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Is a high level of milk production compatible with good reproductive performance in dairy cows?

Authors:
Is a high level of milk production
compatible with good reproductive
performance in dairy cows?
Stephen J. LeBlanc
Population Medicine, Ontario Veterinary College University of Guelph, Canada N1G 2W1
Key words: epidemiology, management, milk yield, pregnancy rate,
selection
Introduction
Milk production and reproductive performance are two major deter-
minants of dairy cow protability. There is much debate among dairy
producers and researchers about possible antagonism between high milk
production and reproductive performance. There are questions about pos-
sible conicts of genetic selection for production and fertility and whether
management can meet the needs of cows for both high production and
timely and efcient pregnancy. This paper reviews and critiques data on
measuring the association of the level of milk production with pregnancy
rate in dairy cows.
Many papers refer to a decline in fertility in dairy cattle over the last
20 to 30 years but valid primary data to support this assertion are scarce.
While there are reports from 50 to 80 years ago expressing concern about
reproductive performance and the effect of increasing production on it, it
seems that measures of reproduction in large datasets only began to de-
cline in the mid-1970s to mid-1980s (Nebel and McGillard, 1993; Lucy,
2001; Stevenson, 2001). This coincided with acceleration of trends toward
fewer, larger dairy farms with more non-family employees and increasing
use of technologies to assist management. Despite the continued march
of increasing production per cow per year, recent data suggest that the
trend in the US in some of the same measures of reproduction has begun
to improve (Norman et al., 2009). There are a few frequently referred-to
datasets (e.g., Butler, 1998; Royal et al., 2000; Lucy 2001) that show an
apparent decrease in conception risk over the last 50 years, during which
time milk production per cow has increased substantially. These data war-
rant scrutiny. Temporal associations do not imply causation. To the extent
that producers abandon breeding on low producing animals but continue
to inseminate high producing animals, it is not surprising that there is
an apparent association between higher milk yield and a higher number
of artical insemination (AI) per pregnancy. Also, many aspects of dairy
production have changed in the last two generations, so caution is needed
in inferring a cause-and-effect relationship between high production and
decreased reproductive performance when the potential for confounding
of the relationship is high. The weaknesses and pitfalls of causal reasoning
relative to study design and analysis in the context of the present question
have been well discussed by Morton (2006) and Bello et al. (2012). Some
key points include:
Failure to account for confounders: many studies ignored other vari-
ables that have changed over time along with production and repro-
duction (e.g., herd size, the skill and experience of farm personnel,
increasing connement on concrete oors which may impair estrus
expression), leaving these analyses susceptible to confounding and
therefore overestimation of the strength of the effect of production.
For example, at the cow level it may be body condition that is a
determinant of reproductive function rather than milk yield (Santos
et al., 2009), but the former is often not available in large datasets.
Ecologic fallacy: This is the erroneous drawing of inferences at the
cow level from data that are at the herd or population level. Both
pregnancy and amount of production are fundamentally individual
level variables. Even if reproduction is negatively correlated with
production at the herd level, it is not necessarily the cows with
higher production within the herd or the population that have worse
reproduction. Bello et al. (2012) illustrate how failure to account for
© LeBlanc
doi:10.2527/af.2013-0038
Implications
Both researchers and producers commonly believe that there is an
inherent conict between high milk production and good fertility
in lactating dairy cows. This possible antagonism is attributed to
competing physiologic demands and divergent genetic selection
choices.
The data from which these inferences are drawn have numerous
substantial limitations and aws in study design and analysis.
Recent analyses that employ more powerful and correct methods
highlight the nuances and multifactorial nature of the relation-
ships between production and reproduction and conclude that
there is not necessarily antagonism between them. These relation-
ships vary among herds and between cows within a herd, and may
depend on when during lactation they are measured.
The challenge for management of increasingly large and produc-
tive herds is to provide for the nutritional and behavioral needs of
high-performance animals, but the demands of high production,
good reproduction, and cattle well-being can be addressed with
similar best management practices.
84 Animal Frontiers
herd vs. cow-level relationships may lead to meaningless results or
wrong inferences.
Biases in the data used: Reliable data on why and how a cow was
bred generally do not exist and in any case would reect a mixture of
preferences, perceptions, and policies that vary between farms. For
example, managers may choose to alter the timing of rst insemina-
tion as a function of milk production. As discussed below, many
of the most-used measures of production (e.g., 305 day yield) and
reproduction (calving interval) have inherent biases in the subset of
cows from which these data are available.
