This presentation covers how data can be used to manage mastitis in dairy farms.

1. Jeffrey Bewley, BoviSync and CowFocused Housing
Data, Decisions,
and Mastitis
“In God We Trust: All Others Bring Data”—William Edward Deming

2. Analytics is the next scientific breakthrough

3. Business Intelligence and Analytics
Optimization What’s the best that can
happen?
Predictive Modeling What will happen next?
Forecasting/Extrapolation What if these trends continue?
Statistical Analysis Why is this happening?
Alerts What actions are needed?
Query/Drill Down What exactly is the problem?
Ad hoc Reports How many, how often, where?
Standard Reports What happened?
CompetitiveAdvantage
Degree of Intelligence
SAS, Competing on Analytics, Davenport, Harris

4. Data Opportunity
Losing ground compared to other
businesses
Often limited to creating production
tables, attention lists, and working
schedules
Generally retrospective
Diagnostic capabilities remain largely
untapped

5. Mastitis is Perfect
Application
 One of the most common and costly
diseases on a dairy farm
 Massive amount of data
 Subclinical mastitis also costly
o Determined by somatic cell count

6. Case Study

7. Hot Sheet

8. DHI 427 Udder Health Monitor

9. DHI 427 Udder Health Monitor

10. DHI 427 Udder Health Monitor

11. Herd or group or shift
monitoring

12. 50
70
90
110
130
150
170
190
210
230
250
5/6 5/12 5/18 5/24 5/30 6/5 6/11 6/17 6/23 6/29
Time Series Plot of BTSCC data
BTSCC
Date
Is BTSCC getting worse or staying same?
How sure are you?
Reneau, 2013

13. The Control Chart
Reneau, 2013

14. Animal Health
Economics
• Quantifying the financial effects
of disease
• Developing methods for
optimizing decisions when
individual animals, herds, or
populations are affected
• Determining costs and benefits
of disease control measures

15. Mastitis Cost
Cost type First lactation
Second and later
lactations
Lost milk $ 131.41 $ 133.46
Vet and drug $ 77.74 $ 77.74
Discarded milk $ 54.55 $ 65.44
Labor $ 11.53 $ 11.53
Death $ 12.05 $ 12.82
Culling $ 10.26 $ 10.32
Days open $-1.54 $ 86.16
Total $ 352.75 $426.50
Liang et al., 2017, Journal of Dairy Science , Volume 100 , Issue 2 , 1472 - 1486

16. Definitions
C = L + E
Costs (C): represent all economic
effects of disease
Loss (L): benefit is taken away
(discarded or unrealized milk,
feelings/stress from death)
Expenditure (E): extra inputs into
production (drug costs,
preventative measures)
McInerney et al., 1992, Prev. Vet. Med, 13:137-154

17. The Loss Expenditure Frontier
McInerney et al., 1992, Prev. Vet. Med, 13:137-154
Maximum
Loss
Technical
Limit
Technical
Optimum
Minimum Level of Losses
Attainable for Given
Expenditure
Economic
Optimum

18. Cost of Culling
 Oversimplified methodology: difference between
slaughter value and cost of replacement
 Better methodology: retention pay-off
 Retention pay-off is the difference between:
 The predicted future income of the animal in question
 The predicted future income of her potential
replacement
 Requires the use of simulation and/or dynamic
programming

19. Decision Support Tools
Allows producers to enter their own farm
information
Result is specific to the information that the
producer enters
o Economic
o Decision
Important to be as realistic and accurate as
possible

20. https://afs.ca.uky.edu/dairy/decisiontools/milk-quality-calculator

21. https://afs.ca.uky.edu/dairy/extension/decisiontools/hotsheet

22. Cow Inputs
https://afs.ca.uky.edu/dairy/extension/decisiontools/hotsheet

23. Example Output
https://afs.ca.uky.edu/dairy/extension/decisiontools/hotsheet

24. https://afs.ca.uky.edu/dairy/decisiontools/mastitistreatment

28. Jeffrey Bewley, PhD, PAS
jbewley@bovisync.com
jbewley@cowfocused.com