Evaluating progesterone profiles to improve automated oestrus detection Claudia Kamphuis, Wageningen University Kirsten Huijps, CRV Henk Hogeveen, Wageningen and Utrecht University
Evaluating progesterone profiles to improve automated oestrus detection
Claudia Kamphuis, Wageningen UniversityKirsten Huijps, CRVHenk Hogeveen, Wageningen and Utrecht University
- First insemination between 40-70 DIM is optimal*
- oestrus detection is a key driver
- Challenges of detection- Time consuming- Error prone- Increased herd sizes
The importance of oestrus detection
*Inchaisri et al., 2012
- 20% of Dutch dairy farms have automated systems*
- Appears to be a success story
- 20% of Dutch dairy farms have automated systems*
- Appears to be a success story when it all works- Sensitivities 80-90% with specificities > 90%**
- No technical / human errors
Adoption automated oestrus detection
*Huijps, 2014, CRV, personal communication*Huijps, 2014, CRV, personal communication**Rutten et al., 2013
- Normal cycle takes 21 days
- Does progesterone affect oestrus behaviour- Affect automated oestrus detection?
Progesterone and oestrus
Days
Pro
ges
tero
ne
(ng
/m
l)
Behavioural changes
Aims of this study: gain insight in
- Performance of oestrus detection in the field
- Timing of oestrus alerts
- Use of alerts by farmers
- Effect of combining oestrus alerts on performance
- Effect of progesterone profiles on oestrus detection
Materials and Methods
- 31 cows, 40-70 DIM, not inseminated
Farm A (450) Farm B (AMS; 250)
Milk samples for 24 days Morning milkingsResidual milk12 cows
First milkingsWhole milk19 cows
Oestrus alerts System A: 12 cowsSystem B: 12 cows System B: 8 cows
System C: 19 cows
Oestrus observations Farm Staff Farm Staff
Average DIM at start 44 53
Average Parity 5.5 2.4
Hormonost-Microlab Farmertest, Biolab, Unterschleissheim, Germany
Progesterone profiles from milk samples
- Commercial on-farm kit
- Analyses 3x a week- Including forgone 1 / 2 days
- Profiles created
- Visual assessment of heat- According to manual- Gold standard
Results: heats, observations and alerts
- Based on Progesterone (P): 30 heats from 30 cows
Farm Staff System
A B C
Heat observed/alerts generated 15 14 12 31
Heat alerts on day with P‐heat 3 9
Heat alerts on day with P‐heat +/‐ 1 day 9 17
False positive observations / alerts 6 4 5 18
Results: timing alerts and observations
0
1
2
3
4
5
6
-23 -13 -3 7 17
Nu
mb
er o
f al
erts
/ob
serv
atio
ns
days around day of P-heat (day = 0)System A
Results: timing alerts and observations
0
1
2
3
4
5
6
-23 -13 -3 7 17
Nu
mb
er o
f al
erts
/ob
serv
atio
ns
days around day of P4heat (day = 0)System A System B
Results: timing alerts and observations
0
1
2
3
4
5
6
-23 -13 -3 7 17
Nu
mb
er o
f al
erts
/ob
serv
atio
ns
days around day of P4heat (day = 0)System A System B System C
0
1
2
3
4
5
6
-23 -13 -3 7 17
Nu
mb
er o
f al
erts
/ob
serv
atio
ns
days around day of P4heat (day = 0)Farm staff System A System B System C
Results: timing alerts and observations
84% of all alerts87% of all observations
are 3 days around P-heat
False or True?
Results: combining detection systems
Using a 1 day time window around a P-heat
Farm A System A: 5 out of 12 P-heats (42%) System B: 3 out of 12 P-heats (25%) One P-heat additionally detected
Farm B System B: 2 out of 18 P-heats (11%) System C: 9 out of 18 P-heats (50%) No additionally P-heat detected
Results: effect of progesterone profiles
0
5
10
15
20
25
30
-23 -20 -17 -14 -11 -8 -5 -2 1 4 7 10 13 16 19 22
Pro
ges
tero
ne
leve
l (n
g/
mL)
Days around P-heat (day = 0)Average P levels
Results: effect of progesterone profiles
0
5
10
15
20
25
30
-23 -20 -17 -14 -11 -8 -5 -2 1 4 7 10 13 16 19 22
Pro
ges
tero
ne
leve
l (n
g/
mL)
Days around P-heat (day = 0)Average P levels Detected P-heats
Results: effect of progesterone profiles
0
5
10
15
20
25
30
-23 -20 -17 -14 -11 -8 -5 -2 1 4 7 10 13 16 19 22
Pro
ges
tero
ne
leve
l (n
g/
mL)
Days around P-heat (day = 0)Average P levels Not detected P-heats Detected P-heats
Conclusions
* Rutten et al., 2012; Kamphuis et al., 2012
- All 3 systems performed less than expected- Expected: 80%*; Found: 25-50%
- Farm staff missed 48% of true positive alerts- Not checked alerts / behavioural changes
already passed
- Most alerts and observations around 3 days of P-heat
- Progesterone profiles did not differ between (non)detected cows
Take Home Message
- Farmers miss correctly identified oestrus's
- Progesterone does not affect oestrus behaviour
- Confirm with larger numbers- Successful inseminations as gold standard
Acknowledgements
- Hands-on- Farmers- Farm staff- Marije Popta and Gea Miedema
(Van Hall-Larenstein, Leeuwarden)
- Funding