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. 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. 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. 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. 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. 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. 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 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. Big Data for Climate Smart Agriculture Access to global data should help providing local information © IISA © CIAT
- 9. How CGIAR projects currently apply Big Data Principles ?
- 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. 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. 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. 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. 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. 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. Future of Big Data in CGIAR CGIAR 2017-2022 Programmes and Platforms
- 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. 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. 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. 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. 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. 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
Editor's Notes
- Enormous amounts of data generated with new technologies for measurement, collection and storage that defy conventional analysis techniques
- A number of scientific organizations developed 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
- Agrciutlre is comlex and requires access to multidsciplinary data sets and models.
- 710,000 accessions in CGIAR genebanks offer the greatest, largely untapped opportunities. and mobilize vast range of plant genetic variation to accelerate the rate of crop Improvement
- The multispectral camera captures and measures light at visible and near-infrared wavelengths. That’s important because each plant variety has a small but measurable difference in the wavelength of light it reflects when in sunlight — a kind of distinctive “signature.” Measuring this spectral signature in field conditions in Africa can help researchers identify from the air whether a crop is sweetpotato, cassava or something else. It can also help them identify what variety of OFSP the crop is. - See more at: http://cipotato.org/press-room/blogs/cip-drone-study-over-sweetpotato-fields-of-east-africa-a-success/#sthash.JnM1vy72.dpuf?platform=hootsuite
- The multispectral camera captures and measures light at visible and near-infrared wavelengths. That’s important because each plant variety has a small but measurable difference in the wavelength of light it reflects when in sunlight — a kind of distinctive “signature.” Measuring this spectral signature in field conditions in Africa can help researchers identify from the air whether a crop is sweetpotato, cassava or something else. It can also help them identify what variety of OFSP the crop is. What’s more, this spectral signature may reveal whether individual sweetpotato plants are thriving and likely to produce many storage roots or whether they are stressed by drought, have a nutritional deficiency or are under attack by a virus or insect. Such changes can be detected in multispectral images before they can be seen in the visible spectrum, scientists say. Getting spectral signatures with the drone is a key part of the CIP-led remote-sensing project — building what the researchers call a “spectral library” containing signatures for each variety of OFSP. - See more at: http://cipotato.org/press-room/blogs/cip-drone-study-over-sweetpotato-fields-of-east-africa-a-success/#sthash.AbGklV4b.dpuf
- Harvest results of annual surveys and agronomic experiments from commercial fields Get planting times for specific sites and seasonal forecasts Pairing historical records with state-of-the-art seasonal forecasts Analyses with advanced algorithms from biology, robotics and neurosciences Search for weather patterns in previous years and checked which varieties did best in those years
- how agricultural biodiversity can help minimize the risks associated with climate change. The concept is simple – if farmers have better information and access to a wide range of varieties, they are more able to choose what best suits their conditions and cope with unpredictable weather. Citizen science – Seeds4Needs run in 11 countries and the corwd sourcing started in Costa Rica, Kenya - 1500, Ethiopia 1500, India(15,000 farmers in India, Tanzania (1500) – weather data are recorded every hour. Parallel to the mother and baby trials, 500 farmers per site will be given 3 blind varieties in small quantities to be tested under their own conditions (the crowdsourcing approach) and will be asked to evaluate the material and provide feedback on their preferences, they will become citizen scientists. Data and feedback will be collected by ERMCSD with the engagement of extension services after receiving appropriate training by Bioversity. The feedback will be collected using a simple questionnaire using mobile phones/tablets for immediate submissions to the data manager. This data will be linked to a global portal developed by Bioversity and CIAT to upscale the approach and will be analyzed using ClimMob software developed by Bioversity (van Etten, 2014).
- ‘I wonder whether wide-spread adoption of data collection and sharing by poultry farmers could have helped stop the spread of the avian flu ?’ (Todd Janzen, Janzen Ag Law, US)
- To scaled up and out AFSP outputs to other countries and regions.
- 15 international research centers working together under the CRP structure, is well positioned to generate multidisciplinary, complementary big datasets and to demonstrate their complementary use in ongoing research programs. There are, however, major needs for modern approaches to data gathering, storage, and analysis across CGIAR
- 1. Supporting (Big) Data Generation and Management: To facilitate broad adoption of modern rapid, large-scale survey and crowd-sourcing tools for collecting new (big) data, efficiently managing, harmonized with other types of (small) data organized, aggregated, and digitized from existing sources. 2. Providing Tools for (Big) Data Discovery and Analytics: To facilitate the use of big data and analytics in agricultural research and development by providing tools for data exploration, visualization, delivery, and analytics and training. 3. Developing Case Studies for Monitoring Agriculture using (Big) Data and Analytics: To support the development of robust case studies on the use of big data analytics in multidisciplinary integrated modeling analysis frameworks and products with indicators to monitor the progress toward the Sustainable Development Goals (SDGs) and CGIAR’s Intermediate Development Outcomes (IDOs). 4. Organizing Activities for Capacity Development: To undertake a series of capacity development activities to ensure the long-term adoption of new techniques, especially with partnering organizations. Scalable Satellite-based Crop Yield Mapper (SYCM): Stanford University developed the SYCM method, which uses crop model simulations to train statistical models and apply to satellite imagery within the Google Earth Engine platform in the cloud. By partnering with Google and Stanford University, the IFPRI-led Spatial Production Allocation Modeling platform will be further developed into a satellite-based crop mapping application, and to make such estimations consistent with agricultural statistics and surveys. ?Crop Water Productivity (CWP): IWMI has developed a body of research in the water productivity of different crops and over multiple scales, including tools and methods to assess this. By partnering with Google and UNESCO-IHE, this work will be further developed to provide a global, daily, ensemble ET product, which will be combined with SYCM to provide scalable assessments of crop water use from field to national and global levels. ?Remote Sensing for Agro-biodiversity Monitoring: Carnegie Airborne Observatory (CAO) at Carnegie Institution for Science uses LiDAR imagery to map the 3D structure of natural habitat. By partnering with CAO, we will be able to study and monitor the agrobiodiversity distribution at sentinel sites.