Genetic parameters were estimated for first lactation
survival defined as a binary trait (alive or dead to second calving) and the curve
shape traits of milk yield, fat and protein percentages using information
from 25 981 primiparous Tunisian Holsteins. For each trait, shape curves
(i.e. peak lactation, persistency), level of production adjusted to 305 days in
milk (DIMs) for total milk yield (TMY), and average fat (TF %) and protein (TP %)
percentages were defined. Variance components were estimated with a
linear random regression model under three bivariate animal models.
Production traits were modelled by fixed herd × test-day (TD)
interaction effects, fixed classes of 25 DIMs × age of
calving × season of calving interaction effects, fixed classes of
pregnancy, random environment effects and random additive genetic effects.
Survival was modelled by fixed herd × year of calving interaction
effects and age of calving × season of calving interaction effects,
random permanent environment effects, and random additive genetic effects.
Heritability (h2) estimates were 0.03 (±0.01) for survival and
0.23 (±0.01), 0.31 (±0.01) and 0.31 (±0.01) for TMY,
TF % and TP %, respectively. Genetic correlations between survival and
TMY, TF % and TP % were 0.26 (±0.08), -0.24 (±0.06) and
-0.13 (±0.06), respectively. Genetic correlations between survival
and persistency for fat and protein percentages were -0.35 (±0.09)
and -0.19 (±0.09), respectively. Cows that had higher persistencies
for fat and protein percentages were more likely not to survive.
Introduction
The cow's life is divided into two distinct periods: a non-productive
period, from birth to first calving, and a productive period from first
calving to death or slaughter. The latter period is commonly called
productive lifetime or herd life (Jenko et al., 2013). Herd life can also be
defined as the cumulated days in lactation or as the number of
lactations (Jairath et al., 1994). It is closely related to the concept of
survival to certain point of life, such as subsequent calving or a certain age
(Van Pelt et al., 2016). It results from a combination of characteristics
directly associated with the ability of the cow to remain in the herd
(Tsuruta et al., 2005; Ahlman et al., 2011), i.e. being able to calve
normally, while resisting metabolic disorders, diseases such as mastitis,
infertility or lameness, and producing enough and good-quality milk (Weigel,
2006). There are many reasons for a dairy farmer to wish for animals that
survive well. The most important is economic benefit even though achieving a long herd life is still a major challenge. In Belgium, it was reported that
less than one-third of the cows reached the fourth lactation (Gengler et
al., 2005). In Canada and the Netherlands, 25 % and 13 % of primiparous
cows left the herd during their first lactation, respectively (Jairath et al.,
1998; Van Pelt et al., 2016). Ajili et al. (2007) reported that in Tunisia
more than 57 % of cows were culled after the first two lactations, and
only 7 % of them reached their fifth lactation. This is a situation that
has fundamentally not changed in recent years.
Animal breeding can be a tool to improve cow survival since it is additive
and cumulative. However, the selection and breeding decisions have to be
made early in a cow's life although survival information is only available at
the end of life. That makes selection for survival difficult. A crucial element
is the modelling of the dairy cow's survival. Two extreme types of models are
used. The first type is proportional hazard function based on survival
models (Ducrocq et al., 1988; Ducrocq and Sölkner, 1994). These models
are defined as single-trait as they model survival from day to day (Ducrocq
and Sölkner, 1994). The second type is multi-trait models in which the
survival to a fixed point in the productive life (e.g. each lactation) is a
different trait (Jairath et al., 1998). Equivalent random regression
implementations of the latter strategy were proposed by Gengler et al. (2005)
and Van Pelt et al. (2016). By considering fixed points in the life
of dairy cows (e.g. alive or dead to second calving), these models represent
longevity as survival to that point. These models have the major advantage
that survival is genetically no longer considered to be the same trait during the
whole productive life. This is a valid hypothesis given the fact that the
culling risk is higher in later parities than in earlier parities (De Vries
et al., 2010). Furthermore, it has been reported that correlations of
lactation survival between parities are different from unity (Jairath et
al., 1998; Gengler et al., 2005; Van Pelt et al., 2015). Given this situation,
survival may be defined as a binary trait and, therefore, in theory, should
be fitted by a non-linear model (Gianola, 1982). Nevertheless, no clear
advantage of using univariate non-linear over linear models was reported for
binary or even categorical traits (Matos et al., 1997; Phocas and Laloë,
2003; Vanderick et al., 2014). However, bivariate linear-threshold models
may show greater advantages than bivariate linear–linear models (Varona et
al., 1999; Ramirez-Valverde et al., 2001), but they require greater computations
and rely on specific assumptions. Therefore, most countries use linear
models in their national genetic evaluation routine for lactation survival
although such data violate the assumption of normality (Interbull, 2017).
