CIP TOA and Beyond 5 29 07

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Tradeoff Analysis and Beyond: Integrating Science and Economics to Support Informed Policy Decision Making John M. Antle Department of Ag Econ & Econ Montana State University CIP Lima May 2007

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How can we provide information needed to support informed decision making? Understanding agriculture as a complex system  “full data” and coupled, site-specific models Matching analysis to levels of spatial and system complexity to provide timely information that is sufficiently accurate to inform decision making  “minimum data” analysis

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Example: Wetlands conservation in Uganda How to prevent encroachment in wetlands to protect water quality & quantity, biodiversity?

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The Ecosystem Service Challenge… When ES not priced, farmers choose practices to max private returns, over-exploit wetlands resources from social point of view What mechanisms can be used to induce farmers to use wetlands resources efficiently? The stick: “Command-and-control” regulation or punitive incentives The carrot: positive incentives (PES) What role can agricultural science and technology play in the solution? Can institutions be created to link farmers (suppliers of ES) to beneficiaries (demanders)?

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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 What level of data and model complexity are needed to support decision making? External Drivers and Market Equil.

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In the beginning…Tradeoff Analysis 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 A participatory process, not a model

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What are the sustainability indicators for the wetland agro-ecosystem? Economic and social indicators: Agricultural prod. & productivity Income Food security Poverty Conflicts over use Soil productivity/degradation Environmental indicators: Water quality (eutrophication, contamination, sedimentation) Water quantity (less water in boreholes (wells)) Biodiversity (fish, birds, plants) Micro-climate Health indicators Malaria Malnutrition Bilharzia There was a consensus that most of the indicators are worsening.

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What can be done to improve the system (scenarios)? Create awareness and provide training and incentives for improved soil and water conservation (for both uplands and lowlands). Restoration of wetlands and water catchments (e.g. agro-forestry). Nutrient management: improved access and affordability of fertilizers, use of cover crops and green manure. Improved rice varieties for uplands. Improved markets for produce.

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Research Question: Can PES provide a viable alternative approach to protecting wetlands areas? Need to identify and quantify wetlands ES (note advantage over C). Are farmers willing to participate in PES? At what price? Impact on poverty and sustainability of their production systems? Who would pay for the ES? How to design and implement payment mechanisms? Who are beneficiaries? What are they willing to pay? (local people and communities, national policy organizations, people downstream in the Nile watershed, global organizations and individuals?) Can local institutions manage PES?

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Economics of ES supply: Spatial distribution of opportunity cost for changing practices

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System 1: current practice including lowlands rice + uplands subsistence crops System 2: uplands crop only Three possibilities: System 2 is profitable for some farms without additional incentives System 1 is more profitable without a payment for environmental services, but System 2 is more profitable with environmental payments System 1 is more profitable even with a payment

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Technical Potential Derivation of the Supply of Environmental Services from the Spatial Distribution of Opportunity Cost Source: Antle and Valdivia, Aust. J. Ag & Res Econ. 2006

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Minimum Data Methods to Simulate the Supply of Environmental Services How to estimate the spatial distribution of opp cost of changing practices? “Full data” -- to construct site-specific simulation models, simulate opportunity cost MD approach: use available data to estimate parameters of opportunity cost distribution Validation studies show MD can approximate “full data” analysis of ES

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Could put dual-purpose sweet potato in the system?

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Simulated Participation Rates in Contracts for Wetlands Protection in Pallisa District, Uganda

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Simulated Change in Crop Income from ES Contract Participation for Wetlands Protection in Pallisa District, Uganda

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Conclusions TOA: an integrative, participatory approach to support informed policy decision making TOA software provides a transparent, modular approach to model agriculture as a complex system Spatial and system complexity  model design MD approach provides a low-cost (data, learning) way to implement analysis to support policy decision making Current research themes: System dynamics, multiple steady states, market equilibria (RV!) Refining MD and applying to new problems…

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