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Using successful
inseminations to evaluate
sensitivity of automated heat
detection systems
Claudia Kamphuis, WUR
Kirsten Huijps, CRV
Challenges of detection
- Time consuming
- Error prone
- Increased herd sizes
The importance of heat detection
New Zealand survey 500 farmers
25% wants it
7% has it
70% listed it in top 3 of technologies
that gained benefit for farm
(Edwards et al., 2014, APS)
Survey 212 farmers stating to have
sensor technology
41% of AMS farmers has it
70% of CMS farmers has it
(Steeneveld and Hogeveen, 2015, JDS)
Survey 109 farmers globally
41% has it
Rated as useful to very useful
(Borchers and Bewley, 2015, JDS)
35% of US respondents
(Bewley, 2014, EAAP/EU-PLF conference)
Adoption rates
What are the success factors?
Associated with clear management actions
What are the success factors?
Associated with clear management actions
Investment is economically beneficial (Rutten et al., 2014)
for Dutch circumstances
What are the success factors?
Associated with clear management actions
Investment is economically beneficial (Rutten et al., 2014)
for Dutch circumstances
Sensitivity 80% with specificity 95% (Rutten et al., 2013, JDS)
performance is OK
But a specificity of 95%....
Still requires visual confirmation
719-9-2015
But a specificity of 95%....
Still requires visual confirmation
100 cow herd:
~1 cow in heat / day
99 cows not in heat / day
5 falsely alerted cows / day
819-9-2015
Alerts around P4 determined heat events
(Kamphuis et al., 2014, EAAP/EU-PLF, Copenhagen, Denmark)
84% of all alerts generated +/- 3 days around P4 heat
Only 30 P4 heats included
Use successful inseminations
Avoid high costs and intensiveness of P4 method
Relatively easy way to increase numbers
Additional: study differences in performance between
parities and lactation stages
Data collection: January-July 2014
Insemination data from CRV
Heat alert data from systems’ software
Farm A Farm B
Herd size 450 250
Milking system Conventional AMS
Milk production
(kg/cow/year)
9,500 9,800
Heat detection system A and B B and C
Defining successful inseminations
For cows with just 1 insemination
31 July1 January
Insemination
>56 d
Successful
GS+
Defining successful inseminations
For cows with >1 insemination
Insemination
>56 d
Successful
GS+
Insemination
≤56 d
Unsuccessful
31 July1 January
Insemination
≤ 56 d
Unsuccessful
Insemination
>56 d
Successful
GS+
Insemination
≤56 d
Unsuccessful
1 January 31 July
Matching inseminations with alerts
Insemination data at daily level
Heat alerts per h or per 2h time blocks
‘summarized at daily level’
≥1 alert per day is an alerted day
Data collection: January-July 2014
Farm A Farm B
Herd size 450 250
Milking system Conventional AMS
Milk production
(kg/cow/year)
9,500 9,800
Heat detection system A and B B and C
Cows with ≥1 successful
inseminations
145 119
GS positive
inseminations
153 129
Heat alerts A: 352
B: 532 B: 117
C: 887
Matching inseminations with alerts
Sensitivity using a 1 day time-window
GS+
day
Matching inseminations with alerts
Sensitivity using a 2 day time-window
GS+
day
Detection performance differed between
heat detection system
Sensitivity (%)
A: 31
B: 58
C: 92
Detection performance differed between
heat detection system
Sensitivity (%)
A: 35
B: 87
C: 94
Detection performance did not differ between
parity / lactation stage
Sensitivity 2 day time- window
System A System B System C
Parity
1 23 85 90
2 48 90 97
≥3 36 90 94
Lactation stage
≤56 days 44 100 91
>56 days 34 87 94
Discussion / conclusion
Lower performance of System A
• consistent with EAAP/EU-PLF 2014
• technical problems in data exchange
Discussion / conclusion
Lower performance of System A
Sensitivity may be overestimated
Summarizing h / 2h alerts to daily alerts
excluding data from cows that were
not detected by a system
not visually confirmed by farmer
inseminated incorrectly
not inseminated at all
Discussion / conclusion
Sensitivity in line with previous research(Rutten et al., JDS, 2013)
around 80-90% using a 2 day time-window
System C also alerts often 1 day after a successful insemination
No apparent differences in parity/lactation stage
no apparent need for specific algorithms
focus on getting more accurate and precise alerts
Ongoing data collection
Confirm or refute current findings statistically
Analyze data using hourly or 2hourly alert basis
Where to from here
Thank you for your attention
www.slideshare.net/claudiakamphuis ckamphuis

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Kamphuis & huijps, ECPLF, milan, 2015 ss