Royal et al. (2000) measured milk progesterone thrice weekly through
the postpartum period in 714 Holstein-Friesian cows in seven herds and
described the commencement and patterns of luteal activity. They com-
pared their data with milk progesterone proles from data from 2,305
lactations in 1,682 cows in 20 herds between 1975 and 1982. They also
compared the timing and probability of pregnancy at rst AI. Despite a
small difference in time of rst AI (mean ± SD, 78 ± 27 vs. 74 ± 21 days
postpartum), the probability of pregnancy at rst AI was signicantly less
(40% vs. 56%) for the 1995 to 1998 vs. 1975 to 1982 data, respectively.
Comparisons did not consider other variables such as management or in-
semination practices, herd size, feeding, labor, or housing. Data on calv-
ing interval which apparently were based on subsequent parturition were
available for 540 cows in the 1995 to 1998 data (390 ± 60 d) and a small
subset of 259 cows published in 1977 from the 1975 to 1982 data (370
± 35 d). No statistical analysis was performed. Nevertheless, the authors
claim to present “a clear decline in pregnancy rates”
Butler (2003) illustrated diverging trends in conception rate (CR)
production/cow per year between 1951and 2001. However, CR was not
quantied in the same manner. The CR of 66% in 1951 was for rst AI
only (Foote, 1978), and while it was reported as being distinct from and
2.6% less than non-return rate, the basis of the CR is not clear. The 1975
data point in Butler (2003) was dened as a live calf, abortion, conrmed
pregnancy check, or no heat in the 100 days following breeding (Spalding
et al., 1975), whereas the measurement and data source for conception
rate in 1996 and 2001 are not reported in Butler (1998) or Butler (2003),
although it is likely that these CR were based on diagnosed pregnancy.
It has been assumed that greater milk production is a cause of the ap-
parent decline in reproductive performance over time because it seems
plausible and the two occurred concurrently. Other variables that are
obvious candidates to inuence the probability and timing of pregnancy
such as nutrition, housing, and skilled labor are ignored, or acknowledged
but not assessed because the data are difcult to obtain. The main ques-
tions are whether fundamental fertility of dairy cows as dened by their
capacity for reproductive function and successful pregnancy has in fact
declined, as opposed to the relative success of management systems and
people at meeting the metabolic, nutritional, housing, and social needs
of increasingly productive animals, and if fertility really has diminished,
then to what extent has this decline been caused by increased milk pro-
duction. There is no doubt that production per cow has increased, but it
is unclear how much of this increase can explain the apparent decrease in
fertility. It is important to separate the biology of reproductive function
from the effects of economically based management decisions about cull-
ing and continuation of breeding. Higher producers are more likely to be
inseminated and less likely to be culled (Grohn and Rajala-Schultz, 2000).
Figure 1. Trends in herd-level reproductive performance in approximately 3,000 dairy herds in Canada, measured using standard inclusion criteria and calculated in
DairyComp 305 (Valley Ag Software). Data courtesy of Canwest DHI.
October 2013, Vol. 3, No. 4 85
USDA.gov
86 Animal Frontiers
It is not clear whether pregnancy rate is falling in all or any dairy produc-
tion systems around the world. In Canada, herd annual 21 day pregnancy
rates have been measured using consistent methods for 13 years (Figure
1). While these data are only descriptive, pregnancy rates have neither
increased nor decreased as average production has continued to increase.
Measuring the Association of Milk Production
and Reproductive Performance
Genetics
The estimated heritability of fertility is low with estimates of < 5%
compared to 25 to 50% for production traits (Marti and Funk, 1994;
Kadarmideen et al., 2003; Jamrozik et al., 2005). Genetic correlations be-
tween production and reproduction have consistently been reported to be
of moderate strength with absolute values of 0.2 to 0.6 and in an unfavor-
able direction [summarized by Pryce et al. (2004) and Bello et al. (2012)].
However, it is difcult to assess how to apply such information when it is
largely based on incomplete or biased data. While one hypothesis for the
improvement of phenotypic measures of reproduction in the US is the co-
incident inclusion of ‘daughter pregnancy rate’ in genetic selection indices
(Norman et al., 2009), the apparent inection in days open also coincided
with increasing use of synchronization programs for timed insemination,
among other things. In any case, for a given cow or herd-year, reproduc-
tive phenotype seems clearly to depend overwhelmingly on management
rather than genetic factors (Bello et al., 2012)
Measures of production
Milk yield is measured numerous ways, each with benets and weak-
nesses. Milk yield in early lactation such as rst test day milk yield or 60
day milk yield has the benet of including more animals in the analysis
but it may not provide accurate predictions for a complete lactation. Often
early lactation point or cumulative milk yields are used in an attempt to
measure production before breeding starts; this avoids confounding of re-
production outcomes by culling because it is too early to cull animals for
failure to conceive. Milk yield per day is affected by stage of lactation so
305 day projections are better. However, parity has a large effect on both
production and culling, so to compare across parities the best measure
of production is 305 day milk yield mature equivalent, or parity should
be accounted for in another way. Quist et al. (2007) showed that projec-
tions from test days later in lactation are more accurate than projections
in early lactation. Completed 305 day milk yields have the advantage of
being a more accurate measure of production over a full lactation (Bello
et al., 2012) but limiting analysis of reproduction to cows with complete
lactation records introduces a bias due to exclusion of information from
cows removed up to 304 days in milk (DIM) because of low production or
lack of reproductive success. Additionally, energy-corrected milk yields
or yields of milk solids likely better reect the physiologic demands of
production, so milk components should be accounted for rather than con-
sidering milk volume alone. For example, data from Australia indicate
that milk protein, but not necessarily fat concentration, was positively as-
sociated with pregnancy in the rst 6 weeks of seasonal breeding (Morton,
2011).
