Interpreting Bulk Tank Bacteria & SCC - Southeast...
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Interpreting Bulk Tank Bacteria & SCC -Jack of all Trades Kristy H. Campbell
Transcript of Interpreting Bulk Tank Bacteria & SCC - Southeast...
Interpreting Bulk Tank Bacteria & SCC
-Jack of all Trades
Kristy H. Campbell
What do we really know? Has it changed?
Bulk Tank
Bacteria Category
Bacteria Cultures
Standard Plate Count
Lab Pasteurized Count
Prelim. Incubation Count
Contagious Pathogens
Environmental
Pathogens
“Odd-ball” Pathogens
High
Medium
Low
Somatic Cell Count
Bulk Tank Somatic Cell Count
Bulk Tank SCC % Infected Quarters
200,000 6
500,000 16
1,000,000 32
1,500,000 48
ESTIMATING INFECTION LEVEL USING BULK TANK MILK SCC
Cost of Mastitis – Production losses
1st lactation 2nd lactation
SCC Class Milk Yield (lbs)Diff
(lbs)Diff (%) Milk Yield (lbs)
Diff
(lbs) Diff (%)
<50,000 19,180 0 0 23,031 0 0
51 – 100 18,677 -503 -2.6 22,026 -1005 -4.4
101 –200 18,567 -613 -3.2 21,649 -1382 -6.6
201 – 400 18,501 -679 -3.5 21,420 -1611 -7.0
401 - 800 18,470 -710 -3.7 20,844 -2187 -9.5
>800 n.a. n.a. n.a. 19,978 -3053 -13.3
18,702 cows (Jahnke, 2004)
- The Market RealityBulk Tank Somatic Cell Counts
• > 400,000 cells/ml
– Danger Zone if not already on ‘probation’
• 250,000 – 350,000 cells/ml
– Warning Level
• <250,000
– “Acceptable” depending on the time of year
• <200,000
– Financial incentives
PICKUP DATE WEIGHT BF PRO LAC SNF OS SCC (X 1000) SPC (X 1000) PI COUNT (X 1000) LPC FRZP MUN TEMP
20161017 50155 3.36 3.31 4.87 9.11 5.8 220 0 0 0 0 0 38
20161017 49999 3.36 3.31 4.87 9.11 5.8 220 0 0 0 0 0 38
20161017 50000 3.5 3.29 4.88 9.08 5.79 260 0 0 0 0.552 8.9 38
20161018 49991 3.46 3.3 4.87 9.09 5.79 240 0 0 0 0 0 38
20161018 49984 3.46 3.3 4.87 9.09 5.79 240 0 0 0 0 0 38
20161018 50010 3.46 3.3 4.87 9.09 5.79 240 0 0 0 0 0 38
20161018 50034 3.46 3.26 4.84 8.97 5.71 230 0 0 0 0.549 10.7 38
20161019 49412 3.39 3.27 4.86 9.04 5.77 250 9 7 0 0.55 9.2 38
20161019 50020 3.31 3.24 4.87 9 5.76 240 8 8 0 0.551 10.2 38
20161019 50016 3.42 3.29 4.88 9.06 5.77 220 4 13 0 0.552 8.3 38
20161020 50040 3.37 3.27 4.87 9.06 5.79 240 0 0 0 0 0 38
20161020 50003 3.37 3.27 4.87 9.06 5.79 240 0 0 0 0 0 38
20161020 49964 3.37 3.27 4.87 9.06 5.79 240 0 0 0 0 0 38
20161020 49992 3.44 3.3 4.88 9.09 5.79 210 0 0 0 0.553 8.1 38
20161021 49951 3.39 3.28 4.88 9.08 5.8 220 0 0 0 0 0 38
