This document summarizes an integrated modeling framework to assess biofuels that links agricultural and energy system models. It describes: 1) The motivation to link the models given the interconnectedness of agricultural and energy markets due to biofuels. 2) The objectives to set up a coupled modeling framework capturing dynamic linkages between the sectors and assess environmental impacts. 3) An overview of the key agricultural (CARD) and energy (MARKAL) models and how they are iteratively run and calibrated to reach convergence on variables like biofuel production levels and prices.

1. Integration of agricultural and energy
system models for biofuel assessment
A. Elobeid, S. Tokgoz, R. Dodder, T. Johnson, O. Kaplan, L. Kurkalova, S. Secchi
Presented by: Simla Tokgoz, Research Fellow
International Food Policy Research Institute
1st International Symposium on Energy Challenges and Mechanics
July 8, 2014
Aberdeen, Scotland

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Modeling Team
 Amani Elobeid: Center for Agricultural and Rural Development,
Iowa State University
 Simla Tokgoz: International Food Policy Research Institute
 Rebecca Dodder & Ozge Kaplan: National Risk Management
Research Laboratory, Office of Research and Development, U.S.
Environmental Protection Agency
 Tim Johnson: Nicholas School of the Environment, Duke University
 Silvia Secchi: Department of Agribusiness Economics, Southern
Illinois University
 Lyubov Kurkalova: Departments of Economics and Energy and
Environmental Systems, North Carolina A&T State University

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Motivation
 In the past, crude oil and natural gas markets have
impacted cost of producing and transporting agricultural
commodities.
 Now, energy prices (gasoline and biodiesel) impact
demand for crops used in biofuel production (Tokgoz et al.
2008, Hayes et al 2009 among many).
 Plus, supply of biofuels impact price and quantity of fossil
fuels (Hochman et al. 2010; Rajagopal et al. 2011;
Thompson et al. 2011; Chen, Huang, Khanna 2012).

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Motivation
 As a result of the biofuel expansion, agricultural
production is now being driven by a new set of economic
incentives, while energy production for transportation
purposes is becoming dependent on weather, climate
change, growing global demand for food and feed, and
other variables that affect agricultural output and prices
(Schmidhuber 2007; Delzeit, Britz, and Holm-Muller 2012).
 Thus, the examination of the impact of biofuels on
agricultural and energy markets should not ignore the
interconnectedness between the two sectors.

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Agriculture - Energy linkages

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Objectives
 To set up a coupled modeling framework to capture
the dynamic linkages between agricultural and energy
markets, and the environmental impacts resulting
from this coupling.
 This framework incorporates the interactions between
agricultural and energy markets at the macro-level,
and the assessment of production practices and
environmental impacts at the micro (field)-level.

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Modeling systems and philosophies
 MARKAL - MARKet ALlocation
 Mixed-integer linear programming model that solves for the
least-cost system-wide solution for meeting end-use energy
service demands given primary resources in a given region
• Transportation sector includes
– Vehicles by fuel type, vehicle class, size, and efficiency
• Model system-wide optimization means coordination of fuel
mix and vehicle fleet
MARKAL

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Modeling systems and philosophies
 CARD US agricultural model
• Partial equilibrium, non-spatial, multi-market model
• Temperate crops, sugar, biofuels
• Behavioral equations for
– crop planted and harvested acreage and yields
– domestic feed, food, industrial uses
– stocks
– trade
• Model solves for prices that balances supply and demand
annually with reduced form equations that mimic trade
responses from world markets.
CARD

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Modeling systems and philosophies
 CARD US crop model uses variable costs of production
(COP) from a model which projects these costs by crop and
by region
• Linked to CARD agricultural model and MARKAL energy
model
 COP model uses 3 energy prices from MARKAL
• Crude oil, natural gas, electricity

10. Comparison of modeling logic
MARKAL
Cannot provide forecasts,
but still can be used for
policy analysis
Least-cost optimization
framework that reaches an
OPTIMAL solution for the full
energy system
Time horizon till 2050
(includes future technologies)
Provide forward looking
projections
CARD
Provide forward looking
projections
Market outlook and policy
analysis tool
Baseline projections based
on long-term historical and
econometric relationships
Time horizon till 2024

