<%BANNER%>

Value of the Dairy Herd Improvement (DHI) hot list as a dairy management tool

University of Florida Institutional Repository

PAGE 1

VALUE OF THE DAIRY HERD IMPROVEMENT (DHI) HOT LIST AS A DAIRY MANAGEMENT TOOL By JESSICA ELIZABETH BELSITO A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLOR IDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE UNIVERSITY OF FLORIDA 2005

PAGE 2

Copyright 2005 by Jessica Elizabeth Belsito

PAGE 3

This document is dedicated to the greatest man I never met, Robert John Belsito (19251973) and to the strongest woman I ever knew, Marcia Walsh (1920-2004).

PAGE 4

iv ACKNOWLEDGMENTS Gratefulness is extended to my superv isory committee, Dr. Roger Natzke, Dr Albert deVries and Dr Nick Place, for their time and effort. Many thanks are given to Dave Bray, fo r taking me under his wing and giving me a wealth of knowledge about the more pract ical side of the dairy industry. I Thank my fellow students Bruno Amaral a nd 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 th ank 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 thr oughout 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.

PAGE 5

v TABLE OF CONTENTS page ACKNOWLEDGMENTS.................................................................................................iv LIST OF TABLES............................................................................................................vii LIST OF FIGURES.........................................................................................................viii ABSTRACT....................................................................................................................... ..x CHAPTER 1 INTRODUCTION........................................................................................................1 2 LITERATURE REVIEW.............................................................................................4 Background...................................................................................................................4 Pathogenesis.................................................................................................................5 Mode of Infection..................................................................................................5 Immunity...............................................................................................................6 Pathogens...............................................................................................................9 Mastitis Prevention and Treatment...............................................................................9 Economics...................................................................................................................15 Milk Composition and Quality...................................................................................17 Milk Culturing and Somatic Cell Counting Techniques............................................18 Factors Affecting Somatic Cell Counts......................................................................19 Variability in Somatic Cell Counts.............................................................................21 3 FLORIDA MILK QUALITY LAB ANALYSIS.......................................................23 Introduction.................................................................................................................23 Materials and Methods...............................................................................................24 Results and Discussion...............................................................................................24 4 VARIATIONS IN SOMATIC CELL COUN TS FROM MILKING TO MILKING29 Introduction.................................................................................................................29 Materials and Methods...............................................................................................30 Results and Discussion...............................................................................................31

PAGE 6

vi 5 ANALYSIS OF THE VALU E OF HOT LIST USE..................................................40 Materials and Methods...............................................................................................42 Results and Discussion...............................................................................................43 6 GENERAL DISCUSSION AND CONCLUSIONS...................................................55 LIST OF REFERENCES...................................................................................................58 BIOGRAPHICAL SKETCH.............................................................................................61

PAGE 7

vii LIST OF TABLES Table page 3-1 Lab Analysis of Official Samples............................................................................28 4-1 Raw and adjusted (for milk product ion) SCC correlations for consecutive milkings....................................................................................................................36 4-2 Raw and adjusted (for milk producti on) SCC correlations for morning, afternoon and evening milkings...............................................................................................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 Pr ocedure on daily bulk tank data.................................52 5-3 Results of the LS means pro cedure on daily bulk tank data....................................53

PAGE 8

viii LIST OF FIGURES Figure page 3-1 SCC for Samples and their dup licates analyzed at lab 1..........................................25 3-2 SCC for Samples and their dup licates analyzed at lab 2..........................................26 3-3 SCC for Samples and their duplicat es 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 so matic 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 devi ation versus her average SCC ...................................38 4-9 Individual cow coefficient of variation versus her average SCC.............................38 5-1 Example of a m onthly DHI 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 Prev ious 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 Prev ious Month Excluded for Dairy B..........................46

PAGE 9

ix 5-6 Frequency of number of appearances on the hot list................................................48 5-7 Frequency of number of appearances on the hot list................................................49

PAGE 10

x Abstract of Thesis Presen ted 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 MANAGEMENT TOOL By Jessica Elizabeth Belsito August 2005 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 wh ite blood cells) increase during an infection. For this reason, and becaus e bacteriological procedures are time consuming and expensive, the somatic cell counts (SCC) are us ed as a general indica tor of udder health. Currently, Dairy Herd Improvement (DHI) offe rs 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 Fl orida 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 highe st 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

PAGE 11

xi differences between a sample and its duplicat e was not statistically significant between the labs (P=.098). In addition to this, vari ation from the standard was not significant (P=.387). These two factors indicate that on ly a small portion of the milking-to-milking variation can be attributed to the sampling and cell c ounting procedure. In Chapter 4, 390 cows were sampled for 15 consecutive milkings. Hot lists were created for each milking and each day to evalua te the repeatability of cows on the list. The repeatability was also very low. Wh en individual cow SCC was compared to the standard deviation for all 15 milkings, we sa w 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 S CC over time. The hot list identifies the 20 cows in the herd that contribu te the most cells to the bulk ta nk. Very few cows repeated on the hot list month after month. In addition to this, statistical an alysis of daily bulk tank SCC showed no positive effect from hot list use. Therefore, if the SCC of many cows decreases without interven tion (based on the results from the hot list analysis), one must question the economic value of intervening. We concluded that SCCs are highly variab le. 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.

PAGE 12

1 CHAPTER 1 INTRODUCTION Quality food products are always a con cern in the United States for producers, consumers and the government alike. One wa y milk quality is meas ured 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 bl ood cells, epithelial cells or othe r types of cells. There are many different reasons for keeping somatic ce ll 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 a ll 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 Counc il (NMC), a well known orga nization that focuses on mastitis and milk quality, has been pushing th e 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.

PAGE 13

2 One reason many dairy producers try to lo wer 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 tes ting, where a DHI technician co mes to their farm and takes a milk sample from every cow. These samp les are sent to DHI labs where they are analyzed and the result s are sent to the producer and st ored in DHI databases. This provides the dairy producer with a milk weig ht and SCC for each individual cow. The goal of monthly somatic cell counts is to help producers identify i ndividual cows with high SCC. By identifying these cows, manage rs can then make a management decision (culling, antibiotic therapy, etc.) to try to lo wer 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 cont ributed 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 problem s outlined above and because of the lack of

PAGE 14

3 literature on the subjects, the goal of this thesis was to observe and describe patterns and/or changes in individual cow SCC over tim e and to analyze the effectiveness of the hot list in managing bulk tank SCC. Through a combination of these studies we we re 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 th at the labs would be accurate and the milking to milking somatic cell counts would sh ow slight variability. It was also thought that if the hot list was im proved by adding other important information it would then become a more useful tool for dairy producers.

PAGE 15

4 CHAPTER 2 LITERATURE REVIEW Mastitis is one of the most costly di seases to milk producers. Losses are associated with decreased milk production, discarding milk from cows treated with antibiotics, the cost of the antibiotics and in creased 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 descri be the events leading to mastitis, economic losses due to infection, methods used to dete ct mastitis, variability in SCC and milk quality and compositional changes. Background The National Mastitis Council defines mas titis as an inflammation of the udder, most commonly caused by infecting microorgani sms. The word mastitis itself means inflammation of the mammary gland. Gi esecke (1975) gives a much more specific definition of mastitis. His definition states th at 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 chan ges can take place in the animal. Giesecke adds to his definition the distin ction between subclinical and clinical mastitis. Subclinical mastitis involves subt le physiological changes (Giesecke, 1975). Clinical mastitis, however, is a more serious inflammatory response. Signs of clinical

PAGE 16

5 mastitis include: changes in milk composition (watery milk, flakes or clots), visual signs of infection (red, swollen quarter s) and a more dramatic increase in SCC (Neave, 1975). Pathogenesis Mode of Infection Mastitis begins at the teat end. The bact eria must first come in contact with the sphincter. Once the bacteria pass through the sphincter it must trav el up the teat canal which is lined with keratin. Keratin is a wax-like substance designed for trapping bacteria and also contains substances with an timicrobial properties (Sordillo et al., 1997). If the bacteria pass by the keratin in th e teat canal it can gain access to the mammary gland. There are many ways in wh ich 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, al lowing them to establish themselves more permanently (Kehrli and Shuster, 1994). The milking machine also aids the bacter ia 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 cau sed when the teat cup liners do not collapse completely around the cows teat. When the line r 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 ar e 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

PAGE 17

6 used to avoid exposing the teat to a constant v acuum. It allows the teat canal to be open and closed rhythmically and prevents th e 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 wh en teats are contaminated with mastitis causing pathogens the rate of new infecti on is higher when exaggerated vacuum fluctuations are applied to the cows teats. In addition to this, contaminated milk coming in contact with the cows 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 pr oducing 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 be ing 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, 1994). Immunity Once in the gland, there are two main ways in which the cows immune system will try to clear the bacteria. The two basic groups of immunity in the cows 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 ne utrophils (PMN), and natural killer cells. Specific

PAGE 18

7 immunity requires time to develop after the initial infection. Th is 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 udde r are not able to eradicat e the bacteria, inflammation and infection will result. The milk in th e gland provides an optimal environment for bacteria multiplication. If the infection becomes severe enough, the cow may show systemic signs of infection including a re d, hard udder and a fever. Inflammation is caused by the flood of cells invo lved in the immune response into the udder (Paape et al., 1979). 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 sc ar 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 lact ations. In addition to this, bacteria have the ability to produce toxins and other substances that can kill milk producing cells, again reducing milk produc tion. Membrane permeability is also affected, allowing more blood components into th e milk. If the infection becomes severe enough, visible changes may occur in the cows 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 un til the next lactation, if ever,

PAGE 19

8 that this tissue begins producing milk agai n. The blood and immune system components can also cause secretory ducts to be come 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. Anothe r 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 antibac terial peptides (S ordillo et al., 1997). Macrophages, cells which are present in healthy mammary glands, are also phagocytic. These cells also engulf and di gest 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 fo reign bacteria with sp ecific receptors on their membranes. The other cells involved in spec ific immunity are antigen-presenting cells (Sordillo et al., 1997). There are two types of lymphocytes, T a nd B. T lymphocytes produce and secrete cytokines. There is much speculation surr ounding the function of T lymphocytes. What is known, however, is they play a role in pr otecting epithelial surface s. They may also mediate the activity of other cells involved in the immune system. B lymphocytes produce antibodies specific to the bacteria that are invadi ng the udder. B lymphocytes present cells to T lymphocytes to be eliminated (Sordillo et al., 1997).

PAGE 20

9 Pathogens There are a variety of pathogens that cau se mastitis. The most common pathogens include Staphylococcus aureus, St reptococcus agalactiae, coliforms, and other streptococci These species are considered to be the major mastitis pathogens because they are the most common. Othe r noteworthy bacteria include Pseudomonas, Corynebacterium bovis, Mycoplasma and coagulase-negative sta phylococci. 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 cows environment. Most of these in fections are caused by cows laying in manure or other types of unclean be dding, 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 Enterococcus faecalis (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 pr oducers attempt to lower the incidence of mastitis on their farm. These include pre a nd post dipping of teats with a disinfectant

PAGE 21

10 using antibiotics at dry-off to prevent futu re infections, preventing the transmission of bacteria from one cow to another and k eeping freestalls clean (Oliver et al., 1993). An additional study evaluating the effi cacy of pre-dipping was completed in Tennessee in 1993. This study was a natural ex posure 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 spli t udder design. The left teats were the controls. They were stripped and dried. Bo th 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 cl inical 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 tr eated 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 met hod that can help control mas titis is antibiotic use. In 1966, Smith wanted to evaluate the effectiv eness of intramammary antibiotic use at the start of the dry period. They had observed pr eviously that half of the cows in most dairies (excluding first calf heif ers) calved with one quarter infected. Smith believed that

PAGE 22

11 eliminating late lactation infections as we ll as preventing new inf ections 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 cows lactation. Cows were assigned randomly at dry-off into three groups 1) control (no disi nfectant no antibiotics; 2) .2g Cloxicillin intramammary and dipped with 5% hypochlorite; 3) intramammary infusion of 1 g Cloxicillin and teats di pped with the 5% hypo chlorite solution. When the cows were dried off, half of them had positive bacteriological milk samples. At calving, bacteriol ogical samples were taken within 7 days. Over 60% of the control group had positive bacteriological m ilk 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 E nglands 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 strategi es. They utilized post-dipping, antibiotic therapy for infected lactating cows and antibio tic therapy for all cows at dry-off. The second group used only teat dip (hypochlorite containing 4% availa ble chlorine). In addition to these factors, milking machines were evaluated annually at each herd and adjusted as necessary. The researchers visite d each herd weekly to assess the implication of the mastitis prevention strategy and to take milk samples for culture (Wilson and Kingwell, 1975).

PAGE 23

12 At the beginning of the study a blitz ther apy was utilized. All infected quarters in half the herds in each group were treated w ith 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 lactati on 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 elimin ated 68% of all infections in the cows which received it. However, a larger numbe r of subclinicals were cured by the use of antibiotics at dry off in the non-blitzed herds. At the end of the firs t year the percentages of infection were relatively similar in th e 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 rate s 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 an tibiotics, nutrition may also play a small role in mastitis management. In 1991 Ol dham et al. conducted a study looking at the effects of vitamin A and -carotene during the dry peri od 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 -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

PAGE 24

13 measure the serum concentrations of vitamin A and -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 lact ation and once during the sixth week of lactation. Milk was also samp led for culture. All samples we re 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 eac h 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 v itamin A levels between treatments throughout his study. Treatment effect on serum -carotene showed a tendency towards being significant (p <.08). Both serum -carotene levels and vitamin A levels tended to drop from 2 weeks prior to dry o ff until calving (Oldham et al., 1991). To determine if -carotene and/or vita min 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 vitami n 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

PAGE 25

14 sampled for analysis three times during the year. Plasma sa mples 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 quart ers 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 sele nium 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 seleni um 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 hour s 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 pr event it (Weiss et al., 2004).

PAGE 26

15 Economics Mastitis is the most economically devasta ting 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 me thods 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 (B losser, 1979). This number agrees with Ravinderpal (1990) who projected that ma stitis accounted for 70 to 80% of the $140$300 dollars lost per cow per year due to dis ease. 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, dis carded milk, and increased labor. It is projected that if a quarter becomes infected the average milk lo ss 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 obtai n a higher response rate. Lactating dairy cows were used from five commercial herds th at all used teat dip and dry cow therapy. Composite milk samples were taken monthl y for electronic SCCs. The somatic cell sampling was unannounced. Within seven days of the sampling individual quarter samples were collected and the bacterio logy 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 (c ephapirin) the first time their SCC rose above 400,000 cells/ml. Th is was done only once in the lactation.

PAGE 27

16 Clinical cases in both the c ontrol and experimental groups were treated by the farm managers themselves. Treatment was base d on SCC. Bacteriology was completed to assess any possible benefits of the antibiotic in eliminating infection. Treating cows with SCC of 400,000 and a bove 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 Univer sity 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 antib iotic enjoyed a benefit/c ost 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 shoul d be used as part of the re gular routine on the farm and should be viewed as a long term applicati on and not a quick fix for a mastitis problem (Asby et al., 1975). More recently, other mastits control and treatment techni ques have been evaluated. A study done in 1998 used computer modeling to test the effectiveness of several mastitis control strategies. This study evaluated cont rol strategies for the following bacteria types: Streptococcus agalactiae, Streptoc occus spp, Staphylococcu s aureus, coagulasenegative staphylococci, and Escheria coli. Prevention of mastitis (forestripping,

PAGE 28

17 predipping and postdipping), vaccination for E. coli, lactation therapy and dry cow therapy were all evaluated for econo mic 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 pe r 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 thera py were beneficial for all pathogen groups. In addition to this it was found that in herd s where environmental mastitis was dominant, vaccination for the prevention of E. 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 sli ght decrease as does lactose (although some studies show no change in fat c ontent). 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 unde sirable protein for cheese making, tends to increase. Blood components such as album ins and immunoglobulins find their way into the milk because of an increase in permeab ility of the milk blood barrier (Harmon, 1994). Mineral balance in the milk is also aff ected. Potassium and calcium are markedly reduced in milk with a high SCC. Potassi um leaks through damaged epithelial cells. These same cells also allow sodium and chlo ride from the blood into the milk. This increase of blood components in the milk also ca uses 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 75 0,000 cells/mL) to low SCC milk (about 45,000

PAGE 29

18 cells/ml) it was found that after pasteurizat ion 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 increas e in number and cause an increase in casein hydrolysis. After 21 days the organoleptic qua lity of the low SCC milk remained high. The high SCC milk was rancid, as determ ined by a sensory panel (Ma et al., 2000). Milk Culturing and Somati c 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 involve s the use of blood agar plates Aseptic milk samples are plated (.01 ml of milk) on ag ar plates containing 5% sheep blood and .1% esculin. Samples are then incubated at 37 C for 48 hours. Usually the plates are read at 24 hours of incubation and after the full 48 hours. Th e 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 qua rter. 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. Anothe r test that is very similar to this is the Wisconsin Mastitis Test (Kitchen, 1981).

PAGE 30

19 In laboratories somatic cells can be determ ined either by a tec hnician 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 Fosso matic. 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 buffe r, ethidium bromide is added to make the somatic cells fluorescent. They are th en 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 a nd 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 th e 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 in itial challenge. In addition to this, the level of the response vari es 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 a nd cows in late lactation have higher somatic cell counts. In many cases this is tr ue because the cow has had more time to be exposed to pathogens. However, if lactat ing cows remain uninfected throughout the course of their lactation, SCC varies very little (Sheldrake et al., 1983).

PAGE 31

20 Another important aspect of SCC and stag e 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 rapidl y and return to normal within 35 days in milk (Reneau, 1986). There is also thought to be a dilution fact or that affects SCC. This means that if a cow were to experience a reduction in m ilk 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 toward s the end of lactation (Schultz, 1977). A reduction in water or feed has also been show n to increase SCC, probably because of the reduction in milk production (Martin, 1973). Various sources of stress can also ca use 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 decr ease 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 ut ilized DHI records to evaluate somatic cell variation. DHI records with at least 220 da ys 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

PAGE 32

21 this study was a high SCS at calving which st eadily decreased until peak milk production. After peak milk production, SCS tended to increase throughout th e remainder of the lactation. The authors admit, however, that so me of this increase in SCS could be due to the dilution factor associated with a decr ease 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 avai lability of this data due to DHI (Reneau, 1986). However, it is suspect ed that individual cow somatic cell c ounts can be highly variably due to sampling errors or more w ithin 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 c ounts. This study utilized aseptic milk samples as well as daily SCC from the morning milking to determine SCC variations. The results implied that monthly individua l cow SCC would not reflect all of the infections that affected the herd in that m onth. However, the most interesting observation is between the within cow variance for uninf ected 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 b ecause 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 va riations of somatic cells throughout lactation on a weekly basis. Eleven Holsteins were used an d samples were collected on a weekly basis using DHI approved sample colle ction jars. The sample time alternated between morning and evening milkings. Differential somatic cell counts were

PAGE 33

22 performed. Aseptic milk samples were also taken for bacteriological cultures when visual evidence of inflammation was observe d or when the SCC doubled for any one cow (Duitschaver and Ashton, 1972). Large variations in SCC were observed th roughout the lactati ons. The amount of neutrophils present mirrored th e 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.

