This document discusses the Global Futures Project, which aims to help the CGIAR determine how additional funding should be allocated to provide the greatest return on investment. It outlines methods for evaluating potential technological improvements through expert elicitation and virtual crop modeling. It also addresses challenges in modeling climate change impacts and socioeconomic scenarios. The project aims to develop strategic foresight tools to quantitatively evaluate promising technologies and support capacity building. It emphasizes the importance of quantitative modeling and improved data for strategic decision making.

1. From Global Futures to
Strategic Foresight
Moving Beyond Norman
Borlaug
Gerald Nelson
Senior Research Fellow , IFPRI
Theme Leader, CRP2 and CRP7
INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

2. What is the Global Futures Project?
 Develop methods, tools, and a consistent
system to help the CGIAR answer the
following question
If 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. 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. 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. How to evaluate potential technological
improvements: 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. How to evaluate potential technological
improvements: The virtual crop method
2. Convert these responses into crop model
genetic 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. How to evaluate potential technological
improvements: 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. 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

9. WHY SCENARIOS?
The future is an uncertain place
INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

10. Challenges in Modeling Climate Change
Average temperature change, 2 modeling groups, scenario A2
10

11. Yield Effects, Rainfed Maize, CSIRO A1B
(% change 2000 climate to 2050 climate)

12. Yield Effects, Rainfed Maize, MIROC A1B
(% change 2000 climate to 2050 climate)
Page 12

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. 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. January Global Futures Meeting
Proof of concept test
 Investment - $10 million
 Promising technology choices
• Drought tolerance
• Herbicide resistance

16. ROI, Drought Tolerant Groundnut
(Proof of Concept Only)
Welfare and returns on Climate change scenarios
investment
No climate MIROC MIROC CSIRO CSIRO
change 369 A1B 369 B1 369 A1B 369 B1
Changes in producer surplus (NPV,
-3,876 -4,275 -3,790 -4,540 -4,698
m 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 7,809
Net welfare change (NPV, m US$)
Cost (NPV, m US$) 15 15 15 15 15
Benefit-cost ratio 448 482 430 509 533
Net benefits (NPV, m US$) 6,553 7,048 6,277 7,443 7,795
IRR (%) 54 55 53 55 56

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. 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

19. FROM GLOBAL FUTURES TO
STRATEGIC FORESIGHT
Moving Beyond Norman Borlaug
INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

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. 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. The importance and implications of
quantitative 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. 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. 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. 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. 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

27. DO WE REALLY NEED MORE
DATA?
INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

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. COMPARING LAND COVER DATA
IN AFRICA
Globcover 2005 – (300m)
GLC2000 2000 – (1km)
MODIS 2001 – (5km)
Africover 1999-20 01 – (30m)
Page
INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE
29

30. Kenya
Globcover GLC2000
Zhe Guo, HarvestChoice
MODIS Africover 2011 (unpublished).”

31. Uganda
Rwanda
Globcover GLC2000
MODIS Africover
Zhe Guo, HarvestChoice
2011 (unpublished).”

32. Tanzania
Globcover GLC2000
MODIS Africover
Zhe Guo, HarvestChoice
2011 (unpublished).”

33. Ethiopia
Globcover GLC2000
MODIS
Zhe Guo, HarvestChoice
2011 (unpublished).”

34. Thanks!