INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE From Global Futures to Strategic Foresight Gerald Nelson Senior Research Fellow , IFPRI Theme Leader, CRP2 and CRP7 Moving Beyond Norman Borlaug
Jan 23, 2015
INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE
From Global Futures to Strategic Foresight
Gerald Nelson
Senior Research Fellow , IFPRI
Theme Leader, CRP2 and CRP7
Moving Beyond Norman
Borlaug
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
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
How to evaluate potential technological improvements: The Delphi method
Ask the experts; aka the Delphi approach
With an additional $20
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!
How to evaluate potential technological improvements: The virtual crop method
1. Ask the experts for details on what they can accomplish
What specific changes in
plant phenotype are
relatively easy to
implement to improve
drought tolerance?
Heat shock proteins for increased protection
Reduce partitioning photosynthate into grain
Early planting and
morning flowering to
avoid pollen sterility
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)
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
Supply/ demand
interactions
FPU level yield and area
scenarios FPU
boundaries SPAM crop distributions
DSSAT yield scenarios
Planting months
Climatic conditions
Soils Management
practices
Incorporating productivity effects: Combine biophysical and socioeconomic
Virtual crop
activities
Socioeconomic
modeling
INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE
WHY SCENARIOS?
The future is an uncertain place
Challenges in Modeling Climate Change
10
Average temperature change, 2 modeling groups, scenario A2
Yield Effects, Rainfed Maize, CSIRO A1B (% change 2000 climate to 2050 climate)
Yield Effects, Rainfed Maize, MIROC A1B (% change 2000 climate to 2050 climate)
Page 12
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
Climate change scenario effects on prices differ (price increase (%), 2010 – 2050, Baseline economy and demography)
Page 14
Minimum and maximum
effect from four climate
scenarios
January Global Futures Meeting Proof of concept test
Investment - $10 million
Promising technology choices
• Drought tolerance
• Herbicide resistance
ROI, Drought Tolerant Groundnut (Proof of Concept Only)
Welfare and returns on
investment
Climate change scenarios
No climate
change
MIROC
369 A1B
MIROC
369 B1
CSIRO
369 A1B
CSIRO
369 B1
Changes in producer surplus (NPV,
m US$) -3,876 -4,275 -3,790 -4,540 -4,698
Changes in consumer surplus
(NPV, m US$) 10,443 11,338 10,082 11,997 12,507
Net welfare change (NPV, m US$) 6,567 7,063 6,292 7,457 7,809
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
Welfare Effects: Drought-tolerant groundnut (Proof of concept only)
Regions Target
countries
D Kcals per $
invested
D Malnourished
children per $
invested
D At Risk of
Hunger per $
invested (million)
ESA
Malawi 1.9 -1,285 -6.1
Tanzania 0.6 -672 -3.6
Uganda 1.7 -1,824 0.0
WCA
Burkina Faso 3.6 -1,666 -1.8
Ghana 3.1 -904 0.0
Mali 2.5 -935 0.0
Nigeria 4.4 -13,604 -4.9
Senegal 5.6 -973 0.0
SSEA
India 0.9 -8,840 -32.0
Indonesia 1.2 -1,429 0.0
Myanmar 3.0 -971 -6.9
Vietnam 1.0 -511 -1.4
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
INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE
FROM GLOBAL FUTURES TO STRATEGIC FORESIGHT
Moving Beyond Norman Borlaug
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
What has changed?
Benefits of Borlaug approach fully exploited -> costs are rising
Mega environments are changing
• Climate change, resource availability, demand
Information technology revolution
• Computing and data storage steadily cheaper
Genetics revolution
• Fundamental understanding of biological processes
New system needed to recognize
and exploit these changes
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
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
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 Develop guidelines, protocols and modeling expertise to
complement that of each center for both socioeconomic and biophysical production system models
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
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
INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE
DO WE REALLY NEED MORE DATA?
How much irrigated area in India?
Intl. Water Management Inst.
113 M ha (net)
Government of India
57-62 M ha
Source: Thenkabail 2009
INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE
COMPARING LAND COVER DATA IN AFRICA
Page 29
Globcover 2005 – (300m)
GLC2000 2000 – (1km)
MODIS 2001 – (5km)
Africover 1999-20 01 – (30m)
Globcover
MODIS
GLC2000
Africover
Kenya
Zhe Guo, HarvestChoice
2011 (unpublished).”
MODIS
GLC2000 Globcover
Uganda
Rwanda
Zhe Guo, HarvestChoice
2011 (unpublished).”
Africover
MODIS
GLC2000 Globcover
Tanzania
Zhe Guo, HarvestChoice
2011 (unpublished).”
Africover
Ethiopia
MODIS
GLC2000 Globcover
Zhe Guo, HarvestChoice
2011 (unpublished).”
Thanks!