1. EveryTHING is someWHERE on the planet in space and in time
Big Agricultural Data
Mark Neal, DairyNZ
Brian Dela Rue, Callum Eastwood, Simon Woodward
EveryTHING is someWHERE on the planet in space and in time

2. What is Big Data?
Standard definition Data sets that are so voluminous and complex that traditional data processing application software are inadequate to deal with them

3. What is Big Data?
Continuously recorded Routinely collected Automated collection Potential use in decision making

4. What is Big Data?
Continuously recorded Routinely collected Automated collection Potential use in decision making

5. Today in brief
Framework for thinking
Strategic needs?
Big Data examples
A way forward?

6. Framework: Hype curve

7. Framework: Hype curve

8. Framework: Hype curve

9. Framework: Action cycle
Analytics
Sensing
Decision
Implement

10. Adapted from David Pannell
Framework: Payoff curve
Optimal
Response curves
$ Profit
Input
Adapted from David Pannell

11. Adapted from David Pannell
Framework: Payoff curve
Optimal
Response curves
95%
$ Profit
Input
Adapted from David Pannell

12. Adapted from David Pannell
Framework: Payoff curve
Optimal
Response curves
95%
$ Profit
Input
Adapted from David Pannell

13. Adapted from David Pannell
Framework: Payoff curve
Optimal
Response curves
95%
$ Profit
Input
Adapted from David Pannell

14. Vision: Improving lives with every drop of New Zealand milk
Strategic Needs: Dairy Tomorrow
Vision: Improving lives with every drop of New Zealand milk

15. Vision: Improving lives with every drop of New Zealand milk
Strategic Needs: Dairy Tomorrow
Vision: Improving lives with every drop of New Zealand milk

16. Examples: Animal breeding
Dairy cow breeding:
Milk, fertility
New traits:
Efficiency (RFI), Heat tolerance, Milking frequency (OAD index)
Future traits?
Extended lactation, Robustness,
Voluntary milking

17. Examples: Animal breeding (2)
Customer traits:
Novel milk types (e.g. a2)
Enviro traits:
N use efficiency, Dilute urine
Grazing location
(Hill country vs flat,
In creek vs out of creek)
Bailey et al.
Draganova et al.

18. Examples: Getting animal data
Activity monitors (pedometer, accelerometer),
Proximity, GPS, IoT, Active RFID with mesh net.
Use: Reproduction, welfare?

19. Examples: Getting animal data
Activity monitors (pedometer, accelerometer),
Proximity, GPS, IoT, Active RFID with mesh net.
Use: Reproduction, welfare?
Camera for condition; 3D, 2D? For lameness?
Use: welfare?

20. Examples: Using animal data
Virtual gates
Virtual herding
Virtual fences

21. Examples: Using animal data
Virtual gates
Virtual herding
Virtual fences
Infer pasture harvest
(day, year)
Monitor feed availability
in real time?

22. Examples: Pasture
Why care?
Forage Value Index
Trial data
Farmer data (Ireland)

23. Examples: Using pasture data
Feed available, growth
Fine tune paddock choice,
monitor residuals
Determine paddock performance (annual)

24. Examples: Getting pasture data
Measurement method
Height
Structure
3D image
2D image
Walk
Ground vehicle
Aerial vehicle
Space
Where device measures from

25. Examples: Getting pasture data
Measurement method
Height
Structure
3D image
2D image
Walk
Ground vehicle
Aerial vehicle
Space
Where device measures from
Consider: Ease of collection, Accuracy, paddock-level vs pixel level

26. Examples: Pasture, Pixel-level data
Dennis et al.

27. Examples: Managing pasture projects
Variability under pivot (LandCare)

28. Examples: Precision pasture projects
Variability under pivot (LandCare)
Irrigating when required
(NIWA)

29. Examples: Pasture potential
Identify the gap between actual and potential pasture
Process to examine how to close the gap

30. Examples: Links to and from consumers
Proof of practice; cow welfare, stock exclusion
Proof of product; integrity of single source, natural/pasture fed, novel milk types
Demand responsive; novel milk and products
Blockchain:
Secure,
authenticated,
transaction data

31. Infrastructure openness Sustainable business models
A way forward?
Challenges:
Value proposition
Governance of data
Infrastructure openness
Sustainable business models
Ecosystems with critical mass
Wolfert et al.

32. A way forward: Players in data landscape
Wolfert et al.

33. A way forward: Middleware
Opportunities
Integrating on- and off-farm data
Sensor fusion
Identify performance gaps
Timely decision-making
Apply new analytical techniques
Traceability/transparency

34. A way forward: Role for Industry good

35. A way forward: Developers focus on value
Understand farmer needs and the product environment
Highly valued: Functionality, simplicity, integration, and trialability
Provide evidence-based performance information
Minimise hype and support the product

36. A way forward: Final thoughts
Product development:
Commercial space <-> Value proposition
but:
Transparent development & demonstration pilots
Pre-commercial R&D + standards
Non-rival data easily available
Blue Sky R&D

37. Questions
Mark Neal