PAGE 34

23 CHAPTER 3 FLORIDA MILK QUALITY LAB ANALYSIS Introduction As stated in Chapter 1, the goal of this thesis is to describe the patterns of fluctuation in individual cow SCC over relatively short peri ods of time. Traditionally, most dairy producers use monthly milk SCC from DHI. In addition to this, these monthly SCC usually are not daily composite s, meaning that many dairy producers only see one SCC from one milking per cow per mont h. In a herd that is milked two times daily, a dairy producer is then using only 1 m ilking 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 testi ng costs money, theref ore it is easily understandable that dairy producers do not wa nt to pay for excessive testing. The problem, however, is that more money may be lo st when cows are treated with antibiotics or culled unnecessarily. If th e samples that were tested for SCC were a composite sample from 2 or more milkings th e variation would be reduced. These observations were the basis for this th esis. There is very little data, however, on milking to milking variation in SCC. Ther efore, 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 accu rate then we know that any variation that we identified is due to factors other than th e accuracy of the laborat ory. The objective of this study was to determine the accuracy and th e precision of the DHI labs in Florida that

PAGE 35

24 analyze milk samples for SCC. Our hypothe sis was that the lab would prove to be accurate and precise. Materials and Methods Four labs in the state of Florida were evaluated for accu racy and repeatability of SCC results. Thirty milk samples (one ga llon each) were obtained from 30 different dairies. The samples were stored below 40 de grees 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 ra ndomly assigned a number from 31 to 60 so technicians would not know which sample it wa s a duplicate of. In addition to the sixty samples, each laboratory also received four st andard samples that were purchased from a commercial laboratory. 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 th e 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 pe rsonnel 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 fdistributions 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.

PAGE 36

25 Data Lab 1 0 200 400 600 800 1000 1200 1471013161922252831 Sample NumberSCC (cells/mL) Sample 1 Duplicate Figure 3-1. SCC for Samples and th eir 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 c onclusion 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 wa s not mixed properly before it was split into sub-samples. Milk tends to settle afte r short periods of time if the milk is not homogenizd. Therefore, not mixing the m ilk thoroughly before the sample was split could easily have caused this error.

PAGE 37

26 0 200 400 600 800 1000 1200 1357911131517192123252729 Sample NumberSCC (cells/mL) Sample 1 Duplicate Figure 3-2. SCC for Samples and th eir 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 ex actly 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 th is lab is 141,000. The average difference between a sample and its duplicate at lab tw o was 16,000 cells/ml. These differences are so slight, however, that they are not even sign ificant. Small variations like this one may be due to the way in which the sample wa s mixed before it was split and the small amount of variation that is possible with electronic soma tic cell counting techniques. Again, one can visually observe that there wa s an error with sample nine. Aside from sample nine, the majority of the data seems very accurate.

PAGE 38

27 Data Both Labs 0 100 200 300 400 500 600 700 800 900 1000 1357911131517192123252729 Sample NumberSCC (cells/mL) Lab 1 Sample 1 Lab 1 Duplicate Lab 2 Sample 1 Lab 2 Duplicate Figure 3-3. SCC for Samples and their dup licates 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 va riance 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 al most 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 differenc es 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 th e trial was conducted. The value the lab obtained for each standard was not statistically different from the actual value of the standard (P = .387).

PAGE 39

28 Table 3-1. Lab Analysis of Official Samples. Lab Replicate Official SCC (in 1000s) Lab SCC (in 1000s) Difference (in 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 bot h 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.

PAGE 40

29 CHAPTER 4 VARIATIONS IN SOMATIC CELL COUN TS FROM MILKING TO MILKING Introduction SCC are often used as an indicator of udder health. It is important, therefore, that we understand more scientifically the variations in SCC. Un derstanding these variations will help scientists develop the best way in which to use somatic cell counts as an udder health indicator. Very little is known about SCC variation fr om milking to milking or even day to day because limited daily or even weekly SCC va riation 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 varia tions 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 c ounts. 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 wh ich 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 b ecause it is generally thought that uninfected

PAGE 41

30 cows do not experience fluctuations in their S CC. 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 milki ng 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 fac ilities and milked 3 times dail y. 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. Sample s were collected by seven different persons working in pairs. Two were trained DHI t echnicians. The others attended a mandatory orientation before the start of the trial to out line 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). Elect ronic cell counting uses ethium bromide to stain the DNA of somatic cells. The instrument then uses laser flow cytometry to count

PAGE 42

31 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 Micr osoft 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 observati ons were dropped from the study. Data was then analyzed using Microsoft Excel to calcu late correlations and adjusted correlations. Results and Discussion Throughout the five day period, the aver age bulk tank SCC was 450,000 cells/ml. Although the bulk tank SCC remained fairly co nstant, large variations in SCC from milking to milking on an individual cow level were observed. Figure 4-1 depicts the average SCC on an i ndividual 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 Basis0 20 40 60 80 100 0-250,000251,000500,000 500,001750,000 751,0001,000,000 1,000,000+ SCC (cells/ml)Number of Cows Figure 4-1. Average SCC for the 15 milki ngs on an individual cow basis (380 cows total).

PAGE 43

32 Number of Cows Over or Under Threshold SCC at all 15 Milkings0 20 40 60 80 100 120 < 200000 < 400000 < 750000 > 1000000 > 750000 > 500000 > 250000 SCCNumber of Cows Figure 4-2. Number of cows over or under certain somatic ce ll counts at all 15 milkings (380 cows total). Number of Cows Above or Below a Certain Daily SCC All 5 Days0 50 100 150 200 > 200,000 < 400,000 < 750,000 > 750,000 > 750,000 > 500,000 > 250000 SCC (cells/ml)Number of Cows Figure 4-3. Number of cows over or under certain daily so matic cell counts each day (380 cows total). Figures 4-2 and 4-3 describe how many co ws were over or under randomly chosen SCC thresholds. This analysis was done on an individual milki ng basis and on a daily basis. For the duration of this reserach, da ily SCC will refer to the average SCC for all

PAGE 44

33 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 peri od 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). On e 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 frequenc y 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 da ily 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 extremel y high. Many cows experienced consistently low SCC (see Figure 4-7, almost 80 cows ne ver had a daily SCC above 250,000). It is peculiar, however, that no cows had a SCC a bove one million every day in the 5 day trial, but we see over 100 cows with at least one da ily average above one million. This is most

PAGE 45

34 likely due to the variation in SCC that ha s been observed consis tently throughout this research. Number of Cows With A Daily SCC Above One Million0 50 100 150 200 012345 Number of Occurences Over One MillionNumber of Cows Figure 4-4. Number of cows with a da ily SCC over one million (380 cows total). Number of Cows With A Daily SCC Above 750,0000 50 100 150 200 012345 Number of OccurencesNumber of Cows Figure 4-5. Number of cows with a da ily SCC over 750,000 cells/ml (380 cows total).

PAGE 46

35 Number of Cows With A Daily SCC Above 500,0000 20 40 60 80 100 120 140 012345 Number of OccurencesNumber of Cows Series1 Figure 4-6. Number of cows with a da ily SCC over 500,000 cells/ml (380 cows total). Number of Cows With A Daily SCC Above 250,0000 20 40 60 80 100 012345 Number of OccurencesNumber of Cows Figure 4-7. Number of cows with a da ily 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

PAGE 47

36 milking as well as from morning to morni ng, 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 tabl es display the values. The values in Table 4-1 and Table 4-2 are not the same; however th ey remain in a relatively small range of 0.3 to -0.01. These values reflect a very w eak 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 pr oduction) SCC correlations for consecutive milkings. Milking Raw Correlation Adjusted Correlation 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 shif t, which were each milked by a different operator, to observe the rela tionship between SCC in the mo rning, afternoon and evening. Milkings 1,4,7,10 and 13 were morning milki ngs (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.

PAGE 48

37 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 correlati ons for morning milkings are more positively related than the correlations between successive milkings. However, the correlation between the fourth morning (milking 10) and th e 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 ne gative 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 Correlation 1 to 4 0.227 0.256 4 to 7 0.089 0.104 7 to10 0.096 0.017 Morning 10 to13 -0.004 -0.035 2 to 5 -0.006 0.008 5 to 8 0.043 0.057 8 to11 0.179 0.164 Afternoon 11 to 14 0.008 0.032 3 to 6 0.216 0.282 6 to 9 0.060 0.052 9 to 2 0.142 0.112 Evening 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. Sca tter plots were then constructed to observe the coefficient of variation and standard de viation versus the cows average somatic cell count. Figures 4-8 and 4-9 display the results.

PAGE 49

38 SCC Standard Deviation0 500 1000 1500 2000 2500 3000 3500 4000 05001000150020002500 Average SCCStandard Deviation Figure 4-8. Individual cow st andard deviation versus her average SCC (380 cows total). Coefficient of Variation Versus Average SCC0 0.5 1 1.5 2 2.5 3 3.5 4 05001000150020002500 Average SCCCoefficient of Variation Figure 4-9. Individual cow co efficient of variation versus her average SCC(380 cows total). Figure 4-8 illustrates that the higher a cows 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 count s. Almost the same trend is observed in Figure 4-9. When

PAGE 50

39 the SCC is above a million, however, the coeffi cient of variation tends to level off at about 2. 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 repeat able; but the data collected so far, however, suggests that the hot list ma y prove to be of no use.

PAGE 51

40 CHAPTER 5 ANALYSIS OF THE VALUE OF HOT LIST USE Monthly DHI somatic cell counts have b een 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 quick ly identifying the top 20 cows with highest number of somatic cells per milking. The list gives a va riety of information on the cow such as her lactation number, her days in milk, and what he r SCC was on that test day. In addition to this, the hot list also calculates what the bulk tank SCC would be if that cows milk, and the cows above her, were not added to the tank on that particular day as well as the percentage of cells in th e bulk tank that the individua l 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. Afte r that is done the total amount of somatic cells from each cow is calculated. The i ndividual cow SCC is then divided by the bulk tank SCC and multiplied by 100. This number repr esents the percentage of somatic cells in the bulk tank that the indivi dual cow is responsible for. The hot list then ranks the top 20 cows in the herd by percent cel ls contributed to the bulk tank. Figure 5-1 is an example of a hot list th at is sent monthly to dairy producers by DHI in the state of Florida. The first column is the cows 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 cows m ilk, and the cows above her, in the bulk tank. The percent cells column lets the da iry producer know what percentage of the

PAGE 52

41 somatic cells in the bulk tank that particular cow is responsible for. Only five cows are shown on this example hot list. Ac tual hot lists s how twenty cows. Cow ID Lactation Number DIM Milk (lbs) 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 t hought 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 co ws 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 descri be 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 lis t 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 th e 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.

PAGE 53

42 Materials and Methods Two commercial dairies (Dairy A and Dairy B) in the state of Florida were used for the first analysis. Dairy Herd Improvem ent lactation records from 1998 to 2003 were utilized. Somatic cell counts were taken mont hly by DHI technicians. Both dairies used in the analysis milked 450 to 550 cows three ti mes daily. In addition to this, both dairies used floor mounted cow washers, pre-stripped, and post dipped with a te at disinfectant. Dairy A used the hot list for making mana gement 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 procedures. 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 th at did not have a SCC for all 5 milkings were dropped from the study. The same samplin g devices were used in both studies and samples were analyzed at the same lab usi ng electronic somatic cell counting. This data was also analyzed using Microsoft Excel. Finally, the last data se t was obtained from Southeas t Milk Inc. (Belleview, Florida) and was analyzed using the mixed pr ocedure in SAS (version 9.0). Three years of complete data were ava ilable (2002-2004) and forty-three herds were used in the

PAGE 54

43 analysis. Somatic cell count data was availabl e from daily or every other day milk pick ups. Somatic cell counts were analyzed at the SM I lab in Belleview, Florida. This lab is one of the labs that was analy zed 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 a ny test day, there are two options for a cow. She could be tested or not tested. If she is not tested sh e cannot appear on the hot list. Cows that are not tested have been dried off, are in the hos pital herd or have a missing sample. If a cow was tested she could be on th e 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 te sted 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 agai n 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 rema inder 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 follo wing month. About 80% on both dairies were not on the list in the current month and the following month.

PAGE 55

44 Because dairy A made management decisions regarding all the cows on the hot list every month, the results that were observed we re different from what was expected. The hypothesis was that Dairy A w ould have fewer cows than Dairy B repeating on the hot list because more of those cows would be trea ted. The results suggest that dairy B was effectively finding and treating mastitis case s using other management tools besides the hot list. An alternate explanat ion 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 de pict the bulk tank SCC of that test date as well as a weighted bulk tank SCC. The wei ghted 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 refl ects the current months SCC with the milk removed from the cows who were on the hot list the month before. Actual Bulk Tank SCC and Calculated Bulk Tank SCC with Hot List Cows RemovedDairy uses hot list 0 100 200 300 400 500 600 700 800 900 19981999200020012002 YearBulk Tank SCC (1000s) Actual SCC Calculated SCC Figure 5-2. Actual Bulk Tank SCC and Calc ulated Bulk Tank SCC Over a Five Year Period with Hot List Cows Rem oved for Dairy A (used hot list).

PAGE 56

45 Actual Bulk Tank SCC and Calculated Bulk Tank SCC with Hot List Cows Removed 0 100 200 300 400 500 600 700 19981999200020012002YearBulk Tank SCC (1000s) Actual SCC Calculated Dairy does not use hot list Figure 5-3. Actual Bulk Tank SCC and Calc ulated Bulk Tank SCC Over a Five Year Period with Hot List Cows Removed fo r 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 th ere 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 la rge herds. Their reduc tion in SCC would not be as great because the 20 cows on the hot list represent a smaller pe rcentage of cows in their herd. 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 54 and Figure 5-5 depict actual SCC on a test day and a calculated SCC where all milk fr om cows that were on the hot list in the previous month has been removed.

PAGE 57

46 0 100 200 300 400 500 600 700 800 900 19981999200020012002 YearBulk Tank SCC (1000s) Actual SCC Calculated SCCActual Bulk Tanks SCC and Calculated Bulk Tank SCC with Hot List Cows In the Previous Month Excluded Dairy uses hot list Figure 5-4. Actual Bulk Tank SCC and Calc ulated Bulk Tank SCC Over a Five Year Period with Hot List Cows in the Previous Month Excluded for Dairy A (used hot list). 0 100 200 300 400 500 600 700 19981999200020012002 YearBulk Tank SCC (1000s) Actual SCC Calculated SCCActual Bulk Tanks SCC and Calculated Bulk Tank SCC With Hot List Cows From the Previous Month Excluded Dairy does not use hot list Figure 5-5. Actual Bulk Tank SCC and Calcul ated Bulk Tank SCC Over a Five Year Period with Hot List Cows in the Prev ious Month Excluded for Dairy B (did not use hot list).

PAGE 58

47 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 effect s. 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 re duction in SCC would be about only 81,800 cells/ml the following month (for Dairy A a nd Dairy B). This was found by calculating the differences between the actual SCC and calculated SCC and averaging the differences. From the analysis of DHI records, we obs erved that for both dairies cow movement on and off the hot list was very similar. It wa s also calculated that cows on the hot list are responsible for approximately ha lf 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 signi ficant effect on bulk tank SCC in the long term on these two dairies. This may be because of the high variab ility in somatic cell counts that was previously discussed in Chapter 4. If the co rrelation 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 th e results in Chapter 4, it is possible that her somatic cell count could re turn to a normal level with in 8 hours. Therefore, a producer may be treating or even culling a co w that is in a perfectly normal state of

PAGE 59

48 health. Making assumptions about the heal th 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 th e 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. E ach 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. Th is 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. Th ere were no cows on the hot list more than 6 times, meaning there were no cows on the hot lis t for more than half of the milkings. These data show great variation in which co ws appear on the hot list from milking to milking. Frequency of Cows Appearing on the Hot List (Hot Lists were calculated for each of the 15 milkings)0 50 100 150 200 250 0123456 Number of Times on the Hot ListNumber Of Cows Figure 5-6. Frequency of number of appearances on th e hot list (lists ca lculated at each milking; 380 cows total).

PAGE 60

49 Daily hot lists were also created and an alyzed. 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 cl early be seen that if all the cows on the hot list were treated 94% of them woul d have been treated needlessly. Frequency of Cows Appearing on the Hot List (Hot Lists calculated for each day)0 50 100 150 200 250 300 350 0123 Number of times on the Hot listNumber of Cows Figure 5-7. Frequency of number of appearances on th e 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 (Gaine sville, FL). This study will be called study 2. It was conducted at the same da iry as used in Chapter 4 (Webb, 2005). To conduct a direct comparison between th e 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

PAGE 61

50 were then compared with two se ts of 5 hot lists from study one. Each set of hot lists from the first study began with an evening milki ng so the analysis would not be confounded by time of day. Table 5-1 again depicts the va riability 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 twi ce although there are a few cows that appear 3, 4 and 5 times. The re sults of this small analysis seem somewhat inconclusive. If anything, th ey support the idea that management decisions based solely on the hot list are not ec onomically sound decisions. Table 5-1. Number of times cows were on hot lists for study one and study two. Number of Appearances on the hot list Study 1, First set of hot lists Study 1, Second set of hot lists Study 2 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 co ws on the hot list observed in the original hot list analysis and from creating hot lists from th e two sets of SCC vari ability data further indicates that the hot list is not a beneficial t ool for dairy producers to utilize. This data coupled with the data from the previous st udy (SCC Variability Chap ter 4) provides solid evidence that the hot list is not economically a dvantageous for the dair ies analyzed in this study. 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

PAGE 62

51 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 m ilkings. 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 re peat on the hot list more than twice are an insignificant amount. Table 5-1 takes this analysis one step furt her 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 agai n, 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 receive s 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 thir ty days was divided into six groups of five days each. Group 1 consists of days 1-5 afte r the testday. Group two consists of days 610 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 ta nk 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

PAGE 63

52 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 wa s less than 500,000. The model inputted into SAS was difference = herdcode highmont h daysaftergroup hi ghmonth*daysaftergroup where difference = average SCC in for a group the 10 day average SCC before the test day, herdcode is each individual herds iden tification and daysaftergroup is group 1-6, depending on how many days after the testda y 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. Th e fixed effects of the model that were significant were herdcode, high month/low mo nth, and group number. The interaction between month and group was not significan t but approached significance therefore it was included in the model. The most intere sting 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 determin ing if the herds SCC will decrease after the testday. Table 5-2. Results of the Proc Mixe d Procedure on daily bulk tank data. Effect Pr > F Herdcode 0.0002 Month <0.0001 Group 0.0184 Month*Group 0.0868 Least squares means were also calculated for high month and low month, groups 16 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, varia tions in SCC tend to

PAGE 64

53 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 interacti ons 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 35 were also significant. None of the interactions between low months and groups were significant. This data is perplexing. Th e 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 tim e 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 mo st feasible reason for this

PAGE 65

54 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 coun t 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 w ith 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.

PAGE 66

55 CHAPTER 6 GENERAL DISCUSSION AND CONCLUSIONS In this study, two labs in Florida were evaluated for accuracy and precision. By testing each lab with thirty dupl icate samples and four standard s, we found that these labs were accurately and precisely evaluating thes e milk samples. The variation in the duplicates within lab and between labs was extremely low. The labs also accurately determined the number of soma tic 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 ev aluate the hot list we sampled 380 cows for 15 consecutive milkings. The results were surpri sing, 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 mo re variable than previously thought and because of this maybe the hot list is not the best tool for dair y producers to use. Some argue that the hot list should be used only in emergency situa tions 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 ta ke 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 recei ves 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 produc er receives the hot

PAGE 67

56 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 soma tic cell counts seem to be possi ble, the hot list may not be the most beneficial tool for dairy producers to be using in any situati on. 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 sa mpled at only one milking per month or every other month. The number that the dairymen receive from this sampling is therefore highly unreliable. In addition to this, there are many othe r 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 a nd frequent metabolic disorders also should be taken into consideration. Dairy produc tion 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. Basi ng any decision off of one factor, such as SCC, is almost always imprudent on a dair y. Factors such as SCC should be part of making a decision but never th e 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

PAGE 68

57 regarding the cows on their da iry. Other producers use met hods 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 (M cDermott 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 culture s to determine if there is bacter ia present in the udder take at least 48 hours and are more costly. What da iry 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 inflammati on 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 da iry producers have a herd manager which examines every cow which drops in producti on 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 c ounts. Ultimately, dair y producers will need a new way to analyze the mastitis status of individual cows in their herd.

