Milk production in dairy industries is encountered by a number of factors such
as types of cows, animal health, environmental conditions, level of inputs,
milking speed of animals, farm inputs namely and labour cost (Dodenhoff
et al., 1999; Antalik and Strapak, 2010).
The other cost involving factors such as supply of power and milking equipments
can also be handled by decreasing the milking time (Boettcher
et al., 1998). Considerable economic influence on the success of
a dairy enterprise can be attributed to milking speed of dairy cows (Banos
and Bunside, 1992). Therefore, dairy farmers would appreciate to lay more
emphases on genetic selection against this characteristic. To avoid slow and
long milking, cow should be milked gently, quickly and completely with minimal
machine stripping or over-milking (Meyer, 1998). Furthermore,
milk can also be produced on cost effective basis if the milking cows are classified
into different categories on the basis of total milk production per milking
cow. This can be achieved by maintaining daily records of each cow during lactation
period (Matthew 2001; Banos and
Bunside, 1992; Erf et al., 1992). The objectives
of this study were to estimate (co)variance components of the first three lactations
data with random regression models and to characterize some genetic aspects
of test-day milking duration across lactation in Hungarian Holstein-Friesian
MATERIALS AND METHODS
Data consisted of 103563 Test Day Records (TDR) on milking duration (MkDr
). The current data set involved the 1st, 2nd and >3rd lactation of Hungarian
Holstein Friesian cows provided by Hungarian Holstein Association. All studied
traits were recorded on each test day between 5 and 365 days in milk (DIM).
Cows must have at least two lactations, while the average was 3.7 lactations
with 7.16 test-day records. Data were recorded on cows calving between 1996
and 2000. Number of test day record per lactation was not less than five observations.
Days in milk (DIM) were classified into 12 monthly groups with 30 days interval.
Structure of the current data is given in Table 1.
Statistical analysis: The random regression model used in this study
where, Yijkl is the mth (test-day observation) of the kth cow in
the lth lactation, HTD is the independent fixed effect of jth herd-test date
for the lth lactation, np is the number of parameters fitted on days
in milk function, βj is the oth fixed regression coefficient
on jth days in milk effect within lth lactation, χklm is the
oth dependent trait on days in milk, αk is the oth random regression
coefficient of additive genetic effect of the kth cow in the lth lactation on
days in milk, nk is the oth random regression coefficient of permanent
environmental effect of the kth cow in the lth lactation on days in milk, εijkl
is the random residual. 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
Multi-lactation heritability estimates using random regression model:
Estimates of heritability for milking duration (h2DR)
(Table 2) were very low during early lactation months (from
0.01 to 0.11) and were intermediate across the 2nd half of lactation (from 0.35
to 0.39). Heritability estimate for milking duration (MkDr) was low
during the first half of lactation while it increased during the beginning of
the 2nd half of lactation.
|| Data Structure for Different Variables Recorded during Study
||Heritabilities (h2), permanent-environmental effect
(PE), additive (ó2 A) and phenotypic
(ó2p) variances across days in milk groups
: is the permanent environmental variance, MKDr means milking
duration of a cow
|| Heritability (h2) estimates and permanent environmental
effect (pE) within the 1st, 2nd and >3rd parity
|DIM: Days in milking; Pr1: Parity-1; Pr2:
Parity-2; Pr3: Parity-3
Similar findings were reported by Zwald et al. (2005)
who found that low heritability estimates for milking time may be due to the
wideness of the interval between positive and negative predicted transmitting
ability or breeding values that is associated with increasing estimates of permanent
environmental effect. Moore et al. (1983) found
that estimated heritability of the "2-min milk" was 0.23 which was significantly
higher than the corresponding estimate of 0.13 for milking duration.
Estimates of permanent environmental effect (PEDR) effect (as the
ratio between permanent environmental variance and phenotypic variance) for
milking duration were high ranging from 0.52-0.73 and 0.50-0.56 during the first
and the last three months of lactation, respectively. On the other hand, PEDR
decreased greatly at the middle of lactation reaching to 0.35 during the
6th month of lactation. It appears that the environmental conditions contributed
appreciably in variations of milking duration among different months of lactation.
