principle a
variable S is generated
S = b1 * average breeding value - b2 *average relationship
where b1 is the economic value of a breeding value unit and b2 is the cost of inbreeding.
Depending of the weights chosen, EVA finds the particular use of the potential parents,
which optimise the variable S and thus the breeding scheme. As the average relationship of a
particular candidate to the group of selected candidates changes depending on the set of
selected candidates, the S value also change. Therefore complex calculations are needed. The
selected matings are printed on a mating list.
It is difficult to specify the weights b1 and b2, and it depends on various subjective estimates.
The literature gives, however, methods describing functions to specify these weights. The
value of the genetic gain, the chosen time horizon as well as the cost of the inbreeding
depression all form part of these functions. Again it is a subjective estimate, which time
horizon is to be considered. The longer time horizon, the lower weight on the genetic gain
and the higher weight on relationship. The reason for that is that the genetic variation will be
reduced with inbreeding, and the longer time horizon considered the more important it is to
keep the inbreeding increase under control. Another possibility is to choose the weights in
such a way that a maximum rate of inbreeding per generation will not be exceeded. The
literature gives arguments that the rate of inbreeding should not exceed 0.5 to 1% per
generation, e.g. Bijma, 2000.
Another and more practical access to the problem is that the dairy cattle association cannot
accept to reduce the genetic gain for the next generation of young bulls too much. This is so
because there is a limit to how much genetic gain they are willing to loose in the short term to
get more back in the long term. Considering it over a longer period of time it might be
profitable to give up more genetic gain in the short term against having more genetic gain in
the long term. To do this it is essential to have the acceptance of the users. Without this, there
is a risk that they might choose another supplier, who would provide a larger genetic gain just
now – a reaction that is easy to understand if farmers are economically strained or are
supposed to be out of business the day the genetic gain would pay off.
5.2.4 Implementation
EVA is used as a facility in Dansire’s breeding programme. The results from the programme
are not being used uncritically but more as a pointer in the work in optimising the selection.
As can be seen from the two following examples EVA now gives guidelines for which bulls
38
should be used and in which scale to keep a rational and acceptable genetic gain and rate of
inbreeding. We would, however, like to keep improving the applications, while we continue
to develop the programme and the procedures used in connection with the calculations by
making them more efficient.
5.2.5 The Use of EVA for Red Dane
When selecting young bulls more or less weight might be attached to the relationship between
the selected bulls and the bulls previously used in the mating scheme for Red Danes (RDM).
The more weight being attached to the relationship the lesser degree of inbreeding will be the
result in the future and the larger genetic gain in the long term. Unfortunately, it will cost
genetic gain in the short term. The table below shows the distribution of sires of the young
bulls selected depending on no weight, a moderate weight or a large weight being attached to
relationship. The 30 bulls have been selected among 90 bull calves after cows on bull dam
level in September 2004.
39
Table 1 Distribution of sire of sons for 30 RDM young bulls depending on how much weight
has been attached to the relationship
Sire of Sons
FYN Aks
Fyn Cent
Jobladin
Syd Hallas
Vest Andy
Micmac
Fyn Cima
SYD Garant
T Hejnsvig
T Moberg
VEST Bæk
ØDA Best
Number of young bulls
No weight attached Moderate weight
to relationship
attached to
relationship
4
4
2
1
3
1
4
7
7
4
5
1
4
1
5
5
2
Great weight
attached to
relationship
4
2
1
1
1
6
1
4
1
2
4
3
The table shows that the sires of the selected bulls are distributed more evenly on more sires
the more weight has been attached to the relationship.
5.2.6 The use of EVA for Danish Holstein
By using EVA for Danish Holstein (SDM-DH), 150 (it is of course possible to vary this
number) mating combinations are selected. The mating combinations would vary depending
on the ratio of the weights on genetic gain and the average relationship in next generation.
Out of these mating combinations the distribution of sires of sons can be calculated, as well
as the distribution of sires of the selected cows.
Table 2 shows the distribution of sire of sons and sire of dams at the varying weight on
relationship
40
Table 2 The distribution (%) of sires and maternal grandsires to the selected combinations at
different weight attached to relationship 0 indicates that all weight has been attached to
genetic gain, at increased number of +’es the weight is being increased on relationship. ++++
indicates that weight has only been attached to relationship. Furthermore, the table shows the
average total merit index and the average relationship. The calculations have been made using
predicted breeding values from 15-4 2004.
0
+
++
+++
++++
Av. Ebv Progeny 115,95
115,55
114,12
113,65
113,20
Average
0,118
0,110
0,109
0,109
relationship
Distribution of
sires*
Bob
117
67
45
Laudan 116
33
Bjørn
111
1
6
Dynasty 112
1
3
O. Justi 116
55
35
23
11
Chuck
114
16
13
7
Okendo 111
2
Puck
110
7
15
Chassee 113
2
3
3
Boliver 109
47
52
53
Other
distribution of
sires of dams**
Farmer (16)
4
5
8
9
9
Huxley (34)
2
4
4
5
7
Lord Lily (10)
3
1
Marshall (21)
5
5
3
1
Funkis (52)
35
35
20
17
8
Lambada (11)
2
2
1
1
TB Steven (13)
3
1
1
Hesne (34)
3
5
10
12
15
Bojer (14)
3
4
1
1
Others (150)
39
39
53
55
58
* After the name the bull’s total merit index has been indicated in brackets.
**
The number of potential bull mothers after which each sire of dam has been
indicated in brackets.
The table shows that the more weight is being attached to relationship the more the sires of
sons and the maternal grand sires are used and in more even proportions. If great weight has
been attached to the genetic gain almost only sires of sons with a high total merit-index have
been selected, whereas the total merit-index of the sire of sons is of no importance, if all
weight has been attached to the average relationship. When all weight has been attached to
relationship, the bulls are selected with the smallest degree of relationship with the population
in general. The population in this case is the animals in the pedigree file. In Table 3 the
average relationship between some sires of sons and the animals in the population distributed
on year of birth is shown.
41
Table 3 The average relationship between some sire of sons and animals in the pedigree file
distributed on year of birth
Bull sire
Sire
Maternal
2004
2003
2002
2001
grand sire
RGK Bob
Lukas
Marconi
12.3
10.4
10.5
8.8
Laudan
Lukas
Raider
12.5
10.5
10.2
9.2
Chuck
Manfred
E. Elton
7.4
6.5
7.1
6.7
O Justi
Novalis
E Lutz
8.8
6.2
6.9
6.7
V Exces
Luxemburg Leadman
9.5
8.6
8.8
9.1
V Elo
Lukas
Luke
13.6
10.5
10.6
9.1
Chassee
Novalis
Jabot
8,5
6.5
7.5
6.6
Dynasty
Lucky Leo Luke
7.6
6.8
7.5
7.3
VAR Elvis
Luxemburg Dannix
9.0
7.8
8.3
8.6
Okendo
Winchester Mountain
7.7
8.1
6.8
6.8
RGK Bjørn
Mattie G
Luke
8.2
6.9
7.7
6.2
Puck
Patron
Merrill
7.4
7.0
7.1
6.2
Tresor
C M Val
Fatal
8.7
7.7
8.0
8.1
P Bandores
C M Val
Hunter
9.4
8.6
9.3
8.8
E. Boliver
Amel
P Mathie
3.7
3.7
4
3.7
When estimating these relationships it is important to draw the attention to the fact that some
animals have lacking pedigree information with only two complete generations of pedigree.
generation. It may reduce the relationship to the population considerably. In general it can be
seen, however, that sires of sons with a high degree of average relationship with the
population as e.g. RGK Bob, V Elo or Laudan are not used at all as sires of sons (Table 2)
when the weight is only attached to relationship.
5.3 References
Bijma, P. 2000. Long-term genetic contributions, prediction of rates of inbreeding and genetic
gain in selected populations, Ph.D. Thesis, Wageningen, Holland.
Grundy, B., Villanueva, B. & Woolliams, J.A., 2000. Dynamic selection for maximizing
response with constrained inbreeding in schemes with overlapping generations. Anim.
Sci. 70:373-3832.
Meuwissen, T.H.E. & Sonneson, A.K., 1998. Maximizing the response of selection with a
predefined rate of inbreeding: overlapping generations. J. Anim. Sci. 76:2575-2583.
Smith, L.A., Cassell, B.G. & Pearson, R.E. 1998. The effects of inbreeding on the lifetime
performance of dairy cattle. J. Dairy Sci. 81: 2729-2737.
Sonesson, A.K., Grundy, B., Woolliams, J.A. & Meuwissen, T.H.E., 2000. Selection with
control of inbreeding in populations with overlapping generations - a comparison of
methods. Anim. Sci. 70:1-8.
Sørensen, A.C., Madsen, P., Sørensen, M.K., Berg, P. 2005a. Danish Holstein Show Inbreeding
Depression for Udder Health. In preparation.
