This presentation covers how dairy monitoring technologies can provide new phenotypes for dairy cattle genetic evaluations.

1. Potential for new
phenotypic data from
automated
technology
measurements
Jeffrey Bewley

2. Genetic Interest
• The first sire book that I studied cover
to cover was as an 8 year old
• Internship with AI company (NOBA)
during college: Tank Boy

3. Genetics
Environment
Phenotype

4. Heritability
• Degree (0 to 100%) to which
offspring resemble their parents
for a specific trait
• Strength of the relationship
between performance
(phenotype) and breeding value
(genotype) of an individual
animal
• Percentage of all differences
between animals that is caused
by gene effects that transfer
from generation to generation

5. Genetics and Technologies

6. Precision Dairy Monitoring

7. Fatness or
Thinness
MobilityHoof
Health
Mastitis
Respiration
Rumination/pH
Temperature
Milk
content
Heart rate
Animal
position/location
Chewing
activity
Lying/
standing
behavior
Methane
emissions
Feed
intake
Possible
Measurements

8. Array Of Options

9. Massive Amounts of Data

10. New Genetic Frontiers?

11. The Opportunity
• Considerable opportunities for
measuring novel phenotypes
• Can provide previously unavailable,
consistent, objective measures for
traits that have been difficult to
measure in the past
• Traits may be incorporated into
robustness assessments focused
on measuring animal health,
reproduction, behavior, and
longevity (Hocquette et al., 2012)

12. 0 0.1 0.2 0.3 0.4 0.5
Udder health
SCC
Elecrical conductivity
Fertility diseases
Ovarian Issues
Luteal activity start
Metabolic diseases
Lameness
Temperament
AMS flow rate
AMS behavior
Activity
Fatty acids
Residual feed intake
Methane emissions
HERITABILITY
TRAIT

13. Possible measures
• Mastitis
• MIR
• Body condition scoring
• 3 D cow
• Estrus intensity
• Fresh cow diseases
• Heat stress
• Robotic milking
selection
• Calf data

14. Milk Based
Measures

15. Inline Somatic Cell Count
CellSenseDeLaval OCC
Lely
MQCC
Mastiline

16. • Visible, near-infrared, mid-infrared,
or radio frequency
• Indirect identification through
changes in milk composition
• AfiLab uses near infrared
– Fat, protein, lactose
• Fatty acids
• Amino acids
• Subclinical disease biomarkers
Spectroscopy

17. AfiLab Fat:protein ratio for cows
with and without subclinical
ketosis
Tsai et al., 2015

18. Milk measurements
• Progesterone
–Heat detection
–Pregnancy detection
• LDH enzyme
–Early mastitis detection
• BHBA
–Indicator of subclinical ketosis
• Urea
–Protein status

19. Genetics of heat stress?

21. “Clearly,some cows are better at regulating their
bodytemperature than others.“The question is,
to what extent is it dueto genetics?”
Pete Hansen
University of Florida

22. Rectal temperatures from Florida study
(Dikmen and Hanse, 2009)

23. Ambient and Reticulorumen
Temperatures in Kentucky
Liang et al., 2013

25. Temperature Monitoring

26. 3D BCS Automation
Lau, Shelley, Stone, and Bewley, 2014

27. Body Condition Scoring
• 100% of predicted BCS were within 0.50 points of actual BCS.
• 93% were within 0.25 points of actual BCS.
Bewley et al., 2008

28. Body Condition Scoring
BCS 2.50
Predicted BCS 2.63
Posterior Hook Angle 150.0°
Hook Angle 116.6°
BCS 3.50
Predicted BCS 3.32
Posterior Hook Angle 172.1°
Hook Angle 153.5°
Bewley et al., 2008

29. DeLaval
Body Condition Scoring

30. Body condition score for the top (× and *)
and bottom (+ and ●) two sires ranked on
profit index (PIN)
M.P. Coffey, G. Simm, W.G. Hill, S. Brotherstone
Journal of Dairy Science 2003 86, 2205-2212
Copyright © 2003 American Dairy Science Association

