This is the second presentation I was invited to give at the CAVI conference held in Galway, Ireland on October 12. it deals with precision dairy farming. A field that is coming up and growing in importance in modern dairy farming

1. Precision agriculture
Henk Hogeveen

2. What can you expect from me
 (The need for) Precision dairy farming
 Some examples
● Automatic milking
● Mastitis detection
● Estrus detection
 Lessons to learn

6. Trend worldwide
 Less farms
 Farm seize increase
 Milk production increases
● Per cow
● Per labour unit
● Per farm
 Increasing need for efficiency
 Cows are managed in groups .....
.........becomes a disposable product?

7. Current demands to dairy industry
 Animal well-being
 Consumer demands
 Environment
 Labor
 Economics

8. Current demands to dairy industry
 Animal well-being
 Consumer demands
 Environment
 Labor
 Economics
 We have to reduce the use of scarce resources
 So: explore the full potential of each individual
dairy cow

9. Is individual cow management possible?
Easy
(too) difficult
Don’t even
think about it

10. Precision dairy farming
 Technology, measuring:
● Physiology
● Behaviour
● Production
 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

11. Cow as individual animal
 Enables management adjusted to the cow’s production
level
● Milking management (times per day)
● Disease management (treatment or not)
● Reproduction management (insemination or not,
intervention or not
● Feeding management
● Management by exception

12. Examples of technologies
 Milk yield recording systems
 Milk component monitors
 Activity monitors
 Lying and rumination behavior monitors
 Milk conductivity indicators
 Heat monitors

13. Review of sensor systems until now
Rutten et al., 2013

14. What can you expect from me
 (The need for) Precision dairy farming
 Some examples
● Automatic milking
● Mastitis detection
● Estrus detection
 Lessons to learn

15. Benefits
 Labor
● No more milking
● Reduction milking time 50 % - 80 %
 Milk production
● Increased milking frequency
 Udder health
● Less overmilking
● Separated quarters
● Increased milking frequency
● ….

16. Disadvantages
 More control tasks
 Replacement value (investment)
 Depreciation time
 Maintenance
 Energy and water
 Udder health
● More cows per cluster
● Milking intervals
● …….

17. Previous studies
 Normative (what-if calculations)
 Automatic milking is not cost effective

18. Farm comparison using real data
Bijl et al., 2007
AMS
Total land use, ha
Milk quota, kg
No. of dairy cows
Milk/cow, kg
CMS
60.0
61.7
828,761
853,620
105
110
8,011
7,894

19. Farm comparison
Bijl et al., 2007
AMS
CMS
60.0
61.7
828,761
853,620
105
110
Milk/cow, kg
8,011
7,894
Total labor FTE
1.45
1.87
Family labor FTE
1.26
1.69
Employee labor FTE
0.19
0.18
Total land use, ha
Milk quota, kg
No. of dairy cows
Source: Bijl et al., 2007

20. Farm comparison
Bijl et al., 2007
AMS
CMS
60.0
61.7
828,761
853,620
105
110
Milk/cow, kg
8,011
7,894
Total labor FTE
1.45
1.87
Family labor FTE
1.26
1.69
Employee labor FTE
0.19
0.18
Dairy cows/total FTE
74
59
586,241
459,117
Total land use, ha
Milk quota, kg
No. of dairy cows
Milk/total FTE, kg
Source: Bijl et al., 2007

21. No difference in margin
Bijl et al., 2007
AMS
CMS
Milk revenues
31.53
32.27
Miscellaneous revenues
2.82
2.27
34.35
34.54
4.67
4.83
Total feed costs
6.47
6.33
Health costs
0.84
0.93
2.01
2.25
1.28
1.46
Total costs
9.76
10.04
Margin on dairy production
24.60
24.50
Total revenues
Concentrate costs
Total livestock costs
Land use costs
Source: Bijl et al., 2007

22. Other costs higher for AMS
Bijl et al., 2007
AMS
CMS
Margin on dairy production
24.60
24.50
Gross margin
26.51
26.34
Contractor costs
2.55
1.81
Gas, water, electricity
1.24
1.01
- machinery and equipment
3.15
2.72
- land, buildings, installations
0.88
0.60
9.29
7.46
Maintenance/insurance of:
Total non-accountable costs
Available for rent, depreciation, interest,
17.22
labor and profit Excluding € 14,000 higher depreciation and interest for AMS
18.87
€ 15,500/farm
Source: Bijl et al., 2007