Milk production per cow has increased over time in most of the world,
typically 1 to 2% per year on average (Butler, 1998; Royal et al., 2000;
Lucy, 2001). Milk yield that would be considered high in one production
system would be low under another, even among countries with devel-
oped dairy industries. A cow producing 8,000 L of milk per lactation in a
pasture-based system may be considered either exceptionally productive
or at risk of excessive metabolic demands, whereas a cow with the same
level of production in an intensive production system may be average in
one herd and a candidate for culling because of low production in another
herd in the same region.
Measures of reproduction
Measures of phenotype for fertility should reect the ability of a cow
to become pregnant efciently at an economically optimal time postpar-
tum. It is difcult, but important, to differentiate physiologic function and
capacity to become pregnant from management constraints such as con-
nement housing, slippery oors, large numbers of cows per worker, lack
of observation, or poor insemination technique or timing that may result
in fertile animals not expressing primary signs of estrus, estrus not being
detected, or a low probability of pregnancy at insemination. To evaluate
reproductive performance and quantify factors that may affect it requires
accurate data and valid analytical techniques. Traditional methods to mea-
sure reproduction in lactating cows rely on indirect or biased measures
such as time to rst insemination, non-return rates, and calving interval.
Time to rst insemination may be highly confounded by herd manage-
ment, primarily by the low intensity of estrus detection in many herds (av-
erage insemination rate in Canadian dairy herds is 35 to 40% per 21 day
period; Figure 1), but also by decisions about when to inseminate some or
all cows in a herd. If a cow is not detected in estrus until 100 DIM, is it be-
cause she did not undergo estrus until then, or despite several estrus cycles
and ovulations, that was the rst time a person observed the cow in heat?
Conception risk and non-return rate. Conception risk is the prob-
ability that an inseminated cow is diagnosed pregnant to that breeding,
typically by examination by a veterinarian between 28 and 50 days post
insemination. The stage of diagnosis of pregnancy is important for com-
parison between studies because of the high rate of pregnancy loss within
this interval (Santos et al., 2004).
Some reports have focused on conception risk as a measure of fertility,
but at best this only reects part of the process of making open cows preg-
nant, in that it does not account for cows that were not inseminated, or for
when insemination occurred. Conception risk reects the efciency of use
of semen, and may reect the accuracy of estrus detection, insemination
technique, or compliance with a synchronization protocol but it cannot
necessarily be taken as a measure of the fundamental fertility of cows.
Non-return rate at rst insemination, which is traditionally used by
AI units to measure reproductive performance, overestimates conception
risk. It is calculated as the proportion of inseminated animals for which
re-insemination is not requested, typically within 56 days, which are then
assumed to be pregnant. Non-return rate grossly overestimates the actual
proportion of cows becoming pregnant, but it is widely available, easy
to measure, and is not as biased as calving interval (CI) because it does
not exclude all failures. Use of assumed pregnancy status can result in
substantial inaccuracy in calculation of time to pregnancy (Wiggans and
Goodling, 2005). It is disheartening that inferences continue to be made
on the basis of data that are blatantly inaccurate.
Calving interval and days open. Both calving interval (time between
successive calvings) and “days open” (count of days from calving to con-
ception) are severely biased by including only cows that become preg-
nant. It is difcult to overstate the weakness of using calving interval as a
measure of reproductive performance when all cows that fail to become
pregnant are excluded from the measure. Calving interval is further biased
by considering only multiparous animals. Additional potential bias comes
from that fact that pregnant cows are very unlikely to be culled, notwith-
standing their level of production (Grohn et al., 1998). Yet, higher produc-
ing cows are more likely to be inseminated more times and for longer than
lower producers (Eicker et al., 1996). Culling lower producers that do not
become pregnant quickly sooner than higher producers leaves a data set
which appears to have an increased number of high producing cows with
more inseminations and longer times to pregnancy.
