moving beyond estrus detection
TRANSCRIPT
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