23. Economic results second study
Cows (number)
Total number of cows
Land (ha)
Total land use
Capital costs (€/100 kg milk) Expenses on buildings
Depreciation on buildings
Expenses on machinery and equipment
Depreciation on machinery and
equipment
Miscellaneous depreciation
Total capital
Labor costs (€/100 kg milk) Customer work
Paid labor
Own labor1
Total labor
Steeneveld et al.,
2012)
AMS
(n=63)
71
110
1.56
2.69
4.57
3.88
0.01
12.71
CMS
(n=337)
70
113
1.54
2.51
3.48
2.53
0.04
10.10
2.89
0.46
6.95
10.30
2.96
0.70
7.06
10.72
Materials costs (€/100 kg
milk)
Revenues (€/100 kg milk)
Total materials
17.17
16.99
Total revenues
44.87
45.33
Net output (€/100 kg milk)
Total revenues – total materials
27.70
28.34

24. Study focusing on grazing
(1,017 farms)
Grazing (yes/no)
21,6280
0,001
Grazing time* farm seize
-0,0674
0,000
-16,1506
0,004
Grazing * AMS
Van de Pol-van Dasselaar et al, 2013

25. Study on motivations to invest in AMS
Cows
Hectares
Quotum (kg)
Milk/ ha
Milk/ cow
No grazing
AM-system
87
51
752,000
15.671
8.682
33
CM-system
91
55
738,000
13.867
8.118
8
Hogeveen et al., 2003

26. Personal circumstances
Age farmer
Married
Children
No successor
No need for
replacement old
system
AM-system
44.1
55
2.6
12
CM-system
41.3
47
2.4
2
25
11

27. Motivations automatic milking
Motivation
Less (heavy) labour
Flexibility
Milking more than twice
Less labour available
Need new milking system
Improved udder health
Higher milk production
Building new stable
Future
Other
Total
Reason 1
18
7
7
7
9
0
0
2
3
7
60
Reason 2 Reason 3
10
5
10
4
6
5
5
6
2
4
4
5
6
3
4
1
2
1
10
7
59
41
%
21
13
11
11
9
6
6
4
4
15
100

28. Motivations conventional milking
Motivation
Costs AM-system too high
Dependency AM-system
Uncertainty AM-system
Inflexible with growing
2nd AM-system expensive
Position in barn
Other
Total
Reason 1
18
7
7
7
9
0
7
60
Reason 2 Reason 3
10
5
10
4
6
5
5
6
2
4
4
5
10
7
59
41
%
21
13
11
11
9
6
15
100

29. What can you expect from me
 (The need for) Precision dairy farming
 Some examples
● Automatic milking
● Mastitis detection
● Estrus detection
 Lessons to learn

30. Mastitis detection
 Developed in 1980’s
 Sensors did not provide useful information
● Clinical mastitis, why automated detection
● Subclinical mastitis, no associated management
 Never a success until automatic milking (need)
● Good enough (but far from perfect)
 High capacity milking parlors: selection of cows to check

31. Problem: needle in a haystack
 Every miling is a test
 60 cows, 2,6 milkings per cow per day -> 57,000 milkings
per year
 20 mastitis cases -> 0.1 % of all milkings

32. What’s found in the past















Sensitivity
Specificity
Cavero et al., 2006
81
94
De Mol & Ouweltjes, 2001
100
96
De Mol & Woldt, 2001
100
99
De Mol et al., 1997
59
98
De Mol et al., 2001
71
97
Kamphuis et al., 2008
80
92
Kamphuis et al., 2008
50
99
Maatje et al., 1992
100
?
Maatje et al., 1997
90
98
Mottram et al., 2007
56
82
Nielen et al., 1995
77
69
Nielen et al., 1995
84
97
Norberg et al., 2006
43
93
Sheldrake & Hoare, 1981
49
79

