Synergies, Surprises and Schwartzenegger:

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Synergies, Surprises and Schwartzenegger Designi
ng Policy-Relevant Science John M. Antle Susan
M. Capalbo Department of Ag Econ Econ Montana
State University www.climate.montana.edu www.trade
offs.montana.edu
Presented at the Center for Natural Resources
Policy Analysis, UC Davis, October 23, 2003.
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This presentation is based on work supported in
part by NSF, USAID, USDA, and the Montana AES
thanks! Also, thanks to our colleagues who
contributed to various pieces of the work
presented here Charles Crissman, Int. Potato
Center Sian Mooney, U. Wyoming Keith Paustian,
Colorado State U. Jetse Stoorvogel, Wageningen
U.R. Roberto Valdivia, Montana State U. David
Yanggen, Int. Potato Center
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The Challenge Policy-Relevant Science
Do we have the science to predict the
sustainability of this system or how farmers
would respond to incentives to adopt more
sustainable practices on land not well suited to
agricultural uses or how this system will be
impacted by external shocks (economic,
technological, environmental)? Do we have the
analysis to determine what is the least cost way
to mitigate buildup of GHG?
Cajamarca, Peru
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• Science is needed to understand and predict the
  properties of managed ecosystems -- such as
  agricultural production systems -- in all of the
  relevant dimensions that have come to be
  represented by the concept of sustainability
• this policy-relevant science will transcend our
  current level of understanding and predictive
  capabilities
• this science will make possible the transition
  to the science-based policies that are needed to
  make an efficient, equitable and sustainable use
  of the natural resource base.

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• Why scientists should talk to economists
• Human behavior often offsets or accentuates
  bio-physical changes, may introduce
  nonlinearities discontinuties into systems,
  e.g.,
• technology adoption, dis-adoption,
  non-adoption
• impacts of and adaptation to climate change
• farmer response to macroeconomic shocks
• permanence of soil carbon

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Mean versus coefficient of variation of net
returns by Montana sub-MLRA, for climate change
(CC) and CO2 fertilization scenarios with (A) and
without (N) adaptation.
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• Why economists should talk to scientists
• Empirical economic models fail to incorporate
  unobserved nonlinearities, and thus fail to
  predict outside the range of observed behavior
• people seek to avoid risk, so we dont often
  observe important thresholds (nonlinearities),
  but these thresholds are key to understanding
  behavioral responses to unanticipated shocks and
  dynamics of human systems.
• e.g., farmers tend to operate on the plateau
  of yield response functions, but economic,
  technological environmental shocks may push
  them over thresholds

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The effect of differences in the thickness of the
fertile A-horizon on the dry matter production of
potatoes as simulated with the DSSAT crop model
in the northern Andean region of Ecuador.
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The temporal dynamics in carbofuran leaching for
4 different fields as a result of tillage erosion
and management changes in the northern Andean
region of Ecuador.
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• From both perspectives, there are two reasons to
  couple disciplinary science
• Synergies answers to many important public
  policy questions about environment and
  agriculture cannot be answered by either
  bio-physical sciences alone or by
  economics/behavioral sciences alone
• Surprises complex systems may exhibit
  unexpected properties e.g., nonlinearities,
  multiple steady states that cannot be inferred
  from individual disciplinary components

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Remainder of presentation I. A framework
undertaking policy-relevant science 2. Selected
examples of synergies and surprises 3. Thoughts
on policy impacts of our research
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1. Designing Policy-Relevant Science
How is it done? Coordinated disciplinary
research. One example Tradeoff Analysis.

• Tradeoff Analysis is a process that can be used
  to
• set research priorities according to
  sustainability criteria
• support policy decision making
• use quantitative analysis tools to assess the
  sustainability of agricultural production
  systems.

