Cow body shape and automatic condition scoring

Full text

Cow Body Shape and Automation of Condition Scoring
I. Halachmi,*1 P. Polak,† D. J. Roberts,‡ and M. Klopcic§
*Institute of Agricultural Engineering, Agricultural Research Organization, Volcani Center, Bet Dagan 50250, Israel
†Research Institute for Animal Production, Slovak Agricultural Research Center, 949 92 Nitra, Slovakia
‡Scottish Agricultural College Dairy Research Centre, Crichton Royal Farm, Dumfries DG1 4SZ, UK
§Department of Animal Science, Biotechnical Faculty, University of Ljubljana, 1230 Domzale, Slovenia
ABSTRACT
The feasibility of including a body shape measure in
methods for automatic monitoring of body reserves of
cattle was evaluated. The hypothesis tested was that
the body shape of a fatter cow is rounder than that of
a thin cow and, therefore, may better fit a parabolic
shape. An image-processing model was designed that
calculates a parameter to assess body shape. The mod-
el was implemented, and its outputs were validated
against ultrasonic and thermal camera measurements
of the thickness of fat and muscle layers, and manual
body condition scoring of 186 Holstein-Friesian cows.
The thermal camera overcomes some of the drawbacks
of a regular camera; the hooks and the tailhead nadirs
of a thin cow diverged from the parabolic shape. The
correlation between thermal camera’s measurements
and fat and muscle thickness was 0.47. Mean body
condition scorings were 2.18, 2.15, and 2.23, with no
significant difference found across the assessment
methods. Further research is needed to achieve fully
automatic, accurate body condition scoring.
Key words: body condition scoring, thermal camera,
dairy cow, image processing
INTRODUCTION
Body condition scoring is a technique to estimate
energy reserves of cattle by estimating their fatness or
thinness according to a 5-point scale (Edmondson et al.,
1989). Body condition scoring is used as a feeding man-
agement tool. In high-yielding dairy cows, the peak of
daily feed intake usually occurs after the peak of milk
output. This asynchrony leads to a period early in lacta-
tion when cows cannot meet their energy requirements
from ingested feed and mobilize body energy reserves
to meet the deficit. This state is commonly known as
negative energy balance and is associated with a range
of negative health traits (Gillund et al., 2001) and poor
fertility (Dechow et al., 2002). Body condition influ-
ences productivity, reproduction, health, and longevity
(Heinrichs and Ishler, 1989). However, the current
method of measuring BCS is manual and subjective:
the scores depend on the person who performs the
measurements, and sometimes a given person might
give different scores to the same cow, depending on the
previous cows seen (Schröder and Staufenbiel, 2006).
Manual estimation of BCS is time-consuming in large
farms and requires trained labor. Therefore, the devel-
opment of a device for automatic, objective monitoring
of BCS is of economic interest.
Several attempts to automate BCS of dairy cows were
reported in the literature. Coffey et al. (2003) captured
digital images of the rear aspect of cows and extracted
curves manually by using image editing software and a
mouse to isolate the lines. Ferguson et al. (2006) record-
ed multiple images from the rear of the cow at an angle
of 0 to 20° relative to the tail head, and 3 nutritional
advisors independently assessed BCS from the images.
Bewley et al. (2007) used a digital camera placed above
a stationary weighing station and identified 23 points
corresponding to identifiable anatomical features for
potential influences on BCS. These points were used
to calculate 15 angles around the hooks, pins, and tail-
head. Keren and Olson (2007) used thermal imaging
in assessing energy requirements for cattle on pasture.
Sharony (2003) patented the application of a digital
camera for BCS; it was not a thermal camera and they
used a different algorithm, and Kriesel and McQuilkin
(2005) patented the application of another nonthermal
digital camera for measuring livestock dimensions, but
not for BCS determination.
With regard to nonphotographic measurements, Miz-
rach et al. (1999) measured subdermal fat thickness
in dairy cows by digitizing cross-sections of ultrasonic
scans. Of 2 sites selected for measurement, one was on
the flat area of the rear of the rump between the pin
bone and the tailhead, and the other was between the
12th and 13th ribs, below the rump. Williams (2002)
described ultrasound applications as a noninvasive
method for estimating fat and muscle accretion and
body composition in live cattle. Polák (2006) used ul-
J. Dairy Sci. 91:4444–4451
doi:10.3168/jds.2007-0785
© American Dairy Science Association, 2008.
