Toekomst met Big Data in Agri&Food

Presentatie over: "Toekomst met Big Data in Agri&Food"— Transcript van de presentatie:

1 Toekomst met Big Data in Agri&Food
7 Juni 2017, Sander Janssen & Karin Andeweg Met thema team

2 Doel: reflectie sessie op strategische kennis vragen rond Big Data in Agri&Food
Start punt: lopende projecten binnen topsector en daarbuiten Genereert kennis voor nu en dicht bij toepassing Strategisch kennisvraag: Wat moeten WUR nu ontwikkelen om over 3-5 jaar in te kunnen zetten? Kennis, competenties, skills Leidende positie als enabler van technologie

3 Programma, parallele lunch sessie
Presentatie Wageningen UR Strategisch thema Big Data 3 specifieke voorbeelden Discussie en reflectie: Feedback van iedere deelnemer Vragen: Hebben we de juiste uitdagingen? Inhoudelijke invullingen/aanvullingen? Link met topsector activiteiten?

4

5 Big data and related terms
Linked data Open data Data revolution Digital agriculture Data science Digital foods Digitization Data analytics

6 Big Data technologies & methodologies
Strategisch thema van Wageningen UR In afstemming met Min EZ Methodologisch thema, ondersteunt thematische insteek

7 Big data: ambition 2025 Big data is business-as-usual
Big data replaces experimental and one-off data collection for research Knowledge is mostly developed on the basis of Big Data Wageningen UR is trend setting in Big Data analytics and use in the life sciences world wide Big data als MUST, NOODZAAK onderstrepen om niet buiten spel te komen te staan.

8 Big data in 2018: end of program
Number of leading projects on Big Data, developing: Pieces of infrastructure Demonstrators Consortia Capacities in hardware, software and orgware to work with and on Big Data General awareness of what it is, and what it can deliver Established partnerships with a number of key players

9 Big Data timeline

10 4 V’s of Big Data

11 Our understanding of Big Data

12 Full screen image with title

13 Access to Big Data: voortgang
In internationale context Global Open Data in Agriculture and Nutrition (godan.info) met actieve WUR en NL bijdrage FAIR principes gelanceerd Guidelines voor data governance in samenwerkingsverband Data governance als obstakel (cross sectoraal)

14 Access to Big Data: plannen 2017
Outcome Activity Big Data collections available for testing in private or public sector setting Agro-DataCube project, activities in related projects Interoperable data available for easy linkage with associated good practices Scaling out of work with FAIR data points in the Plant Sciences group to other groups Guidelines and best practices on data governance available for stakeholders Develop guidelines based on inventories in 2016 and distribute, potential follow up of Data-Governance workshop

15 Smart use of Big Data: voortgang
Eerste installaties met Big Data specifieke IT tools (Spark, Hadoop, Cassandra), tests met WUR relevante data Identificatie en uitwerking van showcases Start Roadmap ontwikkeling

16 Outcome Activity Prototype of Big Data show case on machine learning for animal genetics Setting up data analytics around animal genetics and experimenting with different machine learning processes Finalised prototype on data visualization on climate impacts across a range of data different domains Finalising the activities in AgMIP Impact explorer on climate change impacts in Africa and South Asia Prototype on Big Data analytics for yield gap analysis in arable agriculture Finding the available data, see the best cropping systems (most likely sugarbeets) and setting up analytical soloutions Pilots scoped for large scale innovations with IoT in agriculture and food With the start of Internet of Food Large scale pilots, these pilots will be further scoped and research/innovation challenges identified

17 Cultural change: voortgang
Seminar series over Big WUR Symposium over Data governance Wageningen Data Competence Centre

18 Cultural Change: plannen 2017
Outcome Activity Awareness of Big data Research at Wageningen UR Started in 2016 and finishing seminar series in 2017 about Big Data at WUR. Seminar are attended well (50-80 persons). Position of Wageningen UR in national landscape on Big Data established Started in 2016 with Big Data governance workshop, and to be continued in 2017 with another workshop Online materials and presentation of main results of Big Data strategic theme Big Data Dossier made on Wageningen UR website, blog posts added, in 2017 new projects need to be added.

19 Voorbeelden van specifieke projecten
Plant Sciences, Ron Wehrens Food Sciences, Nicole Koenderink Animal Sciences, Roel Veerkamp

20 Big Data@Plant Sciences
Richard Finkers, Corne Kempenaar, Ron Wehrens

21 Access to data FAIRification of data in the plant sciences:
Definition of terms and ontologies Building and maintaining networks with global players Application to current projects Dissemination of knowledge Wilkinson et al., Nature Scientific Data (2016)

22 Smart use of data Novel algorithms for high-throughput phenotyping
Deep learning, self-organising maps, ... “Decent-ware”: professional-level quality software development (software carpentry) Application to bioinformatics pipelines

23 Change in Systems Thinking
Case studies Apply results from “Access” and “Smart use” subthemes Feedback to research in these subthemes Real-life applications (yield gap prediction, ...)

