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VALUE OF THE DAIRY HERD IMPROVEMENT (DHI) HOT LIST AS A DAIRY
JESSICA ELIZABETH BELSITO
A THESIS PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
MASTER OF SCIENCE
UNIVERSITY OF FLORIDA
Jessica Elizabeth Belsito
This document is dedicated to the greatest man I never met, Robert John Belsito (1925-
1973) and to the strongest woman I ever knew, Marcia Walsh (1920-2004).
Gratefulness is extended to my supervisory committee, Dr. Roger Natzke, Dr
Albert deVries and Dr Nick Place, for their time and effort.
Many thanks are given to Dave Bray, for taking me under his wing and giving me a
wealth of knowledge about the more practical side of the dairy industry.
I Thank my fellow students Bruno Amaral and his wife Michelle, Ben Butler, Liz
Johnson, Christy Bratcher, Nathan Krueger and Wimberly Krueger. Lastly, I thank Jamie
Foster for being a great Sigma Alpha sister.
I thank my officemates Kelly Jimenez and Tiffany Herrera, for the company and
camaraderie. I would be remiss if I did not thank Kasey Moyes, for inspiring me to go to
graduate school in the first place and being a sincere friend.
I also wish to thank my parents for their support and love throughout this process.
It was their guidance and the example they set forth which allowed me to become a
successful person. I wish to extend my love to the rest of my family, Kerri, Bobby, Trish,
Cassandra, Becky, Jerimy, Tatum, Colby, and the little one who is on her way.
Lastly, I wish to thank Sergio, who has been understanding, patient, supportive and
has made my life complete.
TABLE OF CONTENTS
A C K N O W L E D G M E N T S ................................................................................................. iv
LIST OF TA BLE S ....................................................... .. .......... ............ .. vii
L IST O F FIG U R E S .............. ............................ ............. ........... ... ........ viii
A B STR A C T ................................................. ..................................... .. x
1 IN TRODU CTION ................................................. ...... .................
2 LITERATURE REVIEW ........................................................................4
B ack g rou n d ................................................................................................. . 4
P athogenesis ................................................................. 5
M ode of Infection .................. ..................................... .. ........ .... 5
Im m u nity .................................................................... 6
P a th o g e n s .................. ... .................................................................. ............. 9
M astitis Prevention and Treatm ent.........................................................................9
E co n o m ics ................................................. ................................15
Milk Composition and Quality .............. .............................................. .......17
Milk Culturing and Somatic Cell Counting Techniques .........................................18
Factors Affecting Somatic Cell Counts .......................................................19
V ariability in Som atic C ell C ounts....................................... ................................2 1
3 FLORIDA MILK QUALITY LAB ANALYSIS ................................................. 23
Intro du action ...................................... ................................................ 2 3
M materials and M methods ....................................................................... ..................24
R results and D iscu ssion .............................. ......................... ... ........ .... ............24
4 VARIATIONS IN SOMATIC CELL COUNTS FROM MILKING TO MILKING 29
In tro d u ctio n ........................................................................................................... 2 9
M materials and M methods ....................................................................... ..................30
R results and D discussion ..................................... ......... ...... .. ............... 1
5 ANALYSIS OF THE VALUE OF HOT LIST USE ..............................................40
M materials an d M eth od s ...................................................................... ....................42
R results and D iscu ssion ............................. ...... .................. .. .......... ...... ...........43
6 GENERAL DISCUSSION AND CONCLUSIONS..............................55
L IST O F R E F E R E N C E S ...................... .. ............. .. ....................................................58
B IO G R A PH IC A L SK E T C H ...................................................................... ..................61
LIST OF TABLES
3-1 Lab Analysis of Official Samples. ........................................ ....................... 28
4-1 Raw and adjusted (for milk production) SCC correlations for consecutive
m ilk in g s .......................................................................... 3 6
4-2 Raw and adjusted (for milk production) SCC correlations for morning, afternoon
and evening m ilkings. ................................... .. .. .. ... ................... 37
5-1 Number of times cows were on hot lists for study one and study two...................50
5-2 Results of the Proc Mixed Procedure on daily bulk tank data. .............................52
5-3 Results of the LS means procedure on daily bulk tank data. ..................................53
LIST OF FIGURES
3-1 SCC for Samples and their duplicates analyzed at lab 1.......................................25
3-2 SCC for Samples and their duplicates analyzed at lab 2 .......................................26
3-3 SCC for Samples and their duplicates analyzed at Lab 1 and Lab 2......................27
4-1 Average SCC for the 15 milkings on an individual cow basis..............................31
4-2 Number of cows over or under certain somatic cell counts at all 15 milkings .......32
4-3 Number of cows over or under certain daily somatic cell counts each day ............32
4-4 Number of cows with a daily SCC over one million ..........................................34
4-5 Number of cows with a daily SCC over 750,000 cells/ml ......................................34
4-6 Number of cows with a daily SCC over 500,000 cells/ml ......................................35
4-7 Number of cows with a daily SCC over 250,000 cells/ml ......................................35
4-8 Individual cow standard deviation versus her average SCC .................................38
4-9 Individual cow coefficient of variation versus her average SCC .............................38
5-1 Exam ple of a m monthly D H I hot list. ............................................... .....................41
5-2 Actual Bulk Tank SCC and Calculated Bulk Tank SCC Over a Five Year Period
with Hot List Cows Removed for Dairy A. ................................. .................44
5-3 Actual Bulk Tank SCC and Calculated Bulk Tank SCC Over a Five Year Period
with Hot List Cows Removed for Dairy B.................................... .................45
5-4 Actual Bulk Tank SCC and Calculated Bulk Tank SCC Over a Five Year Period
with Hot List Cows in the Previous Month Excluded for Dairy A........................46
5-5 Actual Bulk Tank SCC and Calculated Bulk Tank SCC Over a Five Year Period
with Hot List Cows in the Previous Month Excluded for Dairy B ........................46
5-6 Frequency of number of appearances on the hot list..............................................48
5-7 Frequency of number of appearances on the hot list..............................................49
Abstract of Thesis Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Master of Science
VALUE OF THE DAIRY HERD IMPROVEMENT (DHI) HOT LIST AS A DAIRY
Jessica Elizabeth Belsito
Chair: Roger P Natzke
Major Department: Animal Sciences
Mastitis, an inflammation of the udder, is the most costly disease in the dairy
industry. Somatic cells (which are mainly white blood cells) increase during an infection.
For this reason, and because bacteriological procedures are time consuming and
expensive, the somatic cell counts (SCC) are used as a general indicator of udder health.
Currently, Dairy Herd Improvement (DHI) offers milk sampling to determine individual
cow SCC. Many dairy producers use these SCCs to make management decisions such as
culling, treatment or early dry-off. However, it is not clear what the value is of these SCC
to base these management decisions on. The objectives of the following studies were to
analyze the accuracy of the DHI labs in Florida in determining SCC, observe more
closely milking to milking SCC variation and to use DHI records to determine the value
of the DHI hot list, which ranks the highest SCC cows in the herd each test day.
Chapter 3 was completed to assess the accuracy of our lab. Milk samples were
analyzed in duplicates by electronic somatic cell counting. The variance of the mean
differences between a sample and its duplicate was not statistically significant between
the labs (P=.098). In addition to this, variation from the standard was not significant
(P=.387). These two factors indicate that only a small portion of the milking-to-milking
variation can be attributed to the sampling and cell counting procedure.
In Chapter 4, 390 cows were sampled for 15 consecutive milkings. Hot lists were
created for each milking and each day to evaluate the repeatability of cows on the list.
The repeatability was also very low. When individual cow SCC was compared to the
standard deviation for all 15 milkings, we saw standard deviation increase with average
SCC. An additional study was completed by Southeast DHI (Gainesville, FL) over a
period of five milkings so there would be a comparison for the first study. Many of the
results in the two studies in Chapter 2 were similar.
Chapter 5 contains a statistical analysis of lactation records from 1998 until 2003
for two Florida dairy herds. The objective of the study was to determine the value of the
use of the hot list to reduce the bulk tank SCC over time. The hot list identifies the 20
cows in the herd that contribute the most cells to the bulk tank. Very few cows repeated
on the hot list month after month. In addition to this, statistical analysis of daily bulk
tank SCC showed no positive effect from hot list use. Therefore, if the SCC of many
cows decreases without intervention (based on the results from the hot list analysis), one
must question the economic value of intervening.
We concluded that SCCs are highly variable. These data indicate that the hot list
may not be as useful as previously thought and dairy producers should evaluate and
utilize other methods to manage their bulk tank SCC.
Quality food products are always a concern in the United States for producers,
consumers and the government alike. One way milk quality is measured is through the
counting of somatic cells. The word somatic means of the body. Therefore, it follows
that somatic cells in milk are any kind of cells from the body other than milk. Somatic
cells could possibly be white blood cells, epithelial cells or other types of cells. There are
many different reasons for keeping somatic cell counts (SCC) low. Producers, consumers
and other groups each have their own reason for wanting low SCC.
Producers are concerned about quality. A recent study (Ma et al., 2000) concluded
that lower SCC leads to an increase in the shelf life of milk. Producing a product that
lasts longer is economically beneficial to all consumers. Some consumers may also be
concerned about the health of the animal producing the milk and the cleanliness of farms
(both of these factors can affect SCC)
The National Mastitis Council (NMC), a well known organization that focuses on
mastitis and milk quality, has been pushing the US government to lower the legal limit of
somatic cells from 750,000 cells/ml to 400,000 (NMC, 2003). The reason is that many
countries in other areas of the world have lowered their legal limit to 400,000 and some
people believe that for the United States to successfully sell milk products on an
international level we need to lower our legal somatic cell limit. Clearly SCC is currently
a pressing issue for dairy producers.
One reason many dairy producers try to lower somatic cell counts is premiums.