PAGE 69

58 LIST OF REFERENCES Allore, H.G. and H.N. Erb. 1998. Partial Budget of the Discounted Annual Benefit of Mastitis Control Strategies. J. Dairy Sci. 81:2280-2292. Asby, C.B., P.R. Ellis, T.K. Griffin, R.G. Kingw ill. The Benefits and Costs of a System of Mastitis Control in Indi vidual Herds. University of Reading, Department of Agriculture and Horticulture. Study No 17, 1975. Berry, E.A., J.E. Hillerton and M. Gravenor. 2004. Variation of Individual Cow Cell Count. Proc. 43rd National Mastitis Council M eeting. Verona, WI. P 284. Blosser, T.H. 1979. Economic Losses From and the National Research Program on Mastitis in the United States. J. Dairy Sci. 62:119-127. Dinsmore, R.P., P.B. English, R.N. Gonzalez and P.M. Sears. 1992. Use of Augmented Cultural Techniques in the Diagnosis of th e Bacterial Cause of Clinical Bovine Mastitis. J. Dair y Sci. 75:2706-2712. Dohoo, I.R., and A.H. Meek. 1982. Somatic Cell Counts in Bovine Milk. Can. Vet. J. 23:119. Duitschaever, C.L., and G.C. Ashton. 1972. Variations of Somatic Cells and Neutrophils in Milk Throughout Lactat ion. J. Milk Food Technology. 35:197-202. Elvinger, F., P.J. Hansen, and R.P. Natzke 1991. Modulation of Function of Bovine Polymorphonucleur Leukocytes and Lym phocytes by High Temperatures in Vitro and in Vivo. Am. J. Vet. Res. 52:1962. Emanuelsson, U. and E. Persson. 1984. St udies on Somatic Cell C ounts in Milk from Swedish Dairy Cows. Nongenetic Causes of Variation in Monthly Test-Day Results. Acta Agric. Scand. 34:33. Giesecke, W.H. 1975. The Definition on Bovine Mastitis and the Diagnosis of its Subclinical Types During Normal Lactati on. Proc. Seminar on Mastitis Control. Int. Dairy Fed. Reading, England. Bull. Doc. 85:62. Harmon, R.J. 1994. Symposium: Mastitis and Genetic Evaluati on for Somatic Cell Count. J. Dairy Sci. 77:2103-2112. Heeschen, W. 1975. Determination of Somatic Cells in Milk. Proc. Seminar on Mastitis Control. Int. Dairy. Fed. Bu ll. Reading, England. Doc. 85:79.

PAGE 70

59 Hogan, J.S. W.P Weiss, and K.L. Smith. 1993. Role of Vitamin E and Selenium in Host Defense Against Mastitis. J. Dairy Sci. 76:2795:2803. Janzen, J.J. 1970. Economic Losses Resulting From Mastitis. A Review. J. Dairy Sci. 53:1151. Kehrli, M.E. and D.E. Shuster. 1994. Fact ors Affecting Milk Somatic Cells and Their Role in Health of the Bovine Mamma ry Gland. J. Dairy Sci. 77:619-627. Kelton, D.F., K.D. Lissemore and R.E. Martin. 1998. Recommendations for Recording and Calculating the Incidence of Selected Clinical Diseases. J. Dairy Sci. 81:25022509. Kingwill, R.G., F.H. Dodd, and F.K. Neave. 1979. Machine Milking and Mastitis. In Machine Milking. Nat. Inst. For Res. In Dairying, Reading, England. P. 231. Kitchen, B.J. 1981. Review of Progress of Dairy Science: Bovine Mastitis: Milk Compositional Changes and Related Diagnosti c Tests. J. Dairy Research. 48:167. Ma, Y., C. Ryan, D.M. Barbano, D.M. Galton, M.A. Rudan, and K.J. Boor. 2000. Effetct of Somatic Cell Count on Quality and Shelf-Life of Pasteurized Fluid Milk. J. Dairy Sci. 83:264-274. Martin, J.M. 1973. Milk Yield Interrelati onships With Somatic Cells and Chemical Constituents Over a Lactation and During Restricted Water Consumption. M.S. Thesis, North Carolina State Univ., Raleigh. McDermott, M.P., H.N. Erb, R.P. Natzke, F.D. Barnes and D.R. Bray. 1983. Cost Benefit Analysis of Lactation Therapy with Somatic Cell Counts as Indications for Treatment. J. Dairy Sci. 66:1198-1203. Miller, R.H., U. Emanuelsson, E. Persson, J. Brolund, Philipsson and E. Funke. 1983. Relationships of Milk Somatic Cell Counts to Daily Milk Yield and Composition. Acta Agric. Scand. 33:209. National Mastitis Council. 2003. SCC Regul atory Limit in US to Remain at 750,000. Newsletter. Verona, WI. Volume 26, No 2, p 1. Neave, F.K. 1975. Diagnosis of Mastitis by Bacteriological Me thods Alone. Proc. Seminar on Mastitis Control. Int. Dair y. Fed. Bull. Reading, England. Doc. 85:79. Oldham, E.R., R.J. Eberhart and L.D. Muller. 1991. Effects of Supplemental Vitamin A or -Carotene During the Dry Period and Earl y Lactation on Udder Health. J. Dairy Sci. 74:3775-3781. Oliver, S.P., M.J. Lewis, T.L. Ingle, B.E. Gillespie and K.R. Matthews. 1993. Prevention of Bovine Mastitis by a Prem ilking Teat Disinfectant Containing Chlorous Acid and Chlorine Dioxi de. J. Dairy Sci. 76:287:292.

PAGE 71

60 Ott, S.L. 1999. Costs of Herd-Level Pr oduction Losses Associated With Subclinical Mastitis in U.S. Dairy Cows. Proc. Natl. Mastitis Council. Natl. Mastitis Council, Verona, WI. P 152. Paape, M.J., A.J. Schultze, A.J. Guidry and R.E. Pearson. 1979. Leukocytes; Second Line of Defense Against Invading Mastitis Pathogens. J. Dairy Sci. 62:135. Phipps, L.W. 1968. Electronic Counting of Ce lls in Milk: Examination of a Chemical Treatment for Dispersal of Milk Fat. J. Dairy Res. 35:295. Ravinderpal, G., W.H. Howard, K.E. Leslie and K. Lissemore. 1990. Economics of Mastitis Control. J Dairy Sci. 73:3340-3348. Reneau, J.K. 1986. Effective Use of Dair y Herd Improvement Somatic Cell Counts in Mastitis Control. J. Dairy Sci. 69:1708. Schultz, L.H. 1977. Somatic Cells in Milk Physiological Aspect s and Relationship to Amount and Composition of Milk. J. Food Prot. 40:125. Schutz, M.M., L.B. Hansen, G.R. Steuernage l and A.L. Kuck. 1990. Variation of Milk, Fat, Protein, and Somatic Cells for Da iry Cattle. J. Dairy Sci. 73:484-493. Sheldrake, R.F., R.J.T. Hoare and G.D. Mc Gregor. 1983. Lactation Stage, Parity and Infection Affecting Somatic Cells, Elect rical Conductivity and Serum Albumin in Milk. J. Dairy Sci. 66:542. Smith, A., F.K. Neave, F.H. Dodd and G.C. Brander. 1966. Methods of Reducing the Incidence of Udder Infection in Dry Cows. Vet. Rec. 79:233. Sordillo, L.M., K. Shafer-Weaver and D. DeRosa. 1997. Immunobiology of the Mammary Gland. J. Dairy Sci. 80:1851-1865. Webb, D. Unpublished data. University of Florida. Gainesville, FL. 2004. Weiss, W.P., J.S. Hogan and K.L. Smith. 2004. Changes in Vitamin C Concentration in Plasma and Milk from Dairy Cows After an Intramammary Infusion of Escheria Coli. J. Dairy Sci. 87:32-37 Weiss, W.P., J.S. Hogan, K.L. Smith a nd K.H. Hoblet. 1990. Relationships Among Selenium, Vitamin E, and Mammary Gland H ealth in Commercial Dairy Herds. J. Dairy Sci. 73:381-390. Wilson, C.D., R.G. Kingwell. 1975. A Practical Mastitis Control Routine. Proc. Seminar on Mastitis Control. Int. Dair y Fed. Bull. Reading, England. Doc. 85:62.

PAGE 72

61 BIOGRAPHICAL SKETCH Jessica Elizabeth Belsito was born in Millbury, Massachusetts, on January 31st, 1981. She grew up surrounded by th e dairy industry. While in Connecticut, the author spent many evenings and weekends milking at the Universitys dairy, which further solidified her love of the dairy industry. She was also heavily involved with the Universitys dairy club, Block & Bridle and Si gma 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 Sc ience in animal scie nces and a minor in dairy management. Immediately following her graduation, she moved to Gainesville where she began work on her Master of Science.


