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

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

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

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

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

Framework: Hype curve

Framework: Hype curve

Framework: Hype curve

Framework: Action cycle Analytics Sensing Decision Implement

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

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

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

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

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

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

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

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.

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

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?

Examples: Using animal data Virtual gates Virtual herding Virtual fences

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

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

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

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

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

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

Examples: Managing pasture projects Variability under pivot (LandCare)

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

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

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

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.

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

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

A way forward: Role for Industry good

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

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

Questions Mark Neal mark.neal@dairynz.co.nz