CGIAR Big Data Convention 2017

Advances in computer science, connectivity, and geospatial analysis are enabling impressive new capabilities that the world needs to build more adaptive food systems.  The agriculture sector, however, trails behind the majority of economic sectors in overall digitization.  What must we do differently?   Panelists from varied backgrounds will address the challenges and opportunities of the digitization of agriculture, examining:

• What is the role of different actors (public, private, non-profit, and research)?
• How can we better use data to accelerate and enhance our research insights, and develop robust tools and services that can be mobilized across multiple domains and stakeholder communities to address food and market system needs?      
• What are the real and perceived cultural and technical roadblocks, and how might they be overcome?
• What can we learn from other sectors?

The CGIAR put out a wide-reaching call for partnership-driven innovations to solve four challenges:

• Revealing food system flows
• Monitoring Pests and Diseases
• Disrupting Impact Assessment
• Empowering Data-Driven Farming

We received 120 submissions from around the world of great sophistication and quality.
12 teams have joined us at the Convention for the final round.

CGIAR’s Big Data Platform aims to accelerate and enhance the impact of international agricultural research by enabling researchers to discover, analyze and visualize agricultural data on a large scale to generate rapid, actionable insights. A key aspiration is to build and nurture partnerships across CGIAR Centers and a variety of external partners to deliver effective data management, analytics and ICT-focused solutions to target geographies and communities. This session focuses on the "Organize" module of the Platform, which addresses the need for a culture that values data as a product in itself with global public good potential, and applies best practices to managing and making it widely available following FAIR (Findable, Accessible, Interoperable, Reusable) data principles. An infrastructure is also conceived as a crucial need to enable the discovery of research resources across interoperable repositories and platforms, coupled with tools for seamless analysis and visualization. A prototype of some of these functionalities will be demonstrated as a preliminary value proposition.

• What are the most acute challenges facing the sector in finding and interacting with agricultural data?
• Who are the public and private sector stakeholders that we want to use the data, and how could they play a role in mobilizing it?  
• What failures and successes have different sectors encountered along the path to mobilizing data fully? 
• What lessons were learned from the failures and what capabilities were unlocked by the successes?

Hackathons and data jams, maker spaces and accelerators, prizes, challenges and co-creation--digital technologies have been the subject of a dizzying array of innovation approaches.  Good practices have begun to emerge.  In this session we will review some of the current research into—and evaluation of—this array of approaches for fostering digital innovations, and together we will examine what specific strategies might be well-suited to catalyzing digital innovations in agriculture. 

Tentative Agenda Crop Modeling CoP Big Data Meeting

1:30 pm: Welcome and introduction to the Crop Modeling CoP objectives and workplan – Matthew Reynolds, CIMMYT

1:45 – 3:15 pm: Lightning talks

•  “Advances in dealing with genetics in the DSSAT crop models” Gerrit Hoogenboom, University of Florida
• “Crop Modeling for Zoning the Hazardscape of climate change” – Anton Eitzinger, CIAT
• “WOFOST crop growth model and its reimplementation in a modular and granular way” – Sander Janssen, Wageningen UR
• “Crop Modeling in the CGIAR, next steps” – Kai Sonder, CIMMYT
• “Overview of LandPKS & our associated efforts to use the information to inform models”, Jason Neff –  Modeling lead for LandPKS

3:15 pm:  Overview of the progress in the Crop Modeling Reviews – Anabel Molero, CIMMYT

3:30 - 4:15 pm: Group Discussions Part 1

4:15- 4:30 pm: Feedback and Perspectives on Crop Modeling

Data-driven Agronomy Community of Practice is led by CIAT and has been launched as a part of the CGIAR Platform for Big Data in Agriculture. The main purpose of this Community of Practice is to harness modern information technologies to advance agricultural practices, in relation to particular socio-economic and environmental contexts in a way that promotes the democratization and transparency of agricultural information.

In this session, we will provide a general agreement upon the vision of Data-driven Agronomy, after it has been enriched with different shared prospects by speakers from the private and academy sectors; such as Zia Mehrabi and Guillaume Garin from the University of British Columbia and ITK, respectively. This space will be used to share, discuss and arrange short and long term data-driven agronomy in order to build a joint agenda.

Objectives:

• The participants have a better understanding of key actors involved in DDA and their strength;
• The participants agree on a common definition of DDA;
• The participants agree on a common vision for the DDA CoP;
• The participants identify next concrete actions at short and long term.

Agenda: 

13:30- 13-45 (15 min) Short introduction of the session and its objectives by Daniel

13:45- 14:05 (20 min) Private sector (ITK) and University present shortly and from their perspective (10 min each):

14:05- 14:15 (10 min) Daniel present actor map and main strengths of each type of actor.

Participants identify actors that are not presented in the map. They can come at the end to adjust/add.

14:15- 14:45 (30 min) Facilitated session to come up with the main elements that define DDA

BREAK 

15:00- 15:20 (20 min) Co-formulate the vision of DDA CoP

15:20- 15:50 (30 min) Present from different sector perspective the formulated vision and mission and agree on a common ones.

