1. Introduction
Increases in longevity, defined as lifetime number of litters,
lead to increased profit through reduced sow replacement cost.
There is some concern among swine breeders that genetic selection
for leanness may be reducing sow longevity. If longevity declines,
then replacement costs increase at an accelerating rate. For example
if the farrowing rate is 2.1 litters/year, a decrease in longevity
from 6 litters to 5.8 increases the replacement rate by only 1.2%,
but if longevity is allowed to drop to 3 litters then a subsequent
decrease to 2.8 increases the annual replacement rate by 5%. Also,
as pigs in Canada become closer to an optimum level of leanness,
the economic value of leanness decreases, which means other traits,
including sow longevity in dam lines, receive a higher relative
weighting.
In a purebred selection herd, longevity is a function of voluntary
culling for genetic improvement as well as involuntary culling
due to health and fertility problems. Kennedy et al (1996) analysed
backfat and age at 100kg data on Canadian purebred swine and identified
long generation intervals as a major factor limiting rates of
genetic improvement. Given that this is true, it is possible that
selection herds with greater longevity may be fatter because they
have a lower rate of genetic improvement due to a long generation
interval with a low amount of voluntary culling. This is completely
due to herd management, and any genetic correlation between backfat
and longevity must operate through involuntary culling.
In order to estimate the relationship between backfat and involuntary
culling, this study looks at differences in longevity within herd-management
groups. This is a preliminary analysis before estimating genetic
correlations. The approach used follows that of Richard (1995,
pers. comm.) who carried out similar calculations for multiplier
herds in Québec. Richard (1995, pers. comm.) showed that
gilts with leaner backfat probe measures in performance test,
went on to have fewer litters as multiplier sows.
2. Methods
All farrowing records and backfat probe records on Yorkshire and
Landrace females were extracted from the national database. These
records had passed various edits for inclusion in the database,
including checks for valid animal identification, valid herd number,
sex conflicts, valid ranges on some numeric fields, and valid
ranges and intervals between dates of birth, probing, farrowings
and weanings. The probe records included backfat adjusted to 100kg
probe weight and estimated age at 100kg. The farrowing records
included the farrowing date and herd number.
All herd numbers which did not appear in any farrowing records
from 1996 were identified, and all farrowing records on sows with
these herdnumbers were deleted. This removed data which would
cause underestimation of longevity because the herds ceased recording
sow productivity. For each remaining herd with farrowing records,
the number of consecutive years prior to 1996 in which the herd
had records was counted. When a year was encountered where there
were no farrowing records from the herd, then any farrowing records
from that herd prior to that year were deleted. After this stage,
all probe records from herds which did not have any farrowing
records were deleted, and all probe records where the year of
probing was prior to the year of the earliest farrowing records
in the same herd were deleted. All probe records with the backfat
figure missing were deleted. Then augmented probe records were
produced by adding a field to each remaining probe record to indicate
whether or not the gilt had at least 1 farrowing record in the
edited data. Augmented probe records with birthdates in 1996 were
deleted because if selected, these animals would not have enough
time to appear in the farrowing records. The remaining augmented
probe records formed what will be referred to as the gilt selection
dataset. The herd in this dataset is the herd of probing.
To produce the sow longevity dataset, the farrowing records were
taken after having passed through the edits mentioned above, and
all records on sows which had some farrowing records missing (non-consecutive
parity numbers) or which had the parity number missing in some
of their records, were deleted. All records on sows where the
days of age at farrowing (calculated from the farrowing date and
the birthdate) in any parity was not between 200+85(parity-1)
and 560+300(parity-1) were deleted. Then a smaller dataset was
extracted with one record per sow, consisting of the sow identification,
the last parity number, the year of last farrowing, the herd of
last farrowing and whether or not the herd was the same for all
farrowings. All records on sows where the year of last farrowing
was 1996 were deleted. These sows were likely still breeding at
the data cutoff. If it is assumed that farrowing records in 1996
contain representative proportions of each parity, then this is
a valid procedure to avoid the underestimation of longevity. All
records on sows which farrowed in more than one herd were deleted.
