activity meters are often used for automated oestrus detection. But is there more benefit from monitoring activity of cows? This presentation was part of the SUND Dairycare conference held in 2015, in Cordoba, Spain
8. Technology and dairy farming
Automation to increase labour efficiency
Increased number of cows per labour input
9. Technology and dairy farming
Automation to increase labour efficiency
Increased number of cows per labour input
Less time per cow to monitor health
10. 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
11. Tools monitoring production, health and welfare
automatically, continuously, and (near) real-time
Precision livestock farming (PLF) technologies
12. Tools monitoring production, health and welfare
automatically, 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
13. Improve health & welfare
Increase efficiency
Improve product quality
Objective monitoring
Improve social lifestyle
Benefits of PLF technologies
18. 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)
20. Attached to the ear
Attached to collar
Attached to the leg
Why is automated oestrus detection different?
Still many options to chose from, but
21. Why is automated oestrus detection different?
Still many options to chose from, but
Associated with clear management action
22. 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)
23. Lincoln University Dairy Farm, New Zealand
37-d breeding period - start Oct. 25 2010
635 cows with SCR – collars
320 activity only (AO)
315 activity and rumination (AR)
Milk progesterone as gold standard
Twice weekly during breeding period
Field evaluation of two collar-mounted activity meters
(Kamphuis et al., 2012, JDS)
24. 3 time-windows allow for mismatch of Gold Standard
AO: 52
AR: 67
AO: 58
AR: 71
AO: 62
AR: 77
Sensitivity (%)
27. Why is automated oestrus detection different?
Still many options to chose from, but
Associated with clear management action
OK performance
80% Sensitivity 80% Success rate
(Kamphuis et al., 2012, JDS)
28. Why is automated oestrus detection different?
Still many options to chose from, but
Associated with clear management action
OK performance
80% Sensitivity 80% Success rate
(Kamphuis et al., 2012, JDS)
Investment is economically beneficial
(Rutten et al., 2014, JDS)
30. General culling
Calving
Ovulation
Heat detection
P(1st ovulation)
P(heat)
P(heat detected)
P(culling)
P(culling)
P(culling)
Simulated cow
Parity, 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 price
Labour costs
Cost for AI
Costs/revenues of calves
Costs feed
Costs for culling
Costs 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
10years
Checking each
alert visually
31. Investing in automated oestrus detection
Cash flow: 2,287 € / year
Cost-Benefit ratio: € 1.23
Discounted payback period: 8 years
Investment pays off
(Rutten et al., 2014, JDS)
SN 80%;SP 95%
€ 108/cow
€ 3600/herd
10years
Checking each alert visually
32. Why is automated oestrus detection different?
Still many options to chose from, but
Associated with clear management action
OK performance
80% Sensitivity 80% Success rate
(Kamphuis et al., 2012, JDS)
Investment is economically beneficial
(Rutten et al., 2014, JDS)
33. New Zealand survey 500 farmers
25% wants it
7% has it
70% 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 farmers
41% of AMS farmers has it
70% of CMS farmers has it
(Steeneveld and Hogeveen, 2015, JDS)
Survey 109 farmers globally
41% has it
Rated as useful to very useful
(Borchers and Bewley, in press, JDS)
35% of US respondents
(Bewley, EAAP/EU-PLF conference, 2014)
35. Moving beyond oestrus detection
Explore other fields
improve utilization of activity data
36. Lameness in the dairy industry
Impacts welfare, productivity, profitability
~$28,000 per year on average NZ farm€16,500
37. Lameness in the dairy industry
Impacts welfare, productivity, profitability
~$28,000 per year on average NZ farm
Visual detection is common practice
Challenging for large herds
NZ farmers fail to identify ~75% of lame cows
(Fabian, 2012; Whay et al., 2002)
Whay et al.,
2002)
Lame?
€16,500
38. Automated lameness detection
5 Waikato farms
4,900 cows
1.5 million milkings
Sensor data every milking
activity and milking
order
live-weight yield
45. Values recorded during milking were averaged
a daily value per sensor
Predictive variables were straightforward
Proportional differences Day-1 to D-14
Absolute value on Day-1
n = 14 variables per sensor
Detecting lameness
46. Values recorded during milking were averaged
a daily value per sensor
Predictive variables were straightforward
Proportional differences Day-1 to D-14
Absolute value on Day-1
n = 14 variables per sensor
Daily probability estimate for lameness
Detecting lameness
47. Values recorded during milking were averaged
a daily value per sensor
Predictive variables were straightforward
Proportional differences Day-1 to D-14
Absolute value on Day-1
n = 14 variables per sensor
Daily probability estimate for lameness
Leave-one-farm-out cross validation
Detecting lameness
55. Detecting lameness
Combining sensors outperformed single sensors
consistently across farms
Potential of using data already on-farm
Improvements required
better predictive variables
Autocorrelation matrix
standard operating procedures
56. Moving beyond oestrus detection
Explore other fields
improve utilization of activity data
57. Predicting moment of calving
Current status: expected calving date
267-295 days after successful insemination
58. Predicting moment of calving
Current status: expected calving date
267-295 days after successful insemination
33% of calvings are
difficult (Barrier et al., 2013)
59. Predicting moment of calving
Current status: expected calving date
267-295 days after successful insemination
33% of calvings are
difficult (Barrier et al., 2013)
Can sensor data better predict moment of
calving?