4444
Received October 17, 2007.
Accepted July 18, 2008.
1
Corresponding author: halachmi@volcani.agri.gov.il

trasound measurements at 5 positions for determining
subcutaneous fat and muscle thickness.
None of the above studies effectively addressed the
automation of BCS determination. Therefore the aim
of the present study was to advance the development of
an apparatus and methods for automatic and objective
monitoring of body reserves. The hypothesis tested was
that the body shape of a fatter cow is more likely to be
round than that of a skinny cow; therefore, a parabolic
shape may fit better. The hooks and the tailhead na-
dirs of a skinny cow diverge from the rounded shape
defined by the parabola.
MATERIALS AND METHODS
Data
Data for this study were collected at the Scottish Ag-
ricultural College, Crichton Royal Farm in Dumfries,
Scotland, UK, in September 2007. The study involved
186 cows.
Statistical Terms and Methods
The abbreviations STD, SE, and MAE stand for
standard deviation, standard error, and mean absolute
error statistics. The deviation of the cow contour from
the fitted parabola was expressed in MAE. Matlab
(2005) software was used to calculate the cow contour,
MAE, and the STD. Each cow was sampled during 3
successive days at the same time—0500 to 0700 h at
the milking parlor exit, after the morning milking. The
values averaged over 3 d were used for cross correla-
tion with image data and presented in Figures 4 to 6.
The validation test was performed by comparing the
results with human observations and ultrasound mea-
surements. The SPSS software was used to calculate
nonparametric Spearman’s rho correlation coefficient
and ANOVA. “Reference numbers” stands for the hu-
man observations (i.e., manual BCS measured by 2
BCS technicians, and ultrasound BCS measured by
using ultrasound).
Anatomic Terms
The anatomic terms used in this study are as follows:
Hooks are the point of the hip; the most lateral point of
the ilium also known as the tuber coxae or coxal tuber.
The tailhead is the dorsal aspect of the root of the tail.
The pins are the caudal point on the floor of the pu-
bis, also known as the tuber ischium, or pin bone. The
Latin names are described by Schröder and Staufenbiel
4445
OUR INDUSTRY TODAY
Journal of Dairy Science Vol. 91 No. 11, 2008
Figure 1. A cow contour from bird’s eye view; 23 key anatomical points (Bewley et al., 2007) and the location of the ultrasound probe
(Mizrach et al., 1999). One = left (l) forerib; 2 = l short rib; 3 = l hook start; 4 = l hook anterior midpoint; 5 = l hook; 6 = l hook posterior
midpoint; 7 = l hook end; 8 = l thurl; 9 = l pin; 10 = l tailhead nadir; 11 = l tailhead junction; 12 = tail; 13 = right (r) tailhead junction; 14 =
r tailhead nadir; 15 = r pin; 16 = r thurl; 17 = r hook end; 18 = r hook posterior midpoint; 19 = r hook; 20 = r hook anterior midpoint; 21 = r
hook start; 22 = r short rib start; 23 = r forerib.

(2006): anterior coccygeal vertebrae (tailhead), tuber
sacrale (hook bones), and tuber ischia (pin bones); see
Figure 1.
Ultrasound
The reference numbers for determining body re-
serves were the thicknesses of the muscle and fat lay-
ers, and the manually assessed BCS. The thicknesses
were measured ultrasonically with a Sonovet 2000
instrument (Medison, Korea), fitted with a 96-element
PB-MYL 2–5/1 170-mm linear probe, operating at 2 to
5 MHz. The sonogram of the longissimus dorsi muscle
(LDM) was obtained between the 12th and the 13th
vertebrae. The thickness of the fat was taken as the
distance between the dorsal fascia of the LDM and
the ventral skin layer. The thicknesses of the fat and
muscle layers (in millimeters, the so-called Tot_mm)
were related to the 1 to 5 BCS scale by:
Ultrasound scoring = 5 × [log(Tot_mm) – 3.6] [1]
in which Tot_mm was the thickness of the fat and
muscle layers (in millimeters, not pixels), and the num-
bers 5 and 3.6 normalized the ultrasound units into the
1 to 5 BCS scale. Use of the log function compressed
the wide variation found in our ultrasound measure-
ments.