Estimate of random regression heritabilities and permanent environmental effects
for milking duration within parities across DIM groups are presented in Table
3. Results of heritability estimates of milking duration (h2DR)
within different lactations were mostly near to zero while the corresponding
estimates of PEDR were high. Some high values for h2DR
were obtained slightly during edges of the 1st lactation (from 0.13-0.21
and 0.13-0.20) and during the 2nd half of the 2nd lactation (0.12-0.27). Previous
studies showed that milking duration may have an intermediate optimum, because
most producers prefer cows with relatively uniform milking duration that do
not decrease the flow of cows through the milking parlor. However, selection
for extremely short milking duration may be undesirable, because an antagonistic
relationship may exist with general udder health (Zhang
et al., 1994).
Estimates of PEDR were relatively high reaching 0.80, 0.59 and 0.67
within the 1st, 2nd and the later parity, respectively. These results refer
to the impact of some environmental conditions which may affect the extent of
genetic improvement of this trait across and within all lactations.
Meyer and Burnside (1987) concluded that several environmental
factors affecting the milk ability characteristics of individual cows may vary
during lactation or between subsequent lactations. Zavadilova
et al. (2005) found that variances in milking time of the small permanent
environmental effect went up substantially between the first and subsequent
lactations, with the differences between the 2nd and the 3rd lactations.
Correlations between repeated measures of milking duration across different
days in milk: Estimates of additive genetic (RA) and permanent
environment correlations (RPe) between measures of milking duration
in different months of lactation are illustrated in Fig. 1.
Estimates of additive genetic correlations between repeated measures of milking
duration decreased in magnitude with increasing interval between measurements.
Additive genetic correlations between early and late measures of MkDr
were low and directly changed to negative direction. Therefore, MkDr
in early and late stages of lactation could be considered as different traits.
Multi-lactations expected breeding values using random regression analysis:
Estimates of expected breeding value for milking duration ranged from 0.15 to
0.81 and -0.10 to -0.67 across 360 days in milk. Estimates of breeding values
(+EBV) increased rapidly within the 2nd half of lactation
||Estimates expected breeding value (EBV) that generated
from random regression analysis for milk duration using pooled lactation
||Relationship (Pre: permanent environmental, Add: additive,
Ph: phenotypic correlations) between repeated measures of milking duration
across different days in milk
||Random regression positive expected breeding values (BV) across
months of lactation within different parities
Similar findings were reported by Zwald et al.
(2005) who found that the estimated heritability of milking duration was
0.17 and predicted transmitting abilities of individual sires ranged from -0.48
min for sires with the short time milking daughters to 0.59 min for sires with
the long time milking daughters.
Changes of (¯EBV) were slightly lower than the corresponding
positive estimates during the 2nd half of lactation. These results indicate
to mass selection especially during the late of lactations could reduce the
goal of milk production genetic improvement. These results indicate that the
best individual selection results will be achieved during the late part of lactation.
In general, results of expected breeding values indicate that high estimate
of heritability is not a measure of genetic improvement rate. However, the positive
or negative breeding values in the herd could increase estimates of heritability.
Therefore, breeding strategies must be practiced based on the individual breeding
The floor (blue) area in Fig. 2 shows the widest area for
+EBV. These estimates were obtained within the first 5
lactations and ranged from 0.0-0.5. The second category of +EBV
ranging from 0.5-1.0 were mainly distributed with the 3rd lactation across most
of lactation curve. High breeding value for milking duration >1.0 was mainly
obtained within early and late of productive life during end of lactation. Therefore
successful breeding strategies for improving milking duration could be possible
during the late part of productive life within the late area of lactational
curve. While, the results of random regression on Breeding Values (BV) shows
development of additive genetic effect on milk duration ( MkDr) during
the end of lactations. On the other hand, the analysis of heritability results
from different studied models showed weakness in the inheritance of MkDr.
Estimates of h2DR were very low during early lactation
months (from 0.01-0.11) and were intermediate across the 2nd half of lactation
(from 0.35-0.39). Heritability estimate for milking duration was low during
the first half of lactation while it increased during the beginning of the 2nd
half of lactation. Estimates of expected breeding values for milking duration
increased in different rates with progressing days in milk groups. Additive
genetic correlations between measures at different lactation months continuously
decreased with an increase in interval between the test days. Correlations between
expected breeding values ranged from 0.41-0.83 (mean = 0.69) across different
lactation months. Overall, application of random regression animal model proved
a useful tool for genetic evaluation of milking duration in Holstein Friesian