31. Feed Related Monitoring

32. Neck or Ear Based
Behavior Monitoring

33. ENGS Track a Cow:
Feeding Time
Cable

34. Intake Monitoring
Investigators: Amanda Sterrett, Anthony Shelley,
Daniel Lau, Jeffrey Bewley
• Develop a system for automated dry
matter intake monitoring
• Using machine vision (3D imaging)
• Inexpensive system for monitoring
feed intake for multiple animals
• Automatic recording of feed
disappearance
• Allow for more precise feeding
• Quantify individual animal intake
Original
Feed
Surface
3D Depiction of Feed
Disappearance

35. Feed Intake: 3D Imaging
(99% R2)
Shelley et al., 2013

37. Methane Emissions

38. • Greenfeed measures methane (CH4)
• Select for cows that are more
environmentally friendly
• Monitor impacts of farm changes (rations)
on greenhouse gas emissions

39. Lameness

40. Lying time as a predictor of general
symmetry gait aspect for lame versus
sound cows
Wadsworth et al., 2016

41. StepMetrix
• Lameness detection
• BouMatic

42. Belgian Locomotion

43. Estrus and Fertility
Photo credit: Lauren Mayo

44. High activity for cows and heifers
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.16
0.18
0.20
Timetofirstepisode
Duration
Strength
Timetofirstepisode
Duration
Strength
Timetofirstepisode
Duration
Strength
Heritability
First episode,
first parity
First episode,
all parities
First episode,
all parities

45. Distribution of the time intervals from calving to first high
activity (CFHA) and the time intervals from calving to first
insemination (CFI). The CFHA (dashed line) represents
percentages from 3,533 dairy cows; the CFI (solid line)
represents percentages from 11,363 cows
Journal of Dairy Science 2015 98, 3470-3477
Copyright © 2015 American Dairy Science Association

46. Distribution of estrus episode durations.
The distribution represents percentages
from 12,127 high activity episodes
detected in 3,533 dairy cows
Journal of Dairy Science 2015 98, 3470-3477
Copyright © 2015 American Dairy Science Association

47. Distribution of the estrus episode
strengths. The distribution represents
percentages from 12,127 high activity
episodes detected in 3,533 dairy cows.
Journal of Dairy Science 2015 98, 3470-3477
Copyright © 2015 American Dairy Science Association

48. The 3D Cow Model

50. New linear trait measurement

51. Robotic Milking

52. These traits especially include Rear Teat Placement
(RTP), Teat Length (TL), Udder Depth (UD), Rear
Leg Rear View (RLRV) and Milking Speed (MSP)

53. Based on data from six million robot milkings a
week for 500,000 cows on over 4000 dairy farms
H2 = 0.12 H2 = 0.07 H2 = 0.27

54. Automated calf feeders
• Daily intake
• Drinking speed
• Average daily
gain
• Meal size
• Disease

55. Genetic Evaluations
• May provide information previously unavailable for
genetic evaluations
• New or improved traits (i.e. feed intake, lameness,
BCS, heat tolerance, fertility)
• Improved data accuracy (i.e. yield, fat, protein, SCC,
health traits)
• More data, fewer erroneous measurements

56. Branded Genetics
• Could bull studs supplement
technology costs in large progeny
test herds in exchange for data?
• Reduction in data collection costs
• May be a new form of product
differentiation

57. Genomics
• Precision Dairy Farming/genomic selection
synergies may lead to improvement in health
traits
• But, need enough high quality phenotypic data to
calculate the SNP effects
– Maybe contract certain farms to record data
– May have to pay for data
• More data needed for lowly heritable traits

58. Data Integration Challenge

59. Challenges and Limitations
• Brand differences in measures
• Technology failures
• Standardization
• Calibration
• Integration

60. Questions?
Jeffrey Bewley, PhD, PAS
jbewley@bovisync.com
jbewley@cowfocused.com