1. EveryTHING is someWHERE on the planet in space and in time
AgritechSummit
Palmerston North, New Zealand
8 December 2017
#LPAgTech
EveryTHING is someWHERE on the planet in space and in time

2. Jim Wilson - Ag Gateway Digital agriculture Perspective –
farmers want to profit, feed people, sustain environment, use historical data, actionable insight, equipment that works together
Developers want clear context, semantics, purpose. Standards help, but which ones.
Ag Gateway – build stuff and develop standards from the practice
Resources – ADAPT models for standardizing import data
Plugins to ”adapt” proprietary formats to ADAPT models.
Github repository for ADAPT framework

3. Jim Wilson
Collaboration – Ag Gateway with AEF – Ag Electronics Foundation
CSIRO collaboration to use O&M
Data claims
Farmer -> 3rd party, party analyzes data and delivers insight back
3rd party profits from data by selling insights back.
Farmers don’t trust 3rd parties to deliver value and protect data
Social dynamics, legal frameworks, tech challenges

4. Andrew Cooke - Rezare
Software product and service development, mathematical models for ag organizations in NZ, AUS, UK
Data use – on-farm strategic, tactical decisions from the data
Inputs – fertilizer, feed, seeds needs, timing, placement
Outputs – provenance, timing, productivity, quality
Initiatives
Code of Practice – transparency on rights and policies, security practices, accreditation (similar low uptake to AFB transparency calculator)
Farm Data Standards – common vocabulary targeting developers, analysts
DataLinker Framework – datalinker.org “handshake”
Based on Oauth2, data access agreement standards for easier data sharing connections, JSON-LD standard schemas & message API’s
INSPIRE catalog definitions

5. Peter Dalhaus
Given transparency, where do farmers see the value of sharing data to receive valuable insight that makes money?
Some ag industries are more connected to data than others (cotton, cane

6. Ian Yule – Massey University Centre Precision Ag
Information from data
E.g. yield effects from treatments – sensors have come a long ways
E.g. fertilizer sensor “GreenSeeker” not widespread adoption – needs path to insight
“Pasturemeter” optical sensor becoming more robotic to better fit farmer need
Cow monitoring – more comprehensive for individual animals – big boost from 5g networking
Data and geospatial scale: PA is zone – paddock – farm level now but data collected at many different scales and needed at many scales for particular value
Food security & logistics & value chains, e.g. beef
Tech example – hyperspectral imaging for hill country soil fertility analysis and pasture PA at meter scale instead of extrapolating from handful of samples

7. Matt Flowerday – GPS-IT Drones
Agritech changes and advances
Tech firms moving into ag space
Data ownership a hot topic, but the issues (enforcement vs value) are not very clear.

8. Owen Dance – GS1 NZ Global barcode / RFID standards organization
Item identification – data capture – data sharing
EPC electronic product code – EPCIS data sharing system
GTIN is a globally unique identifier with specific code lengths (12, 13,..)
GS1-128 as much information as needed (ASCII encoding).
Serial shipping container code – GS1 Logistic Label
GLN – Global Location Number (uses GTIN-13)
Capture – e.g. fruit labels
Databar: track individual produce, meat items globally
All codes can be represented in RFID as well
EU 1169 rules for “sold at a distance” food information transparency

9. Frank Bollen – Zespri International
Kiwi fruit exporter for NZ – 230K tons / yr to 60 countries
Spatial information in Kiwi fruit supply chain
Fruit – tree – block (of shade cover) – orchard features
Fruit -> bin -> box -> pallet tracking / quality checking through long-term cool storage then transport then distribution
Goal to manage variability through supply chain to different market windows (e.g. Dry Matter or size)
Biosecurity: e.g. spatial analysis of PSA infestations
Provenance of fruit – how to connect producers and consumers, so far linking to grower regions rather than individual growers / orchards
Some spatial analysis of correlations between size, yield, profit, other parameters such as canopy size
Challenge still to link supply chain “performance” in detail back to orchard – case for consumer “feedback” not yet clear