The location between the 12th and the 13th verte-
brae was selected because this point provides both easy
recognition and the presence of subcutaneous fat and
muscles. We assume that subcutaneous fat and proteins
from muscles are mobilized to support milk production,
especially in lactation peak. Therefore, changes in the
volumes of subcutaneous fat and muscles, as indicated
by the measured thickness between the bone and the
skin at this point, may be correlated with changes in
the BCS.
The location of the probe is on the back near the
spinal column on the assumed line between 12th and
13th ribs. The probe and the spinal column created an
acute angle. The background of the muscle edges in the
sonogram is the vertebra, the LDM, and the intercos-
tal muscle. Muscle thickness is the distance between
body of the 13th vertebra—represented by a specific
V-shape in the sonogram—and the dorsal surface of
the LDM, measured perpendicularly from the top of
the sonogram.
Manual Body Condition Scoring
The manual BCS was assigned according to a 5-point
scale by 2 different technicians (Edmondson et al.,
Journal of Dairy Science Vol. 91 No. 11, 2008
HALACHMI ET AL.
4446
Figure 2. A thin cow (left, cow number 1358) and a fat cow (right, cow number 1640). Upper pictures are the model inputs: thermal im-
ages taken from overhead. Lower pictures are the model outputs: cow contour vs. fitted parabola. The fat cow (1640): manual BCS = 3.0,
ultrasound-measured fat plus muscle thickness = 74 mm (3.52 in BCS units). Model thermal BCS = 3.50. The thin cow (1358): manual BCS
= 1.25, ultrasound-measured fat plus muscle thickness = 40 mm (1.44 in BCS units). Model thermal BCS = 1.3.

1989). The BCS assessment and the ultrasound and
thermal camera measurements were performed in the
same week.
Thermal Camera and Image Processing
A model InfraCAM SD thermal camera (FLIR Sys-
tems Inc., Wilsonville, OR) equipped with a focal plane
array detector with resolution of 120 × 120 pixels and a
spectral range of 7.5 to 13 μm was attached to the barn
ceiling, above the weighing scale at the exit of the milk-
ing parlor. The cows were identified electronically by
means of the radio frequency identification technique.
The antenna was built into the weighing scale.
The video imagery from the camera was divided into
frames by using Movie Plus 4 software (Serif, 2007).
The frames were manually examined to select the best
frame from each cow. Identified frames—those that
were associated with a cow number and that matched
the timing of the cow on the scale—were fed into Mat-
lab (2005) software for image processing analysis. Each
frame contained 787 × 576 pixels, from which a 1.3-MB
bitmap graphic file was generated. Figure 2 presents
the raw thermal images of 2 typical cows, one fat and
one thin. The images were read with the Imread func-
tion then converted to a gray scale by using the Rgb2g-
ray function. The black cursors that indicate the points
of temperature measurement were erased by using the
Roifill function. The place where the cow was expected
to be found was cropped from the original picture to a
specified rectangle by means of the Imcrop function.
The Imcontour function was used to define each unique
cow’s boundary, and the Polyfit function was used to fit
a parabola to the boundary of each individual cow. The
visual difference between fat and thin cows is presented
in Figure 2: it can be seen that for a fat cow, only the
tailhead diverges from the rounded shape of the fitted
parabola, whereas there are many deviations from this
shape with a thin cow. The “distance from a parabola”
was converted to the 1 to 5 BCS scale by
TBCS = 5 × 9 × (1/MAE) [2]
in which MAE stands for mean absolute error; TBCS
stands for thermal BCS; 9 is the best fit reached in our
herd (i.e., only the tailhead diverged from the parabolic
shape); and 5 is the normalization factor from model
output to 1 to 5 on the BCS scale. The deviation of the
cow contour from the fitted parabola was expressed in
MAE units. Thus, the hypothesis tested in this study
is as follows: If a cow is fatter, her body shape is more
likely to be round and the parabola might fit the cow’s
shape better; therefore, the MAE would be smaller.
