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Genetic Analysis of Cow Survival in the Israeli Dairy Cattle Population

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The linear model method of VanRaden and Klaaskate for analyzing herd life was expanded. Information on conception and protein yield was included in the estimation of predicted herd life of Israeli Holsteins. Variance components were estimated by a multitrait animal model. Heritability was slightly higher for herd life than for number of parities, but genetic correlations were close to unity. Animal model heritability estimates of herd life were higher than were sire model estimates. The expected herd life of pregnant cows was 420 d greater than for open cows. Each kilogram of increase in protein yield increased expected herd life by 9.5 d. Heritability of expected herd life increased from 0.11 for cows 6 mo after first calving to 0.14 for cows 3 yr from first calving. The genetic correlation of expected and actual herd life increased from 0.87 for records cut after 6 mo to 0.99 for records cut 3 yr after first calving. Phenotypic correlations increased from 0.61 to 0.94. Sire genetic evaluations based on predicted herd life of live cows were strongly biased if all records were weighted equally, and evaluations derived by weighting incomplete records to account for the effects of current herd life on variance components were nearly unbiased.
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82
Genetic Analysis of Cow Survival in the Israeli
Dairy Cattle Population
Petek Settar
1
and Joel I. Weller
Institute of Animal Sciences A. R. O., The Volcani Center, Bet Dagan 50250, Israel
Abstract
The linear model method of VanRaden and Klaaskate to analyze herd life was expanded.
Information on conception and protein yield was included in the estimation of predicted herd life of Israeli
Holsteins. Variance components were estimated by a multitrait animal model. Heritability was slightly
higher for herd life than for number of lactations, but genetic correlations were close to unity. Animal model
heritability estimates of herdlife were higher than previous sire model estimates. The expected herd life of
pregnant cows was 420 d greater than open cows. Each kg increase in protein yield increased expected herd
life by 9.5 d. Heritability of expected herd life increased from 0.11 for cows 6 mo after first calving to 0.14
for cows 3 yr from first calving. The genetic correlation of expected and actual herdlife increased from 0.87
for records cut after 6 mo to 0.99 for records cut 3 yr after first calving. Phenotypic correlations increased
from 0.61 to 0.94. Sire genetic evaluations based on expected herd life of live cows were strongly biased if
all records were weighted equally, while evaluations derived by weighting incomplete records to account for
the effects of current herd life on variance components were nearly unbiased.
1
Current adress: Ege University Faculty of Agriculture, • zmir,Turkey
1. Introduction
Longevity, or herd life (HL), is of major
economic importance in dairy cattle (VanRaden and
Wiggans, 1995). Three basic strategies have been
suggested to evaluate longevity for live cows. First,
cow survival to a specific age can be analysed as a
binary trait by either linear or threshold models
(Boettcher, et.al., 1998, Harris,et.al., 1992, Jairath,
et.al., 1998, Vollema and Groen, 1998). Second,
VanRaden and Klaaskate (1993) proposed
estimating life expectancy of live cows and
including these records in a linear model analysis.
Estimates based on incomplete data are regressed
toward the mean, and therefore have lower
heritability and variance than do complete records
(Meijering and Gianola, 1985, VanRaden et.al.,
1991, Weller, 1988). The third method is survival
analysis or consideration of cows still alive as
censored records (Boettcher, et.al., 1998,
Emanuelson et.al., 1998, Vollema and Groen, 1998,
Vukasinovic, et.al., 1997). Although the previous
methods could be applied to either animal or sire
models, survival analysis can only be applied to sire
models, and evaluations will be biased if the number
of daughters per sire with complete records is low
(Vukasinovic, et.al.,1997).
There have been numerous suggestions for
a definition of the longevity trait, based chiefly
either on the number of parities or the actual length
of HL (Vollema and Groen, 1996). Many studies
have proposed analysing functional HL, which is
generally computed as longevity adjusted for milk
yield (Boettcher, et.al., 1998, Dekkers, 1993,
Emanuelson et.al., 1998, Jairath, et.al., 1998,
Strandberg and Hakansson, 1994, Vollema and
Groen, 1996, Vollema and Groen, 1998). This trait
accounts only for culling that is due to causes other
than milk yield. The problem of double counting
of production in a selection index that includes
both milk yield and uncorrected longevity
correlated with yield can also be handled by
computing appropriate economic values for these
traits (VanRaden and Wiggans, 1995). Analysing
HL adjusted for yield is complicated because
selection goals change over time. Until 1980, milk
yield was the primary selection objective of most
breeding programs. Now protein yield is the chief
goal, and many countries put a negative economic
weight on milk yield (Leitch, 1994).
VanRaden and Klaaskate (1993) used
cumulative months in milk, current months in
milk, age at first calving, current months dry for
dry cows, first parity milk yield, and lactation
83
status (dry or milking) to predict the HL of live
cows.
In addition to low yield, the main causes of
cow culling are mastitis and nonconception. In
Israel, all milk recorded cows are checked for
pregnancy 60 d after insemination unless the cow is
reinseminated prior to 60 d (Weller and Ezra, 1997).
Thus, the pregnancy status of all cows is known in
real time, and this information can be used to
increase the accuracy of HL predictions.
Objectives of this study were to measure of
longevity for Israeli Holsteins; to study the effect of
incorporating data on pregnancy, days open (DO),
and protein yields in the computation of expected
HL; to compute adjustment factors for live cows;
and to determine the effects of the adjustment
procedure for incomplete records on genetic
evaluations. Adjustment factors were derived from
variance components for complete and incomplete
HL records estimated by a multitrait animal model
analysis, with records of different lengths considered
as correlated traits.