33. Specificities re-arranged
Specificity (%)
100
95
90
85
80
0
5
10
15
20
Total time window (days)
25
30

34. Sensitivities added
Specificity/sensitivity (%)
100
80
60
40
0
5
10
15
20
Total time window (days)
25
30

35. Mastitis is not a black-and-white situatiom
Severe
clinical
mastitis
Healthy

36. Detection of mastitis by farmer
Check
report
Interpret
report
•Conductivity
•Colour
•Milk
production
deviation
•Total number
of alerts
•SCC (optional)
•Check history
alert and/or
check alert in
the barn
Check
history
alert
Interpret
history
alert
Check
alert in
the barn
•Milkquality
•Milk visits
•Conductivity
chart
•Check alert in
the barn
•Check cow
•Check udder
•Spurt and
check milk
•CMT
Interpret
check
Take
action
•Mastitis?
•Take action!
•Take milk
sample
•Treat mastitis

37. Study on 7 farms
Check
report
Interpret
report
•Conductivity
•Colour
•Milk
production
deviation
•Total number
of alerts
•SCC (optional)
•Check history
alert and/or
check alert in
the barn
Check
history
alert
Interpret
history
alert
Check
alert in
the barn
•Milkquality
•Milk visits
•Conductivity
chart
•Check alert in
the barn
•Check cow
•Check udder
•Spurt and
check milk
•CMTsTUDY ON
Quick glance
10 times a day – 2 times a week
Interpret
check
Take
action
•Mastitis?
•Take action!
•Take milk
sample
•Treat mastitis

38. Check and interpret history alert
Check
report
Interpret
report
•Conductivity
•Colour
•Milk
production
deviation
•Total number
of alerts
•SCC (optional)
•Check history
alert and/or
check alert in
the barn
Check
history
alert
Interpret
history
alert
Check
alert in
the barn
•Milkquality
•Milk visits
•Conductivity
chart
•Check alert in
the barn
•Check cow
•Check udder
•Spurt and
check milk
•CMT
Only when alarming
Definition of alarming varies between farmers
Interpret
check
Take
action
•Mastitis? Take
action!
•Take milk
sample
•Treat mastitis

39. Check alert in the barn
 Only 3,5% of the alerts are checked by the farmer!

40. Alerts checked by farmer (n=15)
20%
Clinical
Subclinical
13%
67%
Negative
CMT

41. Alerts checked by researcher
Overview of the checked mastitis alerts
Clinical
mastitis
Subclinical mastitis
Negative CMT
Total
Number of individual
quarter alerts
30
47
150
227
Number of repeated
quarter alerts
9
81
104
194
46%
Total
39
10%
128
254
60%
421

42. Checked clinical
mastitis
26%
Unchecked clinical
74%
mastitis
Subclinical
mastitis
100%

43. Question?
 How bad is this?

44. What can you expect from me
 (The need for) Precision dairy farming
 Some examples
● Automatic milking
● Mastitis detection
● Estrus detection
 Lessons to learn

45. Oestrus detection
 Advantages twofold
● Labour savings
● Better estrus detection rates -> preg rates
 Clear management (decision support) associated with
information
 Adoption rate: ± 15 % in US and Netherlands
(personal communication Knijn and Bewley)

46. Two simulations
Titelstijl van model
bewerken
Visual SN 50%, SP 100%
Sensor SN 80%, SP 95%
• Klik om de tekststijl van het model
te bewerken
– Tweede niveau
• Derde niveau
– Vierde niveau
» Vijfde niveau

47. Financial results (*1000 €/herd/year)
Titelstijl van model
Milk
bewerken 330
334
• Klik om de tekststijl van het model
Feed
-128
-129
te bewerken
Calves
-7
-8
Inseminations niveau -7
• Derde
-7
– Tweede niveau
Culling
– Vierde niveau
-7
» Vijfde niveau
Labour
-1
-6
-0.7

48. Investment analysis
Titelstijl van model
bewerken
Cash flow
(€/year)
Internal Rate of Return
(%)
Pay back period
(Years)
11%
7
• Average om 3,151 tekststijl van het model
Klik
de
te bewerken
– Tweede niveau
• Derde niveau
– Vierde niveau
» Vijfde niveau

49. What can you expect from me
 (The need for) Precision dairy farming
 Some examples
● Automatic milking
● Mastitis detection
● Estrus detection
 Final words

50. Go back to the individual cow
 One size does not fit all!!
 We are throwing away a part
of the potential of our dairy cows!!!!

51. Precision dairy farming is going to increase
 What is the vet going to do?
 Use data from sensor systems
 Adapt herd health programs
 …….

52. Thank you for your attention
@henkhogeveen
animal-health-management.blogspot.com
On-line courses on
Veterinary Economics on:
www.elevatehealth.eu