Many milk buying organizations offer monetary premiums for lower SCC. Perhaps even
more important is the issue of mastitis (which is a costly disease). It is generally believed
that if a herd SCC is low less mastitis will be found on the farm. These are just some of
the reasons that the Dairy Herd Improvement Association (DHI), an association which
aids dairy producers in record keeping and data collection, has tried to develop and
implement tools to aid dairy producers in monitoring their SCC.
One of the first things that DHI did was to set up record keeping systems and labs
to evaluate the SCC of milk samples. Many herds across the US participate in DHI
monthly somatic cell count testing, where a DHI technician comes to their farm and takes
a milk sample from every cow. These samples are sent to DHI labs where they are
analyzed and the results are sent to the producer and stored in DHI databases. This
provides the dairy producer with a milk weight and SCC for each individual cow. The
goal of monthly somatic cell counts is to help producers identify individual cows with
high SCC. By identifying these cows, managers can then make a management decision
(culling, antibiotic therapy, etc.) to try to lower the SCC if necessary. To help managers
identify these cows more efficiently, DHI provides a "hot list" which ranks the top
twenty cows in the herd by total cells contributed to the bulk tank (the bulk tank SCC
refers to the average somatic cell count of the entire herd as the bulk tank is where all the
milk is stored, kept cool and agitated). This "hot list," in theory, aids dairy producers in
quickly identifying problem cows based on their SCC.
The literature lacks peer-reviewed articles on milking to milking SCC variability
and hot list analysis. Because of the problems outlined above and because of the lack of
literature on the subjects, the goal of this thesis was to observe and describe patterns
and/or changes in individual cow SCC over time and to analyze the effectiveness of the
hot list in managing bulk tank SCC.
Through a combination of these studies we were able to observe variability in SCC,
the value of the hot list to the dairy producer and the effect of the hot list on the bulk tank
SCC over time. Our hypothesis for the study was that the labs would be accurate and the
milking to milking somatic cell counts would show slight variability. It was also thought
that if the hot list was improved by adding other important information it would then
become a more useful tool for dairy producers.
Mastitis is one of the most costly diseases to milk producers. Losses are
associated with decreased milk production, discarding milk from cows treated with
antibiotics, the cost of the antibiotics and increased labor expenses (Ravinderpal et al.,
1990). In addition to this, milk quality can be negatively affected by high somatic cell
counts, which indicate the presence of infection (Ma et al., 2000). It is therefore the
purpose of this literature review to describe the events leading to mastitis, economic
losses due to infection, methods used to detect mastitis, variability in SCC and milk
quality and compositional changes.
The National Mastitis Council defines mastitis as an "inflammation of the udder,
most commonly caused by infecting microorganisms." The word mastitis itself means
inflammation of the mammary gland. Giesecke (1975) gives a much more specific
definition of mastitis. His definition states that mastitis is indeed an infection of the
mammary gland. First, there is damage to the mammary epithelium. An inflammatory
reaction (either clinical or subclinical) follows. Depending on the severity of the
infection, local or widespread changes can take place in the animal.
Giesecke adds to his definition the distinction between subclinical and clinical
mastitis. Subclinical mastitis involves subtle physiological changes (Giesecke, 1975).
Clinical mastitis, however, is a more serious inflammatory response. Signs of clinical
mastitis include: changes in milk composition (watery milk, flakes or clots), visual signs
of infection (red, swollen quarters) and a more dramatic increase in SCC (Neave, 1975).
Mode of Infection
Mastitis begins at the teat end. The bacteria must first come in contact with the
sphincter. Once the bacteria pass through the sphincter it must travel up the teat canal
which is lined with keratin. Keratin is a wax-like substance designed for trapping
bacteria and also contains substances with antimicrobial properties (Sordillo et al., 1997).
If the bacteria pass by the keratin in the teat canal it can gain access to the
mammary gland. There are many ways in which bacteria may be introduced into the
mammary gland. Pressure on the teat caused by cow movement may aid the bacteria in
traveling up the teat canal. In addition to this, bacteria may multiply continually until
they have reached milk producing tissues, allowing them to establish themselves more
permanently (Kehrli and Shuster, 1994).
The milking machine also aids the bacteria in gaining access into the teat. The
exposure of the teat to the vacuum (usually between 12-17 Hg) can cause damage to the
skin on the teat. Even more damage is caused when the teat cup liners do not collapse
completely around the cow's teat. When the liner is open, the pressure inside the teat is
greater than the ambient pressure due to the vacuum, allowing the milk to be expelled.
When the liner collapses and the milk is no longer being expelled, milk from other
quarters may splash back and infect uninfected quarters. Large vacuum fluctuations also
can affect the new infection rate. There are many causes for these fluctuations. All
milking machines have vacuum fluctuations because of the pulsation cycle, or the cycle
in which the vacuum is turned on and off to open and close the teat canal. Pulsation is
used to avoid exposing the teat to a constant vacuum. It allows the teat canal to be open
and closed rhythmically and prevents the congestion of blood in the teat. These
fluctuations are caused on an irregular basis because of air that may get into the vacuum
system via the inflations. Additionally, the movement of milk causes vacuum fluctuation
(Kingwill et al., 1979).
Data on these fluctuations shows that when teats are contaminated with mastitis
causing pathogens the rate of new infection is higher when exaggerated vacuum
fluctuations are applied to the cow's teats. In addition to this, contaminated milk coming
in contact with the cow's teat could be the cause of a new infection. When both causes of
vacuum fluctuation occur at the same time the rates of new infection are at their highest
(Kingwill et al., 1979).
Bacteria that are able to reach milk producing tissues, or alveoli, now must find
ways to stay in the tissue. Some bacteria, such as Streptococcus agalactiae and
Staphylococcus aureus are well adapted to attach themselves to the epithelial lining of the
alveoli. This prevents the bacteria from being flushed out during the milking process.
Other types of bacteria do not adhere as well to tissue. These bacteria have adapted other
ways of staying in the udder, such as extremely fast multiplication (Kehrli and Shuster,
Once in the gland, there are two main ways in which the cow's immune system will
try to clear the bacteria. The two basic groups of immunity in the cow's udder are innate
and specific immunity. Innate immunity is more nonspecific and responds to the
bacterial challenges almost immediately. Innate immunity involves macrophages,
neutrophils, polymorphonucleur neutrophils (PMN), and natural killer cells. Specific
immunity requires time to develop after the initial infection. This response involves
antibody molecules, macrophages and lymphoids. These cells attack only the type of
bacteria they are programmed to eliminate (Sordillo et al., 1997).
If the leukocytes in the udder are not able to eradicate the bacteria, inflammation
and infection will result. The milk in the gland provides an optimal environment for
bacteria multiplication. If the infection becomes severe enough, the cow may show
systemic signs of infection including a red, hard udder and a fever. Inflammation is
caused by the flood of cells involved in the immune response into the udder (Paape et al.,
Bacteria cells that adhere and multiply in the udder can cause a variety of negative
effects. Perhaps the worst is the formation of scar tissue in the udder. This is a result of
bacteria adhering themselves to the lining of the alveoli (Sordillo et al., 1997). Bacteria
are not the only organisms that can cause scar tissue. The movement and adhesion of
PMNs to milk producing tissues may damage the tissue, form scar tissue, and lead to an
increase in milk SCC (Harmon, 1994). This scar tissue, or fibrosis, inhibits milk
production in the current lactation as well as in subsequent lactations. In addition to this,
bacteria have the ability to produce toxins and other substances that can kill milk
producing cells, again reducing milk production. Membrane permeability is also
affected, allowing more blood components into the milk. If the infection becomes severe
enough, visible changes may occur in the cow's milk (Kehrli and Shuster, 1994).
In addition to the effects already listed, mastitis pathogens can cause healthy tissue
to involute, or go into a state of resting. Usually, it is not until the next lactation, if ever,
that this tissue begins producing milk again. The blood and immune system components
can also cause secretary ducts to become blocked (Sordillo et al., 1997).
PMNs are one of the first responders in the non-specific immune response. PMNs
are phagocytic cells that engulf and digest bacteria. Another non-specific immune
response involves neutrophils. They account for greater than 90% of leukocytes in the
mammary gland. Neutrophils are bactericidal and travel from the blood to the mammary
gland after substances such as cytokines (an inflammatory mediator) are present in the
blood. Neutrophils also have small antibacterial peptides (Sordillo et al., 1997).
Macrophages, cells which are present in healthy mammary glands, are also
phagocytic. These cells also engulf and digest foreign bacteria. Neutrophils and
macrophages both destroy milk solids, making them less desirable than blood leukocytes
(Sordillo et al., 1997).
The specific immune response is more complex. Lymphocytes are the only
immune cells with the ability to identify foreign bacteria with specific receptors on their
membranes. The other cells involved in specific immunity are antigen-presenting cells
(Sordillo et al., 1997).
There are two types of lymphocytes, T and B. T lymphocytes produce and secrete
cytokines. There is much speculation surrounding the function of T lymphocytes. What
is known, however, is they play a role in protecting epithelial surfaces. They may also
mediate the activity of other cells involved in the immune system. B lymphocytes
produce antibodies specific to the bacteria that are invading the udder. B lymphocytes
present cells to T lymphocytes to be eliminated (Sordillo et al., 1997).
There are a variety of pathogens that cause mastitis. The most common pathogens
include Staphylococcus aureus, Streptococcus agalactiae, coliforms, and other
streptococci. These species are considered to be the major mastitis pathogens because
they are the most common. Other noteworthy bacteria include Pseudomonas,
Corynebacterium bovis, Mycoplasma and coagulase-negative staphylococci. In addition
to being major pathogens, Staphylococcus aureus and Streptococcus agalactiae are also
contagious. These bacteria can be spread to other cows through unsanitary milking
procedures (Harmon, 1994).
Environmental pathogens also cause acute clinical mastitis. These infections tend
to affect the cow more quickly and severely than other mastitis pathogens. The duration
of these infections also tends to be shorter. These pathogens are found in various places
in the cow's environment. Most of these infections are caused by cows laying in manure
or other types of unclean bedding, mud, or standing in unclean ponds or cooling ponds.
