global futures & strategic foresight quantitative modeling to inform decision making in the...
TRANSCRIPT
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www.iita.org A member of CGIAR consortium
Global Futures & Strategic Foresight
Quantitative modeling to inform decision making in the CGIAR and its partners
23 November 2015 (R4D Week 2015)
Keith Wiebe, IFPRI
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Global Futures & Strategic Foresight Quantitative modeling to inform decision making
in the CGIAR and its partners
Keith Wiebe, IFPRI
IITA, Ibadan, 23 November 2015
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Outline • Introduce the Global Futures & Strategic Foresight
(GFSF) program
• Share some recent projections from the IMPACT model
• Describe some of the work that IITA is doing as part of GFSF
• Reflect on how we might help inform decision making in the CGIAR
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Selected drivers of change • Today, this season, this year
• Weather, pests, markets, conflict, migration…
• Medium term • Agricultural policies, trade policies, markets…
• Long term • Population, income, resources, climate, preferences,
technology…
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Socioeconomic and climate drivers
Shared
Socioeconomic
Pathways (SSPs)
Representative
Concentration
Pathways (RCPs)
Source: Downloaded from the RCP Database version 2.0.5 (2015). RCP 2.6: van Vuuren et al. 2006; van Vuuren et al. 2007. RCP 4.5: Clark et al. 2007; Smith and Wigley 2006; Wise et al 2009. RCP 6.0: Fujino et al 2006; Hijioka et al 2008. RCP 8.5: Riahi and Nakicenovic, 2007.
CO2 equiv. (ppm) Radiative forcing (W/m2)
Population (billion) GDP (trillion USD, 2005 ppp)
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Global Futures & Strategic Foresight
1. Improved tools for biophysical and economic modeling
2. Stronger community of practice for scenario analysis and ex ante impact assessment
3. Improved assessments of alternative global futures
4. To inform research, investment and policy decisions in the CGIAR and its partners
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1. Improved modeling tools • Complete recoding of IMPACT v3
• Disaggregation geographically and by commodity
• Improved water & crop models
• New data management system
• Modular framework
• Training
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2. Stronger community of practice
• All 15 CGIAR centers now participate in GFSF • Bioversity, CIAT, CIMMYT, CIP,
ICARDA, ICRAF, ICRISAT, IFPRI, IITA, ILRI, IRRI, IWMI, WorldFish; AfricaRice and CIFOR are joining
• Collaboration with other global economic modeling groups through AgMIP • PIK, GTAP, Wageningen, IIASA, UFL,
FAO, OECD, EC/JRC, USDA/ERS, …
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• Role of agricultural technologies
• Africa regional reports
• Analyses by CGIAR centers
• CCAFS regional studies
• AgMIP global economic assessments
• Private sector
Rainfed Maize (Africa)
Irrigated Wheat (S. Asia)
Rainfed Rice (S. + SE. Asia)
Rainfed Potato (Asia)
Rainfed Sorghum (Africa + India)
Rainfed Groundnut (Africa + SE Asia)
Rainfed Cassava (E. + S. + SE. Asia)
3. Improved assessments
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4. Informing decisions
• National partners • Regional organizations • International organizations
and donors • CGIAR
• Centers • CRPs • System level?
