Big Data for Horticulture

Krijn Poppe Wageningen Economic Research

Based on work with WUR team and others

November 2016 Vineland Research. Ontario - Canada

DATA CAPTURING TOOLS FOR

BETTER CONTROL

Disruptive ICT Trends:

Mobile/Cloud Computing – smart phones, wearables, incl. sensors

Internet of Things – everything gets connected in the internet (virtualisation, M2M, autonomous devices)

Location-based monitoring - satellite and remote sensing technology, geo information, drones, etc.

Social media - Facebook, Twitter, Wiki, etc. Block Chain – Tracing & Tracking, Contracts.Big Data - Web of Data, Linked Open Data, Big data

algorithmsHigh Potential for unprecedented innovations!

everywhere

anything

anywhere

everybody

Content: line of reasoning

ICT developments – why now? Effects on management and business models Effects on markets Effects on chain organisation Governance: centralisation or not? Implications for government policy Implications for public research

5

Prescriptive AgriculturePredictive Maintenance

IoT in Smart Farming

cloud-based event and data

management

smart sensing & monitoring

smart analysis & planning

smart control

Virtual Box

Location A Location B

Location & Stateupdate

Location & State

update Location & Stateupdate

IoT in Agri-Food Supply Chains

6Drones, Big Data and Agriculture

Cloud Event Management System

Location A Location B

VirtualPlant

VirtualLocation A

VirtualLocation B

Environment

updatePlant

location update

Environment

update

IoT and the consumer

8Drones, Big Data and Agriculture

Source: Hisense.com

Smart Farming

Smart Logisticstracking/& tracing

Domotics Health Fitness/Well-being

Tuinbouw Digitaal © 2013

BIG DATA

Social media

Ongestructureerd

Event-drivenOPEN DATA

Databestanden

Gestructureerd

Transactie-driven

CONSUMER CHAIN

TRANSFORM

MAP

ANONY MISE

AGGRE GATE

STRUCTURE

COMBINE

INTER PRETERE

GATHER

LISTENING GATHERINGSocial media - Unstructured - Event-driven Informatiesystems–Structured - Transaction-driven

BIG DATALARGE AMOUNTS

OPEN DATACOMING TO YOU

Amsterdam January – the tulip pop-up event:

And its impact via Twitter

tijd

Mate van verspreidingvan technologische revolutie

Installatie periode

Volgendegolf

Uitrol periodeDraai-punt

INDRINGER

EXTASE

SYNERGIE

RIJPHEID

Door-braak

WerkeloosheidStilstand oude bedrijfstakken

Kapitaal zoekt nieuwe techniek

Financiele bubbleOnevenwichtighedenPolarisatie arm en rijk

Gouden eeuwCoherente groei

Toenemende externalities

Techniek bereikt grenzenMarktverzadiging

Teleurstelling en gemakzucht

Institutionele innovatie

Naar Perez, 2002

Crash20081929189318471797

time

Degree of diffusion of thetechnological revoluton

Installation period

Nextwave

Deploymentperiod

Turningpoint

IRRUPTION

FRENZY

SYNERGY

MATURITY

Big Bang

UnemploymentDecline of old industries

Capital searches new techniques

Financial bubbleDecoupling in the systemPolarisation poor and rich

Golden ageCoherent growth

Increasing externalities

Last products & industriesMarket saturationDisappointment vs

complacency

Crash

2008

1929

1893

1847

1797

Institutional

innovation

Based on Perez, 2002

The opportunity for green growth

1971 chip ICT1908 car, oil, mass production1875 steel1829 steam, railways1771 water, textiles

4 grand challenges: tomorrow’s business

Transport

Input industriesFarmer

Food processor Retail / consumerSoftwareprovider

Logistic solu-tion providers

Collaboration and Data Exchange is needed!