xml version 1.0 encoding UTF-8
REPORT xmlns http:www.fcla.edudlsmddaitss xmlns:xsi http:www.w3.org2001XMLSchema-instance xsi:schemaLocation http:www.fcla.edudlsmddaitssdaitssReport.xsd
INGEST IEID E20101124_AAAACL INGEST_TIME 2010-11-24T19:25:18Z PACKAGE UFE0011398_00001
AGREEMENT_INFO ACCOUNT UF PROJECT UFDC
FILES
FILE SIZE 23292 DFID F20101124_AABZLO ORIGIN DEPOSITOR PATH belsito_j_Page_25.QC.jpg GLOBAL false PRESERVATION BIT MESSAGE_DIGEST ALGORITHM MD5
ab96721feb55de8515915d0b862afd80
SHA-1
fee89c7a89bd30396cbf71fbb652784d445a6850
109162 F20101124_AABYZT belsito_j_Page_24.jp2
6b3aae5d6cf7502a867d01e2bc4366d4
82ebe13c161036478569fabc94ad313e6e3f77b8
25271604 F20101124_AABZBT belsito_j_Page_05.tif
017ab3af9aec0d5c4901d1a3e00402cc
f7a9c0c4d9d0da16245485d52ff9100c0097d799
47120 F20101124_AABZGR belsito_j_Page_66.pro
c70b334d26ede16599578538d1297807
072cbe1cfcbf39bfbe6c6427bac1b8c3fd435622
4739 F20101124_AABZLP belsito_j_Page_33thm.jpg
cd2a7d67eef910a5093e2bdda9be89c9
573c7a746dc036a771f3571b6e11a75be58acfba
109252 F20101124_AABYZU belsito_j_Page_25.jp2
0297f02c2ef958a256b86fd623868acb
cc76741f7627457e0fe1928cb5f9bf757ff0b9a9
F20101124_AABZBU belsito_j_Page_06.tif
411f0b6b062138b228e5faf984cec46d
a5fcb6893367d3c5c55a96e88189e89d3c9bb97f
49309 F20101124_AABZGS belsito_j_Page_67.pro
7602f53b6989a3bde1cbbd2259bc7c71
a156b0297132a277b7f9cefd9a6e31d8a6b84331
9661 F20101124_AABZLQ belsito_j_Page_07.QC.jpg
4f0a5df75b79553d057e2fe05de93c5e
adedb8adadb5d7e7d7f68fe237479077ee8ef040
110743 F20101124_AABYZV belsito_j_Page_26.jp2
47675b04298c43152a5091d38b300679
23faac3a2f614442df83fdb4f840076b6426509b
F20101124_AABZBV belsito_j_Page_07.tif
9ae75e442a166f846dbb281b7391a03b
e8732b98975c7666c4c1aea97b62e95911168ae4
49113 F20101124_AABZGT belsito_j_Page_68.pro
e6d18cde04021d25c1e046b87fd7432a
a960560021ddde14b440957e96fb73b4f7125acf
24434 F20101124_AABZLR belsito_j_Page_71.QC.jpg
ae0f43a4a67d8797c94e7a16d11f75b5
0bda6fe0426da4f677f8712aa215900c3511959e
103903 F20101124_AABYZW belsito_j_Page_27.jp2
882b855ec6ae17797dd784458b009d5c
55302775bb846f9284153ad0b73f125bf9788b13
F20101124_AABZBW belsito_j_Page_08.tif
b316176055e4619ef24fe97d6ddad85d
5608bc2bf4c1c257619c7d43670f2e050d03d0f8
20982 F20101124_AABZLS belsito_j_Page_42.QC.jpg
396fe3f7e46ee8ab6ccc5f7f2f4cdb48
be3653a8fb56f7633ea29777dcc69f5a3d28d7a0
109807 F20101124_AABYZX belsito_j_Page_28.jp2
4d52e2d7f740eb72093685213a1275a7
e06f45b916e4f3c5b5ad8ef17b8c6d11a71bad40
F20101124_AABZBX belsito_j_Page_09.tif
01bcc2631a86dbe4f84ef645a89736fd
268e1368dadee011620494d307a17af1ed17bb62
52686 F20101124_AABZGU belsito_j_Page_69.pro
bdde793ff1974edaa9169c8bdc73580e
1cbbc10745bbdaa25326a630840e83fef03d85c3
6846 F20101124_AABZLT belsito_j_Page_37thm.jpg
658923bc1702b253c2a9ca8cfdb07ad6
8a5b597736e1709cd95becafc0bb6fabc99c4074
104674 F20101124_AABYZY belsito_j_Page_29.jp2
d966f4ab28584185d57f7732b808a2a9
fbf6631dc83c58cd0349f57ff339f89e182ff668
1053954 F20101124_AABZBY belsito_j_Page_10.tif
cafe48581aaf88c8388eff85ce59ff29
cbbc1c4cd821b75108d63e34799d1ed86c105243
62566 F20101124_AABZGV belsito_j_Page_70.pro
68f69f8d713d345231f9d2a7d6521779
9f630764c9b6009ebff4c647cc5b70ce7d5f845e
70952 F20101124_AABYXA belsito_j_Page_22.jpg
272e5ece1560eab288334e7c42ddd005
54431f52dca08da5bc733ba188436c778f111584
6865 F20101124_AABZLU belsito_j_Page_38thm.jpg
dd8bae4547a7bd3240afbfe1ef281f37
d922de12c32791db696386e1eb83fd7000dab768
106089 F20101124_AABYZZ belsito_j_Page_30.jp2
103468d35c0b15eee395066ee0f8bcaa
79014649d2fcb11291d65bb04db98d4f073e6886
F20101124_AABZBZ belsito_j_Page_11.tif
2df48d2ccb264418b769c451eca2bc82
5e0630f691904c80eeb7c0357eface1fe3373406
55293 F20101124_AABZGW belsito_j_Page_71.pro
fd2bc3b4150bda0e40c1159b0b9fdae3
7f7d4b11542484f9161ba7fe13dc7e017136a30b
72624 F20101124_AABYXB belsito_j_Page_23.jpg
5ac5c04da675c0f50d6fda5392250c37
260c98d3ded55cf77c6ee59343da2ec06c38f6d3
5630 F20101124_AABZLV belsito_j_Page_12thm.jpg
762724d51421e8e55e29dc540af5ad6d
90232dd11ad5f1d9e691297f67f8dc8795b886eb
20665 F20101124_AABZGX belsito_j_Page_72.pro
5ffe6dc96ce5b81d35340215899e8a8b
d94ad5c88e4fac856e25e8f5ce49b3fa143b003c
72095 F20101124_AABYXC belsito_j_Page_24.jpg
8bb1895dcfcd9522661124b7a34180b5
cb9e4a09d3844c9d87ab5375ba8ca12ba927db42
21606 F20101124_AABZLW belsito_j_Page_51.QC.jpg
b4b0866b1fd70e0d2a1f30dc350f78b4
8cb1d31b74fc9beb3ce7e00080aa5873f7478be8
F20101124_AABZEA belsito_j_Page_66.tif
401cda8c965638da5149b947971c7523
1c0faf38789f41c26dd1fa5213081227822eac0f
467 F20101124_AABZGY belsito_j_Page_01.txt
7d6a0560820db953a26ff6cc3dcd814c
446368e03478238ddc42274ec77ef4364c5212df
72105 F20101124_AABYXD belsito_j_Page_25.jpg
d0e6dc2c1b6e9791ed4db78678a3f8eb
34ef92132afaffef51b337874548f298f71abfa9
23353 F20101124_AABZLX belsito_j_Page_26.QC.jpg
7bb4ba8cfd7546d46d4c6a66f33f4844
aa3a1c26ba380499d3a5d99f4ae5131fc341a9f6
F20101124_AABZEB belsito_j_Page_67.tif
0ac3ae118e06e4bad341c6e9aea201a3
43d8ab46fb57d1035a78b39e95ae3613b52a16ec
227 F20101124_AABZGZ belsito_j_Page_03.txt
bc47e3472136c66558bcc3d935107f06
c27cd5366cbeb935cbdfc5540428a9c851b8f24b
72834 F20101124_AABYXE belsito_j_Page_26.jpg
7dbbdf9893802df736c82599d98a2482
3c4ca35ef394f0bd4158b13d7dd48a69ae33a109
20846 F20101124_AABZLY belsito_j_Page_59.QC.jpg
8938ba96aa1d140b6e54680fdef51ccf
973879893e442974686741ac44e679645de4dda6
F20101124_AABZEC belsito_j_Page_68.tif
12919316cf3cd5480ebddb83ce5f8246
3c2074b54bbcc9c4fec84888ad1fcd45fc781e51
67877 F20101124_AABYXF belsito_j_Page_27.jpg
4ef5e66920269ee57621ef9274c4b410
ab8691a478e84d1853c71bc9d2d902bc35586d41
1859 F20101124_AABZJA belsito_j_Page_58.txt
ae61b9acf2d42d4bd6c97d0aadbf48e0
8c251f4e6a75c3b8e700c890e9d7db649a0d4597
F20101124_AABZED belsito_j_Page_69.tif
79892019560433af5aca8b798d1a7a05
448f598212258ab4ad05abacebd961fe1f6af350
23580 F20101124_AABZLZ belsito_j_Page_23.QC.jpg
8c4a357c22c70c25189eb55fec11a188
36da70768095fc37f0859228aa300a86a5da5814
1790 F20101124_AABZJB belsito_j_Page_59.txt
58a83f9a05fa665d9ef5366f7288831b
83c212a216657946a8e5bf80155f6f7562a96d02
F20101124_AABZEE belsito_j_Page_70.tif
44a9899804b283746a48d3577665b1f8
f99094742cb3591cd4306987b66aecf28a201bbc
72920 F20101124_AABYXG belsito_j_Page_28.jpg
c47708784f38c98e32036a9a6f303877
a134cb073eca38befb198cc1450677a098bd92bf
1760 F20101124_AABZJC belsito_j_Page_60.txt
9232d8b6051e68eb71316f3847e13bf8
f1df7f24ec4964f530931dd6e88e626c7de1900b
F20101124_AABZEF belsito_j_Page_71.tif
d88ec40378fc07eb64f39e19f8608891
fc1cc68d6ad9464bcbd42f7d0b7e3a94945b7cc6
69453 F20101124_AABYXH belsito_j_Page_29.jpg
e8475a329047b3c52c3685a81e8d0b01
b4ed0f9de726c7483ea444e64065a1e29809d424
18801 F20101124_AABZOA belsito_j_Page_15.QC.jpg
7b246952620af9b7a6dd34a276483296
b52897f612179b6c8834624648962c8c575bbf5f
2099 F20101124_AABZJD belsito_j_Page_61.txt
ae3c7807148774c85162b6c0f8c591ff
f726325165ba8a3badccbbe01fb506ebc65e0e71
F20101124_AABZEG belsito_j_Page_72.tif
6e3fa9576e1b98ce94b967f109a05f37
2ae24603352e80bbda23fc47cbea99154a467760
67534 F20101124_AABYXI belsito_j_Page_31.jpg
6d3407fcb1c1e0c5d41c9f2147df6a69
3a33f8cfc3ae7e0c896c0e79ad075fa8777196e3
5332 F20101124_AABZOB belsito_j_Page_15thm.jpg
47d4d0978cf584b6e16180600e559d6b
a779e02f280093af1bcb310371325427b72949cc
2007 F20101124_AABZJE belsito_j_Page_62.txt
dc205f4950a0a869a1d4c1d44864e1eb
aa272a5f9d7ecc3c02edecdf38cd650c699998e0
8271 F20101124_AABZEH belsito_j_Page_01.pro
de2e2cf0c5fc17056e8232ad7258cab1
e6184779714c8f4797730a3bc210e452a566b829
74245 F20101124_AABYXJ belsito_j_Page_32.jpg
7aa79b5cc2b207496c9e4508ce75531a
2ade024976dde0e6e3d048db7f9514831fc9d431
6569 F20101124_AABZOC belsito_j_Page_16thm.jpg
888dec1c06dfc3c3c33c9066f6b0fa5c
a5f897c4ec3e0f383213e9cc93cb8800e427c71f
1942 F20101124_AABZJF belsito_j_Page_63.txt
1085aeccf14d647e590f7f6e21e7499b
1ee1447c074a985937eaca63a911b96644be313a
1434 F20101124_AABZEI belsito_j_Page_02.pro
6af6f879c86ca21f4dd1cb89f7f5394a
27a1c9fee94cf6273a7f399b7516ad9c4d18bd74
48352 F20101124_AABYXK belsito_j_Page_33.jpg
7ca56ba9c55144cbda1b7011f7b568df
a9dda3e7abf99a10a03a1d7514e38bc6f7b8e7ac
6344 F20101124_AABZOD belsito_j_Page_18thm.jpg
50c8b9613bfdc04a990661fbe8a9e172
ad531a2cd4039d11589a3aed6c5bfd6d149585ab
668 F20101124_AABZJG belsito_j_Page_65.txt
1ea1eef131a888af8adc7e7216718d89
ee780196e9f5f1a07dc14588c3680cf000d87753
4409 F20101124_AABZEJ belsito_j_Page_03.pro
8b638392347a8978d7af15ea179aee7d
782eaf73eb58fbcf6d181126f1f0b9304dc62c8d
62106 F20101124_AABYXL belsito_j_Page_34.jpg
3027e2dd5cdb2c208281288135582d8c
5d2bbd41c65bb1db1a4eecc97c538da92e4a0c86
21328 F20101124_AABZOE belsito_j_Page_19.QC.jpg
82d7926f19dc0547958f399f108c0892
5af698e2f7ea1f434d73aa6caba1047df5ad688b
1946 F20101124_AABZJH belsito_j_Page_67.txt
3fbe34265121a3b06f2b1595140a66eb
d0633ef5e07c3fd00e736abe94cdd5a7efa9c18d
31523 F20101124_AABZEK belsito_j_Page_04.pro
3c3776e6607a698701c57f50fa9796b8
3d033c0382222feb87abe33eb3272ae23710bce6
64588 F20101124_AABYXM belsito_j_Page_35.jpg
8f32ce4df7095c64ee8687e070fd6eb9
a6ccf72d16bfd7564daff21cadf5b2196ab1a9b9
6351 F20101124_AABZOF belsito_j_Page_22thm.jpg
e8f57d70c703bfd34cad2e80eee8dd87
2a97157dc1715fb8aea595e1b7afb029ab834b6b
72498 F20101124_AABZEL belsito_j_Page_05.pro
daa981c1e6a9a8eba8417618d690033c
c307c95bdaef9d03af858d591d8c7031a0b55d5b
61806 F20101124_AABYXN belsito_j_Page_36.jpg
9ae9bb78f967df961d935d3be3e4d91f
bfd5e2f4e15762a28d3673ae2e71e47fa903230b
1939 F20101124_AABZJI belsito_j_Page_68.txt
fa055a7078fedcb798b51a9ef024c0ba
eb72b7e808784251daaf8d522de92011dbdcef9d
6470 F20101124_AABZOG belsito_j_Page_25thm.jpg
d39c1c1d296f0f5f3dd702815ac14eeb
0a2cc0c3268febcc7fe51fff55753bcfe665035b
17445 F20101124_AABZEM belsito_j_Page_06.pro
0ae86b273084935a01f1c158a6eb7f42
b496b7bdd47cc9e48e836273c4489a6598064a91
69700 F20101124_AABYXO belsito_j_Page_37.jpg
f520a99968f562721841bda05fa2923f
b4ba7a2192ec4b8f2cc8d8d625cbc21834eb52ed
2137 F20101124_AABZJJ belsito_j_Page_69.txt
1272216ec77b90f86b346aa98053439e
690f47470790ad6d53a0c0ca768ba8533dac7dfc
23517 F20101124_AABZOH belsito_j_Page_28.QC.jpg
80c3a5c177cf62dc0b4ad1f4c66dbaad
e6c8ce31cbacea4dbc45d6ca881944b3d0959c2b
21694 F20101124_AABZEN belsito_j_Page_07.pro
c25ef6d731ef7c8914e5a5a588ccaca6
9ce85a2221db09c49d6ee2971d6b37d17b272dc5
39925 F20101124_AABYXP belsito_j_Page_39.jpg
64b5a2c0700b5d45c6d3aeaacf8f6f9a
cb4e71379adfe05951c119f8ef10e4a981729497
2523 F20101124_AABZJK belsito_j_Page_70.txt
4bb0b0285033f78c999c22fae6c9500f
e696103728afa43fb7df64ea255b0a3d15d3aeb1
6753 F20101124_AABZOI belsito_j_Page_28thm.jpg
5440106a2649af609bdf640919bfe8ed
ad0eb0f14a8745f7b7885a585670e35eccd05d5f
54507 F20101124_AABZEO belsito_j_Page_08.pro
168463cf364b14112226c9a179d96c72
cb05ce0e2c10f8aac83243cde04a2bb4cb3d67e1
60886 F20101124_AABYXQ belsito_j_Page_40.jpg
0c7af6366f6edf5624ee4806ce8e0ef8
85e2aed10a8b48be0360e99f7f64e8be9a12159e
2238 F20101124_AABZJL belsito_j_Page_71.txt
6d8ad6b2896658526e75d432111f2041
7c9d31e7d7add6567bb7cbbeb822ed487123deee
20306 F20101124_AABZOJ belsito_j_Page_34.QC.jpg
e6d8caf63d635143b5f54799d07bdc2e
cc83f5f7dd69e8061f466258ed003976b044ed38
5687 F20101124_AABZEP belsito_j_Page_09.pro
52bcf118317f58e8d0e224dd3542c93b
ca1122203881cf430696748748b9f8ea6f682c7b
68036 F20101124_AABYXR belsito_j_Page_41.jpg
df8c4d2760976d4e0cd7f84b15a7bacc
421d597a85448cb8d8c1779e7a541306963beaa7
877 F20101124_AABZJM belsito_j_Page_72.txt
68bbb0c1012e50c29ec97438f98b43fd
20f1f052e27cdb4803f9cf8eb476697882900281
6198 F20101124_AABZOK belsito_j_Page_43thm.jpg
00c2f7b9f32b5fc2640ba081f6cd48d8
37ac2e045fc1496068dc71ddd7c5de59ee712df4
38510 F20101124_AABZEQ belsito_j_Page_10.pro
ebca79e11387d1f0af03165eafe8a96a
d5d90052e4cd3f74d7a3324bf3e259960ad72ffa
65423 F20101124_AABYXS belsito_j_Page_42.jpg
90bde6e30c54bbfcbb707dde58e13aba
61982c259bb455e08b2a716c2a9f2b15b71dd2ec
2401 F20101124_AABZJN belsito_j_Page_01thm.jpg
0a6befc527bc242e0c1f9c7c59705175
9431f1302ecc38643b6e26664e8fcf2be364a41b
6412 F20101124_AABZOL belsito_j_Page_44thm.jpg
ad6cdcff93003e0bc2f86f23bec90df1
ad64d682abc36dbbaa656543726fa3d2b52ea5aa
47422 F20101124_AABZER belsito_j_Page_11.pro
343ffe3c46c5dffd007a6ba1b8f1d4ac
4bb15af1f2dce89b7de1460ecc90269ae814c2f0
58677 F20101124_AABYXT belsito_j_Page_43.jpg
ef43882acb396d16d11e56ba4e31c39a
cf615841db8aaa86db7b40ea48f60426112abb66
268436 F20101124_AABZJO belsito_j.pdf
0a0f1eb0be6f946404ef73880a7e3928
26e4c8abf77da0dfa4d2e68e3960df96135c46f9
14671 F20101124_AABZOM belsito_j_Page_45.QC.jpg
b33760e98212dd4ea9372955be4e2489
047292b78860d3b69d0d658ac166beaf35e3df67
71264 F20101124_AABYXU belsito_j_Page_44.jpg
a3250fbd411dbc59f1b15fac22a52bf3
a4be8e548634c1cce873c8669588d5c592fe9089
16593 F20101124_AABZJP belsito_j_Page_04.QC.jpg
9eb8d276b823117018625de182ddf9be
47100e377c219714281a14b5f91af4549deef7ba
19581 F20101124_AABZON belsito_j_Page_48.QC.jpg
32213db2c37b2b351361561b4b6357da
f606fce139aa876128d6e510826ada98ed14be16
41223 F20101124_AABZES belsito_j_Page_12.pro
97e8aeb0488255ec18df7dfad5be7313
fc06ec271d4797ccbd11a5070c8f5ba9b75f655c
43385 F20101124_AABYXV belsito_j_Page_45.jpg
24fef72d3bd2f58af9cc2bb91fb2dd5e
a7fc8497c7152db524050b70c0966a56a2247d5b
6180 F20101124_AABZJQ belsito_j_Page_41thm.jpg
b1d9167de2993e0c388f27b7705a72ab
baef5f5c40adc6d6146badc1a8b3efed6b38c658
18549 F20101124_AABZOO belsito_j_Page_49.QC.jpg
57394e6f843298de3e0f065b613fe95c
b2c2bb115412e941b4b3ea09a889740211b7f1c6
51141 F20101124_AABZET belsito_j_Page_13.pro
f448b6e79aac5498b8c91d147b0a38ec
640ea7c7bbdfd969ee01648a290bf352a7ad3a4d
48468 F20101124_AABYXW belsito_j_Page_46.jpg
4f7c3dc77707bef36142ce43c881fc1a
294279c1ee2216cd2af71b7b2943529f55158bb9
6295 F20101124_AABZJR belsito_j_Page_42thm.jpg
944df2fce46fcb0b9b4a0d67d0292a8e
c3e554816bbe95d24da1f96b794be45d1889f300
5640 F20101124_AABZOP belsito_j_Page_49thm.jpg
0c47127be25dbea88dd6488a6a6e9996
54ac7db4033eb18887ba299aba08c21ab17486d2
18632 F20101124_AABZEU belsito_j_Page_14.pro
207cbae34f73e23f07189d78560491ba
eeecbbc4533b8d3c99b4e26ad87516e5244887f9
58425 F20101124_AABYXX belsito_j_Page_47.jpg
ad9cf0f426dab8a82a98180903db85f5
b63baf9cb1d1e228a0d73ef317cd09388bb57d52
3070 F20101124_AABZJS belsito_j_Page_65thm.jpg
c83b5cd440f028a9a348d15f5efcffbe
a4ef91894c9f3f6ffb477bc409467b8bb6229d15
10681 F20101124_AABZOQ belsito_j_Page_50.QC.jpg
d28ad45e1ec7fbd3f4ebcc52cc77036f
b5832ab2371f3c2b59dc8cd320c69045788d2268
39843 F20101124_AABZEV belsito_j_Page_15.pro
bd87ac16c65993f49a4cb48ab364deb9
14aa7b5cb3b810eac0b05824546164eb6bd940f1
59650 F20101124_AABYXY belsito_j_Page_48.jpg
43d73a0b69284a26dc3a19b6520c4afc
a5ea049b6599af3433628384aa6fe46ca7747a88
20138 F20101124_AABZJT belsito_j_Page_12.QC.jpg
2936e561a449d50199ca82c159a6360d
38f9ee4e1b3365c41138464577eec11d01124db3
6330 F20101124_AABZOR belsito_j_Page_53thm.jpg
70a8005982bc9f2353b775cad8f90cf5
184ae30d4e19b37fc32764107a624d2745fa9fd1
50139 F20101124_AABZEW belsito_j_Page_16.pro
94e874f5fdb47503e9477318091efe1c
98ee4e5f3ed96405920be2853050baa3ef8d37df
56883 F20101124_AABYXZ belsito_j_Page_49.jpg
56aa67a12186dcb50a0cd44ba069a0e5
ab2efd896721f8d5cd046b7fe71baa867e238f7d
6040 F20101124_AABZJU belsito_j_Page_59thm.jpg
947e55326778d626bd9b4a5ffe7a533a
e2de66dd13dce22e77a83afef74936beec2d9c1a
21915 F20101124_AABZOS belsito_j_Page_56.QC.jpg
c904879f8f2486046fa62178ef17c119
