Farming involves entrepreneurship, setting milestones and preparing for the future. In addition, farming is continuously subject to change, due to growth, society, regulations, finance, subsidy, etc. Therefore solid advice is key for a sustainable, profitable and enjoyable future in farming. A variety of speakers from different disciplines will share interesting insights and knowledge to help you in supporting farmers to reach their chosen milestones.

1. Sensors technologies
replacing or complementing human senses
to monitor animal health
claudia.kamphuis@wur.nl

2. What can you expect
Claudia Kamphuis
Sensor technologies in dairy
Theory and Economic potential vs. reality
Performance of sensor technologies
Working with sensor technologies
Current work

3. 3

4. 2004: Graduated, Preventive Animal Heath
and Welfare, Wageningen University
2006: PhD, Utrecht University
2010: Defended successfully
PhD, Utrecht University
2011: Scientist role at DairyNZ, New Zealand
2013: Post-Doc,
Business Economics
Wageningen University

5. What can you expect
Claudia Kamphuis
Sensor technologies in dairy

6. Boosted by development of automatic milking systems in 1990s

7. 6 main brands
Boosted by development of automatic milking systems in 1990s

8. 6 main brands
1992 first farm in NL (Bottema, 1992)
>10,000 farms globally 2013 (Rodenburg, 2013)
3,615 (19.5%) Dutch farms (Stichting KOM, 2015)
Forced to replace human senses
Boosted by development of automatic milking systems in 1990s

9. And further pushed by increased animal welfare concerns
Increasing herds
Government
Society

10. Cheap technology
Low in maintenance costs
Udder or quarter level
Most used to detect abnormal milk or mastitis
Limited performance for mastitis detection
(Rutten et al., 2013)
Electrical Conductivity
handheldIn-line

11. Other (more sophisticated and expensive) sensor technologies
were introduced to monitor cow health and productivity
Udder Health
- Electrical Conductivity
- Milk yield
- Somatic Cell Count
- (Milk) Temperature
- Colour

12. Other (more sophisticated and expensive) sensor technologies
were introduced to monitor cow health and productivity
Udder Health
- Electrical Conductivity
- Milk yield
- Somatic Cell Count
- (Milk) Temperature
- Colour
Milk Composition
- Milk yield
- Fat and protein content
- Lactose content
- Somatic cell count

13. Other (more sophisticated and expensive) sensor technologies
were introduced to monitor cow health and productivity
Fertility
- Progesterone
- Activity
- Rumination
Cow ‘Composition’
- Weight
- Body Condition Score

14. Other (more sophisticated and expensive) sensor technologies
were introduced to monitor cow health and productivity
Metabolic disorders
- Activity
- Rumination
- Milk yield
- SCC
- pH
Cow Mobility
- Weight
- Activity
- Rumination
- Milk yield

15. There are A LOT of sensor technologies
15

16. With A LOT of benefits
Improve health, welfare
Increase productivity
Increase efficiency
Improve product quality
Objective monitoring
Improve social lifestyle

17. Use of sensor technologies in the Netherlands
(Steeneveld and Hogeveen, 2015)
Survey study
1,672 farmers approached via email
512 farmers replied (31%)
202 farmers (41%) replied to have sensor technologies
17

18. When did CMS farmers invest in sensors (n = 81)
(Steeneveld and Hogeveen, 2015)
0
5
10
15
20
25
30
35
40
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
Mastitis Rumination Estrus
Year
Farmers(n)

19. When did AMS farmers invest in sensors (n = 121)
(Steeneveld and Hogeveen, 2015)
0
5
10
15
20
25
30
35
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
Mastitis Rumination Estrus
Year
Farmers(n)

20. Use of sensor technologies (%) in the Netherlands
(Steeneveld and Hogeveen, 2015)
20
Sensor AMS
(n = 121)
CMS
(n = 81)
Colour 60 1
Electrical Conductivity 93 35
Milk temperature 50 6
Weighing platform 27 5
Fat and protein 20 0
Somatic cell count 17 1
Activity meters/pedometers dairy cows 41 70
Activity meters/pedometers young stock 12 28
Temperature 6 14
Rumination 9 12
Lactate dehydrogenase (LDH) 2 1
Progesterone 2 1

21. What can you expect
Claudia Kamphuis
Sensor technologies in dairy
Theory and Economic potential vs. reality

22. Reasons why AMS farmers invested in sensors
(Steeneveld and Hogeveen, 2015)
22
Investment reason EC
(n = 112)
Rumination
(n = 11)
Activity
(n = 50)
Reduce labor 1 9 6
Improve health /
reproduction
14 55 72
Insight in health 14 82 42
Not a conscious decision 97 54 48
Improve farm profitability 13 45 48

23. Automated mastitis detection: theory
Not a conscious decision (we have to?)
Managing bulk milk SCC levels
Mastitis detection
Dry-cow therapy decisions
23