Sørensen, A.C., Sørensen, M.K., Berg, P. 2005b. Inbreeding in Danish Dairy Cattle Breeds. J.
Dairy Sci. Accepted.
Sørensen, M.K. (1999). Stokastisk simulering af avlsplaner for malkekvæg. DJF, Rapport nr.
13, 183 pp.
42
6.0 New techniques in dairy cattle breeding schemes
6.1 Use of Marker Assisted Selection in Nordic Breeding Schemes
Most traits in animal breeding are influenced by both genotype and environment. Traditional
quantitative genetics is based on the infinitesimal model, assuming that a trait is controlled by
an infinite number of genes, each with a small effect. The additive effect of these genes
determines the total genetic effect.
The infinitesimal model has proven to work and the success of modern dairy cattle breeding is
one proof. The knowlegde of genetic regulation has, however, increased, and today it is well
known that most traits are regulated by a combination of genes with large effect and genes
with small effect. In 1990, designs to identify Quantitative Trait Loci (QTL), or single loci
with a large influence on a trait, was suggested for cattle (Weller et al., 1990). These designs,
the Daughter Design and the Grand Daughter Design, have been used to identify QTL
affecting various traits, such as milk yield, milk composition, various conformation traits, and
funcitonal traits (e.g. Freyer et al., 2003; Ron et al., 2004; Schrooten et al., 2000;Van Tassel et
al., 2004).
Today, genetic evaluation in dairy cattle breeding is based mainly on progeny testing, and it is
generally accepted that it is possible to obtain genetic progress for any heritable trait in a
progeny testing scheme. The limitations are that the traits are reliably recorded, bulls are
progeny tested with a sufficient number of daughters to gain high accuracy on the index, and
the trait is given sufficient weighting in the net merit index.
Although it is possible to select successfully for most traits in a progeny testing scheme, there
are some challenges: most of the economic important traits (e.g. milk production, daughter
fertility, mastitis resistance) can only be measured on females after first calving. With the
long generation interval in cattle, sires will be 5-6 years old before genetic proofs are
available, and the elite sires can be selected. Progeny testing programs are also expensive to
run: bulls are kept alive until they get their proofs at 5-6 years of age, and then only a few
percent of the bulls are selected to be used in the breeding program as elite sires. To obtain
maximum genetic improvement in a population, the best sires must be used to a maximum
(with constrains to avoid inbreeding). However, in a progeny testing scheme, a certain amount
of the cows have to be bred by young bulls to produce daughters for progeny testing.
If it was possible to select elite sires as young calves based on genetic markers linked to
favourable alleles of a gene, or on the gene itself, the generation interval for elite sires would
be dramatically reduced, and progeny testing would be redundant. The costs of running a
breeding scheme would then be represented by the cost of genotyping a sufficient number of
bulls to find the best sires for the next generation, and to produce and distribute semen from
these sires. It would also be possible to use the best elite sires to breed all cows, resulting in
maximum genetic progress in the population. This approach, however, requires that
markers/genes for all traits in the breeding goal are known, and that breeding values can be
estimated with high accuracy based on this information.
Based on the challenges in the progeny testing scheme, several studies have suggested
implementation of markers and/or genes in breeding schemes, where sires are selected based
on genetic markers (Marker Assisted Selection; MAS) in addition to, or as a substitute for
43
progeny testing schemes. Various schemes have been suggested, the two major being withinfamily selection and MAS in BLUP evaluations. The BLUP based evaluations require
genotypes on all animals (Fernando and Grossman, 1989), which would represent a major
cost. In within family MAS conventional breeding value estimation (progeny testing) is
combined with MAS within families to select sons carrying the favourable QTL-allele (e.g.
Spelman and Garric, 1998).
To our knowledge, MAS has not so far been implemented for sufficient many generations to
study long-term effect in dairy cattle breeding. However, various simulation studies have
investigated the effect of MAS on genetic gain (e.g. Abdel-Azim and Freeman 2002;
Fernando and Grossman 1989; Meuwissen and Van Arendonk 1992; Spelman and Garrick
1998; Stella et al. 2002). In general, results show that the superiority in genetic gain is large
for the first generations, then the superiority decline as the QTLs reach fixation in the
population (e.g. Abdel-Azim and Freeman 2002). Simulation results show highest genetic
gain when MAS is included in a scheme where all animals are genotyped, for example in a
closed nucleus system (e.g. Fernando and Grossman 1989); when the QTL explaines a large
proportion of the genetic variance; when the position of the QTL is determined by high
accuracy; and when the trait is rare or have a low heritability.
Although MAS might be a useful supplement in dairy breeding schemes, this technique has
several challenges. QTL/genes influencing important traits need to be identified, and the
position of the QTL has to be accurate to avoid the wrong selection decision due to cross-over
between the marker and the QTL. At the moment, QTL has been found for a limited number
of traits included in the Nordic Red breeding schemes. Another limiting factor is the price of
genotyping and the number of available markers/SNPs. The bovine genome is being
sequenced at the moment (http://www.genome.gov). The sequencing will provide important
knowledge on the genes on the bovine genome, and also provide important genetic markers
(SNPs), however, these genes and SNPs have to be validated in the Nordic Red populations,
and linked to important traits before this information could be implemented in the breeding
scheme.
The breeding schemes in the Nordic countries have a special position worldwide because they
also include functional traits with low heritability in the net merit index. As mentioned, traits
with low heritabilities are challenging to include in progeny testing schemes. Good recording
systems and large daughter groups, as well as sufficient weight in the net merit index, are
necessary to obtain genetic progress for these traits. In the Nordic countries, functional traits
with low heritability have been included in the breeding schemes for decades, and genetic
improvement for these traits have been documented in some of the breeds (e.g. AndersenRanberg et al., 2005). Several competing breeding organizations do not include these traits in
their breeding schemes at the moment due to lack of good recording systems. The need for
developing good recording systems in these countries could decrease if MAS could be
implemented to obtain genetic progress for these traits. If QTL/genes affecting traits like
fertility or health are identified with high accuracy, it would be possible for competing
organizations to select for functional traits without including these in the progeny testing
program (i.e. only select sires in to the progeny testing program that have been shown to carry
the favourable QTL/allele for these traits). This would result in a new competitive situation
for the Nordic Countries.
It should also be noted that the genetic regulation of traits is complicated. Favourable and
unfavourable genetic correlation between traits are well known in quantitative genetics.
44
Selection for a gene that would be beneficial for one trait, could have a unfavourable effect on
another important trait. To fully implement MAS in animal breeding schemes, it would
therefore be extremely important to know how various genes interact, and how selection for
one trait would influence other traits.
Although it is not likely that MAS will be used as a substitute for progeny testing for the next
decade in the Nordic countries, it is likely that genetic information could be (are) used as a
supplement to progeny testing schemes. Bulls entering the progeny testing program could be
pre-selected based on which allele they carry for a known gene. This could be genes
controlling a certain disease, like BLAD (Bovine Leukocyte Adhension Deficiency, genetic
test presently used by breeding organisations) or other traits controlled by a single gene (like
polled/horned).
6.2 Use of sexed semen in the Nordic Red Breeds
Sexed semen are now public available for farmers (http://www.cogentuk.com/). Semen in
cattle is separated using fluorescent activated cell sorting (Johnson et al., 1987a, 1987b). The
X chromosome contains more DNA than the Y chromosome, and it is therefore possible to
treat sperm with a DNA specific fluorescent dye, and subsequently sort the sperm through
high throughput flow cytometry. It has to be noted that the method is not perfect: a proportion
of the sperm will be damaged or unsorted.
It is well known (discussed by Weigel 2004) that sexed semen are considerably more
expensive than non-sexed semen, and is known to give lower conception rates. Results for
fertility from several field trails in Holstein, concludes that conception rates in virgin heifers
are expected to drop from 55-60% with unsexed semen to 35-40% with sexed semen (Weigel
2004). Despite the lowered conception rates and the higher price, the commercial availability
of sexed semen has been impatiently expected by the industry. The main reason for this is
probably the reduced fertility (lower conception rates and more days open) in the Holstein
breed in recent years, resulting in to few replacement heifers. Commercial availability of
sexed semen could change this, resulting in more female calves being born, and thereby more
replacement heifers.
In the Nordic Red breeds, there has not been a decreasing genetic trend for fertility, and there
is no reason to expect that sexed semen will be used to increase the number of replacement
heifers. The Nordic Breeding Associations will, however, face other challenges when sexed
semen is commercially available at affordable prices.
Semen from young sires for progeny testing is currently used in regular herds all accross the
Nordic countries (within country random use). The use of semen from young sires vary
between the Nordic countries. Today semen from young sires are used to 30-40% of the cows.