20161021 49998 3.39 3.28 4.88 9.08 5.8 220 0 0 0 0 0 38
20161021 50008 3.25 3.26 4.88 9.03 5.77 200 0 0 0 0.552 8.2 38
20161022 49963 3.37 3.28 4.88 9.08 5.8 210 0 0 0 0 0 38
20161022 50015 3.42 3.29 4.87 9.04 5.75 260 3 3 0 0.553 10.9 38
20161022 50024 3.29 3.35 4.89 9.11 5.76 230 2 2 0 0.552 8.5 38
20161022 50010 3.47 3.36 4.9 9.16 5.8 220 0 0 0 0.556 8.7 38
20161023 49743 3.39 3.33 4.89 9.15 5.82 240 0 0 0 0 0 38
20161023 50015 3.22 3.25 4.87 9.02 5.77 230 2 2 0 0.549 8.4 38
20161023 49998 3.42 3.33 4.9 9.12 5.79 220 0 0 0 0.554 7 38
20161024 50374 3.31 3.31 4.89 9.13 5.82 230 0 0 0 0 0 38
20161024 50137 3.46 3.31 4.89 9.09 5.78 230 0 0 0 0.557 9.9 32
20161024 50038 3.38 3.33 4.85 9.11 5.78 240 0 0 0 0.549 8.5 38
20161024 50016 3.58 3.39 4.89 9.22 5.83 220 0 0 0 0.557 8.1 38
20161025 50011 3.43 3.28 4.88 9.05 5.77 260 3 3 0 0.553 9.5 38
20161025 50017 3.25 3.29 4.88 9.05 5.76 220 3 3 0 0.553 10.5 38
20161025 49969 3.38 3.49 4.58 8.97 5.48 200 4000 4000 0 0.584 1 38
20161026 50440 3.24 3.36 4.85 9.12 5.76 220 1 2 0 0.552 8.3 38
20161026 49962 3.6 3.36 4.86 9.15 5.79 270 3 2 0 0.552 8.7 38
20161026 50131 3.18 3.3 4.88 9.04 5.74 220 4 2 0 0.556 11.5 38
20161026 50012 3.44 3.33 4.91 9.14 5.81 230 0 0 0 0.557 8.7 38
20161027 50003 3.43 3.35 4.87 9.15 5.8 240 0 0 0 0 0 38
20161027 50010 3.13 3.29 4.91 9.09 5.8 230 0 0 0 0.554 8.5 38
20161027 50000 3.38 3.3 4.88 9.06 5.76 260 0 0 0 0.554 10.4 38
20161027 50006 3.47 3.34 4.89 9.14 5.8 240 0 0 0 0.555 8.5 38
20161028 51065 3.02 3.29 4.9 9.04 5.75 200 7 17 0 0.555 11.8 38
20161028 50180 3.23 3.38 4.9 9.16 5.78 200 2 2 0 0.554 10 38
20161028 50017 3.46 3.31 4.88 9.08 5.77 260 10 5 0 0.554 12.1 38
20161029 50001 3.43 3.36 4.88 9.14 5.78 240 3 2 0 0.554 10.7 38
20161029 49995 3.33 3.31 4.89 9.13 5.82 240 1 1 0 0.554 11.2 38
20161029 50436 3.33 3.35 4.87 9.07 5.72 230 0 0 0 0.556 11.6 38
20161029 49986 3.34 3.37 4.91 9.16 5.79 220 0 0 0 0.557 10.3 38
20161030 49962 3.37 3.36 4.89 9.18 5.82 230 0 0 0 0 0 38
20161030 50040 3.13 3.27 4.92 9.06 5.79 210 0 0 0 0.556 11.8 38
20161030 50034 3.34 3.31 4.9 9.08 5.77 210 0 0 0 0.554 10.4 38
20161031 50043 3.27 3.32 4.91 9.16 5.84 210 0 0 0 0 0 38
20161031 49999 3.27 3.32 4.91 9.16 5.84 210 0 0 0 0 0 38
20161031 50001 3.27 3.32 4.91 9.16 5.84 210 0 0 0 0 0 38
20161031 50019 3.51 3.34 4.9 9.12 5.78 240 2 2 0 0.553 9.8 38
20161101 50000 3.33 3.31 4.91 9.15 5.84 220 0 0 0 0 0 38
