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Big Data challenges and solutions in
agricultural and environmental research
IGAD / RDA Big Data Workshop, 22 September 20...
Outline
 Historic perspective (agricultural & environmental
modelling)
 Expectations for the (near) future
 Some Big Da...
3
1960
-
1980
Crop
science
Animal
science
Food
Science
Economics
Institutional
data
collection
Institutional
data
collecti...
4
Crop
science
Animal
science
Food
Science
Economics
2010-
2015
Open data across sectors
IT improvements
(meta data,
seman...
Data analysis and integration, Models,
Artificial Intelligence, Linked Open
Data, Semantic web technologies, ...
Policy op...
Food Security example: Monitoring Agricultural
ReSources (MARS)
Wisdom
Knowledge
Information
Data
 Owned and operated by ...
Example: Monitoring Agricultural ReSources (MARS)
Wisdom
Knowledge
Information
Data
weather archives live data streams
cro...
Example: Monitoring Agricultural ReSources (MARS)
Wisdom
Knowledge
Information
Data
weather archives live data streams
cro...
Example: Monitoring Agricultural ReSources (MARS)
Wisdom
Knowledge
Information
Data
weather archives live data streams
cro...
Example: Monitoring Agricultural ReSources (MARS)
Wisdom
Knowledge
Information
Data
weather archives live data streams
cro...
Food production example: Smart Farming: Monitoring, planning & control
11
cloud-based
event
management
smart sensing
& mon...
Big Data technologies
Technologies used (agricultural research):
 RDBMS, geo-databases
 Various “old & proven” programmi...
Expectations versus reality in 2015...
 New technological solutions (RDF databases, ontology
alignment, NLP)
 Successful...
Source: Gartner (August 2015)
14
Thank you for
your attention
15
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SC2 Workshop 1: Big Data challenges and solutions in agricultural and environmental research

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“Lightning talk” in the Big Data Europe (BDE) workshop on “Big data for food, agriculture and forestry: opportunities and challenges” taking place on 22.9.2015 in Paris by Rob Lokers and Sander Janssen from Alterra, Wageningen UR
The Netherlands.

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SC2 Workshop 1: Big Data challenges and solutions in agricultural and environmental research

  1. 1. Big Data challenges and solutions in agricultural and environmental research IGAD / RDA Big Data Workshop, 22 September 2015 Rob Lokers, Sander Janssen Alterra, Wageningen UR The Netherlands
  2. 2. Outline  Historic perspective (agricultural & environmental modelling)  Expectations for the (near) future  Some Big Data examples from the agri-food domain  Big Data technologies in modelling and remote sensing  Expectations versus reality in 2015 2
  3. 3. 3 1960 - 1980 Crop science Animal science Food Science Economics Institutional data collection Institutional data collection Institutional data collection Institutional data collection 1980 - 2000 2000- 2010 2010- 2015 First computer models Institutional applications Integrated modelling frameworks First computer models Institutional applications First computer models Institutional applications First computer models Institutional applications Open data across sectors IT improvements (meta data, semantics) IT improvements (meta data, semantics) IT improvements (meta data, semantics) IT improvements (meta data, semantics)
  4. 4. 4 Crop science Animal science Food Science Economics 2010- 2015 Open data across sectors IT improvements (meta data, semantics) IT improvements (meta data, semantics) IT improvements (meta data, semantics) IT improvements (meta data, semantics) 2015 - 2020 BIG DATA: one massive linked data pool across disciplines and strong computational capabilities Computational capabilities: • Amazon • Microsoft Azure • Google Earth Engine • EC research infrastructures New data sources: • Remote sensing • Crowd sourcing • Rapid phenotyping/ Omics • Social media Potential to solve problems on agriculture, nutrition, food security, climate change?
  5. 5. Data analysis and integration, Models, Artificial Intelligence, Linked Open Data, Semantic web technologies, ... Policy options, Products, Services, Costs, Benefits, Scenarios, Impact Assessments, Decision Support Systems, Integrated models, ..... Decision domain (policy/industry) Process of data based value creation and roles involved Policy makers/industry/societal stakeholders Wisdom Knowledge info + application Information data + added meaning (Big) Data raw material Knowledge domain (science / consultants) Interests (economic, social, environmental), values, preferences, trade-offs, risks, intangibles, ethics, .... Databases, Satellites, Sensor networks, Social media, Citizen Observatories, ... Open(data)Standards,(meta)datarepositories, Businessdevelopment,Visualizationtoolsand methods,Contextualization,KnowledgeBrokerage,...
  6. 6. Food Security example: Monitoring Agricultural ReSources (MARS) Wisdom Knowledge Information Data  Owned and operated by EC-JRC  Crop forecasts at EU level needed to take rapid decisions on Common Agricultural Policy instruments during the year  Provide information on vulnerability in specific food insecure areas  In support of: ● European Common Agricultural Policy on commodities & subsidies (focus on Europe, Asia) ● Food aid (focus on Africa)  Monitoring weather and crop conditions of current growing season (early warning, extreme events)
  7. 7. Example: Monitoring Agricultural ReSources (MARS) Wisdom Knowledge Information Data weather archives live data streams crop, soil databases Models
  8. 8. Example: Monitoring Agricultural ReSources (MARS) Wisdom Knowledge Information Data weather archives live data streams crop, soil databases Models
  9. 9. Example: Monitoring Agricultural ReSources (MARS) Wisdom Knowledge Information Data weather archives live data streams crop, soil databases Models
  10. 10. Example: Monitoring Agricultural ReSources (MARS) Wisdom Knowledge Information Data weather archives live data streams crop, soil databases Models
  11. 11. Food production example: Smart Farming: Monitoring, planning & control 11 cloud-based event management smart sensing & monitoring smart analysis & planning smart control Genome sequences Feed uptake Performance Manure Temperature Activity Heart rate pH Antibodies Biomarkers Medicine use ........ ........ Size Location Performance Manure Water Energy Nutrition Health management . . . . . . . . . . . . Distance to . . Public health Living environment Mineral cycles Healthy products Disease risks Economic figures Environmental issues . . . . . . . . . . . . . . Crop or Animal level Farm level Environmental level Supporting sustainable food production and contributing to the realization of (inter)national policy agenda’s. Market prices Logistics Regulations . . . . . . . . . . . . . . Market level
  12. 12. Big Data technologies Technologies used (agricultural research):  RDBMS, geo-databases  Various “old & proven” programming languages (esp. for modelling, data processing)  Remote sensing: dedicated tools & environments for processing and analysis, ENVI, R, GDAL etc.  Harmonized information / data models (but still per discipline)  High Performance clusters / grids Experimental (ICT research for agriculture):  RDF databases  Vocabularies and ontologies (no alignments)  NLP algorithms  etc
  13. 13. Expectations versus reality in 2015...  New technological solutions (RDF databases, ontology alignment, NLP)  Successful initiatives use hybrid solutions, often build on “proven” technologies  “Magical” semantic (and linguistic) query processing  “Technical” query processing (e.g. through SPARQL)  Transparent access to big, distributed, heterogeneous datasets  Mainly successful on metadata level and bibliographic sources, cumbersome first attempts to harmonize big heterogeneous data streams  Custom-build data collection and processing chains still remain dominant
  14. 14. Source: Gartner (August 2015) 14
  15. 15. Thank you for your attention 15

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