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Tradeoff analysis process

• Public stakeholders
• Policy makers
• Scientists

Team Building

• Identify sustainability criteria
• Formulate hypotheses as potential tradeoffs
• Identify disciplines for research project

• Identify models and data needs define units of
  analysis
• Collect data and implement disciplinary research

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Implementing the TOA Approach the TOA Software
The Tradeoff Analysis software was originally
designed to assess the sustainability of
agricultural production systems. It can be
readily adapted to any production system that
involves spatially varying biophysical and
economic processes. The software and example
applications can be downloaded from
www.tradeoffs.nl.
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Carbon Analysis in the TOA Framework
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• Key features of the TOA Modeling Approach
• modular
• standard inputs and outputs
• transparent
• replicable
• loosely coupled models
• version with feedbacks under development

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• 2. Synergies and surprises Assessing the
  economic potential for soil carbon sequestration
• can adoption of alternative practices sequester
  carbon at a cost competitive with other sources
  of emissions reductions or offsets? (synergies)
• how do biophysical and economic uncertainties
  affect carbon sequestration potential?
  (surprises)
• will soil carbon be permanent? (surprise)
• will higher productivity increase the supply of
  C sequestration services? (surprise)

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Hypothesis Changing farm land-use and management
practices can restore soil C lost from use of
conventional practices
Soil C
C0
CC
CV
Time
T0
T1
T2
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Factors Determining the Cost of C Sequestered in
Agricultural Soil

• Farm Opportunity Costs What does the producer
  have to do to increase soil C, and how does that
  affect profitability?
• Change tillage practices
• Change crop rotation
• Change fertilizer rates
• Invest in terraces, other conservation practices
• Transactions Costs
• Organizing negotiating contract or project
• Verifying compliance (monitoring practices,
  measuring soil C accumulation)

Antle, J.M., S.M. Capalbo, E. Elliott and K.
Paustian, Spatial Heterogeneity, Contract
Design, and the Efficiency of Carbon
Sequestration Policies for Agriculture. Journal
of Environmental Economics and Management, in
press, 2003.
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• Decision rule choose practice to max
• NPVi ?t ?it /(1r)t, i conVentional,
  Conservation
• If ?it is constant over time, then the decision
  rule is to choose practice to max ?i
• If an informed farmer initially chooses V (and
  there are no signif market distortions), then ?C
  lt ?V
• So to induce adoption give incentive payment g
  such that ?C g gt ?V
• This is a per-hectare payment

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Let ?C (CC CV)/?T, and let g P?C, where P
value of carbon (/tonne C). - this is a per
tonne payment. Then the decision rule is to
adopt the C-practice if ?C g gt ?V or g gt
?V - ?C or P gt (?V - ?C)/?C
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Tradeoff Analysis of Carbon Sequestration in
Perus Terraced Cropping System
Carbon rate (?C)
?C conservation practice
?C(P0, ?C)
P0
?V conventional practice
?C(P0, ?V)
P0 - high fert. price
Net return (?)
??(P0)
Opportunity cost per tonne C ??/?C
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• Where should/would C be sequestered?
• soil scientists should be where potential ?C
  higheste.g., on most degraded lands?

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• Where should/would C be sequestered?
• soil scientists should be where potential ?C
  higheste.g., on most degraded lands?
• economists would be where ??/?C lowest!
• -- ?? and ?C are correlated, so its not obvious
  where the ratio is lowest
• -- high spatial variability, at field, farm,
  regional scales means lowest-cost suppliers of
  soil C could be widely dispersed across local,
  regional, global landscapes

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Spatial Heterogeneity and Technology Adoption
Carbon rate (?C)
Net return (?)
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Marginal costs of soil C in MT Sub-MLRAs
increasing MC reflects heterogeneity within
regions Antle et al, Economic Analysis of Carbon
Sequestration in Agriucltural Soils An
Integrated Assessment Approach, JARE, Dec 2000.
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Marginal cost of soil C sequestration under a
per-tonne contract in IA and MT Antle, J.M. et
al., A Comparative Examination of the Efficiency
of Sequestering Carbon in U.S. Agricultural
Soils. American Journal of Alternative
Agricultural, 2002.
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SURPRISES Sensitivity of Carbon Sequestration
Costs to Scale and to Economic and Biological
Uncertainties
We test the sensitivity of the model to show how
the costs vary depending upon scale of analysis
and uncertainty of input parameters
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Integrated Assessment Paradigm