10. Mark Neal – DairyNZ Big Data
Payoff curves for precision agriculture – hard to see some value in profits
Millions of cows generate millions of data points -> breeding improvements for new traits such as heat tolerance, milk type, urine concentration, grazing preference (hill vs flat)
Internet of Cow Things – GPS, accelerometers, mesh RFID, imaging
Virtual fencing and herding
Pasture condition vs yield (paddock performance)
Pixel level data doesn’t necessarily express the pasture potential yield, e.g. with more fertilizer. Need detailed actual yields vs observable properties.
Data landscape of funders, innovators, farmers, regulators, etc.
Data middleware – integration, fusion, analysis, metrics (e.g. performance gap)
Need for developers / innovators to provide farmer value (but that can be hard to determine or predict)

11. Jochen - NIWA High resolution farm forecasts (1.5km)
System integrations, e.g. irrigation
Farm connectivity
Profitability of irrigation decisions
“Co-innovation”

12. Peter Dalhaus – Federation University
Online Farm Trials – federating farm trial data for all Australia
Soil health eLibrary – community contributed soil test data (shared in de-identified form)
Interest in soil change over time

13. Sean Hodges – Horizons Regional Council
Hats on – public safety, regional planning, regulatory compliance, environmental monitoring
Lightbulb – regional data sharing
Councils need to use carrots more than sticks for standards compliance. Collaboration (e.g. between councils) is effective.
Value of FAIR principles for better outcomes, more reuse, e.g. can use common broker
Farm-scale data: land erosion management, farm mapping of nutrients, etc. so much farm data collected. Agreements affect what can be shared and with whom (e.g. real estate agents).

14. So, what about standards…

15. The Role of Standards
Standards are (persistent) agreements among people and their tools]
Shared value, compromise, consensus, trust
Information standards are agreements for exchanging / sharing data
Where is sharing needed? By whom? Under what conditions?
Who benefits, who loses from sharing data?
Where and when can agreements persist (self-interest, public interest, regulation)?
Agreements between friends, competitors, enemies.
Enforcement, compliance not foregone conclusions.

16. The Context of Standards - Sharing
Standards are necessary but not sufficient for interoperability
Physical sharing versus shared understanding – syntax is not enough, but machine understanding (expected behavior) hard to evaluate.
Sharing vs open sharing
Value of data – innate or comes from integration / application to decisions -> sharing
Competitive advantage of proprietary knowledge
Standards vs business value
What value does a farmer get from participation in “learning”? View of risk / reward applies to contributing to and/or using standards

17. The Role of Science and Technology
Geospatial information science matters
MAUP
Autocorrelation
Geometry
Projections
Lack of standards vs lack of technology
Technologically infeasible or not interoperable
E.g. yield uncertainty: failure to exchange yield measurement quality or infeasibility of calculating uncertainty itself.
Separation of concerns – architecture matters
Interchangeability - interfaces
Technological resilience - layers
Functionality – distributed processing
Transparency - comparability

18. The Social (Standards) Network
Agreements are made by people even if implemented by machines
Consensus “for” standards precedes consensus “on” standards
Playing fields may or may not be leveled (e.g. ease of implementation, regulatory hurdles)
“Coopetition” makes strange bedfellows
Perceived value may be a matter of time horizon
Perceived value also depends on legal / regulatory context
Consultant’s guide to standards: “sell the standard, then break the standard”
Hard to get innovation credit for following someone else’s standard
Hard to get academic credit for developing a (consensus) standard
Standards work done well, like diplomacy, is usually worth trying, whether or not specifically successful

19. Now what (for standards)
Pick a standards development organization…
For geospatial, geographic standards, agricultural features, various OGC domain working groups that host specific standards activities:
Agriculture Domain Working Group (DWG)
Sensor Web Enablement DWG / Sensor Model activity
UxS DWG
Hydrology DWG
Geosciences DWG
MetOcean DWG
Geosemantics DWG
Ag Gateway ADAPT:
PAIL – irrigation data standards
SPADE – fertilization data standards
PICS – imagery tagging