20161101 50003 3 3.3 4.92 9.02 5.72 160 0 0 0 0.553 11.8 38
20161101 50039 3.21 3.29 4.91 9.03 5.74 170 0 0 0 0.552 12.5 38
20161102 50022 3.24 3.31 4.91 9.15 5.84 190 0 0 0 0 0 38
20161102 49993 3.24 3.31 4.91 9.15 5.84 190 0 0 0 0 0 38
20161102 49991 3.36 3.3 4.88 9.07 5.77 170 0 0 0 0.549 11.7 38
20161102 50153 3.16 3.35 4.88 9.1 5.75 170 0 0 0 0.55 10.9 38
20161103 49991 3.24 3.31 4.89 9.13 5.82 170 0 0 0 0 0 38
20161103 50345 3.07 3.23 4.87 8.99 5.76 180 2 1 0 0.547 10.4 38
20161103 50009 3.22 3.29 4.84 9 5.71 190 2 2 0 0.546 10.8 38
20161103 50006 3.27 3.35 4.88 9.13 5.78 190 1 1 0 0.551 10.8 38
20161104 49979 3.19 3.29 4.86 9.07 5.78 190 0 0 0 0 0 38
20161104 49993 3.19 3.29 4.86 9.07 5.78 190 0 0 0 0 0 38
20161104 50067 3.16 3.26 4.88 9.04 5.78 170 1 2 0 0.548 10.5 38
20161105 49995 3.31 3.31 4.88 9.08 5.77 220 0 0 0 0.55 10.4 38
20161105 49957 3.14 3.31 4.89 9.07 5.76 200 0 0 0 0.549 9.8 38
20161105 49946 3.26 3.31 4.9 9.11 5.8 160 0 0 0 0.553 11.8 38
20161105 50029 3.36 3.25 4.83 8.96 5.71 240 0 0 0 0.545 11 38
20161106 50012 3.21 3.31 4.87 9.06 5.75 160 1 1 0 0.549 11.7 38
20161106 50026 3.19 3.24 4.88 9 5.76 220 2 2 0 0.551 12.1 38
20161106 50092 3.5 3.29 4.89 9.06 5.77 230 2 2 0 0.551 11.6 38
20161107 50072 3.41 3.36 4.88 9.14 5.78 200 2 1 0 0.55 10.6 38
20161107 50006 3.25 3.32 4.85 9.07 5.75 210 2 2 0 0.548 10.9 38
20161107 50005 3.26 3.29 4.88 9.01 5.72 190 0 0 0 0.552 13.8 38
20161107 50954 3.23 3.32 4.89 9.1 5.78 210 0 0 0 0.551 11.6 38
20161108 49927 3.26 3.25 4.87 9 5.75 240 0 0 0 0.551 12.7 38
20161108 50021 3.35 3.27 4.87 9.01 5.74 210 0 0 0 0.549 13.3 38
20161108 49814 3.35 3.26 4.81 8.97 5.71 220 0 0 0 0.541 11.1 38
20161109 50010 3.32 3.26 4.85 9.03 5.77 220 0 0 0 0 0 38
20161109 50007 3.32 3.26 4.85 9.03 5.77 220 0 0 0 0 0 38
20161109 50010 3.32 3.26 4.85 9.03 5.77 220 0 0 0 0 0 38
20161109 50022 3.26 3.23 4.82 8.92 5.69 200 2 5 0 0.544 13.4 38
20161110 50070 0 0 0 0 0 0 0 0 0 0 0 38
20161110 50003 3.31 3.28 4.87 9.04 5.76 200 2 2 0 0.547 11.6 38
20161110 0 3.25 3.24 4.88 9.01 5.77 210 0 0 0 0.552 13.2 0
20161110 0 3.41 3.26 4.89 9.05 5.79 200 0 0 0 0.551 13.1 0
20161111 50006 0 0 0 0 0 0 0 0 0 0 0 38
20161111 50001 0 0 0 0 0 0 0 0 0 0 0 38
20161111 0 3.28 3.37 4.88 9.13 5.76 190 0 0 0 0.55 12.3 0
20161111 0 3.28 3.3 4.89 9.08 5.78 250 0 0 0 0.55 13.7 0
Are monthly averages enough?
Are daily counts too much?