Pregnancy rate. The best available single measure of overall repro-
ductive performance at the herd level is pregnancy rate (PR), which mea-
sures the probability that open cows become pregnant per unit of time
(LeBlanc, 2005). Given that, on average, cows should be in estrus every
21 days, pregnancy rate is commonly calculated on a 21 day basis. The
number of successful breedings that occur in the 3 week period is divided
by the number of non-pregnant animals past the voluntary waiting period
that were present in the herd at least 11 days of that 21 day period. A
value of 15% would be interpreted as “on average, 15% of open eligible
cows became pregnant within the 21 day period”. Furthermore, PR allows
comparisons between articially inseminated herds and natural service
herds. A major advantage to the use of 21 day pregnancy rate is that non-
October 2013, Vol. 3, No. 4 87
pregnant cows are included and contribute time eligible for pregnancy to
the denominator for as long as they are in the herd.
Statistics matter
Reproductive performance parameters that have economic value in-
clude the occurrence of pregnancy or culling for non-pregnancy, the time
from calving to subsequent pregnancy, and to a lesser extent, the num-
ber of inseminations required to produce the pregnancy. As summarized
above, most commonly reported measures of reproductive performance
are incomplete or biased. Furthermore, pregnancy is a dichotomous event,
days open are generally not normally distributed, and the number of in-
seminations never follows a normal distribution. Despite this, simple cor-
relations and linear regression analysis of variance have often been used
to evaluate these parameters. Moreover, many reports considered only one
variable when reproductive outcomes are clearly multifactorial (Bello et
al., 2012). There are no longer computational limits that excuse the use of
inappropriate analytic methodology.
The correct method for analysis of pregnancy data at the individual
level is multivariable survival analysis, which is becoming well-estab-
lished in research and beginning to be applied on the front lines of dairy
management (Weigel, 2004). Survival analysis is a statistical methodol-
ogy that measures time to an event, accounting for those subjects that do
not experience the event of interest or are lost to follow-up during the
study period, termed censored observations. Survival analysis can correct
for some of the confounding effects of decisions to delay or stop insemi-
nation, and culling. Information from all cows, regardless of pregnancy
or insemination status at the end of the study, can be used, unlike the
standard regression methods. Specically, cows that are culled or remain
non-pregnant contribute data on time at risk of pregnancy until they leave
the herd or the study period ends. Time-to-event statistical methods allow
for random effects of herd in the model, which importantly accounts for
the correlation or clustering of cows within a herd. Because they experi-
ence the same environment, they are not independent. They also allow for
generalization of the results of the statistical model which can be used to
generate “population averages” rather than herd-specic inferences.
Assessment of the impact of one factor on reproductive performance
may be highly confounded by individual and herd level factors with ef-
fects on reproduction, including cow age, season, diseases, nutrition, body
condition, environment, herd management decisions, the intensity and ac-
curacy of heat detection, and the use of reproductive management pro-
grams. In many studies, most of these factors were not measured. Most
research on the association of production and reproduction is done us-
ing a cross-sectional study design or with retrospective longitudinal data.
Retrospective observational studies have the inherent weakness of using
historical data, which may be confounded by differentially greater culling
of lesser producers and lesser culling of pregnant cows, irrespective of
production (Eicker et al., 1996).
Recently Bello et al. (2012) applied sophisticated novel statistical tech-
niques to simultaneously model and describe herd and cow-level effects
of production and reproduction using a large dataset from dairy herds in
Michigan. They demonstrated the heterogeneity of these relationships: the
associations at the herd and cow levels were different and sometimes op-
posite, and both were substantially inuenced by management practices.
For example, among herds using bovine somatotropin (bST) in more than
half the cows, at the herd level, higher average 305 milk yield was associ-
ated with decreased average calving interval, yet at the cow level, there
was a smaller magnitude but opposite effect, which was partially offset by
milking three vs. two times per day. Random effects of herd year had the
greatest inuence on variability in cow-level associations between 305
day yield and calving interval. In the same vein, Morton (2011) found that
over a 10 year period, the trend in herd reproductive performance differed
and that there was much greater variability between herds than by year.
Additionally Bello et al. (2012) showed that whole lactation variables do
not provide the same results as assessing milk yield and pregnancy at the
time of rst insemination of the lactation.
The methods employed by Bello et al. (2012) advanced the state of the
art on the present question. The methods are not readily applied widely,
and still employed an outcome such as calving interval with important
A newborn calf at Penn State's University Park facility (photo credit: Penn State).
88 Animal Frontiers
weaknesses. Nevertheless, there should be no going back to over-simpli-
ed analytic methods that fail to correctly account for herd effects or fail
to include covariates to the extent possible.
