Tradeoff Analysis: From Science to PolicyTradeoff Analysis: From Science to Policy

John M. AntleJohn M. Antle

Department of Ag Econ & EconDepartment of Ag Econ & Econ

Montana State UniversityMontana State University

How can we link relevant agricultural, environmental and economic sciences to support informed policy decision making?

E.g., do we know what policies will reduce poverty and encourage adoption of more sustainable practices in the Machakos region?

• Ag Scientists: improve crop varieties and management

• Environmentalists: need LISA

• Economists: need to “get prices right”

The Challenge: Policy-Relevant Science

The TOA Approach: Agriculture as a complex system…

• interconnected physical, biological and human systems varying over space and time

- the role of heterogeneity in relevant populations

the fallacy of the “representative unit”

- the role of human decision making

- the role of system dynamics and nonlinearities

- relevant scales of analysis to support policy decisions

The Challenge: Policy-Relevant Science

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Nutrient Depletion (kg/ha/yr)

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Heterogeneity: Nutrient Depletion and Net Returns in Machakos

Variation within and between systems…

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Mean Net Returns ($ per acre)

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Base CC-A CC-N CO2-A CO2-N CC+CO2-A CC+CO2-N

Human Behavior: 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. (Source: Antle et al., Climatic Change, 2004).

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Thickness A-horizon (cm)

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Nonlinearities: 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.

Complexity: 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. (Source: Antle and Stoorvogel, Environment and Development Economics, in press).

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A: shallow topsoil, low tillage erosion rate

B: deep topsoil, low tillage erosion rate

C: shallow topsoil, high tillage erosion rate

D: deep topsoil, high tillage erosion rate

How is it done? Coordinated disciplinary research.

How is it implemented: 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.

Designing and Implementing Policy-Relevant Science

•Public stakeholders•Policy makers•Scientists

Research priority setting

Project design & implementation

Communicate to stakeholders

•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

Tradeoff analysis process

It’s not a linear

process…e.g. NUTMON

TOA is based on an integrated assessment approach to modeling agricultural production systems, using spatially

referenced data and coupled disciplinary models.

Soils & Climate Data Economic Data

Crop/Livestock Models Economic Model

Land Use &Management

Environmental Process Models

EconomicOutcomes

EnvironmentalOutcomes

Yield

Implementing the TOA Approach: the TOA Software

The Tradeoff Analysis model is a tool to model agricultural

production systems by integrating spatial data and disciplinary

simulation models.It helps scientific teams to

quantify and visualize tradeoffs between key indicators under alternative policy, technology

and environmental scenarios of interest to policy decision

makers and other stakeholders.

Example: Assessing Impacts of Policy and Technology Options on the Sustainability of the

Machakos Production System

Nutrient Dep

Poverty

Define a tradeoff curve by varying a price (e.g., maize price) for a given technology and policy environment. What is the

form of the tradeoff?

Factors Affecting Slope of Tradeoff Curve:

• Productivity of each system at each site

• Nutrient balance of each system at each site

• Effects of maize price on farmers’ choice of system at each site (extensive margin)

• Effects of maize price on farmers’ choice of management at each site (intensive margin)

• Spatial distribution of systems, prices

Technology and Policy Scenarios:

Manure Management, Fertilizer Prices

Nutrient Dep

Poverty

How do these scenarios shift the tradeoff curve?

Do curves differ spatially?

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Nutrient Depletion (kg/ha/yr)

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arm

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Machakos: Base Technology and Prices, Individual Farms

basevv

DEP10W1301201101009080706050

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Base Technology and Prices, Aggregated by Village

Base Technology and Prices, Aggregated by Village

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Nutrient Depletion

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Nutrient Depletion

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Aggregated by Tradeoff Point and Village

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Aggregated by Tradeoff Point

Aggregated by Tradeoff Point with Alternative Policy and Technology Scenarios

BASE MANURE FERT PRICE MANURE + FERT PRICEMAIZE PRODUCTIVITY

DEP10W15014013012011010090807060504030

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Conclusions:

• TOA is a tool that can integrate data and modeling tools to support informed policy decision making

• The challenges:

• Make the tools available to clients.

• Create a demand for better information.

• Improve the tools:

• lower cost of adoption and use

• expand applicability

Process for Transfer of TOA Tools to Users:

• Informing potential clients (web sites, etc)

• Training (workshops, on-line course)

• Collaborative agreements with clients

• Use by client staff with TOA support

• Follow-up to assess strengths and weaknesses

Key Issue: High adoption (training) and implementation costs (data)

• Data

• Soils and climate

• Economic: farm surveys

• Model complexity (training)

• DSSAT models

• Economic models

• Environmental models

Solutions

• Data

• Soils and climate: down-scaling techniques

• Economic: minimum data approach

• Linkages to existing data: NUTMON

• Model complexity

• Bio-physical: landscape-scale empirical models

• Economic: minimum data approach

Experience

• Downscaling & linkages: Peru, Senegal, Kenya

• soil & climate data

• adaptation of existing farm survey data

• Kenya: complex model implemented in 3 months with NUTMON data, but model complexity remains

• Minimum data: Panama

• simple model implemented with 1 week training, 1 month data collection & model development

• but limited applicability

Implications

• Optimal strategy for institutionalization

• utilize minimum data approach for training and initial applications

• develop more detailed applications if needed as clients acquire capability, data

Conclusions

• TOA successfully implemented as an operational tool applied to various policy problems

• environmental & human health impacts of pesticide use (Ecuador)

• terracing and related conservation investments (Peru, Senegal)

• soil carbon sequestration (USA, Peru, Senegal, Kenya)

• nutrient depletion (Senegal, Kenya)

Conclusions (cont.)

• Adoption by national and international institutions is in progress

• Development of downscaling & minimum data methods will lower adoption costs

• Further experience needed to fully assess impact

But…note methodological issues to be confronted in assessing impact of policy research (see Pardey and Smith, IFPRI, 2004)