2. Material and methods
2.1
Estimation of expected HL for live cows and
estimation of variance components
Preliminary data set consisting of 559,035
Israeli Holstein lactation records with first calving
dates from 1984 through 1989, was generated to
predict HL for live cows. Lactation records were
discarded from the analysis if the first parity record
was missing; age at first calving was <570 or >1000
d; any calving intervals were <250 d; mean calving
interval was >500 d; last recorded parity was <7, and
exit day was missing for that parity; cows with >1
parity with valid exit dates; protein yield was <50 or
>600 kg for the last valid parity record; or if days in
milk >500 or if a cow was scored pregnant but days
open = 0. After edits, there were 51,888 valid cow
records in this data set (Table 1).
A linear model was used to estimate HL
from incomplete records. Multiple records were
derived for each cow by cutting the records at 6-mo
intervals beginning 6 mo after first calving until 4 yr
after first calving and then cutting records at yearly
intervals until 6 yr after first calving. At each cut
date, a record was generated only for cows that
survived until that cut date. Thus, up to 10 records
were generated per cow. This data set included
231,458 cow records.
The dependent variable was remaining HL
(RHL), computed as days from the cut date to the
exit date. The independent variables were current
HL (CHL), defined as days from first calving to
cut date; last parity prior to cut (PPC); expected
protein yield of the last parity prior to cut (EPY);
pregnancy status (PS) where 1 = pregnant, and 0 =
not pregnant; DO of the last lactation for cows
pregnant by the cut date, or days in milk for cows
not pregnant at the cut date, days pregnant (DP) at
cut date for pregnant cows; and days dry (DD) at
the cut date if the record was cut during the dry
period. Days pregnant = 0 for cows not pregnant at
the cut date. The EPY was computed as described
previously (Weller, 1988). The HYS were defined
relative to the first parity calving date, and were
absorbed. PPC, PS, and HYS were analyzed as
discrete effects; all other effects were analyzed as
continuous variables.
If predicted RHL <0 then predicted RHL
was set to zero. Estimated HL (EHL) was
computed as HL for cow records that were culled
prior to the cut, and as the sum of predicted RHL
and CHL for records cut after the cut date. Genetic
and environmental variance components among
HL and EHL computed for the 10 cut dates were
estimated by multitrait REML (Misztal, et.al.,
1995) using the animal model for these 11 traits.
2.2. Estimation of Adjustment Factors for Genetic
Evaluations of Incomplete Records
The EHL records as a function of CHL
were first adjusted so that the genetic covariance
between EHL and actual HL records would equal
the genetic variance of the actual records.
Multiplicative adjustment factors were estimated
PROC NLIN of SAS (1988) based on the
following nonlinear function of CHL:
RG
i
= b0 + (b1/CHL
i
) + e
I
[1]
where RG
i
is the ratio of the square root of the
genetic covariance between HL and EHL
i
, EHL at
cut date i, b0 and b1 are regression constants, and
e
i
is the residual. With this formula, as CHL
increases, the term b1/CHL becomes negligible
with respect to b0, and b0 should be approximately
equal to unity. The EHL records were then
multiplied by the predicted values of RG for
Equation [1], PRG, which also increased the mean.
The following nonlinear model was used to adjust
for the increase in the mean as a function of CHL
based on data set 2:
EHLX
j
= b2 + b3/CHL
j
+ e
j
where EHLX
j
= EHL
PRG, for cow j, b2 and b3
= regression constants, and the other terms are as
described previously. Predicted HL (PHL) was
84
then computed as EHLX - b3/CHL; b2 was not
subtracted because, as a constant, it would have the
same effect on all records.
In the animal model analysis, the PHL
records were weighted by the inverse of the ratio of
the residual variances of PHL and HL as a function
of CHL. Weighting factors for the square root of the
residual variances of PHL were computed using the
model of Equation [1]. If this model is appropriate,
then b0 should be approximately equal to the
residual standard deviation of HL.
2.3. Genetic Evaluation for Longevity
Data set 2 was generated for computation of
animal model genetic evaluations for HL and PHL.
The HL was computed for each cow as described
previously, except that if HL >2557 d (7 yr), then
the HL was set equal to 2557 d. The CHL was
computed as days from first calving to January 1,
1990. Cows with CHL <35 d were deleted, leaving
45,300 cow records.
The animal model was used to compute
genetic evaluations for HL, PHL with equal weights
for all records, and PHL weighted as described
previously. Pedigree information from all known
parents and grandparents was included. The pedigree
file included a total of 379 sires. Twenty-five
phantom parent groups were defined by year of birth
and sex of parent (Wiggans, et.al., 1988). Number of
cows, records, sire, and HYS included in data set 2
are also given Table 1. The two methods for
analyzing PHL were compared by correlations of the
sires’ EBV for these methods and the EBV for HL
and by the regression of the HL sire EBV on the
PHL EBV.
Genetic evaluations for HL based on the
method developed were also computed on the
complete Israeli-Holstein dairy cattle population in
September 1998, including 284,541 cows with first
calving dates since January 1, 1985, and at least 35
DIM at the evaluation date. Other edits were the
same as for data set 2. The numbers of cows,
records, bulls, HYS, and genetic groups for this data
set (data set 3) are also given in Table 1. Genetic
trends were computed as the regression of the cows
EBV on their birth dates, including all cows born
since 1981. Phenotypic trends were computed as the
regression of the cows’ HL on their birth dates, but
including only cows with valid HL records. For live
cows EHL was used instead of HL to calculate the
phenotypic trend. Correlations were computed
between bull EBV for HL and milk and protein
production for bulls with reliability > 0.5 for all
three traits. Bull EBV for milk and protein were
computed by a standard animal model (Weller and
Ezra, 1997).
