Livestock Production Science 69 (2001) 179–186
www.elsevier.com / locate / livprodsci
Estimates of genetic parameters for reproduction traits at
different parities in Dutch Landrace pigs
E.H.A.T. Hanenberg*, E.F. Knol, J.W.M. Merks
IPG, Institute for Pig Genetics B.V., P.O. Box 43, 6440 AA Beuningen, The Netherlands
Received 3 September 1999; received in revised form 31 May 2000; accepted 6 July 2000
Abstract
Data from Dutch Landrace sows were used to estimate genetic parameters for reproduction traits in the first six parities.
Analyses were performed with DFREML using a model with equal design and herd–year–season of first parity as a fixed
effect. Estimates of genetic parameters were calculated for different traits and parities using data from 58 194 sows.
Heritabilities were found to be low for farrowing after first insemination (FFI), mothering ability (MA) and number of still
born piglets (NSB); moderate for number of piglets born in total (NBT) or alive (NBA) and interval from weaning to first
insemination (IWI); and high for gestation length (GL) and age at first insemination (AFI). Heritability increased slightly
with parity number for NBT and NBA, increased markedly for NSB and MA, and decreased for IWI. Genetic correlations
between the same traits measured in different parities were close to unity for parities higher than 2, for all traits. Genetic
correlations below 0.70 were found between parity 1 and higher parities, for NBT, NBA, NSB, MA and FFI. Undesirable
correlations were found between NBT and NSB (0.53) and NBT and MA (20.49). Indirect selection on MA would be
possible using GL (r g 5 0.40). IWI was positively correlated with AFI (0.31). It is concluded that selection on litter size,
piglet mortality and also number of litters per year would be worthwhile.  2001 Elsevier Science B.V. All rights reserved.
Keywords: Genetic parameters; Reproduction; Pig
1. Introduction
In current commercial pig breeding programmes,
great emphasis is placed on improving reproduction
traits in dam lines. In general the breeding goal is to
increase the number of piglets weaned per sow per
*Corresponding author. Tel.: 131-24-6779-999; fax: 131-246779-800.
E-mail address: egiel hanenberg@ipg.nl (E.H.A.T. Hanen]
berg).
year. Several reports have shown the effectiveness of
selection on litter size (Knap et al., 1993; Lamberson
et al., 1991; Sorensen and Vernersen, 1991). Undesirable correlated responses in other traits such as
piglet mortality can decrease the overall effectiveness of selection on litter size, as shown by the
selection experiment of Johnson et al. (1999).
In addition to litter size, many more traits affecting reproductive performance could be used in a
breeding programme. In this work different traits
influencing the number of pigs weaned per sow per
0301-6226 / 01 / $ – see front matter  2001 Elsevier Science B.V. All rights reserved.
PII: S0301-6226( 00 )00258-X
180
E.H. A.T. Hanenberg et al. / Livestock Production Science 69 (2001) 179 – 186
year were analysed. The aim was to create a comprehensive set of genetic parameters for reproduction
traits for direct use in breeding programmes.
6 or more days were transformed using the following
function:
ln(interval) / [ln(6) 2 ln(5)]
2 [ln(6) / hln(6) 2 ln(5)j 2 6]
2. Materials and methods
Reproductive performance data were available
from purebred Dutch Landrace sows recorded on
breeding farms participating in the Stamboek breeding programme. Records of reproductive performance in parities 1 to 6 were taken from sows which
had their first parity recorded between January 1992
and December 1996. The average parity was 2.9.
Synchronisation of puberty, oestrus stimulation and
induced farrowing with hormonal drugs was not
allowed on the breeding farms. Piglets found dead
and wet behind the sow were registered as still born.
Dead piglets of which the sex could not be determined were registered as mummified and were not
included in the total number of born piglets. On
average 2.0 piglets per litter were crossfostered. Pigs
were weaned at an age of about 4 weeks on almost
all farms. Small farms with less than 500 recorded
litters were excluded from the dataset.
2.1. Traits analysed
Litter size was analysed as the number of piglets
born in total (NBT) and number of piglets born alive
(NBA). Mortality related traits were the number of
piglets stillborn (NSB), mothering ability (MA) and
gestation length (GL). Traits influencing the number
of litters per year were the interval from weaning to
first insemination (IWI), whether or not the sow
farrowed after first insemination (FFI, treated as a
binary trait) and the age at first insemination (AFI).
