The IFPRI IMPACT Model: A Global Simulation Modelling System for

Analysis of Climate Change Scenarios

Sherman Robinson, Daniel Mason-D’Croz, Tingju Zhu, Tim Sulser, Shahnila Islam, Tim Thomas, Keith Wiebe

International Food Policy Research Institute (IFPRI)

8th Forestry and Agricultural GHG Modeling Forum

Shepherdstown, West Virginia, 2016

Global Models for Scenario Analysis

• Two families of global models that focus on agriculture• Global Computable General Equilibrium (CGE) models

• Economywide—incorporate all economic activity

• Include ag/non-ag links—indirect and direct effects, value chains

• Simplified representation of ag technology

• GHG based on ag and non-ag sectors

• Global partial equilibrium (PE) models of agriculture• Many crops, but few or no non-ag sectors—limited value chains

• Focus on agricultural technology and world commodity markets

• GHG based on land use and crop patterns

• MagPIE (PIK, Potsdam), GLOBIOM (IIASA), IMPACT (IFPRI)

• Scenarios: “what-if projections”, NOT “forecasts”• Empirical exploration of alternative futures

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Market Simulation Models

• Both PE and CGE models simulate the operation of national and global markets: supply and demand behavior of various economic “agents”• Solve for equilibrium world prices, supply, demand, and

international trade for various commodities • Large, non-linear, “square” simulation models

• CGE models solve for all quantities (supply, demand, trade), prices (commodities and factors—wages & profits) and all income flows

• PE models include only agricultural commodities and, perhaps, some value chains (e.g., oil seeds to oil)

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The IMPACT 3 Model

• International Model for Policy Analysis of Agricultural Commodities and Trade

• Need for a multi-disciplinary approach:• CGIAR and other collaborators: • Economics, agronomy, water, livestock, fish,

crop models, nutrition/health • Civil engineering: infrastructure• Climate change (GCMs)• Energy (biofuels, inputs)• Water and land use

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IMPACT 3.2: A Suite of Models

• Multimarket model• Core global PE model

• SPAM:• Spatial Production

Allocation Model

• Land-Use• Irrigated/rainfed crops

• Crop allocation

• DSSAT Crop Models

• Welfare• Post solution, CS/PS

• Water models• Hydrology

• Water Basin Management

• Water Stress on yields

• Sugar and oilseeds• Processing value chains

• Livestock/meat/dairy• Systems and value chains

• Nutrition/health: • Post solution

• Linked global CGE model

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Advantages of Modularity• “Standalone” modules can be run independently of

IMPACT, but are designed to interact with IMPACT• Can be developed, calibrated, and tested by specialists (e.g,

from various CGIAR centers)

• Designed to be used in Center research programs

• Design: separate modules can reflect their disciplines• No need to compromise to “fit” one model into another

• E.g. water in economic models or economics in water models—always unsatisfactory: detail and time steps (monthly versus annual)

• E.g., livestock model: jointly developed with ILRI

• “Domain of applicability” of models/modules

• Model development, testing, and debugging is greatly facilitated if the modules can be run separately

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Modularity: Module Desiderata

• A “module” should be designed to:

• Read its own parameters;

• Initialize its own variables;

• Accept variables passed to it from other modules and the environment;

• Pass variables that are computed within the module to other modules or to the main model;

• Own its set of state variables;

• Operate in “standalone” mode

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Modularity: Linking Modules• Exogenous: Information flows in one direction

• To IMPACT: hydrology, DSSAT, GCMs, SPAM

• From IMPACT: welfare, nutrition/health, GLOBE/CGE

• Linked dynamically: Two-way data flow between years• Water basin management, water stress on crop yields

• Land use by type (forest, pasture, crops, “other”)

• GDP/economy-wide links: GLOBE/CGE

• Endogenous: Modules are solved “within” IMPACT• Sugar cane/beet to sugar, oilseeds to oils

• Land allocation to crops

• Livestock sector: meat supply, demand for crops

IMPACT 3: Basic Model System

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IMPACT version 3

159• Countries

154• Water Basins

320

• Food Production Units

• 58 Agricultural commodities

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Source: Nelson et al., PNAS (2014)

Modeling climate impacts on agriculture:biophysical and economic effects

General circulation models (GCMs)

Global gridded crop models

(GGCMs)

Global economic models

Δ TempΔ Precip

…

Δ Yield(biophys)

Δ AreaΔ YieldΔ Cons.Δ Trade

Climate Biophysical Economic

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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 eq. (ppm)Radiative forcing(W/m2)

Population (billion) GDP (trillion USD, 2005 ppp)