3. Results and Discussion
The RHL was estimated from data set 1 as
described previously. After removing
nonsignificant effects from the analysis, the final
equation for estimating RHL from the incomplete
records was
RHL = PPC - 908 + 0.131
CHL + (-0.00029)
CHL
2
+ (-0.872) DP + (-5.218) DO +
(0.0169) DO
2
+ (-0.0000313) DO
3
+
(0.00126) CHL
DO + (-0.536) DD +
(9.532) EPY + (-0.011) EPY
2
[3]
All effects included in the final model
were significant at p < 0.0001. The discrete parity
and pregnancy status effects are given in Table 2.
Parity effects show no discernible trend because
this effect is highly confounded with CHL, which
was also included in the model. Pregnancy at the
cut date increased RHL by 420 d, but the effect DP
was negative.
Genetic correlations between HL and EHL
increased from 0.87 for records cut after 6 mo to
0.99 for records cut after 3 yr. The phenotypic
correlation was 0.61 for records cut after 6 mo, and
increased to 0.94 for records cut after 3 yr. The
genetic and phenotypic correlation estimates
between complete and incomplete HL records were
higher than were those reported by VanRaden and
Klaaskate (1993), but they did not include data on
pregnancy status.
Heritability increased from 0.11 for
records cut at 6 mo to 0.14 for records cut after 3
yr and then remained constant. VanRaden and
Klaaskate (1993), using a sire model, reported that
heritability of cut records increased from 0.03 to
0.08.
The genetic covariances between the
complete records and incomplete records were then
used to compute genetic adjustment factors based
on Equation [1]. The coefficients are given in
Table 3. The constant coefficient was equal to 656,
and the square root of the residual variance of HL
was 666. For convenience, the residual weighting
factors were then computed as 656/(656
+45256/CHL). Although both the genetic
covariances and the environmental variances were
monotonic functions of CHL, the ratio of the
residual variances after adjustment to equal genetic
covariances is no longer monotonic. The
85
coefficients of determination for all three nonlinear
models are also given in Table 3. The coefficient of
determination was lowest for the residual variance of
PHL, but all values were >0.9.
The EBV for all animals included in data set
2 were computed for HL and PHL with all records
cut at January 1, 1990. Correlations between sire
EBV for HL and PHL with and without weighting
factors are given in Table 4 for all sires and for sires
with >10 daughters. Because evaluations of young
sires should be most effected by incomplete records,
correlations are also given for sires born after 1981,
1982, 1983, and 1984. Correlations between HL and
PHL computed with and without weighting factors
were 0.94 and 0.93, respectively. Correlations were
lower if only bulls with >10 daughters were
included, and were again marginally higher for PHL
computed with weighting factors. As expected,
correlations decreased with decreases in the bulls’
ages. In all cases differences between correlations
for PHL computed with and without weighting
factors were no more than 1%.
The regressions of sire EBV for HL on PHL
with and without weighting factors are also given in
Table 4. Without weighting factors, slopes for all
bulls were about 1.3 but were nearly equal to unity
for evaluations computed with weighting factors.
Thus, nearly unbiased evaluations are derived with
weighting factors, and evaluations based on equal
weights of all records are biased. For the young
sires, without weighting factors, regressions
increased up to 1.75 for bulls born after 1984 and
were, therefore, highly biased. With weighting
factors, regressions decreased slightly with the bull’s
age but were still 0.85 for bulls born after 1984.
Thus bias was much smaller with weighting factors.
In the analysis of the complete Israeli
Holstein population the phenotypic trend for HL was
–15 d/yr, and the genetic trend was 9 d/yr. The
genetic correlations between the sire EBV for HL
and milk and protein production by birth year of
bulls born since 1986 are given in Table 5. The
correlations for all bulls born since 1986 are also
given. Correlations were lowest in 1988 and 1989,
but no clear trends are evident. In our analysis, HL
was not adjusted for milk production, and EPY was
used to predict HL for live cows. This should tend
to increase the similarity between EBV for
production and HL, especially for young sires whose
daughters are in first lactation. However, the genetic
correlations between protein and HL were nearly
equal for sires born in 1993, as compared to sires
born in 1986.
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accuracy and economic value. J. Dairy Sci.
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Table1:. Number of levels of effects included in the three data sets that were analyzed.
Data set
Effects 1 2 3
Cows 85,965 75,825 370,406
Records 51,888 45,300 284,541
Sires 393 379 965
Herd year-season 3319 3074 16,816
Genetic groups 2 25 42
Table 2:. Estimated effects for levels of the class effects on remaining days of herd life.
Effect
Parity Pregnancy status
Level 1 2 3 4 5 6 open pregnant
Effect (d) 62 -56 -108 -123 -51 0 0 420
Table 3: Regression coefficients, coefficients of determination (R
2
), and the
correlations between actual and predicted function of variance components based on
the nonlinear analysis model
Dependent Coefficients
2
R
2
Correlation
variable
1
b0 b1
RG 0.95 293 0.93 0.96
EHLX 1081 224,010 0.98 0.99
Square root of residual
variance of PHL
656 45,256 0.86 0.93
1
The dependent variables are explained in the text.
2
The analysis model was: y = b0 + (b1/CHL
i
) + e
i
where y is the dependent variable, and CHL is
days for first calving to cut date
87
Table 4: Regression coefficients and correlations between estimated sire breeding values of HL and
PHL with and without inclusion of weighting factors.