The coliforms are Gram-negative and make up the largest group of environmental
pathogens they include: Escheria coli, Klebsiella spp., Streptococcus bovis, Enterococcus
faecium, Enterobacter spp., Citrobacter spp., Streptococcus dysgalactiae, Streptococcus
uberis and Enterococcusfaecalis (Harmon, 1994).
Mastitis Prevention and Treatment
To avoid losing money because of mastitis, prevention is the key. The rate of
clinical mastitis varies greatly from farm to farm. In 62 citations from 1982 to 1996 the
incidence of clinical mastitis ranged from less than 2.5% to over 50% (Kelton et al.,
1998). There are many ways in which dairy producers attempt to lower the incidence of
mastitis on their farm. These include pre and post dipping of teats with a disinfectant
using antibiotics at dry-off to prevent future infections, preventing the transmission of
bacteria from one cow to another and keeping freestalls clean (Oliver et al., 1993).
An additional study evaluating the efficacy of pre-dipping was completed in
Tennessee in 1993. This study was a natural exposure study following the procedures set
by the National Mastitis Council. The active ingredients in the teat dip were sodium
chlorite (0.64%) and lactic acid (2.64%). The study was conducted over a period of
fifteen months and the average number of cows enrolled in the study at any given time
was 175. There was a total of 423 cows involved in the study. Cows were milked twice
daily and housed in a freestall with sawdust bedding (Oliver et al., 1993).
The study was a negative control using a split udder design. The left teats were the
controls. They were stripped and dried. Both right teats were forestripped, dipped and
wiped. Duplicate samples of foremilk were collected aseptically from all quarters
monthly, twice within ten days after parturition, and when clinical mastitis was observed
(Oliver et al., 1993).
New intramammary infections caused by Staph aureus (P < 0.05) and Strep species
(P < 0.025) were both lower in teats that were treated with the predip. In addition to this,
the total number of quarters with new infections caused by other major pathogens was
also lower in the treatment group (P < 0.01) (Oliver et al., 1993).
Besides teat dipping, another method that can help control mastitis is antibiotic use.
In 1966, Smith wanted to evaluate the effectiveness of intramammary antibiotic use at the
start of the dry period. They had observed previously that half of the cows in most
dairies (excluding first calf heifers) calved with one quarter infected. Smith believed that
eliminating late lactation infections as well as preventing new infections during the dry
period would help to decrease the number of infections present at calving.
Smith used 900 cows from 36 herds. Cows were chosen by duplicate
bacteriological milk samples taken in the last week of the cow's lactation. Cows were
assigned randomly at dry-off into three groups 1) control (no disinfectant no antibiotics;
2) .2g Cloxicillin intramammary and dipped with 5% hypochlorite; 3) intramammary
infusion of 1 g Cloxicillin and teats dipped with the 5% hypochlorite solution.
When the cows were dried off, half of them had positive bacteriological milk
samples. At calving, bacteriological samples were taken within 7 days. Over 60% of the
control group had positive bacteriological milk samples compared to only 23% of group
2 and 15% of group three. This indicates that the use of a 1 gram dose of Cloxicillin is
advantageous when given at dry off (Smith et al., 1966).
Perhaps the most complete assessment of advantageous mastitis prevention
practices is a study which was completed by England's National Institute for Research in
Dairying in 1975. This study included thirty herds over a three year period. The herds
were divided randomly and assigned to one of two treatment groups. The first group
employed several mastitis prevention strategies. They utilized post-dipping, antibiotic
therapy for infected lactating cows and antibiotic therapy for all cows at dry-off. The
second group used only teat dip hypochloritee containing 4% available chlorine). In
addition to these factors, milking machines were evaluated annually at each herd and
adjusted as necessary. The researchers visited each herd weekly to assess the implication
of the mastitis prevention strategy and to take milk samples for culture (Wilson and
At the beginning of the study a "blitz" therapy was utilized. All infected quarters
in half the herds in each group were treated with an antibiotic. This was done to evaluate
the initial reduction in the number of subclinical cows (Wilson and Kingwell, 1975).
Percent of cows infected during lactation and percent of quarters infected during
lactation both dropped steadily each year. The average reduction of udder disease in all
herds was about 70%. The blitz therapy eliminated 68% of all infections in the cows
which received it. However, a larger number of subclinicals were cured by the use of
antibiotics at dry off in the non-blitzed herds. At the end of the first year the percentages
of infection were relatively similar in the blitzed and non-blitzed herds (Wilson, 1975).
The reduced rate of infection was shown in the mean bulk tank SCC, which fell from
680,000 to 310,000 cells/ml over the three year period. Treatment of clinical cases of
mastitis fell by about one half over the three year period (Asby et al., 1975).
The new infection rate of each pathogen declined each year except for Coliforms
and others. The infection rates of these two groups remained relatively the same over the
three years. Streptococcus agalactiae was virtually eliminated from all herds (Wilson
and Kingwell, 1975).
In addition to teat dipping and use of antibiotics, nutrition may also play a small
role in mastitis management. In 1991 Oldham et al. conducted a study looking at the
effects of vitamin A and P-carotene during the dry period and early lactation on udder
health. This study had three treatment groups. The first received 50,000 IU of vitamin
A, the second 170,000 IU of vitamin A and the third 50,000 IU of vitamin A plus 300 mg
of P-carotene (per cow per day). This group of 82 Holsteins was supplemented starting
two weeks before dry off until 6 weeks post calving. Blood samples were taken to
measure the serum concentrations of vitamin A and P-carotene 2 weeks before dry off, at
dry off, the 28th day of the dry period, once between day 10 to 4 pre-calving, within 24
hours of calving, once during week 3 of lactation and once during the sixth week of
lactation. Milk was also sampled for culture. All samples were in duplicate. They were
collected at dry off, the 28th day of the dry period, once between day 10 to 4 before
calving, within 24 hours of calving and once each week during week three and six of
lactation. After culturing, the remainder of milk was used to determine somatic cell
count. Quarters with clinical mastitis were sampled also (Oldham et al., 1991).
Oldham found no difference in serum vitamin A levels between treatments
throughout his study. Treatment effect on serum p-carotene showed a tendency towards
being significant (p<.08). Both serum p-carotene levels and vitamin A levels tended to
drop from 2 weeks prior to dry off until calving (Oldham et al., 1991).
To determine if P-carotene and/or vitamin A were effective in treating mastitis
frequency of new intramammary infections (IMI), SCC, and frequency of clinical
mastitis were used. It was observed by these authors that treatment had no effect on all
three of these parameters. It was also noted that treatment had no effect on which
pathogens were the cause of the mastitis (Oldham et al., 1991).
Vitamin E and selenium have both been shown to have positive effects on udder
health when supplemented alone and together. In Ohio, nine dairy herds were used to
evaluate the effects of supplementing vitamin E and selenium. Bulk tank SCC score was
measured as well as clinical mastitis rate. Serum levels were also observed. The herds
involved with the study were monitored for one year. Strep agalactiae and Staph aureus
mastitis had to be well controlled for a herd to be enrolled in the study. Rations were
sampled for analysis three times during the year. Plasma samples were also drawn from
10 cows in each herd three times during the year. Samples were taken from cows who
were 60 days pre-calving up until 60 days post-calving. Duplicate quarter samples were
taken for bacteriological culture from all quarters of all cows that were diagnosed with
clinical mastitis before treatment was given. Bulk tank samples were also taken weekly
(Weiss et al., 1990).
Increasing the amount of vitamin E and selenium in the diet raised blood serum
levels. Blood serum levels for selenium, however, were independent of supplementation
once intake exceeded 5 mg/day. It was also observed in this study that increased levels
of blood serum selenium had a highly significant positive effect on bulk tank SCC (p <
.005). In addition to this, cows fed a higher diet of vitamin E tended to have less clinical
mastitis (p <0.1) (Weiss et al., 1990). It must be noted, however, that approximately two
thirds of the soil in the United States is selenium deficient. If the animals were selenium
deficient at the start of the study, the results may be biased (Hogan et al., 1993).
Researchers in Ohio found that 24 hours after an intramammary injection with E.
coli plasma vitamin C concentration decreased by 39% (p < 0.01). Vitamin C and
ascorbic acid concentrations in milk from the infected quarters decreased by 52 and 62%
respectively. Unchallenged quarters were unaffected. Twenty-one Holsteins were used
in this study. Blood and milk was sampled before challenge, 24 hours after the challenge
and 7 days after challenge to be analyzed for vitamin C concentration. Because vitamin
C concentrations decreased so dramatically following the E. coli challenge, these results
indicate that vitamin C may play an important role in helping cows recover from mastitis
as well as possibly help to prevent it (Weiss et al., 2004).
Mastitis is the most economically devastating disease facing dairy farmers. There
have been several publications on the subject. All agree that mastitis is very costly but,
because all the estimations use different methods and different numbers, the end results
can vary greatly. The following estimation is one of the more common found in the
literature. It is estimated that at a milk price of $12.07 per hundred weight, $184 dollars
are lost per cow in the herd per year (Blosser, 1979). This number agrees with
Ravinderpal (1990) who projected that mastitis accounted for 70 to 80% of the $140-
$300 dollars lost per cow per year due to disease. Thus, the annual loss per year due to
mastitis in the United States can easily exceed 2 billion dollars. The majority of this loss
(estimated to be about 2/3rds) is accounted for by reduced milk production. Other factors
include cost of treatment, discarded milk, and increased labor. It is projected that if a
quarter becomes infected the average milk loss for that quarter is anywhere from .34 to
2.66 kg per day (Janzen, 1970).
In 1983, McDermott et al. analyzed the economics of treating every cow with a
SCC above 400,000. This fifteen month study analyzed milk production and possible
profits resulting from treating early to obtain a higher response rate. Lactating dairy
cows were used from five commercial herds that all used teat dip and dry cow therapy.
Composite milk samples were taken monthly for electronic SCCs. The somatic cell
sampling was unannounced. Within seven days of the sampling individual quarter
samples were collected and the bacteriology was completed. Cows were assigned
randomly to the control or experimental group based on their ear tag number. In the
experimental group all cows were infused with a lactating antibiotic (cephapirin) the first
time their SCC rose above 400,000 cells/ml. This was done only once in the lactation.
