Principal Investigators:  John M. Antle, Susan M. Capalbo, Siân Mooney, William Hunt, Keith Paustian, and Edward T. Elliot
Funding:  U.S. Environmental Protection Agency

The objective of our research is to significantly advance the state of the art in modeling impacts of climate change in agroecosystems, by moving beyond the loose coupling of unrelated and independent disciplinary models. In this research we propose to develop both a conceptual framework for closer model coupling, and to implement the close coupling of an ecological model with an economic decision model. The research will investigate how our ability to simulate behavior in response to climate change is affected by the temporal and spatial scales of analysis, the degree of coupling of the models, and the dynamic properties of the models. We propose to do this for one of the most important agroecosystems, the crop-based system of the central United States.

Specific objectives of the project are:

1. Develop methods to more closely couple existing ecological and economic models that can be used to assess the impacts of climate change in agricultural ecosystems. This involves linking processes in ecological models with land use and input use decisions in economic models, so that the type and strength of feedback between ecological and economic processes is suitably represented.
2. Simulate the ecological and economic impacts of climate change on agriculture in the central United States, using data at various scales (field/farm, county and Major Land Resource Area (MLRA)), and using a range of climate change scenarios and sensitivity analyses. The climate scenarios will be derived from historical climate data and from the results of global circulation models (GCMs) that have been appropriately down-scaled. Climate data sets will be developed to conduct analysis of sensitivity to changes in mean temperature and precipitation changes, and changes in variability.
3. Investigate the dynamic and spatial properties of agricultural ecosystems to assess how estimates of the impacts of climate change are affected by the choice of spatial scale, temporal scale, and degree of model coupling.These properties will be compared at the farm/field, county and MLRA scales in the central United States using primary data collected by the PIs, and secondary data collected by various state and federal agencies.