Bulk Tank Somatic Cell Counts
Bulk Tank Somatic Cell Counts
“Typical dairy management reports provide a plethora of data usually in a tabular form. The inevitable result of having such a large volume of data presented in such a small area is that it requires much study and effort to interpret. The human mind does not do well absorbing large amounts of data. In addition, most of us tend to be numerically illiterate. It’s not that we don’t understand the mechanics of arithmetic. Rather we don’t intuitively know how to extract knowledge from data. The most common use of dairy management data is compare this month’s average with last month’s average. Are we doing better or worse? The problem with such limited comparisons is that they are out of context.”
- Jeffrey Reneau, NMC 2000 Process Control: Timely Feedback for Quality Milk Production at the Farm
Bulk Tank Somatic Cell Counts
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Bulk Tank Somatic Cell Count July - Nov
Bulk Tank Somatic Cell Counts
• Trend lines are a minimum needed tool (can be producer generated)
• “When we mistake random variation for a real change, we may diagnose a non-existing change! Application of unnecessary corrective action would truly be a waste of labor, time & money…..(it) detracts from productive management. It adds complexity & creates management chaos.”
• “….failure to identify an emerging problem quick enough to initiate timely corrections is also a great concern. The indecisive strategy of “Let’s wait to see what it looks like next month” may squander the opportunity to nip the problem in the bud.”- Jeffrey Reneau
• Statistical Process Control (SPC) should be used as a milk quality monitoring tool and could (should) be provided by milk cooperatives
Milk Quality Standards
Bulk Tank Bacteria Counts
• Standard Plate Count – mesophilic bacteria
• Lab Pasteurized Count – thermoduric bacteria
• Preliminary Incubation Count – psychrotropic bacteria
• All can attribute to lower quality end product, and are therefore important indicators to milk processing.
• However, there are no clear indicators of causation.
What needs cleaning? The machine or the cows?
……Good question!
Sources of High Bulk Tank Bacteria Counts
Reality of Producing High Quality Milk
Time
Temperature
Concentration
Water
Volume
Velocity
Drainage
Pre-cooling
Refrigeration
Time
Agitation
Udder Hygiene
Cow Comfort
Milking Hygiene Milking
Procedure
Mastitis
Cooling
Contamination
Cleaning
What do the numbers mean?Sources of Microbial Contamination As Detected by Bacteriological Procedures
Test Result
Natural Flora of
Teat Skin Mastitis Dirty Cows Dirty Equipment Poor Cooling
SPC >10,000 Unlikely Possible Possible Possible Possible
SPC>100,000 Unlikely Possible (rare) Unlikely Very Likely Very Likely
LPC >200-300 Unlikely Unlikely Possible Very Likely Unlikely
Higher PI than SPC Unlikely Unlikely Possible Very Likely Very Likely
Higher SPC than PI Unlikely Possible Possible (not likely) Possible (not likely) Possible (not likely)
Coliform Unlikely Possible (rare) Possible Possible Possible (not likely)
Source: Raw Milk Bacteria Tests & Sources and Causes of High Bacteria Counts – An Abbreviated
Review.
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HOLY SMOKES!! MY STANDARD PLATE COUNT IS 200,000!!
WHAT’S CAUSING THIS?
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HOLY SMOKES!! MY STANDARD PLATE COUNT IS 200,000!!
WHAT’S CAUSING THIS?
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HOLY SMOKES!! MY STANDARD PLATE COUNT IS 200,000!!
WHAT’S CAUSING THIS?
Is this a problem? Yes
Is this THE problem?
What do the numbers mean?Sources of Microbial Contamination As Detected by Bacteriological Procedures
Test Result
Natural Flora of
Teat Skin Mastitis Dirty Cows Dirty Equipment Poor Cooling
SPC >10,000 Unlikely Possible Possible Possible Possible
SPC>100,000 Unlikely Possible (rare) Unlikely Very Likely Very Likely
LPC >200-300 Unlikely Unlikely Possible Very Likely Unlikely
Higher PI than SPC Unlikely Unlikely Possible Very Likely Very Likely
Higher SPC than PI Unlikely Possible Possible (not likely) Possible (not likely) Possible (not likely)
Coliform Unlikely Possible (rare) Possible Possible Possible (not likely)
Source: Raw Milk Bacteria Tests & Sources and Causes of High Bacteria Counts – An Abbreviated
Review.