Clinical cases in both the control and experimental groups were treated by the farm
managers themselves. Treatment was based on SCC. Bacteriology was completed to
assess any possible benefits of the antibiotic in eliminating infection.
Treating cows with SCC of 400,000 and above had no effect on milk production in
that lactation when compared to the control group. In addition to this, the cost of treating
cows in this manner translated into almost $20 per cow. Most of this cost was incurred
because 49 false positive cows were treated. Included in the $20 were SCC survey, labor,
discarded milk and drugs (McDermott et al., 1983).
The study completed in 1975 at the University of Reading also included an
economic analysis. It was calculated that dairy producers who followed a basic routine
of post-dipping with an iodine teat dip, treating infected lactating cows with antibiotics
and treating all cows at dry-off with an antibiotic enjoyed a benefit/cost ratio of 2.55 to 1.
That is for every one dollar spent on mastitis prevention, the dairy producer should see a
$2.55 profit. The results of this study suggest that mastitis management procedures are
economically beneficial but should be used as part of the regular routine on the farm and
should be viewed as a long term application and not a quick fix for a mastitis problem
(Asby et al., 1975).
More recently, other mastits control and treatment techniques have been evaluated.
A study done in 1998 used computer modeling to test the effectiveness of several mastitis
control strategies. This study evaluated control strategies for the following bacteria
types: Streptococcus agalactiae, Streptococcus spp, Staphylococcus aureus, coagulase-
negative staphylococci, and Escheria coli. Prevention of mastitis (forestripping,
predipping and postdipping), vaccination for E. coli, lactation therapy and dry cow
therapy were all evaluated for economic efficiency (Allore and Erb, 1998).
Each strategy evaluated in the computer model was compared to a control herd to
find the annual benefit (dollars per cow per year). The criteria for budgets were
determined by changes in milk composition and production due to mastitis as well as the
number of cows that were culled for mastitis reasons. Using this criteria, it was
determined that prevention and dry cow therapy were beneficial for all pathogen groups.
In addition to this it was found that in herds where environmental mastitis was dominant,
vaccination for the prevention ofE. Coli is also beneficial (Allore and Erb, 1998).
Milk Composition and Quality
Elevated SCCs are not only accompanied by milk loss; compositional changes also
take place. Milk fat tends to show a slight decrease as does lactose (although some
studies show no change in fat content). The protein content of milk fluctuates very little.
The types of protein present, however, tend to change. Casein, the desirable milk protein
for making cheese, decreases. Whey, an undesirable protein for cheese making, tends to
increase. Blood components such as albumins and immunoglobulins find their way into
the milk because of an increase in permeability of the milk blood barrier (Harmon, 1994).
Mineral balance in the milk is also affected. Potassium and calcium are markedly
reduced in milk with a high SCC. Potassium leaks through damaged epithelial cells.
These same cells also allow sodium and chloride from the blood into the milk. This
increase of blood components in the milk also causes the pH of milk to rise to 6.9 or even
higher (Harmon, 1994).
Quality of milk is also affected in the presence of mastitis. In a study comparing
shelf life of high SCC milk (about 750,000 cells/mL) to low SCC milk (about 45,000
cells/ml) it was found that after pasteurization the high SCC milk had a much shorter
shelf life than the low SCC milk by as many as seven days. It is thought that there are
free fatty acids in high SCC milk that increase in number and cause an increase in casein
hydrolysis. After 21 days the organoleptic quality of the low SCC milk remained high.
The high SCC milk was rancid, as determined by a sensory panel (Ma et al., 2000).
Milk Culturing and Somatic Cell Counting Techniques
Milk samples are cultured for a variety of reasons. Culturing milk identifies which
organism or organisms the quarter is infected with. This can help in identifying the cause
of the mastitis and which drugs to use during the treatment process. The most widely
used culturing technique involves the use of blood agar plates. Aseptic milk samples are
plated (.01 ml of milk) on agar plates containing 5% sheep blood and .1% esculin.
Samples are then incubated at 37C for 48 hours. Usually the plates are read at 24 hours
of incubation and after the full 48 hours. The most effective culture results are obtained
from aseptic quarter milk samples (Dinsmore et al., 1992).
Somatic cells are mainly white blood cells. Because white blood cells often appear
in response to infection, SCC are used as a general indicator of udder health. There are
several methods in which SCC can be determined.
The most popular on farm test is the California Mastitis Test (CMT). This test
gives an estimation of the SCC for each quarter. The milk sample is mixed with a
solution that congeals in the presence of somatic cells. The more gel-like the sample
becomes the higher the SCC is thought to be. When this reaction occurs it is assumed
that the cow has mastitis in that quarter. Another test that is very similar to this is the
Wisconsin Mastitis Test (Kitchen, 1981).
In laboratories somatic cells can be determined either by a technician counting cells
in a small milk sample (DMSCC) or by an automated machine (ESCC). The DMSCC
has several recognized drawbacks. These include non-homogenous distribution of cells
in the sample, subjective decisions and human error (Kitchen, 1981).
There are two main ways to count somatic cells electronically. They are the use of
the Coulter Counter and the Fossomatic. The coulter counter uses particle size to identify
somatic cells and count them (Phipps, 1968). The Fossomatic uses fluorescence to count
cells. Once the milk is diluted with a buffer, ethidium bromide is added to make the
somatic cells fluorescent. They are then counted (Heeschen, 1975). The Bentley
Somacount (Bentley Instruments Inc, Chaska, MN), an instrument used presently, also
counts cells using fluorescence.
Factors Affecting Somatic Cell Counts
Somatic cell counts are highly variable and can fluctuate immensely in a short
period of time. There are many factors which affect SCC. The most important factors
are the infection status and the type of organism which causes the infection. SCC tends
to peak shortly after the cow has been challenged by a pathogen. Depending on the
organism present, this peak may occur hours or days after the initial challenge. In
addition to this, the level of the response varies from cow to cow. The time it takes for
SCC to return to normal can vary from days to months or never if the cow becomes
chronically infected (Harmon, 1994).
It is generally thought that older cows and cows in late lactation have higher
somatic cell counts. In many cases this is true because the cow has had more time to be
exposed to pathogens. However, if lactating cows remain uninfected throughout the
course of their lactation, SCC varies very little (Sheldrake et al., 1983).
Another important aspect of SCC and stage of lactation is fresh cows. After
calving, cows tend to have a very high SCC. If the cow does not become infected during
her fresh period, her SCC should decrease rapidly and return to normal within 35 days in
milk (Reneau, 1986).
There is also thought to be a "dilution" factor that affects SCC. This means that if
a cow were to experience a reduction in milk production and the SCC remained the same,
we would observe an increase in SCC. It has been suggested that this phenomenon is
responsible for the increase in SCC towards the end of lactation (Schultz, 1977). A
reduction in water or feed has also been shown to increase SCC, probably because of the
reduction in milk production (Martin, 1973).
Various sources of stress can also cause an increase in SCC (Dohoo and Meek,
1982). Heat stress has been shown to have dramatic effects on SCC (Elvinger et al.,
1991). Heat stress also causes a marked decrease in milk production; therefore, it is
unclear whether we can attribute a rise in SCC to the stress itself or a decrease in
production, which is thought to cause the "dilution factor."
More recently, researchers in Minnesota utilized DHI records to evaluate somatic
cell variation. DHI records with at least 220 days in milk and at least 4 test days were
used. Provisions were added into the data analysis to ensure that records with
questionable data were not utilized. SCC was transformed into a somatic cell score
(SCS) using a base two log scale. Scores range from 0 to 9. Lactation records were
separated by parity and by breed (Schutz et al., 1990).
The mean SCS increased with age, this finding agrees with previous research
(Emanuelsson and Persson, 1984 and Miller et al., 1983). The general trend observed in
this study was a high SCS at calving which steadily decreased until peak milk production.
After peak milk production, SCS tended to increase throughout the remainder of the
lactation. The authors admit, however, that some of this increase in SCS could be due to
the "dilution factor" associated with a decrease in milk production (Schutz et al., 1990).
Variability in Somatic Cell Counts
Individual cow somatic cell counts have been used as a management tool for many
years, mainly due to the relatively cheap availability of this data due to DHI (Reneau,
1986). However, it is suspected that individual cow somatic cell counts can be highly
variably due to sampling errors or more within cow variation than previously thought.
In 2004, a study was conducted in the United Kingdom which attempted to observe
the variation in individual cow somatic cell counts. This study utilized aseptic milk
samples as well as daily SCC from the morning milking to determine SCC variations.
The results implied that monthly individual cow SCC would not reflect all of the
infections that affected the herd in that month. However, the most interesting observation
is between the within cow variance for uninfected and infected cows. The variance for
both groups was about 0.5. This is somewhat of a surprise, as most would expect the
variance of uninfected cows to be lower because it is generally thought that uninfected
cows do not experience spikes in their SCC. This study suggests that to accurately assess
the mastitis status of a cow multiple somatic cell counts from a short period of time are
more accurate (Berry et al., 2004).
In 1972, researchers in Canada observed variations of somatic cells throughout
lactation on a weekly basis. Eleven Holsteins were used and samples were collected on a
weekly basis using DHI approved sample collection jars. The sample time alternated
between morning and evening milkings. Differential somatic cell counts were
performed. Aseptic milk samples were also taken for bacteriological cultures when
visual evidence of inflammation was observed or when the SCC doubled for any one cow
(Duitschaver and Ashton, 1972).
Large variations in SCC were observed throughout the lactations. The amount of
neutrophils present mirrored the rise and fall of total somatic cells present. It can be
interpreted from this data that fluctuations in SCC are due mainly to white blood cells.
The average sample to sample coefficient of variation for AM milkings was 136%, for
PM milkings it was 98%. The sample to sample coefficient of variation was lower for
PM milkings, however the average SCC was higher for evening milkings (509,000 versus
557,000). It is thought that differences in milk yield are not responsible for the
differences in SCC (Duitschaver and Ashton, 1972).