Weighting Birth year All bulls bulls with >10 daughters
factors of sires No. sires Intercept Slope r No. sires Intercept Slope r
With All 379 12.68 0.98 0.94 212 10.39 0.99 0.87
>1981 157 9.54 0.99 0.86 145 10.09 0.99 0.87
>1982 97 12.31 0.95 0.77 86 13.41 0.94 0.76
>1983 78 14.11 0.95 0.73 68 15.70 0.94 0.72
>1984 39 3.95 0.85 0.61 31 5.50 0.84 0.61
Without All 379 4.85 1.31 0.93 212 3.46 1.30 0.86
>1981 157 0.59 1.30 0.86 145 1.70 1.29 0.86
>1982 97 -5.01 1.47 0.76 86 -3.87 1.46 0.75
>1983 78 -5.22 1.55 0.72 68 -3.94 1.54 0.71
>1984 39 -23.62 1.75 0.63 31 -24.79 1.73 0.62
Table 5: Correlations between EBV for herd life and milk and protein production
based on genetic evaluation of the complete Israeli Holstein population.
Birth Year No. of bulls Correlation with herd life
Milk Protein
1986 40 0.65 0.67
1987 42 0.72 0.65
1988 53 0.43 0.44
1989 33 0.54 0.46
1990 31 0.48 0.64
1991 40 0.59 0.60
1992 42 0.63 0.65
1993 52 0.55 0.68
Total 340 0.53 0.57
... The traits are listed in Table 2. All the index traits were recorded on first parity cows, except for herd-life, for which only a single record per cows is generated (Settar and Weller, 1999;Weller and Ezra, 2015). Data set 5 included 1,585 Holstein sires born since 1991 with reliabilities >0.5 in the individual animal (IAM) analysis of data set 4, and valid genotype records. ...
... There were ~680 bulls that met these criteria for the index traits, and 590 bulls that met these criteria for the conformation traits. The EBV for these traits were computed as described previously (Settar and Weller, 1999;Weller and Ezra, 2004Weller et al., 2006). The optimum selection index considering selection on the phenotypic records for AFI and AFC was computed using the classic selection index equation (Weller, 1994) ...
... As the MTC program generates only positive definite matrices, no bending of the final matrices was needed. Heritability estimates for all 11 traits are given in Table 5. Heritability estimates for the index traits are similar to previous estimates for this population for first parity (Weller and Ezra, 1997, 2004Settar and Weller, 1999;Weller et al., 2006). Heritability estimates for AFI and AFC were 0.06 and 0.03, which were both slightly lower that the estimates from data set 2. This difference might be related to the fact that data set 3 included only cows with valid records for all 11 traits. ...
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A total of 1922 first generation crossbred cows born between 2005 and 2012 produced by inseminating purebred Israeli Holstein cows with Norwegian Red semen, and 7487 purebred Israeli Holstein cows of the same age in the same 50 herds were analyzed for production, calving traits, fertility, calving diseases, body condition score, abortion rate and survival under intensive commercial management conditions. Holstein cows were higher than crossbreds for 305-day milk, fat and protein production. Differences were 764, 1244, 1231 for kg milk; 23.4, 37.4, 35.6 for kg fat, and 16.7, 29.8, 29.8 for kg protein; for parities 1 through 3. Differences for fat concentration were not significant; while crossbred cows were higher for protein concentration by 0.06% to 0.08%. Differences for somatic cells counts were not significant. Milk production persistency was higher for Holstein cows by 5, 8.3 and 8% in parities 1 through 3. Crossbred cows were higher for conception status by 3.1, 3.6 and 4.7% in parities 1 through 3. Rates of metritis for Holsteins were higher than the crossbred cows by 7.8, 4.6 and 3.4% in parities 1 to 3. Differences for incidence of abortion, dystocia, ketosis and milk fever were not significant. Holstein cows were lower than crossbred cows for body condition score for all three parities, with differences of 0.2 to 0.4 units. Contrary to comparisons in other countries, herd-life was higher for Holsteins by 79 days. A total of 6321 Holstein cows born between 2007 and 2011 were higher than 765 progeny of crossbred cows backcrossed to Israeli Holsteins of the same ages for milk, fat and protein production. Differences were 279, 537, 542 kg milk; 10.5, 17.7, 17.0 kg fat and 6.2, 12.9, 13.2 kg protein for parities 1 through 3. Differences for fat concentration were not significant, while backcross cows were higher for protein percentage by 0.02% to 0.04%. The differences for somatic cell score, conception rate, and calving diseases other than metritis, were not significant. Holstein cows were lower than backcross cows by 1.5% to 2.5% for conception status in parities 1 to 3 and lower for body condition score for parities 1 and 2, with differences in the range of 0.06 to 0.09 units. Culling rates were higher, and herd-life lower for the crossbred cows. The gains obtained in secondary traits for crossbred cows did not compensate for the major reduction in production.
... Before the BLUP analysis of data set 3, cow records were adjusted for the effects of birth month, parity of dam, twin or single birth, and calving ease based on the mean values in this data set. Records were also adjusted for gestation length using the coefficients derived from the analysis of data set 1. The algorithm used to analyze data set 4 was described in detail by Settar and Weller (1999). Expected herd life was computed for cows that were not culled before the analysis, as described by Weller and Ezra (2015). ...