For the remaining sows with records, the year, parity number and
herd of last farrowing were added to the probe record on the sow.
When searching for the probe record on the sow, probe records
where the year of probing was prior to the year of the earliest
farrowing records in the same herd were included. Sows which did
not have a probe record were then removed from the data. The augmented
probe records on the sows formed what will be referred to as the
sow longevity dataset. In this dataset herd refers to the herd
in which all farrowings of the sow took place.
For each breed, Table 1 shows the number of breeding sows and
unselected gilts in the data by period of birth. There are no
unselected gilts in the sow longevity dataset.
The records of the gilt selection dataset were grouped by backfat
level into 6 classes, and the proportion of gilts selected for
breeding in each class was calculated (Table 2). The records of
the sow longevity dataset were grouped in the same way, and in
each class the proportion of sows surviving beyond each parity
was calculated (Table 3).
The records of the gilt selection dataset were sorted into herd-birthyear groups. Where there was only 1 record in a group, that record was deleted. The proportion of gilts selected for breeding in each remaining herd-birthyear group was calculated, and the proportion selected from each backfat class within each herd-birthyear was also calculated. Then the deviation of the proportion selected in each backfat class within the herd-birthyear from the proportion selected in the herd-birthyear overall was calculated. These deviations were averaged across herd-birthyears for each backfat class, weighting each one by the number of animals involved. i.e.
where dij is the deviation of the proportion selected in backfat
class i in herd-birthyear j from the overall proportion selected
from herd-birthyear j, nij is the number of candidate gilts in
backfat class i and herd-birthyear j, and nhyr is the number of
herd-birthyear groups. The weighted average deviations (di) for
each backfat class are given in Table 4. A similar process was
repeated for the sow longevity dataset. This involved calculating
the proportion of sows in each herd-birthyear group which survived
beyond each parity, and the proportion of sows within each backfat
class within each herd-birthyear which survived beyond each parity.
Then the deviation of the proportion surviving in each backfat
class within the herd-birthyear from the proportion surviving
in the herd-birthyear overall was calculated, and the weighted
average deviation for each backfat class across herd-birthyears
was calculated as in equation (1). The weighted average deviations
for each backfat class are given in Table 5.
It was necessary to use separate datasets for the study of gilt
selection and the study of longevity because the gilts selected
from those probed, included animals with farrowing records in
1996. Those animals therefore had to be included in the gilt selection
data, but they could not be included in the longevity data because
they were still breeding at the data cutoff. Similarly, the gilts
selected from those probed, included sows which changed herds,
but these sows were not included in the longevity analysis because
they appeared in more than one herd-birthyear class.
Table 2 includes data on 3 Yorkshire gilts and 2 Landrace gilts
which were deleted from the analysis for Table 4 because they
were each the only animal in their herd-birthyear group. Table
3 includes data on 60 Yorkhire sows and 58 Landrace sows which
were deleted from the analysis for Table 5 because they were each
the only animal in their herd-birthyear group.
3. Results
3.1 Production of the datasets
Initially, for Yorkshire there were 135,393 farrowing records and 677,770 probe records. After removing farrowing records from herds which ceased sow productivity recording there were 95,344 farrowing records from 25,752 sows. After removing farrowing records prior to the period of consecutive years of records for the herd, there were 95,300 records. After removing probe records from herds which did not have any farrowing records there were 206,027 probe records, after removing probe records where the year of probing was prior to the first year of farrowing records in the herd, there were 171,561, after removing probe records where the backfat was missing there were 171,403, and after removing probe records with birthdates in 1996 there were 164,476. A field indicating if the gilt had at least 1 farrowing record, was added to each record, to form the Yorkshire gilt selection dataset. There were 23,468 gilts in this data which appeared in the edited farrowing records. The proportion of probed gilts which became breeding sows was therefore 23,468/164,476 = 14.3%
After removing farrowing records on sows which had parity numbers
missing from their records or had non-consecutive parity numbers,
there were 90,627 farrowing records. After removing all records
on sows with an invalid age at farrowing there were 90,539 records
from 24,847 sows. The smaller dataset then produced consisted
of a record containing the identification, last parity number,
year of last farrowing, herd of last farrowing and whether or
not the herd was the same for all farrowings, for each of these
sows. After removing all records on sows with 1996 as the year
of last farrowing there were 18,361 records, and after deleting
sows which changed herds there were 18,266 records. 17,414 of
these sows had probe records with non-missing backfat, and records
on these sows formed the Yorkshire longevity dataset.
Initially, for Landrace there were 155,464 farrowing records and 530,450 probe records. After removing farrowing records from herds which ceased sow productivity recording there were 107,150 farrowing records from 25,504 sows. After removing farrowing records prior to the period of consecutive years of records for the herd, there were 106,910 records. After removing probe records from herds which did not have any farrowing records there were 194,652 probe records, after removing probe records where the year of probing was prior to the first year of farrowing records in the herd, there were 169,744, after removing probe records where the backfat was missing there were 169,673, and after removing probe records with birthdates in 1996 there were 163,005. A field indicating if the gilt had at least 1 farrowing record, was added to each record, to form the Yorkshire gilt selection dataset. There were 24,553 gilts in this data which appeared in the edited farrowing records. The proportion of probed gilts which became breeding sows was therefore 24,553/163,005 = 15.1%
After removing farrowing records on sows which had parity numbers
missing from their records or had non-consecutive parity numbers,
there were 102,989 farrowing records. After removing all records
on sows with an invalid age at farrowing there were 102,808 records
from 26,683 sows. The smaller dataset was then produced in the
same way as for Yorkshire, with records containing the identification,
last parity number, year of last farrowing, herd of last farrowing
and whether or not the herd was the same for all farrowings, for
each of these sows. After removing all records on sows with 1996
as the year of last farrowing there were 20,195 records, and after
deleting sows which changed herds there were 20,028 records. 19,031
of these sows had probe records with non-missing backfat, and
records on these sows formed the Yorkshire longevity dataset.
3.2 Distribution of Animals by Birthyear
Table 1 shows that the Yorkshire and Landrace datasets are similar
in size, and in both cases, more animals come from the more recent
birthyears, where there are more herds on performance recording.
This is true for both the gilt selection and the longevity data.
In the gilt selection datasets for both breeds, the ratio of the
number of breeding sows to the number of unselected gilts increases
with birthyear period up to 1990-1992 and then decreases. This
could be due to expanding herd sizes, or a reduction in the proportion
of the sow herd which is selected from gilts which are not probed,
or increases in replacement rates. Further data analysis is needed
to determine the reason.
3.3 Gilt Selection
From Table 2, in Yorkshire the average proportion of probed gilts
selected for breeding is 14.3%, and the proportion is highest
(15.8%) in the 12-14mm backfat range, and lowest (7.6%) in the
fattest backfat range ( > 18mm). In Landrace the average proportion
selected is 15.1%, and it is also highest (17.7%) in the 12-14mm
range and lowest (9.4%) in the fattest backfat range. In both
cases, lean gilts are less likely to be selected than those with
an intermediate level of backfat, but discrimination against lean
gilts is higher in the Landrace data.
When looking at differences in rates of gilt selection within
each herd and year in Table 4, the discrimination against very
lean gilts is still substantial (-3.2% in Yorkshire and -4.2%
in Landrace), and so is the discrimination against fatter gilts (-4.8% and -2.9%).
3.4 Longevity
From Table 3, in both Yorkshire and Landrace the proportion of
sows surviving beyond any given parity increases dramatically
as the backfat level increases.
The proportion of Yorkshire surviving beyond parity 2 is about
59% on average (Table 3), and increases by 26% (from 45% to 71%)
as the backfat level increases. The proportion of Yorkshire surviving
beyond parity 4 is about 35% on average, and increases by 31%
(from 20% to 51%) as the backfat level increases. For survival
beyond parity 6 the average is about 19%, and increases by 23%
(from 9% to 32%) as the backfat level increases.
In Landrace, average survival beyond parity 2 is about 62% (Table
3), and increases by 30% (from 43% to 73%) as the backfat level
increases. For survival beyond parity 4 the average is about 39%
and increases by 36% (from 18% to 54%) as the backfat level increases.
For survival beyond parity 6 the average is about 22%, increases
by 29% (from 7% to 36%) as the backfat level increases.
For Yorkshire, when examining survival within each herd and year,
the survival rate increases by about 8% for parity 2, by about
10% for parity 4, and by about 7% for parity 6, as the backfat
level increases (Table 5). For Landrace, the increase in survival
rate as backfat increases is only 3% for parity 2, 4% for parity
4, and 3% for parity 6.
It can be concluded that there is a small but significant relationship
between backfat thickness and longevity, particularly in the Yorkshire
breed.
4. Discussion
4.1 Implications of the Results
Breeders tend to have a lower rate of selection of lean gilts
than would normally be expected. On average they tend to select
gilts in the 12 to 16mm backfat class and discriminate against
very lean (<10mm) or very fat (>18mm) gilts. It is possible
that breeders are not selecting the leanest gilts because of the
perception that there is a negative relationship between backfat
thickness and longevity.
When looking at the overall data, there is in fact a strong association between leanness and longevity. It would appear that the lean sows do not remain in the herd for as many parities. After the 4th parity, the survival rate of sows in the leanest category (<10mm) was about 30% lower than those in the fattest category (>18mm) in the Yorkshire breed and 33% lower in the Landrace breed. However, this association is due mainly to the fact that breeding herds that have a higher replacement rate also have leaner sows, probably because the genetic change for backfat is higher in those herds.
When examining the survival of sows on a within-herd basis, the
association between degree of leanness and longevity is much weaker.
In the Landrace breed, the trend is very small. The difference
in survival after the 4th parity between the lowest backfat category
(<10mm) and the highest (>18mm) is about 4% in favour of
the latter. In the Yorkshire breed the trend is more pronounced.
The difference in survival after the 4th parity between the highest
and lowest backfat categories is about 10% in favour of the latter.
4.2 Future Work
Given the above, there is merit in further investigating the relationship
between backfat thickness and survival rates in sows. To investigate
genetic relationships, differences in survival rates will be correlated
to backfat EBV instead of phenotypic backfat. Another approach
will be to develop a genetic model to estimate the genetic correlation
between backfat and longevity. If, based on the genetic correlation
estimate and the economic values of the two traits, it is decided
that longevity should be included in the breeding goal, then the
genetic model would be used to calculate EBV for longevity.
Three alternative types of genetic models exist for longevity.
One is a multiple binary trait system defining survival or culling
at each parity (Everett et al, 1976), as is currently done in
genetic evaluations of herdlife in Canadian dairy cattle. For
best results, this approach requires a threshold model. The use
of a conventional linear model with binary traits gives evaluations
of lower accuracy. The second is a single trait approach modelling
the lifetime number of parities, with censored records being replaced
by projected records (Van Raden and Klaaskate, 1993; Van Raden
and Wiggans, 1995), as is currently done in genetic evaluations
of herdlife in dairy cattle in the US. A third alternative is
to use survival analysis methods. Smith and Quaas (1984) estimated
heritability using these methods, and Ducrocq and Solkner (1994)
have developed freely available software which will estimate genetic
parameters and predict breeding values using this approach with
an animal model.
Cull codes are recorded in some of the farrowing data, and future
work could include an analysis of the reasons for culling in each
backfat group. Currently there is no cull code representing culling
for genetic improvement purposes in the national data formats,
and to include one in future would allow for more accurate investigation
of longevity.
Before sow longevity is included in the breeding goal for dam
lines however, it will be important to validate this work by examining
the relationship between the longevity of F1 sows and the backfat
EBV of their purebred parents. This will require the use of sow
productivity data from commercial herds where the parents of each
F1 can be traced back to the multiplier herds.
5. References
Ducrocq, VP and J. Solkner (1994) "The Survival Kit". A Fortran Package for the Analysis of Survival Data. Proc. 5th World Cong. Gen Appl. Livest. Prod. 22:51
Everett, RW, JF Keown and EE Clapp (1976) Production and Stayability Trends in Dairy Cattle. J. Dairy Sci. 59:1532
Kennedy, BW, VM Quinton and C Smith (1996) Genetic Changes in Performance Tested Pigs for Growth Rate and Fat Depth. Canadian J. of Animal Science 76:41
Smith, SP, and RL Quaas (1984) Productive Lifespan of Bull Progeny Groups: Failure Time Analysis. J. Dairy Science 67:2999
Van Raden, PM, and EJH Klaaskate (1993) Genetic Evaluation of Length of Productive Life Including Predicted Longevity of Live Cows. J. Dairy Sci. 76:2758
Van Raden, PM, and GR Wiggans (1995) Productive Life Evaluations:
Calculations, Accuracy and Economic Value. J. Dairy Sci. 76:2758
Table 1. Number of Breeding Sows and Unselected Gilts by Birthyear
a) Gilt Selection Datasets
| Landrace | Yorkshire | |||||
| Birthyear | Breeding sows (A) | Unselected gilts (B) | Ratio (A/B) | Breeding sows (A) | Unselected gilts (B) | Ratio (A/B) |
| before 1984 | 2,073 | 17,447 | 0.12 | 1,443 | 11,405 | 0.13 |
| 1984-1986 | 2,528 | 16,330 | 0.15 | 1,877 | 13,624 | 0.14 |
| 1987-1989 | 4,734 | 26,831 | 0.18 | 4,682 | 26,848 | 0.17 |
| 1990-1992 | 6,992 | 29,765 | 0.23 | 7,221 | 36,856 | 0.20 |
| 1993-1995 | 8,226 | 48,079 | 0.17 | 8,245 | 52,275 | 0.16 |
| Total | 24,553 | 138,452 | 0.18 | 23,468 | 141,008 | 0.17 |
b) Sow Longevity Datasets
| Birthyear | Landrace breeding sows | Yorkshire
breeding sows |
| before 1984 | 2,360 | 1,469 |
| 1984-1986 | 2,591 | 1,979 |
| 1987-1989 | 4,748 | 4,737 |
| 1990-1992 | 6,447 | 6,361 |
| 1993-1995 | 2,885 | 2,868 |
| Total | 19,031 | 17,414 |
Table 2. Proportion of Gilts Selected for Breeding by Backfat Class (number of candidates in parenthesis)
| Backfat class (mm) | |||||||
| < 10 | (10,12) | (12,14) | (14,16) | (16,18) | >18 | Over all pigs | |
| Yorkshire | 12.0% (16,544) | 14.8% (40,179) | 15.8% (47,513) | 15.4% (34,042) | 12.2% (16,584) | 7.6% (9,614) | 14.3% (164,476) |
| Landrace | 10.8% (15,343) | 15.4% (34,927) | 17.7% (42,133) | 16.3% (34,097) | 14.4% (21,375) | 9.4% (15,130) | 15.1% 163,005 |
Table 3. Proportion of Sows Surviving after each Parity by Backfat Class
a) Yorkshire
| Parity | Backfat class interval (mm), followed by number of pigs in parenthesis | ||||||
| < 10 (1061) |
(10,12) (3769) | (12,14) (5663) | (14,16) (4366) | (16,18) (1837) | > 18 (718) | Over all
pigs (17414) | |
| 1 | 65.4% | 70.1% | 76.1% | 77.2% | 80.2% | 83.4% | 75.2% |
| 2 | 45.0% | 51.7% | 59.2% | 61.8% | 66.5% | 71.4% | 58.6% |