24. Automated mastitis detection: economics
24

25. Automated mastitis detection: reality
(Steeneveld et al., 2015)
Farms AMS farms CMS farms
No sensors Before sensors After sensors Before sensors After sensors
Number of cows
% growth in size
Milk production
(kg / cow / year)
86
3.5
8,343
82
2.6
8,398
97
4.2
8,558
127
6.0
8,371
159
9.7
8,179

26. 190
195
200
205
210
215
220
225
230
235
240
No sensor system AMS farms before
investment
AMS farms after
investment
CMS farms before
investment
CMS farms after
investment
Somaticcellcount(x1,000cells/ml)Automated mastitis detection: reality
(Steeneveld et al., 2015)

27. Automated oestrus detection: theory
Advantages two-fold
 Improve farm profitability
 Better detection rates -> improved pregnancy rates

28. Automated oestrus detection: theory
Advantages twofold
 Improve farm profitability
 Better detection rates -> improved pregnancy rates
Clear management (decision support) associated with
information

29. Automated oestrus detection: theory
Advantages twofold
 Improve farm profitability
 Better detection rates -> improved pregnancy rates
Clear management (decision support) associated with
information
OK performance: 80% SN with 95% SP
(Rutten et al., 2013)

30. Automated oestrus detection: theory
Advantages twofold
 Improve farm profitability
 Better detection rates -> improved pregnancy rates
Clear management (decision support) associated with
information
OK performance
Investment is economically beneficial
(Rutten et al., 2014)

31. Automated oestrus detection: economics

32. General culling
Calving
Ovulation
Heat detection
P(1st ovulation)
P(heat)
P(heat detected)
P(culling)
P(culling)
P(culling)
Simulated cow
Parity, production level
Insemination
after voluntary waiting period
Culling due to fertility issues
- Max 6 inseminations
- Not pregnant in wk 35
Replacement heifer
Cow pregnant
P(pregnant)
P(early embryonic death)
Next parity
∆ Milk yield
∆ Number of inseminations
∆ Number of calves produced
∆ Feed intake
∆ Number of culled cows
∆ Number of false alerts from PLF
Output
cow place /year
Milk price
Labour costs
Cost for AI
Costs/revenues of calves
Costs feed
Costs for culling
Costs of false alerts PLF (labour or AI)
x €
At farm level
Probabilities are
adjusted for each
simulated week
Costs of PLF technology: investment, maintenance,
depreciation, replacement of faulty sensors
Cow Model
SN 50%
SP 100%
SN 80%
SP 95%
€108/cow
€3600/herd
10years
Checking each
alert visually

33. Automated oestrus detection: economics
Cash flow: 2,287 € / year
Cost-Benefit ratio: € 1.23
Discounted payback period: 8 years
Investment pays off
(Rutten et al., 2014)
SN 80%;SP 95%
€ 108/cow
€ 3600/herd
10years
Checking each alert visually

34. Automated oestrus detection: reality
(Steeneveld et al., 2015)
Farms AMS farms CMS farms
No sensors Before
sensors
After
sensors
Before
sensors
After
sensors
Number of cows
% growth in size
Milk production
(kg/cow/year)
85
3.5
8,342
86
2.8
8,473
102
5.3
8,632
104
4.0
8,245
131
6.1
8,177

35. Automated oestrus detection: reality
(Steeneveld et al., 2015)
70
80
90
100
110
120
130
No sensor system AMS farms before
investment
AMS farms after
investment
CMS farms before
investment
CMS farms after
investment
Daystofirstservice

36. Investment in sensor technologies: reality
(€/100 kg milk)
(Steeneveld et al., unpublished)
No sensor AMS CMS
Before After Before After
Capital costs 10.38 9.72a 13.97b 11.08c 11.35c
Labour costs 12.38 11.69a 11.30a 11.30c 10.43c
Variable costs 1945 18.66a 19.80a 18.28c 19.24c
Revenues 46.28 43.93a 46.38b 45.77c 47.18c
Profit 4.07 3.86a 1.31b 5.11c 6.16c

37. So, just a mid re-cap
1,672 farms approached
512 farmers replied
202 indicated to have sensors
(Steeneveld and Hogeveen, 2015)
Economic theory is not matching
reality
12%

38. What can you expect
Claudia Kamphuis
Sensor technologies in dairy
Theory and Economic potential vs. reality
Performance of sensor technologies

39. Sensors are state-of-the-art pieces of technology that develop
and improve rapidly

40. Sensors are state-of-the-art pieces of technology that develop
and improve rapidly

41. It’s all about monitoring parameters associated with events of
interest, but sensors
May not accurately or precisely monitor these parameters

42. It’s all about monitoring parameters associated with events of
interest, but sensors
May not accurately monitor these parameters
Monitor a proxy for these parameters
viscosity measurements;
Whyte et al., 2004

43. It’s all about monitoring parameters associated with events of
interest, but sensors
May not accurately monitor these parameters
Monitor a proxy for these parameters
Monitor parameters that are not unique for the event