Physiology of the Cow and Management by
People
A central question is whether increasing or “high” levels of milk pro-
duction necessarily or irretrievably cause reduced fertility or whether
higher production capability increases the demands on metabolism and
management, which may not always be met. Negative energy balance
(NEB) happens after calving when cows do not consume enough feed
to completely support their capacity and homeorhetic drive to produce
milk. Essentially all cows experience some degree of NEB in early lacta-
tion. The duration and severity of postpartum NEB, specically the timing
of the nadir, is associated with the timing of rst ovulation (Butler and
Smith, 1989). A common perception is that high producers have the great-
est energy decit, but higher producing cows are not necessarily those
with more severe or prolonged NEB or with the greatest loss or the lowest
nadir of body condition (Lucy, 2001). Rather, high producing cows are
likely those with the greatest feed intake postpartum and therefore not the
greatest energy decit. Santos et al. (2009) reported data on nearly 6,400
cows in high-producing herds in California on similar synchronization
programs that allow for some distinguishing between the associations of
BCS and production with reproduction. Cows in the lowest quartile of
early lactation milk yield (mean of 32 kg/d) were less likely to be ovula-
tory by 65 DIM than higher producing cows (73% of cows vs. 75 to 78%,
respectively). There were no associations of production with pregnancy
at rst insemination or with subsequent pregnancy loss, but all three out-
comes were unfavorably associated with BCS < three at calving or at rst
AI, or with > one point of BCS loss between these times.
High producing cows may experience greater challenges to at least
some aspects of reproductive function. Greater feed consumption, which
is characteristic of higher production, leads to increased blood ow
through the liver and increased steroid catabolism, increasing the rate of
clearance of progesterone and estradiol, resulting in decreased circulating
concentrations (Sangsritavong et al., 2002; Wiltbank et al., 2005). The
reduced circulating sex steroid concentrations have the potential to af-
fect reproductive physiology at several levels. The same research group
demonstrated that cows with higher production (46 vs. 34 kg/day at ap-
proximately 94 DIM) had a shorter duration of estrus, stood to be mounted
fewer times, and despite having larger ovulatory follicles, had reduced
plasma estradiol concentrations (Lopez et al., 2004). However, even the
lower producers on average only had a cumulative time in standing to be
mounted of 28 seconds vs. 22 seconds for the higher producers, a consid-
erable challenge for estrus detection in both cases.
Fonseca et al. (1983) found that rst ovulation occurred about 3 weeks
postpartum regardless of production. There was no association between
timing of rst ovulation or duration of the rst two postpartum estrous
cycles and higher milk yield. Age at calving and clinical abnormalities
had greater inuence than milk yield on reproductive measures. Tracking
of the reproductive cycle was done with serial blood sampling instead of
visual estrus detection, which avoided the problems of low intensity and
possible inaccuracy of estrus detection that are typical in the eld even if
record keeping is done well (Fonseca et al., 1983).
There are few data available to support the notion that high producing
cows suffer negative effects from milk production (Fetrow and Eicker,
2003). On the contrary, it may be argued that the only way to achieve and
sustain high milk yields is to meet the nutritional and behavioral needs of
the cows. Stressors such as heat, lack of access to feed, inadequate nutri-
ent supply, unavailability of comfortable resting space, and poor ventila-
tion reduce milk production, and when these are removed cows approach
their genetic potential. However, some assume cows that produce a lot of
milk to be operating at their maximum potential and near their breaking
point. This is not true unless producers were doing something like feed-
ing cows to bolster milk production at the risk of causing disease (Fetrow
The South Mountain Creamery milking facility in Middletown, MD (photo credit: Adam Fagen).
October 2013, Vol. 3, No. 4 89
and Eicker, 2003). Therefore if cows are managed appropriately and their
needs met as fully as possible, they will produce to their capacity because
the demands of their production are met. The effects of milk production
cannot be blamed wholly for a reduction in health that may affect repro-
ductive performance. Rather, good health and the management to provide
for it are prerequisites for both good production and good reproduction
(Nebel and McGillard, 1993).
Herd Level
Stevenson (1999) reported summary cross-sectional data from 1.2 mil-
lion Holstein cows in 9,684 herds and 50,000 Jersey cows from 546 herds.
On average, higher producing herds were larger. As rolling herd average
milk production went from less than 6,800 kg to greater than 11,300 kg,
days open decreased from 195 to 156 days, interval to rst service went
from 102 to 94 days, number of services per conception increased from
1.8 to 2.2 and heat detection efciency went from 19 to 41%. This report
suggested that better reproduction in the higher producing herds may be
a reection of better nutrition, healthier cows, and superior reproductive
management.