4447
OUR INDUSTRY TODAY
Journal of Dairy Science Vol. 91 No. 11, 2008
Figure 3. An algorithm for automatic evaluation of thermal body
condition score (TBCS). The algorithm incorporated the following
tasks: (a) extracting cow frames from the video with the MoviePlus4
software (Serif, 2007); this was the only task that was performed
manually (broken line arrow—). (b) Cropping the place where the
cow was expected to be found, with the imcrop function (Matlab,
2005). (c) The black cursors, temperature numbers, etc., were erased
by using the roifill function (Matlab, 2005), (d) the Imcontour func-
tion (Matlab, 2005) found the unique cow individual boundary, and
(e) the polyfit function (Matlab, 2005) fitted a parabola to that bound-
ary. MAE stands for mean absolute error between the cow contour
and its fitted parabola. If the cow is thin, the cow contour has protru-
sions that result in a high MAE. A fat cow is characterized by round-
smooth contour, and then the MAE is lower.

Conversely, if a cow is thin, her body shape is less
round, and therefore, the MAE is larger.
The entire image process flowchart is presented in
Figure 3: it can be seen that the process is automatical-
ly executed apart from 1 manual phase—the selection
of the best frame for each cow, which requires further
programming.
Treating Artifacts
Only 9 cows were removed from the database because
of wrong positioning of the cow below the camera: hooks
out of sight or the cow not aligned as required. Five
cows were removed from the ultrasound database be-
cause of a large deviation between their measurements
made on the first day and those made on other days.
Such deviation could be attributed to wrong cow iden-
tification or incorrect location of the ultrasound probe
on the animal. The total number of cows removed from
the database for these reasons was 14 (n = 186 – 14 =
172).
RESULTS
This chapter compares between the thickness of
the body reserve as measured by (1) the manual BCS,
(2) the ultrasound (US), and (3) the thermal camera
(TBCS). The chapter is divided into 2 sections: (a) vali-
dation by visual analysis and (b) statistical validation.
Visual Analysis
Figure 4 shows the correlation between the TBCS
and the manual BCS: the regression line slope should
tend to 45° and the intersect should approach 1, 1. Be-
cause the TBCS is a continuous scale but manual BCS
Journal of Dairy Science Vol. 91 No. 11, 2008
HALACHMI ET AL.
4448
Figure 4. The link between the thermal camera scoring and the
manual BCS.
Figure 5. The link between the thermal camera BCS and the
scoring as measured ultrasonically.
Figure 6. The link between the body condition scoring as mea-
sured ultrasonically and the manual BCS.

is categorical, distribution at each BCS for the TBCS
can be seen in Figure 4.
Figure 5 shows the correlation between the TBCS
and the US. The regression line slope should tend to
45°, and the intersect should approach 1. Figure 6
shows the correlation between the US and the manual
BCS results. Boxplots (Figure 7) provide a visual sum-
mary of the 3 BCS methods.
Statistics
The significance value of the F-test in the ANOVA
table is 0.443; thus, no significant difference found
across the BCS assessment methods (Table 1). The
difference between group means was not significant
(Table 2). However, the means of each BCS level (Table
3) suggest that the US was less accurate in the range
2.00 to 2.25 BCS and the thermal BCS was less accu-
rate in 1.75 and 2.50 BCS cows.
The correlation coefficient between the manual and
the thermal scoring was 0.315 (Table 4). The highest
correlation (0.471) was found between the thermal and
the ultrasound results. The manual BCS had a con-
stant shift of 0.25 score, technician 2 was higher. The
correlation between the 2 technicians was 0.78.
DISCUSSION
Coffey et al. (2003) found that the correlation be-
tween tail head curvature and condition score was
0.55, and that between pin bone and BCS was 0.59. In
the present case the correlation between the manual
BCS and the TBCS was 0.315, and that between the
TBCS and the ultrasound measurements was 0.471.
The difference between the findings of Coffey et al.