Mothering ability was calculated as the percentage
of piglets weaned out of the total number of piglets
nursed including those crossfostered. The total number of piglets nursed was calculated as the total
number of live born piglets plus or minus the
crossfostered piglets. Mothering ability was calculated only for litters of more than two nursed piglets.
A logarithmic transformation was performed on
the trait interval from weaning to first insemination
as suggested by ten Napel et al. (1995). Intervals of
Individual parity records were excluded when
NBT exceeded realistic limits (1–30 piglets). Other
traits were recoded to show a missing value when
they exceeded realistic limits. Limits were set to
0–30 piglets for NBA and NSB, 105–125 days for
GL, 2–56 days for IWI and 180–365 days for AFI.
After data editing, a total of 202 399 farrowing
records from 58 194 sows at 147 herds were represented in the data set. The number of records, mean
values and standard deviations of reproduction traits
analysed are given in Table 1.
2.2. Analyses performed
Genetic parameters were estimated using restricted
maximum likelihood (REML) analyses based on an
animal model. Univariate analyses were performed
for all traits for (1) first parity and (2) parity two to
six (repeatability model). The mixed model can be
written in matrix notation as: Y5Xb1Za1Wc1e,
where Y is the vector of observations; X, Z and W
are known incidence matrices; b is the vector of
fixed effects; a is the vector of random additive
genetic effects |(0, As 2a ); c is the vector of random
permanent non-genetic effects of each sow |(0,
Table 1
Number of records (n), frequency (%) or mean and standard
deviation (S.D.) of the reproduction traits analysed (parity 1–6)
Trait
n
Mean
S.D.
NBT
NBA
NSB
MA
GL
IWI a
FFI
AFI
202 399
202 399
202 399
196 198
202 309
178 001
177 997
58 194
10.81
10.25
0.56
91.88
115.2
6.35
3.06
2.97
1.11
11.00
1.5
2.73
234.3
20.8
a
Frequency
0.89
Mean and S.D. of IWI before transformation.
E.H. A.T. Hanenberg et al. / Livestock Production Science 69 (2001) 179 – 186
I c s c2 ), and e is the vector of the residuals |(0,
I e s 2e ); I c and I e are identity matrices; and A is the
additive genetic relationship matrix. The vector c
was only included in the repeatability model. Analyses were performed with DFREML software (Meyer,
1991) with an average information algorithm. The
software was extended to estimate the significance of
fixed effects and covariables.
The fixed effects included in b are given in Table
2. Parity number (PN) was only included in the
repeatability model. For FFI and IWI parity number
was defined as the parity number of the preceding
litter. Herd–year–season of farrowing (HYS) was
defined in months. Successive YS-classes with small
numbers were joined within herds so that at least 15
records were present in each HYS, resulting in 8168
HYS-classes. The effect of crossbred / purebred was
confounded with service boar (BOAR). Service
boars with less then 15 litters in the data were pooled
into two groups, one for purebred litters and another
for crossbred litters. In total 950 service boars with
at least 15 litters were included. The number of
inseminations within 2 days (NI) was included as a
fixed effect with two classes (one versus more than
181
one insemination). All covariables were included
with a linear and quadratic component. Covariables
in the model were lactation period in days (LP and
LP 2 ), number of piglets potentially weaned, defined
as the number of piglets born alive plus / minus
crossfostered piglets (PW and PW 2 ), number of
piglets weaned (NW and NW 2 ) and interval from
weaning to insemination in days (IWID and IWID 2 ).
Non-significant factors (P.0.05) were excluded
from the model. The pedigree-matrix was built with
two generations of pedigree. In total 67 154 animals
were included in the pedigree-matrix. Sows with
performance records were descended from 338 sires
and 12 221 dams.