06efc91450b8ac7ae4734e8fe933f6fa7d26510f
47640 F20101124_AABZEX belsito_j_Page_17.pro
b93d80f73a3a7f4a6f8fab8a8eaedc38
4b30fb182b0a48498f3d31025cc058add2c7419c
23786 F20101124_AABZJV belsito_j_Page_24.QC.jpg
f251378a843b6189b95f728247dc4850
b2e6406b746c4c3730a5e89d16cd2ab544ed32e8
6118 F20101124_AABZOT belsito_j_Page_58thm.jpg
ff95ae09adc882573b7339909c9e9dbb
c39dfa4e6eefef6ad09b17eadb3d13d00c99bf9a
47957 F20101124_AABZEY belsito_j_Page_18.pro
147ad5acb8ba0f6cf4c005b640e5c7f5
b6869fd76b7f26ffa6ab8162c31a21e1455a7f8c
19091 F20101124_AABZJW belsito_j_Page_43.QC.jpg
f0efabf10e560504cf6a38fddd9a3f37
13cfa6939625cb09d891f05952deb68072cc6ca9
F20101124_AABZCA belsito_j_Page_12.tif
54865c89a9e61a94aae7b840b158d37d
360bd7d5304f5485dfd0e17e3cb8eaabd2565a8b
21547 F20101124_AABZOU belsito_j_Page_60.QC.jpg
4ab12a340d89dd294d6cedd95c425b16
5b34f76b6715543fc5e0d223bd8f8ea5e19a0f7b
45082 F20101124_AABZEZ belsito_j_Page_19.pro
082b43058e1edb309734ad2e8f745923
a570bf0eeaecabb990f7c2d85003c19cf330db00
F20101124_AABZCB belsito_j_Page_13.tif
4a48234c47a13befa7bfc68b6d10bfe3
b650fffc7fbee423337f3b7642be8dcc27382c7e
23638 F20101124_AABZOV belsito_j_Page_62.QC.jpg
356ec76a09e9ff3d4b73ea033f78645d
946ded8881b754738c301655198948d5dda91f62
18631 F20101124_AABZJX belsito_j_Page_47.QC.jpg
73751935f6428bc075e1eb00a7edcb1d
ff3ee94063313dee2a076a5101da445b9135322f
F20101124_AABZCC belsito_j_Page_14.tif
b4975654151ac43bd117e76d5dbbe071
ecc93fda71d3486da60098b4c3446443b0aca384
6185 F20101124_AABZOW belsito_j_Page_63thm.jpg
bc3dd943c8ff4150881eaf5d4bf7f752
5b7e120136f56a1aeb9902243071467c229af90f
1326 F20101124_AABZHA belsito_j_Page_04.txt
5fea993286b5388c2accc584026ebb13
bc719a6ad082a47147922fe19b7f9643ac2251c3
5649 F20101124_AABZJY belsito_j_Page_64thm.jpg
2cfc2c9ae3448def5ccb2048e7c4b5c9
2cc1ead533e3fb0f3a2f70e151384de2a366465f
F20101124_AABZCD belsito_j_Page_15.tif
295c0c46da16ca11cf024a3d315e678c
9a98d615c0ef7309c7e951b84dfcca061060ceab
6194 F20101124_AABZOX belsito_j_Page_69thm.jpg
61a2a13378b505c559c24b68cc551f17
cf8cd47a517de50056b5f480139ea1c6970f79ce
3043 F20101124_AABZHB belsito_j_Page_05.txt
376a278be25d772d396489109fbb44ea
eb0629644dc27fa27ba552914cda2ba9255a5a9b
6792 F20101124_AABZJZ belsito_j_Page_55thm.jpg
1f79ca4e5ca5338bbc75ef31d57f0b65
204cbd4d38338dcaab49dc6dd6846027572f36ad
F20101124_AABZCE belsito_j_Page_16.tif
abdfeb85b4ddf945e1d514c08d4c509b
bb9123b9b4a94c5f3fe3556db6a80f49aee1960b
12094 F20101124_AABZOY belsito_j_Page_72.QC.jpg
55b9d0cea32cc7ae07e546b9948b2fc9
e375256e2233a9df68ab9ee9798b49918f742186
698 F20101124_AABZHC belsito_j_Page_06.txt
d78d2cbb723491e1566b76e40718945a
458b05b49542fd0e1406fd02d538b9b487d4bd49
F20101124_AABZCF belsito_j_Page_17.tif
e143ba94e5016ad6ff5e72ae3e3a9c94
4e7a1f951953e4b85b562cfcdc7ec89e5710c2c5
3683 F20101124_AABZOZ belsito_j_Page_72thm.jpg
e6112ae72644a03c3b9987e789e3bd0d
4ffef0007b8f62ee9214bc948a6c293edede3d3d
24290 F20101124_AABZMA belsito_j_Page_32.QC.jpg
5baae8685f724edb729950e82c53aff8
5d2b10c4fba074d11bf93bff53a75151d89c88e2
914 F20101124_AABZHD belsito_j_Page_07.txt
ea8afd94167a258430ad0a9cf5478dc8
eef975f6f4532695c02e4636f26d5e499be22ba1
F20101124_AABZCG belsito_j_Page_18.tif
20633a64bdd942b6ae8e9ae5d376a903
41deae31d68495da63e169d6b755700c0f70bf80
23694 F20101124_AABZMB belsito_j_Page_54.QC.jpg
568df4636534aea3f30d7613779d8185
a71e60f8d84e0f8bb694337255426f95349df172
2181 F20101124_AABZHE belsito_j_Page_08.txt
60e484e327db77e73db68e69eeb94993
f3bd522e83823e3ce99392a010ed12a1c32c621f
F20101124_AABZCH belsito_j_Page_19.tif
bc5535c38dbc17123662c549114a5476
bb30acdb60a0bd14ac6b456d7ad11172ad2513ce
20669 F20101124_AABZMC belsito_j_Page_61.QC.jpg
f7a6614326de108800f8230af8649d86
5de7b1becfd542e3fafcad9e9cda8917359f78e2
F20101124_AABZHF belsito_j_Page_09.txt
c8961ad60e8620fe949977828151d5c6
de16fb13580b07a4a3ecd85101daeeda2ed62f4c
F20101124_AABZCI belsito_j_Page_20.tif
3d5923bbcb524688e438500a20655b0f
48870d36e6adb70dcc843750b99d4e23d5d582a6
5636 F20101124_AABZMD belsito_j_Page_46thm.jpg
ddb7b3c394d6745b798ba10af1d1f300
5bdd51f4e417f635648396c28462d30c394f19db
23233 F20101124_AABYVL belsito_j_Page_44.QC.jpg
e2f38ce8d9f5742cbaef7976e3bdbc8c
a0266aee012eaa6d3b90e2d4d1d620fd62e1c58d
1883 F20101124_AABZHG belsito_j_Page_11.txt
5db69699d8acea9bcbcb5b0018938766
b03737945f1adacd8084a31541d1aa94c2c5da71
F20101124_AABZCJ belsito_j_Page_21.tif
44c4dc39624a0945458607724c7da1d3
95d9c3aa3dbec2a1a0bc123d4422ae242ef758bd
6563 F20101124_AABZME belsito_j_Page_36thm.jpg
501a6fe562991587569e0aac578e215a
017e8e5d2627a4495f9b5fe2ed15abba383b05b0
1711 F20101124_AABYVM belsito_j_Page_10.txt
d764edd4fd37bf4362ede7b572dec8f0
c43379b4607964563cd3e1d886412a036f0f61e2
1712 F20101124_AABZHH belsito_j_Page_12.txt
92dcbc263a1274f730b5d8bf7f3c8d9b
b466be49244951691ddfb72099315906e33390ff
F20101124_AABZCK belsito_j_Page_22.tif
87740a7133bf20f810c855c4af62b89c
79c4b65aa3538ab7327206e39cc9953236f0d82d
21852 F20101124_AABZMF belsito_j_Page_69.QC.jpg
5491c3c282d9c09eb5e1bf64e63e3136
5f51ddc11ecac11456f3869fb5b7702481f1b524
128 F20101124_AABYVN belsito_j_Page_02.txt
5eccc31f0ddc7fc8f7040515dc549fef
1494bb4294eb4fcd107eddf613f8be754d0c1441
2021 F20101124_AABZHI belsito_j_Page_13.txt
7cb07c7d77aa92810987902679ab2aae
81cfd74854d9d3368cdddd1fc07ba98722a7f263
F20101124_AABZCL belsito_j_Page_23.tif
fc92a0b3e84114d8c677915725bbce93
aac0a852646f49a4d7a0f5773d0736f6b132e164
22605 F20101124_AABZMG belsito_j_Page_08.QC.jpg
0ba0b1eefe3a2eb2ca092ccc18d06705
f600f396a036f7db936d94a26c002466e3b56850
69976 F20101124_AABYVO belsito_j_Page_16.jpg
696c5ebde919cc5e78676095c369443e
8e19c2d158d182ff326148713aef90555addbfcf
748 F20101124_AABZHJ belsito_j_Page_14.txt
bc1efdced5d37c56e755c41fbac50ec8
f29ea35a8ef2f7eca5e3a0a73cd5b2510acbbd38
F20101124_AABZCM belsito_j_Page_24.tif
ed732a5f8e1ce9e051ec555eaeae0713
e16ae6d1f1f16566a23646d89cc0553fb24550e2
1393 F20101124_AABZMH belsito_j_Page_02thm.jpg
6649c00ff2513b5494abdbeae9e93abd
c399d9ed11acc6a10db7136b1f700749c056914d
70448 F20101124_AABYVP belsito_j_Page_30.jpg
0c889ba2d6a67199ceb63f4784c073d4
fb445919aacac8ac76ec39702e6be0187725474b
1696 F20101124_AABZHK belsito_j_Page_15.txt
9c25b8a0fbdf6005b4cc08475e66dc39
a2d0ce061b006689f29a629a76bf6c94d412654d
F20101124_AABZCN belsito_j_Page_25.tif
3dfa9a79c876b3c04063a03d532b4260
d7697592eab3dd0e5807b300ed73b6c37bb918e2
6385 F20101124_AABZMI belsito_j_Page_31thm.jpg
d2db878f434d020c91871ea66bfcda30
2941b381baba1e94c4d0c37bc4f86ddbe7b35db9
2014 F20101124_AABZHL belsito_j_Page_16.txt
8e2ce0b08291776e087b13554c7fea27
cc7ea8d927e670d0cfa3fbdda8e9f452583af074
F20101124_AABZCO belsito_j_Page_26.tif
e6a54cd27fe36e3796d03ffc3e296c18
60f53acd88d75b4d64457c847696d0d70a21577c
74350 F20101124_AABYVQ belsito_j_Page_38.jpg
cecaf7695c35fa38f99789c39d4eceaa
d8c751f41adbc59ec369cff422ef743a1f66d24b
22978 F20101124_AABZMJ belsito_j_Page_38.QC.jpg
1fd06a8d800abbd687a362d1c2b153f0
95c08280000e23103fb8a3c97724177de8e12007
1881 F20101124_AABZHM belsito_j_Page_17.txt
7ed60cc53eea0298f1e10e93a25868e9
154c21f876c16c1a7030f4b5f3acb2cf84d598ed
F20101124_AABZCP belsito_j_Page_27.tif
b174855093e58425f4705e4e81408f5f
13e9637327dca5e57335ca3dca64e4d92f7e2949
53226 F20101124_AABYVR belsito_j_Page_39.jp2
6eb69d3323f4de09916f17501b387d07
e941730edac6cfcb39bb02235678a874b8bf31b5
3396 F20101124_AABZMK belsito_j_Page_50thm.jpg
4446deb9fc2cfc0cf6fa73c14be95fef
8a00883830455624c299248387ced799e8729db3
1900 F20101124_AABZHN belsito_j_Page_18.txt
52e0bc594cb0b4bd95d83276baca3d50
4a3f9c6498f106a4063ac47637aee2f8b9f79604
43886 F20101124_AABYVS belsito_j_Page_48.pro
c329fe7031e4c9d91357c9187a3dd866
9fb501128be31cf815dbccfedd4a2f9a3e89de8b
5887 F20101124_AABZML belsito_j_Page_08thm.jpg
dd9fe0788e1170e5f4b44e09ee34d4e8
4b818d48fa224f6cc6e21e9d50c7601e11870d05
F20101124_AABZHO belsito_j_Page_20.txt
53ef416b675b2a80fdfa2622dc8438de
409a2b4680dde1e6f9cbf7ad1c8f3205a6f2b2fe
F20101124_AABZCQ belsito_j_Page_28.tif
1b5698929501bd6b9986cf7b61a6d5c2
b8d78f6d722585a594a9302fdce37510d662eb3b
21492 F20101124_AABYVT belsito_j_Page_35.QC.jpg
fd4ff16029d11155afa78609a107bea4
51c89e447eb74718f14fe5cd7d92bbc1554dbece
6427 F20101124_AABZMM belsito_j_Page_27thm.jpg
bbea8873ed2a15159be3900dfa755a59
c9da19c13cd1ff203fd963f5c1feb5dedba8e340
1904 F20101124_AABZHP belsito_j_Page_21.txt
a65db882b6ba1370b1361cadddfc98da
c66b8b396b514bd0261fde3dde44f483eacbf141
F20101124_AABZCR belsito_j_Page_29.tif
3bd1b6f5ab9d29583f3be29852af613d
391b1a45f062831acbf819bb35d51c83828eff05
F20101124_AABYVU belsito_j_Page_33.tif
91d65c3e0c40b7d38521bfaf96d05b62
5a3bfc48152da18aa3fb03be367a1959e04df506
20894 F20101124_AABZMN belsito_j_Page_36.QC.jpg
e8dfd5156a0ea00df8584d864c73f3b4
8d9e64cbf9cdc07217f07c6235fcc99a0b6b270d
1933 F20101124_AABZHQ belsito_j_Page_22.txt
d4ef63c1cbd3deed615f4cc4d45716fc
a92642ffa524010be2bd25eb86e66b15e12fa1d5
F20101124_AABZCS belsito_j_Page_30.tif
34118b8a9096094a6a3f6077d90221bc
386d36e1c9097ae6edb91d2a48c6435a84026603
5305 F20101124_AABYVV belsito_j_Page_47thm.jpg
98cbd366b6ef2c72c803a26db0933edb
6ea63d3a25967d6683e9af3e3ee28a481d751db9
5645 F20101124_AABZMO belsito_j_Page_34thm.jpg
44af398c23b1d9bc80d01b057c1eec7a
5d9097a683cd7f08dfded3c1704bde6848a4130b
2013 F20101124_AABZHR belsito_j_Page_23.txt
b699a8d7cceb0d55cffcf31ef9876ead
a49debfc1f75950a5411dff1ef9ab3ed751e48ff
F20101124_AABZCT belsito_j_Page_31.tif
e0e5e4f498efd221fdf22309bdf04111
ebccd061f2cd93737ea89083e4d960aa3398d05a
20494 F20101124_AABYVW belsito_j_Page_50.pro
3b0d0cc874dc87ba59584c333b2d023e
1043834cb70dee4cfbaec1ce7745c67ff88c2504
9595 F20101124_AABZMP belsito_j_Page_65.QC.jpg
e6547ca3823c8a42cf5e094b22e41e17
9c2e75f8d1786b3a8f30e6885e313b3ce5eb9aa8
2004 F20101124_AABZHS belsito_j_Page_24.txt
99ff627828ed5bc952f122d59182bd70
047ee2edd1d6d173270ce16dc01b129a2aa1b248
F20101124_AABZCU belsito_j_Page_34.tif
d775c33a3ff7a625909e039818cc04d4
ba994ba94e49e6210c36ba55ea9c45d3cbd62a93
2191 F20101124_AABYVX belsito_j_Page_64.txt
3ba2bc008fa6154f474024f3665e2490
afaceea1cd859f1a9f883656c3d0ed077d0e9092
16071 F20101124_AABZMQ belsito_j_Page_33.QC.jpg
3ef9c7e6c25f9d7d90fa52801b4c2d79
238528b680033c3815070b195a2ab06cf1e1dae6
1995 F20101124_AABZHT belsito_j_Page_25.txt
c7c972e98c798c9a372f716a487d0b51
d39ccb74a69066c1cd81a781281d33a701f5cf42
F20101124_AABZCV belsito_j_Page_35.tif
2289f8561189df0b4d2eaeaf72606c3f
a0b634cd83500d7a10fac6a0589abe1c7eea3a3a
1797 F20101124_AABYVY belsito_j_Page_19.txt
a969d15cdff6afcf3cada9936bbe26ea
de12647d297d0540b8684627b65523dac72a51ba
7118 F20101124_AABZMR belsito_j_Page_70thm.jpg
0d244a76ade21534bf5940a1dcf757a5
40a3ec347e78f9f97f41021f4082b9021884f5e9
2058 F20101124_AABZHU belsito_j_Page_26.txt
24135055f68f4653838b34a1d1a16005
83fe50c85d971e448ff94a866470ed8537939ab7
F20101124_AABZCW belsito_j_Page_36.tif
62fba9018dd2d61c4ccef06d9f09a94c
87758520a32672d9b6f1000433e9d3f10ab25d5b
29268 F20101124_AABYVZ belsito_j_Page_65.jpg
a14c18e9bf8aa35a351ad989801034b9
f0fa58a0f3feacb666ffc6c7d3f1c02fc23b49d4
22119 F20101124_AABZMS belsito_j_Page_21.QC.jpg
aae8b57e69acdc4afe6e1f6e6451934e
54bcc4cfb74e6caef0a9d3917b1ca00a2b90157b
F20101124_AABZCX belsito_j_Page_37.tif
8d8b51ea7d5091b5602cf6651622a680
bea68a10d9797b4fea791b7bd07c138fe59eed76
6602 F20101124_AABZMT belsito_j_Page_67thm.jpg
b70f2e12271b92028a6dd730b67c3729
e6c70869811cfe1b11b0c38bf5aa01ae15ff6a8e
1885 F20101124_AABZHV belsito_j_Page_27.txt
b19669293ac7b319d878f2b7f03c599c
20c0416eab1e8be509740a0fa397c88e491b9cf5
33505 F20101124_AABYYA belsito_j_Page_50.jpg
d6cc78d51c83cd6ecec2e110f382159a
37a58cc158281bf70d0cc11a31fb7faaa04456d4
102769 F20101124_AABZAA belsito_j_Page_31.jp2
ed004c34e3bb607569d351965c0498cb
9dc6fa590e4e54dcfe10cc98415feeb5cd78831e
F20101124_AABZCY belsito_j_Page_38.tif
d42340cbc95c93881dcf6c163e804e9f
ff2844a1417bec90ea8ba2c129c61603cd05f2b1
22635 F20101124_AABZMU belsito_j_Page_53.QC.jpg
a0c98ecda75b6e762d392cf92fb18196
0d2d811ffa101e4937d3e1cd8389fd4a2f6ddd9c
2051 F20101124_AABZHW belsito_j_Page_28.txt
e5343ae1ce136799a5aeacf6c4c57877
ab344c96e8fe0b05dcf44018891c949d9cfe92ca
67090 F20101124_AABYYB belsito_j_Page_51.jpg
e22a0c88afb81cd0caa5a1d5dbe3a760
4676c9c694d063e8655b2411727a6e4fa6a033ad
111743 F20101124_AABZAB belsito_j_Page_32.jp2
6c48eae07659161aad1ac9b098f7ad41
c0ee8cdefa61fbc46d170db04b816a5eb644d40b
F20101124_AABZCZ belsito_j_Page_39.tif
0723e40b5aef0c50d377c7fb65098d56
ed997e4d2a1fb33cd41e0eae93dbbd04cce68ac4
4752 F20101124_AABZMV belsito_j_Page_04thm.jpg
be54aff47ed957790e1be773872baf53
884b8bb24c49c36209b21997a8228e5a4f87ac7d
1954 F20101124_AABZHX belsito_j_Page_29.txt
54cd7cb2c8a5c2ce82dfd1df4e041b0f
af66be356f9874529a7a9c7f5cf7a6c7a8bb8073
66797 F20101124_AABYYC belsito_j_Page_52.jpg
45046fca636440df2b80eb10205c754d
472eec3b00cd9e0c5e7d71945b97f0facfd43b24
70549 F20101124_AABZAC belsito_j_Page_33.jp2
2194657d2dae57672fa129d3903a82ea
7fb51b5e3c75706ac1a3389d47f3e675d0a05298
6503 F20101124_AABZMW belsito_j_Page_29thm.jpg
57654c9eefb4ef6458b15face86784e6
f521d5e337f076c4a5b6a77f6329ef4062e6ce3a
1978 F20101124_AABZHY belsito_j_Page_30.txt
e5a44c24c7bef2d403e4ddcec691db63
0cab595c95ef7346f64368526e7ef663b6aca263
69279 F20101124_AABYYD belsito_j_Page_53.jpg
ef8200ea9d68a359e1c68bac6ec90010
962202039f6e78073b7e8034b9fd3c407340f1c5
90360 F20101124_AABZAD belsito_j_Page_34.jp2
1c2e8ad660975c50685733be4320be67
b66eb2559bdeb0aea63ebbdb4369cd1625f125ad
48372 F20101124_AABZFA belsito_j_Page_20.pro
491a97d41bd31d1868751782b581697d
d1ae6e2dd8b9463c354a4cef218f569b75dcc9f8
23197 F20101124_AABZMX belsito_j_Page_16.QC.jpg
4c438bd0b24b3a11cbf4bb6e365af7ab
42c8851b5f51845eea9b8f7ede140dea2b14f57b
1884 F20101124_AABZHZ belsito_j_Page_31.txt
a1d8d247f65beabcd0def2982e47fbff
946f5a77409681593151151e0d9669cc6bd94e24
71623 F20101124_AABYYE belsito_j_Page_54.jpg
f51754b58988cfdf18f365c066567d1e
976ea512a1bd3a674e54d93f0e6c6caa314a6d49
96981 F20101124_AABZAE belsito_j_Page_35.jp2
18e11c6053edaee18cb26246d29f93e7
10f906f7a3770b5d4406a3433a62e32ef9093d62
47913 F20101124_AABZFB belsito_j_Page_21.pro
7d635eb4e56908684eac45a4b58a8cb1
e100e08d2984956b01d0ab0e7aa6c2da832c0a05
1750 F20101124_AABZMY belsito_j_Page_03thm.jpg
615f61024ed5a1da77ef562ccb017d9b
682b881b33bf6161a6cf97846e84ed9319a6884b
76941 F20101124_AABYYF belsito_j_Page_55.jpg
377056c475f6734fe89545eb71affb08
5d90b4a534f8a5554dc5fa7c6e616fd8790f8f24
895893 F20101124_AABZAF belsito_j_Page_36.jp2
5cf913ec475a6a54bedceed0dd1d69ad
0b8eb0a2b0ebb192f1f2a7e1f508b2d442f148bf
48781 F20101124_AABZFC belsito_j_Page_22.pro
55235d951d8d470e42fae5f289128a2d
d962acf7f15eaa8ccc604ea175e61d7c4cc7bd32
6383 F20101124_AABZMZ belsito_j_Page_68thm.jpg
b9efcd5d428140aa406dc3cc53eb3796
f36e587771b25f275c82596e04057924ea710f3d
70135 F20101124_AABYYG belsito_j_Page_56.jpg
f7295b57fbe18ea0c8a4377a1277aa54
cbe77cbdbcdb4e3ae802ab7b21830d753d4b2eef
1021618 F20101124_AABZAG belsito_j_Page_37.jp2
5236e4cb0c23bcaf819ea3ab99f7f38e
23f560b372e9a5efbd34fce500c3023158e72896
20441 F20101124_AABZKA belsito_j_Page_64.QC.jpg
35b798cf4b5f3c5bec65bda2db741a83
023c7cace42aa25d624d8adc07d9a33c277c784f
50825 F20101124_AABZFD belsito_j_Page_23.pro
3059d992ef365970366598aa84772af8
b972e551a38aedc7f5eb6d7769b71c1ff53570be
1051985 F20101124_AABZAH belsito_j_Page_38.jp2
425d9c2255657c9165ed14b818c5c67b
e19873ecf4224c1e7cc1ffb6e67fea02e3d2f940