11. Comparison of modeling logic
MARKAL
Determines what a
rational planner seeking to
minimize total system
costs in all periods should
do over the model’s time
horizon given
technological constraints -
no annual optimization
You cannot capture profit
seeking market behavior of
individual agents or sectors
CARD
Solves for net annual
returns to individual
producers in each
sector/commodity and
the least-cost alternative
for final consumers
You can impose zero profit
margins or not, by choice

12. Relative Strengths
MARKAL
Technological detail for
the LDV fleet (including
penetration of FFVs)
Interactions among all
sectors of the energy
system (transportation,
residential, commercial,
and industrial demand
sectors, electric power
sector and refineries)
CARD
Harvested area for the
major crops by region
and state.
Co-products and by-
products resulting from
the processing of
agricultural crops

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Integrated MARKAL-CARD modeling framework
 We define
• (a) static variables that could be harmonized across models (biofuel and
energy policies, historical and technological data)
• (b) variables that could be exchanged iteratively by running the two
models
• (c) criteria that determine convergence between the two models.
 Sensitivity analyses were run to identify those parameters
that most affected the individual model results with
respect to biofuels, and those parameters were, then,
prioritized for inclusion in the data exchanges.

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Integrated MARKAL-CARD modeling framework
DATA

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Integrated MARKAL-CARD modeling framework
 Evaluate “convergence” between two models and
identify “equilibrium” when generating a baseline
• Update the 2 models iteratively by exchanging dynamic
variables to achieve convergence (a “joint” baseline)
• 2 conditions were met to decide we reached an
equilibrium
– Corn ethanol, total ethanol, and biodiesel production
projections were the same in both models and stopped
changing between iterations
– Prices for corn, ethanol, biofuel by-products, biodiesel,
soybeans that drive the convergence between 2 models
stopped moving with iterations

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Iteration of Corn Production
0
5,000
10,000
15,000
20,000
25,000
2000 2005 2010 2015 2020 2025
Milliongallons
Version 1a
Corn Ethanol Production (CARD)
Corn Ethanol Production (MARKAL)
0
5,000
10,000
15,000
20,000
25,000
2000 2005 2010 2015 2020 2025Milliongallons
Version 1b

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Iteration of Corn Production
0
5,000
10,000
15,000
20,000
25,000
2000 2005 2010 2015 2020 2025
Milliongallons
Version 2a
0
5,000
10,000
15,000
20,000
25,000
2000 2005 2010 2015 2020 2025Milliongallons
Version 2b
Corn Ethanol Production (CARD)
Corn Ethanol Production (MARKAL)

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Iteration of Corn Production
0
5,000
10,000
15,000
20,000
25,000
2000 2005 2010 2015 2020 2025
Milliongallons
Version 2c
0
5,000
10,000
15,000
20,000
25,000
2000 2005 2010 2015 2020 2025Milliongallons
Version 2d
Corn Ethanol Production (CARD)
Corn Ethanol Production (MARKAL)

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Iteration of Corn Production
0
5,000
10,000
15,000
20,000
25,000
2000 2005 2010 2015 2020 2025
Milliongallons
Version 3a
0
5,000
10,000
15,000
20,000
25,000
2000 2005 2010 2015 2020 2025Milliongallons
Version 3b
Corn Ethanol Production (CARD)
Corn Ethanol Production (MARKAL)

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Iteration of Corn Prices
Corn Farm Price (CARD annual)
Corn Farm Price (CARD 5 year average)
0.00
0.50
1.00
1.50
2.00
2.50
3.00
3.50
4.00
4.50
5.00
2000 2005 2010 2015 2020 2025
US$perbushel
Version 1a
0.00
0.50
1.00
1.50
2.00
2.50
3.00
3.50
4.00
4.50
5.00
2000 2005 2010 2015 2020 2025
US$perbushel
Version 1b

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Iteration of Corn Prices
Corn Farm Price (CARD annual)
Corn Farm Price (CARD 5 year average)
0.00
0.50
1.00
1.50
2.00
2.50
3.00
3.50
4.00
4.50
5.00
2000 2005 2010 2015 2020 2025
US$perbushel
Version 2a
0.00
0.50
1.00
1.50
2.00
2.50
3.00
3.50
4.00
4.50
5.00
2000 2005 2010 2015 2020 2025
US$perbushel
Version 2b

