The Role of Technology in Quantifying Mastitis Related Decisions
•
0 likes•128 views
This document discusses the role of technology in quantifying decisions related to mastitis detection and management. Precision monitoring technologies like electrical conductivity, milk color, temperature, spectroscopy, biosensors and inline somatic cell count can help detect mastitis earlier than visual observation alone. However, challenges remain around meeting sensitivity and specificity goals, calibration over time, and determining appropriate actions in response to alerts. Further research is needed to quantify the economic benefits of early mastitis detection and determine optimal treatment protocols.
Recommended
Recommended
More Related Content
Similar to The Role of Technology in Quantifying Mastitis Related Decisions
Similar to The Role of Technology in Quantifying Mastitis Related Decisions (20)
More from Jeffrey Bewley
More from Jeffrey Bewley (15)
Recently uploaded
Recently uploaded (20)
The Role of Technology in Quantifying Mastitis Related Decisions
- 6. Mastitis Detection isMostWanted Most Useful Parameters Mean ± SD Mastitis 4.77 ± 0.47 Standing heat 4.75 ± 0.55 Daily milk yield 4.72 ± 0.62 Cow activity 4.60 ± 0.83 Temperature 4.31 ± 1.04 Feeding behavior 4.30 ± 0.80 Milk components (e.g. fat, protein, and SCC) 4.28 ± 0.93 Lameness 4.25 ± 0.90 Rumination 4.08 ± 1.07 Hoof health 4.06 ± 0.89 1Results calculated by assigning the following values to response categories: Not useful: 1, Of little usefulness: 2, Moderately useful: 3, Useful: 4, Very useful:5. Matthew Borchers et al.
- 7. Shift Away from Treatment Historical focus on treatment Physical observations Cow-side tests Need more rapid and continuous measures Recent proactive movement Advancement in technologies
- 8. Mastitis Detection Daily use of CMT and EC Monthly testing of SCC More recent focus Introduction of in-line analyzers Addition of EC to milk component data Behavior
- 9. Early Disease Detection Detect diseases earlier than with visual observation alone Improve individual cow treatment results Indicate a larger, herd-level, problem leading to improved prevention strategies
- 10. Mastitis Detection Benefits Reduced labor Early treatment and intervention Cultured based therapy? NSAID administration? Pathogen-specific approach?
- 11. Mastitis Detection Benefits Increase in bacteriological cure Less chronic cases? Increase in well-being Fewer severe cases Preemptive NSAID administration based on culture results Reduced chronic cases Faster more appropriate treatment regimes
- 12. Mastitis Detection Benefits Reduced mastitis transmission Contagious animals separated sooner Separate abnormal milk Threshold in component changes For example, presence of blood Use of management protocols already in place to address sick cows
- 13. Drying off Abrupt cessation is US industry norm Milk leakage and discomfort are concern Increase risk of IMI with > 17.5 kg/d Primiparous animals show reduced risk of IMI with gradual cessation Role in tailoring drying off approach Selective dry cow therapy (Gott et al., 2016) (Rajala-Schultz et al., 2005)
- 14. Herd or group or shift monitoring
- 15. What Technologies are Out There?
- 16. Electrical Conductivity Ion concentration of milk changes, increasing electrical conductivity Inexpensive and simple equipment Wide range of sensitivity and specificity reported Results improve with quarter level sensors Improved results with recent algorithms Most useful combined with other metrics
- 17. Milk Color Color variation (red, blue, and green) sensors in some automatic milking systems Reddish color indicates blood (Ordolff, 2003) Clinical mastitis may change color patterns for three colors (red, green and blue) Specificity may be limited www.lely.com
- 18. Temperature Not all cases of mastitis result in a temperature response Best location to collect temperature? Noise from other physiological impacts
- 20. Thermography May be limited because not all cases of mastitis result in a temperature response Difficulties in collecting images Hovinen et al., 2008; Schutz, 2009 Before Infection After Infection
- 21. Inline Somatic Cell Count CellSenseDeLaval OCC Lely MQCCMastiline
- 22. < 200,000 200,000 — 400,000 400,000 — 800,000 800,000 — 2,000,000 > 2,000,000 SCC value Works like a traffic light
- 23. Spectroscopy Visible, near-infrared, mid-infrared, or radio frequency Indirect identification through changes in milk composition AfiLab uses near infrared Fat, protein, lactose
- 24. BiosensorsandChemicalSensors Biological components (enzymes, antibodies, or microorganism) Enzyme, L-Lactate dehydrogenase (LDH), is released because of the immune response and changes in cellular membrane chemistry Chemical sensors: changes in chloride, potassium, and sodium ions, volatile metabolites resulting from mastitis, haptoglobin, and hemoglobin (Hogeveen, 2011)
- 25. Milk measurements Progesterone Heat detection Pregnancy detection LDH enzyme Early mastitis detection BHBA Indicator of subclinical ketosis Urea Protein status
- 28. On-farm evaluation of lying time: Identification of cows requiring attention (lameness, illness, estrus) Assessment of facility functionality/cow comfort Assess animal well-being LyingBehaviorMonitoring
- 30. Current Research Limitations Examines changes retrospectively Changes may be seen around subclinical and clinical disease Extrapolation? Is it appropriate? How much? Breed, housing type, region? We find differences….but so what?
- 31. MastitisChallenges Meeting sensitivity (80%) and specificity (99%) goals (Rasmussen, 2004) Calibration across time Automatic diversion or alert? Recommended action when an alert occurs with no clinical signs
- 32. Mastitis Challenges Dynamics of clinical and subclinical mastitis Potential for overtreatment Employee education Gold standards imperfect
- 33. Mastitis Challenges No growth Different pathogens behave differently Changes affected by Heat stress, estrus, production, overall health, calving, etc.
- 34. How good are we at finding events of interest?
- 35. Reasonsalertswerenotchecked 36% 15% 2%1% 9% 8% 6% 5% 5% 2% 2% 3% 3% 3% No flakes/clots on filter sock Milk production not alarming Repeat No time Combination alert not alarming Temporary physical problems Conductivity alert not alarming AMS disorders Too many cows treated Green alert Checked before, not clinical Not clinical at last check Will be culled In heat Hogeveen et al., Precision Livestock Farming ‘13 *Farmers only checked 3% of alerts and missed 74% of mastitis cases
- 37. HowManyCowsWithConditionDoWeFind? Example: 100 mastitis events 80 Mastitis Events Identified by Technology 20 Mastitis Events Missed by Technology
- 38. HowManyAlertsCoincidewithanActualEvent? Example: 100 mastitis alerts 90 Alerts for Cows that Have Mastitis 10 Alerts for Cows that Do No Have Mastitis
- 39. Are we focused too heavily on disease detection?
- 40. Other Cautions • Huge within cow and within herd variation • Many management factors and environmental conditions affect these variables • Group/pen changes affect behaviors • Some cows don’t read the book • Not all changes are linear
- 41. Where do we go from here? Identify disease prospectively Determine course of action after alert Culture? Treatment? Another intervention? Assess economic benefit of identification
- 42. Questions? Jeffrey Bewley, PhD, PAS jbewley@bovisync.com jbewley@cowfocused.com