The correlation estimates between early indicator traits and survival traits
are of great interest. A long history of studies has linked survival to milk
yield (Pool et al., 2003; Ajili et al., 2007). More specifically, M'hamdi et
al. (2010) reported that survival was mainly influenced by milk yield in
Tunisia. These authors did not address in detail the relationships between
survival and a more detailed description of the milk yield across lactation
(lactation curve shape and milk composition). To our knowledge, there are
few studies linking survival to the shape of the lactation curve (Reents et
al., 1996; Cole and Null, 2009). In the past, there were some efforts to make
use of pre-established lactation curve shape traits, i.e. by defining the lactation
persistency as the ability to maintain constant yield during lactation
(Gengler, 1996) or in the context of linking lactation curve shape to
disease traits (Harder et al., 2006). These efforts could not be considered
a success since persistency definitions are much more diverse and since
estimated correlations suffered from the lack of control by other variations
(e.g. overall level of production) as mentioned by Gengler (1996).
Therefore, the aim of the present study was to link the lactation curve shape of
milk yield (represented as level and persistency) and major milk components
(fat and protein percentage level and persistency) to survival of first
lactation in primiparous Tunisian Holstein cattle.
Material and methodsData
Pedigree and daily milk yield (MY), fat percentage (F %) and protein
percentage (P %) test-day (TD) records of 25 981 primiparous Tunisian
Holstein cows that participated in the official milk recording in the
period from 2000 to 2014 were provided by the Tunisian Genetic Improvement
Center (Tunisian Livestock and Pasture Office). The animals came from
34 herds located in the north (24), central (3) and eastern regions (7) of
Tunisia. Each herd consisted of at least 50 cows in order to keep informative herds (Van Pelt et al., 2016). Cows
aged less than 20 or more than 42 months at first calving were excluded.
Ages at calving were divided into five classes: <=25; 26 to 27;
28 to 29; 30 to 31; and >31 months. Seasons of calving were
separated into four classes: fall (from September to November), winter (from
December to February), spring (from March to May) and summer (from June to
August). The pregnancy status was defined by gestation month: first to
ninth (n=9 classes). Test-day records of daily MY < 3 and
>80 kg, F % < 1.5 and >9 %, and P % < 1 and
>7 % were excluded following Hammami et al. (2008), who worked on similar data. Test-day records between 5 and 305 days
in milk (DIMs) remained for further analyses. Lactations with the first TD exceeding 82 days from calving were deleted. Only cows having
at least three TDs were kept. Pedigree information was traced back five
generations, resulting in a pedigree file with 1054 sires of which 202 had
more than 30 daughters. Descriptive statistics of the edited dataset are
presented in Table 1.
Descriptive statistics.
TraitMeanSDMinimumMaximumMilk yield (n=150955) Number of test-day records per lactation6.231.323.008.00Milk yield test-day records20.707.503.106.90Fat percentage (n=121208) Number of test-day records per lactation4.931.753.008.00Fat percentage test-day records3.380.711.519.00Protein percentage (n=130769) Number of test-day records per lactation5.391.603.008.00Protein percentage test-day records3.120.391.017.00Trait definition
Persistency and peak. In this study, persistency was defined following
Jamrozik et al. (1997) as the difference between the values at DIM 280
(i.e. MY280 – MY at DIM 280; F280 – F % at DIM 280; P280 – P % at DIM 280) and at the peak (i.e. MY60 – MY at DIM 60; F60 – F % at DIM 60; P60 – P % at DIM 60).
Level of production. These traits were defined as total MY (TMY) and
average F % (TF %) and P % (TP %) over 305 DIMs.
Lactation survival. The survival trait was defined as a binary trait (alive or dead to second
calving). We have chosen to analyse survival during first lactation, due to the
rapid availability of information for survival and the lower complexity of data
modelling. Moreover, a high genetic correlation with third-lactation survival
was found (results not shown). Similar results were also reported by Gengler
et al. (2005). A cow was considered to have survived its first lactation if
a cow had at least one TD from the second lactation and was coded as 2. A
cow was considered to be dead if she did not have a subsequent lactation and was coded as 1. Finally, the cows studied inevitably had a first lactation but not
necessarily a second lactation because they could leave the herd before
calving again. Since TD records used in this study were taken until the end
of 2014 and the occurrence of a second calving was checked up to
21 October 2017, we considered that cows had sufficient opportunity to calve
again. Given the fact that there is little inter-farm commerce of older cows
in Tunisia we expect that this way of procedure generates reliable survival values.
