Intramammary infection and clinical mastitis in dairy cows leads to
considerable economic losses for farmers. The somatic cell
concentration in cow's milk has been shown to be an excellent
indicator for the prevalence of subclinical mastitis. In this study,
a new somatic cell count index (SCCI) was proposed for the accurate
prediction of milk yield losses caused by elevated somatic cell
count (SCC). In all, 97 238 lactations (55 207 Holstein cows) from
2328 herds were recorded between 2010 and 2014 under different
scenarios (high and low levels of SCC, four lactation stages,
different milk yield intensities, and parities (1, 2, and
Development of intramammary infection (IMI) and occurrence of clinical mastitis in dairy cows leads to considerable economic losses for farmers (Nielsen et al., 2010; El-Awady et al., 2011), mainly owing to the reduction of milk production and lowering of milk technological traits (Bobbo et al., 2016). IMI has been shown to adversely affect fertility (Wolfenson et al., 2015) and reduce the longevity of dairy cows (Archer et al., 2013); it also increases the costs for implementation of veterinary services and extra labour.
The concentration of somatic cells in cow's milk has been shown to be an excellent and the main indicator for the estimation of the prevalence of subclinical mastitis. Cows with subclinical mastitis show no visible signs, but their somatic cell count (SCC, defined as the number of somatic cells per millilitre of milk) is elevated. Elevated SCC in milk suggests the presence of pathogens in the udder and is an indicator of IMI as well as a measure of the response to infection (Pyörälä, 2003; Heringstad et al., 2006). Thus, subclinical mastitis is considered as a hidden threat to healthy cows in a herd (Nyman et al., 2014).
For estimating the possible milk yield losses caused by subclinical
mastitis, a definition of healthy or non-infected is
essential. The threshold for a healthy udder was considered to be an
SCC of
Together with high milk yield, the fat-to-protein ratio (FPR) can serve as an important risk factor for mastitis (Windig et al., 2005). Cows with mastitis are characterized by lower milk yield, elevated SCC, and a higher FPR (Jamrozik and Schaeffer, 2012). Thus, the FPR of milk was considered to be a suitable measure of the energy balance status of animals, especially during the initial and most metabolically stressful stage of lactation (Buttchereit et al., 2010).
A close association is known to exist between high milk yield and SCC. High-milk-yielding cows are more susceptible to mastitis (Jamrozik et al., 2010). The average SCCs calculated based on SCCs at different lactation stages are often used in mastitis control programs and in programs for the improvement of udder health. The drawback of using the lactation average of the SCCs is that it does not account for the SCC variability during lactation (De Hass et al., 2004). Variation in the shapes of the lactation curve during different lactation periods can be influenced by subclinical and/or clinical mastitis. Moreover, the types of pathogens associated with clinical mastitis occurrence can also differentially affect the lactation curve (De Hass et al., 2002). The early detection of elevated SCC during lactation is possible only by using test-day records. For detection of subclinical mastitis and possible IMI, comparing different test-day records of SCC is necessary. Timely detection and analysis of peaks in SCC during the different stages of lactation are important for the successful management of dairy farms.
However, considering the relationship between milk yield and SCC might
lead to erroneous results since high milk yield might decrease the
SCCs because of the dilution effect (Miller et al., 1993). The
estimated SCCs of high-yielding dairy cows without IMI were found to be
lower than those of low-yielding dairy cows (Green et al., 2006;
Halasa et al., 2009; Boland et al., 2013). If the SCC concentration
due to lower milk production in infected cows is neglected, the milk
production loss might be overestimated. Overestimation of milk
production loss can be avoided by using a dilution factor
Several other effects are also related to subclinical mastitis development and elevated SCCs. Numerous studies have shown that different factors, such as stage of lactation, subsequent parity (PAR), milk yield (Nielsen et al., 2010; Boland et al., 2013), calving month (Rupp and Boichard, 2000) and calving season, feeding and housing (Hortet and Seegers, 1998; Hagnestam-Nielsen et al., 2009), milking (Nyman et al., 2009), milk composition (Windig et al., 2005; Nyman et al., 2014), and test-day season, breed, pregnancy status, and health disorders, affect the SCC (Hagnestam-Nielsen et al., 2009). Subclinical mastitis is a very complex problem. Therefore, developing a simple, cost-effective, and efficient method for the estimation of the relationships between elevated SCC, subclinical mastitis, and potential milk yield loss in dairy cows is of great interest to the dairy sector.
Therefore, this study aimed to develop an index for excessive SCC, namely, the somatic cell count index (SCCI), for estimating the effect of subclinical mastitis on milk yield loss. Intervals of 30 days in milk test-day records were used to determine the relationships among SCC, calving year, calving season, age at the first calving, milk composition, stage of lactation, and milk yield; the effect of herd was also assigned for a more reliable prediction of milk yield losses.