23294 F20101124_AABZKB belsito_j_Page_30.QC.jpg
ab9fb18b002707fd0e947dc8b4a3ff25
6ecff8456bf8123f0f605b7f1d544c6402db4f64
50766 F20101124_AABZFE belsito_j_Page_24.pro
031108c927cca69ea53fe9591d37c268
7038dc9161c4c8e83c14dd8e5de6940d56866d31
64813 F20101124_AABYYH belsito_j_Page_57.jpg
563e24cfb5a035a99691cf7779d7325c
e000eed2d63993cccc5d880f4b2d1d75a7441bd1
89727 F20101124_AABZAI belsito_j_Page_40.jp2
7f5205ec4f5f6bd36097d573bf7e9bb7
333e2342bb171cd093cd4ee13fdaf596d529ffd3
21674 F20101124_AABZKC belsito_j_Page_17.QC.jpg
f1ec34eedbbcefdba7a3aae65171a17d
8b0dd11f59f6662decf578e664a22b0e2609df84
50649 F20101124_AABZFF belsito_j_Page_25.pro
5919187e8f08bd947d9aec13f3fdbf37
c502c6208849b0c37567e84ced6603bb261eddaf
66383 F20101124_AABYYI belsito_j_Page_58.jpg
f1d861c8a7f442fd29d1cac645091e5f
c0b8b410b0c7190afda6a06d3b6fbac55d2da484
102028 F20101124_AABZAJ belsito_j_Page_41.jp2
6cb4d722d28d2a488bc11a9bcfe686d9
c47314139b1ed14e32274938a74998fa64789547
6479 F20101124_AABZKD belsito_j_Page_30thm.jpg
6f0e59dbedfffb5c5f731abd769aa329
6fca7700f0334dd8b036c7316a85431fc30b9a6e
51445 F20101124_AABZFG belsito_j_Page_26.pro
72cc0b749f49d5a8a882df108bd1ff73
c3237b9a344cbc75d129bdb27432b791e193e5a1
64952 F20101124_AABYYJ belsito_j_Page_59.jpg
977ab5ba89408386394ff3fdfae6730e
bb6320e51059aa0f105d2edecffaa7aa4f196e79
851334 F20101124_AABZAK belsito_j_Page_42.jp2
f19d7973af052a6d4d7cf75d53bf8487
3498595ffe1b630f5e35d31b0da94a750b291f55
18747 F20101124_AABZKE belsito_j_Page_10.QC.jpg
8dcb173405e0cdcadca416d26924c9e9
e9522d0381d9b7d03174d5f0741a015127aea6dd
47554 F20101124_AABZFH belsito_j_Page_27.pro
66d032da2cd4cb19857225e2202e7a08
f43e0d74bd3f1b5dd48498dafb3f1609f6387e49
68411 F20101124_AABYYK belsito_j_Page_60.jpg
60a5b1f2989f3e18f4d77d5bdd8f90ea
2d3eb3c1bc9540d76c4dd2455711f9cfbd0db8e8
641589 F20101124_AABZAL belsito_j_Page_43.jp2
5e42be229bbfb0018ff347b5a8e36fba
d80c5eaa28b787fe7ffd99925752c442a8fb8cdd
22090 F20101124_AABZKF belsito_j_Page_52.QC.jpg
4eda82776faf07ae435610bd909b8e38
85085caf23a2deb535ee7eb063335c2533578121
51116 F20101124_AABZFI belsito_j_Page_28.pro
1bbf32a0e75507d293c9bcd7c92371e0
089b2a26d95792e3f6b932938ef9850cf6e06e6d
66341 F20101124_AABYYL belsito_j_Page_61.jpg
fac9c8bbe25ab13c13ccc11367a8fdd0
ea2ceef68ccc984c1929f6f0baaa9f6b61b869b1
108469 F20101124_AABZAM belsito_j_Page_44.jp2
60eb2ac4bcd49cf3ab549c2d38b99cd1
3b9be559e8077d1059c15742b686f8b75acb8ab1
19368 F20101124_AABZKG belsito_j_Page_40.QC.jpg
4617f818e4e0f2d27177f7bc5dfb15b2
978d666789fe6d97958a0f445b6d0ea519baf0f0
48964 F20101124_AABZFJ belsito_j_Page_29.pro
d28e5faf0016dc622ccef3558caa8f7e
89d2b4a578b318daf07e9f8dddd50f8f34161bdb
460835 F20101124_AABZAN belsito_j_Page_45.jp2
23dd56045e4d17e2af992936dfe47fc4
901ac3d8350aba5116c60ae156f2fa0ce7de1896
5282 F20101124_AABZKH belsito_j_Page_10thm.jpg
8450cfd9974dc7805cdb82042e3b6ae1
5bb6d26ee9a33196f9729f5b308dd11aafac30cc
49305 F20101124_AABZFK belsito_j_Page_30.pro
50c7074364b91d522602f50f08f1825e
3f48bc321bb87fdb53b9347f23a2ff0f766a7583
71354 F20101124_AABYYM belsito_j_Page_62.jpg
6a70279f3ef31d240e29ade9fe925f31
8908f61404f5a2efcdbc1c06bd12088da123a1a1
F20101124_AABZKI belsito_j_Page_40thm.jpg
061e50d8ffd55c97a58643c8183b6384
4581831ed1cea9fcc2c648d88290413c98bcf6d5
47250 F20101124_AABZFL belsito_j_Page_31.pro
afffaae0a93c3856e08767eb03a201b1
81ebda1e40dcd8889f9d428640d30926dc6208a6
69412 F20101124_AABYYN belsito_j_Page_63.jpg
ea673032ed5064c21670609e9cb17d18
ab08feae8e7894ee7cb74e9c327d0486a4fd39b6
547074 F20101124_AABZAO belsito_j_Page_46.jp2
0826f8d46a77ad8ea67ba36dea323812
874ae72aa5ed8fadf8b00e545cbee16994a505e3
6549 F20101124_AABZKJ belsito_j_Page_23thm.jpg
a8437772fa48ea32eaf78f69e6ad1de3
12ccb65bb9fe80ab4a6508e02adf04823d23da70
51994 F20101124_AABZFM belsito_j_Page_32.pro
7804aa693cea58a47fb4dd165c1af250
65d3e402ff429c1ef2afd428be80c15a9c604ead
64454 F20101124_AABYYO belsito_j_Page_64.jpg
3e91dbe35eb9b943024c97e3d683235f
30847a978b2e3c36687b996ca9462bc7b6230d58
84432 F20101124_AABZAP belsito_j_Page_47.jp2
8fb8fd902488ad62c43fa1e22cdb600a
9842850acd4c0a5f70e2a7c41f62150e6d7f065e
23103 F20101124_AABZKK belsito_j_Page_29.QC.jpg
3c365ab969f120292ca4008342811e1e
a0ef8d0c7a84f694a3000d4936af2837b2c8b6eb
31255 F20101124_AABZFN belsito_j_Page_33.pro
97750f3348a0c36e16366ce57f8bc852
d9e2fb864dc9c59b4cef5ac3164051104b367758
67250 F20101124_AABYYP belsito_j_Page_66.jpg
b1f57d25debce29eb73aebe5cfa4cd2b
bc3c46432f27403700737bfb4e73524d6043a1d7
87398 F20101124_AABZAQ belsito_j_Page_48.jp2
a8a7f86dddb575acd4d81b493fe7b507
0af683783df7fd300fd20418b2bef243a2dbb6f7
6233 F20101124_AABZKL belsito_j_Page_66thm.jpg
894a84ef6dc8d7fe24b50d1f59a942f7
4997c4cb1848362ce5721085954ba506b736fa14
42189 F20101124_AABZFO belsito_j_Page_34.pro
f09adc737fc9e537b932f7811f50f265
8772d164818e61188afe84f16f900ab1c723a59d
70284 F20101124_AABYYQ belsito_j_Page_67.jpg
f4f4ae0c32d43d2dadcf46f48d6d60b4
f8861b18643fd47612538bf8130de88bcfc519ed
833437 F20101124_AABZAR belsito_j_Page_49.jp2
4f3b5295ea277c5b248f1d319049ad6a
948f98b26a50a9c0d2b8b96d1ef50d52256022fd
22806 F20101124_AABZKM belsito_j_Page_22.QC.jpg
19b7fb78462dc5f4dc1bfe025af3c47b
4084dc7ed61d8cde4c7d8cf97065fe6348419aa9
43996 F20101124_AABZFP belsito_j_Page_35.pro
ee598d51b5ca1140fbab1aaed2a4db69
dfa0c9045a669c816d87f1d9c6172fc41363b7dc
70576 F20101124_AABYYR belsito_j_Page_68.jpg
3ba3f8b30a308953bfd3ec6df18e5249
b4f7580082ad6bffc999506d26cc718d601f8c14
46158 F20101124_AABZAS belsito_j_Page_50.jp2
50df628aa7a467d588c32d49281b1c5a
c9c81efc13f9b895841ba8ae084a5d05b4867772
6299 F20101124_AABZKN belsito_j_Page_11thm.jpg
6fb806a4fcb59ce9dcd3d673f8824381
eb7e720763a7eb722a942c0fa0452ec68a593739
21350 F20101124_AABZFQ belsito_j_Page_36.pro
a14983e83ad79f3c5b9d85af3c675f46
6c1823fe4520268214612a697acc8976565c3d0a
71201 F20101124_AABYYS belsito_j_Page_69.jpg
bb261d83536399d259288b528b8ca3a3
4102e7083020c06f9f393c7699c54f0dc28b8f62
F20101124_AABZKO belsito_j_Page_51thm.jpg
40805699a318e65cc1fcc546ec277010
6240d7923b7553a13d309a1f1639220dd056ecb9
26555 F20101124_AABZFR belsito_j_Page_37.pro
924788ae98f54cef282184cda9ce7d45
c0ba243086ab6aa80dd4d60e4401ad1be99ef915
89713 F20101124_AABYYT belsito_j_Page_70.jpg
b0dc12bce1bb85c530b0b36df19a035b
ea55cb75e7a8cfcaf54c1a39be6a3cd97bf6f59c
97110 F20101124_AABZAT belsito_j_Page_51.jp2
aa0446fe657956dd83b7959008979359
691b38a7639f26f9a1a2a39ae4a3ad279041a884
10421 F20101124_AABZKP belsito_j_Page_14.QC.jpg
5585b4442b8c22fd16ffd595a31354ea
3d0b40f18960445d5db473bf38f4581ab076fbc2
25940 F20101124_AABZFS belsito_j_Page_38.pro
99f63b09afa7e7de49f86bff689bb030
c999a8a8faa1050d10152ce4cb4496ebc3b60158
80447 F20101124_AABYYU belsito_j_Page_71.jpg
ce38b56c335657cb3859ee3bdbeb485c
ac92d8bf542cd1ce28f2926318ca45ba7ad94844
96412 F20101124_AABZAU belsito_j_Page_52.jp2
974f726d8e96719d36ad7d0bdec820cb
493fa506f999de2f87e91a992e09508cdb4c0ca0
6281 F20101124_AABZKQ belsito_j_Page_17thm.jpg
09df9e075779c2938325c05f3d90015a
58c5dcb0f8e5e41fe492660a1e656d5ad7873e67
36165 F20101124_AABYYV belsito_j_Page_72.jpg
f3012605887e8d1f62879e4834fb563d
fa963732c48957ddb129dab71050122ccd92a712
104037 F20101124_AABZAV belsito_j_Page_53.jp2
031c978258407defc97b28de67f94d12
f95e67e3369a5b26ad1de1579c66e331f45ae8e3
17179 F20101124_AABZKR belsito_j_Page_46.QC.jpg
a4f6edc7b21d38f63e5a7e63be247cab
9d6a34cfa2aed66915fbf346b2d9e6fbe171c5c0
33895 F20101124_AABZFT belsito_j_Page_39.pro
f489023612d1327a3303da3c71cdccc8
2ed70b21304ef7de8349e5a698b2b7b268ee923a
24315 F20101124_AABYYW belsito_j_Page_01.jp2
8d46af1be50cd4334de0df37829b8fe8
f511a0039b5dc0fbcdbde65c7cc0ad8ffef46fd1
107302 F20101124_AABZAW belsito_j_Page_54.jp2
ab52aadb4a9c8e85b6592bd697ff585e
c6b4e520db1a9ffaee22ea2d7c4bc5998b9514d1
2330 F20101124_AABZKS belsito_j_Page_06thm.jpg
3ced45d43e14d30d64765669b1c49128
0b968a0d405c9a59f9cf0247410514223502dcdf
40440 F20101124_AABZFU belsito_j_Page_40.pro
9d59dfeba59b682620189871ef5c974e
8a109dd580b7953465fc5fa4e05a7e146abd30b2
6221 F20101124_AABYYX belsito_j_Page_02.jp2
8d9ce822ae1c798cb6a7cb11a94365cb
a76e2b3b78a1890ffc06dbd86b3ac765ccbde932
1051958 F20101124_AABZAX belsito_j_Page_55.jp2
7428335c469fbd4ba4599a8d35e70954
4f5a9b121b6774f6e207a465d3973636a268f29b
6159 F20101124_AABZKT belsito_j_Page_52thm.jpg
db8c35a44b3742283aead727a467c6d1
c27b39d293e36466b0bff09491f3527be4bc0e12
34318 F20101124_AABZFV belsito_j_Page_42.pro
c2b955e98927c574b637a5c326c82d7e
0e13ad093ae05cae33c525b3d523f55db9c56c43
F20101124_AABYWA belsito_j_Page_32.tif
f57dcacd7a80d7a06dde9c3fbcbd7f9d
f4c70a119c4169b7404b01e46ad326b15fea09cc
12551 F20101124_AABYYY belsito_j_Page_03.jp2
58cd5581c0c28cc39256bd56b6e0ba38
ee8e870405f9aa8e7b0465c3aca2ea9c4e8beb4c
1051949 F20101124_AABZAY belsito_j_Page_56.jp2
32e402df92303bdc6f64e4b94c073323
a52525f9c20c5a7cd3df9d901bc98b627f00b730
6283 F20101124_AABZKU belsito_j_Page_21thm.jpg
8c24fd694046e4b6daefcffe3bee6eb4
0adfe49676eead0acb916fae9acd89a5318d3972
23314 F20101124_AABZFW belsito_j_Page_43.pro
094742645a74ffd027f782c85a32bc35
5e478f7b701ee373d6d348af6bf5e5c85685f098
46662 F20101124_AABYWB belsito_j_Page_41.pro
f6ffd7cf0ff66985eb5f96056db8f244
9b2719e4ee2f3b9523e51fa08760801b3d610f78
70901 F20101124_AABYYZ belsito_j_Page_04.jp2
6b611c1906ffb2cef3a40439ac934bb3
c0bb2792589af44601bc551355ac4ef99869f104
F20101124_AABZAZ belsito_j_Page_57.jp2
83eadf3794576089c90d36b78c1bdc9d
1d89d2e466edc343d1eef2552e6cf971357b4f89
24866 F20101124_AABZKV belsito_j_Page_55.QC.jpg
60e1507795b2166a601296acd40a6d64
f63815f7743904ee2efd01bec56022d640dbb819
49415 F20101124_AABZFX belsito_j_Page_44.pro
3522cd241776a5a37483c183e09e61fe
0054c066304562bb4b80a99d964cd118b5a84ce4
1911 F20101124_AABYWC belsito_j_Page_66.txt
4901c68ead29a07e1ba205f68cebc878
fe36385a5f1a4c4367ec32e28506a8e3117132bd
6134 F20101124_AABZKW belsito_j_Page_35thm.jpg
aaaf612c1f864d4d2aaca9b18eb7aefb
9ad784c112d9fb6936b620b2fd97d2146714bf24
F20101124_AABZDA belsito_j_Page_40.tif
dcefd8a40c3fea7ba5a4806d854d8c82
4359b78e094880babac6438000c8c53de98ead29
15435 F20101124_AABZFY belsito_j_Page_45.pro
b3bb1e51f16e3a0384ed6f78f7876c33
f58ff5eaa993ab6ad82362b6451db7be85ab446d
85210 F20101124_AABYWD UFE0011398_00001.mets FULL
f0112aa5bb1c6639f07d4cb633d4c89b
7a8dc168fc38087457d4cfa8b5fc8cceb6909b81
22353 F20101124_AABZKX belsito_j_Page_31.QC.jpg
2088c661d6ee130276de70cde2f446bd
f60cf4540389edaa5265e4d11be01515cffba503
F20101124_AABZDB belsito_j_Page_41.tif
8949a59eb256b77f607ea31c70ccad98
050f943904906b151e8aaf1aff1634f6f80e5e51
16696 F20101124_AABZFZ belsito_j_Page_46.pro
e3bbe7a423bf4c82ecfa556f2c289dda
1b4e132f62577c730b5d94351ded516a3292f1bf
F20101124_AABZDC belsito_j_Page_42.tif
f09ad99404bb80bf82f87d391f387b1f
698a9a956a70b05b292766b9a39100bc002a351c
21878 F20101124_AABZKY belsito_j_Page_11.QC.jpg
776024038f086914fe7a4b1a3d356a12
55dcf8c744567c04311cde37eecc9e34aa842346
F20101124_AABZDD belsito_j_Page_43.tif
7e42655d583cbb664c67d8f5369fc8de
4ff72c83ee150ebf558a861a90d34fe53e8f1f9e
2077 F20101124_AABZIA belsito_j_Page_32.txt
d19d1b3f9773d15e70ec61685cd26931
4a6fe3b19d4c20fb74e9788f2c873dbe1ded5766
6878 F20101124_AABZKZ belsito_j_Page_06.QC.jpg
91d5585fe5ebd0ea1996d9d4396fada6
d18eb21758321bdefbd2a4181bcba4849ecb4a4a
F20101124_AABZDE belsito_j_Page_44.tif
94c13017170adc6807bacc4a90530718
1bcda1e617c1f79ce1835dd909cd6deece7fd909
22889 F20101124_AABYWG belsito_j_Page_01.jpg
c2f9c027a48fd8bc9403181ccd3ea971
da23b8123a6111da42705718721c551337c29669
1254 F20101124_AABZIB belsito_j_Page_33.txt
480f159e5d74c9a33f751df0de1a337b
b234b5432945bd96b49e38c7e5bdda0c36f912cd
F20101124_AABZDF belsito_j_Page_45.tif
116c614c729c93501edcff03478cbeb6
3b2117f02d2a73a5eebba486cc20426f921cc654
10652 F20101124_AABYWH belsito_j_Page_02.jpg
d51120af8b592252f77bcbd845468d45
99811b3eda9dcc0041c900b9dd801fd11314c941
1765 F20101124_AABZIC belsito_j_Page_34.txt
fd9a9a412dcd7d25f6b6722f0f2d06c3
2e5ea7ffd1fbee5a5c6cb3ebe8a3f7b9b41f877d
6521 F20101124_AABZNA belsito_j_Page_62thm.jpg
f7bfdfccad4e2ed4b172c1a2f436d7ea
9d676e6329bb4247ee11f5bfd684e67d5cabb050
F20101124_AABZDG belsito_j_Page_46.tif
ac0c5b83d8cda0113b661e4489689e78
486d7411fd03f2bc7f7a4650d3198cc4a31adc45
14824 F20101124_AABYWI belsito_j_Page_03.jpg
88fdd26906b217fb14fb4721ceae3ec4
7edb4ac4b6c815d350042bc67c2acc60ef4d3361
1821 F20101124_AABZID belsito_j_Page_35.txt
03b45eab6851d0f171cd3ab6e6c51474
7f03ba3352c8dbc0960dc3bb7b323faeaf65679c
6341 F20101124_AABZNB belsito_j_Page_20thm.jpg
36583f054a70433e96ef097de30479a0
90502e74286555708cf6fd66967eae8de90522da
F20101124_AABZDH belsito_j_Page_47.tif
03f15e4198c718828549841ec0b644b7
b720a60f8c5c1a357d0497a78e4dfd54a113b561
49623 F20101124_AABYWJ belsito_j_Page_04.jpg
090fdb9c227eba27a1fca9a4914d54b7
20134c47f47bfe4164c2a6272a61be651cbbb941
889 F20101124_AABZIE belsito_j_Page_36.txt
ff186b152c541bf0b6c49217570773e4
e7692bff49bd45cf7fa944ceb61a05fafad73b10
22069 F20101124_AABZNC belsito_j_Page_27.QC.jpg
0c7f89d7fcc4ad72c7351d2128ee5ea6
1354d5b25b356397807a744acaa30205b5f98d44
F20101124_AABZDI belsito_j_Page_48.tif
cf4eb31a15fad62d84aa561bb2f039d9
1ef55e106e0ab311eb33b429655c861668f36b68
63082 F20101124_AABYWK belsito_j_Page_05.jpg
2bfb30a3762965ec653ec42dec053904
005a177b88ef9d4f22b11a21b3a1811915d4f465
1090 F20101124_AABZIF belsito_j_Page_37.txt
a2b4e9e006946e749210993432bf3501
499d948cdc7306d41c2c0d456e50e665a4625303
F20101124_AABZND belsito_j_Page_13thm.jpg
fd2acbf2e8137cbe31bd7bd8841f2d55
c475690fd34905cf460616b4aed4251703719028
F20101124_AABZDJ belsito_j_Page_49.tif
425288749ebba5ca1300a1059c7734e6
79fc0830d194279a7ac91251af92974ae686beee
23620 F20101124_AABYWL belsito_j_Page_06.jpg
2df0beb56f4e3f5d8b34df8fc476a172
a3ed2cb407ffd9985d1832b12db362665e33f986
1068 F20101124_AABZIG belsito_j_Page_38.txt
13af7bafcdba8fce95a7e7989291a95d
226357d5bd39e1f7bf1ae5428c8443e9ed5a50c2
4264 F20101124_AABZNE belsito_j_Page_03.QC.jpg
04ab9cd44c05c5605181435445e3c2bd
c79223b6880569833ea5f3f595f153173c9ba4f2
F20101124_AABZDK belsito_j_Page_50.tif
6cb5b9ce1ca45752a45335c0738a2aac
a3bebcbd29e8d69d877487c946cc9f2f589dd986
32142 F20101124_AABYWM belsito_j_Page_07.jpg
3460d6a4f9f23e28b7a5a08c09eb065c
4e2d9b40d8f37bafa2e6ed80d7c78351940f3821
1895 F20101124_AABZIH belsito_j_Page_39.txt
301eeb70faf52603a98d1a816e8a0dbd
5ac4f07f806ad62bb260ad457fce1ac9707a8de8
6112 F20101124_AABZNF belsito_j_Page_19thm.jpg
b20aa892dc491234ac7d6201fdf53718
39ede2dc3663bfc0efbe80851b50b1c739d1c1c1
F20101124_AABZDL belsito_j_Page_51.tif
f8359c88bff0ff98750910258529e5ef
fa60c99acf47c327d75e2621fb49f2739d74ec02
74903 F20101124_AABYWN belsito_j_Page_08.jpg
39a66ecdc8210895f8f4625a6975b1b7
93a1bc4f53a833bb0bc3d459bb18ab0c43586863
1691 F20101124_AABZII belsito_j_Page_40.txt
7648ed5c960bc5184ae68551f9c5c0bb
710c91751e1fcdbd233ac5bb4fc375981613aa1e
3357 F20101124_AABZNG belsito_j_Page_14thm.jpg
d599263e7afc093acc68c2684ed15b2e
f331af3f73b962048f3269cd4b884c0b432002ad
F20101124_AABZDM belsito_j_Page_52.tif
f2f6dad0318330962b44550625d65c3b
5479210c5df13e12c4f954bd36892aa0027725a1
13798 F20101124_AABYWO belsito_j_Page_09.jpg
4d31784023f87b276585b9f12e3c2fd6
1fbcc60a948b0e11eaf4dd21ed5e13708d7ace7a
F20101124_AABZIJ belsito_j_Page_41.txt
8e07634cdf66e204213925e297bdfc98
313ccb6d4e6358defbcf72e0a8e85109e30b35a4
21685 F20101124_AABZNH belsito_j_Page_58.QC.jpg
