This document discusses whether technology pays for itself in dairy farming. It provides an overview of the history of sensor use on dairy farms since the 1970s and their increasing adoption. Success factors for precision technology include system specifications that provide useful information, cost efficiency where benefits outweigh costs, and non-economic factors like risk tolerance. Studies show sensor systems for mastitis and estrus detection can increase productivity and profitability on farms, though their benefits are not always fully realized in practice due to limited use of sensor information and farmer attitudes. In conclusion, sensors have the potential to improve farms economically and enhance dairy cattle welfare but not all systems may be cost-effective.

1. Does technology pay for itself?
Henk Hogeveen, Wilma Steeneveld, Mariska vd Voort and Claudia Kamphuis

2. What can you expect from me
 A little history and overview
 Success factors for precision technology
 Adoption of sensor systems
 Effects of mastitis detection systems
 Effects of estrus detection systems
 Wrap up

3. Initiated by cow identification systems in 1970s
Recording of individual milk yield
Allocating feed/concentrates to individual cows

4. Boosted by development of automatic milking systems in 1980s

5. 6 main brands
Automatic milking systems are marketed since 1992

6. 1992 first commercial farm in NL (Bottema, 1992)
>10,000 farms globally (Rodenburg, 2013)
3,615 (19.5%) Dutch farms (Stichting KOM, 2015)
Boosted by development of automatic milking systems in 1990s

10. Replacement of human senses
Sensor development was boosted by
Automatic milking

11. And further pushed by other factors
Increasing herd
seize
Government
Society
Mom, where does the milk come from?
From the factory, honey

12. There are A LOT of sensor technologies
12

13. 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

14. 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

15. 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

16. Other (more sophisticated and expensive) sensor technologies
were introduced to monitor cow health and productivity
Fertility
- Progesterone
- Activity
- Rumination
Milk Composition
- Milk yield
- Fat and protein content
- Lactose content
- Somatic cell count

17. 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

18. Metabolic disorders
- Activity
- Rumination
- Milk yield
- SCC
- pH
Other (more sophisticated and expensive) sensor technologies
were introduced to monitor cow health and productivity
Cow ‘Composition’
- Weight
- Body Condition Score

19. 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

20. There is A LOT of potential
Improved health, welfare
Increase productivity
Increased efficiency
Improved product quality
Objective monitoring
Improved social lifestyle
……..

21. What can you expect from me
 A little history and overview
 Success factors for precision technology
 Adoption of sensor systems
 Effects of mastitis detection systems
 Effects of estrus detection systems
 Wrap up

22. Success factors
 System specifications
 Cost efficiency
 Non-economic factors

23. System specification
 Description of (prototype) technology
 Algorithms that transform data to information
Is this information useful?
 Integration with other data sources
This can improve performance
Problems: Integration of various systems, co-operation
between companies.
 Decision support
With or without interference of the farmer
This is the ultimate of precision dairy farming

24. Cost efficiency
 Benefits > costs
 Sounds easy but ......
● Costs are clear
● Benefits often indirect
● Belief of effect
● .........
 There are a few economic analyses (scientifically)
published
 Portfolio problem: other fields of investment in
comparison to precision dairy

25. Non-monetary factors
 Risk
 Availability of labor/capital
 Farmers’ goals/preferences

26. What can you expect from me
 A little history and overview
 Success factors for precision technology
 Adoption of sensor systems
 Effects of mastitis detection systems
 Effects of estrus detection systems
 Wrap up

27. Use of sensor systems in the Netherlands
 Questionnaire study: 1,672 Dutch dairy farmers (Accon-
AVM)
 512 (31%) responded
● 212 had sensor systems (41 %)
 Permission to use MPR data: 414 (37 % with sensors)
 Available accountancy data: 217 farms
Steeneveld et al., 2015, J. Dairy Sci.
Steeneveld & Hogeveen, 2015, J. Dairy Sci
Steeneveld et al., 2015, COMPAG

28. When did CMS farmers invest in sensors (n = 81)
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)

29. 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)

30. Milk sensors used (n = 512)
0
20
40
60
80
100
AMS
CMS

31. Estrus detection sensors used (n = 512)
0
20
40
60
80
Activity cows Activity young Temperature Progesterone
AMS
CMS

32. Oestrus detection sensors used (n = 512)
0
20
40
60
80
Rumination Weighing
AMS
CMS

33. Reasons to invest (CMS farms; %)
Rumination (10) Activity (57) Temp (4)
Reduce labor 30 39 6
Improve health/reprod 70 81 25
Insight in health 60 46 0
Not a concious decision 10 4 50
Improve profitability 30 47 0
Other 10 5 25

34. Reasons to invest (AMS farms; %)
34
Investment reason EC
(n = 112)
Rumination
(n = 11)
Activity
(n = 50)
Reduce labor 1 9 6
Improve health/ reprod 14 55 72
Insight in health 14 82 42
Not a conscious decision 97 54 48
Improve farm profitability 13 45 48

35. What can you expect from me
 A little history and overview
 Success factors for precision technology
 Adoption of sensor systems
 Effects of mastitis detection systems
 Effects of estrus detection systems
 Wrap up

36. Automated mastitis detection: effects
Farms AMS farms CMS farms
No sensors Before After Before After
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

37. 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: Somatic cell count

38. Estrus detection sensors
Farms AMS farms CMS farms
No sensors Before After Before After
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

39. Effects on reproduction
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

40. What can you expect from me
 A little history and overview
 Success factors for precision technology
 Adoption of sensor systems
 Effects of mastitis detection systems
 Effects of estrus detection systems
 Wrap up

41. Automated oestrus detection: model calculations

42. 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

43. Results
Cash flow: 3,202 $CA / year
Cost-Benefit ratio: $CA 1.72
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

44. Economics of estrus detection: Practise
($CA/100 kg milk)
No sensor AMS CMS
Before After Before After
Capital costs 14.53 13.61a 19.56b 15.51c 15.89c
Labour costs 17.33 16.37a 15.82a 15.82c 14.60c
Variable costs 27.23 26.12a 27.72a 25,59c 26.94c
Revenues 64.79 61,50a 64.93b 64.08c 66,05c
Profit 5.70 5.40a 1.83b 7.15c 8.62c

45. What can you expect from me
 A little history and overview
 Success factors for precision technology
 Adoption of sensor systems
 Effects of mastitis detection systems
 Effects of estrus detection systems
 Wrap up

46. Difference between theory and practise
 The potential of sensors is not always reached
 Why??

47. 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%)

48. Many mastitis cases are not detected
(Hogeveen et al., 2013)
75%
less clinical mastitis
and higher SCC

49. 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)

50. Too much information without knowing
what to do with it (Russell and Bewley, 2013)
50

51. Farmers’ attitude is important
Being in control Letting-go
Convenience seekersBusiness optimisers

52. Farmers’ attitude
Eager to understand and
learn the system
Not having the
time or skills
Innovators/ambassadors

53. Sensors can pay for themselves…..
….. and at the same time:
 Improve dairy cattle health and welfare
 Improve the efficiency in the dairy value chain
 All sensors?
I am not sure about that

54. Thank you for your attention
I wish you a great conference
@henkhogeveen
animal-health-management.blogspot.com
www.slideshare.net/henkhogeveen
Thank you for your attention
www.precisiondairyfarming.com/2016