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Webinar@AIMS: Perspective on Big Data in the CGIAR

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To help reaching the Sustainable Development Goals, CGIAR must tap into Big Data. Within the programme on Climate Change for Agriculture and Food Security (CCAFS), researchers have already applied Big Data analytics to agricultural and weather records in Colombia, revealing how climate variation impacts rice yields. After defining its Open Data-Open Access strategy, CGIAR has launched an internal call for proposals for big data analytics platforms that will provide services to the Agri-Food system programmes and parners, and will interconnect the CGIAR data to other multi-disciplinary big data. The seminar will present the pespectives of the envisioned platforms.

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Webinar@AIMS: Perspective on Big Data in the CGIAR

  1. 1. Big Data in CGIAR Elizabeth Arnaud1, Medha Devare2, Jacob Van Etten1, Jawoo Khoo3, Andy Jarvis4 1 Bioversity International, Consortium Office2, IFPRI3, CIAT4 4th February 2016, AIMS Webinar, FAO
  2. 2. What is CGIAR ?  An international organization that advances international agricultural research for food security. Photo CGIAR  15 Centers worldwide  40 years of research  Over 8,000 scientists, researchers, technicians, and staff
  3. 3. CGIAR Goals 1. Reduced poverty 2. Improved food and nutrition security for health 3. Improved natural resource systems and ecosystem services Photo B. Wawa/CIMMYT
  4. 4. Big Data (as per IBM) Volume, Velocity, Variety, Veracity  Identify patterns and gain new knowledge to answer complex and unanswered questions  Entreprises remain agile in a rapid evolving environment
  5. 5. Strategic Collaborations  Existing high performance computing facilities and big data analytical capabilities  Leverage this investment in capability and infrastructure  Strong partnerships across the Consortium and beyond it  Example of the Strategic Alliance between Google Maps and FAO to make geospatial tracking and mapping products more accessible FAO's José Graziano da Silva and Google's Rebecca Moore celebrate the partnership formalization at COP21 in Paris
  6. 6. Big Data for Agriculture  Massive Historical Agricultural Data in distributed databases and repositories  New Highthrouput Data  Genomics/Genetics/metabolomics  Phenotyping  Geophysical through Remote sensing  Social and Economics  Citizen Sciences  Challenge: Secure Food and Health by addressing Yield Gap and Climate Change Photo: ICRISAT
  7. 7.  7 million samples of crop varieties and wild relatives in genebanks worldwide  Using the emerging deluge of omics data to unlock crop diversity on a massive scale  Biodiversity Informatics Platform  Currently 62 institutions  Initiative facilitated by Global Crop Diversity Trust, Global Plant Council, International Treaty for PGRFA, CGIAR  http://www.divseek.org/ Mining the omics data as collaborative effort IRRI Illustration: Australian Phenomics Facility
  8. 8. Big Data for Climate Smart Agriculture  Access to global data should help providing local information © IISA © CIAT
  9. 9. How CGIAR projects currently apply Big Data Principles ?
  10. 10. Remote sensing project across CGIAR Research Programmes  Bill &Melinda Gates Foundation-supported remote sensing project spans across the CGIAR  Linked to CGIAR Research Programs on Climate Change, Agriculture and Food Security (CCAFS) and on Water, Land and Ecosystems (WLE).  A complete lay of the land will be recorded by Use Unmanned Aerial Vehicles (UAVs) to discern crop types, any present diseases, and the effects of climate change. Photo: CIP
  11. 11. Drone over sweet potato fields in East Africa • To validate a low-cost, effective method of monitoring sweet potatoes • Great quality of data enabling discrimination of land uses and estimation of the area for each use • Identifying ‘spectral signature’ of variet Multispectral camera © CIP Project leader: Roberto Quiroz See more at: http://cipotato.org/press-room/blogs/cip-drone-study-over-sweetpotato- fields-of-east-africa-a-success/ Photo CIP: Mwanza region of northern Tanzania
