Tradeoff Analysis:

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Tradeoff Analysis Coupling Bio-Physical and
Economic Models to Support Agricultural and
Environmental Policy John M. Antle Department
of Ag Econ Econ, Montana State University Jetse
J. Stoorvogel Laboratory of Soil Science,
Wageningen University
Presented at University of Florida June 12 2006
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Acknowledgements Soil Management CRSP,
USAID Collaborating people and institutions
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• The challenge science and policy for sustainable
  agriculture
• Provide marketable products, ecosystem services
  and livelihoods to farm households and the wider
  community in ways that balance present and future
  needs.
• Transition from commodity-based subsidies to PES
  in US agriculture (2007 farm bill)
• Enhancing sustainability and reducing poverty in
  developing countries

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• Meeting the challenge through science and policy
  
• Knowing the short-term and long-term consequences
  of our actionsWhat are the options?
• Integrate science across disciplines and scales
  to understand agriculture as a managed ecosystem
• Design and implement appropriate technologies
• Making informed choicesHow can we individually
  and collectively choose the best options?
• Empower consumers to express their demand for
  products with desired attributes and their demand
  for ecosystem services
• Support informed decision making by individuals
  and communities
• Design and implement science- and
  information-based policies

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Agriculture as a Managed Ecosystem Loose and
close coupling of bio-physical data and processes
to economic processes
drivers
Fertilizer use
Bio-geo-physical
Soil Nutrients
Crop
Crop
State
Ecosystem
Soil
Model
Moisture
Environmental
Crop Yield and Quality
Impact
Antle et al., Ecosystems 2001
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Integrated assessment approach using coupled
site-specific bio-phys and econ processes to
characterize spatial and temporal distributions
of environmental and economic outcomes
External drivers
Antle, J.M. and S.M. Capalbo. (2001).
Econometric-Process Models for Integrated
Assessment of Agricultural Production Systems.
American Journal of Agricultural Economics
83(2)389-401.
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The Challenge Support informed decision
makingThe Approach Tradeoff Analysis
A participatory process, not a model

• Public stakeholders
• Policy makers
• Scientists

• Identify key sustainability indicators and
  tradeoffs
• Identify technology and policy scenarios

• Identify key disciplines in research team
• Define spatial and temporal scales of analysis
  for
• disciplinary integration and policy analysis

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• Tradeoff curves feasible combinations of
  sustainability indicators
• Technology and policy scenarios using data and
  modeling tools to explore options and find
  win-win solutions.

People may choose to trade off income for health
or environmental quality, or vice-versa!
Health Environment
Why not use BCA?
Farm Income
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Implementing the TOA Approach the TOA Software
A modular approach to integrate spatial data and
disciplinary models to simulate agricultural
systems. See www.tradeoffs.nl for on-line course
and downloadable software
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Economic, Environmental and Health Tradeoffs in
Agriculture Pesticides and the Sustainability of
Andean Potato Production
Carchi

• 10 year program funded by
• Rockefeller Foundation
• USAID SM-CRSP
• Ecoregional Fund
• IDRC

The problem
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Tradeoffs and Win-Wins Neuro-behavioral health
risk versus economic returns with alternative
management scenarios
A methodology ideally suited to characterizing
risk
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Environmental Impact tillage erosion and
pesticide leaching

• Preliminary analysis showed little chemical
  leaching in deep volcanic soils.
• Analysis accounting for heterogeneity within
  fields showed much higher potential for
  environmental impact.

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• Preliminary analysis showed little chemical
  leaching in deep volcanic soils.
• Analysis accounting for heterogeneity within
  fields showed much higher potential for
  environmental impact.

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Dynamics thresholds tillage erosion and
pesticide leaching
Antle and Stoorvogel, Env and Dev Econ, 2005
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By linking spatially-explicit bio-physical and
economic simulation models we can also study
complex interactions between environmental and
economic processes
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By linking spatially-explicit bio-physical and
economic simulation models we can also study
complex interactions between environmental and
economic processes
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By linking spatially-explicit bio-physical and
economic simulation models we can also study
complex interactions between environmental and
economic processes
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By linking spatially-explicit bio-physical and
economic simulation models we can also study
complex interactions between environmental and
economic processes
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By linking spatially-explicit bio-physical and
economic simulation models we can also study
complex interactions between environmental and
economic processes
20
By linking spatially-explicit bio-physical and
economic simulation models we can also study
complex interactions between environmental and
economic processes
21
By linking spatially-explicit bio-physical and
economic simulation models we can also study
complex interactions between environmental and
economic processes
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By linking spatially-explicit bio-physical and
economic simulation models we can also study
complex interactions between environmental and
economic processes
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By linking spatially-explicit bio-physical and
economic simulation models we can also study
complex interactions between environmental and
economic processes
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Spatially-explicit approach provides basis for
environmental risk assessment
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Implementation Alternative Modeling Strategies

• Goal support informed policy decision making
• Tradeoff between timeliness and accuracy

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Minimum Data Approach

• Need quantitative back of the envelope
  analysis to support policy decision making order
  of magnitude?
• Antle, J.M. and R.O. Valdivia. 2006. Modeling
  the Supply of Ecosystem Services from
  Agriculture A Minimum-Data Approach. Aust. J.
  Ag. and Res. Econ. 50 1-15.
• MD approach exploit structure of the problem to
  characterize spatial distribution of opp cost
• Example simulation of carbon supply curve
• Key insight supply of ES depends on spatial
  distribution of opportunity cost
• MD approach uses available data to estimate
  parameters rather than complex econometric models

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Derivation of the Supply of Ecosystem Services
from the Spatial Distribution of Opportunity Cost
Technical Potential
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Comparison of carbon supply curves from the
Montana econometric-process model and from the
minimum data model (6 ecoregions aggregated) Full
model 125 parms, MD model 25
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Effect of changing the correlation between crop
returns on the carbon supply curves in a Montana
ecoregion.
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Comparison of full EP to MD model Carbon
contract participation in Machakos, Kenya Case
Study (Full model 700 parms, MD 75)
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Conclusions

• TOA support informed policy decision making
• an approach, not a model
• TOA software provides a transparent,
  reproducible, modular approach to model
  agriculture as a complex system or agro-ecosystem
• Spatial and system complexity ? model design
• MD approach provides a low-cost way to implement
  analysis for the analysis of PES
• Current research themes
• System dynamics, multiple steady states, spatial
  dependence
• Applying MD to other problems

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For more information
www.tradeoffs.montana.edu
www.climate.montana.edu