1. LESSONS FROM THE USE OF PRECISION DAIRY TECHNOLOGIES J.M. Bewley, R.A. Black, M.C. Cornett, K.A. Dolecheck, C.N. Gravatte, A.E. Sterrett, and B.A. Wadsworth UK Department of Animal and Food Sciences

3. Sometimes When You’re Sitting on the Cutting Edge, It Hurts your Butt Dr. Mike Schutz

4. Technology Pitfalls “Plug and play,” “Plug and pray,” or “Plug and pay” Technologies go to market too quickly –not fully-developed –software not user-friendly Developed independently without consideration of integration with other technologies and farmer work patterns Too many single measurement systems

5. Technology Pitfalls Lack of large-scale commercial field trials and demonstrations Technology marketed without adequate interpretation of biological significance of data Information provided with no clear action plan Just simple information overload

6. Be prepared for little things to go wrong Be careful with early stage technologies Need a few months to learn how to use data Data integration is challenging

7. Be cautious without sensitivity/specificity data Algorithms make a difference Use according to manufacturer directions Customer service matters

8. Outline 1.Temperature Monitoring 2.Body Condition Scoring 3.Lying Behavior 4.Real Time Location Tracking 5.Precision Project

9. Reticulorumen Temperatures TenXsys SmartBolus DVM Systems/MaGiiX

10. Milking Parlor Holding Pen Scale MaGiiX Panel 1 MaGiix Panel 2 (installed for fall, winter) Rectal Temps GLA Thermometer

11. Overall Means (N=1473) Mean (±SD) Reticular Temperature 39.30°C (±0.37) Rectal Temperature 38.81°C (±0.34) Difference0.49°C (±0.30) Correlation0.65

12. LSMeans for Milking Reticular Temperature Rectal Temperature Difference Morning (N=716) 39.29°C (±0.02)38.68°C (±0.01)0.61°C (±0.01) Night (N=757) 39.38°C (±0.02)38.97°C (±0.01)0.41°C (±0.01) P<0.0001 for all differences

13. Considerable variation in both rectal and reticular temperatures Reticular temperatures higher than rectal temperatures Rectal and reticular temperatures are moderately correlated –Correlation appears to be lower during colder temperatures –Correlation appears to be higher at night

14. Examine the relationship between water intake and reticulorumen temperatures collected with SmartBolus® Transponders (TenXsys, Eagle ID): Water temperature Water volume Time to return to baseline Cornett et al. 2011 Impact of water intake on dairy cattle reticulorumen temperature

15. University of Kentucky Coldstream Dairy Tie-Stall Barn 4 mid-lactation, multiparous, Holstein cows SmartBolus® Transponders recording at 2 minute intervals Drinking behavior monitoring: Two observers per 4 hour shift 48 consecutive hours Ad Lib Water Study

16. Example Drinking Event Calculations

17. First Study: Descriptive Statistics (N=84) VariableMean ± SD Volume of water consumed per drinking event0.27 ± 0.31 L Drop in temperature following drinking event2.29 ± 1.82 °C Reticular temperature before drinking event39.76 ± 0.49 °C Water temperature before drinking event3.63 ± 3.14 °C Time to return to baseline temperature57.75 ± 38.70 min

18. Drinking behavior of cows housed in tie-stall barns is characterized by frequent but small drinking bouts Even small quantities of water consumed influence reticulorumen temperatures With free-choice access to water in a tie-stall facility in the winter, reticulorumen temperature returns to normal in about an hour

19. Twelve multiparous, mid-lactation, Holstein cows SmartBolus® transponders recording at two minute intervals four consecutive days Modified Latin square design (random cow assignment) Three water quantities: 5.7 L, 11.4 L, 22.7 L Four water temperatures: 1.7°C, 7.2°C, 15.6°C, 29.4°C No cow received the same temperature treatment twice Controlled Water Intake Study

20. Feed and water intake restricted from 8:00 am to 3:30 pm Water drenched to each cow via Cattle Pump System® (Springer Magrath, Mcook, NE) Each cow’s baseline temperature was calculated from the mean temperature of the 48 hours before experiment Two 95% confidence intervals (CI) were calculated using the SD for this time period Time to return to baseline was calculated as the first recorded temperature within the 95% CI after water drenching Controlled Water Intake Study