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HELP!!! MY LPC’S ARE 400!!!
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HELP!!! MY LPC’S ARE 400!!!
The bulk tank thermometer
is stuck on 38F (and has
been for awhile), and the
milkers only look at it at the
END of milking – Ay OK!
You find that the milk
temperature is actually 52F
4 hours after milking ends.
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HELP!!! MY LPC’S ARE 400!!!
The bulk tank thermometer
is stuck on 38F (and has
been for awhile), and the
milkers only look at it at the
END of milking – Ay OK!
You find that the milk
temperature is actually 52F
4 hours after milking ends.
Is this a problem?
Yes.
Is this THE problem?
What do the numbers mean?Sources of Microbial Contamination As Detected by Bacteriological Procedures
Test Result
Natural Flora of
Teat Skin Mastitis Dirty Cows Dirty Equipment Poor Cooling
SPC >10,000 Unlikely Possible Possible Possible Possible
SPC>100,000 Unlikely Possible (rare) Unlikely Very Likely Very Likely
LPC >200-300 Unlikely Unlikely Possible Very Likely Unlikely
Higher PI than SPC Unlikely Unlikely Possible Very Likely Very Likely
Higher SPC than PI Unlikely Possible Possible (not likely) Possible (not likely) Possible (not likely)
Coliform Unlikely Possible (rare) Possible Possible Possible (not likely)
Source: Raw Milk Bacteria Tests & Sources and Causes of High Bacteria Counts – An Abbreviated
Review.
X
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• SPC >10,000 but <100,000 cfu/mL
• Mastitis Causing Organisms
• Streptococcus spp., most notably S. agalactiae and S. uberis
• These organisms do not grow significantly on soiled milking equipment or with poor cooling. Their presence in
bulk tank milk is considered strong evidence that they originated from infected cows.
• High SPC with Low PI
• Mastitis Causing Organisms
• Typically from mastitic/high SCC cows
• Typically environmental Streps
• High PI & High LPC
• Not Mastitis Causing organisms
• Less efficient cleaning, using lower temperatures and/or the absence of sanitizers tends to select for the faster
growing, less resistant organisms, principally Gram-negative rods (coliforms and Pseudomonads) and lactic
streptococci.
• High LPC
• Persistent Cleaning Failures & Inconsistencies
• Faulty equipment; worn out parts such as leaky pumps; old pipeline gaskets, inflations and other rubber parts;
milkstone deposits. Can also be influenced by soiled cows - use coliform count to confirm this correlation.
• High LPC and High Coliform
• Milking dirty cows
• Incubation of buildup or biofilm in the system
• High Coliform
• Manure or Contaminated Environment
• Poor milking practices, attaching units to dirty or wet teats, very high coliforms indicate dirty equipment.
• High PI with Low SPC and/or Low LPC
• Failure to cool milk rapidly, marginal cooling, prolonged storage times, milking wet teats, extremely wet & humid
weather conditions, agitator problems during milking and/or storage
• High SPC and High PI
• A PI count equal or slightly higher than a high SPC (more than 50,000 cfu/mL) suggests that the high SPC is
possibly due to mastitis.
• High LPC with High PI
• Cleaning failures.
• High PI with Pseudomons
• Pseudomonas from well water. Seeds into rubber goods. 1) treat the well 2) replace ALL rubber parts
“Further studies are necessary to identify biologically meaningful
thresholds to define increased bacterial counts in raw milk and investigate
on-farm risk factors associated with bacterial counts in bulk milk.” – Pantoja, Reinemann, Ruegg. JDS 2009. Associations among milk quality indicators in raw bulk milk.
We need more frequent information!Bulk Tank Bacteria Count
PICKUP
DATE WEIGHT SPC (X 1000)
PI COUNT (X
1000) LPC
20161019 50230 21 41 0
20161019 50056 0 0 0
20161020 50195 0 0 0
20161020 49741 0 0 0
20161020 49660 0 0 0
20161020 49781 0 0 0
20161020 49809 0 0 0
20161021 49711 0 0 0
20161021 49905 0 0 0
20161021 50024 0 0 0
20161021 49813 0 0 0
20161021 50118 0 0 0
20161022 50095 0 0 0
20161022 49991 0 0 0
20161022 49878 4 3 0
20161022 49630 3 4 0
After the high PI count – 13 loads were shipped before the next sample.
