Using activity meters to monitor health

Moving beyond oestrus detection

Claudia.Kamphuis@wur.nl

Roadmap

Precision Livestock Farming Technologies

Roadmap

Precision Livestock Farming Technologies

A success story: Automated oestrus detection

Roadmap

Precision Livestock Farming Technologies

A success story: Automated oestrus detection

Moving beyond oestrus detection

Roadmap

Precision Livestock Farming Technologies

A success story: Automated oestrus detection

Moving beyond oestrus detection

Take home message

Precision livestock farming technologies

Technology and dairy farming

Automation to increase labour efficiency

Technology and dairy farming

Automation to increase labour efficiency

Increased number of cows per labour input

Technology and dairy farming

Automation to increase labour efficiency

Increased number of cows per labour input

Less time per cow to monitor health

Automation to increase labour efficiency

Increased number of cows per labour input

Less time per cow to monitor health

Need for management-support technologies

Technology and dairy farming

Tools monitoring production, health and welfareautomatically, continuously, and (near) real-time

Precision livestock farming (PLF) technologies

Tools monitoring production, health and welfareautomatically, continuously, and (near) real-time

Emerging field:126 studies, 139 technologies (Rutten et al., 2013, JDS)

Precision livestock farming (PLF) technologies

(Inter)national projects International conferences

Improve health & welfare

Increase efficiency

Improve product quality

Objective monitoring

Improve social lifestyle

Benefits of PLF technologies

Adoption of PLF technologies

Why has it been so slow?

Not familiar with available options (Russel and Bewley, 2013, JDS)

15

Too much information without knowing what

to do with it(Russel and Bewley, 2013, JDS)

16

Waiting for improved systems (Steeneveld and Hogeveen, 2015, JDS)

Undesirable/unknown cost-benefit ratio (Russel and Bewley, 2013, JDS; Steeneveld and Hogeveen, 2015, JDS)

Most important limiting factor for commercialisation (Banhazi et al., 2012, Int J Agric & Biol Eng)

A success story: automated oestrus detection

Attached to the ear

Attached to collar

Attached to the leg

Why is automated oestrus detection different?

Still many options to chose from, but

Why is automated oestrus detection different?

Still many options to chose from, but

Associated with clear management action

Why is automated oestrus detection different?

Still many options to chose from, but

Associated with clear management action

OK performance(Rutten et al., 2013, JDS)

Lincoln University Dairy Farm, New Zealand

37-d breeding period - start Oct. 25 2010

635 cows with SCR – collars320 activity only (AO)315 activity and rumination (AR)

Milk progesterone as gold standardTwice weekly during breeding period

Field evaluation of two collar-mounted activity meters (Kamphuis et al., 2012, JDS)

3 time-windows allow for mismatch of Gold Standard

AO: 52AR: 67

AO: 58AR: 71

AO: 62AR: 77

Sensitivity (%)

Changing activity alert threshold – AR collars

25

26

Changing activity alert threshold – AR collars

Why is automated oestrus detection different?

Still many options to chose from, but

Associated with clear management action

OK performance80% Sensitivity 80% Success rate(Kamphuis et al., 2012, JDS)

Why is automated oestrus detection different?

Still many options to chose from, but

Associated with clear management action

OK performance80% Sensitivity 80% Success rate(Kamphuis et al., 2012, JDS)

Investment is economically beneficial(Rutten et al., 2014, JDS)

A model for the Dutch situation

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

Investing in automated oestrus detection

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

Investment pays off(Rutten et al., 2014, JDS)

SN 80%;SP 95%€ 108/cow

€ 3600/herd10years

Checking each alert visually

Why is automated oestrus detection different?

Still many options to chose from, but

Associated with clear management action

OK performance80% Sensitivity 80% Success rate(Kamphuis et al., 2012, JDS)

Investment is economically beneficial(Rutten et al., 2014, JDS)

New Zealand survey 500 farmers25% wants it7% has it70% listed it in top 3 of technologies that gained benefit for farm(Edwards et al., 2014, APS)

Adoption rates of automated oestrus detection systems

20% of all Dutch farms(Huijps, CRV, personal communication)

Dutch survey 512 farmers41% of AMS farmers has it70% of CMS farmers has it(Steeneveld and Hogeveen, 2015, JDS)

Survey 109 farmers globally41% has itRated as useful to very useful(Borchers and Bewley, in press, JDS)

35% of US respondents(Bewley, EAAP/EU-PLF conference, 2014)

Moving beyond oestrus detection

Moving beyond oestrus detection

Explore other fields improve utilization of activity data

Lameness in the dairy industry

Impacts welfare, productivity, profitability~$28,000 per year on average NZ farm€16,500

Lameness in the dairy industry

Impacts welfare, productivity, profitability~$28,000 per year on average NZ farm

Visual detection is common practiceChallenging for large herdsNZ farmers fail to identify ~75% of lame cows (Fabian, 2012; Whay et al., 2002)

Whay et al., 2002)

Lame?