21. Time to Baseline Time to Return to Baseline = 2.63 + (23.59×Water Quantity) + (0.03 × Water Temperature °F) - (0.23 × (Water Quantity × Water Temperature))

22. Temperature Drop Temperature Drop = 7.59 + (2.85×Water Quantity) - (0.08 × Water Temperature °F) - (0.02 × (Water Quantity × Water Temperature))

23. Water Temperature (°F) 35404550556065707580859095100 Drop in Temperature (°F) 10.30.91.72.8 20.00.40.91.42.23.34.8 30.20.50.81.31.92.63.54.86.8 40.00.30.50.81.21.72.22.93.84.96.48.7 50.10.30.60.81.21.52.02.53.13.94.96.28.010.7 60.60.81.11.41.82.22.73.34.04.96.17.69.612.6 71.11.41.72.02.42.93.54.15.06.07.28.911.214.6 81.61.92.22.63.13.64.25.05.97.08.410.312.816.6 92.12.42.83.23.74.35.05.86.88.09.611.614.418.5 102.62.93.33.84.45.05.76.67.79.010.713.016.020.5 113.13.53.94.45.05.76.57.48.610.111.914.317.622.5 123.64.04.55.05.66.47.28.39.511.113.015.619.224.4 134.04.55.05.66.37.18.09.110.412.114.217.020.826.4 144.55.05.66.26.97.88.79.911.313.115.418.322.428.4 155.05.56.16.87.68.49.510.712.314.116.519.724.030.3 165.56.16.77.48.29.110.211.613.215.217.721.025.632.3 176.06.67.28.08.89.811.012.414.116.218.922.427.234.2 186.57.17.88.69.510.511.813.215.017.220.023.728.836.2 197.07.68.49.210.111.212.514.015.918.221.225.130.438.2 207.58.28.99.810.811.913.314.916.819.322.326.432.040.1 Water Intake Conversion Chart

24. As water quantity increased, average time to return to baseline temperature increased (up to 67 ± 38 minutes) As water temperature decreased, time to return to baseline temperature increased (up to 63 ± 42 minutes) Impact of water intake on reticulorumen temperatures is considerable and impacted by both quantity and temperature of water consumed

25. Detection of Clinical and Subclinical Mastitis DVM Systems, LLC (Boulder, CO) reticulorumen bolus monitoring system (Phase IV Engineering Inc., Boulder, CO) Temperatures collected twice-daily from 110 cows, including 71 Holstein cows, 22 crossbred cows, and 17 Jersey cows from September 15, 2011 to June 28, 2012 A composite milk sample was obtained from each cow in the herd every 14 days for SCC analysis Subclinical mastitis events were established when SCC was greater than 200,000 cells/mL Sterrett et al., 2012

26. Detection of Clinical and Subclinical Mastitis Milkers recorded clinical mastitis events if a cow’s milk was abnormal Reticulorumen temperatures were adjusted for the change in herd temperature at each milking A 30-day rolling mean baseline reticulorumen temperature was calculated along with the number of SD from which each respective reticulorumen temperature varied from this baseline The maximum reticulorumen temperature and number of SD among all reticulorumen temperatures within the previous 2 days were used as a baseline to assess whether a reticulorumen temperature alert was observed for clinical and subclinical mastitis events

27. Descriptive statistics for observed temperatures associated with mastitis events Maximum observed within 2 days before mastitis event NMeanSDMinMax Clinical Mastitis Temperature (° C) 3439.250.4738.2740.20 Subclinical Mastitis Temperature (° C) 9139.200.5638.3440.51

28. Example of mastitis event with temperature threshold reached before a subclinical mastitis event

30. Detection Capability Z-score Threshold Obs. Window Clinical MastitisSubclinical MastitisNeither Event % Above Threshold % Below Threshold % Above Threshold % Below Threshold % Above Threshold % Below Threshold 22 d1486595793 32 d2981.08991 25 d297117831585 35 d992298397 210 d425834662674 310 d1288793694 215 d445641593565 315 d1486991892

31. Detection Capability Temp. Threshold Obs. Window Clinical MastitisSubclinical MastitisNeither Event % Above Threshold % Below Threshold % Above Threshold % Below Threshold % Above Threshold % Below Threshold 40.27ºC 2 d595694397 40.00ºC 2 d16841288794 39.72ºC 2 d416022781387 39.44ºC 2 d435748522377