Food & nutrition security

Climate change

Healthy diet for a healthy life

Environmental issues

There is a need for software ecosystems for ABCDEFs: Agri-Business Collaboration & Data Exchange Facilities• Large organisations have

gone digital, with ERP systems

• But between organisations (especially with SMEs) data exchange and interoperability is still poor

• ABCDEF platforms help

law & regulation

innovation

geographic cluster

horizontal fulfillment

Vertical

Effect of ICT on management

Towards smart autonomous objects

Source: Deloitte (2014), IT Trends en Innovatie Survey

Tracking & Tracing

Monitoring

I am thirsty: water me within 1 hour!

I am product X at locatie L of Z

My vaselife is optimal at a

temperature of 4,3 °C.

I am too warm: lower the

temperature by 3 °C

Event Managemen

t

I am too warm: I lower the cooling of my truck

X by 2 °C.

I don’t want to stand besides that banana!

I am thirsty!

I am warm!

Optimalisation

Autonomy

Connected Nursery

17

Order info

Cultivation info

Stock info

Quality info

Financial info

Systems in Supply Chain Network

Trader

Retail

Service provider

Accountant

Serviceprovider

Trader

Etail

Detail

Rules issuing authorities

Systems Authorities

Compliance info

Supplier info

Supplier Systems

Seed supplier

Packaging supplier

Pesticides supplier

Info exchange Supply Chain

Network

Enterprise System Grower

Systems and machines in the primary production / cultivation

Internal info exchange

cultivation related

machines Work definition Work planningWork capacity Work performance

Adoption of Connected Nursery solutions

Economic reality

High ICT complexityHigh path

dependency

Relative small scale of farms

Low rate of investments of

growers

Decrease high-end market

Low willingness to cooperate

Limited development of

integration solutions

Negative perception

relative advantage

Effect on business models: how to earn money with data?

basic data sales (commercial equivalent of open data; new example: Farm Mobile)

product innovation (heavy investments by machinery industry, e.g. John Deere, Lely’s milking robots)

commodity swap: data for data (e.g. between farmers and (food) manufacturers to increase service-component)

value chain integration (e.g. Monsanto’s Fieldscript) value net creation (pool data from the same consumer:

e.g. AgriPlace)See: Arent van 't Spijker: "The New Oil - using innovative business models to turn data into profit“, 2014

Redefining Industry Boundaries (1/2)(according to Porter and Heppelmann, Harvard Business Review, 2014)

20

3. Smart, connected product

+

+

+

2. Smart Product

1. Product

Redefining Industry Boundaries (2/2)(according to Porter and Heppelmann, Harvard Business Review, 2014)

21

5. System of systems

farmmanagement

system

farm equipment

system

weather data

system

irrigation system

seed optimizing

system

fieldsensors

irrigation nodes

irrigation application

seedoptimizationapplication

farmperformance

database

seeddatabase

weather dataapplication

weatherforecastsweather

maps

rain, humidity,temperature sensors

farm equipment

system

planters

tillers

combineharvesters

4. Product system

Is this‘mono-equipment system’ reality?

How to cope with changes in industry

boundries?

How many platforms should

users and developers enter?

Dynamic landscape of Big Data & Farming

22

Farm

Farm

Farm

Farm

DataStart-ups

Farming

AgBusinessMonsanto

CargillDupont

...

ICT Companies

GoogleIBM

Oracle...

Ag TechJohn Deere

TrimblePrecision planting

...

ICTStart-upsFarm

Ag softwareCompanies

AgTechStart-upsVenture

CapitalFounders FundKleiner Perkins

Anterra...

Farm

USA Start ups in different activities

Disruptive scenario: Toshiba’s lettuce growing

Farm data harvesting initiatives

Platforms as central nodes in network economy: some agricultural examples• Fieldscripts (Monsanto)• Farm Business Network (start-up with Google Ventures)• Farm Mobile (start-up with venture capitalist): strong

emphasis on data ownership• Agriplace (start up by a Dutch NGO with a sustainability

compliance objective)• DISH RI – Richfields (consumer data on food, lifestyle

and health)• FIspace (recently completed EU project ready for

commercialisation via a Linux-like Open Source model)Note the different business models / governance structures!

The USA battleground: Monsanto (et al.)

