Agritech Summit Palmerston North New Zealand 8 December
![Agritech. Summit Palmerston North, New Zealand 8 December 2017 #LPAg. Tech Every. THING is Agritech. Summit Palmerston North, New Zealand 8 December 2017 #LPAg. Tech Every. THING is](https://slidetodoc.com/presentation_image_h2/a8b9a012f58dcd1b6d58f96089fff149/image-1.jpg)
Agritech. Summit Palmerston North, New Zealand 8 December 2017 #LPAg. Tech Every. THING is some. WHERE on the planet in space and in time
![Jim Wilson - Ag Gateway • Digital agriculture • Perspective – • farmers want Jim Wilson - Ag Gateway • Digital agriculture • Perspective – • farmers want](http://slidetodoc.com/presentation_image_h2/a8b9a012f58dcd1b6d58f96089fff149/image-2.jpg)
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
![Jim Wilson • Collaboration – Ag Gateway with AEF – Ag Electronics Foundation • Jim Wilson • Collaboration – Ag Gateway with AEF – Ag Electronics Foundation •](http://slidetodoc.com/presentation_image_h2/a8b9a012f58dcd1b6d58f96089fff149/image-3.jpg)
Jim Wilson • Collaboration – Ag Gateway with AEF – Ag Electronics Foundation • CSIRO collaboration to use O&M • Data claims • • Farmer -> 3 rd party, party analyzes data and delivers insight back 3 rd party profits from data by selling insights back. Farmers don’t trust 3 rd parties to deliver value and protect data Social dynamics, legal frameworks, tech challenges
![Andrew Cooke - Rezare • Software product and service development, mathematical models for ag Andrew Cooke - Rezare • Software product and service development, mathematical models for ag](http://slidetodoc.com/presentation_image_h2/a8b9a012f58dcd1b6d58f96089fff149/image-4.jpg)
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 • Data. Linker Framework – datalinker. org “handshake” • Based on Oauth 2, data access agreement standards for easier data sharing connections, JSON-LD standard schemas & message API’s • INSPIRE catalog definitions
![Peter Dalhaus • Given transparency, where do farmers see the value of sharing data Peter Dalhaus • Given transparency, where do farmers see the value of sharing data](http://slidetodoc.com/presentation_image_h2/a8b9a012f58dcd1b6d58f96089fff149/image-5.jpg)
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
![Ian Yule – Massey University Centre Precision Ag • Information from data • E. Ian Yule – Massey University Centre Precision Ag • Information from data • E.](http://slidetodoc.com/presentation_image_h2/a8b9a012f58dcd1b6d58f96089fff149/image-6.jpg)
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 “Green. Seeker” 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 5 g 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
![Matt Flowerday – GPS-IT Drones • Agritech changes and advances • Tech firms moving Matt Flowerday – GPS-IT Drones • Agritech changes and advances • Tech firms moving](http://slidetodoc.com/presentation_image_h2/a8b9a012f58dcd1b6d58f96089fff149/image-7.jpg)
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.
![Owen Dance – GS 1 NZ • • • Global barcode / RFID standards Owen Dance – GS 1 NZ • • • Global barcode / RFID standards](http://slidetodoc.com/presentation_image_h2/a8b9a012f58dcd1b6d58f96089fff149/image-8.jpg)
Owen Dance – GS 1 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, . . ) GS 1 -128 as much information as needed (ASCII encoding). Serial shipping container code – GS 1 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
![Frank Bollen – Zespri International • • • Kiwi fruit exporter for NZ – Frank Bollen – Zespri International • • • Kiwi fruit exporter for NZ –](http://slidetodoc.com/presentation_image_h2/a8b9a012f58dcd1b6d58f96089fff149/image-9.jpg)
Frank Bollen – Zespri International • • • Kiwi fruit exporter for NZ – 230 K 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
![Mark Neal – Dairy. NZ Big Data • Payoff curves for precision agriculture – Mark Neal – Dairy. NZ Big Data • Payoff curves for precision agriculture –](http://slidetodoc.com/presentation_image_h2/a8b9a012f58dcd1b6d58f96089fff149/image-10.jpg)
Mark Neal – Dairy. NZ 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)
![Jochen - NIWA • High resolution farm forecasts (1. 5 km) • System integrations, Jochen - NIWA • High resolution farm forecasts (1. 5 km) • System integrations,](http://slidetodoc.com/presentation_image_h2/a8b9a012f58dcd1b6d58f96089fff149/image-11.jpg)
Jochen - NIWA • High resolution farm forecasts (1. 5 km) • System integrations, e. g. irrigation • Farm connectivity • Profitability of irrigation decisions • “Co-innovation”
![Peter Dalhaus – Federation University • Online Farm Trials – federating farm trial data Peter Dalhaus – Federation University • Online Farm Trials – federating farm trial data](http://slidetodoc.com/presentation_image_h2/a8b9a012f58dcd1b6d58f96089fff149/image-12.jpg)
Peter Dalhaus – Federation University • Online Farm Trials – federating farm trial data for all Australia • Soil health e. Library – community contributed soil test data (shared in de-identified form) • Interest in soil change over time
![Sean Hodges – Horizons Regional Council • Hats on – public safety, regional planning, Sean Hodges – Horizons Regional Council • Hats on – public safety, regional planning,](http://slidetodoc.com/presentation_image_h2/a8b9a012f58dcd1b6d58f96089fff149/image-13.jpg)
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).
![So, what about standards… So, what about standards…](http://slidetodoc.com/presentation_image_h2/a8b9a012f58dcd1b6d58f96089fff149/image-14.jpg)
So, what about standards…
![The Role of Standards • Standards are (persistent) agreements among people and their tools] The Role of Standards • Standards are (persistent) agreements among people and their tools]](http://slidetodoc.com/presentation_image_h2/a8b9a012f58dcd1b6d58f96089fff149/image-15.jpg)
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.
![The Context of Standards - Sharing • Standards are necessary but not sufficient for The Context of Standards - Sharing • Standards are necessary but not sufficient for](http://slidetodoc.com/presentation_image_h2/a8b9a012f58dcd1b6d58f96089fff149/image-16.jpg)
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
![The Role of Science and Technology • Geospatial information science matters • • MAUP The Role of Science and Technology • Geospatial information science matters • • MAUP](http://slidetodoc.com/presentation_image_h2/a8b9a012f58dcd1b6d58f96089fff149/image-17.jpg)
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
![The Social (Standards) Network • Agreements are made by people even if implemented by The Social (Standards) Network • Agreements are made by people even if implemented by](http://slidetodoc.com/presentation_image_h2/a8b9a012f58dcd1b6d58f96089fff149/image-18.jpg)
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
![Now what (for standards) • Pick a standards development organization… • For geospatial, geographic Now what (for standards) • Pick a standards development organization… • For geospatial, geographic](http://slidetodoc.com/presentation_image_h2/a8b9a012f58dcd1b6d58f96089fff149/image-19.jpg)
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 Ux. S DWG Hydrology DWG Geosciences DWG Met. Ocean DWG Geosemantics DWG • Ag Gateway ADAPT: • PAIL – irrigation data standards • SPADE – fertilization data standards • PICS – imagery tagging
- Slides: 19