  12. 12.  To produce recommendations much more quickly on rice varieties in Colombia 1. Access annual surveys and agronomic experiments from commercial fields 2. Getting 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. Search for weather patterns in previous years and checked which varieties did best in those years Analyzing large, uncontrolled, real-world data for rice
  13. 13.  Most productive rice varieties and planting times for specific sites and seasonal forecasts Identified.  Recommendations could potentially boost yields by 1 to 3 tons per hectare.  Scaling up the techniques from their pilot to Argentina, Nicaragua, Peru and Uruguay Analyzing large, uncontrolled, real-world data for rice The project won UN Global Pulse’s Big Data Climate Challenge CIAT
  14. 14. Crowdsourcing Farmers’ Preferences for varieties Seeds for Needs projects 500 farmers per site will be given 3 blind varieties in small quantities to be tested under their own conditions and feedback returned by phone
  15. 15. What Big Data can do for Livestock Farmers ?  1 billion small-scale livestock keepers  Smalls-cale ‘mixed’ crop-and-livestock farmers  Livestock-based strategies for adapting to, and mitigate, climate change -  Livestock feeding systems that both increase productivity and reduce greenhouse gases. From Kunming to Da Li, Yunnan Province, China (photo credit: ILRI/Stevie Mann). Remote sensing/GIS for  Monitoring welfare of livestock  Grazing locations, distribution  Epidemiology/diseases distribution Another Webinar !
  16. 16. Future of Big Data in CGIAR CGIAR 2017-2022 Programmes and Platforms
  17. 17. Agri-food System Programmes  Big Data platforms will support 8 Agri-Food System Programmes (AFSP) 1. Dryland Cereals and Legumes 2. Fish Forest and Agroforestry Landscapes 3. Livestock 4. Maize 5. Rice 6. Roots, Tubers and Bananas 7. Wheat
  18. 18. Global Integrative Programmes 1. Genebanks 2. Agriculture for Nutrition and Health program (A4NH) 3. Water Land and Ecosystems (WLE) 4. Climate Change, Agriculture and Food Security program (CCAFS) 5. Policies, Institutions and Markets (PIM)
  19. 19. CGIAR data & service platforms 1. Genebanks 2. Genetic Gain  Establishing CGIAR system genetic resources capability 3. A platform on Big-Data, Information and Knowledge  genetics & genomics, Agrobiodiversity data  spatial, biophysical, social and economics  Open Data-Open Access
  20. 20. IFPRI/CIAT led Proposal for CGIAR Big Data Platform ‘Tools for Driving Interdisciplinary and Collaborative Big Data Analytics ‘ - some elements  CGIAR Survey Platform Data  for data collected through mobile devices  from connected sensor network across trial sites  Provide ontologies/standards/tools for Data Discovery & Analytics across data repositories  Proposed use cases for monitoring agriculture:  Scalable Satellite-based Crop Yield Mapper (SYCM):  Crop Water Productivity (CWP)  Remote Sensing for Agro-biodiversity Monitoring  Capacity development activities
  21. 21. More on the CGIAR Web Site  First Expression of Interest and Comments  http://www.cgiar.org/our-strategy/second-call- for-cgiar-research-programs/crp-2nd-call-pre- proposal-submissions/  Second call for full proposal  http://library.cgiar.org/bitstream/handle/10947/4 127/CGIAR-2ndCall- GuidanceFullProposals_19Dec2015.pdf?seque nce=1
  22. 22. Thank you Medha Devare Data and Knowledge Manager Open Data-Open Access Strategy Coordinator CGIAR Consortium Office Jawoo Koo HarvestChoice project Co-PI IFPRI Andy Jarvis Director, Decision &Policy Analysis Area, CIAT Flagship leader, CCAFS Mali, E. Arnaud, Bioversity Jacob van Etten Theme Leader, Adaptation to Climate Change Bioversity International

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