27

PRESCRIPTIVE FARMING

based on VARIABLE RATE APPLICATION

USA: Farmers Business Network

28

Farmers’ owned, investment by Google Ventures Summer 2015:FBN has aggregated data from 7 million acres of farm land across 17 states, and they’re growing 30% month over month. The platform is currently able to assess the performance of 500 seeds and 16 different crops.Costs farmer $ 500 / year.

USA: Farm Mobile

29

“Farmers believe their trust has been violated”: their data go to multinationals, that announcebig future income from big data, while they have pay for everything.

Farmers collect ‘crop stories’ and decide wherethey travel (and get afew cents per item?)(Anterra Capital invests)

Discussions among (US) farmers:

30

Code of Conduct

• Do I own my tractor? (IPR on software)• Do I own my own data? Who has access to my data?• Does the government have access?• Do companies gain market power on future markets ?• Is there a lock inn – can I take my data with me?• Do I become a franchiser with the risks but not the

returns?

Agriplace – compliance in food safety etc. made easy

Two platform examples from our work

Donate to (citizen) research

RICHFIELDS: manage your food, lifestyle, health data and donate data to research infrastructure

audit

FMIS

FIspace: an eco-system of apps to push data

FARMER SCANS PESTICIDES PACKAGE IN THE FIELD

APP CONNECTS BASF FOR E-INSTRUCTION, CROP AND SOIL SPECIFIC

APP ASK METEO FOR 24 hour WEATHER FORECAST

BASF SENDS INSTRUCTION TO SPRAYING MACHINE ON WATER / PESTICIDE RATIO >> Machine adjusts

APP CHECKS ADVISE WITH GOV.AGENCY

FARMER CAN SHARE DATA WITH GOVERNMENT, SGS-AUDITOR GLOBAL GAP AND PUBLIC

CAN I USE MY CURRENT

SERVICE ?

CAN I USE MY FMS ?

DOES IT WORK WITH

BAYER / DEERE

DOES IT WORK WITH BRC / ISAcert

Can we link apps / services in a clever way ?Leading to a market for services (apps and data)?Can this market be European (not MS), so that development costs of services (apps and data) are shared ?

Towards highly integrated solutions

Platforms in the cloud of input suppliers and food processors:• What is the scope (connect only machinery or also with chemical

companies and accountants ?)• Reduce costs of linking individually with many other platforms and

software packages (especially in chains that are not integrated)• Is it possible to use apps with their own business model, so that the

platform does not have to pay all their costs? >> can (non-strategic) apps be available on several platforms?

• How to prevent that farmers complain to have to pay for basis apps (e.g. weather service) more than once?

MyJohnDeere.com Farmers

Biz architectbundles apps in a platform

...

80 Accelerator companies

Apps

Towards highly integrated solutions

Highly Integrated Service Solutions• Event-driven• Configurable• Customizable• Service model

Data (Standardisation) Services

AdaptEPCIS

MyJohnDeere.com

Data Standardsto connect

BusinessCollaborationServices -Based on OpenSource Software

Farmers

Biz architectbundles apps in a platform

...

80 Accelerator companies

Apps

Modules:Single SignOnBiz Collab.Event Proces.System-Data integrationApp repository

Value propositionPlatforms solve the issue of connecting individually with a lot of business

partners to exchange data : connect easily to apps (and data services in apps) based on EDI-standards or let farmers / end-users make the connection

App-developers Develop one app for different platforms Reach a European / Global market

Governments (and industry organisations)

See above for your government platform (paying agency, public advisory service etc.)Promote innovation by a competitive market for apps with new servicesPrevent lock-inn situations for farmers and unbalanced power relations in the information exchange in food chains

Farmers Not a direct FIspace client. Platforms using FIspace inside provide you more choice

Software writers in platforms and app-companies

Helps you to be part of an open source community that cares for sustainable food production with up to date ICT – be recognized by your peers

FIspace App Store

80 Accelerator companies

Configure &Use Systems

First Commercial MVP by ... ?