32. Natural variation in cow body temperatures may limit the utility of a reticular-based temperature monitoring system with twice-daily recordings. More frequent or continuous temperature recording may be necessary to unlock the full potential of dairy cattle temperature monitoring Different bacteria species may have varying effects on temperature

33. Influence of breed, milk production, season, and ambient temperature on reticulorumen temperature Reticulorumen temperatures (RT) were recorded every 15 minutes using SmartBolus® transponders (TenXSys Inc., Eagle, ID) for 97 cows (67 Holstein, 20 crossbred, and 10 Jersey) at the University of Kentucky Coldstream Dairy from November 06, 2009 to July 14, 2011 Raw RT (n=1,646,145) were edited to remove erroneous reads and temperatures potentially influenced by water intake by removing temperatures (1) < 38.3°C and (2) < 3 SD from each individual cow’s 28 days rolling RT average 14426 cow days were included in the final model Liang et al., 2012

34. Seasonal reticulorumen temperature diurnal rhythms

35. Reticulorumen and Ambient Temperatures within Day The nadir and zenith RT appeared 10:00 and 23:30, respectively.

36. Relationship between daily ambient temperature and daily reticulorumen temperature

37. Reticulorumen Temperature by Breed

38. Relationship among daily ambient temperature, daily milk production and daily reticulorumen temperature

39. Reticulorumen temperatures are impacted by ambient temperature, milk production level, breed, and parity Crossbred cows may be more heat tolerant than Holstein cows, even after accounting for differences in milk yield These results provide new insight into cow core body temperature and may be useful in interpreting data from automatic temperature monitoring systems

40. Automated Body Condition Scoring Reduced labor requirements Less stressful on animal More objective, consistent measure Increased observation frequency Early identification of sick animals Tracking BCS trends of individual animals and management cohorts

41. Image Collection Images collected with a digital camera Camera triggered to capture image when gates closed Image timestamps matched with weigh station timestamps to ID cows Images only available during afternoon milking because of lighting Camera Weigh Station

42. Anatomical Points Identified

43. Anatomical Point Coordinates 1 & 23Foreribs7 & 17Hook Ends 2 & 22Short Rib Starts8 & 16Thurls 3 & 21Hook Starts9 & 15Pins 4 & 20Hook Anterior Midpoints10 & 14Tailhead Nadirs 5 & 19Hooks11 & 13Tailhead Junctions 6 & 18Hook Posterior Midpoints12Tail

44. Calculated Angles 1 & 15Hook Anterior Angles5 & 11Hook Posterior Angles 2 & 14Hook Anterior Curvatures6 & 10Thurl to Pin Angles 3 & 13Hook Angles7 & 9Tailhead Depressions 4 & 12Hook Posterior Curvatures8Tailhead Angle

45. Predicted vs. Actual USBCS 100% of predicted BCS were within 0.50 points of actual BCS. 93% were within 0.25 points of actual BCS.

46. Examples USBCS2.50 Predicted BCS2.63 Posterior Hook Angle150.0° Hook Angle116.6° USBCS3.50 Predicted BCS3.32 Posterior Hook Angle172.1° Hook Angle153.5°

47. Next: Automated Image Extraction

48. BCS through imaging is possible Lighting is important Image extraction is most difficult part Funding is next most difficult part

49. Lying is a high priority for dairy cows. (Munksgaard, 2005) Cows require 12-14 hours of lying time/day. (Grant, 2007) Providing cows time to rest may increase milk production

50. IceTags The IceTag™ from IceRobotics (Scotland) uses accelerometer technology to monitor: –Lying –Standing –Stepping behavior Validated by comparison with direct visual observations ( McGowan et al., 2007, Munksgaard et al., 2006)

51. # of lying bouts Maximum and minimum lying bouts Average lying bouts Hours lying Hours standing Number of steps Calculated each day

52. IceTag Means ParameterDaily Mean (  SD) Hours Lying 10.50  2.06 Hours Standing 12.59  1.96 Number of Lying Bouts 10.95  3.89 Avg. Duration of Lying Bout (minutes) 62.40  20.23

53. Raw Means by BCS Category Thin (N=25) Moderate (N=25) Heavy (N=27) Hours Lying 9.9110.4411.09 Average Lying Bout 64.3362.5260.54 Number Lying Bouts 10.2110.7711.79 BCS2.512.923.52

