Using activity meters to monitor health. Moving beyond oestrus detection.

Transcription

1 Using activity meters to monitor health Moving beyond oestrus detection

2 Roadmap Precision Livestock Farming Technologies

3 Roadmap Precision Livestock Farming Technologies A success story: Automated oestrus detection

4 Roadmap Precision Livestock Farming Technologies A success story: Automated oestrus detection Moving beyond oestrus detection

5 Roadmap Precision Livestock Farming Technologies A success story: Automated oestrus detection Moving beyond oestrus detection Take home message

6 Precision livestock farming technologies

7 Technology and dairy farming Automation to increase labour efficiency

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 Technology and dairy farming Automation to increase labour efficiency Increased number of cows per labour input Less time per cow to monitor health Need for management-support technologies

11 Precision livestock farming (PLF) technologies Tools monitoring production, health and welfare automatically, continuously, and (near) real-time

12 Precision livestock farming (PLF) technologies Tools monitoring production, health and welfare automatically, continuously, and (near) real-time Emerging field:126 studies, 139 technologies (Rutten et al., 2013, JDS) (Inter)national projects International conferences

13 Benefits of PLF technologies Improve health & welfare Increase efficiency Improve product quality Objective monitoring Improve social lifestyle

14 Adoption of PLF technologies Why has it been so slow?

15 Not familiar with available options (Russel and Bewley, 2013, JDS) 15

16 Too much information without knowing what to do with it (Russel and Bewley, 2013, JDS) 16

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

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)

19 A success story: automated oestrus detection

20 Why is automated oestrus detection different? Still many options to chose from, but Attached to the leg Attached to collar Attached to the ear

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 Field evaluation of two collar-mounted activity meters (Kamphuis et al., 2012, JDS) Lincoln University Dairy Farm, New Zealand 37-d breeding period - start Oct cows with SCR collars 320 activity only (AO) 315 activity and rumination (AR) Milk progesterone as gold standard Twice weekly during breeding period

24 3 time-windows allow for mismatch of Gold Standard Sensitivity (%) AO: 52 AR: 67 AO: 58 AR: 71 AO: 62 AR: 77

25 Changing activity alert threshold AR collars 25

26 Changing activity alert threshold AR collars 26

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)

29 A model for the Dutch situation

30 Cow Model Probabilities are adjusted for each simulated week Simulated cow Parity, production level Calving P(1 st ovulation) P(culling) SN 50% SP 100% SN 80% SP 95% 108/cow 3600/herd 10years Checking each alert visually P(early embryonic death) Ovulation Heat detection P(heat) P(heat detected) Insemination after voluntary waiting period P(pregnant) P(culling) P(culling) General culling Culling due to fertility issues - Max 6 inseminations - Not pregnant in wk 35 Cow pregnant Output cow place /year Next parity Replacement heifer Milk yield Number of inseminations Number of calves produced Feed intake Number of culled cows Number of false alerts from PLF x Milk price Labour costs Cost for AI Costs/revenues of calves Costs feed Costs for culling Costs of false alerts PLF (labour or AI Costs of PLF technology: investment, maintenance, depreciation, replacement of faulty sensors At farm level

31 Investing in automated oestrus detection Cash flow: 2,287 / year Cost-Benefit ratio: 1.23 Discounted payback period: 8 years SN 80%;SP 95% 108/cow 3600/herd 10years Checking each alert visually Investment pays off (Rutten et al., 2014, JDS)

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 Adoption rates of automated oestrus detection systems 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) 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) 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)

34 Moving beyond oestrus detection

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 16,500 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?

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

39 Lameness events Trained Farmers Farmer observations Cow identification Date Affected limb Lameness score

40 Methods 1 lame cow 10 non-lame cows Matched by farm, date

41 Activity Methods High Non-Lame, n = 3,180 Lame, n = 318 Day of observation Low Day

42 Change in Activity (steps / hour) Day of observation Lame (n = 318); Non-lame (n = 3,180) Day Patterns through time were different (P<0.05)

43 Changes in other sensor measurements 500 Weight (kg) 65.0 Milking order Day Milk yield (kg) Day Patterns through time were different (P<0.05) for all sensor measurements Day Lame (n = 318); Non-lame (n = 3,180)

44 Detecting lameness Values recorded during milking were averaged a daily value per sensor

45 Detecting lameness 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

46 Detecting lameness 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

47 Detecting lameness 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

48 Detecting lameness Sensor Lame cows Sensitivity SP = 80% SP = 90% Lame cows 3 Lame cows Lame cows 3 Activity Live weight Milking order All three

49 Detecting lameness Sensor Lame cows Sensitivity SP = 80% SP = 90% Lame cows 3 Lame cows Lame cows 3 Activity Live weight Milking order All three

50 Detecting lameness Sensor Lame cows Sensitivity SP = 80% SP = 90% Lame cows 3 Lame cows Lame cows 3 Activity Live weight Milking order All three

51 Detecting lameness Sensor Lame cows Sensitivity SP = 80% SP = 90% Lame cows 3 Lame cows Lame cows 3 Activity Live weight Milking order All three

52 Detecting lameness Sensor Lame cows Sensitivity SP = 80% SP = 90% Lame cows 3 Lame cows Lame cows 3 Activity Live weight Milking order All three

53 Detecting lameness Combining sensors outperformed single sensors consistently across farms

54 Detecting lameness Combining sensors outperformed single sensors consistently across farms Potential of using data already on-farm

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 days after successful insemination

58 Predicting moment of calving Current status: expected calving date days after successful insemination 33% of calvings are difficult (Barrier et al., 2013)

59 Predicting moment of calving Current status: expected calving date 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 Predicting moment of start calving two logit models Dependent: hour in which calving started Basic: days to expected calving date (ECD) ECD = insemination date + 280

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

68 Predicting hour of start calving

69 Predicting hour of start calving Impractical Too early? Reasonable

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 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) Improvements required modelling techniques predictive variables

74 Take home message

75 What I would like you to remember Adoption of PLF is expected to increase