App developer Business Configurator End User

Advertiser

Access fee

Use Fee Use Fee

Access fee (e.g. CargoSwApp)

Pay for app use (e.g. Spraying Advice)Sponsored app

FIspace FoundationMVP – open source

My JohnDeere365 Farmnet

AkkerwebDacom/CROP-R

Datalab Pantheon

ICT company Service model ?

Towards highly integrated solutions

Highly Integrated Service Solutions• Event-driven• Configurable• Customizable• Service model

Data (Standardisation) Services

AdaptEPCIS

MyJohnDeere.com

Data Standardsto connect

BusinessCollaborationServices -Based on OpenSource Software

Farmers

Biz architectbundles apps in a platform

...

80 Accelerator companies

Apps

Modules:Single SignOnBiz Collab.Event Proces.System-Data integrationApp repository

Is this commercially feasible?

Or is it too much a common pool investment in a market where

everybody wants to grab a stake, over-estimates the value of its own data and

finds it easier to builds its own website ?

Effects on markets

38

• In terms of suppliers, buyers, market organisation and market regulation (government involvement)

Make an analytical difference between:• current (old) product markets (e.g. social effects old

producers, is gov. using up to date ICT for auditing)• market for new products/services (milking robots,

milk with credence attributes like ‘nature-friendly’) • new ict/data markets (e.g. platforms)

• Products change: the tractor with ICT – from product to service

• New products: smart phones, apps, drones: should markets be created or regulated ?

New entrants:• Designers on Etsy• Landlords on AirBnb• Drivers on Uber

New entrants:• Direct international

sales by website• Long tail: buyers for

rare products

• Due to ICT new options to fine tune regulation / monitor behaviour

• Regulation can be out of date

• New types of pricing and contracts: on-line auctions, dynamic pricing, risk profiling etc.

• Shorter supply chains (intermediaries as travel agencies and book shops disappear)

• Strong network effects in on-line platforms (rents and monopolies)

Effects on Chain organisation

40

ICT lowers transaction costs• In social media (Facebook etc.): the world is flat

with spiky metropolises• In ‘sharing’ platforms (peer-to-peer like AirBnb,

Uber, crowd funding): creates new suppliers (reduce overcapacity) and users. Long tail effects.

• In chain organisation: centralisation to grab advantages of data aggregation or more markets?

• Platforms: centralisation via data management

Programmability: Low HighAsset specifity: Low High Low

HighContribution partnersseparableHigh spot long-t. spot

jointmarket contract mrkt

venture

Low coope- coop./ insidevertical

rationvertical contractowner-

© Boehlje ownershipship

Organisational arrangements in the food chain are changing

Chain organisation changes (©Gereffi et al., 2005)

inpu

ts

E

nd p

rodu

ct

PRICE

Shops

Complete Integration

Lead company

Leadcompany

Turnkey supplier

Relationalsupplier

Market Modular Relational Captive Hierarchy

Low Degree of explicit coordination and power asymmetry High

Leadcompany

Farmers

2 Scenarios, with significant impacts ?

1. Scenario CAPTIVE PRODUCT CHAINS: ● Farmer becomes part of one integrated supply chain as a

franchiser/contractor with limited freedom ● one platform for potato breeder, machinery company, chemical

company, farmers and french fries processor.● Weak integration with service providers, government ?2. Scenario OPEN NETWORK COLLABORATION:

• Market for services, apps and data• Common, open platform(s) are needed• Higher upfront, common investment ??• Business model of such a platform more difficult?• More empowerment of farmers and cooperatives?

F

F

Governance issues

2 Scenario’s to explore the future: HighTech: strong influence new technology owned by

multinationals. Driverless tractors, contract farming and a rural exodus. US of Europe. Rich society with inequality. Sustainability issues solved. Bio-boom scenario.

Self-organisation: Europe of regions where new ICT technologies with disruptive business models lead to self-organisation, bottom-up democracy, short-supply chains, multi-functional agriculture. European institutions are weak, regions and cities rule. Inequalities between regions, depending on endowments.

(Based on EU SCAR AKIS-3 report that also included a Collapse scenario: Big climate change effects, mass-migration and political turbulence leads to a collapse of institutions and European integration).