  • 1. Using successful inseminations to evaluate sensitivity of automated heat detection systems Claudia Kamphuis, WUR Kirsten Huijps, CRV
  • 2. Challenges of detection - Time consuming - Error prone - Increased herd sizes The importance of heat detection
  • 3. New Zealand survey 500 farmers 25% wants it 7% has it 70% listed it in top 3 of technologies that gained benefit for farm (Edwards et al., 2014, APS) Survey 212 farmers stating to have sensor technology 41% of AMS farmers has it 70% of CMS farmers has it (Steeneveld and Hogeveen, 2015, JDS) Survey 109 farmers globally 41% has it Rated as useful to very useful (Borchers and Bewley, 2015, JDS) 35% of US respondents (Bewley, 2014, EAAP/EU-PLF conference) Adoption rates
  • 4. What are the success factors? Associated with clear management actions
  • 5. What are the success factors? Associated with clear management actions Investment is economically beneficial (Rutten et al., 2014) for Dutch circumstances
  • 6. What are the success factors? Associated with clear management actions Investment is economically beneficial (Rutten et al., 2014) for Dutch circumstances Sensitivity 80% with specificity 95% (Rutten et al., 2013, JDS) performance is OK
  • 7. But a specificity of 95%.... Still requires visual confirmation 719-9-2015
  • 8. But a specificity of 95%.... Still requires visual confirmation 100 cow herd: ~1 cow in heat / day 99 cows not in heat / day 5 falsely alerted cows / day 819-9-2015
  • 9. Alerts around P4 determined heat events (Kamphuis et al., 2014, EAAP/EU-PLF, Copenhagen, Denmark) 84% of all alerts generated +/- 3 days around P4 heat Only 30 P4 heats included
  • 10. Use successful inseminations Avoid high costs and intensiveness of P4 method Relatively easy way to increase numbers Additional: study differences in performance between parities and lactation stages
  • 11. Data collection: January-July 2014 Insemination data from CRV Heat alert data from systems’ software Farm A Farm B Herd size 450 250 Milking system Conventional AMS Milk production (kg/cow/year) 9,500 9,800 Heat detection system A and B B and C
  • 12. Defining successful inseminations For cows with just 1 insemination 31 July1 January Insemination >56 d Successful GS+
  • 13. Defining successful inseminations For cows with >1 insemination Insemination >56 d Successful GS+ Insemination ≤56 d Unsuccessful 31 July1 January Insemination ≤ 56 d Unsuccessful Insemination >56 d Successful GS+ Insemination ≤56 d Unsuccessful 1 January 31 July
  • 14. Matching inseminations with alerts Insemination data at daily level Heat alerts per h or per 2h time blocks ‘summarized at daily level’ ≥1 alert per day is an alerted day
  • 15. Data collection: January-July 2014 Farm A Farm B Herd size 450 250 Milking system Conventional AMS Milk production (kg/cow/year) 9,500 9,800 Heat detection system A and B B and C Cows with ≥1 successful inseminations 145 119 GS positive inseminations 153 129 Heat alerts A: 352 B: 532 B: 117 C: 887
  • 16. Matching inseminations with alerts Sensitivity using a 1 day time-window GS+ day
  • 17. Matching inseminations with alerts Sensitivity using a 2 day time-window GS+ day
  • 18. Detection performance differed between heat detection system Sensitivity (%) A: 31 B: 58 C: 92
  • 19. Detection performance differed between heat detection system Sensitivity (%) A: 35 B: 87 C: 94
  • 20. Detection performance did not differ between parity / lactation stage Sensitivity 2 day time- window System A System B System C Parity 1 23 85 90 2 48 90 97 ≥3 36 90 94 Lactation stage ≤56 days 44 100 91 >56 days 34 87 94
  • 21. Discussion / conclusion Lower performance of System A • consistent with EAAP/EU-PLF 2014 • technical problems in data exchange
  • 22. Discussion / conclusion Lower performance of System A Sensitivity may be overestimated Summarizing h / 2h alerts to daily alerts excluding data from cows that were not detected by a system not visually confirmed by farmer inseminated incorrectly not inseminated at all
  • 23. Discussion / conclusion Sensitivity in line with previous research(Rutten et al., JDS, 2013) around 80-90% using a 2 day time-window System C also alerts often 1 day after a successful insemination No apparent differences in parity/lactation stage no apparent need for specific algorithms focus on getting more accurate and precise alerts
  • 24. Ongoing data collection Confirm or refute current findings statistically Analyze data using hourly or 2hourly alert basis Where to from here
  • 25. Thank you for your attention www.slideshare.net/claudiakamphuis ckamphuis