It must be expected that in a situation where sexed semen is commercially available at a
reasonable price, farmers will buy sexed semen from the best elite sires to produce
replacement heifers. Cows that are not inseminated with sexed semen from the best elite sires
can be expected to be inseminated with the purpose of beef production.
In this situation it will be difficult to get the necessary number of daughters after young sires
in production to perform progeny testing, and in the worse case, it would not be possible to
run a progeny testing scheme. Facing a situation like this, it might be necessary for the
breeding associations to change their progeny testing system into contract use of semen from
45
young sires, where farmers have a contract to inseminate with semen from young sires and
use these heifers for replacement for the purpose of progeny testing. It must be assumed that
farmers on contract must get an economic compensation for the use of semen from young
sires, which would result in a additional cost for the breeding associations. It should however
be noted that semen from young sires in a contract situation could be sexed to reduce the
amount of semen to produce daughters for progeny testing.
For sexed semen to be commercially used in the Nordic countries, the increased cost of sexed
semen has to be considerably lower compared to today, where sexed semen represents a major
increase in price per straw. With the current technology, 150-200 straws can be sorted per
machine per day, resulting in a high increase in price (Weigel, 2004). It would also be
reasonable to expect that a major increase in conception rate for sexed semen would be a
requirement for the successful commercial implementation of sexed semen in the dairy
industry in the Nordic countries. A reduction in virgin heifer conception rate to 35-40% with
the use of sexed semen would be dramatically for the dairy farmers in the Nordic countries.
Although there are limitations with the commercial use of sexed semen at the moment, it must
be expected that some of these problems will be solved. When semen can be sexed at higher
speed resulting in a lower price, and if the reduction in conception rate is decreased, it should
be expected that dairy farmers in the Nordic countries wants the opportunity to purchase
sexed semen from their breeding associations. It is therefore necessary that the Nordic
breeding associations have a strategy to meet this situation.
6.3 References
Abdel-Azim., G., and A. E. Freeman. 2002. Superiority of QTL-assisted selection in dairy
cattle breeding schemes. J. Dairy Sci., 85:1869-1880
Andersen-Ranber, I. M., G. Klemetsdal, B. Heringstad. and T. Steine. 2005. Heritabilities,
genetic correlations, and genetic change for female fertility and protein yield in
norwegian dairy cattle. J. Dariy Sci., 88:348-355
Fernando, R. L., and M. Grossman. 1989. Marker assisted selection using best linear unbiased
prediction. Genet. Sel. Evol., 21: 467-477
Freyer, G., P. Sørensen, C. Kühn, R. Weikard, and I. Hoeschele. 2003. Search for Pleiotropic
QTL on chromosome BTA6 affecting yield of milk production. J. Dairy Sci., 86:9991008
Johnson, L. A., J. P. Flook, and M. V. Look. 1987a. Flow cytometry of X and Y chromosome
bearing sperm for DNA suing and improved preparation method and staining with
Hoechst 33342. Gamete Res. 17:203-212
Johnson, L. A., J. P. Flook, M. V. Look, and D. Pinkel. 1987b. Flow sorting of X and Y
chromosome-bearing spermatozoa into two populations. Gamete Res. 16:1-9
Meuwissen, T. H. E., and J. A. M. Van Arendonk. 1992. Potential improvements in rate of
genetic gain from marker-assisted selection in dairy cattle breeding schemes. J. Dairy
Sci., 75:1651-1659
Ron, M., M. Golik, I. Tager-Cohen, D. Klinger, V. Reiss. R. Domochovsky, O. Alus, E.
Seroussi, E. Ezra, and J. I. Weller. 2004. A complete genome scan of the Israeli
Holstein population for quantitative trait loci by a daughter design. J. Dairy Sci., 87:476490
Schrooten, C, H. Bovenhuis, W. Coppiters, and J. A. M. Van Arendonk. 2000. Whole genome
scan to detect quantitative trait loci for conformation and functional traits in dairy cattle.
J. Dairy Sci., 83:795-806
46
Spelman, R. J., and D. J. Garrick. 1998. Genetic and economic responses for within-family
marker-assisted selection in dairy cattle breeding schemes. J. Dairy Sci., 81-2942-2950
Stella., A., M. M. Lohuis, G. Pagnacco, and G. B. Jansen. 2002. Strategies for continual
application of marker-assisted selection in an open nucleus population. J. Dairy Sci.,
85:2358-2367
Van Tassel, C. P., T. S. Sonstegard, and M. S. Ashwell. 2004. Mapping Quantitative Trait
Loci affecting dairy conformation to chromosome 27 in two holstein grandsire families.
J. Dairy Sci., 87:450-457
Weigel, K. A. 2004. Exploring the role of sexed semen in dairy production systems. J. Dairy
Sci., 87:(E.Suppl):E120-E130
Weller, J. I., Y. Kashi, and M. Soller. 1990. Power of daughter and granddaughter designs for
determining linkage between marker loci and Quantitative Trait Loci in dairy cattle. J.
Dairy Sci., 73: 2525-2537
47
7.0 Nucleus breeding in Practice – a Nordic approach
Morten Kargo Sørensen, Dep. of Genetics and Biotechnique,and Søren Borchersen, Dansire
7.1 Introduction
For more than 25 years the breeding plans for dairy cattle in most Western countries have
been based on the waiting bull principle. According to this highly selected young bulls are
used on a limited scale and subsequently wait until breeding values based on daughter
phenotypes are estimated. Thereafter final selection of proven bulls and sire of sons can take
place. The breeding schemes will constantly be adapted, and modern reproduction technology
will be used in many breeding plans.
The theoretic possible genetic gain in a conventional breeding scheme with the use of
artificial insemination is 0.26 genetic standard deviation units in total economic merit per
year. However, many international examinations show that the genetic gain in practice has
been far below the theoretically obtainable. Through the latest 10 years the implemented
breeding growth has for most breeds in Denmark been about 0.17 standard deviation units in
total economic merit per year. This is due to both lack of consequence in the management of
the breeding scheme and to the fact that the accuracy of bull dam selection in private breeding
herds is considerably lower than expected.
Because of that there has been an increasing interest in nucleus breeding schemes with use of
embryo-transfer in many countries. Numerous simulating studies have proven that open
nucleus breeding with test herds and heifer flushing has a considerable potential, and several
nucleus breeding programmes have been initiated throughout Europe. If Nordic cattle
breeders plan far ahead to maintain their competitiveness and position as a serious
international partner it is important to continue the existing Nordic nucleus breeding
programmes at a highly professional and commercial level.
A description will be given below on the following:



Accuracy in the selection of bull dams
The theoretic background of nucleus breeding programmes
Practical experience of those subjects
7.2 The accuracy in the selection of bull dams
The selection of bull dams is very important for the total breeding result. In the so-called
conventional breeding schemes the theoretically expected contribution of the bull dams to the
genetic gain varies from 25 to 40%. The importance of the bull dams is highest in the
breeding plans with a wide use of embryo-transfer and a large share of young bull
insemination (Pedersen, 1992).
In the latest decade many results have been published showing that the accuracy of the
selection of bull dams is lower than expected. A Danish study from the early 1990ies has
shown that the estimated breeding values of bull dams for milk was overestimated by 225 to
350 kg (Pedersen, 1992), and this tendency has just been confirmed (Hostrup, 2003). In both
examinations the deviation was largest for SDM-DH.
48
The expectation of the estimated breeding value of a progeny equals the average of the
estimated breeding value of the parents. The similar regression coefficients are shown in table
1. The regression coefficients show how much the breeding value of the dam can tell about a
son’s breeding value. The theoretic value of this regression coefficient is 0.5.
The Regression Coefficient
The Regression Coefficient of y on x is an expression of how much y is expected to change
when x is changed by one unit.
Table 1. The regression coefficients of the breeding value of bulls and cows for the yield of butter
fat on the similar breeding value figures of the parents.
Bull dams
.
Cow dams
RDM
SDM-DH
DJ
Ped1)
0,23
0,25
0,30
Sire of sons.
Hos2)
0,27
0,30
0,30
Ped1)
Hos2)
RDM
0,48
0,42
SDM-DM
0,37
0,47
DJ
0,40
0,42
1) DOA breeding value figures - Pedersen, (1992)
2) Animal Model breeding value figures - Hostrup, (2003)
Hos2)
0,48
0,48
0,48
Sire of cows
Hos2)
0,50
0,51
0,51
As shown the regression coefficient for bull dams is only about half the theoretically expected
of 0.5. The Danish results are in agreement with Finnish results where the average empirical
bias in pedigree indices was estimated to 13.6 kg protein. The correlation between the final
proof of the bull and the EBVs of the bull sire or dam were 0.45 and 0.17, respectively. The
low correlation with bull dam EBV indicates the unreliability of the bull dam EBVs (Uimara
& Mäntysaari, 1995). The reason is primarily preferential treatment of some cows. This has
an unpleasant ring to many people, but fundamentally it just covers up the fact that there is
more awareness to good animals – which can be called good farmer practice. The
implementation of an increased registration in the herds or automatic milking does not solve
any problem, as it is a question of a real additional yield to be obtained on the basis of a
slightly better environment for each animal. In relation to breeding value estimation this is
however a problem as the higher yield is not a result of the animal’s genes.