15:50- 16:20 (30 min) Identify key next concrete actions at short and long term to contribute to agriculture in developing world.

1:30 pm
Introduction
Everyone

1:45 pm
Keynote: Remote sensing in Sub-Saharan Africa
Rolf de By, ITC/University of Twente

2:15 pm
Discussion: How to Increase Use of Remote Sensing in CGIAR and Unlock its Full Value
Liangzhi You, IFPRI
Chandrashekhar Biradar, ICARDA
Nasreen Khan, IRRI
Pierre C. Sibiry Traoré, MANOBI Group

3:15 pm
Lightning Talks: Five Minutes of Fame
Everyone

* Note: Coffee will be available in the room

The Community of Practice (CoP) on Ontology will span the knowledge domains that are relevant to the Platform with the objective of establishing best practices, guidelines in the selection, use and application of semantics for data harmonization at the levels of collection and storage, for data interoperability and data discovery following the FAIR principles.

The CoP will contribute to making the best practices and recommended ontologies a long-term reference resource for the collaborators of the CGIAR Platform for Big Data in Agriculture.

Session Agenda
Moderator: Simone Staiger-Rivas, CIAT
Coffee break will be available in the room 

1:30-1:44       Welcome

1:45- 2:00      Interest in Ontologies:  Learning from each other – Guided Discussion groups

2:00 - 2:15     Introduction to the objectives, workplan and composition of the Ontology CoP - Elizabeth Arnaud, Bioversity International, CoP Guru

2:15 – 2:45    Plenary discussion

2:45 - 3:00     Lightening talks

• Steve Kemp, Programme leader for animal biosciences, ILRI – Managing livestock language - ontologies, vocabularies or useful anarchy?
• Kevin Silverstein, Operation Manager, International Agroinformatics Alliance – An ontology mash-up framework for agriculture
• Medha Devare, IFPRI, Module 1 leader – Collecting agronomic data with an ontology-driven fieldbook

3:15 – 3:45    Group discussions around presented topics

3:45 – 4:15    Plenary – Feedback and Perspectives on ontologies for Big Data in Agriculture

Note : A board will be available for participants to subscribe to the CoP, indicating which objective they can contribute to.

The Community of Practice on Socio-Economic Data #CoP_SED facilitates data management and analysis of the high variety socio-economic data. Furthermore #CoP_SED offers a platform to explore novel ways of harnessing the power of Big data to enhance the livelihoods of the resource poor. #CoP_SED coordinates community-wide activities, through communications, a collaborative space, publications and convening in vatious events representing the CGIAR in the domain of socio-economic data.

In this session, we will provide an overview of our community’s activities, which this year have a focus on making socio-economic data interoperable. Moreover, we have a number of speakers on interesting topics related to the use of socio-economic data.

Program details

• Introduction to the Platform
• Making socio-economic data FAIR and fair
• Novel data analytics to get the most out of data
• Making Livelihood improvements with Big Data and ITC
• Measurement of impact

Improvisaton with Songs:

A small music collective experience workshop (30 - 45min)  around exploitation of individual ideas to generate harmony and team cooperation.

Directed by: Rodrigo Matta. Director of Improvisation With Sings Band: La Percumotora. From Cali - Colombia

1. 8:00 - 8:15 Welcome & Overview
2. 8:15 - 8:20 Introduction of Ernesto, Miguel and Ivonne of the CIAT Partnerships team. 
3. 8:20 - 9:00 Deep Dive into reporting requirements (Technical and Financial), Module deliverables expectations, etc. 
4. 9:15 - 9:30 Open to Questions, Discussion  
5. 9:30 End

Agenda

9:45- 11:00 am: Group Discussions Part 2

11:00 – 12:30 : Work group Crop Modeling reviews

12:30-13:30 Lunch break

13:30 Discussion on future activities and work plan 2018

Discussion topics:

• Strengths available within the CG/partnerships, in terms of modeling capacity.
• Identify the modeling skills, expertize, and data bases that could be shared among CG centers (i.e. for follow up with on-line tools and training workshops).
• Identify near-wins where modelling can directly support crop management, breeding and policy decisions, that could be achieved following on directly from previous modelling work/proof of concept, with some modest additional investment.
• Big picture modelling goals that could have impact across research areas, cropping systems, rural communities, etc..
• Tools/methods required to achieve the above (that may be available and shared from other Centers, and/or requested in project proposals)
• Definition of training needs
• Discuss better organization and collection cross the CGIAR and partners of cultivar, climate and other data that would improve use of nursery trials and other station and on farm generated yield and crop data
• High resolution data & Remote sensing integration into Precision Agriculture (PA)
• Large scale applications and Agricultural monitoring
• Crop prediction (e.g. machine learning)
• Big Data applications for climate risk transfer and management through targeted ICTs and smart insurance solutions
• How to collect data from farm groups to better understand crop productivity.