ISPC 13

1. Quantitative Foresight Modeling to Inform
the CGIAR Research Program Portfolio
Mark W. Rosegrant
Director, Environment and Production Technology Division, IFPRI
The CGIAR @ 45: How to boost its contribution to world welfare
Lima, Peru, May 26, 2016

2.  Overview
 Methodology and Key Activities
 Implications for Portfolio Design
 Concluding Remarks
Outline

3. Overview

4.  IFPRI + other CGIAR Centers – collaboratively conducting
quantitative analyses
• Alternative future scenarios to inform decisions on the design
of CGIAR research program portfolio
• 2017‐2022, 2030 and 2050
 One of multiple inputs into priority setting by CGIAR
• Quantitative exercise
• Consultation with partners
• Qualitative analysis
Overview

5. Methodology and Key Activities

6.  Current scenario exercise funded by USAID
 Draws upon the tools developed by Centers and external
partners collaborating in the Global Futures and Strategic
Foresight program
 Global Futures and Strategic Foresight program
• CGIAR initiative led by IFPRI (at present funded by PIM, CCAFS, and
the Bill and Melinda Gates Foundation)
• Includes 15 CGIAR Centers and supports the prioritization of research
across CRPs as well as within CRPs and Centers
Methodology: Quantitative Assessment

7.  Alternative CRP phase II investments are being assessed
 Two key activities are being undertaken:
1) Compare IFPRI IMPACT Business‐As‐Usual Baseline with targets and
goals reflected in the Intermediate Development Outcomes (IDOs)
and sub‐IDOs described in the CGIAR Strategy and Results
Framework (SRF) document (CGIAR 2015)
2) Analyze the contribution of alternative CRP portfolios toward
achieving the CGIAR SRF IDOs and sub‐IDOs
Key Activities

8. POLICIES AND
DRIVERS
LINKED
MODELS
OUTCOMES
(Annual Projections)
• GDP
• Population
• Climate Change
• Investment in
‐ Agricultural R&D
‐ Irrigation and Water
Management
• Technology
• Post‐harvest Losses
and Marketing
Margins
• Price Policy
• Employment, GDP, and
Household Income in
Agriculture, Industry,
Services
• International Trade by
Sector
• GHG Emissions
• Deforestation
• Biodiversity
• Soil Carbon
• Energy Use
• Water demand and supply
for domestic, industrial,
livestock and irrigation
users
• Water supply reliability
• Water quality
• Crop area / livestock
numbers, yields, and
production
• Agricultural commodity
demand
• Agricultural commodity trade
and prices
• Hunger and malnourishment
• Micronutrient consumption
Quantitative Assessment Scenarios

9. IMPACT Version 3.2 Baseline Scenarios
• Shared Socioeconomic Pathway (SSP) 2 – middle of
the road
• Representative Concentration Pathway (RCP) 8.5
(high)
• 4 General Circulation Models
‐ HadGEM2‐ES
‐ IPSL‐CM5A‐LR
CRP Scenarios

10. Alternative Portfolio Scenarios
• Broadly reflective of alternative CRP portfolios –
but not specific CRP proposals
• Different regional emphases
• Different commodity group and system
emphases
• Policy and natural resource management
CRP Scenarios

11.  Alternative research investment and productivity
growth rates (base, high, low)
 Improved research efficiency: reduced lag times
due to reform of regulatory systems and breeding
systems
 Increased irrigation investment: faster growth in
irrigated area and higher water use efficiency
CRP Scenarios

12.  Improved value chains (reduced marketing
margins and reduced post‐harvest losses)
 Improved soil quality (organic matter and water
holding capacity)
 Improved nutrient use efficiency
 Trade policy: alternative border measures
CRP Scenarios

13. IMPACT
IMPACT Global
Hydrological
Model
IMPACT Water
Simulation
Model
DSSAT Crop
Models
GCM Climate Forcing
Effective P
Potential ET
IRW
Irrigation Water
Demand & Supply
Crop Management
WATER
STRESS
Pop & GDP growth
Area & yield growth
Food Projections
• Crop area /
livestock
numbers, yields,
and production
• Agricultural
commodity
demand
• Agricultural
commodity
trade and prices
• Hunger and
Mal‐
nourishment
Water Projections
• Water demand and supply for domestic, industrial, livestock and irrigation users
• Water supply reliability
GLOBE CGE model
Change in GDP, cost of
agrochemicals and
biofuel mix
Food models
Water models
Macroeconomic
policies and
shocks
Method: IMPACT with CGE linkage

14.  Model baselines are calibrated on agricultural productivity,
GDP and prices and economy‐wide GDP
 Climate shocks on agricultural productivity and prices are
transmitted from IMPACT to GLOBE, with further iteration
back to IMPACT for economy‐wide feedbacks to agriculture
 Macro policy shocks on household income and GDP are
transmitted from GLOBE to IMPACT
 Employment and income effects of agricultural policies and
investments are captured
GLOBE‐IMPACT linkage