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Iteration of Corn Prices
Corn Farm Price (CARD annual)
Corn Farm Price (CARD 5 year average)
0.00
0.50
1.00
1.50
2.00
2.50
3.00
3.50
4.00
4.50
5.00
2000 2005 2010 2015 2020 2025
US$perbushel
Version 2c
0.00
0.50
1.00
1.50
2.00
2.50
3.00
3.50
4.00
4.50
5.00
2000 2005 2010 2015 2020 2025
US$perbushel
Version 2d

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Iteration of Corn Prices
Corn Farm Price (CARD annual)
Corn Farm Price (CARD 5 year average)
0.00
0.50
1.00
1.50
2.00
2.50
3.00
3.50
4.00
4.50
5.00
2000 2005 2010 2015 2020 2025
US$perbushel
Version 3a
0.00
0.50
1.00
1.50
2.00
2.50
3.00
3.50
4.00
4.50
5.00
2000 2005 2010 2015 2020 2025
US$perbushel
Version 3b

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Pre- and post-linkage baseline results in the
CARD model (2020/21)
Initial Baseline
(Coordinated)
Starting Point
Post-linkage (Converged)
Baseline
Corn Soybeans Corn Soybeans Corn Soybeans
Production (million bushels) 18,916 3,164 14,793 3,468 15,818 3,349
Domestic use (million bushels) 16,259 2,441 11,860 2,457 13,164 2,416
Fuel alcohol use
(million bushels)
8,579 NA 4,619 NA 6,320 NA
Exports (million bushels) 2,605 731 2,935 1,017 2,662 939
Farm price ($/bushel) 4.37 10.81 3.92 9.74 4.28 9.96
Variable production expenses
($/acre)
301 140 319 141 320 141

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Pre- and post-linkage baseline results in the
CARD model (2020/21)
Initial Baseline
(Coordinated)
Starting Point
Post-linkage (Converged)
Baseline
Ethanol Ethanol Ethanol
Production (million gallons) 37,153 35,712 28,900
From corn 24,618 13,417 18,357
From cellulosic 12,500 22,295 10,543
Domestic disappearance
(million gallons)
40,527 38,434 35,572
Conventional 24,500 13,155 12,775
Other advanced ethanol 3,526 2,985 12,255
Cellulosic 12,500 22,295 10,543
Net imports (million gallons) 3,500 2,790 6,741
Unleaded gasoline price,
FOB Omaha ($/gallon)
2.71 2.15 2.30
Conventional ethanol price,
rack Omaha ($/gallon)
1.69 1.66 1.60

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Micro (Field) - level Modeling
 Link CARD-MARKAL baseline to a field-level, GIS-based
model
 It operates a spatially explicit 30-meter square grid
from USDA NASS remote sensing crop cover maps, and
land in CRP program.
 Micro model takes input, crop and corn stover prices
as exogenous and uses budget analysis to simulate the
expected profit-maximizing choices of farmers on each
grid unit.
MICRO-MODEL

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Note: C stands for corn, S for soybeans, 1 is for conventional tillage,
2 for conservation tillage, Sr for stover removal and ns for no stover removal.
Crop rotations and management practices in Iowa

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Conclusions
 Intensified linkage between energy and agricultural
markets
• through higher input costs to agricultural production
• through use of agricultural feedstocks as addition to or
replacement of transportation fuels
• through impact of biofuels on price of transportation fuels
 Integration of both systems’ models need to be
utilized to correctly assess the recent transformation

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Conclusions
 Integrated modeling approach better reflects real
world dynamics, where economic agents make
decisions based on multiple factors and where all
markets interact with each other.
 Specifically, analyzing the impacts of biofuels
expansion using energy systems and agricultural
markets models in isolation would lead to
overestimation or underestimation of these impacts.
To better assess the changes brought about by
biofuels, both systems models need to be utilized.

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Simla Tokgoz
s.tokgoz@cigar.org
Thank you!