Approx 650,000 lbs of potentially poor quality milk.
We need more frequent information!Bulk Tank Bacteria Count
Before the high count – 15 loads were shipped between samples.
Approx 750,000 lbs of potentially poor quality milk.
PICKUP
DATE WEIGHT SPC (X 1000)
PI COUNT (X
1000) LPC
20161022 49630 3 4 0
20161022 49914 0 0 0
20161023 49202 0 0 0
20161023 50133 0 0 0
20161023 50046 0 0 0
20161023 49882 0 0 0
20161023 49809 0 0 0
20161023 49738 0 0 0
20161024 49875 0 0 0
20161024 50021 0 0 0
20161024 50002 0 0 0
20161024 50115 0 0 0
20161024 49650 0 0 0
20161025 49608 0 0 0
20161025 49889 0 0 0
20161025 50013 0 0 0
20161025 50163 140 260 0
Bulk Tank Milk Cultures
• Can supply 2 important types of information
– Presence or absence of a bacterial group
– Identification of predominant bacterial groups
• More often it is sampled, the more useful it can be.
• EXTREME caution should be taken when interpreting results from a single BTM sample
– Cultures cannot be used to predict the number of quarters infected within a herd and are not useful indicators of mastitis prevalence within a herd
Low levels Moderate
levels
High levels Very High
levels
Strep ag <50 50-200 200-400 >400
Staph aureus <50 50-150 150-250 >250
Non-ag Strep 500-700 700-1200 1200-2000 >2000
Coliforms <100 100-400 400-700 >700
Staph Species <300 300-500 500-750 >750
Bulk Tank Milk Cultures
Contagious Pathogen ScreeningBulk Tank Milk Cultures
• Streptococcus agalactiae (Strep.ag.)
• Staphylococcus aureus (Staph.a.)
• Mycoplasma species
Environmental PathogensBulk Tank Milk Cultures
• Streptococcus & Enterococcus species (Strep non-ag)
– Subclinical infections. Elevated SCC.
– Bedding material, manure, mud & infected cows
• Escherichia coli, Klebsiella species, Enterobacter
– Gram negative with severe infection to chronic infections
– Bedding, manure, water & soil
• Coagulase-negative Staphylococcus species (Staph sp. or CNS)
– Opportunistic mastitic from bovine skin
– Bedding & manure
“Odd Ball” PathogensBulk Tank Milk Cultures
• Pseudomonas species
– Water & wet bedding
– Mastitic, chronic, does not respond to therapy
• Proteus species
– Gram negative found in bedding, manure, feed & water
– Typically not mastitic, but can be; chronic, does not respond to therapy
• Serratia species
– Gram negative found in soil & water
– Typically not mastitic, but can be; chronic, does not respond to therapy
“Odd Ball” PathogensBulk Tank Milk Cultures
• Yeast
– Found in soil, plants, decaying organic matter & bedding
– Can be mastitic, cows are self-limiting but not responsive to therapy
• Nocardia species
– Found in soil, water, grass and on teat skin
– Rarely mastitic, but can be sub-clinical to clinical and not responsive to therapy
• Prototheca species
– Algae that can cause mastitis (subclinical to clinical to chronic) & not responsive to therapy
KC’s Take Home MessageInterpreting Bulk Tank Bacteria & SCC
• SCC
– We do not provide producers with the right information in the right format to make effective change.
– We either do not provide enough data (ex: monthly averages) or too much data (daily bulk tank SCCs) and we do not provide KNOWLEDGE.
• Bacteria Counts (SPC, LPC, PI)
– We do not sample often enough.
– We do not have enough research to help indicate causation.
– We do not have enough monitoring tools to help producers control mechanical aspects
• Bacteria Cultures
– We do not do them often enough & typically only done in response to a problem
– We do not interpret the data with other management inputs
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