(2003) and the present results can be attributed to the
respective numbers of cows ejected from the databases.
In the present study only 9 cows, which had hooks or
tailheads outside the field of view of the camera lens,
were removed from the database. In contrast, Coffey et
al. (2003) reported that out of 190 cows, only 36 yielded
images suitable for data extraction; these authors
advocated development of the body shape parameter.
Perhaps if more cows had been filtered out of the pres-
ent database our present correlation might have been
higher. The correlation between the 2 observers was
0.78, which is in agreement with the findings of Fergu-
son et al. (2006), whose correlation coefficients between
observer 1, on one hand, and observers 2, 3, and 4, on
the other hand, were 0.78, 0.76, and 0.79, respectively.
The thermal camera’s zoom should capture most of
the cow back. Those cows where the hooks were out-
side the visible area were most likely to be expelled
from the model. This result is in agreement with
Bewley et al. (2007), who also found the hooks were
the easiest to identify and that the angles around the
hooks and tailhead had the highest correlations with
BCS. Edmondson et al. (1989) found that the correla-
tions between BCS and hook posterior angle, between
BCS and hook angle, and between BCS and tailhead
were 0.52, 0.48, and 0.31, respectively. Lowman et al.
(1976) obtained lower but still significant correlations
between BCS and all 3 body traits: 0.46 between BCS
and hook posterior angle, 0.33 between BCS and hook
angle, and 0.19 between BCS and tailhead. Higher
camera mounting or a wider-angle lens might improve
our results, and a 3-D picture, obtained by means of an
additional camera, could further improve the accuracy
of the device. The main advantage of using a thermal
camera rather than a regular digital camera lies in the
ease of recognition of cow patterns; in our case almost
all the cow images were suitable for analysis.
The MAE contains measuring errors, but the errors
are related with the choice of the parabola. The char-
4449
OUR INDUSTRY TODAY
Journal of Dairy Science Vol. 91 No. 11, 2008
Table 1. Statistical validation—descriptive and ANOVA comparisons of thermal, ultrasound, and manual BCS
Item
Manual
BCS
Ultrasonic
BCS
Thermal
BCS Item
Sum of
squares df
Mean
square F (2,454) P-value
Mean 2.18 2.15 2.23 Between groups 0.505 2 0.253
SE 0.03 0.05 0.05 Within groups 140.450 454 0.310 0.815 0.443
n 172 172 172 Total 140.955 456
Table 2. Statistical validation—multiple comparisons (Games-Howell)1
I category J category Mean difference I − J SE P-value
Manual Ultrasound 0.03 0.06 0.851
Manual Thermal −0.05 0.06 0.691
Ultrasound Thermal −0.08 0.07 0.501
1BCS measured by manual, ultrasonic, and thermal methods.

acteristics of the parabola are not used in this study.
In further research, taking into the equations the
characteristics of the parabola in each individual cow
may increase the accuracy of the method. In further
research, the thermal camera output, together with its
body shape parameters should be studied during the
entire course of the cows’ lactation to compare between
different physiological states such as advanced preg-
nancy, developing of negative energy balance, etc.
The existence of precision in the ultrasound mea-
surement of the thicknesses of fat and muscle is an
assumption and not a fact. However, in the process
of developing a measuring device, the accuracy must
be validated against a reference number. The manual
BCS is commonly used but has measurement errors, is
subjective, and uses a scale of discrete points (e.g., 1.5,
1.75, 2, 2.5, etc.). Our study aimed to use a continuous
scale, but the attempt to correlate continuous-scale
findings (i.e., the output of the new device) with those
of a discrete-points system (i.e., our reference number)
obtained by manual BCS is categorical and leads to
statistically inferior results. Grouping the continuous
results into discrete clusters might improve the statis-
tics but would impair an important feature of the new
device (i.e., the continuous scale). Ultrasonic measure-
ment has a continuous scale, and our initial thought
was that it is objective. However, during the study it
emerged that an ultrasound operator might influence
the results: (1) by not returning to exactly the same
measurement location on every cow, and (2) through
his interpretation of the ultrasound picture. In further
research, the adoption of an objective method is crucial
to obtaining higher statistical correlation. Both draw-
backs, the discrete-points scale and the subjectivity can
be overcome by examination of a large number of cows
by several technicians working in parallel.
