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Big Data for Building Inclusive Agriculture in Dry Areas


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25 to 30 August. The World Water Week in Stockholm is an annual focal point for the globe’s water issues. Organized by the Stockholm International Water Institute (SIWI), and supported by the United Nations water programs.
Wednesday 28 August
“Big data for all”, can it help improve agricultural productivity?

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Big Data for Building Inclusive Agriculture in Dry Areas

  1. 1. International Center for Agricultural Research in the Dry Areas A CGIAR Research Center Big Data for Building Inclusive Agriculture in Dry Areas Stockholm | August 28, 2019 Atef Swelam and Chandra Biradar “Big data for all”, can it help improve agricultural productivity?
  2. 2. New 9: 5 SRPs + 4 CCTs Genetic Resources:Mining crop diversity to develop germplasm resistant to heat, drought, cold, disease, higher nutrients; International public goods (open access) Adaption to Climate Change: Conventional and molecular breeding to develop climate-smart crops and livestock Building resilience: Integrated crop-livestock farming systems to address economic, social, and environmental conditions Promoting value chains, policies: Agriculture as an income- generating business for many poor smallholder households Enhancing water, land productivity: Rainfed, irrigated, and agro-pastoral farming; Reversal of environmental degradation; Enhance intensification CCTs BigData Scaling CapDev Gender SRPs-strategic research priorities + CCTs-cross cutting themes
  3. 3. DryArc region and where ICARDA is working
  4. 4. • Big Data can support resilient agri-food systems under uncertain climate variability and change. • Big Data has a huge potential in the dry areas where resource use efficiency is below its actual potential • But they can only deliver if applied to Inclusive Farming Systems Big Data in sustainable agriculture Better integration Better measurements Better modelling Resilient Systems ++
  5. 5. 2015-162014-15 Impact of variability or extreme weather events on agricultural systems productivity
  6. 6. Quantification of Farming Systems @ multiple-scales
  7. 7. Water Productivity (rainfed) Water Productivity (Irrigated) Agricultural Water Productivity in E-T Basin • It is possible to save substantial amounts of water by increasing AWP. • Achieving high water productivity can save about 19 BCM while producing same amounts of the crops. Alternatively, using the same amounts of water may boost the production by about 24 million tons of crops. • Such water savings and/or increases in production is clear response to water scarcity in the E-T basin.
  8. 8. Land use and systems level yield gaps 2000 to 2018 2010 2011 2012 2013 2014 2015 2016
  9. 9. Quantification of crop water productivity in the Nile Delta over last three decades • Determining what crops have the highest water productivity in order to better planning the agricultural policy based on comparative advantages of these crops in a particular climatic conditions • Establishing big data would be use as reference for researchers and decision makers • Helping irrigation water planner for better improve the supply system according to the actual crow water requirements
  10. 10. NENA ET-Network: Validation of Remote-Sensing Evapotranspiration estimates using Field Measurement Vs 0.1 mm/day error can lead to and error of 30% for a 150-days crop
  11. 11. • Several Remote-Sensing ETa data are made freely available at an increasing temporal and ground resolution [SSEBop, WaPOR, ET Enseble, GloDET, OpenET]. This is extremely valuable for progressing in agricultural water management on large areas • Although many ET RS-based estimates are available, that can be used for regional planning and policy development, they suffer from uncertainties owing to the fact that they are not rigorously validated. • The weak point: these RS data suffer from a limited field validation (virtually none in the NENA Region) • ICARDA, in collaboration with FAO-RNE, has recently activated a NENA Regional-Network of field ETa monitoring including (Egypt, Jordan, Lebanon, Morocco and Tunisia) • Objective: capacity development in field ETa measurements, accuracy assessment of RS ETa data of different databases, provide the opportunity for RS algorithms calibration The issue The response
  12. 12. Global FluxNet NENA-ETNet
  13. 13. Sugarbeet Maize Pomegranate ET = Rn - G - H
  14. 14. 3 ha 3 million ha 300 k ha30 k ha 3 billion ha Improving agroecosystems productivity by scaling innovations and measuring impacts
  15. 15. Scale out the MRB in the region Mechanized raisedbed will likely form the core of water savings for wheat production across the MENA region.
  16. 16. • Agri-food systems is the largest user of land and water resources and is also the vehicle to achieves SDGs (considering improved system efficiency) • Big Data is helpful tool required to close the farming system productivity gaps to achieve efficient use of resources in the water scarce world (needs collective actions and efforts) • To build the confidence on the Big Data, it has to be verified in the field • Big Data is useless unless reflected in implementation and decision making to help the targeted communities Take home message
  17. 17. What is the Real Big Data in Agriculture?!
  18. 18. The Farmer Thank You and goes back to the field Big Data comes from the field