Preferential treatment is the most essential source of error in the breeding value estimation of
bull dams, and until now it has not been possible to describe the effect in a way permitting a
correction.
Preferential treatment hinders the ability of the estimated breeding value to foresee the
breeding value of untested bulls, which limits the genetic gain. It has been expected that the
increasing size of herds as well as more cows in free stalls would partly solve the problem.
However, until now there has been no sign that the preferential treatment will be reduced,
why the breeding plan must be adjusted accordingly. However, there is reason to believe that
an improved method to correct the herd variation will reduce the problem. This method will
be applied together with the NAV (Nordisk Avlsværdivurdering) testday model for yield.
49
The positive preferential treatment of bull dams is very much dependent on herds. Fig. 1
shows that the average difference between the estimated breeding values of the sons for
protein yield and the breeding value of the parents for protein yield vary from –1.5 kg to 17
kg in the 9 Danish SDM-DH herds, in which from 1994 to 1997 more than 15 progeny tested
bulls were born. On average this difference should be zero. The reason for not being zero is
preferential treatment. The higher negative differences between the estimated breeding values
of bulls and the average breeding values of parents, the higher degree of preferential treatment
in the herd.
Herd Number.
1
2
3
4
5
6
7
8
9
0
-2
kg protein
-4
-6
-8
-10
-12
-14
-16
-18
Fig. 1. The difference between the bulls’ and parents ’estimated breeding values for protein in
9 different SDM herds (Hostrup, 2003).
The anticipation of preferential treatment within bull dams is confirmed by the fact that
daughter/mother regression for production cows is 0.48 – which is very close to the
theoretically expected. Also the production cow/father regression is high, while the son/father
regression shows some variation, which may be caused by inaccurate estimated breeding
values of some of the imported sire of sons.
The Danish and Finish results are confirmed by international examinations, e.g. the results
from the Delta nucleus breeding programme, where bull dams are tested and selected in test
herds. Here the overestimation of bull dams is only 1/5 of the overestimation found for bull
dams in normal breeding herds (Fig. 2). A dispersed nucleus where the nucleus animals are
placed in private herds does not create a solution. The Canadian nucleus breeding programme
TEAM where bull dams were selected and flushed in private herds has found an extremely
low regression value of about 0.1 (Lohuis, 1998).
50
INET
200
180
160
140
Av. parent EBV
120
Av. bull EBV
100
80
60
Normal breeding
herds
Delta herd
Fig. 2. Pedigree EBV and own breeding value for Dutch AI bulls born in normal breeding
herds or in the DELTA-project. The Dutch EBV for yields is called INET.
By the implementation of special nucleus herds it is possible to avoid preferential treatment.
Because of the equal environment it is also possible to obtain a generally higher heritability.
In the Danish FY-Bi project it was documented for yield, where the heritability for both milk,
fat and protein was about 0.42 against about 0.28 in field surveys (Jakobsen et al. 1997,
Sørensen 1999). The heritability is also expected to be higher for functional traits. e.g.
somatic cell count, so that it might be possible to calculate estimated breeding values for these
traits including own performance.
If a central nucleus is a part of the breeding programme it is important to make sure that the
environment in the nucleus herd reflects the future (10 – 15 years) production circumstances,
as a genotype environment interaction may arise. It may have the effect that the theoretically
possible breeding growth in the population cannot be obtained as indicated in another chapter.
7.3 Simulation of nucleus breeding schemes
The nucleus breeding programmes are characterized by exploiting the possibilities of
advanced reproduction techniques to the optimum so that the generation interval for the
female animals is reduced, and the number of offspring from the best female animals will be
increased. By collecting the bull dam candidates in special nucleus herds (test stations) it may
as mentioned be possible to obtain a more accurate breeding value estimates of bull dams.
There is a differentiation between open and closed nucleus breeding programmes. The open
programmes are characterized by involving competitive breeding material from the total
population according to requirements, as also future insemination bulls are selected between
all progeny tested bulls. In the following only simulation results from open nucleus breeding
schemes will be mentioned.
51
New Finish simulation studies found a very large effect of nucleus breeding schemes - from
40% to 77% larger genetic gain for yield (Stranden et al. 2001). An essential reason for the
large effect was that the comparison standard was normal conventional breeding plans and not
effective screening schemes.
The latest Danish simulations have shown that nucleus breeding plans will increase the
genetic gain by about 5% (Nielsen et al. 2001). For a population of 50.000 cows a nucleus of
100 lactating cows was assumed, and for a population of 190.000 cows the nucleus was 200
cows. However, it must be mentioned that the effect of preferential treatment and any other
advantages of additional registrations in a nucleus have not been taken into account in these
calculations.
The effect of preferential treatment in breeding schemes has been calculated several times.
The results showed that total genetic gain for production was reduced by about 10% if the
estimated breeding value of bull dams has been overestimated in practice due to preferential
treatment (Glacius 1999,Meuwissen and Ruane 1989, Schrooten et al. 1993).
Screening Plan:
A screening plan is a breeding scheme, where breeding animals are selected exclusively on
estimated breeding values.
Thus the conclusion to be drawn is that the genetic gain will increase by at least 5% by
changing an effective screening plan to an open nucleus programme. But as shown above
considerable preferential treatment still exists in many actual breeding herds, why the real
effect of nucleus breeding will be at least 10 to 15%, depending on the weigth put on
production in the breeding goal. To this must be added that to days conventional breeding
schemes only exploits 60 – 70% of the potential of a screening plan. The expected larger
consequence in the selection procedure will contribute additionally to the genetic gain. Danish
calculations show that having a population size of 500.000 cows the value of the extra genetic
gain will be 50 to 75 mill. kr. per year after payment and interest of the costs involved.
7.4 Nucleus breeding in practice
There are many other nucleus breeding programmes in Europe, many of them established
within the latest years. Nucleus breeding programmes in Holland, Finland, Germany and
Sweden have been examined closely; plans and experience from these projects are mentioned
below.
Delta Project in Holland
The Delta project is a nucleus breeding program, which has been running over a longer
period, and from this much practical experience has been gained. In Holland about 270
Holstein Friesian bulls, 60 red Holstein Friesian and 10 Dutch red and white bulls are tested.
About half of the black and white bulls originate from the Delta project.
Holland Genetics now own all animals in the nucleus herd. The heifer calves are placed there
at the age of no more than 6 weeks, raised under standardized conditions, flushed 2 –3 times
at the age of 12-16 months and then inseminated at the age of about 17 months. Subsequently
OPU are carried out on the best third of the heifers. The oocytes are fertilized by the test tube
method (IVP)
52
During first lactation the heifers are performance tested on milk production. About 300
heifers are tested every year, and the best 60 are selected as bull dams.
Delta has made contracts with recipient farms, and many embryos are sold with an option on
the calves born. In 2002 there were about 500 bulls born in the Delta project, which have been
progeny tested, and as mentioned above their pedigree value has been determined with a
higher degree of accuracy than the contemporary young bulls from other breeding herds. This
results in a larger part of the Delta young bulls being selected as proven bulls, which
strengthens both the genetic gain as well as the economy of the AI organisation.
ASMO Project in Finland
The breeding nucleus in the ASMO project consists of 70 first lactation cows placed in the
test station at the age of 8-12 months. Asmo herd is owned by Alkiokeskus Oy - Emryocenter
Ab and MTT together, Alkiokeskus Oy is owned by AI-FABA, Breeding-FABA, dairy
industry and Svensk Avel.
The heifers are flushed twice prior to the insemination at the age of about 17 months.
Furthermore the OPU/IVP is made on the selected first lactation cows. When the cows have
ended their first lactation then the best cows are selected as donors. According to the plan this
should be 15-20 cows per year, in reality this is only about 7, partly because of phenotypic
selection on conformation. A network of recipients consisting of 50 herds has been
established. These recipients buy embryos relatively cheap on the condition that ASMO has
the option.
Finland initiates every year 120 young bulls, and 25% of them should come from ASMO.
Today approximately 27% of the test bulls come from ASMO, so the goal has been reached.
The project has during the latest years experienced a growing support and importance.
NOG Project in Germany
NOG is a breeding cooperation between four North German AI organisations, and they started
in 2001 a nucleus breeding project including bull dam testing.
NOG plans each year to place 280 Holstein Friesian young bulls, and out of this number 5060% is to be found in the nucleus breeding project. Each year about 200 heifer calves are
selected for the nucleus breeding programme. The heifers are flushed twice and subsequently
inseminated. If less than 10 useful embryos is obtained then subsequent OPU/IV is made.