54. Hours Lying by Production Level

55. #486 Bred on 11-6-06

56. After adjustments for DIM and production, body condition score did not impact lying time Production level and stage of lactation influenced lying time Considerable within cow, day, and week variation observed Lying times for this herd were less than recommended levels

57. Evaluation of Dairy Cattle Lying Behavior in Commercial Freestall Barns 360 cows housed in freestalls 15 farms divided into three production categories –High (> 9,318 kg) –Medium (8,409 to 9,317 kg) –Low (< 8,409 kg) Milk yield data –Dairy Herd Improvement Association (DHIA) Gravatte et al., 2010

58. VariableMeanSDMin.Max. Lactation1.91.21.07.0 Daily milk yield, kg/d30.710.34.663.0 Days in milk206.697.749.0468.0 Lying, h/d11.02.41.618.6 Lying bouts, number/d10.27.41.0117.0 Mean duration of lying bout, min 77.532.45.4384.4 Steps, number/d1805.5597.6296.010249.0 Study Means

59. Distribution of Lying Times for All Study Cows

60. Raw Mean (±SD) Lying Times by Herd by Production Level

61. Lying Time per Day by Herd Production Level

62. Predicted Mean Daily Hours Lying by Daily Milk Yield

63. Predicted Mean Daily Hours Lying by Stage of Lactation

64. Predicted Hours Lying by Cow Milk Yield for Varying Herd Production Levels

65. Sample Ethogram of Lying Behavior Throughout a Day

66. Changes in Lying Time After Freestall Renovations Wadsworth et al., 2012

67. As milk production increases lying time decreases within a herd Average lying times vary tremendously between and within herds Ensuring cows have adequate opportunity to rest during periods of high milk production may impact subsequent milk production Technologies like IceTags can provide valuable insight into the interaction between production and behavior

68. Dairy Cow Real-Time Location System RTLS (real time location systems) used to track assets or people and can be used indoors Improve understanding of the interaction between barn design, barn management, and animal movement patterns Understand animal feeding behavior, resting behavior, activity levels, and socialization patterns Assist farmers in locating cows of interest in large herds, cows not eating or drinking, and cows with changing activity levels (i.e. estrus or illness) Black et al., 2012

69. Black et al.

70. Harvest Home Dairy Barn

71. Data Collection

72. Preliminary System Accuracy ParameterNMeanSDMinimumMaximum X Zone Difference 1811.381.4707 Y Zone Difference 1810.520.6403 With tags only, mean RSEM was 0.91 ± 0.48 m, ranging from 0.39 to 1.56 m On cows, mean RSEM was 12.55 ± 10.35 m, ranging from 0.60 to 52.53 m

73. Cow bodies interfere with signal transmission Much easier working with dairy companies Engineers can be a bit too optimistic Still potential for this concept, but not likely with this technology

74. IceQube (IceRobotics) Leg Accelerometer: Lying Time HR Tag (SCR Engineers) Rumination and Activity Monitor AMATS (Netquest Services) Surface Temperature Monitor DVM Bolus (DVM Systems) Rumen Temperature Monitor Sterrett et al.

75. The Milpro4C™ (Milkline) system: individual quarter electrical conductivity Sterrett et al.

76. Cow Data Collected DHIA Disease incidence Weekly BCS and Locomotion Intensive fresh cow exams Fresh cow feed intake KetoTest ketones Precision Xtra BHBA Fresh cow mineral blood panel Bacteriological cultures-mastitis

77. DVM Temperature and Milkline Conductivity for Mastitis Detection Sterrett et al.

78. SCR HR Tag for Milk Fever Detection Sterrett et al.

79. HR Activity and Rumination/DVM Bolus Temperature for Estrus Detection Bred on 7/22/11 Sterrett et al.

80. Comparison of Timed AI and Activity Based Heat Detection Compare reproductive performance between both systems Days to first service, interval between services, cumulative pregnancy rate at 150 days postpartum, percent successful breedings for first, second, and third services Conducted on three commercial Kentucky dairies Economic analysis of both methods Dolecheck et al., 2012

81. Jeffrey Bewley, PhD, PAS 407 W.P. Garrigus Building Lexington, KY 40546-0215 Office: 859-257-7543 jbewley@uky.edu www.bewleydairy.com