Big Data in
CGIAR
Elizabeth Arnaud
Bioversity International
22nd September, IGAD pre-
meeting, INRA, Paris

CIAT: Big Data for Climate
Smart Agriculture
 CIAT analysis large, real-world data sets from annual survey
on rice to produce recommendations much more quickly.
1. Harvest results of annual surveys and agronomic experiments
from National Private Companies
2. Get Planting times for specific sites and seasonal forecasts
3. pairing historical records with state-of-the-art seasonal
forecasts
4. Analyses with advanced algorithms from biology, robotics and
neurosciences
5. Searches for weather patterns in previous years and checked
which varieties did best in those years
 Result: Identify the most productive rice varieties and
planting times for specific sites and seasonal forecasts.
Recommendations could potentially boost yields by 1 to 3
tons per hectare.

Crowdsourcing varieties
500 farmers per site will be given 3 blind varieties in small quantities to
be tested under their own conditions (the crowdsourcing approach)

CGIAR Big Data
 Large amounts of data accumulated by
CGIAR Centers to be published as Open
Data Highthrouput production of data:
 Highthrouput Genotyping
 Highthrouput Phenotyping
 Remote Sensing data
 Citizen Sciences ( Crowd Sourcing)
 Open Data-Open Access Strategy for CGIAR

8 Agrifood System research
programmes
 Big Data platform will support the 8 CRPs
1. Dryland Cereals and Legumes Agri-food
System
2. Fish Agri-food Systems
3. Forest and Agroforestry Landscapes
4. Livestock Agri-food Systems
5. Maize Agri-food Systems
6. Rice Agri-food Systems
7. Roots, Tubers and Bananas Agri-food
Systems
8. Wheat Agri-food Systems

5 Global Integrative
Programmes
 to ensure that research results deliver solutions at
the national level that can be scaled up and out
to other countries and regions.
1. Genebanks ++
2. Nutrition and Health
3. Water Land and Ecosystems (including
soils);
4. Climate Change
5. Policies, Institutions and Markets
research

Big Data and ICT: Call for
Expressions of Interest
 A number of scientific organizations developed high
performance computing facilities and big data
analytical capabilities.
 A major opportunity exists for the CGIAR to leverage
this investment in capability and infrastructure
 Strong partnerships across the Consortium and
beyond it,
 work with existing and promising efforts to support
the creation of a global-agri-informatics platform
and network that ensures compliance with Linked
Open Data and other standard interoperability
protocols.

IFPRI-led EOI: Tools for Driving
Interdisciplinary and Collaborative
Big Data Analytics
 Implementation of CGIAR Survey Platform Data
for data collected through mobile phones
 from connected sensor network across trial sites
 Further development of agricultural ontologies
with research communities’ inputs
 Implementing Linked Open Data and APIs in
data repositories
 Enabling Data Discovery
 Use cases:
 Scalable Satellite-based Crop Yield Mapper
(SYCM):
 Crop Water Productivity (CWP)
 Remote Sensing for Agro-biodiversity Monitoring

Call for Pre-proposals for
CGIAR Research Programmes
 http://www.cgiar.org/our-
strategy/second-call-for-cgiar-research-
programs/crp-2nd-call-pre-proposal-
submissions/