Laben et al. (1982) compared average production in 200 California
herds with levels between 5,000 and 10,000 kg and days open and found
that pregnant cows in the highest producing herds were pregnant approxi-
mately three weeks earlier than cows in lower producing herds. Nebel
and McGillard (1993) reported data from 4,550 herds in 1992 stratied
by herd average production. Despite a decrease in CR as production in-
creased from 6,300 to 10,000 kg, higher producing herds had greater in-
semination rates, producing the net effect of a decreased interval to preg-
nancy in pregnant cows. This is likely explained by better management
leading to both higher production and better net reproduction.
Cow Level
Lopez-Gatuis et al. (2005) examined 2,756 pregnancies in two high
producing (> 11,700kg/year) herds in Spain and found that cows that were
pregnant by 90 days in milk produced a mean of 49.5 kg/day of milk at
Day 50, in contrast to 43.2 kg/day among cows that became pregnant
later, accounting for the effects of parity and retained placenta. This study
would have been strengthened by consideration of the probability and tim-
ing of pregnancy in all cows, rather than only dichotomization of the tim-
ing of pregnancy among pregnant cows. Nevertheless, the study under-
lines the need to consider time-ordering of the path to pregnancy. Whereas
many imply that high production may lead to poorer reproduction, these
data support an alternative hypothesis that cows that are healthy produce
more milk thereby coming closer to fullling their genetic potential and
also become pregnant sooner than cows that may be less healthy. In other
words, selection for lower yield would not lead to better reproduction;
rather, management for greater health may lead to both higher yield and
pregnancy rate.
Additionally, a study in New York reported no signicant association
of 60 day milk production with pregnancy rate, using survival analysis
(Eicker et al., 1996). High milk yield was not a major factor in delaying
conception. Non-pregnant cows had a greater risk of being culled. High
milk yield, high parity, and winter calving were risk factors for several
reproductive disorders, which in turn delayed insemination and concep-
tion. Cumulative 60 day milk yield had no effect on time to pregnancy,
although cows in the highest quartile of 60 day yield (2,541 kg) had a
slightly less, but not statistically signicant, pregnancy rate than the low-
est quartile. Reproductive diseases such as retained placenta and metritis
had larger effects on time to pregnancy than the level of milk production
in early lactation. Because managers might intentionally delay insemina-
tion of high yielding cows, the association between milk yield and time
to rst insemination was considered. As 60 day milk yield increased, so
did insemination rates, meaning that the highest yielders were more likely
to be inseminated sooner than the lowest yielders. They concluded that
farmers were making rational decisions by breeding young, healthy, high
yielding cows (Eicker et al., 1996).
Crude measures of production may obscure relationships between lac-
tation and pregnancy and the mechanisms that may link them. Madouasse
et al. (2010) used monthly DHI data from 799,000 lactations from 441,000
cows in 2,128 herds in the UK with mean 305 day yield of 8,200 kg. They
modeled time to pregnancy between 20 and 144 DIM in 21 day intervals
with survival analysis with a random herd effect using data from the rst 2
test days. The strongest predictors of pregnancy by 145 DIM were yield at
test day one, which was associated with decreased odds of pregnancy and
protein % at test day two and lactose % at test day one. Each of these was
associated with increased odds of pregnancy. The protein concentration
association is supported by a similar nding by Morton (2011).
We measured the relationship of the level of milk production with
reproductive performance in Canadian dairy cows at both the herd and
cow levels (Campbell et al., 2009). Data were extracted from all 6,326
herds on milk recording in Ontario and western Canada. There were 3,297
herds with complete AI and pregnancy data for the year 2005, to which
herd size, demographics, production, milking frequency, and housing type
were added. Herd annual mean (SD) 21 day PR, insemination rate (IR),
and CR were 12.5 (4.7), 33.9 (10.5) and 37.2 (9.9), respectively. Herd PR
was modeled with mixed linear regression with a random herd effect. Ac-
counting for herd size, parity distribution, breed, and housing, each 1,000
kg increase of herd mean mature equivalent milk was associated with an
increase of 0.7 points of PR (P < .0001).
Individual data for the rst three test days were available for 103,060
Holstein cows in 2,076 herds. Times to rst AI and to pregnancy were
modeled with survival analysis with a random herd effect. Production
was described by kilograms of milk and 305 day projections at test days
one, two, and three, and completed 305 day records, each of which had
a signicant univariate association with shorter time to pregnancy. Milk
yield at test day 1 was not associated with time to rst AI. A separate
analysis considered milk production relative to herd-mates by classifying
each cow into intra-herd quartiles of production at the rst test day. Cows
in the highest intra-herd quartile of production in early lactation were in-
seminated slightly earlier, and became pregnant slightly sooner than cows
in the lowest quartile for production. Overall, by either measure, higher
producing cows became pregnant a few days sooner than lower produc-
ing cows. These associations should not be surprising if good nutrition,
cow comfort, and attentive management provide the conditions for high
production and good reproductive performance.