Two sets of multivariate analyses were performed
to estimate the (co)variances between parities and
between traits: (1) multivariate analyses for each
trait between parities and (2) multivariate analyses
between traits in each parity. Given the size of the
dataset, only models with equal design could be
used. Therefore HYS of farrowing was the only fixed
effect considered in the multivariate model. To
achieve equal design only information from animals
with all six parities could be used in the multivariate
Table 2
Significance of fixed effects and covariables used in univariate analyses a
Fixed effects b
NBT1
NBT2–6
NBA1
NBA2–6
NSB1
NSB2–6
MA1
MA2–6
GL1
GL2–6
IWI1
IWI2–6
FFI1
FFI2–6
AFI
a
HYS
BOAR
**
**
**
**
**
**
**
**
**
**
**
**
**
**
**
**
**
**
**
**
**
**
**
**
**
Covariables c
PN
**
**
**
**
**
NI
**
**
**
**
**
**
–
–
**
**
**
**
**
**
**
**
LP
LP 2
–
**
–
–
*
**
–
*
*
**
–
*
NW
**
*
*
–
NW 2
–
**
*
*
IWID
**
**
IWID 2
PW
PW 2
**
**
**
**
–
**
First parity traits are indicated with a 1 and higher parity traits with 2–6 (repeatability model). **P,0.01; *P,0.05; –P.0.05.
HYS, Herd–year–season; BOAR, service boar; PN, parity number; NI, number of inseminations.
c
LP, Lactation period; NW, number of piglets weaned; IWI, interval from weaning to insemination; PW, potential number of piglets
weaned.
b
182
E.H. A.T. Hanenberg et al. / Livestock Production Science 69 (2001) 179 – 186
analyses between parities. It was not possible to
include both NBT, NSB and NBA in the multivariate
model because of dependencies, therefore NBA was
excluded.
3. Results and discussion
3.1. Univariate analyses
Table 3 gives the results of the univariate analyses. Heritability for NBT was estimated to be around
0.10, which is also the average value reported in the
literature reviewed by Rothschild and Bidanel
(1998).
Heritabilities tended to be lower for NBA in
comparison with NBT for both first and later parities.
The same tendency was found in the literature
(Alfonso et al., 1997; Roehe and Kennedy, 1995).
Estimates of permanent environmental effects for
NBT vary in the literature from 0.00 to 0.19 (Estany
and Sorensen, 1994; Knap et al., 1993; Lamberson et
al., 1991) and those found in this study for NBT2–6
are in between these values.
Heritabilities for piglet mortality traits, NSB and
MA, are low but significantly higher then zero. Knol
(2000) estimated slightly higher heritabilities for
NSB (0.05) and litter mortality (0.06), calculated as
the percentage of live piglets dying from birth to
weaning, traced back to their biological mothers.
Mothering ability is in fact a combination of real
mothering abilities and piglet vitality. In most herds
crossfostering is non-random. Sows with better
mothering abilities often nurse weak piglets of sows
with low mothering abilities. As no corrections were
made for the quality of the piglets, the heritability for
mothering ability will be underestimated. The
heritability estimated for GL is high in comparison
with that of other reproduction traits and higher than
estimates of about 0.20 reported by Mercer and
Crump (1990).
The heritability for interval from weaning to first
insemination was significantly higher in the first
parity (0.14) than in later parities (0.07). Both
phenotypic and genetic variances were much higher
in the first parity. Tholen et al. (1996a) also found
different heritabilities for IWI, without logarithmic
transformation, for parity 1 (0.08–0.10) compared
with parity 2 (0.00–0.01). Heritability estimates for
IWI are low compared to the results of ten Napel et
al. (1995) who found a heritability for IWI of 0.36 in
a selection experiment. The use of practical on-farm
data in this study could explain the difference in
estimates. FFI had a low heritability in the first
parity, and an even lower one in higher parities.
Brandt and Gandjot (1998) estimated a slightly
Table 3
Univariate estimates of phenotypic variance (s 2P ), heritability (h 2 ) and permanent environmental ratios (c 2 )
Trait
n
s 2P
h 2 6S.E.
NBT1
NBT2–6
NBA1
NBA2–6
NSB1
NSB2–6
MA1
MA2–6
GL1
GL2–6
IWI1
IWI2–6
FFI1
FFI2–6
AFI
58.194
144.205
58.194
144.205
58.194
144.205
55.881
140.317
58.149
144.160
50.030
127.971
50.026
127.971
58.194
6.951
9.012
7.022
8.399
1.147
1.178
107.82
85.72
2.040
1.817
10.81
3.41
1281
780
260.9
0.09360.009
0.10160.006
0.08460.008
0.08960.005
0.02060.004
0.04860.004
0.01860.004
0.02860.003
0.24560.012
0.28760.009
0.13960.011
0.06660.005
0.02760.005
0.01060.002
0.31860.013
c 2 6S.E.
0.09060.005
0.08560.005
0.05560.004
0.04660.003
0.11660.007
0.12560.005
0.02660.003
E.H. A.T. Hanenberg et al. / Livestock Production Science 69 (2001) 179 – 186
higher heritability for FFI of 0.03 over all parities.
Falconer (1985) shows that heritability of binary
trait, as FFI, can be corrected as:
183
Table 4
Relationship between measurements of each trait recorded in
different parities, estimated using multivariate analyses a
* (1 2 p) /i 2 p
h 2normal 5 h 2binary
Trait and parity
(number of records)
1
2
3
4
5
6
After applying this correction the heritabilities
increased to 0.061 for the first parity and 0.031 for
the later parities. The highest heritability estimate
(0.318) in this study was found for age at first
insemination, which is in agreement with results
from Merks and Molendijk (1995). Estimates for age
at first oestrus found in the literature varied from 0.2
to 0.3 (Lamberson et al., 1991; Rydhmer et al.,
1994).
NBT (n514.739)
1
2
3
4
5
6
0.07
0.83
0.74
0.74
0.62
0.62
0.04
0.04
0.90
0.78
0.74
0.68
0.10
0.07
0.09
0.95
0.94
0.90
0.10
0.12
0.15
0.08
0.97
0.97
0.10
0.12
0.17
0.15
0.11
0.98
0.08
0.10
0.19
0.18
0.20
0.10
NBA (n514.739)
1
2
3
4
5
6
0.06
0.79
0.73
0.71
0.58
0.55
0.04
0.04
0.88
0.75
0.69
0.62
0.08
0.05
0.08
0.93
0.92
0.83
0.09
0.10
0.12
0.08
0.97
0.95
0.08
0.10
0.13
0.13
0.09
0.96
0.06
0.08
0.16
0.17
0.17
0.08
3.2. Multivariate analyses
NSB (n514.739)
1
2
3
4
5
6
0.02
0.81
0.38
0.60
0.58
0.37
0.05
0.01
0.76
0.88
0.89
0.78
0.05
0.06
0.05
0.95
0.93
0.94
0.05
0.06
0.11
0.05
0.96
0.91
0.05
0.07
0.11
0.15
0.08
0.96
0.06
0.07
0.09
0.13
0.14
0.09
MA (n513.796)
1
2
3
4
5
6
0.02
0.87
0.46
0.56
0.60
0.47
0.04
0.02
0.62
0.80
0.77
0.70
0.04
0.05
0.02
0.90
0.95
0.94
0.03
0.07
0.07
0.03
0.95
0.96
0.02
0.06
0.09
0.08
0.05
0.96
0.03
0.06
0.08
0.09
0.13
0.05
GL (n514.716)
1
2
3
4
5
6
0.22
0.95
0.94
0.94
0.92
0.94
0.32
0.23
0.98
0.96
0.97
0.96
0.32
0.36
0.23
0.99
1.00
1.00
0.31
0.35
0.39
0.26
0.99
1.00
0.30
0.35
0.37
0.40
0.25
0.99
0.29
0.33
0.36
0.39
0.41
0.26
IWI (n512.350)
1
2
3
4
5
6
0.11
0.89
0.87
0.79
0.89
0.83
0.19
0.07
0.96
0.95
0.93
0.94
0.16
0.16
0.07
0.97
0.92
0.91
0.11
0.17
0.13
0.05
0.85
0.86
0.08
0.10
0.16
0.10
0.04
0.96
0.08
0.09
0.13
0.16
0.13
0.04
FFI (n512.348)
1
2
3
4
5
6
0.02
0.72
0.73
0.83
0.82
0.94
0.02
0.01
0.44
0.77
0.40
0.68
0.00
0.01
0.00
0.58
0.75
0.75
0.00
0.02
0.01
0.01
0.60
0.81
0.00
0.02
0.04
0.03
0.01
0.85
0.04
0.01
0.02
0.03
0.04
0.02
Table 4 shows the results of the multivariate
analyses for each trait between parities. In general,
heritability estimates were lower than those found in
the univariate analyses. This could be caused by the
simplicity of the model, which we were forced to use
because of the equal design, which gave a poorer fit.
Sows culled before parity 6 could not be used in the
equal design analysis. Bias arising through the
culling of sows with low reproductive performance
in early parities might be another reason for these
lower heritability estimates.
Heritabilities for NBT slightly increased with
parity number, with the exception of parity 2, which
showed a considerably lower heritability. Again,
heritabilities for NBA are slightly lower than those
for NBT in all parities. The heritability of NSB
increased with parity number. As the mean and
phenotypic variances increases with parity number
for NSB, expression of genetic differences between
sows may also increase. The heritability for MA also
increased with parity, although phenotypic variance
did not The heritability for the interval from weaning
to first insemination decreased considerably with
parity. The highest heritability was found in the first
parity (0.11), in which the mean and standard
deviation for IWI were also relatively high. The
heritability of FFI (not transformed) was very low in
Parity
a
Estimates of heritabilities (in bold) on the diagonal, genetic
correlations below the diagonal and phenotypic correlations above
the diagonal.
184
E.H. A.T. Hanenberg et al. / Livestock Production Science 69 (2001) 179 – 186
all parities, whilst that of GL was high and increased
slightly over parities.
Estimates of genetic correlations between parities
varied most for NSB (ranging from 0.37 to 0.96) and
least for GL (from 0.92 to 1.00). For all traits,
genetic correlations between measurements in
parities 3, 4, 5 and 6 were hardly different from
unity. Only between parity 1 and later parities did
genetic correlations change considerably from unity,
for most traits. This means that for those traits parity
1 is genetically different compared to later parities.
Some traits also showed lower genetic correlations
between parity 2 and later parities. In the literature,
genetic correlations for litter size between parities
always exceed 0.70, except in some cases between
first and later parities (Alfonso et al., 1997; Irgang et
al., 1994; Knap et al., 1993; Roehe and Kennedy,
1995; Tholen et al., 1996b). This study showed low
genetic correlations (,0.70) between litter size in
both parity 1 and parity 2 and later parities. For these
traits, use of a multivariate model, instead of a
simple repeatability model which assumes homogeneity of variance across parities, is preferable for
breeding value estimation. In addition to the genetic
correlations, two different aspects should be considered when use of a repeatability or multivariate
model for practical purposes is discussed. Practical
breeding value estimation will be considerably more
costly in computer time with the multivariate model.
Secondly, creating separate correlated traits for each
parity, for analysis with a multivariate model reduces
the number of observations per HYS-class. Estimates
of HYS-effects will be less accurate and the accuracy
of estimated breeding values will decrease.
Table 5 gives the results of the multivariate
analyses between traits. Results were calculated for
each parity, separately. Therefore only genetic correlations between traits within parity are presented. In
comparison with results presented in Table 4,
heritabilities for most traits were somewhat lower for
parities 5 and 6, and the surprisingly low result for
heritability of NBT in parity 2 seen in Table 4 was
not repeated.
Genetic correlations between traits were, in general, quite stable over parities. Genetic correlations
between NBT, NSB and MA were positive. Selection
on litter size would give an undesirable increase in
stillborn piglets and a decrease in MA. The high
correlation between MA and GL is interesting. An
increase in gestation length leads to a better chance
for piglets to survive until weaning. Comparable
correlations were found by Knol (2000) between
NBT, NSB, litter mortality and GL. Genetic correlations between NBT and other traits are low. A high
positive genetic correlation was found between AFI
and IWI (0.31) as was described earlier by Merks
and Molendijk (1995) and Sterning et al. (1998).
4. Conclusions
The genetic parameters found in this study indicate that there are possibilities for improving reproduction traits by selection on more than litter size
at birth. A more general reproduction breeding goal,
increased number of piglets weaned per sow per
year, can be achieved.
Given an undesirable correlation with the number
of stillborn piglets and mothering ability, selection
on litter size only will increase piglet mortality.
Including selection on stillborn piglets or mothering
ability in the breeding goal can avoid or reduce
increased piglet mortality, although both traits have a
quite low heritability and have an undesirable correlation with litter size. Selection on gestation length, a
trait with a high heritability and a high genetic
correlation with mothering ability, gives opportunities for an effective indirect selection on mothering abilities. The low genetic correlation between
gestation length and litter size means that the latter
will not be adversely affected by inclusion of GL
into the selection criteria.
Effective selection for an increase in the number
of litters per year is possible by selection on interval
from weaning to first insemination and age at first
insemination. There is a beneficial positive genetic
correlation between these traits. Greatest response
can be expected in the first parity where the
heritability of IWI is highest, as is the occurrence of
prolonged intervals of weaning to first insemination.
Increasing the number of litters per year by selection
on farrowing after first insemination would not
appear effective, because of the very low heritability
of this trait.
E.H. A.T. Hanenberg et al. / Livestock Production Science 69 (2001) 179 – 186
185
Table 5
Results of the multivariate analyses between traits a
Traits and parity
n
NBT
1
2
3
4
5
6
MA
GL
IWI
FFI
49 993
39 684
31 937
25 260
18 509
12 544
0.09
0.08
0.12
0.10
0.09
0.08
0.23
0.29
0.30
0.31
0.35
0.34
20.16
20.26
20.26
20.27
20.28
20.29
20.13
20.16
20.15
20.14
20.12
20.13
0.05
0.07
0.02
0.01
20.02
20.02
0.00
20.02
0.00
0.00
0.00
0.00
0.09
NSB
1
2
3
4
5
6
49 993
39 684
31 937
25 260
18 509
12 544
0.29
0.53
0.59
0.58
0.60
0.56
0.03
0.02
0.05
0.05
0.07
0.08
20.06
20.09
20.06
20.07
20.06
20.04
20.02
20.07
20.05
20.06
20.05
20.04
0.00
0.01
0.01
20.01
20.01
20.01
0.00
20.01
20.01
0.01
20.03
20.02
0.04
MA
1
2
3
4
5
6
49 993
39 684
31 937
25 260
18 509
12 544
20.30
20.56
20.47
20.56
20.48
20.56
20.12
20.10
20.36
20.16
20.47
20.33
0.02
0.02
0.03
0.02
0.03
0.03
0.09
0.11
0.12
0.11
0.10
0.09
0.03
0.00
0.02
0.01
0.02
0.03
20.02
20.01
20.01
0.01
0.00
20.02
20.01
GL
1
2
3
4
5
6
49 993
39 684
31 937
25 260
18 509
12 544
20.18
20.12
20.18
20.10
20.04
0.05
0.04
20.27
20.14
0.01
20.09
0.05
0.41
0.40
0.36
0.42
0.50
0.31
0.25
0.26
0.24
0.26
0.26
0.24
20.02
20.02
0.01
20.01
0.01
0.01
0.00
0.01
0.00
0.00
20.01
20.02
0.01
IWI
1
2
3
4
5
6
49 993
39 684
31 937
25 260
18 509
12 544
20.08
0.21
20.02
0.04
20.39
20.39
0.13
0.25
20.03
20.34
20.22
20.35
20.03
0.10
0.25
0.03
0.82
0.33
0.01
0.09
0.10
0.06
0.19
0.26
0.13
0.12
0.06
0.04
0.01
0.01
0.03
20.06
20.04
20.03
20.04
20.04
0.08
FFI
1
2
3
4
5
6
49 993
39 684
31 937
25 260
18 509
12 544
0.23
20.09
0.23
20.12
20.09
20.16
0.06
20.06
0.20
0.39
20.23
20.12
20.21
20.39
20.55
20.20
20.36
0.01
20.04
20.17
20.31
20.51
20.19
20.34
20.12
20.22
0.06
20.17
20.27
20.21
0.03
0.02
0.01
0.01
0.01
0.02
0.02
AFI
49 993
20.08
20.10
0.09
0.01
0.31
0.13
0.29
a
NBT
NSB
AFI
Estimates of heritabilities (in bold) on the diagonal, genetic correlations below the diagonal and phenotypic correlations above the
diagonal. Only estimates of genetic correlations within parities are presented.
186
E.H. A.T. Hanenberg et al. / Livestock Production Science 69 (2001) 179 – 186
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