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The objectives were to estimate the effects of various environmental factors on female calf survival of Israeli Holsteins, to estimate the economic value of calf survival under Israeli conditions, to estimate the genetic and environmental variance components for calf and cow survival using the individual animal model, to perform genome-wide association study (GWAS) analyses of survival to first calving and herd life after first calving, to estimate the genetic and environmental trends for calf survival since 1985, to estimate genetic correlations of calf survival with the traits included in the current Israeli breeding index, and to estimate the consequences of inclusion of calf survival in the national selection index. Mean calf survival rate of Israeli Holsteins from 2001 through 2008 was 0.85, and the mean economic value of survival to first calving was $526. Birth month, gestation length, dystocia, and twin birth significantly affected calf survival rate. Dystocia and twin birth each reduced survival rate by 0.034. Survival rate was highest for calves born in October and lowest for calves born in February. The difference between these months was 3.4%. Maximum survival was at a gestation length of 276 d, the mean gestation length for this population. Survival rate was reduced to 0.76 for calves born after a gestation length of 260 d. The individual animal model was applied for all the genetic analyses. Heritability for calf survival to first calving, as estimated by REML, was 0.009, whereas heritability of herd life from first calving was 0.15. The complete data set for genetic analysis of survival to first calving included 1,235,815 calves born between 1985 and 2017. Annual genetic and phenotypic trends for calf survival were 0.019 and 0.015%, respectively. Correlations of transmitting abilities of 226 sires born since 2010 for calf survival with the traits included in the Israeli breeding index were significant only for the maternal effects of dystocia and stillbirth. The GWAS analysis was based on the transmitting abilities of 1,493 bulls with genotypes and reliabilities >0.5 for calf survival and cow herd life. There were 7 single nucleotide polymorphisms with coefficients of determination >0.03 for calf survival and 12 single nucleotide polymorphisms with coefficients of determination >0.05 for cow survival. There was no overlap between the genome-wide significant markers for the GWAS analyses of calf survival and cow herd life. This corresponds to the conclusion from the REML results and the low correlations between the sire evaluations that the genetic control of the 2 traits are not similar. Inclusion of calf survival in the Israeli breeding would result in a 0.5% increase in calf survival over 10 yr but reduce progress for the other traits by 8%.
... Conversely, defining and describing performance benefits and economic advantages of providing artificial shade has significant challenges. First, more efficient animals with better genotypes tend to produce a greater amount of body heat due to increased metabolism (West, 1994;Settar et al., 1999;St-Pierre et al., 2003). Because of this, the effects of ambient temperature, especially heat, are ever-changing as producers continually select for higher performing, more efficient livestock. ...
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Shade is a mechanism to reduce heat load providing cattle with an environment supportive of their welfare needs. Although heat stress has been extensively reviewed, researched, and addressed in dairy production systems, it has not been investigated in the same manner in the beef cattle supply chain. Like all animals, beef cattle are susceptible to heat stress if they are unable to dissipate heat during times of elevated ambient temperatures. There are many factors that impact heat stress susceptibility in beef cattle throughout the different supply chain sectors, many of which relate to the production system, i.e. availability of shade, microclimate of environment, and nutrition management. The results from studies evaluating the effects of shade on production and welfare are difficult to compare due to variation in structural design, construction materials used, height, shape, and area of shade provided. Additionally, depending on operation location, shade may or may not be beneficial during all times of the year, which can influence the decision to make shade a permanent part of management systems. Shade has been shown to lessen the physiologic response of cattle to heat stress. Shaded cattle exhibit lower respiration rates, body temperatures, and panting scores compared to un-shaded cattle in weather that increases the risk of heat stress. Results from studies investigating the provision of shade indicate that cattle seek shade in hot weather. The impact of shade on behavioral patterns is inconsistent in the current body of research, some studies indicating shade provision impacts behavior and other studies reporting no difference between shaded and un-shaded groups. Analysis of performance and carcass characteristics across feedlot studies demonstrated that shaded cattle had increased ADG, improved feed efficiency, HCW, and dressing percentage when compared to cattle without shade. Despite the documented benefits of shade, current industry statistics, although severely limited in scope, indicate low shade implementation rates in feedlots and data in other supply chain sectors do not exist. Industry guidelines and third party on-farm certification programs articulate the critical need for protection from extreme weather but are not consistent in providing specific recommendations and requirements. Future efforts should include: updated economic analyses of cost versus benefit of shade implementation, exploration of producer perspectives and needs relative to shade, consideration of shade impacts in the cow-calf and slaughter plant segments of the supply chain, and integration of indicators of affective (mental) state and preference in research studies to enhance the holistic assessment of cattle welfare.
... Correlations were computed between the breeding values for PEA rate for these sires and the current Israel breeding index, PD16, and 11 economic traits analyzed in Israel. Breeding values for these traits were computed as described previously (Settar and Weller, 1999;Ezra, 2004, 2016;Weller et al., 2006). Data set 7 was analyzed using the MTC REML program (Misztal, 1994) instead of AIREMLf90 due to computing limitations in the joint analysis of 10 traits. ...
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One of the causes of observed low fertility is embryo loss after fertilization. Previous findings suggested that more than half of fertilizations result in embryo loss before pregnancy is detected. We proposed reinsemination between 49 and 100 d after the first insemination as an indicator trait for early abortion (EA) in dairy cattle based on the mean estrus interval of 21 d. This trait was compared with conception rate from first insemination and conception status, computed as the inverse of the number of inseminations to conception. Animal model variance components were estimated by REML, including parents and grandparents of cows with records. First-parity heritability for first insemination conception rate was 3%. In the multitrait analysis of parities 1 to 3 for putative EA, heritabilities ranged from 8.9% for first parity to 10.4% for second parity. All genetic correlations were >0.9, whereas all environmental correlations were <0.12. The variance component for the service sire effect for putative EA rate was less than half the variance component for conception rate. Thus, genetic control of the 2 traits is clearly different, and analysis of EA rate by a single-trait animal model is justified. Genetic evaluation for putative EA was computed using this model, including all first- through third-parity cows with freshening dates from January 1, 1985, through December 31, 2016, that either became pregnant on first insemination or were reinseminated between 49 and 100 d after the first insemination. All known parents and grandparents of cows with records were included in the analysis. The regression of the breeding value for non-abortion rate on the cows' birth year was 0.083%/yr. The genetic correlation between first-parity EA and conception status was 0.995. The genetic correlations between first-parity EA and milk, fat, and protein production were all negative, whereas the genetic correlation between EA and herd life was 0.33. Inclusion of putative EA in the selection index instead of conception status resulted in 10 to 20% greater genetic gain for both fertility traits. In a genome-wide association study based on 1,200 dairy bulls with reliabilities >50% for abortion rate genotyped for 41,000 markers, 6 markers were found with nominal probabilities of <10⁻¹² to reject the null hypothesis of no effect on EA rate. The markers with the lowest probabilities for EA rate were also included among the markers with the lowest probabilities for female fertility, but not vice versa. The marker explaining the most variance for abortion rate is located within the ABCA9 gene, which is found within an ATP-binding cassette (ABC) genes cluster. The ABC family is the major class of primary active transporters in the placenta.
