CCAFS Science Meeting A.2 Jerry Nelson - Global futures


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CCAFS Science Meeting presentation by Gerald Nelson (Senior Research Fellow , IFPRI) - "From Global Futures to Strategic Foresight: Moving Beyond Norman Borlaug"

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CCAFS Science Meeting A.2 Jerry Nelson - Global futures

  1. 1. From Global Futures to Strategic Foresight Moving Beyond Norman Borlaug Gerald Nelson Senior Research Fellow , IFPRI Theme Leader, CRP2 and CRP7INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE
  2. 2. What is the Global Futures Project? Develop methods, tools, and a consistent system to help the CGIAR answer the following questionIf an investor provides an additional $x million to the CGIAR, how should it be spent to provide the greatest return on investment? • Financial ROI • Reduction in poverty • Improvements in sustainability
  3. 3. Unprecedented Collaboration Enhance modeling tools • IMPACT model • HarvestChoice • DSSAT Use expertise at centers and elsewhere • IFPRI, IRRI, ICRISAT, CIMMYT, ILRI, ICRAF, CIP, CIAT, others to be added • DSSAT crop model experts CRP7 – to support a climate change add-on Page 3
  4. 4. How to evaluate potential technological improvements: The Delphi method Ask the experts; aka With an additional $20 the Delphi approach million, what productivity improvements can you come up with? 3 % per year for 20 years 2 % per year for 15 years Nothing! We need more money!
  5. 5. How to evaluate potential technologicalimprovements: The virtual crop method 1. Ask the experts for details on What specific changes in what they can plant phenotype are relatively easy to accomplish implement to improve drought tolerance? Heat shock proteins for increased Reduce protection partitioning photosynthate into grain Early planting and morning flowering to avoid pollen sterility
  6. 6. How to evaluate potential technological improvements: The virtual crop method2. Convert these responses into crop modelgenetic coefficients CERES Maize Model Coefficient Brief description P1 Degree days (base 8°C) from emergence to end of juvenile phase P2 Photoperiod sensitivity coefficient (0-1.0) P5 Degree days (base 8°C) from silking to physiological maturity G2 Potential kernel number G3 Potential kernel growth rate mg/(kernel d) PHINT Degree days required for a leaf tip to emerge (phyllochron interval) (°C d)
  7. 7. How to evaluate potential technologicalimprovements: The virtual crop method 3. Use crop modeling software on HPC to ‘grow’ the virtual variety everywhere and evaluate performance relative to existing varieties GCM with SRES A1B average yield (mt per hectare) DSSAT generic maize varieties 2000 yield 4.2 CSIRO, 2050 4.1 MIROC, 2050 3.7 DSSAT specific varieties 2000 yield 5.4 CSIRO, 2050 5.4 MIROC, 2050 4.9 DSSAT virtual varieties 2000 yield 5.5 CSIRO, 2050 5.6 MIROC, 2050 5.2
  8. 8. Incorporating productivity effects:Combine biophysical and socioeconomic Supply/ demand interactions Socioeconomic FPU level modeling yield and area scenarios FPU boundaries SPAM crop distributions DSSAT yield scenarios Planting months Climatic conditions Virtual crop activities Management Soils practices
  10. 10. Challenges in Modeling Climate ChangeAverage temperature change, 2 modeling groups, scenario A2 10
  11. 11. Yield Effects, Rainfed Maize, CSIRO A1B(% change 2000 climate to 2050 climate)
  12. 12. Yield Effects, Rainfed Maize, MIROC A1B(% change 2000 climate to 2050 climate) Page 12
  13. 13. Challenges in Modeling Socioeconomics: Identifying Plausible Futures Optimistic • High GDP and low population growth Baseline • Medium GDP and medium population growth Pessimistic • Low GDP and high population growth
  14. 14. Climate change scenario effects on prices differ(price increase (%), 2010 – 2050, Baseline economy and demography) Minimum and maximum effect from four climate scenarios Page 14
  15. 15. January Global Futures Meeting Proof of concept test Investment - $10 million Promising technology choices • Drought tolerance • Herbicide resistance
  16. 16. ROI, Drought Tolerant Groundnut (Proof of Concept Only)Welfare and returns on Climate change scenariosinvestment No climate MIROC MIROC CSIRO CSIRO change 369 A1B 369 B1 369 A1B 369 B1Changes in producer surplus (NPV, -3,876 -4,275 -3,790 -4,540 -4,698m US$)Changes in consumer surplus 10,443 11,338 10,082 11,997 12,507(NPV, m US$) 6,567 7,063 6,292 7,457 7,809Net welfare change (NPV, m US$)Cost (NPV, m US$) 15 15 15 15 15Benefit-cost ratio 448 482 430 509 533Net benefits (NPV, m US$) 6,553 7,048 6,277 7,443 7,795IRR (%) 54 55 53 55 56