| 3 | 29.6% | 38.6% | 45.0% | 49.1% | 54.1% | 59.6% | 45.3% |
| 4 | 20.5% | 28.6% | 34.1% | 38.7% | 44.8% | 51.1% | 35.1% |
| 5 | 13.2% | 20.2% | 25.5% | 29.9% | 35.6% | 41.8% | 26.4% |
| 6 | 9.0% | 14.4% | 17.8% | 22.3% | 26.2% | 31.8% | 19.1 |
b) Landrace
| Parity | Backfat class interval (mm), followed by number of pigs in parenthesis | ||||||
| < 10 (952) |
(10,12) (3395) | (12,14) (5559) | (14,16) (4731) | (16,18) (2898) | > 18 (1496) | Over all
pigs (19031) | |
| 1 | 61.2% | 69.3% | 76.1% | 79.6% | 81.8% | 83.4% | 76.5% |
| 2 | 43.2% | 50.2% | 60.3% | 66.6% | 69.8% | 72.6% | 61.6% |
| 3 | 29.5% | 36.9% | 47.2% | 54.7% | 58.4% | 63.3% | 49.3% |
| 4 | 18.2% | 25.6% | 36.4% | 43.9% | 48.4% | 53.9% | 38.7% |
| 5 | 11.4% | 17.1% | 26.7% | 35.1% | 40.0% | 45.1% | 29.8% |
| 6 | 7.0% | 10.8% | 19.3% | 26.6% | 30.1% | 36.0% | 21.9% |
Table 4. Within-Herd-Birthyear Deviations in Proportion of Gilts Selected for Breeding by Backfat Class (number of candidates in parenthesis).
| Backfat class interval (mm) | ||||||
| < 10 | (10,12) | (12,14) | (14,16) | (16,18) | > 18 | |
| Yorkshire | -3.2% (16544) | +0.1% (40178) | +1.4% (47513) | +1.4% (34042) | -1.2% (16582) | -4.8% (9614) |
| Landrace | -4.2% (15343) | -0.4% (34927) | +1.6% (42133) | +1.3% (34095) | +0.5% (21375) | -2.9% (15130) |
Table 5. Within-Herd-Birthyear Deviations in Proportion of Sows Surviving by Backfat Class
a) Yorkshire
| Parity | Backfat class interval (mm), followed by number of pigs in parenthesis | ||||||
| < 10
(1056) | (10,12) (3757) | (12,14) (5649) | (14,16) (4351) | (16,18) (1828) | > 18 (713) | Over all pigs (17354) | |
| 1 | -3.0% | -1.9% | +0.7% | +0.6% | +1.3% | +1.9% | 75.2% |
| 2 | -4.0% | -2.8% | +0.5% | +1.0% | +2.6% | +3.6% | 58.6% |
| 3 | -5.1% | -2.2% | -0.1% | +1.3% | +2.9% | +4.7% | 45.3% |
| 4 | -4.4% | -2.0% | -0.7% | +1.2% | +3.7% | +5.7% | 35.1% |
| 5 | -3.9% | -2.0% | -0.5% | +1.2% | +3.3% | +4.9% | 26.4% |
| 6 | -2.7% | -1.4% | -0.8% | +1.2% | +2.4% | +4.1% | 19.1% |
b) Landrace
| Parity | Backfat class interval (mm), followed by number of pigs in parenthesis | ||||||
| < 10
(949) | (10,12) (3384) | (12,14) (5546) | (14,16) (4721) | (16,18) (2890) | > 18 (1483) | Over all pigs (18973) | |
| 1 | -4.0% | -1.1% | +0.9% | +0.5% | +0.1% | 0.0% | 76.5% |
| 2 | -3.0% | -2.4% | +0.8% | +1.2% | +0.3% | +0.3% | 61.6% |
| 3 | -2.6% | -2.1% | +0.4% | +1.2% | 0.0% | +1.1% | 49.3% |
| 4 | -2.4% | -2.2% | +0.6% | +1.0% | -0.2% | +1.4% | 38.7% |
| 5 | -1.8% | -2.2% | 0.0% | +1.4% | +0.2% | +1.3% | 29.8% |
| 6 | -1.4% | -2.2% | +0.3% | +1.5% | -0.5% | +1.1% | 21.9% |