... For culled cows, longevity was computed as the number of days from first calving to culling. For cows that had not yet been culled, expected longevity was computed as described by Settar and Weller (1999). For the disease traits, the model included a fixed HYS effect as described previously and all known relationships among animals with valid records. ...
Article
Incidences of ketosis, metritis, mastitis, and retained placenta were studied in Israeli Holstein cows calving between 2008 and 2017. These diseases were selected based on their economic impact. Ketosis, metritis, and retained placenta were scored dichotomously. Mastitis was scored as absent, a single occurrence during the lactation, or more than 1 occurrence. Ketosis and metritis were recorded during the first 21 d after calving, retained placenta during the first 5 d after calving, and mastitis up to 305 d in milk. The effects of herd-year-season, calving age, month of calving, gestation length, and occurrence of dystocia were included in the first-parity analysis models. All effects were significant for metritis and retained placenta. For ketosis, all effects were significant, except for gestation length. For mastitis, only the effects of herd-year-season and calving age were significant. Variance components were computed by the multitrait animal model. The 4 diseases were analyzed jointly based on first-parity records, and each disease was analyzed separately for parities 1 to 3 with the different parities considered separate traits. The 4 disease traits in first parity were also analyzed jointly with the 6 major traits included in the Israeli breeding index: milk, fat, and protein production; somatic cell score; female fertility; and longevity. Heritability was highest for metritis and lowest for mastitis, but all heritabilities were <0.07, similar to previous studies. For all 4 diseases, genetic correlations among the first 3 parities were >0.65, and all residual correlations were <0.07. Selection of herd-years assumed to have more accurate recording of mastitis did not result in higher heritability estimates. Genetic correlations between the disease traits and milk, fat, and protein production were economically unfavorable, while correlations between the disease traits and somatic cell score, female fertility, and longevity were economically favorable. Expected genetic changes in the disease traits after 10 yr of selection with the current Israeli breeding index were all <1%, except for ketosis, which was predicted to increase by 1.5%. Inclusion of these traits in a proposed index with the disease traits constituting 7% of the index would result in only marginal improvements for the disease traits and adversely affect genetic gain for fat and protein production. Thus, inclusion of these traits in the breeding index cannot be justified economically.
... The first parity trait heritabilities are also given. Hayes [19]. There was only one record per cow for this trait. ...
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Abstract Estimates of quantitative trait loci (QTL) effects derived from complete genome scans are biased, if no assumptions are made about the distribution of QTL effects. Bias should be reduced if estimates are derived by maximum likelihood, with the QTL effects sampled from a known distribution. The parameters of the distributions of QTL effects for nine economic traits in dairy cattle were estimated from a daughter design analysis of the Israeli Holstein population including 490 marker-by-sire contrasts. A separate gamma distribution was derived for each trait. Estimates for both the α and β parameters and their SE decreased as a function of heritability. The maximum likelihood estimates derived for the individual QTL effects using the gamma distributions for each trait were regressed relative to the least squares estimates, but the regression factor decreased as a function of the least squares estimate. On simulated data, the mean of least squares estimates for effects with nominal 1% significance was more than twice the simulated values, while the mean of the maximum likelihood estimates was slightly lower than the mean of the simulated values. The coefficient of determination for the maximum likelihood estimates was five-fold the corresponding value for the least squares estimates.
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Purpose: study the phenotypic parameters of fertility, milk production and survival of the same cows during their lifetime in the herd (from birth to the fourth calving) depending on the age of the first calving to better understand the factors that will improve the productive longevity of cows. Material and methods. Studies were conducted in a commercial herd with Holstein cattle located in the Central region of the Russian Federation. For the analysis, we used data from cows (culling and alive) of the same year of birth (2014), which were born, grew, calved and reached 4 calving (2019). The generated database contained complete information about the animal throughout its life in the herd (n=842). Depending on the actual age of the first calving (26 months), which reflects the growth rate, cows were grouped into groups: less than 23 months, 23–25 months, 26–29 months and over 30 months. In each group, we studied (1) reproductive parameters of heifers (number of inseminations per conception, age of the first insemination); (2) reproductive parameters of cows (number of inseminations per conception,, number of days from calving to first insemination, number of days from first to last insemination, conception rate at first insemination, number of days from calving to conception); (3) 305-day milk yield of first, second and third lactations; (4) lifetime milk production; (5) longevity index (proportion of days spent on milk production); (6) survival rate (proportion of cows surviving from first calving to the second, third and fourth calving, respectively). Results. Cows with an average age of the first calving of 22.1 (<23 months) and 23.7 (23–25 months) months during the growing period with a minimum interval from the first insemination to conception (68 days) had a high percentage of pregnancy from first insemination ≥84%. Survival rates from 1 to 2 calving ranged from 82.7% to 83.1%, to 3 calving-from 55.3% to 62.7%, to 4 calving — from 6.0% to 11.9%, respectively. Optimal intervals between inseminations allowed to finish 3 lactation, in general, 73% of cows and 45% were still alive and producing milk. For 3 lactation (for 305 days) they produced from 17280 to 17805 kg of milk. What was spent on from 45% to 48% of a productive life. Cows with an average age of the first calving of 26.9 (26–29 months) and 32.5 (≤30 months) months during the growing period had a low conception rate of 44% and 5%, long intervals between inseminations (from 113 to 219 days). In lactation, there was a tendency to increase the average days from calving to the first insemination (in 1 lactation — from 85 to 88 days, in 2 lactation — from 82 to 83 days), from the first insemination to conception (in 1 lactation — from 117 to 122 days, in 2 lactation — from 88 to 92 days), which led to an increase in the days from calving to conception (in 1 lactation — from 156 to 164 days, in 2 lactation — from 125 to 140 days). Such cows had the lowest fertility, survival rate, and therefore the short productive life. Conclusion. Cows with the age of the first calving ≤25 months without serious problems during the rearing period were distinguished by the best indicators of reproduction and productivity. They reached the third lactation faster and ended it by producing the largest amount of milk, which spent 45 to 48% of their productive life.