Clearly, the most effective and inexpensive way to prevent mastitis is through using
teat dip, antibiotic therapy, and keeping cows clean. Other factors, such as nutrition, may
play a small role as well. Mastitis is the most costly disease that affects dairy cattle
therefore preventing it is economically advantageous to all dairy farmers.
FLORIDA MILK QUALITY LAB ANALYSIS
As stated in Chapter 1, the goal of this thesis is to describe the patterns of
fluctuation in individual cow SCC over relatively short periods of time. Traditionally,
most dairy producers use monthly milk SCC from DHI. In addition to this, these
monthly SCC usually are not daily composites, meaning that many dairy producers only
see one SCC from one milking per cow per month. In a herd that is milked two times
daily, a dairy producer is then using only 1 milking out of 60 to evaluate the udder health
of individual cows. If the herd is milked 3 times daily the producer is then only using
one SCC out of 90 milkings. SCC testing costs money, therefore it is easily
understandable that dairy producers do not want to pay for excessive testing. The
problem, however, is that more money may be lost when cows are treated with antibiotics
or culled unnecessarily. If the samples that were tested for SCC were a composite sample
from 2 or more milkings the variation would be reduced.
These observations were the basis for this thesis. There is very little data, however,
on milking to milking variation in SCC. Therefore, we believed the appropriate first step
would be to evaluate the lab that would be used in the study. With little to no data to
compare ours to, if the lab was found to be accurate then we know that any variation that
we identified is due to factors other than the accuracy of the laboratory. The objective of
this study was to determine the accuracy and the precision of the DHI labs in Florida that
analyze milk samples for SCC. Our hypothesis was that the lab would prove to be
accurate and precise.
Materials and Methods
Four labs in the state of Florida were evaluated for accuracy and repeatability of
SCC results. Thirty milk samples (one gallon each) were obtained from 30 different
dairies. The samples were stored below 40 degrees F. These samples were then split into
8 sub samples. Four laboratories received sixty samples each (two laboratories are used
in this analysis), the duplicates were randomly assigned a number from 31 to 60 so
technicians would not know which sample it was a duplicate of. In addition to the sixty
samples, each laboratory also received four standard samples that were purchased from a
SCC were counted electronically with a Bentley Somacount cell counter (Bentley
Instruments Inc, Chaska, MN). The Somacount utilizes laser based flow cytometry to
count cells one by one. First the DNA of the somatic cells is stained with ethium
bromide, which makes them fluorescent. This enables the laser to then count them. The
machine is calibrated at start up and every hour thereafter using a standard. The study
was conducted as a blind trial so lab personnel were unaware which samples were
duplicates. The sample and its duplicate were not run one after the other. Duplicate
samples of four standards were also run at the same two labs using the same procedures.
Data was analyzed using SAS (version 9.0) and Microsoft Excel to perform f-
distributions and t-tests.
Results and Discussion
Figures 3-1 through 3-3 illustrate the values for the sample and its duplicate sample
at both of the labs.
Figure 3-1. SCC for Samples and their duplicates analyzed at lab 1.
In Figure 3-1 duplicate samples are being compared in the same lab. Careful
observation of the data will point to the conclusion that most of the duplicate samples
analyzed were extremely close in value, if not exactly the same. Sample nine seems to
be the only outlier. The average difference between the sample and its duplicate was
12,600 cells/ml at lab one. The difference between sample nine and its duplicate was
97,000 at lab one. Most likely, sample nine was not mixed properly before it was split
into sub-samples. Milk tends to settle after short periods of time if the milk is not
homogenizd. Therefore, not mixing the milk thoroughly before the sample was split
could easily have caused this error.
Figure 3-2. SCC tor Samples and their duplicates analyzed at lab 2.
Many of the trends in Figure 3-2 are the same as those in Figure 3-1. Most of the
samples were very close in value if not exactly the same. In Figure 3-1, however, the
value for the duplicate sample is higher than sample 1 and the opposite is true in Figure
3-2. Again one can see that there seems to be a problem with sample 9. The difference
between sample 9 and its duplicate at this lab is 141,000. The average difference
between a sample and its duplicate at lab two was 16,000 cells/ml. These differences are
so slight, however, that they are not even significant. Small variations like this one may
be due to the way in which the sample was mixed before it was split and the small
amount of variation that is possible with electronic somatic cell counting techniques.
Again, one can visually observe that there was an error with sample nine. Aside from
sample nine, the majority of the data seems very accurate.
Figure 3-3. SCC for Samples and their duplicates analyzed at Lab 1 and Lab 2.
These graphs illustrate that the variation within lab was very small as well as the
variation between labs. The variance between the means in lab one was not significantly
different than the variance between means in lab two (P = .098) and there was no lab bias
(P = .684). Looking at this data it is easily observed that almost every sample has equal
values for its duplicate at each lab. It can be seen even more clearly now that sample
nine is erroneous. Many of the values, such as those for sample 3, 7, 29, and 30 are
almost exactly the same. There are no significant differences between them.
The four standards were also run at each lab so that there would be samples in the
analysis where the value was known before the trial was conducted. The value the lab
obtained for each standard was not statistically different from the actual value of the
standard (P = .387).
Table 3-1. Lab Analysis of Official Samples.
Lab Replicate Official SCC (in 1000s) Lab SCC (in Difference (in 1000s)
1000s) (Official Lab)
1 1 1182 1327 -145
1 1 670 702 -32
1 1 127 121 6
1 1 370 377 -7
1 2 1182 1305 -123
1 2 670 680 -10
1 2 127 125 2
1 2 370 373 -3
2 1 1182 1344 -162
2 1 670 681 -11
2 1 127 142 -15
2 1 370 375 -5
2 2 1182 1351 -169
2 2 670 682 -12
2 2 127 125 2
2 2 370 374 -4
The results from this trial show that the results from both of the labs are accurate.
Very similar results were obtained from both of the labs and the results were very
repeatable. Not only were the individual labs very precise and able to obtain similar
results from the same sample of milk, but both of the labs were accurate, meaning they
both obtained similar results from the same sample, and also were accurate when they
were checked against standards.
VARIATIONS IN SOMATIC CELL COUNTS FROM MILKING TO MILKING
SCC are often used as an indicator of udder health. It is important, therefore, that
we understand more scientifically the variations in SCC. Understanding these variations
will help scientists develop the best way in which to use somatic cell counts as an udder
Very little is known about SCC variation from milking to milking or even day to
day because limited daily or even weekly SCC variation data is available. There are two
studies worth mentioning. The first looked at SCC variation on a weekly basis and the
second on a daily basis.
In 1972, researchers in Canada evaluated SCC fluctuations from week to week
(Duitschaverand Ashton, 1972). Large variations in SCC were observed throughout the
lactations. The average sample to sample coefficient of variation for AM milkings was
136%, for PM milkings it was 98%.
In 2004, a study was conducted in the United Kingdom which attempted to observe
the variation in individual cow somatic cell counts. This study utilized aseptic milk
samples as well as daily SCC from the morning milking to determine SCC variations.
The most interesting observation is that which compares the within cow variance for
uninfected cows and the within cow variance for infected cows. The variance for both
groups was about 0.5%. This is somewhat of a surprise, as most would expect the
variance of uninfected cows to be lower because it is generally thought that uninfected
cows do not experience fluctuations in their SCC. This study suggests that to accurately
assess the mastitis status of a cow multiple somatic cell counts from a short period of
time reduce variation (Berry et al., 2004). The objective of this study was to observe the
variation of somatic cell counts from milking to milking. The expected results were
slight variations in SCC from milking to milking.
Materials and Methods
This study was conducted at the University of Florida Dairy Research Unit. Cows
were housed in free-stall facilities and milked 3 times daily. Milkings were evenly
spaced eight hours apart. 380 lactating cows were sampled for 15 consecutive milkings
over a period of 5 days. At each milking one milker was present. Three different people
milked the herd each day.
Milk samples were collected according to DHI sampling procedures. In-line milk
sampling devices were attached to the milk meters to take a composite sample from the
entire milking. Milk from the sampling device was mixed with a preservative in a milk
vial and analyzed within 2 days. Samples were collected by seven different persons
working in pairs. Two were trained DHI technicians. The others attended a mandatory
orientation before the start of the trial to outline sampling procedures. The quality of the
sample taken could potentially be affected by the amount of milk in the vial, a missing
preservative pill and inadequate mixing of the milk with the preservative (which causes
the fat to separate).
Samples were analyzed using electronic somatic cell counting (Bentley Somacount,
Bentley Instruments Inc, Chaska, MN). Electronic cell counting uses ethium bromide to
stain the DNA of somatic cells. The instrument then uses laser flow cytometry to count
the cells one by one. Anywhere from 100 to over 500 samples per hour can be analyzed
using electronic somatic cell counting.
The data collected was imported to Microsoft Excel. It was evaluated by two
different persons for unusual data or errors in the data (cow numbers that may have been
reversed etc.) Cows without 15 observations were dropped from the study. Data was
then analyzed using Microsoft Excel to calculate correlations and adjusted correlations.
Results and Discussion
Throughout the five day period, the average bulk tank SCC was 450,000 cells/ml.
Although the bulk tank SCC remained fairly constant, large variations in SCC from
milking to milking on an individual cow level were observed.
Figure 4-1 depicts the average SCC on an individual cow basis for the 15 milkings.
The majority of the cows have averages of less than 500,000 cells/ml. Almost half of the
cows, however, have an average above 500,000. The somatic cell counts range from very
low (22,000 cells/ml) to extremely high (9,000,000 cells/ml).
Average SCC on an Individual Cow Basis
0-250,000 251,000- 500,001- 751,000- 1,000,000+
500,000 750,000 1,000,000
Figure 4-1. Average SCC for the 15 milkings on an individual cow basis (380 cows
Figure 4-2. Number of cows over or under certain somatic cell
(380 cows total).
counts at all
Number of Cows Above or Below a Certain Daily
SCC All 5 Days
> < < > > > >
200,000 400,000 750,000 750,000 750,000 500,000 250000
Figure 4-3. Number of cows over or under certain daily somatic cell counts each day
(380 cows total).