In further research, TBCS should be validated
against carcass composition at a slaughter house rath-
er than BCS. The biology behind the use of thermal
methods (the thermal isolation of fat layers) should be
understood.
If the new device is to be inexpensive and mobile,
it should be possible, after its parameters have been
calibrated, to apply it also to beef cattle: on pasture,
to support a decision on when to move a group from
one grazing field to another; and in feedlots, to help
determine the optimal marketing time. The utility of
a low-cost, automatic, and accurate BCS in dairy herd
management is not in question. However, for genetic
purposes, Pryce et al. (2006) stated that there would be
little benefit in including BCS as an independent trait
in the breeding worth dairy index. In New Zealand
BCS is already included as a predictor in the genetic
evaluation of fertility; breeding values for BCS will be
estimated routinely from the fertility model.
Journal of Dairy Science Vol. 91 No. 11, 2008
HALACHMI ET AL.
4450
Figure 7. Boxplot comparison of thermal, ultrasound (US), and
manual body condition scorings. The heavy black line inside each box
marks the 50th percentile, or median, of that distribution. The lower
and upper box boundaries mark the 25th and 75th percentiles of each
distribution, respectively. The whiskers are vertical lines ending in
horizontal lines at the largest and smallest observed values that are
not statistical outliers. Outliers are identified with an O. Extreme
values are marked with an asterisk (*).
Table 3. Statistical validation—ANOVA comparison of thermal, ultrasound, and manual body condition
scorings—with manual BCS levels expressed as classes
Item n
Ultrasonic BCS
Mean SE Minimum Maximum
Manual BCS (classes)
1.75 28 1.74 0.11 0.62 2.61
2.00 35 2.24 0.11 0.83 3.25
2.25 77 2.04 0.06 0.76 3.35
2.50 32 2.60 0.10 1.41 4.09
Thermal BCS
1.75 31 1.98 0.10 1.24 3.96
2.00 39 2.08 0.08 1.31 3.28
2.25 72 2.24 0.08 1.28 3.71
2.50 30 2.68 0.14 1.45 4.43

CONCLUSIONS
A model based on thermal camera and image process-
ing algorithms, intended for evaluation of cows’ body
reserve, was designed and was implemented on a small
number of cows. Results suggest that further study
with more cows may lead to a means for automating
BCS monitoring. The onus is now on the industry to
further develop the methodology described above.
ACKNOWLEDGMENTS
The authors thank the anonymous reviewers of the
JDS for their critical comments that reshaped the
paper into its final version and also encouraged us
to rework the paper. The study was financed by the
Marie Curie project number MTKI-CT-2005–029863
of the European Union. All authors also acknowledge
support under a previous EU project (Young-Train,
contract number 016101, coordinated by Cled Thomas
and Andrea Rosati from the EAAP).
Thanks are also due to the farm workers of the
Scottish Agricultural College, Crichton Royal Farm
in Dumfries, Scotland, UK. Special thanks are due to
Ainsley Bagnall of the Scottish Agricultural College; to
Antonia White of True North Innovation, UK for assis-
tance in writing; to Robert Boyce of IceRobotics, UK for
coordinating the Marie Curie project; to Oliver Lewis,
Chloe Capewell, and Robin Dripps of IceRobotics for
technical support; and to the other partners in the proj-
ect, Jeffery Bewley, University of Kentucky, and Peter
Lovendahl and Lene Munksgaard from the Research
Center Foulum, Denmark, for scientific advice. Thanks
to Hillary Voet from the Hebrew University of Jerusa-
lem (Israel) for her statistical help. Thanks to Wiebe
Koops from Wageningen University (the Netherlands)
for his statistical help.
REFERENCES
Bewley, J. M., A. M. Peacock, O. Lewis, D. J. Roberts, M. P. Coffey,
and M. M. Schutz. 2007. Estimation of body condition score in
dairy cattle using digital images. 58th Annu. Meet. Eur. Assoc.
Anim. Prod. (EAAP). http://www.eaap.org/Dublin/Sessions/
Session_21.htm Accessed Sep. 30, 2007.