Model
Three bivariate analyses (MY, P % or F % TD with survival) using a
linear random regression model were performed. The model was written with matrix notation as follows:
y=Xb+QZa+Zp+e,
where y is a vector of MY, F % or P % and survival; b is
a vector of fixed effects, herd × TD, classes of 25 DIMs × age
of calving × season of calving and pregnancy for
production traits, and herd × year of calving and age of
calving × season of calving for survival; a and p are
vectors of random regression coefficients for additive genetic and permanent
environment effects, respectively; Q is a matrix for the modified
second-order Legendre polynomials (constant = 1; linear and quadratic) for
production traits and for the null-order Legendre polynomial (constant = 1)
for survival; X, Z are incidence matrices linking
observations with respective effects, and e is a vector of residuals.
The covariance matrix structure of the model is as follows:
ape=A⊗Ka000I⊗Kp000R,
where Ka is a 4×4 (co)variance matrix of the additive
genetic random regression coefficients; A is the additive
relationship matrix among all animals; Kp is the 4×4
(co)variance matrix of the random permanent environment regression
coefficients; I is an identity matrix having as dimension the number of
animal with records, and R is a 2×2 diagonal matrix of
residual variance. The genetic variance matrix among all DIMs and production
traits was obtained as following:
G=QKaQ′.
For all traits, additive genetic variance (σa2), permanent
environment variance (σp2), residual variance (σe2),
phenotypic variance (σT2) and cow-specific
variance (σc2=σa2+σp2)
were obtained by the appropriate functions of regression coefficients Ka and Kp.
The variances at the peak and late lactation for MY, F % and P % were
computed at DIM 60 and DIM 280 by using the estimated (co)variances for
those DIMs. The variances for persistency were calculated as follows:
4σa2=QpKaQp′,5σp2=QpKpQp′,
where Qp=Q280-Q60; Q60 and
Q280 are vectors of three modified Legendre polynomials
associated with DIM 60 and DIM 280, respectively.
MY (continuous line; a), F % (dotted line; b) and
P % (dashed line; c) by 25 classes of days in milk (DIMs).
The variances for level of production, TMY, TF % and TP % were the
variances derived from the sums of full Legendre polynomial coefficients
summed from DIM 1 to DIM 305 of each trait.
Heritability (h2) estimates of survival as
well as MY, F % and P % at DIM 60 and DIM 280 were defined as the ratio
of σa2 to σT2. In order to take into
account the fact that TMY, TF %, TP % and persistency are, following
Jamrozik et al. (2001), second-stage traits defined from test days, the h2 estimates were computed as the ratio of σa2 and σc2.
Random effects were assumed to be normally distributed and residual
variances were assumed to be independent and constant along lactation.
The use of a constant residual variance was also chosen so as not to render the
model even more complex.
To avoid environmental covariances being considered as genetic covariances,
within an animal, survival was modelled by a permanent environment effect, as
proposed by Bastin et al. (2012). This effect together with a diagonal
residual matrix allowed us to model non-genetic covariances between test-day
yields, with the permanent environment effect part modelling the non-genetic cow-specific correlations between lactation-curve-based traits as level of
production, persistency and survival. The sum of genetic and non-genetic cow-specific effects was considered as the total cow-specific effect and used in the study.
Variance components were estimated using the REMLF90 software (Misztal et al.,
2002). After convergence of the REMLF90, the average information REML (AIREML)
software was run using final estimates of the REMLF90. The standard
error (SE) of (co)variance estimates and h2 were obtained
using the average information REML matrix (Misztal et al., 2014). Approximated SE
for correlations and h2 were calculated using equations
provided by Falconer and Mackay (1996) and Rustin et al. (2009).
Results
From studied cows, only 75.20 % of cows reached their second lactation.
The quarter of cows that left the herd during their first lactation had
partial lactation data. Indeed, 35.34 % of the latter cows left the herd in the
first 100 DIMs, 19.87 % left the herd in the middle of lactation (between
100 and 200 DIMs) and 44.79 % left the herd during the last part of lactation (after 200 DIMs).
Means for 25-DIM classes of MY, F % and P % are shown in Fig. 1.
