Use of Random Regressions for Estimating Heritability of Body Scores in Adapted Saudi Holstein Cows
The objective of this study was to estimate the heritability and variance component of Body Condition Score (BCS) of adapted Holstein Friesian in the Kingdom of Saudi Arabia (KSA). The analysis of test day records for dairy cattle and covariance functions of random regression analysis allowed a continuous change of variances and co-variances of BCS on different lactation parts, cow ages and levels of daily milk production. Quadratic Legendre polynomials were used to estimate (co)variances of random effects. A model for analyzing test day records containing both the fixed and random regression coefficients was applied for genetic evaluation of first three lactations. Data was 45,349 BCS observations from calvings between 1989 and 1998 from four different dairy herds in KSA. Each evaluated animal received at least two measurements for BCS representing the random regression coefficients. Three genetic measures of BCS such as cow age, days in milk and the levels of daily milk production were compared. Overall means of BCS across early, mid and late part of production live were 2.8±0.10, 3.3±0.05 and 2.9±0.04, respectively. Based on the random regression solutions, the estimated heritability ranged from 0.19-0.53 for various parts of lactation and 0.02-0.48 across different cow age for BCS. The highest estimate of heritability of BCS based on random regression function of daily milk yield was 0.37 at 10 kg day-1. The corresponding lowest estimate of heritability was 0.26 at 35 kg day-1. The study showed a potential of using random regressions for estimating heritability in adapted animals under different environments.
Received: January 09, 2013;
Accepted: January 14, 2013;
Published: May 03, 2013
Body condition scoring is an important management practice used by producers
as a tool to optimize production, evaluate health and assess nutritional status.
This practice helps producers evaluate the amount of fat and muscle carried
by the animal. Failure to recognize these animals that are too fat or too thin
for their stage of lactation and taking action is expensive for disease treatments,
lost milk production and decreased fertility. If body condition scoring is conducted
at planned intervals throughout the production cycle, nutrition and management
can be altered if needed (Lowman et al., 1976).
During the production cycle, re-breeding, mid-gestation, parturition and weaning
are the most critical times to body condition scoring in animals. The practice
of body condition scoring is used mainly to increase economic returns through
increased reproductive performance and realize more efficient feed costs (Fox
et al., 1999). Dairy cows use BCS system ranging from 1.0, a very
thin cow with no fat reserves, to 5.0 a severely over-conditioned cow in increment
of 0.1 or 0.25. One point of BCS equals 100-140 pounds gain in body weight.
Cows should be scored regularly to reflect changes in fat reserves at each stage
of lactation. Ideally score for all cows is at the beginning and end of their
dry period and at least 4 or 5 times during lactation. Cows should be evaluated
based on stage of lactation (days in milk or days dry). Cows should be scored
both by looking at and handling the backbone, loin and rump areas. It is critical
for producers to identify cows with poor body condition scores early to make
important treatment or culling decisions in a timely and responsible manner.
Furthermore, BCS levels and changes in BCS are associated with the health and
fertility status of the cow (Veerkamp et al., 2000;
Collard et al., 2000; Pryce
et al., 1999), so BCS is an obvious target for potential selection
indices. Selection for yield alone has resulted in cows that have a lower BCS
than cows of average genetic merit for production (Pryce
et al., 1999). Edmonson et al. (1989)
reported that the recommended body condition scores at various stages of lactation
are 3.0 to 3.5: at calving, 2.5 at breeding 2.5, 3.0-3.5 at late lactation and
3.0-3.5 during dry period.
Using random regression techniques of analysis on field data with BCS measured
at different times but only once on each animal, Jones
et al. (1999) demonstrated that changes in BCS throughout lactation
are under genetic control and that BCS has a heritability of around 0.3. De
Vries et al. (1999) reported a similar analysis with data from the
Netherlands. Coffey et al. (2001) using data
from an experimental herd showed that BCS is relatively easy and cheap to measure
on large numbers of daughters via progeny testing and national conformation
assessment schemes. Energy balance curves may provide data that could be included
in a multi-trait index aimed at improving health and fertility and thereby reducing
wastage from the dairy herd for both welfare and environmental reasons.
