Effect of Increased Somatic Cell Count on Herd Level Yield of Milk and Milk Components

Hannah Walczyk, Undergraduate Student, and Maurice Eastridge, Professor, Department of Animal Sciences, The Ohio State University

Introduction

Dairy is an essential component to the human diet. With this being said, it is important that farmers continue to improve their productivity in order to supply the fast-growing population with dairy products (De Vliegher et al., 2012). Additionally, increasing the cow’s yield of milk, protein, and fat will benefit the farm economically. Unfortunately, there are many factors that can negatively affect the quantity of milk, as well as its quality. One of these factors is disease, especially mastitis. Mastitis is one of the most common and harmful diseases among dairy cows (De Vliegher et al., 2012). Mastitis is the inflammation of the mammary gland caused by an intra-mammary infection. Infection can be caused by multiple species of bacteria which enter the teat orifice and invade the mammary gland (De Vliegher et al., 2012). Diagnosis of mastitis can be achieved by evaluating the concentration of somatic cells in the milk. If the cow shows high concentrations of somatic cells, then mastitis is present in the gland.

Mastitis is not only harmful to the cow’s biological functions but also to the profitability of the farm. As stated by Losinger (2005), there have been many past reports which have proven that an increase in somatic cell count (SCC) results in a decrease in milk production, thus resulting in financial loss to the farm. In fact, the economic loss in the US per cow per year for one clinical mastitis case has been estimated to be $80 (Sadeghi-Sefidmazgi et al., 2010). In our study, levels of SCC, somatic cell score (SCS), milk yield, milk fat, and milk protein on a herd basis were evaluated in order to determine the impact of increased SCC and SCS on yields of milk, fat, and protein.

Materials and Methods

During this study, data collected from Dairy Herd Improvement (DHI Cooperative, Inc., Columbus, OH) were used to assess the impact of increased SCC and SCS at the herd level on yields of milk, fat, and protein. The data included the rolling herd averages (RHA) for SCC and yields of milk, fat, and protein from Ohio dairy farms for the years 2012 through 2015. The data were sorted to meet the following criteria: > 100 cows, and Holstein or mixed breed. The RHA SCC (1000 cells/mL) was used to calculate the RHA SCS using the following equation (Wiggans and Shook, 1987):

Subsequently, data were analyzed using SAS (SAS Institute, Inc., Cary, NC). The simple statistics for number of cows, SCC, SCS, and yields of milk, fat, protein, and energy corrected milk (ECM) are shown in Table 1. The PROC CORR procedure of SAS was used for determination of the correlation coefficients for the variables of interest. The PROC MIXED procedure of SAS was used to estimate the impact of SCC and SCS on stated yields on a herd basis, with herd set as a random variable and year as a repeated variable. Data were considered to be significant if the P < 0.05 and a trend if P < 0.10.

Table 1. Number of cows, somatic cell count (SCC), somatic cell score (SCS),
and rolling herd average yields of milk, fat, protein, and energy corrected milk (ECM).

Results/Discussion

As expected, milk yield and SCC were negatively correlated (Table 2). This means that as SCC increases, milk yield will decrease. Milk yield and SCS also were negatively correlated, with the coefficient only being slightly higher than for milk and SCC. In addition, it was observed that yields of fat and protein also were negatively correlated with SCC and SCS. Thus, an increase in SCS would be expected to decrease the quality of milk and the yields of milk, fat, and protein, which would all contribute to a loss of income to the dairy farmer. Each of the correlation coefficients provided in Table 2 were significant. 

Finally, the impact of increased SCC on milk yield was determined.  From this analysis of milk yield and SCC, the y-intercept was 23,661 lb, and the slope was -1.56.  Thus, the relationship between milk yield and SCC is expressed as:

Milk (lb) = 23,661 - 1.56x,

where x is defined as SCC (1000 cells/mL).  The same analysis was performed in order to evaluate the relationship between milk yield and SCS.  In this case, the intercept was 24,151 lb and the slope was -207.  The following equation was used to express this relationship:

Milk (lb) = 24,151 - 207x, 

where x is defined as SCS (0 to 9 linear scale).