Two months prior calving the heifers are placed in a test herd with space for 150 cows, owned
by the university in Kiel. The test period is 180 days, and each year 30 cows / bull dams are
selected. They complete the lactation in the test herd, and produce more embryos.
Viken Project in Sweden
In 2002 Svensk Avel started a nucleus breeding project called Viken. The first heifers were
bought for the project in spring 2002, and the first elite doners were ready in December 2004.
The project is shared equally between Läntmannen, Svalöf Wiebull and SEAB (Svensk
Embryoservice AB). SEAB is owned by Svensk Avel ( 51 %), Dansire (36.5 %) and FABA
(12.5 %). The project has a capacity to test 100 Swedish Red Cattle and 100 Holstein Friesian
first lactation cows per year. Heifer calves are placed there at the age of 10 months and prior
to insemination they are flushed twice. An annual production of 1800 embryos is expected.
53
Those embryos are sold to the network herds where Viken has the option on the calves born,
both bulls and heifers. In the first part of second lactation the “elite donors” are selected. The
“elite doners” are subsequently flushed. According to the plan 60 SRB and 60 HF young bulls
from the projects are initiated every year.
7.5 Recommendations and conclusion
The implementation of nucleus breeding programmes results in a major increase in genetic
gain. The reason is higher accuracy in the bull dam selection, shorter generation intervals and
a stronger management of the practical part of the breeding work. To this must be added that
the nucleus breeding programmes and test stations have a considerable marketing effect.
Therefore it is important that the Nordic nucleus herds continue in an integrated cooperation.
This is an essential condition for the Nordic cattle breeding to keep its professional and
commercial competitiveness both nationally and internationally.
Well established nucleus programs (ASMO and Viken) with participation from both
Denmark, Sweden, and Finland already exist. Norway is not involved and will probably not
be in the near future, since preferential treatment does not seem to be a problem in Norway. In
the existing programs it is important only to focus on indices in the selection procedure.
Practice have shown that phenotype in some cases play a role in the selection, this rely reduce
the possible effect of a nucleus program.
Due to the Nordic breeding profile it’s obligate to include more registrations on functional
traits in the existing and coming nucleus programmes in the Nordic countries. In relation to
that feed intake will be an essential measurement, since intake can not be registered accurate
in production herds.
Inbreeding has as mentioned in chapter 5 growing interest in dairy cattle breeding. The more
offspring from the highly selected animals the more important is inbreeding, therefore this has
to be taken into account when heifers are selected for the nucleus herds. The tools mentioned
in chapter 5 just have to be implemented in the selection procedure.
7.6 References
Glacius, A., 1999. Effekt af særbehandling og fysiologiske funktionsprøver i avlsplaner for
malkekvæg. Speciale afhandling, Institut for Husdyrbrug og Husdyrsundhed, KVL. 93
sider.
Hostrup, H. 2003. The relation between bull mothers and the breeding value figures of their
sons – using Animal Model for yield for RDM, SDM-DH and DJ. Tesis, ”Institut for
Husdyrbrug og Husdyrsundhed”, KVL. 54 sider.
Jakobsen, J.H., Liboriussen, T. & Jensen, J., 1997. Genetiske parametre for produktionsegenskaber. I : Liboriussen, T. & Andersen, B.B. (ed.) Ægtransplantation, fysiologiske
funktionsprøver og kerneavl med malkekvæg. Beretning nr. 737 fra Danmarks
JordbrugsForskning, 47-72.
Jørgensen, J.N. & Sørensen, M.K. , 2003. Breeding schemes for improvement of genetic
response in dairy breeds of minor population size. Godkent til publikation i Acta Agric.
Scand.
54
Lohuis, M. & Bagnato, (1998). What have we learned from the TEAM projekt ?
http://www.aps.uoguelph.ca/cgil/pub/mmlpapers/hjjuly98.html.
Meuwissen, T.H.E., Ruane, J., 1989. The value of daughters of bull dams in dairy cattle
progeny testing schemes. Livest. Prod. Sci. 23: 267-274.
Nielsen, L.P., Sørensen, M.K., Berg, P. & Noesgaard, J., 2001. Alternative avlsstrategier for
rød dansk malkerace. DJF-rapport nr. 32 - Husdyrbrug, 99 sider.
Pedersen, G.A., 1992. Assessment of breeding value and selection of bull mothers.
Examination of the expected and realized efficiency in the Danish dairy races. Ph.D.
thesis,” Institut for Husdyrbrug og Husdyrsundhed,” KVL. 83 sider.
Schrooten, C., Steverink, M.H.A. & van Arendonk, J.A.M., 1993. Stochastic simulation of
dairy cattle breeding schemes: Influence of breeding strategy and baised breeding values
in the population. J. Anim. Breed. Genet. 110:268-280.
Stranden, I., Korpioha, P., Pakula, M. & Mäntysaari, E.A., 2001. Bull selection in MOET
nucleus breeding schemes with limited testing capacity. Acta Agric. Scand. 51: 235-245.
Sørensen, M.K., 1999. Stokastisk simulering af avlsplaner for malkekvæg. Ph.D. afhandling,
Institut for Husdyrbrug og Husdyrsundhed, KVL. 226 sider.
Uimari, P., Mäntysaari, E. A. 1995. Relationship between bull dam herd characteristics and
bias in estimated breeding value of bull. Agricultural science in Finland 4, 5-6: 463-472.
55
8.0 Imports from other populations to the Scandinavian Red Dairy Breeds.
Lars-Olof Bårström, Svensk Avel
8.1 Introduction
The Scandinavian Red Dairy Breeds, Finnish Ayrshire (FAY), Norwegian Red (NRF), Red
Dane (RDM) and Swedish Red (SRB), are the most productive red breeds in the world
besides Red Holstein. The breeding programmes in the red populations in the Scandinavian
countries have since many years been based on Total Merit Index. All traits of economical
importance for the milk production have been included in the TMI, which is a
PROFITABILITY INDEX. Important traits except production and functional conformation
are female fertility, calving ease and calf-mortality, udder health, beef production traits and
longevity.
Since many years there has been an exchange of semen from top bulls between the
Scandinavian red populations. In Finnish Ayrshire and Swedish Red a number of the best
bulls from the North American Ayrshire population have also been used as bull sires. Until
end of the 1990th, a number of Swedish Holstein Frisian bulls have been used as bull sires in
the NRF population. In the RDM breeding programme bulls from three more breeds, Brown
Swiss, Montbeliarde and Red Holstein Frisian have been used.
Exchange/use of bulls from different breeds in the Nordic red population
SRB
RDM
FAy
NRF
RHF
Brown Swiss
Montbeliarde
Ayrshire,
US & Can
HF,
Swe,
Dk
In the ongoing work to improve the Scandinavian red breeds it is important to optimise the
national breeding programs. It is also important to look for interesting genetics from other
breeds or populations, which could offer the Scandinavian red population desirable traits.
8.2 What are we particular looking for for the Scandinavian Red breeds?


Positive traits according to our breeding goal
Traits with a positive correlation to longevity and udder health
56


Udder conformation (important to improve)
o For-udder attachment
o Udder depth
Outcross pedigrees
8.3 Where do we find possible sources for importations to the Scandinavian red breeds?
Today all countries with organized breeding programs are members of the Interbull. There are
six breed groups in the Interbull system and the number of bulls in are shown below.
(November 2004)
 Ayrshire - The red group
10780
 Brown Swiss
6430
 Guernsey
816
 Holstein
83000
 Jersey
6870
 Simmental
21350
.
Characteristics for breeds / populations from which it can be possible to import bulls from to
the Scandinavian red breeds.
 Breeding values for Nordic profile traits
 Breeding goals close to the Scandinavian.
 Efficient breeding program
 Large population
Ayrshire
The Scandinavian red breeds are all in the Ayrshire group. Bulls from these breeds are totally
dominating both in number and also the ranking for BV for production. In November 2004
breeding values were calculated for about 10780 bulls in the Interbull Ayrshire group and
77,3 percent of the bulls were from the Scandinavian breeds.
Percentage of bulls from different populations in the Ayrshire / Red group in Interbull
November 2004.
Breed
Percentage
SRB
21,7
Finnish Ayrshire
21,6
NRF
21,2
RDM
12,8
New Zeeland Ayrshire
6,0
Canadian Ayrshire
4,1
Australian red breeds
3,3
US Ayrshire
2,5
Other ay-populations
6,8
A number of Canadian Ayrshire bulls have been used in Sweden and Finland and the
conclusions from the results in Sweden are;
Production
obvious lower production
57
Female fertility
lower fertility in average
Calving traits
lower calving index sire
Conformation obvious higher results for udder
Holstein
Red Holstein bulls have been used to ”improve” many red breeds. Is this also a alternative for
Scandinavian red? What will be positive and negative by using Holstein bull in the
Scandinavian red breeds?