Figures 4-2 and 4-3 describe how many cows were over or under randomly chosen
SCC thresholds. This analysis was done on an individual milking basis and on a daily
basis. For the duration of this reserach, daily SCC will refer to the average SCC for all
Number of Cows Over or Under Threshold SCC at
all 15 Milkings
o 60 -
200000 400000 750000 1000000 750000 500000 250000
three milkings in a 24 hour period for each cow. The majority of the cows tended to stay
under 750,000 cells/ml throughout the duration of the study. The number of cows over
one million or even 750,000 at all milkings or every day is very low (0 and 2,
respectively). Particularly interesting is that only one cow had a SCC higher than
250,000 at every milking. This tells us that even though some cows had extremely high
SCC (>1,000,000), within the five day period of the study that same cow also
demonstrated very low somatic cell counts (< 250,000). Most cows never even had a
daily SCC above one million (189 cows). One hundred and twelve cows had one daily
SCC above one million, 60 cows had two, 15 cows had three, 4 cows had four and no
cows had all 5 daily SCC above one million.
Figures 4-4 to 4-7 indicate the frequency in which cows have daily SCC over a
certain threshold. For example, Figure 4-4 shows that almost 200 cows had no daily SCC
above one million, over 100 cows had one daily SCC above one million, about 60 cows
had two daily SCC over one million and so on. From these figures we can see that most
cows had very few, if any, daily SCC above 750,000. In Figure 4-6 we can see that more
cows had SCC above 500,000 two and three times. Figure 4-7 shows that the amount of
times a cow had a SCC above 250,000 is more spread out, but four and fives occurrences
still show the least amount of animals.
The conclusion that can be drawn from these figures is that, in general, most cows
did not have daily SCC that were extremely high. Many cows experienced consistently
low SCC (see Figure 4-7, almost 80 cows never had a daily SCC above 250,000). It is
peculiar, however, that no cows had a SCC above one million every day in the 5 day trial,
but we see over 100 cows with at least one daily average above one million. This is most
likely due to the variation in SCC that has been observed consistently throughout this
Number of Cows With A Daily SCC Above One
0 1 2 3 4 5
Number of Occurences Over One Million
Figure 4-4. Number of cows with a daily SCC over one million (380 cows total).
Number of Cows With A Daily SCC Above 750,000
0 1 2 3 4 5
Number of Occurences
Figure 4-5. Number of cows with a daily SCC over 750,000 cells/ml (380 cows total).
Number of Cows With A Daily SCC Above 500,000
0 1 2 3 4 5
Number of Occurences
Figure 4-6. Number of cows with a daily SCC over 500,000 cells/ml (380 cows total).
Number of Cows With A Daily SCC Above 250,000
Number of Occurences
Figure 4-7. Number of cows with a daily SCC over 250,000 cells/ml (380 cows total).
Several correlations were calculated to better describe the relationship of SCC
fluctuations. Correlations based strictly on the SCC were calculated from milking to
milking as well as from morning to morning, afternoon to afternoon, etc. Correlations
with the SCC multiplied by milk production were also calculated to adjust for different
levels of milk production. The following tables display the values. The values in Table
4-1 and Table 4-2 are not the same; however they remain in a relatively small range of
0.3 to -0.01. These values reflect a very weak positive correlation and in some cases
even a negative correlation. Taking into account milk production did not seem to have
much of an effect on the correlations.
Table 4-1. Raw and adjusted (for milk production) SCC correlations for consecutive
Milking Raw Correlation Adjusted
1 to 2 0.005 0.009
2 to 3 0.055 0.039
3 to 4 0.143 0.128
4 to 5 0.104 0.085
5 to 6 0.075 0.044
6 to 7 0.024 0.095
7 to 8 -0.011 0.166
8 to 9 0.148 0.133
9 to 10 -0.011 -0.026
10 to 11 0.075 0.058
11 to 12 0.311 0.266
12 to 13 0.215 0.136
13 to 14 0.070 0.063
14 to 15 0.079 0.084
Correlations were also calculated by shift, which were each milked by a different
operator, to observe the relationship between SCC in the morning, afternoon and evening.
Milkings 1,4,7,10 and 13 were morning milkings (approximately 5 AM). Milkings
2,5,8,11 and 14 were afternoon milkings (1 PM) and milkings 3,6,9,12, and 15 were
evening milkings (9 PM). These correlations were calculated to look for differences in
SCC due to time of day.
The results of these correlations do not allow us to draw many conclusions. The
time of day in which the cow was milked does not seem to have an effect on the SCC.
Furthermore, the correlations that are calculated taking the milk weights into
consideration do not seem to give a clearer picture than the others. At first glance, Table
4-2 might seem to indicate that the correlations for morning milkings are more positively
related than the correlations between successive milkings. However, the correlation
between the fourth morning (milking 10) and the fifth morning (milking 13) is actually a
negative value in both tables. The evening milkings seem to be the most highly
correlated as there are no negative values in Table 4-2.
Table 4-2. Raw and adjusted (for milk production) SCC correlations for morning,
afternoon and evening milkings.
Shift Milking Raw Correlation Adjusted
Morning 1 to 4 0.227 0.256
4 to 7 0.089 0.104
7 tol0 0.096 0.017
10to13 -0.004 -0.035
Afternoon 2 to 5 -0.006 0.008
5 to 8 0.043 0.057
8 toll 0.179 0.164
11 to 14 0.008 0.032
Evening 3 to 6 0.216 0.282
6 to 9 0.060 0.052
9to2 0.142 0.112
12 to 15 0.101 0.141
In a further attempt to describe the data, standard deviations and coefficients of
variation were calculated for each cow. Scatter plots were then constructed to observe
the coefficient of variation and standard deviation versus the cow's average somatic cell
count. Figures 4-8 and 4-9 display the results.
SCC Standard Deviation
0 500 1000 1500 2000
Figure 4-8. Individual cow standard deviation versus her average SCC (380 cows total).
Coefficient of Variation Versus Average SCC
Figure 4-9. Individual cow coefficient of variation versus her average SCC(380 cows
Figure 4-8 illustrates that the higher a cow's average SCC is, the greater the
standard deviation. This means that the cows with higher cell counts tend to vary more
dramatically in their cell counts. Almost the same trend is observed in Figure 4-9. When
the SCC is above a million, however, the coefficient of variation tends to level off at
With such large fluctuations in SCC on a daily basis and from one milking to the
next, the hot list is probably not an accurate reflection of the udder health of individual
cows. Observing the data in this chapter it is clear that many cows experience temporary
spikes in SCC. From this analysis, we cannot attempt to explain those events. However,
it is safe to draw the conclusion that if any of the cows on the hot list were experiencing a
temporary spike, they would be treated unnecessarily. Admittedly, more studies will be
needed to insure that these results are repeatable; but the data collected so far, however,
suggests that the hot list may prove to be of no use.
ANALYSIS OF THE VALUE OF HOT LIST USE
Monthly DHI somatic cell counts have been used for many years to assess the
mastitis status of individual cows (Reneau, 1986). More recently, the hot list was created
to aid dairy producers in quickly identifying the top 20 cows with highest number of
somatic cells per milking. The list gives a variety of information on the cow such as her
lactation number, her days in milk, and what her SCC was on that test day. In addition to
this, the hot list also calculates what the bulk tank SCC would be if that cow's milk, and
the cows above her, were not added to the tank on that particular day as well as the
percentage of cells in the bulk tank that the individual cow is responsible for.
To calculate which cows are on the hot list the first step is to calculate the total
amount of somatic cells in the bulk tank. After that is done the total amount of somatic
cells from each cow is calculated. The individual cow SCC is then divided by the bulk
tank SCC and multiplied by 100. This number represents the percentage of somatic cells
in the bulk tank that the individual cow is responsible for. The hot list then ranks the top
20 cows in the herd by percent cells contributed to the bulk tank.
Figure 5-1 is an example of a hot list that is sent monthly to dairy producers by
DHI in the state of Florida. The first column is the cow's identification number.
Following that is her lactation number, days in milk, her milk production for that day
expressed in pounds, and her SCC in thousands. The column labeled W/O is what the
bulk tank SCC would be without that cow's milk, and the cows above her, in the bulk
tank. The percent cells column lets the dairy producer know what percentage of the
somatic cells in the bulk tank that particular cow is responsible for. Only five cows are
shown on this example hot list. Actual hot lists show twenty cows.
Cow ID Lactaton DIM Milk SCC (1000s) W/O % Cells
A 5 101 79 9052 363 6.3
B 4 154 93 7352 341 6.1
C 3 100 72 9052 319 5.8
D 5 205 78 7352 300 5.1
E 1 29 83 5199 286 3.8
Figure 5-1. Example of a monthly DHI hot list (bulk tank SCC 386,000).
There are two ways in which the hot list could possibly benefit dairy producers.
The first way is lowering the bulk tank SCC over time by removing cows with high SCC.
The second way is more short term. It is thought that some producers may use the hot list
to rapidly reduce their bulk tank SCC if they are in danger of shipping illegal milk. This
could be accomplished by either culling cows on the hot list or purposely withholding
their milk from the bulk tank until their SCC returns to normal levels.
The first goal of this study was to describe cow movement on and off the hot list.
The second goal was to observe the effect on bulk tank SCC over a long period of time if
hot list cows were to be removed from the milking herd. The hypothesis was that cows
would not repeat on the hot list as often if the hot list was used to make management
decisions. Little is known about daily or even weekly variations in SCC, therefore it is
important to describe more scientifically the value of the hot list. The final objective was
to describe the effect of the hot list on the bulk tank SCC over short periods of time. It
was expected that the hot list would prove to be of little value because it only described
the SCC of the cow during one milking.
