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Consequences of farmers' interpretation of mastitis alerts in automatic milking

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Consequences of farmers' interpretation of mastitis alerts in automatic milking

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These are the slides that I used to present research work of Klaske Buma (at that time student Veterinary Medicine at Utrecht University). Klaske has collected data on 7 Dutch farms that used an automatic milking system. She followed the farmers in their interpretation of mastitis alerts. The farmers behaviuor and the consequences of that behaviour are presented

These are the slides that I used to present research work of Klaske Buma (at that time student Veterinary Medicine at Utrecht University). Klaske has collected data on 7 Dutch farms that used an automatic milking system. She followed the farmers in their interpretation of mastitis alerts. The farmers behaviuor and the consequences of that behaviour are presented

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Consequences of farmers' interpretation of mastitis alerts in automatic milking

  1. 1. Consequences of farmers’ interpretation of mastitis alerts Klaske Buma, Ruurd Jorritsma en Henk Hogeveen Leerstoelgroep Bedrijfseconomie, Wageningen Universiteit Departement Gezondheidszorg Landbouwhuisideren, Universiteit Utrecht
  2. 2. Automatic milking: Mean SCC ↑ • SCC = MANAGEMENT – Risk of IMI ↑ = Hygiene milking, housing – Detection of mastitis • Common • Expensive • Frustration
  3. 3. Information from milking robot • Sensor information – Conductivity – Colour – Milk production (24h) – Temperature (MQC 2) – SCC (optional) • Algorithm → Udder health report – Potential cases of mastitis
  4. 4. Detection of mastitis by farmer Check Interpret Check Interpret Check alert Interpret Take action report report history history in the barn check •Take milk • Conductivity •Check history alert alert •Check cow •Mastitis? sample • Colour alert and/or •Milkquality •Check alert in •Check udder •Take action! •Treat mastitis • Milk production check alert in •Milk visits the barn •Spurt and deviation the barn check milk • Total number of •Conductivity alerts chart •CMT • SCC (optional)
  5. 5. Objective • Get insight in the decision model of a farmer to check on alerts from the report
  6. 6. Material and methods • 7 farms, North Western part of The Netherlands • 5 farms visits – Questionnaire – Interview – CMT of all alerts – Milksample and culturing clinical mastitis alerts • Analysis of data
  7. 7. Check and interpret alertlist Check Interpret Check Interpret Check alert Interpret Take action report report history history in the barn check •Take milk • Conductivity •Check history alert alert •Check cow •Mastitis? sample • Colour alert and/or •Milkquality •Check alert in •Check udder •Take action! •Treat mastitis • Milk production check alert in •Milk visits the barn •Spurt and deviation the barn check milk • Total number of •Conductivity alerts chart •CMT • SCC (optional) Quick glance 10 times a day – 2 times a week
  8. 8. Check and interpret history alert Check Interpret Check Interpret Check alert Interpret Take action report report history history in the barn check •Take milk • Conductivity •Check history alert alert •Check cow •Mastitis? Take sample • Colour alert and/or •Milkquality •Check alert in •Check udder action! •Treat mastitis • Milk production check alert in •Milk visits the barn •Spurt and deviation the barn check milk • Total number of •Conductivity alerts chart •CMT • SCC (optional) Only when alarming Definition of alarming varies between farmers
  9. 9. Check alert in the barn • Only 3,5% of the alerts are checked by the farmer!
  10. 10. Alerts checked by farmer (n=15) 20% Clinical 13% Subclinical Negative 67% CMT
  11. 11. Alerts checked by researcher Overview of the checked mastitis alerts Clinical mastitis Subclinical mastitis Negative CMT Total Number of individual quarter alerts 30 47 150 227 Number of repeated quarter alerts 9 81 104 194 46% Total 39 128 254 421 10% 60%
  12. 12. Checked clinical mastitis 26% Unchecked clinical mastitis 74% Subclinical mastitis 100%
  13. 13. Reasons to check (n=15) • Combination of: – Milk production decrease alarming – Flakes/clots on milk filter – High conductivity – Failure in milking – History of teat damage – Earlier flakes/clots in milk
  14. 14. Reasons not to check (n=421) • No flakes/clots on milk filter 28% • Milk production decrease not alarming 19% • Repeatedly on list 10% • No time 10% • Conductivity level is not alarming 5% • Malfunctioning AMS 4% • Other 24%
  15. 15. Eye openers • Selection on milk production deviation (6/7) • None of the farmers uses CMT for detection subclinical mastitis • Broken sensor → lot of alerts → no repair • Sensitivity <100% - 2 cases clinical mastitis without alert
  16. 16. Conclusions 60 % of alerts false positive 3% of alerts is checked by farmers checked alerts are often clinical cases 74 % of clinical cases is missed How bad is this? Check Interpret Check Interpret Check alert Interpret Take action report report history history in the barn check •Take milk • Conductivity •Check history alert alert •Check cow •Alarming? sample • Colour alert and/or •Milkquality •Check alert in •Check udder Take action •Treat mastitis • Milk production check alert in •Milk visits the barn •Spurt and deviation the barn check milk • Total number of •Conductivity alerts chart •CMT • SCC (optional)
  17. 17. Thank you for your attention

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