Positive
Milk yield
Udder conformation
Size
Nice-looking cows??
Negative
Lower components
Calving traits
Female fertility
SCC / Udder health
Beef production traits
Feet & legs
Scandinavian Red will not be a real alternative to HF.
Holstein bulls have during a period been used as bull sires in both NRF and RDM but since
some years this is ended.
Guernsey, Jersey , Brown Swiss and Simmental.
These breeds are quite different from the Scandinavian red breeds and there today no reason
to consider importations of bulls from these breeds to the Scandinavian population. Brown
Swiss bulls were during 1980th used in the RDM breed but they couldn’t compete production
wise. A other problem was that many of the US Brown Swiss bull used were carrier of genetic
defects.
Montebeliarde is a French breed in the Simmental family with a breeding goal rather close to
the Scandinavian. Bulls from the breed have been used in the RDM program with positive
results for production, beef traits, female fertility and udder health. For traits like udder
conformation, feet and legs and calving ease the results were negative. During the last year
two for these traits very positive Montebeliarede bulls have been used in the RDM
programme.
8.4 Conclusions
 The breeding work to improve the Scandinavian red breeds have to be done within the
population. There is today no real source for importation of bulls to the Scandinavian
red population. Hopefully some of the best bulls from red populations with great
influence of Scandinavian red can be brought back. (Aussi Red in Australia, New
Zeeland Ayrshire and also Canadian Ayrshire)
 North American Ayrshire bulls will improve udder conformation but aren’t
competitive for other traits. A number of the highest LPI-cows in Canada have been
flushed to some of the best bulls from Sweden and also Finland and resulting young
sires have been tested in Swedish and Finnish breeding programs.
 Holstein bulls are not real alternative in the Nordic Red population. For the future it is
important that the Nordic Red population is ”holstein free”
58
9.0 Competitive advantages in the Nordic red breeds.
Torstein Steine, Geno, and Lars-Olof Bårstrøm, Svensk Avel
9.1 The Nordic Red Breeds
The Nordic red breeds have been selected according to a broad breeding goal over many
years. Unlike breeding programs in other countries, the nordic breeding organisations have all
included health and fertility in the breeding goal. Another important difference is the fact that
these traits are based on direct observations of health and fertility, not only some traits with
some correlations to the real traits.
The nordic breeding objectives have without doubt had a significant positive effect on health
and fertility in the red nordic breeds. On the other hand such a breeding goal also leads to less
weight on milk production, which is quite necessary to be able to obtain anything on health
and fertility.
In this report we might look into all possible details with regard to the development of the
four red nordic breeds. We do not find that to be a very fruitful operation. It is of more value
to see how the red nordic breeds perform compared with the major dairy breeds in the world.
Now we have recent results from both USA and Ireland. In USA both NRF and SRB are used
in crossbreeding with Holstein, especially in California. In Ireland and Nortern Ireland NRF
are compared with Holstein as a pure breed. It may be argued against these comparisons that
it should be done just within the group of red breeds. But today semen is sold globally, and
the commercial dairy farmers will hardly look at colour if there are clear economical
differences between breeds. In order to have a future on the international scene the red nordic
breeds have to compete with Holstein.
Table 1. Results from California for production during the first 150 days of
lactation. Published by Les Hansen and coworkers, University of Minnesota,
USA
NormandeMontbeliarde- ScandinavianTrait
Holstein Holstein
Holstein
Holstein *
Cows
Milk (kg)
294
31.8
171
28.6
194
31.0
120
33.3
Fat (kg)
1.10
1.01
1.10
1.17
Protein (kg)
0.94
0.89
0.95
1.01
SCS
2.13
2.40
2.33
1.88
Fat + Protein (kg)
2.03
1.90
2.05
2.18
̶ 6%
1%
% of Holstein
+7%
* Scandinavian = NRF or SRB
Table 1 show the results from California for crosses between Holstein and NRF or SRB
compared with pure Holstein. There will be more results available in March 2005. At that
time also fertility and full lactation records will be included. This analysis has used records
59
from very large Californian herds, more than 1000 cows. The French breeds, Montbelliard
and Normande have also been used in these herds. Therfore we get a comparison with these
breeds too.
Table 2. Results from Ireland, milk yield 2. lactation, kg.
Published by Frank Buckley and coworkers, Teagasc, Ireland.
Breed
Low level of High level of
concentrates, concentrates,
548 kg
1251 kg
Holstein
5433
6360
NRF
5208
5871
Montbelliarde
4826
5700
Normande
4307
4661
Table 3. Results from North-Ireland, milk production.
Published by Conrad Ferris and coworkers, ARINI, North-Ireland.
NRF
Holstein
Trait
305 day milk yield, kg
5704
5957
Fat %
3,88
3,82
Protein %
3,29
3,22
Days in production
319
316
Full lactation yield, kg
6084
6317
Table 4. Results from North-Ireland. Fertility, culling and still births.
Published by Conrad Ferris and coworkers, ARINI, North-Ireland.
NRF
Holstein
Trait
Heifers:
Non return(%)
62
54
Culled due to infertility
2
3
1. lactation cows:
Non return(%)
50
39
Culled due to infertility
6
17
All:
Culled of the 230 animals per
26
54
breed starting in the project
11%
24%
Still births
5,5%
15,5%
These results demonstrate that the Nordic red breeds are very competitive with Holstein. This
is especially true for functional traits like fertility, somatic cell count and therefore probably
also mastitis resistance, but they are also at almost the same level as Holstein for milk yield.
Economically the Nordic red breeds seem to be quite superior to Holstein.
Experiences from the Nordic countries indicate less clear differences between the breeds than
shown here. This may be a result of a systematical selection of cow sires based on nordic
breeding objectives in the nordic Holstein populations. This has probably had some positive
effect even though most of the bull sires have been imported.
60
But there are more challenges in making an international market for the red Nordic breeds
than just to demonstrate the quality of the animals. In USA the results from the cross breeding
trial in California was reported in a farmers magazine where NRF and SRB were called “whohave-ever-heard-of-them-breeds”. This shows that we have to start from nowhere to sell the
Nordic dairy cattle genetics. The international results and all the publicity around these results
have however helped very much in the marketing, but we still have some way to go.
9.2 Conclusions
The quality of the red Nordic dairy cattle genetics has proven to be at a high international
level.
The Nordic red breeds are outperforming Holstein in functional traits as health and fertility in
trials in California and Ireland.
The Nordic red breeds are close to Holstein in milk yield.
There is still some work to do in marketing the Nordic dairy cattle genetics.
If we are able to continue the efficient breeding work and also succeed in marketing, the
Nordic countries may become a dairy cattle breeding nucleus for a large part of the world.
61
10.0 Prospects for cooperation between Baltic and Nordic countries in dairy
cattle breeding
Jarmo Juga, FABA
10.1 Background
Four Nordic countries, Denmark, Finland, Norway and Sweden, have signed an agreement of
joint testing and use of AI-bulls. The motivation for this agreement is to improve the
efficiency of current breeding programs in four countries by mowing towards a joint breeding
program and to better utilise the existing resources. Joint use of best bulls also guarantees a
better assortment of proven bulls for commercial breeding in a single country. It also
increases the competitivity in export of Nordic semen to other countries.
Accurate comparison and selection of bulls and cows across Nordic countries is of great
importance in such cooperation. To improve the possibilities for across country comparison
the breeding organisations have established a new company Nordic Cattle Genetic Evaluation
(NAV), which has a responsibility to develop and run joint genetic evaluations for Denmark,
Finland and Sweden.
There is a mutual interest to expand this Nordic cooperation also to Baltic countries: Estonia,
Latvia and Lithuania. The aim of this report is to evaluate the possibilities for the future
cooperation in prediction of breeding values and sustainable selection of animals across
Nordic and Baltic countries and also to look at the future market for export in Baltic countries.
10.2 Milk recording in Baltic countries
In Estonia the recording scheme is quite developed and about 90 % of the cows belonged to
milk recording in 2003. One reason for high participation is that belonging to milk recording
is required for state subsidies. The number of cows in Estonian milk recording was 102788 of
which about 26 %, 74 % were Estonian Red and Estonian Holstein, respectively. Estonia has
also some native cattle left in recording. The average production in 2003 was 5119 kg milk
with 4,44% butter fat and 3,39 % protein for Estonian Red and 5906 kg milk with 4,27 %
butter fat and 3,27 % protein for Estonian Holstein.
In Latvia quite small number of herds belongs to milk recording at the moment due to a big
number of small herds. The goal is not to have all the herds in recording, but to have the
biggest and most progressive herds, which will most likely continue the production in the
future. There was an increase in the number of herds in recording in 2003, which is a
challenge for the milk recording system, since many new herds enter to the system with
incomplete information. Due to the high integration of databases all new herds have pedigree
and calving information, however.