44. It’s all about monitoring parameters associated with events of
interest, but sensors
May not accurately monitor these parameters
Monitor a proxy for these parameters
Monitor parameters that are not unique for the event
Monitor one single aspect of a complex event

45. Always a trade-of between
Sensitivity
How many events do you
detect (true positive alerts)
and how many do you
miss (false negative alerts)
Specificity
How many healthy cows do
not receive an alert
(true negative alert)
and how many do receive
an alert falsely
(false positive alert)

46. Trade-off dependants
Event being monitored
Dairying system in which sensor is implemented
Economic consequences of decision-making based on
inaccurate sensor information
Farmer’s preference (risk attitude)

47. Example automated mastitis detection
High SN
no additional labour for
checking alerts
Checking a few false
positives is always better
than checking 2,000 cows
High SP
nuisance of fetching cows
and checking alerts
Willing to accept mildly
infected cows remain
undetected
(Mollenhorst et al., 2012;
Hogeveen and Steeneveld, 2013)

48. Example of automated oestrus detection
Field evaluation of SCR systems in New Zealand:
75% SN and 99%SP
Visual observation using tail paint: 91% SN and 99.8% SP
48

49. Example automated oestrus detection with 75% sensitivity
Year-round calving might
be OK
But what about seasonal
calving?
6wks time to get all cows
pregnant
Economic losses in case
oestrus events are missed

50. Farmers’ attitude
Being in control Letting-go
Convenience seekersBusiness optimisers

51. Farmers’ attitude
Eager to understand and
learn the system
Not having the
time or skills
Innovators/ambassadors
Convenience seekers
/business optimisers

52. Sensors are not about ‘one size fits all’
Waiting for ‘improved’ systems
(Borchers and Bewley, 2015; Steeneveld and Hogeveen, 2015; Russell and Bewley, 2013)
52

53. What can you expect
Claudia Kamphuis
Sensor technologies in dairy
Theory and Economic potential vs. reality
Performance of sensor technologies
Working with sensor technologies

54. Reasons why AMS farmers invested in sensors
(Steeneveld and Hogeveen, 2015)
54
Investment reason EC
(n = 112)
Rumination
(n = 11)
Activity
(n = 50)
Reduce labor 1 9 6
Improve health /
reproduction
14 55 72
Insight in health 14 82 42
Not a conscious decision 97 54 48
Improve farm profitability 13 45 48

55. Use of sensor information is limited
Sensor AMS (%) CMS (%)
Never/
sometimes
Daily Never/
sometimes
Daily
Colour (n=72 / 1) 49 32 100 0
Fat and protein sensor (n = 24) 63 17
Electrical conductivity (n = 112 / 28) 5 77 25 21
Weighing platform (n = 33 / 4) 39 21 25 50
Activity meters/pedometers dairy
cows (n = 50 / 57)
6 74 6 74

56. Use of sensor information is limited
(Hogeveen et al., 2013)
5% of generated mastitis alerts are visually checked

57. Use of sensor information is limited
(Hogeveen et al., 2013)
5% of generated mastitis alert lists are visually checked
Reasons not to check alerts included:
No deviation in yield (19%)No flakes on filter (28%) Repeatedly on list (10%)
Too busy (10%)Malfunctioning (4%) No EC increase (5%)

58. Use of sensor information is limited
(Hogeveen et al., 2013)
5% of generated mastitis alert lists are visually checked
Reasons not to check alerts
Consequence: 75% of
detected mastitis is not
‘seen’

59. 190
195
200
205
210
215
220
225
230
235
240
No sensor system AMS farms before
investment
AMS farms after
investment
CMS farms before
investment
CMS farms after
investment
Somaticcellcount(x1,000cells/ml)Automated mastitis detection: reality
(Steeneveld et al., 2015)

60. Use of sensor information is limited
22% of farm owners indicated that expectations did not
match performance reality
24% of farm owners indicated
that learning support was not
as expected
(Eastwood et al., 2015)

61. Too much information without knowing
what to do with it (Russell and Bewley, 2013)
61

62. What can you expect
Claudia Kamphuis
Sensor technologies in dairy
Theory and Economic potential vs. reality
Performance of sensor technologies
Working with sensor technologies
Current work

63. 63
The cow central
Farmer rules
Real time models of
different parties
Sensors of
different
companies
Other data
sources
InfoBroker: Open
platform for sensor
data
Work
instructions
What’s currently being done?

64. What’s currently being done?
Develop a blueprint for
successful PLF technologies
Social impact
Economic viability

65. What’s currently being done?
Tools to estimate economic and social value
Value Creation Tool  potential economic benefits of
sensor technology in different dairying situations
Break-even Tool  how much change of a parameter is
required to break-even with the investment
Adaptive Conjoint Analysis  assessing utilities of
costumers for economic or social aspects

66. What can you expect
What I would like you to remember

67. Sensors are exciting, high-tech and have potential
But we need their information combined with
To complement management decisions on animal health

68. Thank you for your attention
www.slideshare.net/claudiakamphuis ckamphuis