As a result of continued selection against backfat
thickness, Canadian pigs are getting closer to an optimum level
of leanness for domestic and foreign markets. As this occurs,
it becomes increasingly important to select on all heritable components
of carcass quality, rather than only lean yield. Also, selection
must be geared towards the improvement of traits which directly
increase the profits of the commercial pork producer.
One of the steps taken toward this goal has been the development of national genetic evaluations for feed efficiency and carcass quality traits. Three national genetic evaluations have been carried out so far (one each calendar quarter), and next year they will replace the genetic evaluation for backfat.
The Genetic Evaluations
The new genetic evaluation system produces estimated breeding values (EBVs) for lean yield, feed conversion, and loin eye area, using a multiple trait animal model. It makes use of a relational database which stores all available pedigree and performance data back to the 1970s. The evaluation system uses measurements on both the live animal and the carcass.
Live-animal measurements include ultrasonic backfat depth, loin muscle depth, and age at 100 kg. We began recording loin muscle depth on a large scale in our home test program in 1996, using the A-mode USM2 probe from the Krautkramer Company, with a 126 mm, 3.5 MHz transducer. The research work on this was carried out by the Québec swine centre (CDPQ). In this work, with a well-trained Krautkramer technician and full cut-outs of 180 pigs, Dion et al. (1998) obtained more accurate predictions of lean yield and loin eye with the Krautkramer USM2 than they did with real-time scans from the Aloka 500.
About 90,000 purebreds are performance tested each year on the swine improvement program, and half of them, including all sire line animals, are now probed for loin muscle depth. Krautkramer no longer manufactures A-mode probes, and it is hoped that real-time probes can eventually be used to get more accurate measurements or information on new traits such as marbling. The US50 real-time probe from Alliance Medical is already being used, and could become the standard in future years.
The carcass measurements included in the evaluation are lean yield, weight of lean in the loin, and loin eye area. These measurements are taken on purebred carcasses in slaughter plants. The genetic evaluation currently includes several thousand of those records.
Research at CCSI showed that if the backfat data
recorded prior to 1990 is not included in the evaluation, the
effect on the EBV of current animals is negligible, but there
is a large saving in computing requirements. Consequently, the
data used in the evaluations comprises:
No feed intake data is used at the moment, but
records are being collected and they will be introduced into the
system next year. Because there are no feed intake records at
the moment, the mixed model equations currently do not include
equations for feed intake or feed efficiency breeding values.
Heritabilities for backfat, ultrasonic loin muscle
depth, loin eye, lean in the loin, and lean yield are assumed
to be 52%, 25%, 40%, 40%, and 40%, respectively. A litter variance
component is assumed which is 10% of the phenotypic variance for
each trait. Genetic correlations with lean yield are assumed
to be -0.66, +0.29, +0.65, and +0.78 for backfat, ultrasonic loin
muscle depth, loin eye, and loin yield, respectively.
Genetic correlations with feed intake from 25
to 100 kg liveweight are assumed to be +0.43 for age at 100 kg,
-0.10 for lean yield, and zero for the other carcass traits.
Figure 2 shows the average estimated breeding
value for feed conversion ratio (ratio of feed:gain), plotted
against year of birth for each breed, and expressed in the same
way as in Figure 1.
Figure 3 shows the average estimated breeding
value for loin eye area in square centimeters, plotted against
year of birth for each breed, and expressed in the same way as
in Figure 1.
Annual genetic improvements of -0.5 mm of backfat
and -1 day to 100 kg are translating into +0.2% annual improvement
in lean yield, -0.014 units annual improvement in feed conversion
(about 1.05 kg less feed consumed from 25 to 100 kg liveweight),
and +0.3 square centimeters annual improvement in loin eye area.
Summary and Conclusions
Various major enhancements are being made to
the Canadian Swine Improvement Program. These are summarized
1. Improved selection indexes
Lean yield, feed conversion, and loin eye area
are economically important in pork production. New selection
indexes have been produced which include these EBVs instead of
the backfat EBV which appeared in previous indexes. The new indexes
are more relevant to the commercial producer, since they are based
on EBV for traits which impact directly on his revenue or costs.
2. More performance test data for improved carcass trait EBV
The widespread collection of loin muscle depth
data is increasing the accuracy of genetic evaluations for carcass
lean yield. The collection of carcass data from slaughter plants,
on a more limited basis, provides sib testing in elite litters
which has a large effect on the accuracy of EBV of the best animals.