In this study, 97 238 standard lactation records of 55 207 Holstein
breed cows from 2328 herds were collected over 921 594 test-days
between 2010 and 2014. The data were a part of the national milk
recording from the Slovenian database (Jeretina et al., 1997)
collected according to the International Committee for Animal
recording (ICAR, 2016). The average herd size was 32 cows. The
test-day records with clinical mastitis were discarded from the
data set. Lactations with at least seven milk recordings were truncated
at 305 days. Each record of later analyses included the number of
test-day milk yields (TDMYs, kg), FPR, PARs, stage of
lactation (days in milk, DIMs), the season of calving (S; 1: spring,
2: summer, 3: autumn, and 4: winter), age at first calving (AFC,
days), breeding values of cows for milk yield (BVAs), and SCC (
The model was developed using two steps by using the statistical
application R (R Development Core Team, 2016) and the lme4 libraries
(Bates et al., 2015). In the first step, we determined the standard
shapes of the curves for the natural logarithm of SCC during lactation
in healthy cows for PAR 1, 2, and
We defined the SCCI for an individual lactation as the sum of the differences between the measured interpolated values of ln(SCC) (IP, Fig. 1) and the values of the standard shape of the curve for SCC for a particular period, divided by the area above the standard shape of the curve for SCC (PIS, Fig. 1) (Eq. 1).
Therefore, the SCCI represents the area for IP in the percentage share
of the total area for PIS above the standard shape of the curve for
SCC (Fig. 1). By definition, the values of SCCI are between 0 and 100,
wherein a value of 0 represents a small or inconsequential influence of
SCC on milk yield for standard lactation, and 100 represents the
maximal effect. When the SCCI was calculated, we considered the effect
of dilution for high-milk-yielding cows without IMI. For cows with
a daily milk yield above 10
SCCI of a cow in the third lactation with somatic cell count (SCC) values on test days in relation to the standard shape of the curve for SCC. Jeretina et al. (2016).
To determine the standard shape of the curve for SCC, we included the
completed standard lactations (305-day milk yield – MY305) of cows for
which the average SCC for standard lactation (ASCC) did not exceed
100 000 SCC. This limit was set because in healthy cows, on the
third day after calving, the SCC drops to 166 000 and by the 10th
day of lactation, it reduces to 100 000 SCC (Barkema et al.,
1999). In addition, we excluded the data for all cows in which the SCC
between two consecutive milk recordings increased from less than
50 000 to more than 100 000. These numbers potentially indicate
a suspected case of subclinical mastitis (Halasa et al., 2009). We
also excluded data for which the sum of squared deviations at fitting
of lactation curves through TDMY in MY305 according to the MilkBot
model (Cole et al., 2012) was larger than 150
We included fixed-effect PAR and the linear and quadratic regression
effects of DIMs, which were used to explain the dependent variable
We calculated the phenotypic potential of milk yield for cows by using
regression coefficients estimated from the linear regression model as
follows:
Based on the phenotypic potential of milk yield, we classified the
cows into the following four classes: cows with the lowest – milk
quantity (MQ)
We investigated the effect of SCCI on milk yield loss for standard
lactation within a particular month of lactation (
For milk yield loss on a daily basis (
To determine the effect of IMI on
The number of completed lactations with the average MY305, protein and
fat contents in milk, and the average geometric mean of SCC per
lactation are shown in Table 1. The average MY305 of primiparous cows,
which was 40 % of all lactations in the analysis, was
6828
To estimate the effect of SCCI on
The corresponding parameter values (Table 2) were included in Eq. (5)
and
Estimated daily milk yield loss (
The results showed that in primiparous cows the regression influence
of SCC on
Some descriptive statistics of data: number of lactations, average milk yield in standard lactation (MY305, kg), fat (% F), protein (% P), and geometric mean of the somatic cell count (SCC).
Regression coefficients calculated using Eq. (5) (Ali and Schaeffer, 1987) within parities (PARs) and the rank of milk production level (MQ) for the somatic cell count index estimation according to the stage of lactation.
About 77 % of the variance in milk yield loss caused by SCC could be explained by the effects such as subsequent PAR, MQ, LI, and ASCC (Tables 3 and 4).