• Economic data ? economic production
  models
• Soils climate data ?
• crop ecosystem models
• Output of crop ecosystem models ?
• economic models andenvironmental process models
• Output of economic models ?
  environmental process models

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Result from Earlier Papers
For each quantity of C sequestered, the marginal
opportunity cost of the per-hectare payment
mechanism (MCH) is greater than or equal to the
marginal opportunity cost of the per-tonne
mechanism (MCT), i.e., MCH ? MCT, and MCT /MCH is
decreasing with spatial heterogeneity.
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Marginal Cost of per-hectare and per-tonne
Payment Mechanisms
Sub-MLRA58a-high
Sub-MLRA58a-low
per-hectare payment per-tonne payment
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Sensitivity of Marginal Cost Results to

• Soil C rates
• Scale for measuring soil C rates
• Yield uncertainties
• Output price uncertainties

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Scenario Descriptions
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Changes in Soil C Rates

• Keep spatial heterogeneity
• Adjust by 50 increase in soil C rates
• Adjust by 50 decrease in soil C rates

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Sensitivity of Marginal Costs to Carbon Rates,
Sub-MLRA 52-high
per-tonnecontract
per-hectarecontract

• Base Quantity ? 50 Increase in C ? 50
  Decrease in C

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Sensitivity of Marginal Costs to Carbon Rates,
Sub-MLRA 58a-low
per-tonnecontract
per-hectarecontract

• Base Quantity ? 50 Increase in C ? 50
  Decrease in C

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Results of Change in Soil C Rates

• Changes in soil C rates change the quantity of
  soil C sequestered at various prices (shifts the
  MC curve)
• Under per-hectare policy, as soil C rates
  increase, the impact on soil C sequestered
  increases in proportion to the square of the
  increase in soil C rates
• Under per-tonne policy, we have a linear mapping
  of changes in soil C rate and changes in MC curve

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Marginal Costs for Soil CSoil C Rate Scenarios
Sub-MLRA52-high
Sub-MLRA53a-high
Sub-MLRA58a-high

• Base Scenario ? Scenario 1 ?
  Scenario 2
  (150 of base) (50 of base)

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Sensitivity of Marginal Coststo Scale
Use average rates of soil C across all Sub-MLRAs

• Impacts are specific to Sub-MLRA
• Using mean rates of soil C overestimates the MC
  for Sub-MLRA 52-high and 58a-high
• Using mean rates of soil C underestimates the
  MC for Sub-MLRA 53a-high

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Marginal Costs for Soil CScale Scenario
Sub-MLRA52-high
Sub-MLRA53a-high
Sub-MLRA58a-high

• Base Scenario ? Scenario 3

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Sensitivity of Marginal Coststo Output Price
Changes

• Scenario 4 A 10 increase in the mean of the
  estimated sample distributions of output prices
  respectively.
• Scenario 5 A 10 decrease in the mean of the
  estimated sample distributions of output prices
  respectively.

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Marginal Costs for Soil COutput Price Scenarios
Sub-MLRA52-high
Sub-MLRA53a-high
Sub-MLRA58a-high

• Base Scenario ? Scenario 4 ?
  Scenario 5
  (10 Increase) (10 decrease)

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Sensitivity of Marginal Coststo Change in Yields
Scenario 6 A 10 increase in yields for fields
that are in the program.
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Marginal Costs for Soil CProductivity (Yield
Increase) Scenario
Sub-MLRA52-high
Sub-MLRA53a-high
Sub-MLRA58a-high
? Base Scenario ? Scenario 6 (10 yield change)
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• Research Impacts
• policy research impacts occur cumulatively
  e.g., acceptance of emissions trading for SO2and
  GHGs?
• a growing recognition of the role of economics
  in assessing GHG mitigation potential
• e.g., DOE carbon sequestration partnerships
• importance of having timely research results
  communicated effectively
• butimpacts on policy may depend on political
  climate
• e.g., US and Kyoto Protocol!
• pesticide impact research in developing countries

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This presentation and related information are
available at www.tradeoffs.montana.edu
www.climate.montana.edu