The objectives of this study were (1) To compute heritability estimates of
body condition scores (BCS) across different cow ages or/and lactation orders,
(2) To develop different lactation curve parts (different days in milk) and
(3) To determine different levels of body condition score and test day milk
MATERIALS AND METHODS
Data consisted of 45,349 Body Condition Score (BCS) observations. Records were
considered for the first three lactations of Holstein Friesian cows adapted
under Saudi environmental conditions. Body condition score was recorded at least
two times per lactation during test day examination with interval between 5
and 365 or more days in milk. Cows must have at least the first lactation, while
the average was 1.2 lactations. Data were extracted from cows calving between
1989 and 1998. Body condition scores observations were 21,211, 16,121 and 8,016
records in the first three parities. Full identification was available for most
of the animals. A small part of animals in the currently used data set was partially
identified. Five key areas on the body of cows were assessed namely the area
between the tail head and pin bones, inside of the pin bones, backbone, hips
and depression the hip and pin bones as shown in study of Moran
(2005) and Edmonson et al. (1989). The system
for body condition score ranged from emaciated/very little flesh over the skeleton
(score 1) to very fat heavy fat cover (score 8). Only those cows with scores
from 1.00-5.00 were described (BCS). Cows with scores of 1.5 or less were very
thin and were either severely underfed or suffering from disease or injury.
Cows with scores over 5 were considered in the over fat category and were at
risk of suffering from metabolic diseases around calving. Scores range from
1 (thin) to 5 (obese); scoring increments may be a tenth, quarter or half points.
Statistical analysis: The random regression model used in this study
where, Yijklm is the mth test-day observation body condition score
of the kth cow in the lth lactation, HTDil is the independent
fixed of jth herd-test-date for the lth lactation, np is the
number of parameters fitted on days in milk or cow age or level of daily milk
production function, βjlois the oth fixed regression coefficient
on jth days in milk or cow age or level of daily milk production effect within
lth lactation, χklmo is the oth dependent trait on days
in milk or cow age or level of daily milk production, αklo is
the oth random regression coefficient of additive genetic effect of the kth
cow in the lth lactation on days in milk or cow age or level of daily
milk production, φklo is the oth random regression coefficient of
permanent environmental effect of the kth cow in the lth lactation on
days in milk or cow age, εijklm is the random residual.
The following (co)variance structure was assumed:
where, G is the genetic covariance matrix among random regression coefficients
and traits, A is the additive numerator relationship matrix, P is permanent
environmental covariance matrix among random regression coefficients and traits
and E is residual variance for lactation n assumed to be constant throughout
the lactation due to program limitations. Variance-covariance parameters for
each of the current longitudinal traits (test-day milk yield and body condition
score) were estimated using the software random regression package, DFREML (Meyer,
RESULTS AND DISCUSSION
Descriptive statistics: Means of BCS across the first three parities are
presented in Fig. 1. Overall mean of BCS in this study was
2.92±0.13 with range from 2.81 at the 1st parity to 3.04 at the 3rd parity.
Figure 2 shows changes in BCS means across different lactation
parts and dry period. Mean of BCS at calving and at the beginning of lactation
(3.17) was higher than either mid (2.72) or end of lactation (2.85). Similar
trend of changing BCS were observed through individual presentation (Fig.
3). Means of BCS during dry period were similar to those obtained during
calving and at the beginning of lactation (Fig. 2). Body condition
scores ranging from 2.0-3.0 were more frequent and occurred with the highest
percentage (68.45%) for the current data set. Body condition scores less than
1.75 and greater than 4.5 were in small values and contributed only up to 2.99%
Distribution of means of BCS across different cow ages was illustrated in Fig.
5. Overall means of BCS across early, mid and late part of production live
were 2.8±0.10, 3.3±0.05 and 2.9±0.04, respectively. Means
of BCS increased gradually with progressing cow age from >2.5 at 22 months
of age to >3.5 at 70-80 months of cow age and then decreased toward the end
of productive life.
|| Distribution of means of body condition scores across different
|| Distribution of means of body condition scores across different
days in milk groups
|| Distribution of body condition scores across different days
|| Distribution of body condition scores
|| Distributions of body condition scores across different cow
The current results partially agree with those reported by Jones
et al. (1999), Pryce et al. (2001)
and Berry et al. (2002). Lactational heritability
estimates: Estimates of heritability using random regression analysis of BCS
in the first three lactations are given in Table 1, along
with their standard errors. Overall estimate of heritability for BCS was 0.19±0.04.
There were 4 different estimates of heritability for BCS from random regression
analysis across more than the first three lactations and varied from 0.10-0.32
across different lactations. Estimates of h2 were changed in curve-shape
mode. Among more than the first three lactations, older lactations (>the
3rd lactation) BCS started their performance with the lowest inheritance power
(1st Pr: 0.15 and 2nd Pr: 0.10). The heritability estimates were significant
for BCS, with standard error being less than or equal to 0.12. No permanent
environmental variance existed for BCS with random regression analysis. It appears
that, genetic improvement could be the principal role for enhancing the performance
of BCS. Zavadilova et al. (2005) reported that
additive genetic variances using random regression analysis increased with parity
and heritability estimates increased in turn, especially from the 2nd to the
Heritabilities of BCS across different cow ages: Changes of heritability
estimates of Holstein Friesian body condition scores across different cow ages
were plotted in Fig. 6. Estimates of h2 ranged
from 0.02-0.48 with overall mean equal to 0.23±0.02 across more than
100 months of cow age. Variations in estimates of h2 were very high
during early cow age which decreased gradually with progressing age. The highest
estimates of h-BCS2 were attained around 30th month of cow age. It
means that early genetic selection for improving BCS could be possible. The
general linear change in estimates of h2 mode tended to decrease
toward old age cows. Estimates of permanent environmental effect were null and
ranged from 0.01-0.06. It means that the contribution of environmental conditions
in improving BCS could be negligible.