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One of the important breeding goals in dairy cattle is increasing length of productive life (LPL). In the recent decades, genetic evaluations of dairy cattle longevity have been a major concern for breeders. The trait LPL is defined as the number of days from the first calving to culling, death or censoring. Increasing LPL by reducing the costs of replacement of the heifers and increasing the number of high producing cows plays an important role in increasing the herd incomes and profitability. This study aimed to evaluate genetic variations for LPL based on the survival analysis models was used to evaluate the impact of environmental and genetic factors on the risk of culling and to estimate the genetic parameters for longevity in Holstein dairy herds. Data included 35,137 records of productive lifetime from the first calving during 1991 and 2012, collected from dairy herds in Isfahan province. Culled and un-culled animals were assigned as uncensored and censored cows, respectively. However, it may be of interest to distinguish between disposal mostly beyond the control of dairy managers such as the sale of profitable but sterile can (involuntary culling) and voluntary disposal of a healthy but not profitable cow. The number of observations was considered with at least 20 records per herd and at least 10 daughters per sire. The last lactation was considered for the animals whose culling date was missed. In this case, cow assigned as culled animal only if the time interval between end of the last lactation and date of recording exceeds 365 days. Three types of cows were excluded in this study: sold, without any records and transferred to other herds. The sires with one daughter in a herd were removed. Genetic parameters were estimated based on a sire model which was implemented in Weibull model in Survival Kit software Survival analysis using proportional hazard model was used to analyze data on LPL. The existence analysis models are the best for the genetic PL evaluation; these models are referred to as the Proportional Risk Models, which are categorized in two semi-parametric Cox and Weibull. Following the designed algorithm in this software, the records with known longevity and low FHL limit were used. Hence, the records were considered uncensored data if the cows were either culled or died for any reason. Therefore, censoring the records represented the cows were sold, exported or leased to other herds. Both Cox and Weibull models were implemented in Survival Kit, and they could be used for continuous and discontinuous (time-dependent) variables. The average lifetime in uncensored and censored cows were 937.8 and 1002.8 days, respectively. It is obvious that some cows are culled due to calving difficulties on day one, therefore LPL of One day is considered for them. Heritability could change based on the estimates of ρ and scale (λ). Estimates of heritability of LPL according to logarithmic scale and original scale were 0.074 and 0.18, respectively. In many studies on different populations, the heritability evaluated through survival analysis is higher than what is determined through linear models. Regression of phenotypic changes was -0.03±0.01, which showed that the reduction of relative culling risk has occurred slowly across the studied herds. The genetic trends of culling risk showed that regression coefficient was close to zero and therefore, it can be concluded that according to variance of the estimated breeding values in LPL, it would be possible to increase LPL by selecting the high ranked cows. The range of culling risk were calculated from 0.96 to 0.99. An attempt to estimate the genetic trend for sires was made by grouping sires according to their year of birth. Besides, negative phenotypic trends in this study for the proportional culling risk was achieved which demonstrated that LPL was phenotypically improved but based on the genetic trend, an increase in culling risk was observed that indicated a genetically decreasing in productive lifetime in studied dairy herds. More research is needed to analyze more data in other dairy farms in Iran. Based on the variation of the obtained breeding value, it is possible to increase the lifetime of cows via selecting the higher breeding value cows.
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An animal model was applied to predict genetic merit for Ayrshire milk yield. The model included fixed herd- year-season (32,287) and random herd- sire interaction (32,159), permanent environment, animal, and residual effects. Animals evaluated included 119,541 cows with 301,799 records, 5762 sires, and 11,893 dams without records. Genetic groups (36) were defined for unknown parents and parents not contributing ties or records. Groups were defined by sex of parent and by birth year and sex of animal with unknown parent. Evaluations included combinations of these group effects derived from tracing each path in pedigree back to an unknown parent group. Iteration was by Gauss-Seidel for herd-year-season, permanent environment, and herd-sire interaction effects and by second-order Jacobi for animal and genetic group effects. Iteration was conducted without forming mixed model equations; instead one copy of data sorted by herd and sire was read each
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Methods were developed for the national genetic evaluation of herd life for Canadian Holstein sires. The genetic evaluations incorporate information from survival (direct herd life) and information from conformation traits that are related to herd life (indirect herd life) after adjustment for production in first lactation to remove the effect of culling for production. Direct genetic evaluations for herd life were based on survival in each of the first three lactations, which was analyzed using a multiple-trait animal model. Sire evaluations thus obtained for survival in each of the first three lactations were combined based on their economic weights into an overall sire evaluation for direct herd life. Sire evaluations for indirect herd life were based on an index of sire evaluations for mammary system, feet and legs, rump, and capacity. A multiple-trait sire model based on multiple-trait across country evaluation methodology was used to combine direct and indirect genetic evaluations for herd life into an overall genetic evaluation for herd life. Sire evaluations for herd life were expressed in estimated transmitting ability as the number of lactations and represent expected differences among daughters in functional herd life (number of lactations); the average functional herd life was set equal to three lactations. Estimated transmitting abilities were normally distributed and ranged from 2.31 to 3.43 lactations.