The MY lactation curve had an inverted shape compared with content curves. The MY curve
showed a typical shape: the peak occurred between the third and fourth
classes (DIM 50–60 post-partum) and gradually fell thereafter. Curves
for F % and P % were characterized by an early decrease reaching nadir point
between DIM 35 and 50 after calving, followed by a steady increase to the
end of lactation. Highest values for F % (3.5 %) were observed at the
beginning and at the end of lactation (DIM 305) while the highest P % value
(3.3 %) was reached at the end of lactation (DIM 305).
Heritability (in bold), additive genetics (above the diagonal),
permanent environment correlation (below the diagonal), cow-specific correlation
(in brackets) between first lactation survival and MY60, MY280, F60, F280, P60,
P280, TMY, TF %, TP % and P as well as associated standard errors.
DIMs: days in milk; MY60: milk yield at DIM 60;
MY280: milk yield at DIM 280; F60: fat percentage at DIM 60;
F280: fat percentage at DIM 280; P60: protein percentage at DIM 60;
P280: protein percentage at DIM 280; TMY: total milk yield production;
TF %: total fat percentage production; TP %: total protein
percentage production; ±: standard errors.
Daily estimates of heritability (h2) for MY (continuous
line; a), F % (dotted line; b) and P % (dashed line; c) by days in milk (DIMs).
Additive genetic, permanent environment and cow-specific correlation estimates among all traits, h2 estimates as well as all standard errors
are shown in Table 2. Genetic correlations of survival were
positive for TMY (0.26±0.08) and negative for TF % (-0.24±0.06)
and TP % (-0.13±0.06). On the other hand, permanent
environment and cow-specific correlations were, respectively, 0.16 (±0.01)
and 0.23 (±0.01) between survival and TMY, -0.02 (±0.01)
and -0.05 (±0.01) between survival and TF %, and -0.05 (±0.01) and
-0.06 (±0.01) between survival and
TP %. Genetic correlations between survival and MY persistency not to be seemed different from zero (-0.01±0.09) whereas permanent
environment and cow-specific correlations were positive but low
(0.07±0.01 and 0.09±0.01, respectively). Nevertheless, genetic
correlations between survival and persistency were moderate and negative for
F % (-0.35±0.09) and for P % (-0.19±0.09). Permanent
environment and cow-specific correlations were close to zero between
survival and persistency for MY, F % and P % except for F % where cow-specific correlation between survival and persistency was 0.11 (±0.01).
Genetic correlation between survival and MY (MY60 and MY280) was
positive and had the same magnitude at peak lactation (MY60) and in late
lactation (MY280). For permanent environment and cow-specific correlations, a different trend was observed and correlations were higher in late lactation
(Table 2). The genetic correlation estimates between survival and F % were
negative and of a high magnitude in late lactation (-0.40±0.14).
Genetic correlation of P % with survival seemed not to be significantly different from zero for P60 (0.04±0.16) and was -0.23 (±0.12)
for P 280.
Daily estimates of heritability for MY, F % and P % by DIM were plotted
in Fig. 2. Milk yield was more heritable at the middle and end of lactation. For
P % and F %, trends were different and showed border effects. The lowest
heritability estimates for F % were observed between the 55th and the
95th DIM, while the lowest estimates were observed between the
111th and the 172nd DIM. Generally, daily heritability estimates
of F % were small and did not even exceed 0.06 for two-thirds of lactation.
Heritability estimates for survival ranged from 0.02 to 0.04 while those of
persistency were 0.21 (±0.01) for MY, 0.05 (±0.01) for F %
and 0.14 (±0.01) for P % (Table 2). Estimates of h2 for TMY,
TF % and TP % were 0.23 (±0.01), 0.31 (±0.01) and 0.31 (±0.01), respectively.
Discussion
The proportion of cows with partial lactation found in this study confirmed the
findings of M'hamdi et al. (2010) on the same population, who reported that
the highest risk of culling was found for cows at the beginning and at the
end of the first lactation.
Lactation curve shapes for MY and components plotted in this study (Fig. 1)
were similar to those of Bouallegue et al. (2014), who found similar curve
shapes in a Mediterranean hot climate.
Few studies have used random regression models to investigate the
relationship between milk yield and survival whereas studies linking major
milk components to survival are even scarcer (Dematawewa and Berger, 1998;
Zavadilovà and Zink, 203). The literature provided evidence that survival was
influenced by MY (Pool et al., 2003; Ajili et al., 2007; M'hamdi et al.,
2010). Genetic correlations between TMY and survival are very variable as
survival's definition changes from a continuous trait to survival at certain point
of time (Dematawewa and Berger, 1998; Pool et al., 2003). However, these
correlations are usually positive as also found in the current study.