€16,500

Automated lameness detection

5 Waikato farms

4,900 cows

1.5 million milkings

Sensor data every milking

activity and milking order

live-weight yield

Lameness events

Trained Farmers

Farmer observationsCow identificationDateAffected limbLameness score

1

2

3

4

5

Matched by farm, date1 lame cow 10 non-lame cows

Methods

-14 -13 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0

Day

Methods

Day of observation

Lame, n = 318

Non-Lame, n = 3,180

A

ctiv

ity

High

Low

-14 -13 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0320

340

360

380

Day

Change in Activity (steps / hour)

● Lame (n = 318); ○ Non-lame (n = 3,180)

Day of observation

Patterns through time were different (P<0.05)

● Lame (n = 318); ○ Non-lame (n = 3,180)

Changes in other sensor measurements

-14 -12 -10 -8 -6 -4 -2 045.0

55.0

65.0

Day

Milking order

-14 -12 -10 -8 -6 -4 -2 08.48.68.89.09.29.49.69.8

10.0

Day

Milk yield (kg)

-14 -12 -10 -8 -6 -4 -2 0470

475

480

485

490

495

500

Day

Weight (kg)

Patterns through time were different (P<0.05) for all sensor

measurements

Detecting lameness

Values recorded during milking were averageda daily value per sensor

Values recorded during milking were averageda daily value per sensor

Predictive variables were straightforwardProportional differences Day-1 to D-14 Absolute value on Day-1n = 14 variables per sensor

Detecting lameness

Values recorded during milking were averageda daily value per sensor

Predictive variables were straightforwardProportional differences Day-1 to D-14 Absolute value on Day-1n = 14 variables per sensor

Daily probability estimate for lameness

Detecting lameness

Values recorded during milking were averageda daily value per sensor

Predictive variables were straightforwardProportional differences Day-1 to D-14 Absolute value on Day-1n = 14 variables per sensor

Daily probability estimate for lameness

Leave-one-farm-out cross validation

Detecting lameness

Detecting lameness

SensitivitySP = 80% SP = 90%

Sensor Lame cows

Lame cows ≥3

Lame cows

Lame cows ≥3

Activity 26 39 14 27Live weight 37 38 23 28Milking order 33 44 21 28All three 48 57 31 41

Detecting lameness

SensitivitySP = 80% SP = 90%

Sensor Lame cows

Lame cows ≥3

Lame cows

Lame cows ≥3

Activity 26 39 14 27Live weight 37 38 23 28Milking order 33 44 21 28All three 48 57 31 41

Detecting lameness

SensitivitySP = 80% SP = 90%

Sensor Lame cows

Lame cows ≥3

Lame cows

Lame cows ≥3

Activity 26 39 14 27Live weight 37 38 23 28Milking order 33 44 21 28All three 48 57 31 41

Detecting lameness

SensitivitySP = 80% SP = 90%

Sensor Lame cows

Lame cows ≥3

Lame cows

Lame cows ≥3

Activity 26 39 14 27Live weight 37 38 23 28Milking order 33 44 21 28All three 48 57 31 41

Detecting lameness

SensitivitySP = 80% SP = 90%

Sensor Lame cows

Lame cows ≥3

Lame cows

Lame cows ≥3

Activity 26 39 14 27Live weight 37 38 23 28Milking order 33 44 21 28All three 48 57 31 41

Detecting lameness

Combining sensors outperformed single sensorsconsistently across farms

Detecting lameness

Combining sensors outperformed single sensorsconsistently across farms

Potential of using data already on-farm

Detecting lameness

Combining sensors outperformed single sensorsconsistently across farms

Potential of using data already on-farm

Improvements requiredbetter predictive variablesAutocorrelation matrixstandard operating procedures

Moving beyond oestrus detection

Explore other fields improve utilization of activity data

Predicting moment of calving

Current status: expected calving date267-295 days after successful insemination

Predicting moment of calving

Current status: expected calving date267-295 days after successful insemination

33% of calvings are difficult (Barrier et al., 2013)

Predicting moment of calving

Current status: expected calving date267-295 days after successful insemination

33% of calvings are difficult (Barrier et al., 2013)

Can sensor data better predict moment of calving?

Predicting moment of calving

Two Dutch dairy farms

583 cows with SensOor (Agis Automatisering BV, Harmelen, the Netherlands)

Predicting moment of calving

Two Dutch dairy farms

583 cows with SensOor (Agis Automatisering BV, Harmelen, the Netherlands)

Calvings caught on camera

Predicting moment of calving

Two Dutch dairy farms

583 cows with SensOor (Agis Automatisering BV, Harmelen, the Netherlands)

Calvings caught on camera

Predicting moment of calving

Two Dutch dairy farms

583 cows with SensOor (Agis Automatisering BV, Harmelen, the Netherlands)

110 Calvings caught on camera

Dependent: hour in which calving started

Basic: days to expected calving date (ECD)ECD = insemination date + 280

Predicting moment of start calving– two logit models

Predicting hour of start calving– two logit models

Dependent: hour in which calving started

Basic: days to ECD

Extended: days to ECD + sensor datawhere these are relative changes forRuminatingFeedingHighly activeNot activeTemperature

Predicting hour of start calving– two logit models

Dependent: hour in which calving started

Basic: days to expected calving date (ECD)

Extended: days to ECD + sensor data

Data selection:168 h before and including hour of start calving

Predicting hour of start calving

Model SN at SP = 90%Basic 22Extended 69

Predicting hour of start calving

Predicting hour of start calving

ReasonableToo early?Impractical

Predicting hour of start calving

Model SN at SP = 90%Basic 22Extended (same hour) 69Extended (same + previous hour) 81

Predicting hour of start calving

Potential of using data already on-farm‘Not active’ significantly added to the model

Predicting hour of start calving

Potential of using data already on-farm‘Not active’ significantly added to the model

Not ready for practical implementation yetmodel not validatedperformance not good enough (SP too low)

Potential of using data already on-farm‘Not active’ significantly added to the model

Not ready for practical implementation yetmodel not validatedperformance not good enough (SP too low)

Improvements requiredmodelling techniquespredictive variables

Predicting hour of start calving

Take home message

What I would like you to remember

Adoption of

PLF is expected

to increaseRequire insight

in economics of PLF to improve adoption

Activity for

automated heat

detection

works

Activity to

monitor other health issues has

potential

What Method

sWhat

combination

Action associat

ed