In summary, herd pregnancy rate was signicantly greater in higher
producing herds, and at the cow level there were signicant but small ef-
fects of level of production on time to pregnancy. A study using the same
methods on data from 57,700 cows that calved in 2007 in 1,450 herds in
Quebec reached similar conclusions (J. Dubuc, personal communication)
90 Animal Frontiers
Conclusion
It is not clear whether pregnancy rate in the dairy cattle population is
falling and data are needed to quantify and begin to benchmark perfor-
mance over time. It is not appropriate to measure reproductive perfor-
mance on the basis of conception risk alone and data on the association
of milk production with pregnancy rate are conicting. Questions about
whether metabolic demands for production and reproduction are reaching
a biological or management limit and whether genetic selection criteria
for fertility are optimized, important, and warrant valid, large-scale stud-
ies. Management to provide for high production can be compatible with
good reproductive performance.
Literature Cited
Bello, N., J.S. Stevenson, and R.J. Tempelman. 2012. Invited review: Milk produc-
tion and reproductive performance: Modern interdisciplinary insights into an
enduring axiom. J. Dairy Sci. 95:5461–5475.
Butler, W.R. 1998. Review: Effect of protein nutrition on ovarian and uterine physi-
ology in dairy cattle. J. Dairy Sci. 81:2533–2539.
Butler, W.R. 2003. Energy balance relationships with follicular development, ovu-
lation and fertility in postpartum dairy cows. Livest. Prod. Sci. 83:211–218.
Butler, W.R., and R.D. Smith. 1989. Interrelationships between energy balance and
postpartum reproductive function in dairy cattle. J. Dairy Sci. 72:767–783.
Campbell, M.S., K. Hand, D.F. Kelton, F. Miglior, and S.J. LeBlanc. 2009. The
association of level of milk production with reproductive performance. J. Dairy
Sci. 92. E-Suppl. 1:335 (abstr.)
Eicker, S.W., Y.T. Grohn, and J.A. Hertl. 1996. The association between cumulative
milk yield, days open, and days to rst breeding in New York Holstein cows. J.
Dairy Sci. 79:235–241.
Fetrow, J., and S. Eicker. 2003. High Production and Health- A Curious Paradox.
Bov. Practitioner 37:128–136.
Fonseca, F.A., J.H. Britt, B.T. McDaniel, J.C. Wilk, and A.H. Rakes. 1983. Repro-
ductive traits of Holsteins and Jerseys. Effects of age, milk yield, and clinical
abnormalities on involution of cervix and uterus, ovulation, estrous cycles, de-
tection of estrus, conception rate and days open. J. Dairy Sci. 66:1128–1147.
Foote, R.H. 1978. Reproductive performance and problems in New York dairy
herds. Cornell Univ. Agr. Exp. Stn. Search: 8.
Grohn, Y.T., S.W. Eicker, V. Ducrocq, and J.A. Hertl. 1998. Effect of diseases on
the culling of Holstein dairy cows in New York State. J. Dairy Sci. 81:966–978.
Grohn, Y.T., and P.J. Rajala-Schultz. 2000. Epidemiology of Reproductive Perfor-
mance in Dairy Cows. Anim. Reprod. Sci. 60–61:605–614.
Jamrozik, J., J. Fatehi, G.J. Kistemaker, and L.R. Schaeffer. 2005. Estimates of
genetic parameters for Canadian Holstein female reproduction traits. J. Dairy
Sci. 88:c2199–c2208.
Kadarmideen, H.N., R. Thompson, M.P. Coffey, and M.A. Kossaibati. 2003. Ge-
netic parameters and evaluations from single- and multiple trait analysis of
dairy cow fertility and milk production. Livest. Prod. Sci. 81:183–195.
Laben, R.L., R. Shanks, P.J. Berger, and A.E. Freeman. 1982. Factors affecting
milk yield and reproductive performance. J. Dairy Sci. 65:1004–1015.
LeBlanc, S. 2005. Using DHI Records On-farm to Evaluate Reproductive Perfor-
mance. Adv. Dairy Technol. 17:319–330.
Lopez, H., L.D. Satter, and M.C. Wiltbank. 2004. Relationship between level of
milk production and estrous behavior of lactating dairy cows. Anim. Reprod.
Sci. 81:209–223.
Lopez-Gatius, F., I. Garcia-Ispierto, P. Santolaria, J. Yaniz, C. Nogareda, and M.
Lopez-Bejar. 2005. Screening for high fertility in high-producing dairy cows.
Theriogenology 65:1678–1689.