Issues at several institutional levels

Data ethics, privacy thinking, on-line and wiki culture. Libertarian ‘californisation’

Data “ownership”, right to be forgotten, Open data cyber security laws etc.

Platforms (nested markets), contract design (liability !), open source bus. models

Value of data and information

Effects on government policy

Agricultural policy●Data sharing between government and business●Worries on the future of the family farm

Environmental policy●Precision measurement: internalisation

Regional policy●Risk of rural exodus, need for ICT infrastructure●Some regions can become a big-datahub

Competition policy●Monopolies in platforms ?

Science & Innovation policy: enablers (+ next sheet)

Much is invested by multinational companies – Why should government intervene and plan research in ICT?

• Public objectives like food security, employment, regional development are not automatically guaranteed by the market

• Many SME (also in food and machinery industry) that underinvest in knowledge as IPR cannot easily be protected: quickly copied in the market. Pooling of funds make sense.

• There could be systemic bottlenecks in collaboration agriculture with ICT-sector or logistics.

• There is a need for common pool investments (Standards, infrastructure like ABCDEFs = Agri-Business Collaboration and Data Exchange Facility.

• There are (negative) external effects of ICT that needs attention: privacy, data ownership, power balance, effects on small farms, remote regions…

• There are (negative) external effects in agriculture that can be solved by ICT more attractively than by regulation (environment,

food safety, animal welfare, etc.) • Government is user of ICT: simplification issue CAP; E-science

What does this mean for the AKIS ?

Big Data and other ICT developments will not only influence agriculture but also science, research and development and innovation processes in the AKIS. This goes much deeper than open access and linked open data sets in science. Where the past is characterised by doing research on data from one experimental farm or only a sample of farms (like in the FADN / ARMS) that results into one set of advice for everybody, the future is characterised by doing research on data of all farms, in real time, that results in individually customised advice for individual farms. That blurs borders in AKIS between research and advice and advisors will need continuous training on these developments.(c) EU SCAR AKIS Towards the future – a foresight paper, 2015

Different objectives, methods, and public roles

What is going on in the European Union cs.

• EU SCAR AKIS Towards the future – a foresight paper, 2015• ERA-NET ICT AGRI: strategic research agenda• Future Internet PPP

• Smart AgriFood• Fispace• Accelerator projects: Finish, SmartAgrifood2, Fractals

• H2020: Internet-of-Farm &Food-2020: Internet of Things (30 mln.)

• European Innovation Partnership: seminar data driven data models (Sofia) + benchmarking

• DISH-RI en RICHFIELDS: consumer data on food, lifestyle and health

• Plus several other projects in H2020 where ict is an important work package (e.g. Valerie)

FI-PPP Programme Architecture

90 M€ 80 M€ 130 M€

Accelerators

Building blocks for the Future Internet

52

Linked with the Whitehouse’ Global City Challenge

IoF2020: Overall concept (2017-2020, 30M€ funding)

Elements for an Agri-ICT research strategy

• Promote data-exchange (reduce administrative burdens, create value via combination, aggregation)• Standardisation for interoperability; AgGateway, UN/CEFACT• Platform(s) for data exchange• Open data by government

• Promote innovation with new services• Especially ict-start ups, connect them with farmers and

companies (e.g. FIware 3 stage approach)• Internet of Things • Big Data (use of social media data, machine learning etc.) ?

• Advisory service: “just” another player in data exchange + update own software: go real time

• Research: support all this + real time agronomic models.

Don’t forget:• The interstates made the cars flowing, changed our way of

living more (specialisation, suburbs etc.) than the car itself.• We need utilities in rural areas: 3G/4G/5G, but also ABCDEF

platforms to combine and aggregate data for value creation and to create markets for apps

• It raises issues of data governance (business model, data ownership, organisation model) as (vendor)platforms are only linked to one part of the farm and can be natural monopolies with lock-in effects

• Solutions (market-based or otherwise) are contingent on situation and institutional environment

Thanks for your attention

and we welcome collaboration in your projects !!

krijn.poppe@wur.nl

www.wur.nl