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Modeling climate impacts on agriculture: biophysical and economic effects
General
circulation models (GCMs)
Global
gridded crop models
(GGCMs)
Global
economic models
Δ Temp Δ Precip
…
Δ Yield (biophys)
Δ Area Δ Yield Δ Cons. Δ Trade
Climate Biophysical Economic
Source: Nelson et al., Proceedings of the National Academy of Sciences (2014)
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Projections to 2050 w/o climate change Average of 5 global economic models for coarse grains, rice, wheat, oilseeds & sugar
0
10
20
30
40
50
60
70
80
90
100
Yields Area Production Prices Trade
Perc
ent
chan
ge f
rom
20
05
to
20
50
SSP1 SSP2 SSP3
Source: Wiebe et al., Environmental Research Letters (2015)
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Climate change impacts in 2050 Average of 5 global economic models for coarse grains, rice, wheat, oilseeds & sugar
-10
-5
0
5
10
15
20
Yields Area Production Prices Trade
Perc
ent
chan
ge in
20
50
SSP1-RCP4.5 SSP2-RCP6.0 SSP3-RCP8.5
Source: Wiebe et al., Environmental Research Letters (2015)
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IMPACT model: selected results
• Yields
• Prices
• Total demand
• Per-capita food demand
• Trade
• Food security
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Growth in global cereal production (SSP2, NoCC)
Source: IFPRI, IMPACT version 3.2, September 2015
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Growth in cereal production by region (SSP2, NoCC)
World Latin Am & Caribbean
South Asia Sub-Saharan Africa Source: IFPRI, IMPACT version 3.2, September 2015
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Growth in global production of pulses and oilseeds (SSP2, NoCC)
Source: IFPRI, IMPACT version 3.2, September 2015
Pulses Oilseeds
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Rainfed maize and climate change: Projected yield changes in 2050, before economic responses (HadGEM2, RCP 8.5)
Source: IFPRI DSSAT simulations
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Yield effects of climate change (SSP2)
Cereals
Source: IFPRI, IMPACT version 3.2, September 2015
WLD = World; EAP = East Asia and Pacific; EUR = Europe; FSU = Former Soviet Union; LAC = Latin America and Caribbean; MEN = Middle East and North Africa; NAM = North America; SAS = South Asia; SSA = Sub-Saharan Africa;
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Yield effects of climate change (SSP2)
Cereals Maize
Rice Wheat
WLD = World; EAP = East Asia and Pacific; EUR = Europe; FSU = Former Soviet Union; LAC = Latin America and Caribbean; MEN = Middle East and North Africa; NAM = North America; SAS = South Asia; SSA = Sub-Saharan Africa;
Source: IFPRI, IMPACT version 3.2, September 2015
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Yield effects of climate change (SSP2)
Source: IFPRI, IMPACT version 3.2, September 2015
Cereals Roots & tubers
Oilseeds Pulses
Fruits & veg
Sugar
WLD = World; EAP = East Asia and Pacific; EUR = Europe; FSU = Former Soviet Union; LAC = Latin America and Caribbean; MEN = Middle East and North Africa; NAM = North America; SAS = South Asia; SSA = Sub-Saharan Africa;
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Price impacts of climate and socioeconomic drivers
Source: IFPRI, IMPACT version 3.2, September 2015
SSP
s R
CPs
Cereals Meats
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Total global demand: aggregated commodities (SSP2, NoCC)
20
10
= 1
.00
Source: IFPRI, IMPACT version 3.2, September 2015
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Total global demand: maize, rice, wheat (SSP2, NoCC)
20
10
= 1
.00
Source: IFPRI, IMPACT version 3.2, September 2015
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Composition of food supply (SSP2, NoCC)
Source: IFPRI, IMPACT version 3.2, September 2015
WLD = World; EAP = East Asia and Pacific; EUR = Europe; FSU = Former Soviet Union; LAC = Latin America and Caribbean; MEN = Middle East and North Africa; NAM = North America; SAS = South Asia; SSA = Sub-Saharan Africa
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Maize demand composition (mmt) (SSP2, NoCC)
Source: IFPRI, IMPACT version 3.2, September 2015
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Soybean demand composition (mmt)
Source: IFPRI, IMPACT version 3.2, September 2015
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Cassava demand composition (mmt)
Source: IFPRI, IMPACT version 3.2, September 2015
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Source: IFPRI, IMPACT version 3.2, September 2015
EAP = East Asia and Pacific; EUR = Europe; FSU = Former Soviet Union; LAC = Latin America and Caribbean; MEN = Middle East and North Africa; NAM = North America; SAS = South Asia; SSA = Sub-Saharan Africa
Mill
ion
met
ric
ton
s
Cereals
Net trade and climate change (SSP2)
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Net trade and climate change (SSP2)
Source: IFPRI, IMPACT version 3.2, September 2015
EAP = East Asia and Pacific; EUR = Europe; FSU = Former Soviet Union; LAC = Latin America and Caribbean; MEN = Middle East and North Africa; NAM = North America; SAS = South Asia; SSA = Sub-Saharan Africa
Soybeans
Mill
ion
met
ric
ton
s
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Population at risk of hunger (SSP2, RCP8.5)
Source: IFPRI, IMPACT version 3.2, September 2015
EAP = East Asia and Pacific; SAS = South Asia; FSU = Former Soviet Union; MEN = Middle East and North Africa; SSA = Sub-Saharan Africa; LAC = Latin America and Caribbean
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Exploring the impacts of improved technologies and practices on…
-40.0
-35.0
-30.0
-25.0
-20.0
-15.0
-10.0
-5.0
0.0
Malnourished Children Pop. at-risk-of-hunger
No till Drought tolerance Heat tolerance
Nitrogen use efficiency Integrated soil fertility mgt Precision agriculture
Water harvesting Sprinkler irrigation Drip irrigation
Crop Protection - insects
Source: Rosegrant et al. (2014)
Food Security (Percent difference from 2050 CC baseline)
Source: Islam et al. (draft)
Crop yields (Percent difference from 2050 CC baseline)
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Global Futures & Strategic Foresight in IITA • Mandate crops: cassava, yam, maize, plantain/banana,
cowpea and soybean
• Objectives of GFSF in IITA: • Development of modelling tools adapted to needs of IITA • Community of practice to enhance validity of modelling tools and
results: • Engagement between modellers and non-economists in IITA (breeders;
agronomists; etc.)