60. Predicting moment of calving
Two Dutch dairy farms
583 cows with SensOor (Agis Automatisering BV, Harmelen, the Netherlands)
61. Predicting moment of calving
Two Dutch dairy farms
583 cows with SensOor (Agis Automatisering BV, Harmelen, the Netherlands)
Calvings caught on camera
62. Predicting moment of calving
Two Dutch dairy farms
583 cows with SensOor (Agis Automatisering BV, Harmelen, the Netherlands)
Calvings caught on camera
63. Predicting moment of calving
Two Dutch dairy farms
583 cows with SensOor (Agis Automatisering BV, Harmelen, the Netherlands)
110 Calvings caught on camera
64. 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
65. Predicting hour of start calving– two logit models
Dependent: hour in which calving started
Basic: days to ECD
Extended: days to ECD + sensor data
where these are relative changes for
Ruminating
Feeding
Highly active
Not active
Temperature
66. 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
67. Predicting hour of start calving
Model SN at SP = 90%
Basic 22
Extended 69
70. Predicting hour of start calving
Model SN at SP = 90%
Basic 22
Extended (same hour) 69
Extended (same + previous hour) 81
71. Predicting hour of start calving
Potential of using data already on-farm
‘Not active’ significantly added to the model
72. Predicting hour of start calving
Potential of using data already on-farm
‘Not active’ significantly added to the model
Not ready for practical implementation yet
model not validated
performance not good enough (SP too low)
73. Potential of using data already on-farm
‘Not active’ significantly added to the model
Not ready for practical implementation yet
model not validated
performance not good enough (SP too low)
Improvements required
modelling techniques
predictive variables
Predicting hour of start calving
75. What I would like you to remember
Adoption of
PLF is expected
to increase
Notas del editor
Voorbeeld aanhalen, automatisatie melkput waardoor meer koeien per uur melken, dus uitgebreider maar daardoor minder tijd deze koeien moeten ook gezondh blijven maar daar is dan minder tijd voor monitoring health
Aware entire session is called ‘Precision Livestock Farming’, so perhaps unnecessary.
But, think of PLF similarly for duration of this presentation.
PLF tools measure ‘something’, for example a cow’s activity, automatically, continuously and (near) real-time.
PLF aims at helping end-users in their decision-taking management processes or at reducing dependency on human labour. Examples, pedometers can aid in insemination decisions, automatic milking replace a significant amount of hard and repetitive labour.
PLF is emerging, supported with 126 publications on 139 PLF technologies past decade. Moreover, national and EU-funded projects that focus on implementation of PLF on-farms (SDF and All Smart Pigs). Finally, emerging international conferences dedicated to PLF (smartagrimatics and PDC in 2016, mentioned by Wilma Steeneveld.
So, a lot is going on in the field of PLF, but....
Aware entire session is called ‘Precision Livestock Farming’, so perhaps unnecessary.
But, think of PLF similarly for duration of this presentation.
PLF tools measure ‘something’, for example a cow’s activity, automatically, continuously and (near) real-time.
PLF aims at helping end-users in their decision-taking management processes or at reducing dependency on human labour. Examples, pedometers can aid in insemination decisions, automatic milking replace a significant amount of hard and repetitive labour.
PLF is emerging, supported with 126 publications on 139 PLF technologies past decade. Moreover, national and EU-funded projects that focus on implementation of PLF on-farms (SDF and All Smart Pigs). Finally, emerging international conferences dedicated to PLF (smartagrimatics and PDC in 2016, mentioned by Wilma Steeneveld.
So, a lot is going on in the field of PLF, but....
Lists goes on:
Reduce costs
Reduce stress
Safe labor
Finish with first part of the presentation
Start with second one, the success story of automated heat detection
RUTTEN TOEVOEGEN ALHIER
AR, maar alleen activity meegenomen!
Neem tijd om dit allemaal uit te leggen
Wij zeiden dat SN op zijn minst 80% moest zijn, daarvoor moest th verschoven worden en success rate bleek ook 890% te zijn. Dat leek ons van praktische waarde.
Duidelijk maken dat balans SN en SR kan verschillen voor verschillende situaties
With vervangen door
20% dutch farmers boven steenveld.
Ook kentucky is vertekend beeld want alleen mensen met sensoren algemeen zullen de survey beantwoorden
Meest fair is NZ en CRV
Maar meanstream sensor geworden
Since farmers already have activity data, is it possible to add even more (economic) value by using the same data for other dairy cow health management areas?
Jessica Fabian, thesis of massey university, PN
Make sure you mention that it is difficult to see that specific cow is lame
Jessica Fabian, thesis of massey university, PN
Make sure you mention that it is difficult to see that specific cow is lame
All same sensors!
1.5 years data collection
Scale and relevance, data from the field
All same sensors!
Stress that this is an imaginary example! Mention that non-lame cows were not allowed to have a lameness record throughout data collection period. Make sure that you mention ‘compare pattern changes in behaviour and physiology 14 days before the cow was observed lame by the farmer
Stress that this is an imaginary example! Mention that non-lame cows were not allowed to have a lameness record throughout data collection period. Make sure that you mention ‘compare pattern changes in behaviour and physiology 14 days before the cow was observed lame by the farmer
Mention not interested in risk factors, so not interested that lame cows seem less active then non-lame cow but we’re interested in the pattern difference because that is what a detection model needs to identify
Good result because it tells us that sensors can pick up changes in behaviour and physiology associated with lameness
Since farmers already have activity data, is it possible to add even more (economic) value by using the same data for other dairy cow health management areas?
Negative impact cow health
Higher mortality rate of calves
Negative impact image
Negative impact cow health
Higher mortality rate of calves
Negative impact image
Negative impact cow health
Higher mortality rate of calves
Negative impact image