In the end of 2003 Latvia had 186300 dairy cows, of which 62143 belonged to milk
recording. Of these 42219 were Latvian brown breed, 17377 Holstein and few hundred were
Angler, Danish red or Swedish red and white. The average yield for Latvian brown was 4550
kg with 4,45 % of fat and 3,22 % of protein. The average yield for Holstein in 2003 was 5296
kg of milk with 4,21 % of fat and 3,09 % of protein.
The total number of dairy cows in Lithuania in 2002 was 441800 of which 84237 Holstein
cows and 31634 red cows belonged to milk recording. The average milk yield for Holstein
62
was 5136 kg with 4,2 % of fat and 3,29 % of protein. The milk yield for red breed in 2002
was 4695 kg with 4,38 % of fat and 3,43 % of protein.
To my knowledge the recording scheme is the least developed in Lithuania. Latvia has
applied to ICAR Special Stamp in the end of 2003 with a reasonable good auditing report
from ICAR auditors. There are a lot of small herds in all Baltic countries, but also quite a high
number of very large herds, which are formed from old state farms. Some of these big farms
are very well developed, especially in Estonia, and looking very much forward to future
production under EU system. I have also experienced the same optimism in Latvia among the
larger dairy herds. These herds are investing a lot to management, but also to foreign genetics.
10.3 Genetic evaluation
Sampling data from milk recording database to the evaluation system is problematic in Latvia,
since a lot of data is lost due to different reasons. This might be a reflection of new herds in
recording system with incomplete information, but also a quality problem of recording on
farm. A review of sampling criteria and classification of fixed (environmental) effects might
be advisable. I assume the situation is very similar in Lithuania. Estonia is the most developed
country when genetic evaluation is concerned. Estonia also participates to INTERBULL
evaluation. Latvia and Lithuania are members in ICAR and INTERBULL, but do not send
data to international evaluation.
10.4 Breeding programs in Baltic countries
Both in Estonia and Lithuania the most common breed in milk recording is Holstein, which
has increased very fast. The most common breed in Latvia is still the Latvian brown breed,
which makes 74% from all livestock
Estonia runs a breeding program for Holstein and Estonian red. The breed organisations have
recently merged. Latvia has also a national breeding program on both Latvian brown and
Holstein breed. The number of annually tested bulls is small in all Baltic countries in all
breeds. Increasing the number of tested bulls would speed up the genetic progress, but
obviously the small number of recorded cows makes it difficult to carry out more
inseminations with young bull semen. Increasing the number of herds in recording scheme
will produce more capacity for the testing program, too.
Breeding programs rely very much on imported semen. In Latvian brown many of the red
European populations including red Holstein are accepted as sires. Recently the Swedish SRB
has been found very successful (Strautmanis, 2003). Also Estonia and Lithuania have used
Nordic red semen, especially Swedish and Danish red.
10.5 The possibilities for cooperation
All Baltic countries are willing to develop their recording and evaluation systems, breeding
programs and use of new technology in animal breeding. The biggest obstacle currently is the
lack of money and skilled people. The cooperation with Nordic countries could grow from
joint R&D projects and education. In research and development the possible areas of
cooperation are to map the possibilities for a joint genetic evaluation and sustainable selection
program.
63
The problem on Nordic side is that there is much less money available to projects in Baltic
countries compared to the situation some years ago. All Nordic countries and Nordic
Ministerrådet were funding Baltic projects through development funds, but after Baltic
countries joint the EU these funds are much less. Now the best possibility is to apply money
to joint EU projects, but the competition is much harder and the amount of work needed to
write the application is much bigger.
Genetic evaluation
Before planning any joint evaluation between Nordic and Baltic populations the genetic links
across populations should be studied. Between population links are of two kinds, either
genetic links, through the additive genetic relationship matrix, or data links, where animals
have offspring in the same contemporary group. The amount of such links makes up
connectedness in the data.
The studies have shown that Ayrshire populations in Finland and Sweden have good genetic
links. Norwegian red is quite well linked to Swedish red and moderately well linked to
Finland. Danish red is more isolated due to its past breeding policy, but recent exchange of
semen has increased the amount of links to Swedish red population. In Holstein the
populations in all three Nordic countries Denmark, Finland and Sweden have good genetic
links. Nordic countries have realised the need for increment in exchange of semen between
populations to enable more accurate comparison.
Since only Estonia has delivered data to Interbull evaluations it is of interest to quantify the
genetic links between the Baltic and Nordic red populations. There was a report of genetic
links and model validation of Estonian Red population for Interbull Ayrhshire evaluation
(Uba and Kruus, 2004) in the conference for Animal Breeding in Baltics in 2004.
Selection program
Differences in breeding programs between countries may be due to differences in economic
conditions. Currently the economic values used in the breeding goals are calculated according
to different methods and principles. Different weights will lead to different selection decisions
in different countries, even if the same bulls are available for selection everywhere.
It would be of interest to compare the breeding goals in Baltic and Nordic countries and to
study the possibilities for harmonisation of the methods to calculate economic values. It is
also important to describe the benefit of animals with better health, fertility and other
functional traits, especially when the consumers are putting more emphasis in the future to
non-economic values, such as ethical value of better health and welfare.
Global breeding and more effective selection will also introduce a higher risk of decreasing
the effective population size and hence increasing the change in inbreeding (Meuwissen and
Woolliams, 1994). This risk can be accounted for by utilising the results of modern selection
theory, for example by maximising the response of selection with a predefined rate of
inbreeding or constraining the variance of response (Meuwissen, 1997).
Common use of sires across countries (Mark et. al., 2002) would increase connectedness, but
probably also the rate of inbreeding. It is hence important to look for the optimum exchange
64
of genetic material between populations. One aim in R&D could be to calculate the change in
average inbreeding in Baltic populations and to estimate the contribution of imported sires to
the current breeding populations. This information could be used in guiding the use of
imported material in a sustainable way in Baltic breeding programs.
10.6 Possibilities in marketing
The Baltic countries make a small market with a hard competition from the central Europe.
The Nordic countries are strong in red breeds, but unfortunately the red breeds are playing a
smaller and smaller role in Baltic countries. The Holstein breed is taking very much over and
will do so very fast if the Nordic countries don’t help the Baltic red breeding programs.
There is an interest to import Nordic red genetics to Baltic countries, but the money available
is limited and the numbers of doses will be quite small. The total number of red cows in milk
recording in Baltics is about 100000. If foreign bulls would be used for 10 % of the
population the total marked would be about 20000 doses per year. Some years ago the Baltic
countries used old AI bulls from Sweden and Denmark. Now they import mainly semen.
I think that the best strategy would be to help the Baltic countries to improve their own
breeding programs and sell semen to be used for best cows. In the long run the Baltic
programs could cooperate on a more equal level, but it will take at least ten years. There
might be some room for selling the red genetics to crossing in the very large herds.
10.7 Contact people in Baltics
Estonia:
Tanel-Taavi Bulitko, Animal Breeders´ Association of Estonia, must@estpak.ee
Olav Saveli, Estonian Animal Breeding Association email: saveli@eau.ee
Mart Uba, Estonian Animal Recording Centre (EARC), email: mart.uba@reg.agri.ee
Kaivo Ilves, Estonian Animal Recording Centre (EARC), e-mail: kaivo.ilves@reg.agri.ee
Latvia:
Ilona Miceikiene, Lithuanian Veterinary Academy, e-mail: genetikalab@lva.lt
Rita Zutere, Latvian State information Data Processing Centre of Domestic Animals, e-mail:
Rita.Zutere@vcidac.lv
Erna Galvanovska, Latvian State Information Data Processing Centre of Domestic Animal
Pedigree (LSIDPC), email: erna.galvanovska@vcidac.lv
Lithuania:
Donata Uchockiene, Holstein Breeders Association, Lithuania
Arunas Svitojus, Rural Business Development and Information Centre in Lithuania (RBDIC),
e-mail: arunas@vic.lt
10.8 References
Mark, T., W.F. Fikse, H. Jorjani & J. Philipsson. 2002. Monitoring changes in the structure of
global dairy cattle populations. Proc. 7th World Congress on Genetics Applied to
Livestock Production, 19-23 August 2002, Montpellier, France.
65
Meuwissen, T.H.E. 1997. Maximizing the response of selection with a predefined rate of
inbreeding. J. Anim. Sci. 75, 934-940.
Meuwissen, T.H.E. & J. Woolliams. 1994. Effective sizes of livestock populations to prevent
a decline in fittnes. Theor. Appl. Genet. 89, 1019-1026.
Strautmanis, D. 2003. Improving Latvian Brown dairy breed by using different breeds. In
Proc. Baltic Animal Breeding Conference, Sigulda 2003, pp. 37-40.