||Estimates of heritability (h2), additive (σ2a),
permanent environmental (σ2c) and phenotypic (σ2p)
variance of body condition score across different parities (Pr)
||Heritability and permanent environmental estimates of body
condition score of Holstein Friesian across different ages
Estimates h2 across different DIM: Changes in estimates of
h2 using random regression analysis across different stages of lactation
are plotted in Fig. 7 using pooled lactation data set. Estimates
of BCS heritability increased after calving with progressing days in milk until
240 DIM. Interval from 120 - 320 days in milk involved the highest estimates
of h-BCS2 which was more than 0.50. It appears that BCS additive
genetic variations were very high during the late part of lactation. It means
that genetic selection for improving BCS were available during the late 2/3
part of lactation. The general linear trend of h2 decreased toward
trajectory end by including cows with long lactations (>305 DIM). Estimates
h-BCS2 during the early part of lactation are not low, it ranged
from 0.30 to <0.50 during the first three months of lactation. It suggests
that early genetic selection based on BCS could be possible. Coffey
et al. (2003) found that genetic variance for BCS rose abruptly towards
the end of lactation. This could be the effect of increasing the heritability
in a similar fashion. At the minimum point of the trajectory, the heritability
for BCS was about 0.25. The estimates obtained in the current study are similar
to those reported by Jones et al. (1999) and
Veerkamp et al. (2000). The current results showed
that environmental conditions did not play any significant role in improving
the body condition score during 305 days lactation.
Heritabilities of BCS across milk production levels: Changes of h2
across different test day milk yield levels are presented in Fig.
8. Overall estimate of h2 across different levels was 0.28±0.01.
Estimates of h2 decreased from 0.38-0.26 with progressing level of
daily milk production from 10 kg to 35-45 kg day-1. Estimates of
h2 increased slightly from 50 kg day-1 in curve-shape
mode towards the highest daily production (65 kg). Estimates of h2 across
production levels from 25-50 kg day-1 showed little changes (around
0.25). Genetic selection for improving BCS could be possible within the lowest
(10 to 20 kg day-1) levels of daily production.
|| (a) Body condition score heritability estimates across days
in milk within pooled and (b) Separate lactations of Holstein Friesian using
random regression animal model
|| Estimates of heritability at different levels of daily milk
production using (a) Pooled and (b) Separate parities data sets
Changes of h2 across different levels of daily milk production within
the first three parities separately are illustrated in Fig. 8.
Estimates of h2 ranged from 0.13-0.21, 0.13-0.21 and 0.07-0.27 across
different levels of daily milk production within the first three lactations.
Variability was substantial among estimates h-BCS2 within all parities.
Hence, the shapes of heritability curve for BCS was also variable. On the other
hand, the highest h2 appeared at the lowest milk production level
for all parities except the 2nd parity. For all three lactations, the variability
was the lowest in the 1st followed by either the 2nd or the 3rd lactation which
corresponded to similar h2 changing mode. In general, for all three
lactations, the linear trend decreased with progressing level of milk production.
It means that ability for improving body condition scores was high for the lowest
Three genetic measures of BCS such as cow age, days in milk and the levels
of daily milk production were compared. Overall mean of Body Condition Score
(BCS) was 2.92+0.13 with range from 2.81 at the 1st parity to 3.04 at the 3rd
parity. Mean of BCS at calving and at the beginning of lactation (3.17) was
higher than either mid (2.72) or end of lactation (2.85). However, means of
BCS across early, mid and late part of production live were 2.8±0.10,
3.3±0.05 and 2.9±0.04, respectively. Based on the random regression
solutions, the estimated heritability ranged from 0.19-0.53 for various parts
of lactation and 0.02-0.48 across different cow age for BCS. The highest estimate
of heritability of BCS based on random regression function of daily milk yield
was 0.37 at 10 kg day-1. The corresponding lowest estimate of heritability
was 0.26 at 35 kg day-1. The study showed a potential of using random
regressions for estimating heritability in adapted animals under different environments.
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