Materials and Methods
Two commercial dairies (Dairy A and Dairy B) in the state of Florida were used for
the first analysis. Dairy Herd Improvement lactation records from 1998 to 2003 were
utilized. Somatic cell counts were taken monthly by DHI technicians. Both dairies used
in the analysis milked 450 to 550 cows three times daily. In addition to this, both dairies
used floor mounted cow washers, pre-stripped, and post dipped with a teat disinfectant.
Dairy A used the hot list for making management decisions. More specifically,
every month each cow on the hot list was either treated with antibiotics, culled, dried off
early, or another management decision was made. Dairy B did not use the hot list to
make management decisions.
Data was obtained from Dairy Records Management Systems in Raleigh, North
Carolina. Data was analyzed using SAS (Version 9.0) using the mean and rank
Data from Chapter 4 was also used and analyzed in Microsoft Excel. Another
study, very similar to the study in Chapter 4, is also included in this analysis. All the
data collectors used in this study were trained DHI personnel. The entire herd (the same
herd as was used in the previous study) was sampled for 5 consecutive milkings,
beginning with an evening milking. Cows that did not have a SCC for all 5 milkings
were dropped from the study. The same sampling devices were used in both studies and
samples were analyzed at the same lab using electronic somatic cell counting. This data
was also analyzed using Microsoft Excel.
Finally, the last data set was obtained from Southeast Milk Inc. (Belleview,
Florida) and was analyzed using the mixed procedure in SAS (version 9.0). Three years
of complete data were available (2002-2004) and forty-three herds were used in the
analysis. Somatic cell count data was available from daily or every other day milk pick
ups. Somatic cell counts were analyzed at the SMI lab in Belleview, Florida. This lab is
one of the labs that was analyzed in Chapter 3. Merging this data with the data obtained
from DHI (Raleigh, NC) we were able to determine when each testday was for each herd.
Results and Discussion
To comprehend the results, the possibilities for cow movement on and off the hot
list must be described. On any test day, there are two options for a cow. She could be
tested or not tested. If she is not tested she cannot appear on the hot list. Cows that are
not tested have been dried off, are in the hospital herd or have a missing sample. If a cow
was tested she could be on the list or off the list. In the following month, the same
options are again present for each cow. As an example, if a cow was tested in the first
month and was on the hot list, she could be tested the following month and still be on the
hot list or she could be tested and dropped off the hot list. The last option is that she was
not tested and therefore, not on the hot list.
Statistical analysis of cow movement on and off the hot list was very similar for
both dairies. On dairy A, 26.1% (SD 12.4) of the cows who were on the hot list during
any given month were on the hot list again during the following month. For dairy B,
26.1% (SD 10.8) of the cows were on the hot list for two successive months. On both
dairies, about 60% of the cows that were on the hot list during any given month dropped
off the hot list in the following month (the remainder of the cows were either not tested or
culled since the last test date).
For cows that were not on the hot list in the first month, about 3.5% of them on
dairy A and dairy B were on the list again in the following month. About 80% on both
dairies were not on the list in the current month and the following month.
Because dairy A made management decisions regarding all the cows on the hot list
every month, the results that were observed were different from what was expected. The
hypothesis was that Dairy A would have fewer cows than Dairy B repeating on the hot
list because more of those cows would be treated. The results suggest that dairy B was
effectively finding and treating mastitis cases using other management tools besides the
hot list. An alternate explanation could be that the fluctuation in cows on and off the hot
list is due to the high variation in individual cow SCC. This is in keeping with what was
reported in Chapter 4.
The following analysis observed the effect of the hot list cows on the bulk tank
SCC over a five year period. Figures 1 and 2 depict the bulk tank SCC of that test date as
well as a weighted bulk tank SCC. The weighted bulk tank SCC represents what the bulk
tank SCC would be if all the cows on the hot list in the previous month were culled. To
elaborate, the weighted bulk tank SCC reflects the current month's SCC with the milk
removed from the cows who were on the hot list the month before.
Figure 5-2. Actual Bulk Tank SCC and Calculated Bulk Tank SCC Over a Five Year
Period with Hot List Cows Removed for Dairy A (used hot list).
Figure 5-3. Actual Bulk Tank SCC and Calculated Bulk Tank SCC Over a Five Year
Period with Hot List Cows Removed for Dairy B (did not use hot list).
The graphs clearly show that if the milk from all the cows on the hot list was
removed from the bulk tank on the test day there would be an obvious decrease in bulk
tank SCC. Removing the hot list cows from the bulk tank SCC reduces the SCC by
roughly one half, on dairies of about 500 cows. Differences in the amount of reduction
will vary depending on herd size. Smaller herds would see a more dramatic change in
SCC reduction because the 20 cows on the hot list represent a greater percentage of their
total herds. The opposite is also true for large herds. Their reduction in SCC would not
be as great because the 20 cows on the hot list represent a smaller percentage of cows in
If we look at the effects of removing hot list cows on bulk tank SCC over time we
do not observe the same results. Figure 5-4 and Figure 5-5 depict actual SCC on a test
day and a calculated SCC where all milk from cows that were on the hot list in the
previous month has been removed.
Figure 5-4. Actual Bulk Tank SCC and Calculated Bulk Tank SCC Over a Five Year
Period with Hot List Cows in the Previous Month Excluded for Dairy A
(used hot list).
Figure 5-5. Actual Bulk Tank SCC and Calculated Bulk Tank SCC Over a Five Year
Period with Hot List Cows in the Previous Month Excluded for Dairy B (did
not use hot list).
Looking at the long term effects of hot list cows on the bulk tank SCC, it can be
observed that they are not nearly as dramatic as the short term effects. Many of the actual
SCC are very similar to the calculated SCC. Therefore, if all the cows on the hot list
were culled every month, the observed reduction in SCC would be about only 81,800
cells/ml the following month (for Dairy A and Dairy B). This was found by calculating
the differences between the actual SCC and calculated SCC and averaging the
From the analysis of DHI records, we observed that for both dairies cow movement
on and off the hot list was very similar. It was also calculated that cows on the hot list are
responsible for approximately half of the somatic cells in the bulk tank on the test day.
However, if all the cows on the hot list the previous month were culled, the decrease in
bulk tank SCC would not be nearly as dramatic as expected. We concluded that use, or
non-use, of the hot list did not have a significant effect on bulk tank SCC in the long term
on these two dairies.
This may be because of the high variability in somatic cell counts that was
previously discussed in Chapter 4. If the correlation of SCC from milking to milking is
very weakly correlated and very variable, it is unreasonable to expect that one SCC a
month can give us an accurate picture of what the mastitis status of a cow is. Basing
management decisions on the hot list could prove, in fact, to be detrimental. If a cow
appeared on the hot list one milking, based on the results in Chapter 4, it is possible that
her somatic cell count could return to a normal level within 8 hours. Therefore, a
producer may be treating or even culling a cow that is in a perfectly normal state of
health. Making assumptions about the health of a cow based on a single SCC could
easily be a very expensive mistake.
In an attempt to solidify these statements, hot lists were created from the data set
analyzed in Chapter 4. A hot list was made for each milking and each day. Figures 5-6
and 5-7 depict the possible number of times a cow could be on the hot list and how many
cows were on the hot list for each of those numbers.
The majority of cows never appear on the hot lists. There was one hot list created
for each milking, for a total of 15 hot lists. Each hot list has 20 cows on it. Fifty percent
of the cows on the 15 hot lists only appeared once. Thirty percent of the cows on the hot
list were on the lists twice. This tells us that if a producer is treating all the cows on the
hot list with antibiotics that possibly over 80% of the cows would probably be treated
unnecessarily. Only 21 of the cows were on the list more than twice and a mere eight
cows were on the list 4 or more times. There were no cows on the hot list more than 6
times, meaning there were no cows on the hot list for more than half of the milkings.
These data show great variation in which cows appear on the hot list from milking to
Frequency of Cows Appearing on the Hot List
(Hot Lists were calculated for each of the 15 milkings)
0 1 2 3 4 5 6
Number of Times on the Hot List
Figure 5-6. Frequency of number of appearances on the hot list (lists calculated at each
milking; 380 cows total).
Daily hot lists were also created and analyzed. Figure 5-7 depicts how many cows
were on the daily hot lists zero times, one time, two times and three times. These hot lists
were calculated by averaging the SCC for all three milkings for each cow and then
finding the top twenty cows who contributed the most to the bulk tank each day. There
were five hot lists created. There potentially could have been 100 cows appearing on the
lists (5 days times 20 cows per day). Most of the cows never appeared on the hot list.
Seventy cows appeared once and only 4 of those 70 cows appeared on the hot list a
second time. That is only 6%. Again it can clearly be seen that if all the cows on the hot
list were treated 94% of them would have been treated needlessly.
Frequency of Cows Appearing on the Hot List
(Hot Lists calculated for each day)
0 1 2 3
Number of times on the Hot list
Figure 5-7. Frequency of number of appearances on the hot list (lists calculated for each
day; 380 cows total).
Because very little literature exists on milking to milking SCC variation, a second
study was conducted by the Southeast DHI (Gainesville, FL). This study will be called
study 2. It was conducted at the same dairy as used in Chapter 4 (Webb, 2005).
To conduct a direct comparison between the first and second study, hot lists were
created for each milking in the second study, for a total of 5 hot lists. These hot lists
were then compared with two sets of 5 hot lists from study one. Each set of hot lists from
the first study began with an evening milking so the analysis would not be confounded by
time of day.
Table 5-1 again depicts the variability in the cows on and off the hot list. It seems
as though the majority of the cows are only on the list once or twice although there are a
few cows that appear 3, 4 and 5 times. The results of this small analysis seem somewhat
inconclusive. If anything, they support the idea that management decisions based solely
on the hot list are not economically sound decisions.
Table 5-1. Number of times cows were on hot lists for study one and study two.
