Close-coupling of Ecosystem and Economic Models: Adaptation of Central U.S. Agriculture to Climate ChangeEPA Grant Number: R828745
Title: Close-coupling of Ecosystem and Economic Models: Adaptation of Central U.S. Agriculture to Climate Change
Investigators: Antle, John M. , Capalbo, Susan M. , Elliot, Edward T. , Hunt, William , Mooney, Sian , Paustian, