J.P. Gibson, K. Nadarajah, C.A. Aker and R.O. Ball
Ultrasound probes have been used for many
years in Canada and elsewhere to measure backfat depth on live
pigs for use in genetic evaluation programs. One objective of
the OPCAP was to compare existing measurements of backfat depths
with a wider range of measurements of fat depth, muscle depth
and muscle areas provided by more advanced "real time"
ultrasound machines. Real time ultrasound (also known as B mode
ultrasound) machines provide accurate images of fat and muscle
that should convey more information about the amount of lean and
fat in the carcass. Apart from the equipment used, there is also
the question of which are the best sites to take measurements,
and whether measurements at different sites provide information
about different aspects of carcass composition. Another issue
is ease of use in the field. Current probes are simple to use
and require no interpretation of images. Real time machines are
slightly more complex to use and provide stored visual images
that require detailed interpretation after scanning. Correct
interpretation is particularly important with cross-sectional
images where the correct location and angle to measure fat and
muscle depth must be determined along with the area of the muscle.
Longitudinal real-time measurements provide an image of fat and
muscle depths between 10th and 15th ribs. Such images provide
no information about the cross-sectional shape or area of the
muscle and fat, but they can be interpreted relatively rapidly
with the assistance of computer software such as AUSKey Auto-D
developed by Dr. J. Stouffer.
A summary of scanning sites and methods
is given by Aker (these proceedings). Cross sectional scans at
3-4th last rib, last rib and loin were interpreted manually by
three different interpreters over the course of the experiment.
Interpreters A, B and C interpreted images from 577, 495 and 1383
pigs. Only interpreter C interpreted the images from pigs which
had shoulder and ham measurements. Fat and muscle depths from
the longitudinal scans were obtained using the AUSKey Auto-D software
(J. Stouffer, Animal Ultrasound Services, Ithaca, NY) with interpreter
intervention to check positions of fat and muscle boundaries.
All longitudinal images were interpreted by a novice interpreter
(interpreter C) in Guelph and by an experienced interpreter at
Animal Ultrasound Services, Ithaca.
We explored the possibility of predicting
the following carcass traits using various combinations of ultrasound
The first four traits are measures of overall
leanness, whereas the last three are measures of where the lean
occurs. Details of statistical methods are given in Appendix
3. A large number of alternative analyses were performed and only
brief summaries of key results are presented here. Lean in three
primals, lean in shoulder, lean in loin and lean in ham are all
very closely related to each other and similar results were obtained
for each. We only give results for lean content in three primals
Predicting Lean Content
Some key results are summarized in Table 1. The R2 in Table 1 is the proportion of variation in lean content between animals that is explained by the prediction equation. An R2 of 1 would be a perfect prediction while an R2 of 0 would indicate no predictive power. For each situation the accuracy of prediction equations that include breed and sex is shown. This would generally be the equation recommended for use in practice. Also shown is accuracy of the prediction that only uses ultrasound information. This indicates the minimum accuracy when breed and sex effects are not known, which should apply if predictions were made for breeds other than those studied in the OPCAP.
The predictions from A-mode measurements
of fat depth gave an R2 of 0.62 (see Table 1). This
is based on the average fat depth at the two scanning sites; but
use of only one site caused little loss of accuracy, because measurements
at the two sites were very highly correlated. There was also little
difference in ability to predict leanness between measurements
at 90 kg versus 100 kg liveweight (results not shown).
Using real-time cross-sectional measurements
at a single site (last rib), the best interpreter achieved an
R2 of 0.74. The other two interpreters had R2
0.1 to 0.15 lower than this (results not shown). The best interpreter
was the most experienced, and produced more consistent and accurate
interpretations than the two less experienced interpreters. However,
the difference in accuracy between interpreters was reduced when
information from several sites was included in the prediction
of carcass lean (eg. compare best interpreter with interpreter
C when multiple sites were used, Table 1, R2 = 0.78
versus 0.72 and 0.74).