Number of Study 1, First set of Study 1, Second set Study 2 hot lists
Appearances on the hot lists of hot lists
1 49 61 27
2 17 18 10
3 5 0 6
4 1 0 5
5 0 0 3
The low repeatability of cows on the hot list observed in the original hot list
analysis and from creating hot lists from the two sets of SCC variability data further
indicates that the hot list is not a beneficial tool for dairy producers to utilize. This data
coupled with the data from the previous study (SCC Variability Chapter 4) provides solid
evidence that the hot list is not economically advantageous for the dairies analyzed in this
It could be argued that there are ways in which to improve the hot list. For
example, if the hot list were to tell the dairy producer how many times each cow had
appeared on the hot list and when, producers may be able to identify which cows are truly
the persistent high SCC cows and not the cows that tend to have temporary spikes in their
SCC. This addition to the list might be a good one, in theory. However, referring back to
Figure 5-6, it can be easily observed that most cows appear on the hot list only once in a
period of 15 milkings. Fifty cows do show up on the hot lists twice, but we must
remember that is only twice out of fifteen milkings. On a percentage basis, these cows
only appear on the hot list 13% of the time. An occurrence of high SCC only 13% of the
time is probably not a high enough number to convince dairy producers that a certain cow
should be treated or culled. The cows that repeat on the hot list more than twice are an
Table 5-1 takes this analysis one step further and compares three sets of hot lists
which span 5 milkings each. It looks as though there are far more cows repeating on
these lists than in the set of hot lists used in Figure 5-6. However, if things are broken
down to a percentage basis again, averaging the data for these three sets of lists, 33% of
the cows repeat on the hot list twice in five days. A strong argument can be made that
dairy producers again would not find this amount substantial enough to want to utilize the
hot list to make management decisions.
A final analysis was completed to assess the effect of the hot list on the bulk tank
SCC immediately after the dairy producer receives the hot list. To analyze if the hot list
had an immediate effect on the bulk tank SCC, the bulk tank SCC of 43 dairies for thirty
days after the test day was analyzed. The thirty days was divided into six groups of five
days each. Group 1 consists of days 1-5 after the testday. Group two consists of days 6-
10 and so on until day 30. To see if there was an effect of the hot list in herds that had a
higher or lower average bulk tank SCC, the months were also divided into "high months"
or "low months" for each herd. A month is considered to be high if the 10 day average
SCC before the test day was above 500,000. A month was considered a low month if the
10 day average SCC before the test day was less than 500,000. The model inputted into
SAS was difference = herdcode highmonth daysaftergroup highmonth*daysaftergroup
where difference = average SCC in for a group the 10 day average SCC before the test
day, herdcode is each individual herd's identification and daysaftergroup is group 1-6,
depending on how many days after the testday the current SCC is. The variable name
"highmonth" accounts for high months and low months. The results for the proc mixed
procedure are displayed in Table 5-2. The fixed effects of the model that were
significant were herdcode, high month/low month, and group number. The interaction
between month and group was not significant but approached significance therefore it
was included in the model. The most interesting observation here is the value for high
month/low month. This tells us that the herd's SCC before the test day is probably the
most statistically important factor in determining if the herd's SCC will decrease after the
Table 5-2. Results of the Proc Mixed Procedure on daily bulk tank data.
Effect Pr > F
Least squares means were also calculated for high month and low month, groups 1-
6 and the interaction between high month and group number as well as low month and
group number. The results of the LS means procedure are listed in Table 5-3. The
change in SCC was significant statistically in many cases. The concern is, however, that
although these numbers are significant according to statistics, variations in SCC tend to
fluctuate often. Therefore, by just observing the change in SCC visually, the numbers are
not that dramatic.
Table 5-3. Results of the LS means procedure on daily bulk tank data.
Effect Change in SCC (in 1000s) Pr > t
High Month -16.33 <0.0001
Low Month -0.87 0.6618
Group 1 -4.31 0.1965
Group 2 -4.07 0.2250
Group 3 -13.04 0.0002
Group 4 -8.65 0.01711
Group 5 -16.65 <0.0001
Group 6 -5.07 0.2853
High Month Group 1 -11.14 0.0379
High Month Group 2 -4.97 0.3569
High Month Group 3 -20.43 0.0003
High Month Group 4 -19.62 0.0011
High Month Group 5 -29.9 <0.0001
High Month Group 6 -12.34 0.1262
High months have very significant (P < 0.0001) effects on the bulk tank SCC after
the testday. Herds that were having a low month had no significant groups and no
significance in the interactions between low months and group number. Groups 3-5
(days 11-25) also had a significant effect on bulk tank SCC after the test day. The
interaction between high months and groups 3-5 were also significant. None of the
interactions between low months and groups were significant.
This data is perplexing. The hypothesis was that if the hot list had an immediate
effect on SCC that the effect would be seen sometime between days 5 and 10. The
rationale for this is that the hot list does not reach the dairy producer until at least 3 days
after the testday because the lab must have time to analyze the samples. Therefore, it was
expected that groups one and two would show significance. Instead, groups 3-5 show
significance with group five being the most significant. The most feasible reason for this
is that the bulk tank SCC was high before the testday and it was in the process of
decreasing. If the reason for the decrease in somatic cell count was due to the use of the
hot list it would most likely be before day 10.
The hot list is a good idea in theory and with little research on daily or milking to
milking variation in SCC the usefulness of it could not be accurately assessed. However,
with the data presented in this thesis we can now question the role the hot lists should
play in making management decisions on the farm. Many more parameters other than
SCC must be analyzed before making a management decision.
GENERAL DISCUSSION AND CONCLUSIONS
In this study, two labs in Florida were evaluated for accuracy and precision. By
testing each lab with thirty duplicate samples and four standards, we found that these labs
were accurately and precisely evaluating these milk samples. The variation in the
duplicates within lab and between labs was extremely low. The labs also accurately
determined the number of somatic cells in the standards.
A common mechanism DHI uses to help dairy producers monitor the somatic cell
count of their cows is the hot list. To evaluate the hot list we sampled 380 cows for 15
consecutive milkings. The results were surprising, as they showed great variability in
SCC over a short period of time. This analysis, combined with the analysis of the hot list,
indicated that SCC are much more variable than previously thought and because of this
maybe the hot list is not the best tool for dairy producers to use. Some argue that the hot
list should be used only in emergency situations when the dairy producer may be in
danger of shipping illegal milk. The theory is to use the hot list to find the highest cows
and hold their milk from the bulk tank (or take another management action) until a legal
limit of somatic cells is reached. However, this may not be the case. On the day the
technician sampled the herd, the cows on the hot list were the highest cows in the herd.
However, by the time the dairy producer receives the hot list it could be days or even a
week or longer, after the herd was sampled. After the technician samples the herd he
needs to send the sample to the lab, the lab needs to analyze it, print the report and send it
back to the producer. This process takes days. By the time the producer receives the hot
list the cows that were the highest may not be the highest anymore. For example, the
data from the study where the cows were sample at 15 consecutive milking shows cows
that drop from one million to under two hundred thousand in a period of eight hours.
Since large variations in somatic cell counts seem to be possible, the hot list may not be
the most beneficial tool for dairy producers to be using in any situation. In addition to all
of these factors, many herds enrolled in the DHI program are not sampled at every
milking during a 24 hour period, they are sampled at only one milking per month or
every other month. The number that the dairymen receive from this sampling is therefore
In addition to this, there are many other factors which should be taken into
consideration other than SCC before a cow is culled. Some of these other parameters
include reproductive status, more specifically, is the cow pregnant and how long did it
take to get her pregnant. Cows that take long periods of time to become pregnant again
are not as profitable as cows who are bred quickly. Cows with low milk production (in
what should be their peak phase) also should be considered for culling. Older cows and
cows with other problems such as lameness and frequent metabolic disorders also should
be taken into consideration. Dairy production is certainly not a black and white
operation. There is hardly ever one definite answer to a problem and many things must
be considered when a problem arises. Basing any decision off of one factor, such as
SCC, is almost always imprudent on a dairy. Factors such as SCC should be part of
making a decision but never the sole motivating force.
Extreme variability in SCC can have many implications for dairy producers. Many
producers rely on the once monthly SCC from DHI to make management decisions
regarding the cows on their dairy. Other producers use methods such as the California
Mastitis Test to evaluate the mastitis. This test depends on somatic cell counts for its
results as well. The variability in SCC is a good explanation of why $20 was lost per
cow treated in the McDermott study when every cow with a SCC above 400,000 was
treated with antibiotics (McDermott et al., 1983).
The problem facing dairy producers is that the cheapest and quickest ways to
evaluate mastitis problems on an individual cow level have always been based on somatic
cell counts. Milk cultures to determine if there is bacteria present in the udder take at
least 48 hours and are more costly. What dairy producers need is a test to quickly and
cheaply identify cows with mastitis that is not based on SCC. Currently, the only other
way to detect mastitis besides culturing and somatic cell counts is visual observation.
Perhaps dairy producers would be better suited if they trained their milkers extensively.
Milkers that are able to detect inflammation of the udder and abnormal milk could be a
huge asset on a dairy farm. One other possibility in identifying cows that may be ill is
looking at daily milk weights. Many dairy producers have a herd manager which
examines every cow which drops in production during a 24 hour period. This identifies
cows which may be affected with a number of different ailments, including mastitis.
Future research is necessary to solve this problem. The research must begin with
evaluating the variation in SCC more thoroughly to be certain that the results observed in
this study are repeatable. More research is also needed to discover what the true reason is
for the dramatic swings in somatic cell counts. Ultimately, dairy producers will need a
new way to analyze the mastitis status of individual cows in their herd.
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Jessica Elizabeth Belsito was born in Millbury, Massachusetts, on January 31st,
1981. She grew up surrounded by the dairy industry. While in Connecticut, the author
spent many evenings and weekends milking at the University's dairy, which further
solidified her love of the dairy industry. She was also heavily involved with the
University's dairy club, Block & Bridle and Sigma Alpha. These activities all helped to
further her knowledge of the dairy industry.
The author decided to attend graduate school during her senior year of college. She
graduated in May, 2003, with a Bachelor of Science in animal sciences and a minor in
dairy management. Immediately following her graduation, she moved to Gainesville
where she began work on her Master of Science.