• Engagement between IITA and modellers in NARS: training workshops; etc.
• Informing R4D priority setting • Agronomy versus breeding: resource allocation
• Better targeting of improved technologies depending on agro-ecological characteristics
• National policies to enhance adoption of improved technologies: engagement with policy-makers
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Projected growth in cowpea production and consumption in Nigeria and Niger (SSP2)
0
2000
4000
6000
8000
10000
12000
14000
2005 2010 2015 2020 2025 2030 2035 2040 2045 2050
Nigeria-Cons-NoCC
Nigeria-Cons-HadGEM
Nigeria-Prod-NoCC
Nigeria-Prod-HadGEM
Nigeria (000 MT)
0
500
1000
1500
2000
2500
3000
2005 2010 2015 2020 2025 2030 2035 2040 2045 2050
Niger-Cons-NoCC
Niger-Cons-HadGEM
Niger-Prod-NoCC
Niger-Prod-HadGEM
Niger (000 MT)
Source: IITA (in progress)
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Projected gap between production and consumption of soybean in Africa (SSP2, RCP8.5)
-5000
-4500
-4000
-3500
-3000
-2500
-2000
-1500
-1000
-500
0
2005 2030 2040 2050
De
man
d g
ap (
00
0 M
T)
Africa - SSP2- NoCC
Africa - SSP2- HadGEM
Source: IITA (in progress)
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Future plans for GFSF work in IITA
• Tools: develop accurate ‘base’ results for IITA’s mandate crops
• Biophysical crop modelling: calibrate and validate standard and promising technologies
• Socio-economic modelling: results for base year (2005) for all IITA’s mandate crops; intrinsic productivity growth rates (IPRs);
• Community of practice: training workshop on bio-economic modelling (BUK)
• Priority setting: report on impact of promising cowpea technologies
Arega Tahirou Sika
Alpha Kamara, agronomist
Boukar Ousmane, cowpea breeder
Ken Boote
Prof. Jibrin
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4. Informing decision making
• National partners
• Regional organizations
• International organizations and donors
• CGIAR • Center work planning • CRP Phase 2 proposals
• PIM, RTB, Maize, et al. • System level?
• ISPC and donor interest
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The CGIAR Research Agenda System Level Outcomes (SLOs) and
Intermediate Development Objectives (IDOs)
Increased resilience of the poor to
climate change and
other shocks
Enhanced smallholder
market access
Increased incomes
and employment
Increased productivity
Improved diets for poor and
vulnerable people
Improved food safety
Improved human and
animal health
through better
agricultural practices
Natural capital
enhanced and
protected, especially
from climate change
Enhanced benefits
from ecosystem goods and
services
More sustainably managed
agro-ecosystems
Reduced Poverty
Improved natural resource systems
and ecosystem services
Improved food and nutrition security
for health
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Model improvements under way
• Livestock and fish
• Nutrition and health
• Land use
• Environmental impacts
• Variability
• Gender
• Poverty
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Concluding thoughts • Collective effort, involving all 15 CGIAR centers (and
other partners)
• Multiple scales of analysis
• Opportunity to inform decision making in the CGIAR and its partners • Quantitative model results as one input among several
• On-going effort to build capacity and a community of practice to assess options over time
• Looking forward to collaboration with IITA!