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Canonical transformation for REML can be applied to models with several random effects by simultaneously diagonalizing (co)variance matrices for all random effects. This procedure is an approximation when matrices cannot be diagonalized completely. The level of the approximation was studied with simulated data by comparing multiple diagonalization and exact REML estimates. For a range of diagonalization levels, the error of the estimates was 2 to 10 times lower than the fraction of non-diagonalizable (co)variances because 57 to 91% of these variances were recovered after the backtransformation step. To determine whether REML by multiple diagonalization is successful with real data, a study used 98,113 records of 44,765 Holstein cows for 14 conformation traits. Effects included in the model were herd classification, animal with unknown parent groups, and permanent environmental effects. Estimates of (co)variance components were on average 5% higher for type compared with estimates from an earlier analysis using only first records, but the estimate for udder cleft increased 2.4 times. Correlations for the permanent environmental effect were within .1 of genetic correlations. Multitrait REML by multiple diagonalization provides accurate multiple-trait estimates for repeatability models more efficiently than general model REML.
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Heritabilities and genetic and phenotypic correlations for 48- and 72-mo herd life were estimated with multiple-trait REML from sire models incorporating sire relationships. Two traits were defined for 48- and 72-mo herd life, true herd life and functional herd life, which were adjusted for milk production prior to culling. Heritabilities for 48- and 72-mo herd-life traits were low, ranging from .02 to .07; genetic correlations among herd-life traits ranged from .82 to .95, and phenotypic correlations ranged from .80 to .97. Genetic correlations between the herd-life traits and first lactation milk, fat, and protein production ranged from .37 to .81. Genetic correlations were lower between functional herd life and milk, fat, and protein production than among true herd life and these same variables. Multiple-trait REML from sire models, which included sire relationships, was used to estimate genetic and phenotypic correlations between 48-mo true or 48-mo functional herd life and linear type traits for registered Guernsey cattle. The genetic correlations were used to compute weights for indirect prediction of true and functional herd-life transmitting abilities from linear type traits transmitting abilities. The predictions are equivalent to multiple-trait BLUP with no observations for herd life.
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A theoretical model of herd life was used to quantify biases in genetic parameter estimates from culling on production and to study effects on response to selection. Herd life was modeled as a linear function of production and ability to survive regardless of production (survival). Genetic improvement of survival is of interest. Results of analytically derived formulas showed that estimates of heritability obtained from analysis of herd life are biased for survival. For moderately negative genetic correlations between production and survival, biases tended to be upward. The genetic correlation between production and survival was severely overestimated when based on production, and herd life. Adjustment of phenotypic herd life for phenotypic production removed some of the biases in genetic parameter estimates unless little direct culling for production occurred.Incorporation of estimated breeding values for herd life that were adjusted for production in a selection index, along with estimated breeding values for production, resulted in more response in a breeding goal consisting of production and survival than did inclusion of estimated breeding values for herd life when the standardized direct effect of production on herd life was larger than .15. For smaller values, adjustment of herd life for production reduced response to selection. Given current levels of culling on production, measures of herd life should be adjusted for production when included in selection strategies.
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Restricted maximum likelihood estimates of variances and covariances are often preferred by animal breeders but can be expensive or impossible to compute for large data sets. Less expensive, approximate restricted maximum likelihood estimates can be obtained by using quadratic forms that resemble the restricted maximum likelihood quadratics but have expectations easier to compute. Quadratic forms for the tilde-hat approach resemble the restricted maximum likelihood quadratics more closely than previous approximations. The strategy is not difficult computationally even for models containing additive genetic relationships. Of three approximate strategies tested, tilde-hat gave estimates closest to restricted maximum likelihood in an actual data set. All three approximations were strongly biased downward by selection in simulated data. The restricted maximum likelihood quadratic seems to account for selection well, whereas substitute quadratics account for selection poorly.
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Information on partial lactations often is included in genetic evaluations by predicting the cow's eventual 305-d yield. Such projected yields have less phenotypic and genetic variation than completed yields but were modeled as having greater or equal variation in evaluations. Analysis of first lactations from 48,424 daughters of 844 Holstein sires indicated that yields predicted early (46 to 75 d) in lactation had less than one-half as much additive genetic variance as completed yields. Multiple-trait REML estimates of genetic correlations of predicted and completed yields were all above .92, indicating that early lactation information is valuable if modeled appropriately. Expanded records with genetic variances equal to those of completed yields and new lactation length weights were derived. Expanded records have larger error variances than either projected or completed yields and, thus, are given less weight when included in animal model evaluations. Genetic gains are expected to increase only .2 to .3%, but more stable genetic evaluations should result from use of expanded records, particularly for animals evaluated primarily from first lactation records in progress.
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Mixed model equations are constructed using the convention of regression on dummy variables that are given values of either unity (presence of the effect) or zero (absence of the effect). In the proposed method, incomplete records were included by computing regression coefficients of sire effects as the regression of the effect on the partial record on the same effect on the complete record. Partial and complete records were treated equally for other effects. Regression and the error components of variance were estimated as simple functions of the length of the partial records. The only additional computation required in sire evaluation was the differential weighting of records in the construction of the mixed model equations. This method was tested on field data and was slightly more accurate than evaluations including partial records without differential weighting and significantly more accurate than evaluations obtained with partial records deleted.