Uba, M. and M. Kruus. 2004. Data of Estonian Red breed for Interbull Ayrshire evaluation;
Genetic links and model validation. In Proc. Animal Breeding in the Baltics, Tartu 2004,
pp. 100-1004.
66
11.0 Discussion/summing up
One of the major concerns in modern animal breeding is to avoid a rapid increase in rate of
inbreeding. As it is discussed in chapter 5, breeding associations have to decide how much
increase in inbreeding they are willing to accept in the short and long term scale. This is the
major challenge in modern animal breeding where one sire has the potential to be used for all
cows in the Nordic red population to obtain maximum genetic gain.
There are several aspects to consider when an optimum breeding strategy should be chosen in
the Nordic red breeds. A major concern is the number of sires that should be used across
breeds, and how much they should be used within the country of origin to avoid inbreeding,
and also how much they should be used in other Nordic countries before the other red Nordic
populations does not serve as a genetic pool in case of inbreeding problems. Simulation
results in chapter 2 shows that only a limited exchange of sires across countries will result in a
rapid decrease in the possibility of using the other red population as genetic pools.
At the same time it should be noted that a certain exchange of genetic material is necessary to
obtain genetic links between countries. In addition to genetic links, traits have to be defined
similarly and recorded in similar ways countries to be able to rank sires across countries.
It is also obvious from the discussion in chapter 2 that the correlation between breeding goals
needs to be at a certain level before exchange of sires are important. It should be discussed
whether this is the case today. In the results presented in chapter 2, the correlation between
breeding goals are ≈0.8, however this number are very sensitive for the parameters used as
input. In the simulation scheme in chapter 2, the breeding populations in Norway, Sweden and
Finland are used as a base, and only two traits are considered; milk and mastitis resistance.
We would, however, expect that the realistic correlation between countries are much lower,
because there are large variation amongst countries for breeding goals when the other traits
included in the breeding goals are considered. Further work is to be done to predict the results
of exchange of sires between countries with larger differences in breeding goals and with
more extreme variation in genetic parameters.
Based on the results in chapter 2 there are now reason to doubt that exchange of sires between
breeding associations at a higher level than what we see today, will result in extra genetic gain
at the short term, but in a longer term the opportunity to use the other Nordic red breeds as a
genetic pool if one of the populations should get heavily inbred will disappear. It should also
be noted that the conclusion from chapter 8 is that there are no other alternative populations to
import from if the Nordic red breeds become inbred.
Another aspect that should be considered when it is discussed at what level sires should be
exchanged, is genotype-environment (GXE) interaction. As it is discussed in chapter 3, there
have been found indications of GxE interaction, indication that different genotypes have
different ranking (reranking or scaling) between the Nordic countries. This indicates that a
sire that are ranked as number 1 in one Nordic country doesn’t necessarily rank as number 1
in another Nordic country or within environments in a country. This indicates that the wrong
animals could be selected for a given environment, or that we would observe lower
performance in the environments different to those where the genetic evaluation was
performed. Although certain environmental factors are quite similar in the Nordic countries,
there are undoubtedly differences in, for instance, feeding regimes, production level, amount
of daylight, and temperature. Breeding associations select those sires with the best results in
67
the given country, and when considering exchange of sires it has to be kept in mind that there
may be some differences in ranking between countries.
It is also an important question if and how breeding nucleuses may be integrated in the Nordic
breeding schemes. Today there are such nucleuses in Sweden and Finland. Denmark is an
active part in the Swedish nucleus, and Sweden and Finland have been working together on
the nucleus breeding for some years. Norway is the only country without participation in any
nucleus. This reflects some differences in the view on what is the best way of utilising the
limited resources available in dairy cow breeding. There are some disagreements on the
benefit from nucleuses in a scheme with many low heritable traits in the breeding goal, and
about the benefit from a nucleus when the rate of increase in inbreeding has to be kept low.
These questions can be better answered after some years of experience with the existing
nucleus herds in Sweden and Finland.
The Nordic red breeds (especially NRF and SRB) have during the last 2 years become
important competitors to the Holstein breed. These breeds can offer genetic material bred with
special emphasis on health and fertility, which have become major problems in the Holstein
breed. To maintain this competitive advantage, it is extremely important that the breeding
associations continue to run breeding schemes with large emphasis on functional traits, that
the breeding programs are run as optimal as possible (as discussed in chapter 4), and that the
breeding organisations work to maintain the high participation in the recording systems in the
Nordic countries, so that data of high quality are recorded on a routine basis and can be used
for progeny testing in the future.
Summing up
Modern animal breeding consists of possibilities, risks and challenges. It is possible to
increase the selection intensity by utilising sires across countries, or using the best sires more
intensely within country. But this may also lead to more inbreeding and reduced selection
intensity in the long term.
Theoretical studies on nucleus herds demonstrate that such herds may give an extra lift to the
genetic gain. But is it possible to use this and still keep inbreeding under control? Or will the
risk of increase in inbreeding reduce the extra benefit from the nucleus resulting in no extra
benefit? This report does not give any final answer to these questions, but it highlights some
of the topics of importance when discussing more close breeding cooperation between the
Nordic countries.
We hope this report will start many discussions on the topics included, and that it will help in
continuing and improving a Nordic cooperation in dairy cattle breeding, balancing the risks
and the benefits.
The situation today is quite unique: We have three internationally competitive cow
populations with some relations between them, but also with some differences. With all the
existing knowledge and experiences in dairy cattle breeding it should be possible to develop
this into a way of running dairy cattle breeding which makes the Nordic countries even more
competitive, but without loosing the genetic variation.
68
Appendix 1. The use of EVA-programme in Finnish Ayrshire and
Finncattle breeding schemes
Terhi Vahlsten, Finnish Animal Breeding Association
Ayrshire
The main idea with Ayrshire was to find an answer to the question how to breed bullsires and
bulldams in order to keep genetic progress high and rate of inbreeding low? There were
several different strategies to explore this question. The first strategy was to mate the current
bullsires to current bulldams i.e. cows that were expected to inseminate during the next few
months. This strategy is rather easy to carry out and the animals are already selected by the
breeding committee (bullsires) and breeding advisor (bulldams). Basically, this strategy
produces a mating plan for bulldams and this strategy can be done four times a year after
every breeding value evaluation.
The second strategy was to approximate the number of sons AI should by out of each bullsire.
The idea was to include all cows that have been inseminated during the last 12 months and
which had the bulldam status at the insemination time. The bulls were those bulls which were
selected as bullsires during the last 12 months. This strategy is not very useful because it is
difficult to set limits to the time period because all bullsires are not changed after every
breeding value evaluation. And this strategy answers the question what should have been
done instead of what should be done. The results from this strategy were not promising
although the EBV’s were high but the rate of relationship per generation was too high.
The third strategy included cows calved in a certain time with high TMI and udder
conformation index. Heifers were also included if they filled certain requirements. The bulls
were the best available Finnish and foreign bulls. The results weren’t very good; especially
the rate of relationship per generation was very high. The results changed dramatically when
only half of the cows and heifers were mated. EVA-programme selected the mated females
and the rate of relationship per generation was in acceptable level and the EBV’s maintained
high. This strategy showed that the programme loses some of its powers when the females are
selected strictly and every female has to be mated.
Finncattle
The Finncattle breed is old traditional Finnish breed and there are three different types:
Western, Northern and Eastern types. The Western type is most common and over 2400 cows
are included in milk recording scheme. The Eastern type is endangered as well as the
Northern type. Although the Western type is not endangered the situation is not good for this
type. Especially the average relationship is high and the young bulls are rather close related to
their potential mates. The effect of few old bulls has become too large and the gene pool is
narrow. About 70 % of inseminations in Western population are made with young sire semen
in order to try to keep the genetic base as wide as possible. The young sires are also used as
bullsires and bulldams need to fill certain requirements. The EVA-programme was used for
making mating plan for bullsires and bulldams but the results were not very promising. The
main problem with the Western type is that the best cows come from the very popular
families.
69
The Eastern and Northern populations are so small that the selection is based almost entirely
on avoiding inbreeding. However, the inbreeding is not a problem with these two types. For
both types there are some bulls that are used rather much and their sons are bought for AI. It
is very important that the effect of one bull or family doesn’t become too significant in a very
small population. The object with these types is to make a mating plan for the population with
the EVA-programme. However, there are some requirements for the females, thus the plan is
not made for the entire population. The females include all live cows, heifers and calves
whose sire and maternal grandsire are same type. Thus the females are at least 75 % Eastern
or Northern type. The selected bulls need to have enough sperm doses and they shouldn’t
have many live daughters in the population and their average relationship to the selected
females should be rather low. The object is to do this plan once a year and the farmer receives
a letter where the bull candidate is informed. This is only a proposition and the farmer makes
the final decision of the bull.
70
71