When examining the relationship between milk yield and SCC, it was determined that for every 1000 cells/mL SCC increase, there would be a 1.56 lb RHA milk loss. Thus, if a herd had an SCC of 400,000 cells/ mL, then the RHA milk yield would be expected to decrease by 624 lb (400 x 1.56 = 624). Using this example, a 624 lb decrease in RHA milk yield for a 500 cow herd could result in about a $56,000 loss in income (assuming $18/cwt for milk). Likewise, for every 1 SCS increase, there would be a 207 lb loss in milk. Thus, a SCS of 5 (equivalent to 400,000 cells/ mL SCC) would result in about 1035 lb (5 x 207) decrease in RHA milk yield (Table 3). Using $18/cwt for milk with a 500 cow herd, this could result in about $93,000 of loss revenue. The slopes for both SCC and SCS tended to be significant (P < 0.10), but they differed in magnitude of milk loss (for the example above: 624 vs 1035 lb loss RHA milk). However, it has been determined in previous research that SCS has a better relationship than SCC with milk loss (Wiggans and Shook, 1987). The results of this study were somewhat different compared to the generally accepted milk losses used in the DHI system which are differentiated by parity (Table 3). For every linear score increase, or doubling of SCC, above SCS 2, there is estimated a loss of 200 lb/lactation for parity one cows and 400 lb/lactation of milk loss for those with 2+ lactations. The results of this study showed a loss of 207 lb of milk in RHA per SCS increase. Some of this difference between the estimated milk loss using the DHI system and our findings is because our study was on a RHA basis and not separated by parities of individual cows. If you assume that a typical herd consists of about one third parity one cows and two-thirds of cows with two or more parities, then the weighed estimated milk loss would be 334, 668, 1002, and 1336 lb/lactation for SCS of 3, 4, 5, and 6, respectively. These would compare to the 621, 828, 1035, and 1242 lb RHA milk, respectively, using the SCS coefficient from our study. Understanding the relationship between SCS and milk yield is important for improving mammary health within a herd and improving profitability for the farm.

Conclusions

Mastitis is a costly disease on dairy farms. Its impact on animal health and farm profitability are well recognized. The impact on the farm economics results from the costs associated with the disease on the farm (e.g. loss of milk, cost of treatment, labor, etc.) and the premiums for milk quality from the processor. Understanding the relationship of SCS on milk loss is important since over 50% of the cost of mastitis is caused by loss of milk sales. Monitoring this relationship can assist in culling decisions and making management changes and facility modifications to improve the mammary health status of the herd. Whether using the estimates of milk loss from this study or those referenced in the DHI system, it is very apparent that mastitis can be very costly on a dairy farm.

Table 2. Correlation coefficients and P-values for milk, fat, protein, somatic cell
count (SCC), somatic cell score (SCS), and energy- corrected milk (ECM).

Table 3. Comparison of estimated milk loss for the DHI adopted system 
versus the equation derived in this study.¹

¹SCS = Somatic cell score, SCC = somatic cell count, and RHA = rolling herd average.
²Based on system adopted by DHI (NMC, 2017)
³Based on the SCS equation in this paper; estimated RHA loss only provided for
SCS 3 to 6 because of range of data used for deriving the equation.

Works Cited

De Vliegher, S., L.K. Fox, S. Peipers, S. McDougall, and H.W. Barkema. 2012. Invited review: Mastitis in dairy heifers: Nature of the disease, potential impact, prevention, and control. Journal of Dairy Science, 95:1025-1040. http://dx.doi.org/ 10.3168/jds.2010-4074

Losinger, W. C. 2005. Economic impacts of reduced milk production associated with an increase in bulk-tank somatic cell count on US dairies. Journal of the American Veterinary Medical Association, 226(10), 1652-1658. doi:10.2460/javma.2005.226.1652

NMC. 2017. The value and use of Dairy Herd Improvement somatic cell count. National Mastitis Council Fact Sheet, http://www.nmconline.org/wp-content/uploads/2016/09/The-Value-and-Use-of-Dairy-Herd-Improvement-Somatic-Cell-Count.pdf (accessed August 3, 2017)

Sadeghi-Sefidmazgi, A., M. Moradi-Shahrbabak, A. Nejati-Javaremi, S.R. Miraei-Ashtiani, and Amer. 2010. Estimation of economic values and financial losses associated with clinical mastitis and somatic cell score in Holstein dairy cattle. Animal, 5(01), 33-42. doi:10.1017/s1751731110001655

Wiggans, G., and G. Shook, G. 1987. A lactation measure of somatic cell count. Journal of Dairy Science, 70(12), 2666-2672. doi:10.3168/jds.s0022-0302(87)80337-5