Lucy, M.C. 2001. Reproductive loss in high-producing dairy cattle: Where will it
end? J. Dairy Sci. 84:1277–1293.
Madouasse, A., J.N. Huxley, W.J. Browne, A.J. Bradley, I.L. Dryden, and M.J.
Green. 2010. Use of individual cow milk recording data at the start of lactation
to predict the calving to conception interval. J. Dairy Sci. 93:4677–4690.
Marti, C.F., and D.A. Funk. 1994. Relationship between production and days open
at different levels of herd production. J. Dairy Sci. 77:1682–1690.
Morton, J.M. 2006. Potential bias in observed associations between milk yield and
reproductive performance in dairy cows. Proc 11th Intl. Symp. Vet Epidem.
Econ.
Morton, J.M. 2011. InCalf Fertility Data Project 2011 Final Report. http://www.
dairyaustralia.com.au/Animals-feed-and-environment/Fertility/InCalf-resourc-
es-and-other-tools/InCalf-Research.aspx. Dairy Australia, Southbank, Victoria,
Australia. Accessed June 23, 2013.
Nebel, R.L., and M.L. McGillard. 1993. Interactions of high milk yield and repro-
ductive performance in dairy cows. J. Dairy Sci. 76:3257–3268.
Norman, H.D., J.R. Wright, S.M. Hubbard, R.H. Miller, and J.L. Hutchison. 2009.
Reproductive status of Holstein and Jersey cows in the United States. J. Dairy
Sci. 92:3517–3528.
Pryce, J.E., M.D. Royal, P.C. Garnsworthy, and I.L. Mao. 2004. Fertility in the high
producing dairy cow. Livest. Prod. Sci. 86:125–135.
Quist, M.A., S.J. LeBlanc, K.J. Hand, D. Lazenby, F. Miglior, and D.F. Kelton.
2007. Agreement of predicted 305-day milk yields relative to actual 305-day
milk weight yields and parlour electronic identication accuracy. J. Dairy Sci.
90:4684–4692.
Royal, M.D., A.O. Darwash, A.P.F. Flint, R. Webb, J.A. Woolliams, and G.E. Lam-
ming. 2000. Declining fertility in dairy cattle: Changes in traditional and endo-
crine parameters of fertility. Anim. Sci. 70:487–502.
Sangsritavong, S., D.K. Combs, R. Sartori, and M.C. Wiltbank. 2002. High feed
intake increases blood ow and metabolism of progesterone and estradiol-17β
in dairy cattle. J. Dairy Sci. 85:2831–2842.
Santos, J.E.P., H.M. Rutigliano, and M.F. Sa Filho. 2009. Risk factors for resump-
tion of postpartum estrous cycles and embryonic survival in lactating dairy
cows. Anim. Reprod. Sci. 110:207–221.
Santos, J.E.P., W.W. Thatcher, R.C. Chebel, R.L.A. Cerri, and K.N. Galvão. 2004.
The effect of embryonic death rates in cattle on the efcacy of estrus synchro-
nization programs. Anim. Reprod. Sci. 82–83:513–535.
Spalding, R.W., R.W. Everett, and R.H. Foote. 1975. Fertility in New York arti-
cially inseminated Holstein herds in dairy herd improvement. J. Dairy Sci.
58:718–723.
Stevenson, J.S. 1999. Can you have good reproduction and high milk yield?
Hoard’s Dairyman 144:536.
Stevenson, J.S. 2001. Reproductive management of dairy cows in high milk-pro-
ducing herds. J. Dairy Sci. 84 (E. Suppl.):vE128–E143.
Weigel, K. 2004. Improving the reproductive efciency of dairy cattle through ge-
netic selection. J. Dairy Sci. 87(E. Suppl.): E86–E92.
Wiggans, G.R. and R.C. Goodling RC. 2005. Accounting for pregnancy diagnosis
in predicting days open. J. Dairy Sci. 88:1873–1877.
Wiltbank, M., H. Lopez, R. Sartori, S. Sangsritavong, and A. Gumen. 2005. Chang-
es in reproductive physiology of lactating dairy cows due to elevated steroid
metabolism. Theriogenology 65:17–29.
About the Author
Stephen LeBlanc is an Associate Professor
in the Department of Population Medicine
at the Ontario Veterinary College, Uni-
versity of Guelph. He received a B.S. in
Animal Science from McGill University
in 1992 followed by a DVM and DVSc
from the University of Guelph in 1997
and 2001, respectively. After ve years
of private practice in veterinary medi-
cine, he joined the faculty at OVC where
he teaches veterinary and agriculture stu-
dents and provides clinical farm service.
His research focuses on transition dairy
cow metabolic and reproductive health and management.
Correspondence: sleblanc@uoguelph.ca
October 2013, Vol. 3, No. 4 91
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