Does technology pay for itself

Does technology pay for itself?

Henk Hogeveen, Wilma Steeneveld, Mariska vd Voort and Claudia Kamphuis

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

Initiated by cow identification systems in 1970s

Recording of individual milk yieldAllocating feed/concentrates to individual cows

http://www.google.nl/url?sa=i&rct=j&q=cow+identification+nedap&source=images&cd=&cad=rja&docid=6g7lFGlyEOJ3iM&tbnid=sV8jrpK-VpRX2M:&ved=0CAUQjRw&url=http://www.nedap-lactivator.com/specifications/the-neck-version/&ei=sinIUY-3B4a40QXhjICIAQ&bvm=bv.48293060,d.d2k&psig=AFQjCNFuiJ-0zlu7PDmFiCiMTi-aTC6Yow&ust=1372158749520492

Boosted by development of automatic milking systems in 1980s

http://www.google.nl/url?sa=i&rct=j&q=cow+identification+nedap&source=images&cd=&cad=rja&docid=6g7lFGlyEOJ3iM&tbnid=sV8jrpK-VpRX2M:&ved=0CAUQjRw&url=http://www.nedap-lactivator.com/specifications/the-neck-version/&ei=sinIUY-3B4a40QXhjICIAQ&bvm=bv.48293060,d.d2k&psig=AFQjCNFuiJ-0zlu7PDmFiCiMTi-aTC6Yow&ust=1372158749520492

6 main brands

Automatic milking systems are marketed since 1992

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

Replacement of human senses

Sensor development was boosted by Automatic milking

And further pushed by other factors

Increasing herdseize

Government

Society

Mom, where does the milk come from?

From the factory, honey

There are A LOT of sensor technologies

12

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

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


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


Fertility- Progesterone- Activity- Rumination

Milk Composition- Milk yield- Fat and protein content- Lactose content- Somatic cell count


Fertility- Progesterone- Activity- Rumination

Cow ‘Composition’- Weight- Body Condition Score

Metabolic disorders- Activity- Rumination- Milk yield- SCC- pH


Cow ‘Composition’- Weight- Body Condition Score


Metabolic disorders- Activity- Rumination- Milk yield- SCC- pH

Cow Mobility- Weight- Activity- Rumination - Milk yield

There is A LOT of potential

Improved health, welfare

Increase productivity

Increased efficiency

Improved product quality

Objective monitoring

Improved social lifestyle

……..



Success factors

System specifications Cost efficiency Non-economic factors

System specification

Description of (prototype) technology Algorithms that transform data to information

Is this information useful? Integration with other data sources

This can improve performanceProblems: Integration of various systems, co-operation between companies.

Decision supportWith or without interference of the farmerThis is the ultimate of precision dairy farming

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

Non-monetary factors

Risk Availability of labor/capital Farmers’ goals/preferences



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 SciSteeneveld et al., 2015, COMPAG

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

2004 2005 2006 2007 2008 2009 2010 2011 2012 20130

5

10

15

20

25

30

35

40

Mastitis Rumination Estrus

Year

Farm

ers

(n)

When did AMS farmers invest in sensors (n = 121)(Steeneveld and Hogeveen, 2015)

2004 2005 2006 2007 2008 2009 2010 2011 2012 20130

5

10

15

20

25

30

35

Mastitis Rumination Estrus

Year

Farm

ers

(n)

Milk sensors used (n = 512)

Condu

ctivit

yColo

ur

Tempe

rature

Fat-p

rotein SC

CLD

H0

20

40

60

80

100

AMSCMS

Estrus detection sensors used (n = 512)

Activity cows Activity young Temperature Progesterone0

20

40

60

80

AMSCMS

Oestrus detection sensors used (n = 512)

Rumination Weighing0

20

40

60

80

AMSCMS

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

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



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

190

195

200

205

210

215

220

225

230

Som

atic

cel

l cou

nt (x

1,00

0 ce

lls/m

l)Automated mastitis detection: Somatic cell count

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

Effects on reproduction

No sensor system AMS farms before investment

AMS farms after investment

CMS farms before investment

CMS farms after investment

70

75

80

85

90

95

100

105

Day

s to

firs

t se

rvic

e



Automated oestrus detection: model calculations

General culling

Calving

Ovulation

Heat detection

P(1st ovulation)

P(heat)P(heat detected)

P(culling)

P(culling)

P(culling)

Simulated cowParity, 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 priceLabour costsCost for AICosts/revenues of calvesCosts feed Costs for cullingCosts 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

10yearsChecking each

alert visually

Results

Cash flow: 3,202 $CA / yearCost-Benefit ratio: $CA 1.72Discounted payback period: 8 years

Investment pays off(Rutten et al., 2014)

SN 80%;SP 95%€ 108/cow

€ 3600/herd10years

Checking each alert visually

Economics of estrus detection: Practise ($CA/100 kg milk)

No sensor AMS CMS Before After Before AfterCapital 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



Difference between theory and practise

The potential of sensors is not always reached Why??

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%)

Many mastitis cases are not detected(Hogeveen et al., 2013)

75%

less clinical mastitisand higher SCC

Use of sensor information is limited

22% of farm owners indicated that expectations did not match performance reality

24% of farm owners indicatedthat learning support was not as expected (Eastwood et al., 2015)

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

50

Farmers’ attitude is important

Being in control Letting-go

Convenience seekers Business optimisers

Farmers’ attitude

Eager to understand and learn the system Not having the time or skills

Innovators/ambassadors

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

Thank you for your attentionI wish you a great conference

@henkhogeveen

animal-health-management.blogspot.com

www.slideshare.net/henkhogeveen

Thank you for your attentionwww.precisiondairyfarming.com/2016