There was essentially no difference in
accuracy of predictions between longitudinal measurements interpreted
in Guelph or Ithaca, both giving an R2 of about 0.64.
This accuracy was about 0.1 lower than from cross-sectional measurements
at a single site with the best interpreter, and about 0.14 lower
than when two or more sites were used, regardless of interpreter.
Other than a difference in average leanness,
there was no evidence of differences between breeds in the relationship
between leanness and ultrasound measurements. This means that
a single set of regression equations can be used for all breeds,
which simplifies use on farm.
Predicting Distribution of Lean
Results for predicting lean distribution
are shown in Table 2. The R2 were generally quite low,
even when breed, sex and measurements from several sites were
included and predictions were based on the best interpreter. When
only ultrasound measurements were included, R2 dropped
substantially. For shoulder and loin there was still some potentially
useful predictive ability (R2 = 0.19 and 0.21), while
for ham there was very little predictive ability (R2
Discussion and Implications
The results show that cross-sectional images
produced by real-time ultrasound can be used to predict carcass
leanness with much higher accuracy than fat depths produced by
a conventional A-mode machine. Prediction was also much more accurate
than when using longitudinal real-time ultrasound images. Using
information from two cross-sectional sites (10th rib and last
rib) gives considerably higher accuracy than the best single site
(last rib), even when an experienced interpreter is involved.
Using two (or possibly more) sites becomes even more important
when a less experienced interpreter is involved.
Longitudinal images can be interpreted
with the aid of computer software (AUSKey Auto-D) which requires
less time and skill than interpretation of cross-sectional images,
especially if muscle area is used. Nevertheless, given the much
higher accuracy possible from cross-sectional images, the recommendation
is that real-time cross-sectional images at two sites should be
used whenever possible.
Accuracies of prediction of distribution
of carcass lean in the primals are probably too low at this stage
to recommend application on farm. Accuracies are sufficiently
encouraging, however, that it may be possible to find better predictors
in the future. It should be possible to explore various other
carcass measures in the OPCAP data to determine the limits to
prediction of lean distribution. It is likely, however, that further
trials will be required, perhaps using more advanced or more detailed
ultrasound measurements, before
prediction of lean distribution becomes available on farm.
Accuracy of prediction of percentage lean in three primals from
various ultrasound measurements.
|A-Mode Fat Depth||Cross-Sectional Real-Time|
|Breed, sex, ultrasound1||- Interpreter C2|
|Ultrasound only1||a. Measurements at all 3 back sites|
|Breed, sex, ultrasound|
|Carcass Fat Depth||Ultrasound only|
|Breed, sex, fat depth||b. Measurements at all 5 sites|
|Fat depth only||Breed, sex, ultrasound|
|- Best Interpreter||Longitudinal Real-Time|
|a. Measurements at last rib||- Interpreter C2|
|Breed, sex, ultrasound||Breed, sex, ultrasound|
|Ultrasound only||Ultrasound only|
|b. Best two locations (last rib|
|and 10th rib)3||Longitudinal Real-Time|
|Breed, sex, ultrasound||- AUS Interpretations4|
|Ultrasound only||Breed, sex, ultrasound|
Ultrasound includes all ultrasound measurements at that location
plus all quadratic and interaction terms among measurements within
2 Interpreter C interpreted all the longitudinal images at Guelph and was not as good as the best interpreter with individual cross-sectional images. When images at several locations were included, however, predictions were as good as with the best interpreter.
3 Addition of measurements at loin site did not improve accuracy.
4 Interpretations undertaken by Animal Ultrasound Services, Ithaca using the AUSKey Auto-D software.
Accuracy of prediction of distribution of lean in carcass.
|Measurements and Model|
|All Five Locations2|
|Breed, sex, ultrasound|
|Last Rib - Best Interpreter|
|Breed, sex, ultrasound|
|All Locations - Best Interpreter3|
|Breed, sex, ultrasound|