Sources of variation for milk yield loss estimation included in
the linear regression model with standard errors (SEs),
For consecutive lactations, the model explained almost 44 % of the
variability in milk yield loss (
Based on their literature review, Hortet and Seegers (1998) reported
that in published studies 38 to 84 % of variability in milk yield
loss at the test-day level was explained by regression models. Most of
previously reported regression models can explain about 63 to 84 %
of variability; thus, the 77 % variability explained in the present
study is in good agreement with the findings of previous studies. In
this study, in agreement with the findings of literature reports, a
significant herd effect was noted on the investigated characteristics
(data not presented). The herd effect was highly significant in milk
composition and SCC even in studies that included low numbers of
herds, i.e., with only two commercial herds with 149 and 106 Holstein
cows per herd (Friggens and Rasmussen, 2001), 12 to 58 herds
with
Analysis of variance
In the present study, the SCC was adjusted using a dilution factor,
Cows with an SCC of
The duration of subclinical mastitis was described using the SCCI, for which
the area (IP) above the standard curve for SCC was used. The size of
the area depended on the duration and intensity of the subclinical
mastitis. In addition, all incidences for the increase in SCC were
used for the prediction of the SCCI. Within each 30-day interval, the
effect of the SCCI was estimated as the regression for 1, 2, and
A specific cyclic nature of infection of mammary glands in the data collected was presumed form the estimated SCCI in relation to the eight different scenarios (high or low levels of SCC in four lactation stages for different parities) an equal average SCC was determined for the milk yield loss across these scenarios. The SCCI enables the investigation of different situations related to varying SCCs at different lactation stages, milk intensities, and subsequent PARs (Table 5). It has been demonstrated that milk yield loss is related to the lactation stage at which the elevated SCC appears. This means that milk yield loss cannot be reliably estimated on the basis of average SCC. Information on the course of events is needed. A too-long interval between test-day controls is a period with a lack of information. During this period, subclinical mastitis could heal spontaneously or progress to clinical mastitis and thus lead to an elevated SCC. Determining the number of days before or after the diagnosis of subclinical mastitis is important for clinical mastitis detection. Such data can be accurately obtained on an experimental farm or by using a systematic collection of data on medical treatments by veterinarians and breeders, as in some countries (Scandinavian countries, Austria).
Daily milk yield loss (
It is questionable whether management conditions on experimental farms (Lescourret and Coulon, 1994) are appropriate and comparable with larger numbers of commercial farms (Windig et al., 2005) to study applicable methods for the detection of subclinical mastitis and its effect on milk yield loss in field conditions. Hortet and Seegers (1998) reviewed selected literature on milk yield loss, milk composition, and elevated SCCs. Prediction of milk yield loss can be affected by several factors such as differences in the methodology used for preparing data sets and calculations; the specific design of studies regarding the number and interval between test-day controls; the number and size of investigated farms, herds, and types of farms (experimental vs. commercial); housing systems; number of PARs; duration of lactation; and threshold of SCCs for subclinical mastitis. Therefore, comparing our results (Table 5) regarding the effect of elevated SCC and lactation period, subsequent PAR, milk yield, and different scenarios within standard lactation on reduced milk production with those of literature findings is difficult.
A threshold of 200 000 SCC would suggest that a cow has IMI and is
likely to be infected for at least an udder quarter. Across studies primiparous cows with
200 000 SCC showed, in kilograms per day, a milk loss of 0.13 (Boland
et al., 2013), 0.23 to 1.76 (Rekik et al., 2008), 0.28 (Halasa et al.,
2009), 0.61 (Hortet et al., 1999), 0.46 to 0.72 (Dürr et al.,
2008), 0.35 to 0.80 (Hand et al., 2012). The results of the present
study for thresholds of 200 000 and 400 000 SCC predicted higher
milk yield losses in the range of 0.8 to 1.4 and 1.0 to
2.7
In the second and/or third and subsequent PARs, elevated SCC led to
milk yield losses. Studies show, in kilograms per day, milk yield losses of
0.63 to 1.17 and from 0.60 to 1.85 for PAR2 and PAR
In the present study, elevated SCC had a higher effect on milk yield loss in multiparous than in primiparous cows. Multiparous cows in late lactation might be responsible for the majority of milk production loss at the herd level caused by elevated SCC (Hagnestam-Nielsen et al., 2009). Moreover, if milk losses owing to subclinical mastitis were not estimated appropriately, i.e., by using average loss per lactation, milk loss could be overestimated in the beginning of lactation, thereby remarkably underestimating losses toward the end of lactation (Dürr et al., 2008). Therefore, the SCCI developed in the present study allows corrections for the estimation of milk loss in the population of Holstein cows. Furthermore, it is applicable to other cow populations, but the standard curves and breeding value prediction methods for specific cow populations need to be determined.
Improving herd management requires the recognition of the dynamics and peaks of elevated somatic cell count with relation to daily milk loss during lactation. A standard of the average somatic cell count as a criterion for comparing cows with regard to the health status of their udder glands does not allow identification of time-related consequences of IMI for cow and herd management. The newly introduced somatic cell count index might enable the mutual comparison of milk yield loss across cows in relation to the level of SCC, effect of consecutive parity, stage of lactations, and milk yield intensity. The SCCI has been proposed as an indicator of IMI to provide farmers reliable information to apply appropriate measures regarding cow health management and overall economical cow milk production.
The data of the paper are available upon request from the corresponding author.
JJ and DB designed the experiment. JJ analysed the data, and DŠ drafted the paper.
The authors declare that they have no conflicts of interest.
The comments and suggestions of the three anonymous referees are greatly appreciated. The authors would like to thank the Slovenian Research Agency for the financial support (P1-0164, P4-0133). Edited by: Nina Melzer Reviewed by: Muhamed Brka and one anonymous referee