Coffey, M. P., T. B. Motram, and N. McFarlance. 2003. A feasibility
study on the automatic recording of condition score in dairy cows.
Page 131 in Proc. Annu. Meet. Br. Assoc. Anim. Sci. March 2003
York. British Assoc. Anim. Sci. BSAS, Penicuik, Midlothian,
UK.
Dechow, C. D., G. W. Rogers, and J. S. Clay. 2002. Heritability and
correlations among body condition score loss, body condition
score, production and reproductive performance. J. Dairy Sci.
85:3062–3070.
Edmondson, A. J., I. J. Lean, L. D. Weaver, T. Farver, and G.
Webster. 1989. A body condition scoring chart for Holstein cows.
J. Dairy Sci. 72:68–78.
Ferguson, J. D., G. Azzaro, and G. Licitra. 2006. Body condition
assessment using digital images. J. Dairy Sci. 89:3833–3841.
Gillund, P., O. Reksen, Y. T. Grohn, and K. Karlberg. 2001. Body
condition related to ketosis and reproductive performance in
Norwegian dairy cows. J. Dairy Sci. 84:1390–1396.
Heinrichs, A. J., and V. A. Ishler. 1989. Body condition scoring
as a tool for dairy herd management. Extension Circular 363,
College of Agriculture Cooperative Extension, University of
Pennsylvania.
Keren, E. N., and B. E. Olson. 2007. Applying thermal imaging
software to cattle grazing winter range. J. Therm. Biol. 32:204–
211.
Kriesel, M. S., and G. L. McQuilkin. 2005. Apparatus and methods
for the volumetric and dimensional measurement of livestock.
United States Patent Application 20050257748. Filing Date:
05/19/2005 (Saint Paul, MN). Publication date: 11/24/2005.
Lowman, B. G., N. A. Scott, and S. H. Somerville. 1976. Condition
scoring of cattle. Bull. No. 6. East of Scotland Coll. of Agric.,
Edinburgh, Scotland.
Matlab. 2005. The image processing toolbox user’s guide. MathWorks,
Natick, MA. Mizrach, A., U. Flitsanov, E. Maltz, S. L. Spahr, J. E.
Novakofski, and M. R. Murphy. 1999. Ultrasonic assessment of
body condition changes of the dairy cow during lactation. Trans.
ASAE 42:805–812.
Polák, P. 2006. Evaluation of beef production by sonographic
and photometric method. PhD thesis. Slovak University of
Agriculture, SCPV-RIAP Nitra, Slovakia.
Pryce, J. E., B. L. Harris, D. L. Johnson, and W. A. Montgomery.
2006. Body condition score as a candidate trait in the breeding
worth dairy index. Proc. N.Z. Soc. Anim. Prod. 66:103–106.
Schröder, U. J., and R. Staufenbiel. 2006. Invited review: Methods to
determine body fat reserves in the dairy cow with special regard
to ultrasonographic measurement of backfat thickness. J. Dairy
Sci. 89:1–14.
Serif. 2007. Online. http://www.serif.com/MoviePlus/MoviePlus4/
index.asp Accessed Sep. 2007.
Sharony, D. 2003. Imaging system and method for body condition
evaluation. EU Pat. No 1537531. Filing date: 07/27/2003 (Israel).
Publication date: 06/08/2005.
Williams, A. R. 2002. Ultrasound applications in beef cattle carcass
research and management. J. Anim. Sci. 80(E. Suppl. 2):183–
188.
4451
OUR INDUSTRY TODAY
Journal of Dairy Science Vol. 91 No. 11, 2008
Table 4. Statistical validation—correlation coefficients: comparison of thermal camera, ultrasound, and
manual body condition scorings; nonparametric Spearman’s rho correlation coefficient
Item Ultrasound BCS Manual BCS Thermal BCS
Ultrasound BCS 1 0.379(**) 0.471(**)
Manual BCS 0.379(**) 1 0.315(**)
Thermal BCS 0.471(**) 0.315(**) 1
**Correlation is significant at the 0.01 level (2-tailed). Reject the possibility of zero correlation.