Summary of economic modeling in BioEarth BioEarth Kick-off Meeting: April 11, 2011

1. Summary of economic modeling in BioEarth BioEarth Kick-off Meeting: April 11, 2011 Mike Brady, WSU Yong Chen, OSU Jon Yoder, WSU

2. Project Goal: Improve understanding of C, N, and H20 interactions in the context of global change to better inform decision making with respect the management of natural resources. Economic Modeling • Typical challenge for economics in this type of modeling is the difference in spatial and temporal scale. • Economic decision making is usually • Time: monthly/seasonal/annual/multi-year • Space: • Administrative: field, farm, county, state, national • Physical: watershed, basin, growing region • There are some global economic models. • In comparison, physical and biological models can be at much higher resolution (square meters and hourly time steps). Can also operate over much longer time scales (many decades or longer). • For economics, the interesting challenge is • to find ways to use all this additional information at both smaller and larger scales than we typically deal with. • to take advantage direct links to biophysical environment to be able to model feedbacks between human and environmental systems (Yong Chen’s specialty).

3. Integration of economic and biophysical modeling developing rapidly under preexisting project that is nearing completion. • Integration in this project will only be “soft”, but provides a good foundation for proceeding straight to hard linking models for BioEarth. • Spatial scale • Regional extent • Resolution • Data resolution at field level (NASS CDL/WSDA CDL) • Decision making resolution at level of irrigation management. • Irrigation district • Water Resource Inventory (WRIA)

4. Work at places like UC Davis where economic components linked to land surface hydrology models is a useful template to follow. • This project involves many more additional model components. • Decadal and regional aspect also makes it unique. • Input quantities and prices • Natural resources • Precipitation • Soil • Heat or degree days • Variables with quantities that can be varied at shorter time intervals • Seed • Fertilizer • Herbicides • Energy • Labor • Irrigation water • Capital, or fixed, resources (only be changed year to year) • Machinery • Land • Buildings • Financial • Outputs • Agricultural goods • Sediment runoff, chemical runoff, greenhouse gas emissions

5. Model structure • A region is the basic production unit, so constrained optimization problem is to choose inputs to maximize profit for regional production subject to resource constraints. Model inputs and outputs are region specific. • Partial equilibrium: effect of changes in agriculture on rest of the economy in terms of income, for example, that could feed back on agriculture are not considered. Safe assumption in developed economies where agriculture is small part of overall economy. • Static • Decisions in one year do not carry over into following year as opposed to a dynamic model with control and state variables that carry decisions in one year over into the next. • The modeling approach described is a good starting point and it has a track record. However, there are interesting options for developing more innovative approaches. Considering how to incorporate methods like Agent Based Modeling will be an important part of increasing the overall level of innovation of this project from a social sciences perspective.