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IMPACT model results

• Yields—commodity and sub-region • Long run productivity trends

• Climate and water shocks

• Market interactions—response to price changesPrices—markets driven by SSPs and climate effects

• Total demand—commodities, country and global

• Composition of demand—by commodity and country• Food, livestock, biofuels, industrial

• Net trade—by commodity and country

• Prices—markets driven by SSPs and climate effects

• Food security—by country13

Model improvements under way

• Linked global CGE model

• Livestock and fish

• Land use

• R&D and productivity

• Nutrition and health

• Variability

• Poverty (CGE models)

• Environmental impacts

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CGE Models and IMPACT

• Motivation: need to examine direct and indirect links between agriculture and non-ag sectors• Intermediate inputs, energy, value chains, incomes

• Link IMPACT to a global CGE model (“Globe” model)• One-way: welfare analysis by feeding IMPACT results for agri-

culture to GLOBE, which include direct and indirect links to the rest of the economy: economy-wide welfare effects

• Two-way: IMPACT in “standard” mode assumes exogenous GDP trends from SSPs, regardless of scdenario changes in agriculture• Globe solves for GDP including changes in agriculture, which are then

sent back to IMPACT dynamically, endogenizing GDP growth

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Linked Globe/IMPACT models: GDP

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Land Use in IMPACT

• Allocation of arable land (irrigated and rainfed) to different crops: done “within” IMPACT• Solve for “shadow rental rate” for land by region and type; land

supply is assumed fixed in a given year

• Land allocated to crops which are most profitable: “equilibrium” equates marginal value of land for each crop

• “Between years” land supply: simple land use model• Irrigated share of arable land is exogenous, with time trends

• Conversion of forest and pasture to/from cropland is a function of “profitability” of crop land

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Land Use in IMPACT

• IFPRI is interested in GHG generation in both agriculture and industry, working at country and global levels• “Energy/Food Nexus”: hydro, biofuels, fuels, renewables

• Early work with linked CGE/IMPACT models: country/global

• Land use “module” under development that distinguishes crop land, forest, pasture, and “other”• Currently have developed a logit regression model estimated

using pixel-level data for various countries

• Global regression model using national data

• Need for a more “structural” model if we are to include policies related to GHG mitigation scenarios

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Scenario Results: Maize, Wheat, Soybeans

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US and Global Maize Production

21

0

200000

400000

600000

800000

1000000

1200000

1400000

2010 2015 2020 2025 2030 2035 2040 2045 2050

Maize Production (000 metric tonnes)

USA Maize SSP2-NoCC USA Maize SSP2-GFDL USA Maize SSP2-HADG USA Maize SSP2-IPSL USA Maize SSP2-MIRO

WLD Maize SSP2-NoCC WLD Maize SSP2-GFDL WLD Maize SSP2-HADG WLD Maize SSP2-IPSL WLD Maize SSP2-MIRO

US and Global Maize Yields

22

0

2

4

6

8

10

12

14

16

2010 2015 2020 2025 2030 2035 2040 2045 2050

Maize Yield (mt/ha)

USA Maize SSP2-NoCC USA Maize SSP2-GFDL USA Maize SSP2-HADG USA Maize SSP2-IPSL USA Maize SSP2-MIRO

WLD Maize SSP2-NoCC WLD Maize SSP2-GFDL WLD Maize SSP2-HADG WLD Maize SSP2-IPSL WLD Maize SSP2-MIRO

2050 Wheat Yields: Climate Change Effects for Top 10 Wheat Producers

24Source: IMPACT 3 (2014)

World Price: Maize

25

0

50

100

150

200

250

300

350

2010 2015 2020 2025 2030 2035 2040 2045 2050

Maize Prices (Year 2005 $US/mt)

Maize SSP2-MIRO Maize SSP2-HADG Maize SSP2-IPSL Maize SSP2-GFDL Maize SSP2-NoCC

World Price: Wheat

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0

50

100

150

200

250

300

350

2010 2015 2020 2025 2030 2035 2040 2045 2050

Wheat Prices (Year 2005 $US/mt)

Wheat SSP2-MIRO Wheat SSP2-HADG Wheat SSP2-IPSL Wheat SSP2-GFDL Wheat SSP2-NoCC

World Price: Soybeans

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0

100

200

300

400

500

600

2010 2015 2020 2025 2030 2035 2040 2045 2050

Soybean Prices (Year 2005 $US/mt)

Soybean SSP2-MIRO Soybean SSP2-HADG Soybean SSP2-IPSL Soybean SSP2-GFDL Soybean SSP2-NoCC

Thank you

s.robinson@cgiar.org