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

Prog

este

rone

(ng

/ml)

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 cowsOestrus 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

-23 -18 -13 -8 -3 2 7 12 17 220

1

2

3

4

5

6

System Adays around day of P-heat (day = 0)

Num

ber

of a

lert

s/ob

ser-

vatio

ns

Results: timing alerts and observations

-23 -18 -13 -8 -3 2 7 12 17 220

1

2

3

4

5

6

System A System Bdays around day of P4heat (day = 0)

Num

ber

of a

lert

s/ob

ser-

vatio

ns

Results: timing alerts and observations

-23 -18 -13 -8 -3 2 7 12 17 220

1

2

3

4

5

6

System A System B System Cdays around day of P4heat (day = 0)

Num

ber

of a

lert

s/ob

ser-

vatio

ns

-23 -18 -13 -8 -3 2 7 12 17 220

1

2

3

4

5

6

Farm staff System A System B System Cdays around day of P4heat (day = 0)

Num

ber

of a

lert

s/ob

-se

rvat

ions

Results: timing alerts and observations84% of all alerts

87% of all observationsare 3 days around P-heat

False or True?

Results: combining detection systems

Using a 1 day time window around a P-heat

Farm ASystem A: 5 out of 12 P-heats (42%)System B: 3 out of 12 P-heats (25%)One P-heat additionally detected

Farm BSystem 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

-23 -20 -17 -14 -11 -8 -5 -2 1 4 7 10 13 16 19 220

5

10

15

20

25

30

Average P levelsDays around P-heat (day = 0)

Prog

este

rone

leve

l (ng

/mL)

Results: effect of progesterone profiles

-23 -20 -17 -14 -11 -8 -5 -2 1 4 7 10 13 16 19 220

5

10

15

20

25

30

Average P levels Detected P-heatsDays around P-heat (day = 0)

Prog

este

rone

leve

l (ng

/mL)

Results: effect of progesterone profiles

-23 -20 -17 -14 -11 -8 -5 -2 1 4 7 10 13 16 19 220

5

10

15

20

25

30

Average P levels Not detected P-heatsDetected P-heats

Days around P-heat (day = 0)

Prog

este

rone

leve

l (ng

/mL)

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