  17. 17. Welfare Effects: Drought-tolerant groundnut (Proof of concept only) D Malnourished D At Risk of Target D Kcals per $Regions children per $ Hunger per $ countries invested invested invested (million) Malawi 1.9 -1,285 -6.1 ESA Tanzania 0.6 -672 -3.6 Uganda 1.7 -1,824 0.0 Burkina Faso 3.6 -1,666 -1.8 Ghana 3.1 -904 0.0 WCA Mali 2.5 -935 0.0 Nigeria 4.4 -13,604 -4.9 Senegal 5.6 -973 0.0 India 0.9 -8,840 -32.0 Indonesia 1.2 -1,429 0.0 SSEA Myanmar 3.0 -971 -6.9 Vietnam 1.0 -511 -1.4
  18. 18. Tasks remaining/for next phase Revise and resubmit results Test with more types of virtual cultivars Address issues such as • Ruminants • Land use • GHG emissions • New climate data • Non-tradable goods • Improvements to and new crop models
  20. 20. The Borlaug Paradigm Borlaug key insight - Do trial and error approach with LOTS of trials • Limited collection of data other than yield Exploit mega environments • Regions with similar agronomic characteristics • Do trials where mandate crop is currently important Led to • Breeders are key • Physiologists are not
  21. 21. What has changed? Benefits of Borlaug approach fully exploited -> costs are rising Mega environments are changing • Climatesystem needed toavailability, demand New change, resource recognize and exploit these changes Information technology revolution • Computing and data storage steadily cheaper Genetics revolution • Fundamental understanding of biological processes
  22. 22. The importance and implications ofquantitative modeling for strategic foresight What are models • Reduced form quantification of biological/socioeconomic processes • Calibrated with real world data Why model • When interactions become too complex to understand intuitively • When costs of modeling are less than the benefits
  23. 23. Insights for the CGIAR Institutionalize model use and development Design data collection efforts to support model improvements Employ people who can contribute to improved models Develop systems that make it easy for others to • Use the models • Contribute to model improvement
  24. 24. New Approach with Two Elements: Coordinating Unit• Develop methodologies and tools needed to conduct integrated assessments of potential research outputs• Place those tools in an integrated suite of biophysical and socioeconomic models• Ensure that models are evaluated based on the science behind the components, including uncertainty• Ensure that the models are available as global public goods (open source utilizing GPL licenses)• Support multidisciplinary teams  Developguidelines, protocols and modeling expertise to complement that of each center for both socioeconomic and biophysical production system models
  25. 25. New Approach with Two Elements: Multidisciplinary Center-based Teams Link to experimentalists to provide in-depth, state of the art knowledge about mandate crops, animals, and systems Identify promising options for technology enhancements Adapt/improve production/system-specific models to simulate • existing plant varieties and livestock breeds in targeted ecosystems • new varieties and breeds in those and new ecosystems, taking into account existing and plausible future socioeconomic and natural resource conditions Help design critical experiments and data collection protocols to • Ensure adequacy and availability of data for mandate systems • Contribute data for a global database of agronomic and breeder trial data for evaluating and improving models, that facilitate analyses from household to global of technology, policy, and climate changes
  26. 26. Outputs Strategic foresight quantitative modeling tools Ex ante evaluation of promising technologies Outreach – Food Security Futures Conference • first scheduled tentatively April 15-19, 2013 Capacity building
  28. 28. How much irrigated area in India?Intl. Water Management Inst. Government of India 113 M ha (net) 57-62 M ha Source: Thenkabail 2009
  29. 29. COMPARING LAND COVER DATAIN AFRICA Globcover 2005 – (300m) GLC2000 2000 – (1km) MODIS 2001 – (5km) Africover 1999-20 01 – (30m) PageINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE 29
  30. 30. KenyaGlobcover GLC2000 Zhe Guo, HarvestChoiceMODIS Africover 2011 (unpublished).”
  31. 31. Uganda RwandaGlobcover GLC2000MODIS Africover Zhe Guo, HarvestChoice 2011 (unpublished).”
  32. 32. TanzaniaGlobcover GLC2000MODIS Africover Zhe Guo, HarvestChoice 2011 (unpublished).”
  33. 33. EthiopiaGlobcover GLC2000MODIS Zhe Guo, HarvestChoice 2011 (unpublished).”
  34. 34. Thanks!