Sensors technologies

replacing or complementing human senses to monitor animal health

claudia.kamphuis@wur.nl

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

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 EconomicsWageningen University

What can you expect

Claudia Kamphuis

Sensor technologies in dairy

Boosted by development of automatic milking systems in 1990s

6 main brands

Boosted by development of automatic milking systems in 1990s

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

And further pushed by increased animal welfare concerns

Increasing herds

Government

Society

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

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

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

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

There are A LOT of sensor technologies

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With A LOT of benefits

Improve health, welfare

Increase productivity

Increase efficiency

Improve product quality

Objective monitoring

Improve social lifestyle

Use of sensor technologies in the Netherlands(Steeneveld and Hogeveen, 2015)

Survey study1,672 farmers approached via email

512 farmers replied (31%)

202 farmers (41%) replied to have sensor technologies

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When did CMS farmers invest in sensors (n = 81)(Steeneveld and Hogeveen, 2015)

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)

Use of sensor technologies (%) in the Netherlands(Steeneveld and Hogeveen, 2015)

20

Sensor AMS (n = 121)

CMS (n = 81)

Colour 60 1Electrical Conductivity 93 35Milk temperature 50 6Weighing platform 27 5Fat and protein 20 0Somatic cell count 17 1Activity meters/pedometers dairy cows 41 70Activity meters/pedometers young stock 12 28Temperature 6 14Rumination 9 12Lactate dehydrogenase (LDH) 2 1Progesterone 2 1

What can you expect

Claudia Kamphuis

Sensor technologies in dairy

Theory and Economic potential vs. reality

Reasons why AMS farmers invested in sensors(Steeneveld and Hogeveen, 2015)

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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

Automated mastitis detection: theory

Not a conscious decision (we have to?)

Managing bulk milk SCC levels

Mastitis detection

Dry-cow therapy decisions

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Automated mastitis detection: economics

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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

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: reality (Steeneveld et al., 2015)

Automated oestrus detection: theory

Advantages two-fold Improve farm profitability Better detection rates -> improved pregnancy rates

Automated oestrus detection: theory

Advantages twofold Improve farm profitability Better detection rates -> improved pregnancy rates

Clear management (decision support) associated with information

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)

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)

Automated oestrus detection: economics

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

Automated oestrus detection: economics

Cash flow: 2,287 € / yearCost-Benefit ratio: € 1.23Discounted payback period: 8 years

Investment pays off(Rutten et al., 2014)

SN 80%;SP 95%€ 108/cow

€ 3600/herd10years

Checking each alert visually

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

Automated oestrus detection: reality (Steeneveld et al., 2015)

70

75

80

85

90

95

100

105

Day

s to

firs

t ser

vice

Investment in sensor technologies: reality (€/100 kg milk) (Steeneveld et al., unpublished)

  No sensor AMS CMS  Before After Before AfterCapital 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

So, just a mid re-cap

1,672 farms approached512 farmers replied202 indicated to have sensors(Steeneveld and Hogeveen, 2015)

Economic theory is not matching reality

12%

What can you expect

Claudia Kamphuis

Sensor technologies in dairy

Theory and Economic potential vs. reality

Performance of sensor technologies

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

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

It’s all about monitoring parameters associated with events of interest, but sensors

May not accurately or precisely monitor these parameters

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

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

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

Always a trade-of between

SensitivityHow many events do you

detect (true positive alerts) and how many do you

miss (false negative alerts)

SpecificityHow many healthy cows do

not receive an alert (true negative alert)

and how many do receive an alert falsely

(false positive alert)

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)

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)

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

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Example automated oestrus detection with 75% sensitivity

Year-round calving might be OK

But what about seasonal calving?6wks time to get all cows pregnantEconomic losses in case oestrus events are missed

Farmers’ attitude

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/ambassadorsConvenience seekers/business optimisers

Sensors are not about ‘one size fits all’

Waiting for ‘improved’ systems(Borchers and Bewley, 2015; Steeneveld and Hogeveen, 2015; Russell and Bewley, 2013)

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What can you expect

Claudia Kamphuis

Sensor technologies in dairy

Theory and Economic potential vs. reality

Performance of sensor technologies

Working with sensor technologies

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

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

Use of sensor information is limited(Hogeveen et al., 2013)

5% of generated mastitis alerts are visually checked

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%)

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’

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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: reality (Steeneveld et al., 2015)

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)

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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

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?

What’s currently being done?

Develop a blueprint for successful PLF technologies

Social impactEconomic viability

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

What can you expect

What I would like you to remember

Sensors are exciting, high-tech and have potential

But we need their information combined with

To complement management decisions on animal health

Thank you for your attention

www.slideshare.net/claudiakamphuis ckamphuis