5dca36c70d954e227736bac4c94dec77
93e474b0e04a89142477cbb62724e677cb8089ec
F20101124_AABZDN belsito_j_Page_53.tif
fec859d3f904904ef9bb6d05b2f08bdf
7ba16d0ff089d3d8e79ee858c7c78b5a2bc998e5
58851 F20101124_AABYWP belsito_j_Page_10.jpg
14c84630cfdc5f538f80dd83e4fe9460
ffcda52c7a4fd5abdf03fe1d7219449279199d58
1545 F20101124_AABZIK belsito_j_Page_42.txt
a28fc22c17de3ee217786c2c49b6ef43
199af465c7208fcda47774cbf5e920cb9db609bb
6260 F20101124_AABZNI belsito_j_Page_57thm.jpg
0f94bfd3127f130bbf8a345ae631a89e
83cba2036bd67d323c7fd0774cde5dd948e6819c
F20101124_AABZDO belsito_j_Page_54.tif
4da2b44551b9a3fb0741d1f5609380d9
4755690be86fae19850ded4b5a99331a9d0cbde2
67949 F20101124_AABYWQ belsito_j_Page_11.jpg
24ad80604bc4dcf9a79f4651aedfd38b
3058089d314dee1feab44c66b30944107f4ec8bb
1178 F20101124_AABZIL belsito_j_Page_43.txt
f5b703f58f2be5f182dc7cf85dbd8933
e92f4cf8c0568a7d71280910e11949e30850c845
21871 F20101124_AABZNJ belsito_j_Page_18.QC.jpg
8c3237384c0b4aee115a8a8802377123
9fdb9da2f483ff11b66c80674335f9c76bb54630
F20101124_AABZDP belsito_j_Page_55.tif
dcaaab24b90f0f927a360d104c16db4d
fb0ae159afaf1dbeb00686a769e0b4fe27432573
60575 F20101124_AABYWR belsito_j_Page_12.jpg
18c986fc638f6f276fd6a78b690e8ad6
cadaf1ecdcbc5704859f37513c169e93c4c48312
1944 F20101124_AABZIM belsito_j_Page_44.txt
d64d9e3ecc06846c340d4fdf99eb7f6b
9c62efbba3744f2e439f73095843a9812a34a0e4
6317 F20101124_AABZNK belsito_j_Page_60thm.jpg
baa0e0461770815af92a376c074b0548
f1fc13dfa82cd01fd75ba8e6eb55b6bf45590f77
72811 F20101124_AABYWS belsito_j_Page_13.jpg
bac7767b91e66105add53d4ce129b0d7
01742ae00187bd8a186e9a4e22f99538f783f0da
816 F20101124_AABZIN belsito_j_Page_45.txt
481719dc038e5076b04ee96cc76c9e61
c935ba9f878e1dae8bce34f8b9bd326f2a99af0e
F20101124_AABZDQ belsito_j_Page_56.tif
a3b914c47faa65f30f04a34698d34a00
cb55d8a079be73ae72b41fa14d44b3f251827fea
5530 F20101124_AABZNL belsito_j_Page_48thm.jpg
245f172f821cffd6afd8090899599521
666af711252f050622fa113178db67c7e5a326f3
31845 F20101124_AABYWT belsito_j_Page_14.jpg
cf19414e55a03276cbc17083284e971d
3f24629881e31ceaf8ff47397b639090026be3f2
711 F20101124_AABZIO belsito_j_Page_46.txt
8ef73b42cf426788eb436b838fe87e9e
6a928a632aea0a77cca17022418387d405ead7da
F20101124_AABZNM belsito_j_Page_57.QC.jpg
904332883b2feb7c5e97c3eab3c3a7bc
cd6d6c947b462a1c3b49b19ababe2c5136d29bb8
58149 F20101124_AABYWU belsito_j_Page_15.jpg
04c358c2dc8a4ed8663b2da0e5d5be01
ef4c5f201140bcdd4aea99edc34959872fdaa23e
2116 F20101124_AABZIP belsito_j_Page_47.txt
1a2105884800fa2a81d7ede736f79a31
5e83c05d7bc31ffa02194b989fc055a6150d33f9
F20101124_AABZDR belsito_j_Page_57.tif
739f84f99ce5e14885081d2c5d351b17
ca8153f77f1968da4b94bd0efba6aa5eeef43c48
6403 F20101124_AABZNN belsito_j_Page_56thm.jpg
18fb02216f144f06f71ff1986412551e
e54cae830abc0506ec73996e997160e64183f477
66944 F20101124_AABYWV belsito_j_Page_17.jpg
e01ee8ca326ed630127709082f6b281f
ea1c14a27d195a19957eeba2e3a8c6225b2860a0
2259 F20101124_AABZIQ belsito_j_Page_48.txt
d31cb28addc28e3f3cd2a1f14bc3b02d
b1e86f75746e620e4facf4c11ec1519c94ed988f
F20101124_AABZDS belsito_j_Page_58.tif
4e9b3e2b3701cc30897bf0c936c784c3
4d2418eb7d474e6ad54d2650bef158bd67a33458
22636 F20101124_AABZNO belsito_j_Page_68.QC.jpg
56e47ef27710618f403639f20a0c3379
3d0448223d9a283fcf501bccc08d98319d925bef
67797 F20101124_AABYWW belsito_j_Page_18.jpg
3625ca95884d6578ad41ec05b80b6b52
24c8a15a17c1bbb81ed940709be76c385f3b81a2
909 F20101124_AABZIR belsito_j_Page_49.txt
bb88624a1850664ed6a53a6c4264a692
ad26cf6520f000afd513b7ebf5f5357964794850
F20101124_AABZDT belsito_j_Page_59.tif
bab1a085ffa521b4271d3e4905d088bd
4afbe7c6fb83281d85a81b1072fdec11e0b00bf4
24057 F20101124_AABZNP belsito_j_Page_13.QC.jpg
fb1e53d719a622abeec30ac6b0d88d5d
937cb42c6f3c95856126c81f314b240ba01861a9
66540 F20101124_AABYWX belsito_j_Page_19.jpg
c5102f69a7f4f57c119a65cee14e739a
79418492965774a26cda80e093364f0d9d949e84
822 F20101124_AABZIS belsito_j_Page_50.txt
d321e03d86b0bd1df6afd19106096ca7
698487b3dc2c19c726867e4d4e9982a384e15e4b
F20101124_AABZDU belsito_j_Page_60.tif
55a1516253125da4bebe771df3c0e8c6
d9ccd4de4da859226597e3c6befa4f5a1103f34a
22607 F20101124_AABZNQ belsito_j_Page_37.QC.jpg
2ee3d271d8edc8be5cc021bbb712e9c6
ff489204671c7ce60d658841340afbb4c1c71a60
70634 F20101124_AABYWY belsito_j_Page_20.jpg
02579590837cdcdbc00c6a52026c25c0
473ddbef149431cd244dcb684b6a6cc991156a6b
F20101124_AABZIT belsito_j_Page_51.txt
e26935701d5f7e2ff6f50322800f4a38
d33b2a173183464550f5e5c109053ee5ff0883d7
F20101124_AABZDV belsito_j_Page_61.tif
8c24cc84af884dd5baebdbb5229406ce
cd7e6d37c0e6c2a9a83fc3e6278a17bb2699a0e4
22330 F20101124_AABZNR belsito_j_Page_41.QC.jpg
050305ced23bc0ca6cd0cc5b32dec437
5f0e9eb07beade909dedf359b5c7cfcaa5ba0b9c
68883 F20101124_AABYWZ belsito_j_Page_21.jpg
90d8189442d3fc4c7363a976c4d966eb
9ac4b6ca658af9d2db8d5ccf607e18dbed752baf
F20101124_AABZIU belsito_j_Page_52.txt
6de16a8bd473dc0299ad60b3fa943e37
b75dfc3f0101b1b6ddc09a87ce269a3880654897
F20101124_AABZDW belsito_j_Page_62.tif
9b894267d8b0bb635923053f1ec806a9
62c78b5f33b44f58dd28e1f844a54d68c6c8f6ea
23383 F20101124_AABZNS belsito_j_Page_67.QC.jpg
9cc482abe77668313610c93c8569c708
f3c8815a357652e56be55c156a019c4f4976e1db
1918 F20101124_AABZIV belsito_j_Page_53.txt
ce5a8f543e5e63f2e26bfd41adbb5625
df9310593e020ce571d3fa45854bfcc51cda7bde
F20101124_AABZDX belsito_j_Page_63.tif
536c69c10ed490e8768ac3b08f96718c
173fc866ddea394dc73e1a90fce977a22c1f982f
6133 F20101124_AABZNT belsito_j_Page_61thm.jpg
67258750de47fef70b8680b0aa73e390
73f889ff068d729512e57b0a6363697538468d91
1051983 F20101124_AABYZA belsito_j_Page_05.jp2
0b4bf0726d5aa8128e51ccd29dec9a05
9ef8dc56fe265d20274fc0facf54e676dc2747b5
99370 F20101124_AABZBA belsito_j_Page_58.jp2
fbb171a17bc1789c1c9d2b0a3178fc7d
6899116803c8d98743f8ddee068240bed2d209ee
F20101124_AABZDY belsito_j_Page_64.tif
668bdbb306cb3b9bd65599847d537f6c
07a88dcf8759a590dfcb5afaa15c884e5b1b5a2c
109999 F20101124_AABZNU UFE0011398_00001.xml
4df1ea0a9257e15193496c31bdc97867
8b638666ff9d843fddbce40480d15dd6d1db47b1
2066 F20101124_AABZIW belsito_j_Page_54.txt
6298ae81681a297de2fe67b2e5b3439f
934946619b58e71dbbc7544d74d118748aea9536
465958 F20101124_AABYZB belsito_j_Page_06.jp2
5ee48b79e59daf902d5645861c8278e2
5bcf7ef3380e7c86e6268a0f7551bf035fb22693
889001 F20101124_AABZBB belsito_j_Page_59.jp2
a8955670292849bc382818afe777899d
d7fdcb587b7c12651108a4e8e25bccde2c72db49
F20101124_AABZDZ belsito_j_Page_65.tif
c27d1cc6846b2514dd49d6fd1c214570
9af9c6367686c8f4f69bfedb5bea334875a5ad62
7378 F20101124_AABZNV belsito_j_Page_01.QC.jpg
64d8d88999e6bb893316bd6c8514ed17
075bf36d25a3073aaf7c5091455e4bf5bbdac077
1478 F20101124_AABZIX belsito_j_Page_55.txt
729ce7a6d7d897a6889fce8577bd01b0
4f6848f6038a28dfd67a44be33fd14f1e26db96c
758091 F20101124_AABYZC belsito_j_Page_07.jp2
7fa72a877d645d414b4fde6dd3a8bf3b
afb7648258079e2a434f667143140028c5898df4
901908 F20101124_AABZBC belsito_j_Page_60.jp2
2255ccea19e65516b53540b9fe72fd1a
4daea41859d32b00c6b3cbe0d29a13b419c9fdd9
3409 F20101124_AABZNW belsito_j_Page_02.QC.jpg
feee445a04d1c525ecfef6146125cef8
781a487a1a32215714c8d9acc75862ebe668f676
43547 F20101124_AABZGA belsito_j_Page_47.pro
7b5ef65d9f81762412a2cb5c4a04a5e3
340149b1e095c42501b637dda42d1c29486cd0dc
1245 F20101124_AABZIY belsito_j_Page_56.txt
c4aab966d1dbe3bdd9a0b1297a0622f7
7c8fd972489993c3bdfc1d42121719f3eaa89c53
1051980 F20101124_AABYZD belsito_j_Page_08.jp2
1e9a4eca64c39045e8ac8b5b7b2ef583
21e3803ab64e6fe3d75a01b4490e453fde18b298
94811 F20101124_AABZBD belsito_j_Page_61.jp2
44b700875b29b7ca0af0fd6d7ef521f0
ec5c08e8dd35275a437389cb02dcff3674ac13bc
16539 F20101124_AABZNX belsito_j_Page_05.QC.jpg
2c57a741f35c9fd15f2c35c9eaa96302
2ba5ed7dccbb47aa74eea210b7e8d5fc035d84d7
20854 F20101124_AABZGB belsito_j_Page_49.pro
781ff7745ba436e2faec20e5d5cc8a06
9e18457ee064204c6e95f6313830395ac5ac2b1d
448 F20101124_AABZIZ belsito_j_Page_57.txt
e5a33bea9f7a52af9b16147d080dbe7e
14e484507d0d0be8c7e86bf1065e491d0e44fdac
199383 F20101124_AABYZE belsito_j_Page_09.jp2
328ea3d99031d710898674d846bd1d8c
6aef471c380973995146238696119634614c950e
107778 F20101124_AABZBE belsito_j_Page_62.jp2
bd35b19c89560e215df8b3dc9ea5bfce
5a2cab1dd3e843260d0c657dc72d0f976ac44c96
4514 F20101124_AABZNY belsito_j_Page_05thm.jpg
4b1a10a67e6ff8b55d883ce2ceb5acc1
5c9dd8db8369cd3229b7d950ba1f1c0a08b7c9ec
46678 F20101124_AABZGC belsito_j_Page_51.pro
ce0705f23fd56113b57d25fe4769660d
30d45fcf5efeae24164a4d5d3e6f4b4765c5cac4
85519 F20101124_AABYZF belsito_j_Page_10.jp2
48349d0f3b2b330180bd6b9fd7beef81
2ce8c78fb6e8519ef89b8adbdd9e21be3334628a
100698 F20101124_AABZBF belsito_j_Page_63.jp2
a250447c17a6bc2551dc2257f362ef9e
3615b1c413ef23ca2cfb52961714e84f84710b4d
2973 F20101124_AABZNZ belsito_j_Page_07thm.jpg
56d33e60d485ceaa4842b39e4f9bd353
3dbfa3264de50e139eaad73530b24f29089923eb
6594 F20101124_AABZLA belsito_j_Page_71thm.jpg
62e0bf81ff0607060c90045c1d996886
d34036bfee40e26a7104bdd1ae57047ac8a62722
48127 F20101124_AABZGD belsito_j_Page_52.pro
90f20fd0f9253f51dbfcb8f6beb9ecf2
feea8b413b48eac210996ee4c1b40d540fa2312e
101531 F20101124_AABYZG belsito_j_Page_11.jp2
84b1bb84d70c767fca05346bb8f81aee
8baf5ed1b4d78d6e9424a428bd7683e5cce359fa
95157 F20101124_AABZBG belsito_j_Page_64.jp2
f53206b92a1ebcde653101fbebcbdc42
5bfef66a3f2dd60728bea1c8ce0051b6f1af8373
26487 F20101124_AABZLB belsito_j_Page_70.QC.jpg
dedd4b3a9b39f7d7fee34650c2bd6ec2
ba9984d87931887f8055a6d5fdef4648434a40df
47506 F20101124_AABZGE belsito_j_Page_53.pro
bac59cfd9a16c045919e8fd8dbb90ea4
209dc6f8bb57fe0adf1bffd5aa60566ee183fd38
89805 F20101124_AABYZH belsito_j_Page_12.jp2
efb794a65bfdb60f773f34d129e43429
73923c28c31344d11a7e630ec131206e9877f8d5
38712 F20101124_AABZBH belsito_j_Page_65.jp2
c95bceebe40d55c220e9357fb02e1de3
41fc732693afbf6d643b0bc41d488733d98b6ffd
1727 F20101124_AABZLC belsito_j_Page_09thm.jpg
7517eeffa0772db7ba077fb3ebb1788b
6fd0bd4f5597210cc38c736722c12aa85c3fe71d
51461 F20101124_AABZGF belsito_j_Page_54.pro
a91eb665d5298b64cfe1bddfe6463905
da4370bf5eab173af46b235d776ffc27c2577578
99711 F20101124_AABZBI belsito_j_Page_66.jp2
26edc2d2538fe333a2a31b1f6f91cd74
e658550b904c9cdcc1f6a0f51755e2eef1a02660
21927 F20101124_AABZLD belsito_j_Page_66.QC.jpg
28d11659945a67814d87216b2db2acea
6c267e77424ac35a57fa479ec41a7f7b355b3a9b
34594 F20101124_AABZGG belsito_j_Page_55.pro
6e34f0549297d070216bad6af1954082
c635ec749c5bcb10d82046f65ac4dbd012931da8
109122 F20101124_AABYZI belsito_j_Page_13.jp2
dfab89f1acc8f8ad3c4334fac848ee43
04f568b6e81777136b1d50694c3e553ec9df7193
106470 F20101124_AABZBJ belsito_j_Page_67.jp2
2686a5fd53d7c0897f49c7dd336811d5
a4146a076f1f9b3e857061ef8a1851bc0ac2f3eb
6502 F20101124_AABZLE belsito_j_Page_26thm.jpg
63af27c39163b9ecde28e451573c56ce
993cb38def875bb65b90798c7e9affb3f4d57a2a
30343 F20101124_AABZGH belsito_j_Page_56.pro
d1b1c61a5a533dc24cd3e5790c06233e
2e6187dc19f5ab5e08935827eb165a0136b3a67b
42527 F20101124_AABYZJ belsito_j_Page_14.jp2
4d54ed777219cc5ca8cb015d7e137080
5bf29bb3dd1d371ac57f3258952dde4f7d5d0248
105350 F20101124_AABZBK belsito_j_Page_68.jp2
07769b5e73cd9f080cfd955971546555
d1fa616d41167e8dbc96c959506ecdf9323e4e51
4170 F20101124_AABZLF belsito_j_Page_39thm.jpg
a62b5d68e3a45b612924bc368da5a304
a163b549e5d9a0194a69bfa5a07c9ac8c704b4b6
9506 F20101124_AABZGI belsito_j_Page_57.pro
fff2c495fc2bd57c4181d6c3f4847937
26941bb7da06ac2e84607908e43658307e5b0860
87419 F20101124_AABYZK belsito_j_Page_15.jp2
283c8c808315445366e54eee5636d7bc
19651320c1f7543986127f656abc3eb66d83be35
108834 F20101124_AABZBL belsito_j_Page_69.jp2
d34039f52d990903efb6818912d5ce3e
e7d11f4363d6c3794da64cae81886849c9a578bb
6759 F20101124_AABZLG belsito_j_Page_32thm.jpg
2e75618a2b5156a0db437f8a5c5cb1d5
e9776c2578a4be179d26f456ab205de3ecd9fcf5
46964 F20101124_AABZGJ belsito_j_Page_58.pro
92256bfc66ca5357cf78631ed1053a0e
ddd3c87a57f17f915bcbc5cdf47670942b46bced
106214 F20101124_AABYZL belsito_j_Page_16.jp2
5288fa7c07053a5f324a48790f900f4f
6f9d708550f43df6db833b0b3b9f76d4f1dcd1e2
128335 F20101124_AABZBM belsito_j_Page_70.jp2
3dc408dc1181f9179f9df0ae65e83f92
cbbd18aec626307108f45a6572a4f393f4b80ce3
22628 F20101124_AABZLH belsito_j_Page_63.QC.jpg
113a7ea5a2f423a12fd4e0c8f1a26b8f
e530bed34894087735b7d7f5d35fa0c62e3d453d
41107 F20101124_AABZGK belsito_j_Page_59.pro
2bc8eef96cad5bd06d88598a36a0cb83
3a7fb2347da9c3fced230d602f0e06544b2d65df
102099 F20101124_AABYZM belsito_j_Page_17.jp2
186ac814b11ccd3e41e7f1dcede04a3e
4d6ee915380d88532a8de3be241efada4aae30b7
115728 F20101124_AABZBN belsito_j_Page_71.jp2
c8186315a62eedee3dbd77a2d9bffb19
6397382cbe7650e9700ae7de9cb7597bdffee869
F20101124_AABZLI belsito_j_Page_24thm.jpg
777a4bbb44e44e0887ade8ce4faaabc6
8cd4bc13ea16e01c7dcb7f0f883b5c87747bbe25
39771 F20101124_AABZGL belsito_j_Page_60.pro
982ba9112dbf923effdf791fe6ff4325
1d85309f9b0748a048523f89a098c2227038f5b8
101067 F20101124_AABYZN belsito_j_Page_18.jp2
637f98c12182cdce8509bef9e24e6ccd
55ee32c910d15c5172c8d6ce191f5136b3053ce6
48451 F20101124_AABZBO belsito_j_Page_72.jp2
b04e7491a614d863a95d00b2a8cd6ed7
14df84dd03bd75bd6136384809b56294775b6770
6773 F20101124_AABZLJ belsito_j_Page_54thm.jpg
5e90645cc1936353c31978a260c2676f
93e952c501ac639c9dc0d130141df89ea8b68777
99162 F20101124_AABYZO belsito_j_Page_19.jp2
39ac0383015101c50f154e577dbfd5a0
00acc4fe6c5437c57790e91a6a702452f54fd767
47940 F20101124_AABZGM belsito_j_Page_61.pro
f54aa1fb21bf7ea22cbec03182291e8a
68b6a8d4a0e93b9f642513c60aeccc93672379ba
4765 F20101124_AABZLK belsito_j_Page_45thm.jpg
a6012f61dc1dd44763a6580828cdde8a
97cc274d453d516aef7a4588eb7abd05818d2ef0
108769 F20101124_AABYZP belsito_j_Page_20.jp2
897e65e737a90f7e0b5157469df8c8ec
86e4fff5be96161acb3b2c02e1d5c0360b18f469
F20101124_AABZBP belsito_j_Page_01.tif
aba247b28b1d12f5207961d292e5867a
b721ef927e23f3d68de8bab6b400fb1b2aca3d5e
51018 F20101124_AABZGN belsito_j_Page_62.pro
7dcae5683ca2d3bff594e2abed646acc
6d35c263c4af141d83fafc8d51e1f07a6e59bfba
13399 F20101124_AABZLL belsito_j_Page_39.QC.jpg
cae3b20cb5aa0dee3c7b570606ab8483
14eca162c807ffbd89a2e5a87d2c09f812d6a5cb
103400 F20101124_AABYZQ belsito_j_Page_21.jp2
7e3c326614b3a2044dab02e56981a39c
0221b0a7babe6d747f26c95399d1ab01e4e6e89e
F20101124_AABZBQ belsito_j_Page_02.tif
3d244c3a12bb6e10bf8e474cf5296013
466e4eef0b5a70f7ca1be64c6ffa1a15e469c8f5
47204 F20101124_AABZGO belsito_j_Page_63.pro
cf1e44b8489d25c9620b920eff77c8a2
21621903058f5fa1e76f158f6ae35f7c415e8cd6
22743 F20101124_AABZLM belsito_j_Page_20.QC.jpg
4d3f19eb7740235aca66de55e1d3d682
4939e8840f943fae89594c78385e3b14a58c3fa1
106905 F20101124_AABYZR belsito_j_Page_22.jp2
07892efe1eb6d23de372d2e033752e3a
2b1727e2ffbc15073aa2346e79408ea877a0d4e7
F20101124_AABZBR belsito_j_Page_03.tif
93a6a3a6eba9f768ffaf5f12d2c4baef
a6364358668f0100644bb7a64ad87be6ea00f02c
49058 F20101124_AABZGP belsito_j_Page_64.pro
3a59a42731acc00201b2995cc6484165
4cb81df97c9f1c0ba66680375d55ff7074f9a890
4439 F20101124_AABZLN belsito_j_Page_09.QC.jpg
d0c66e7e50314d3a3ec03cb3a219ea0f
afc9b7b4bbc50a7b8cc2745a47cdb18543d42a42
108398 F20101124_AABYZS belsito_j_Page_23.jp2
77431a363902c56f65eeac9e62a1418f
d9c32bd5b6833fb5baa43242cd7545005d857aaa
F20101124_AABZBS belsito_j_Page_04.tif
2dc5943a8c74e73d565192862f801f7b
ff3c5fcad3b2568d06821f53eee819281ea598db
16615 F20101124_AABZGQ belsito_j_Page_65.pro
fffb85c283fa6837fc04ed0032344d3c
ee4ad073022639f25f15d3115a1db4ed626f2e7b