3. Use of feed intake data for improved feed conversion EBV
Based on the economic values used in the new
selection index, and estimated heritabilities and genetic correlations
between traits, research (manuscript in preparation) at CCSI has
shown that inclusion of feed intake data in genetic evaluations
can substantially increase total economic response to selection
(by 43% if total feed intake over the grower-finisher period is
measured, and by 23% if a feed intake measure is recorded which
accounts for 50% of the genetic and phenotypic variation in total
intake over the grower-finisher period).
However, individual feed intake is costly to
measure, and it is unlikely that large amounts of data will be
available from the field. An alternative is to measure the average
feed intake of pens, with an appropriate family structure in each
pen. Research at CCSI (a manuscript in preparation, results summarized
in Appendix 1) shows that with 12 pigs per pen and littermates
grouped together, total economic response to selection can be
increased by 25%.
Therefore, we plan to use both individual and
pen-average feed intake data in the new genetic evaluation system.
Random regression modelling of growth and feed
intake, using feed and age data from any given weight (i.e., including
young pigs) is currently underway. The random regression approach
also allows early genetic evaluations of young pigs and a more
accurate selection of which boars to castrate and which to keep
Dion, N., D. Pettigrew, G. Dumas, and J.P. Daigle.
1998. Accuracy of prediction of lean yield, loineye area and
marbling from measurements on live pigs. Proc. 6th World Cong.
on Genet. Appl. to Livestock Prod. 23:567-570, Univ. of New
Harris, D.L., and S. Newman. 1992. How does
genetic evaluation become economic improvement? Proc. of the
Symp. on Application of EPD to Livestock Improvement, Pittsburgh,
PA. Published by ASAS.
Stewart, T.S., D.H. Bache, D.L. Harris, M.E.
Einstein, D.L. Lofgren, and A.P. Schinkel. 1990. A bioeconomic
model for swine production: application to developing optimal
multitrait selection indexes. J. Anim. Breed. Genet. 107:340.
The EBV index used is:
-5.37BACKFAT - 1.17AGE AT 100KG -98.25FEED CONVERSION
The weights are in Canadian dollar units, and
the index is designed to measure differences in profit per commercial
litter from terminal sires which have the given index values.
The new selection index for sire lines on the Canadian Swine Improvement Program is:
5.94LEAN YIELD - 1.35AGE AT 100KG - 93.75FEED
CONVERSION RATIO + 0.40LOINEYE(cm2) (2)
The index (1) used in this study is a simplification
of (2) which closely approximates (2), taking into account the
index weights in (2) and the genetic correlations between the
traits in (2) and the traits in (1). Specifically, the vector
b, of index weights in (1) are calculated as:
b = G-1Gca
where G is the genetic covariance matrix for
traits in (1), Gc is the matrix of genetic covariances
between traits in (1) and traits in (2), and a is the vector of
index weights in (2).
The difference between indexes (1) and (2) is
that index (1) does not contain loin eye area EBV, and it contains
backfat EBV instead of lean yield EBV. Because few animals have
direct measurements of carcass loin eye area, and because backfat
is highly correlated to lean yield, index (1) is highly correlated
(> 0.95 across the hometest population) to index (2).
Realistic genetic parameters are assumed, and
selection index methods are used to predict genetic responses,
assuming that feed intake in a pen-average recording environment
is the same genetic trait as feed intake in a situation where
the pig eats from an individual recording machine.
The benefits in Table 1 below are measured as
the superiority of the top 10% of pigs on EBV index, in each situation.
For a given cost investment, pen-average recording gives more genetic improvement than individual recording.
For example, if we use pen-average recording
with 12 pigs per pen and littermates are penned together, the
top 5.5% of pigs selected will have economic superiority of $34.80.
This is equivalent to using individual recording and selecting
10% (see table).
Thus, at 12 pigs per pen, if the cost per pig
of individual recording is more than twice the cost of pen-average
recording, the breeder will get more genetic improvement per unit
cost by recording pen averages. The benefit of penning littermates
together is very substantial.
Breeders should compare annual depreciation costs
on individual intake recording machines with the extra effort
required to put ad-lib feeders in each pen and record the amount
of feed used by each one.
|Table 1. Superiority of the top 10% of pigs on selection index, when
|Type of recording|
|Individual (total intake)|
|Individual (correlation = 0.7 with total)|
|Average of 12 per pen, littermates apart|
|Average of 12 per pen, littermates together|
|Average of 4 per pen, littermates apart|
|Average of 4 per pen, littermates together|