15. Raw data
FAO Food
Balance
Sheets
Alcoholic
beverages
Aquatic
foods
Shared
Socioeconomic
Pathways
(SSPs)
Scenarios of
population
growth and age
and gender
distribution
IMPACT
scenarios
consumption
results
Income
growth
Population
growth
Technology
changes
Nutrient
content tables
IMPACT
commodity to
consumed
product
Cooking
retention
Food
and
staple
groups
Dietary
Reference
Intakes (DRIs)
Estimated
Average
Requirement
(EAR)Recommende
d Daily
Allowance
(RDA)/Adequa
te Intake (AI)
Tolerable
Upper Intake
Level (UL)
Assessment of
Nutritional Impacts
Source: G. Nelson, under preparation

16. Data
Alignment
Commodity
grouping
Age and gender
grouping
Regional
aggregation
Indicators
Consumption diversity
(Share of nutrient from
staple/food group)
Nutritional adequacy
(Share of nutrient
requirement consumed)
Nutritional performance
indicators: Impacts of …
Climate
change
Research
productivity
improvements
Policy
changes
Assessment of Nutritional Impacts
Source: G. Nelson, under preparation

17. IMPACT
IMPACT
SSPs
Land Use Change (LandSHIFT)
Includes cropland, pasture, forest cover, other natural land, set aside
Source: Center for Environmental Systems Research: LandSHIFT
https://www.uni‐kassel.de/einrichtungen/en/cesr/research/projects/.../landshift.html

18. IMPACT
DSSAT
Crop
Models
GCM Climate
Forcing
Irrigation
Water
Demand &
Supply
Crop
Management
WATER
STRESS
Population &
GDP growth
Area & yield
growth
•Crop area
• Crop yield
• Soil C sequestration
• N2O emission
• CH4 emission (IPCC
Tier 1 methodology)
Grid‐based modeling
Cropj GWP in pixelk × Cropj area in pixelkj,k

Cropland GHG emission in FPU =
GHG emissions‐IMPACT linkage

19. Biodiversity Threat Analysis: Framework
Birds
distribution,
richness &
endemicity
Bird’s
bio‐regions
Food
Production
Units (FPU)
Data collection
Current
Land Use map
Bird’s Extinction
risk under current
land use
Baseline & Model
Calibration and
validation
Climate Data
Bird’s Extinction
risk under current
climate
CC and LU
Projections
scenario 1
land use LU scenario n
Birds Risk to
extinction
LU scenario 2
LU scenario 1
scenario 1
land use CC scenario n
Birds Risk to
extinction
CC scenario 2
CC scenario 1
BIO risk
Projections
Source: Developed by Bárbara Willaarts,
Research Centre for the Management of
Agricultural and Environmental Risks
(CEIGRAM), Department of
Agricultural Economics and Social Sciences,
Universidad Politécnica de Madrid, Madrid,
Spain; Flachsbarth, et al. 2015. The Role of
Latin America’s Land and Water Resources
for Global Food Security: Environmental
Trade‐Offs of Future Food Production
Pathways. PLOS ONE 10 (1): 1‐24

20. Agricultural Water Pollution Assessment:
Framework and Sample Results
IMPACT
SWAT
National total
Pixel
N & P in
fertilizer &
manure to
agricultural
land
FAOSTAT (base year data)
Crop area/yield/production
Livestock numbers
Projections for scenario analysis
Sample results on agricultural nitrogen loading in base year
(Source: IFPRI and Veolia 2015)
Downscaling
Metrics: N & P from arable land

21. Implications for Portfolio Design

22.  Results of these scenarios can inform decision
making at the CGIAR System level
 Related to but different from scenarios to inform
decision making at the CRP or Center level
 Needs to be part of an on‐going process of
analysis and discussion
Implications/Insights

23.  Inform decisions about what specific activities are highest
priority
• What flagship activities should get additional funding; at the
consortium level (allocating W1 and 2 funds across CRPs)
• In a CRP
• At a Center
 Basic implementation is technically straightforward, but
efficiency gains would require
• Investment in modeling improvements
• Integrated, ongoing system that tracks activities ex‐ante, en‐passant,
and ex‐post
Inclusion of Fine‐grained Technology Modeling
for Operational Efficiency Gains

24. Concluding Remarks

25.  Completion of report for USAID
 On‐going improvements in modeling tools and
applications
 Multiple consuming households (low and high incomes; urban and rural)
 Incorporate climate variability explicitly
 Economic valuation
 Update/improve trade distortion parameters
 Improve demand parameters
 On‐going dialogue with partners in the CGIAR and
beyond
Next steps in Modeling to Inform Priority
Setting

26. Thank you
Mark W. Rosegrant
Director, Environment and Production Technology Division, IFPRI
The CGIAR @ 45: How to boost its contribution to world welfare
Lima, Peru, May 26, 2016