Permanent Link: http://ufdc.ufl.edu/UFE0011398/00001

Material Information

Title: Value of the Dairy Herd Improvement (DHI) hot list as a dairy management tool
Physical Description: xi, 61 p.
Language: English
Creator: Belsito, Jessica Elizabeth ( Dissertant )
Natzke, Roger P. ( Thesis advisor )
Vries, Albert de ( Reviewer )
Place, Nick ( Reviewer )
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2005
Copyright Date: 2005

Subjects

Subjects / Keywords: Animal Sciences thesis, M.S   ( local )
Dissertations, Academic -- UF -- Animal Sciences   ( local )

Notes

Abstract: 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.
Subject: cell, count, DHI, hot, list, somatic, variability
General Note: Title from title page of source document.
General Note: Document formatted into pages; contains 72 pages.
General Note: Includes vita.
Thesis: Thesis (M.S.)--University of Florida, 2005.
Bibliography: Includes bibliographical references.
General Note: Text (Electronic thesis) in PDF format.

Record Information

Source Institution: University of Florida
Holding Location: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
System ID: UFE0011398:00001

Permanent Link: http://ufdc.ufl.edu/UFE0011398/00001

Material Information

Title: Value of the Dairy Herd Improvement (DHI) hot list as a dairy management tool
Physical Description: xi, 61 p.
Language: English
Creator: Belsito, Jessica Elizabeth ( Dissertant )
Natzke, Roger P. ( Thesis advisor )
Vries, Albert de ( Reviewer )
Place, Nick ( Reviewer )
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2005
Copyright Date: 2005

Subjects

Subjects / Keywords: Animal Sciences thesis, M.S   ( local )
Dissertations, Academic -- UF -- Animal Sciences   ( local )

Notes

Abstract: 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.
Subject: cell, count, DHI, hot, list, somatic, variability
General Note: Title from title page of source document.
General Note: Document formatted into pages; contains 72 pages.
General Note: Includes vita.
Thesis: Thesis (M.S.)--University of Florida, 2005.
Bibliography: Includes bibliographical references.
General Note: Text (Electronic thesis) in PDF format.

Record Information

Source Institution: University of Florida
Holding Location: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
System ID: UFE0011398:00001


This item has the following downloads:


Full Text












VALUE OF THE DAIRY HERD IMPROVEMENT (DHI) HOT LIST AS A DAIRY
MANAGEMENT TOOL
















By

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


2005

































Copyright 2005

by

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).















ACKNOWLEDGMENTS

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

page

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

CHAPTER

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




v









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


Table page

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


Figure page

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
MANAGEMENT TOOL

By

Jessica Elizabeth Belsito

August 2005

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.














CHAPTER 1
INTRODUCTION

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.














CHAPTER 2
LITERATURE REVIEW

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.

Background

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).

Pathogenesis

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,

1994).

Immunity

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.,

1979).

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).









Pathogens

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

Kingwell, 1975).









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).









Economics

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.














CHAPTER 3
FLORIDA MILK QUALITY LAB ANALYSIS

Introduction

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

commercial laboratory.

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.














CHAPTER 4
VARIATIONS IN SOMATIC CELL COUNTS FROM MILKING TO MILKING

Introduction

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

health indicator.

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

100
3 80-
4-60
0


S20-
0
0-250,000 251,000- 500,001- 751,000- 1,000,000+
500,000 750,000 1,000,000
SCC (cells/ml)

Figure 4-1. Average SCC for the 15 milkings on an individual cow basis (380 cows
total).





























Figure 4-2. Number of cows over or under certain somatic cell
(380 cows total).


counts at all


15 milkings


Number of Cows Above or Below a Certain Daily
SCC All 5 Days


a 200

o 150

o 100

E 50
z 0
> < < > > > >
200,000 400,000 750,000 750,000 750,000 500,000 250000
SCC (cells/ml)

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


120
o 100
80 -
o 60 -
40
E 20
z 0

200000 400000 750000 1000000 750000 500000 250000
SCC









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

research.


Number of Cows With A Daily SCC Above One
Million


200

150

100

50
n


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

200

150
0
4-)
o 100

S50 -
z
0 "-
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


140
120
100
80
60
40
20
0


* Series


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


100

80

60

40

20

0


2 3
Number of Occurences


4 5


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
milkings.
Milking Raw Correlation Adjusted
Correlation
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
Correlation
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


4000
a 3500
0
= 3000
> 2500
o 2000
S1500
S1000
, 500
0


0 500 1000 1500 2000
Average SCC


2500


Figure 4-8. Individual cow standard deviation versus her average SCC (380 cows total).


Coefficient of Variation Versus Average SCC


- 4
0 3.5
*" 3
> 2.5
05 2
4-W
S 1.5
"I 1
w 0.5
o 0


500


1000 1500
Average SCC


2000


2500


Figure 4-9. Individual cow coefficient of variation versus her average SCC(380 cows
total).

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


AL-
S+NY









the SCC is above a million, however, the coefficient of variation tends to level off at

about 2.

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.














CHAPTER 5
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.

Lactation Milk
Cow ID Lactaton DIM Milk SCC (1000s) W/O % Cells
Number (lbs)
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

procedures.

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.












+Actual SCC
-calculated SCC




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

their herd.

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

differences.

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

milking.


Frequency of Cows Appearing on the Hot List
(Hot Lists were calculated for each of the 15 milkings)

250
200
150
0
0 100
o M

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)

350
300
O 250
0 200
150
E 100
50 -
0
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
hot list
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

study.

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

insignificant amount.

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

testday.

Table 5-2. Results of the Proc Mixed Procedure on daily bulk tank data.
Effect Pr > F
Herdcode 0.0002
Month <0.0001
Group 0.0184
Month*Group 0.0868

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.














CHAPTER 6
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

highly unreliable.

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.
















LIST OF REFERENCES


Allore, H.G. and H.N. Erb. 1998. Partial Budget of the Discounted Annual Benefit of
Mastitis Control Strategies. J. Dairy Sci. 81:2280-2292.

Asby, C.B., P.R. Ellis, T.K. Griffin, R.G. Kingwill. The Benefits and Costs of a System
of Mastitis Control in Individual Herds. University of Reading, Department of
Agriculture and Horticulture. Study No 17, 1975.

Berry, E.A., J.E. Hillerton and M. Gravenor. 2004. Variation of Individual Cow Cell
Count. Proc. 43rd National Mastitis Council Meeting. Verona, WI. P 284.

Blosser, T.H. 1979. Economic Losses From and the National Research Program on
Mastitis in the United States. J. Dairy Sci. 62:119-127.

Dinsmore, R.P., P.B. English, R.N. Gonzalez and P.M. Sears. 1992. Use of Augmented
Cultural Techniques in the Diagnosis of the Bacterial Cause of Clinical Bovine
Mastitis. J. Dairy Sci. 75:2706-2712.

Dohoo, I.R., and A.H. Meek. 1982. Somatic Cell Counts in Bovine Milk. Can. Vet. J.
23:119.

Duitschaever, C.L., and G.C. Ashton. 1972. Variations of Somatic Cells and
Neutrophils in Milk Throughout Lactation. J. Milk Food Technology. 35:197-202.

Elvinger, F., P.J. Hansen, and R.P. Natzke. 1991. Modulation of Function of Bovine
Polymorphonucleur Leukocytes and Lymphocytes by High Temperatures in Vitro
and in Vivo. Am. J. Vet. Res. 52:1962.

Emanuelsson, U. and E. Persson. 1984. Studies on Somatic Cell Counts in Milk from
Swedish Dairy Cows. Nongenetic Causes of Variation in Monthly Test-Day
Results. Acta Agric. Scand. 34:33.

Giesecke, W.H. 1975. The Definition on Bovine Mastitis and the Diagnosis of its
Subclinical Types During Normal Lactation. Proc. Seminar on Mastitis Control.
Int. Dairy Fed. Reading, England. Bull. Doc. 85:62.

Harmon, R.J. 1994. Symposium: Mastitis and Genetic Evaluation for Somatic Cell
Count. J. Dairy Sci. 77:2103-2112.

Heeschen, W. 1975. Determination of Somatic Cells in Milk. Proc. Seminar on Mastitis
Control. Int. Dairy. Fed. Bull. Reading, England. Doc. 85:79.









Hogan, J.S. W.P Weiss, and K.L. Smith. 1993. Role of Vitamin E and Selenium in Host
Defense Against Mastitis. J. Dairy Sci. 76:2795:2803.

Janzen, J.J. 1970. Economic Losses Resulting From Mastitis. A Review. J. Dairy Sci.
53:1151.

Kehrli, M.E. and D.E. Shuster. 1994. Factors Affecting Milk Somatic Cells and Their
Role in Health of the Bovine Mammary Gland. J. Dairy Sci. 77:619-627.

Kelton, D.F., K.D. Lissemore and R.E. Martin. 1998. Recommendations for Recording
and Calculating the Incidence of Selected Clinical Diseases. J. Dairy Sci. 81:2502-
2509.

Kingwill, R.G., F.H. Dodd, and F.K. Neave. 1979. Machine Milking and Mastitis. In
Machine Milking. Nat. Inst. For Res. In Dairying, Reading, England. P. 231.

Kitchen, B.J. 1981. Review of Progress of Dairy Science: Bovine Mastitis: Milk
Compositional Changes and Related Diagnostic Tests. J. Dairy Research. 48:167.

Ma, Y., C. Ryan, D.M. Barbano, D.M. Galton, M.A. Rudan, and K.J. Boor. 2000.
Effetct of Somatic Cell Count on Quality and Shelf-Life of Pasteurized Fluid Milk.
J. Dairy Sci. 83:264-274.

Martin, J.M. 1973. Milk Yield Interrelationships With Somatic Cells and Chemical
Constituents Over a Lactation and During Restricted Water Consumption. M.S.
Thesis, North Carolina State Univ., Raleigh.

McDermott, M.P., H.N. Erb, R.P. Natzke, F.D. Barnes and D.R. Bray. 1983. Cost
Benefit Analysis of Lactation Therapy with Somatic Cell Counts as Indications for
Treatment. J. Dairy Sci. 66:1198-1203.

Miller, R.H., U. Emanuelsson, E. Persson, J. Brolund, Philipsson and E. Funke. 1983.
Relationships of Milk Somatic Cell Counts to Daily Milk Yield and Composition.
Acta Agric. Scand. 33:209.

National Mastitis Council. 2003. SCC Regulatory Limit in US to Remain at 750,000.
Newsletter. Verona, WI. Volume 26, No 2, p 1.

Neave, F.K. 1975. Diagnosis of Mastitis by Bacteriological Methods Alone. Proc.
Seminar on Mastitis Control. Int. Dairy. Fed. Bull. Reading, England. Doc. 85:79.

Oldham, E.R., R.J. Eberhart and L.D. Muller. 1991. Effects of Supplemental Vitamin A
or p-Carotene During the Dry Period and Early Lactation on Udder Health. J.
Dairy Sci. 74:3775-3781.

Oliver, S.P., M.J. Lewis, T.L. Ingle, B.E. Gillespie and K.R. Matthews. 1993.
Prevention of Bovine Mastitis by a Premilking Teat Disinfectant Containing
Chlorous Acid and Chlorine Dioxide. J. Dairy Sci. 76:287:292.










Ott, S.L. 1999. Costs of Herd-Level Production Losses Associated With Subclinical
Mastitis in U.S. Dairy Cows. Proc. Natl. Mastitis Council. Natl. Mastitis Council,
Verona, WI. P 152.

Paape, M.J., A.J. Schultze, A.J. Guidry and R.E. Pearson. 1979. Leukocytes; Second
Line of Defense Against Invading Mastitis Pathogens. J. Dairy Sci. 62:135.

Phipps, L.W. 1968. Electronic Counting of Cells in Milk: Examination of a Chemical
Treatment for Dispersal of Milk Fat. J. Dairy Res. 35:295.

Ravinderpal, G., W.H. Howard, K.E. Leslie, and K. Lissemore. 1990. Economics of
Mastitis Control. J Dairy Sci. 73:3340-3348.

Reneau, J.K. 1986. Effective Use of Dairy Herd Improvement Somatic Cell Counts in
Mastitis Control. J. Dairy Sci. 69:1708.

Schultz, L.H. 1977. Somatic Cells in Milk- Physiological Aspects and Relationship to
Amount and Composition of Milk. J. Food Prot. 40:125.

Schutz, M.M., L.B. Hansen, G.R. Steuemagel and A.L. Kuck. 1990. Variation of Milk,
Fat, Protein, and Somatic Cells for Dairy Cattle. J. Dairy Sci. 73:484-493.

Sheldrake, R.F., R.J.T. Hoare and G.D. McGregor. 1983. Lactation Stage, Parity and
Infection Affecting Somatic Cells, Electrical Conductivity and Serum Albumin in
Milk. J. Dairy Sci. 66:542.

Smith, A., F.K. Neave, F.H. Dodd and G.C. Brander. 1966. Methods of Reducing the
Incidence of Udder Infection in Dry Cows. Vet. Rec. 79:233.

Sordillo, L.M., K. Shafer-Weaver and D. DeRosa. 1997. Immunobiology of the
Mammary Gland. J. Dairy Sci. 80:1851-1865.

Webb, D. Unpublished data. University of Florida. Gainesville, FL. 2004.

Weiss, W.P., J.S. Hogan and K.L. Smith. 2004. Changes in Vitamin C Concentration in
Plasma and Milk from Dairy Cows After an Intramammary Infusion of Escheria
Coli. J. Dairy Sci. 87:32-37

Weiss, W.P., J.S. Hogan, K.L. Smith and K.H. Hoblet. 1990. Relationships Among
Selenium, Vitamin E, and Mammary Gland Health in Commercial Dairy Herds. J.
Dairy Sci. 73:381-390.

Wilson, C.D., R.G. Kingwell. 1975. A Practical Mastitis Control Routine. Proc.
Seminar on Mastitis Control. Int. Dairy Fed. Bull. Reading, England. Doc. 85:62.















BIOGRAPHICAL SKETCH

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.