24 Betekenisvolle blockchains in Agrifood
Nicole Koenderink, Anton Smeenk, Don Willems, Paul Bartels, Jan Top Wageningen Food & Biobased Research

25 Blockchains in deze presentatie
Technisch: niet interessant, belangrijk dát het werkt Effect: betrouwbare informatie- uitwisseling is mogelijk zonder tussenpartij (financieel en anderszins)

26 Strategische vraagstelling
Hoe kunnen bedrijven en consumenten ketenrelevante informatie en transactiecontracten vastleggen? Betrouwbaar Onveranderlijk Begrijpelijke informatie semantische blockchains data & context (metadata) blockchains

27 Semantische blockchains
Blockchains bieden de mogelijkheid om zonder Trusted Third Party transacties af te spreken en vast te leggen Hiermee wordt ketensamenwerking met willekeurige, onbekende partijen mogelijk Het expliciet vastleggen van de betekenis van data en contracten is cruciaal om elkaar (automatisch) te begrijpen → semantiek is de sleutel ! Doel van dit project: de do’s & dont’s rondom semantische blockchains in de agrifood-sector in kaart brengen

28 Relevantie voor de topsector Agrifood
Blockchains gaan agrifoodketens diepgaand veranderen Zonder goede meta-informatie: garbage-in-garbage-out Wageningen UR biedt governance: hoe organiseer je blockchains in de keten semantiek: hoe zorg je dat er betekenisvolle data wordt gedeeld toepassingen in agrifood: ervaringen delen binnen de sector samenwerking met aanbieders van Blockchain

29 Plan van aanpak Meer informatie via Jan.Top@wur.nl Anton.Smeenk@wur.nl
Use case ‘Druivenketen’ binnen PPS ‘Blockchains in agrifood’: governance: ketenstructuur, rol van partners met partners bespreken welke data gedeeld wordt en welke niet data + context (met vooraf afgesproken betekenis) Demonstratie van toegevoegde waarde en overzicht van openstaande issues Publicatie van resultaten op WUR website en in vakliteratuur Meer informatie via

30 Big Data in the livestock chain
7 June 2017, Wageningen Roel Veerkamp en Claudia Kamphuis

31 Big data Maar ook in de agriculture zien we steeds meer Big Data aspecten, die vooral te maken hebben met sensoren die steeds meer data verzamelen. We hebben drones die data genereren, we weten precies waar een koe zich in de melkstal bevind en de hoeveelheid melk die ze produceert, steeds vaker ook nog de samenstelling van deze melk. We weten waar de kippen zich bevinden in de stal, we kunnen steeds preciezer het gras bemesten.

32 Strategic research question
By connecting and combining Big Data and using clever analytical tools can we innovate the sector on the big themes? Management tools Sensor technologies Food chain

33 Examples running projects:
Predict cow-individual feed intake in dairy cattle Combine animal nutrition, genetics, cow info, KNMI Sort pigs earlier in life to get homogenous groups Combine on-farm data (litter size, birth information, weight, movements) and genetics Need for management tools for resilient AND efficient production system. Existing sensor data, national data, New technology data (drones) ……

34 Project Discussion with PPS in livestock sector:
Can we predict real-time the norms for nutrient utilisations on a dairy farm? Data on grass, cows, youngstock, production, farm, external circumstances, history, soil, manure, silage samples, concentrates … Can we predict the (expected) survival rate for pigs and poultry for a farm? Any data source

35 Discussie Hebben we de juiste uitdagingen? Access to data
Smart use of data Change in systems thinking Inhoudelijke invullingen/aanvullingen? Link met topsector activiteiten/disseminatie? High Tech to Feed the World Andere PPS’en Vakbladen/events?

36 Thank you! @Wurcgi

37 Challenges for Big data in agri-environmental domain
More persistent barriers lie in handling the variety and veracity aspects. Variety: Only bits and pieces of the domain are covered by standards & vocabularies Improved semantic interoperability is needed Veracity: Lack of sufficient, high quality metadata hinders the smooth access to and linkage of data sources. Consequence: Lack of trust Lokers et al, 2016, Big Data technologies for use in agro-environmental science, Env. Mod. & Soft:

38 Challenges (2) Having big data available
Data governance and data sharing across players in the value chain Sensor integration for cross- disciplinary analysis Answers looking for questions