Positive phenotypic and cow-specific correlations between survival and TMY
seemed to indicate that in Tunisia farmers may provide better management for highly producing cows during their first lactation and eventually prioritize
their health treatment over other cows in order to keep them in the herd for as
long as possible. On the other hand, the genetic antagonism found between
survival and TF % was higher than in the findings of Dematawewa and Berger (1998)
and Zavadilová and Zink (2013), who reported genetic correlations of -0.20
and -0.18, respectively. These same authors reported similar genetic
correlations between survival and TP % (-0.15 and -0.13, respectively).
This antagonism can partly be explained by the fact that high F % and lower P % require a constant high consumption of energy, and
consequently cows may undergo an excessive mobilization of reserves leading
to a negative energy balance. This negative energy balance is associated with fertility and therefore with the ability to achieve a next calving and therefore to survive.
According to our results, cows that had higher F % and P % persistency,
are expected to exit the herd earlier. The question that arises is whether
this negative genetic relationship is due to the beginning of lactation or
not. Unexpectedly, results showed that it is rather due to the end of
lactation. A hypothesis could be, that lower MY leads to lower survival, and
at the same time lower MY leads to higher F % and P %. This could
finally end up with a negative correlation of F280 to survival. However,
survival was not genetically associated with F % and P % at their peak. Morton
et al. (2017) reported that low levels of P % in early lactation can be
due to an energy deficit that results in reproductive difficulties. At this
early stage of lactation, farmers seem to postpone the culling decision by
giving the cow more chances to overcome its reproductive problems. This may
explain weak environmental and cow-specific correlations in early lactation
found in our study.
As regards survival, h2 estimates were similar to other
studies using a sire model (Boettcher et al., 1999; Du Toit et al., 2009) and lower than the majority of the results of survival models (Boettcher et
al., 1999). Milk yield persistency had similar h2
compared to those found by Canaza Cayo et al. (2015) (ranged from 0.10
to 0.33). For the persistency of F % and P %, h2 estimates were
low. Given that h2 of persistency varied significantly
depending on definition (Biassus et al., 2010), it could be stated that
persistency's definition was not optimal for this trait as it was for MY and for
P %. However, the SE obtained in this study showed that these estimates
of h2 differ from zero. Therefore, these low estimates could be due to
the stressful climatic conditions, constrained feeding resources and the
sampling process preventing the full genetic potential of cows from being expressed (Hammami et al., 2008). Milk contents and detailed milk composition (fatty acids
and metabolites) were found to be more sensitive to warmer conditions
compared to the milk yield at a phenotypic and genetic level (Hammami et al.,
2015). Tunisia experiences high temperatures and
humidity (THI > 70) going beyond the thermoneutral threshold for nearly half the year.
For TMY, TF % and TP %, h2 estimates were higher than
those found by Hammami et al. (2008) by using a random regression model
(0.17, 0.13 and 0.15, respectively). However, h2 daily estimates were
low, especially for F % and P %. These results were similar to those
found by Hammami et al. (2008) on the same population.
Conclusion
In conclusion, the results of the present study revealed a positive genetic,
environmental and cow-specific relationship between survival and TMY.
Moreover, survival was genetically negatively correlated to TF % and
slightly less to TP %. The most likely reasons could be that highly (milk)
yielding cows are preferentially treated; however, high fat and protein
production through high percentages requires a constant high consumption of
energy. Consequently, cows may undergo an excessive mobilization of reserves
that can lead to a negative energy balance that is associated with the
ability of the cow to calve again.
On the other hand, correlations between survival and MY persistency were
low. However, cows that had higher F % persistency were more likely to
exit the herd earlier. The question that arose was whether this negative
relationship was due to the beginning of lactation. Unexpectedly, results
showed that it was linked to high percentages at the end of lactation.
Data availability
The data (pedigree and phenotypic data) cannot be made
publicly accessible because they are not the property of the authors but that
of the Genetic Improvement Center (Tunisian Livestock and Pasture Office) acting
on the behalf of the owners of the performance-recorded cows.
Author contributions
MG edited the data, developed the model and prepared the
manuscript with contributions from all co-authors.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
Authors are thankful to the Genetic Improvement Center (Tunisian Livestock
and Pasture Office) for providing the data. Computational resources have
been provided by the Consortium des Équipements de Calcul Intensif (CÉCI)
funded by the National Fund for Scientific Research (F.R.S. – FNRS, Brussels,
Belgium) under grant no. 2.5020.11.
Edited by: Steffen Maak
Reviewed by: two anonymous referees
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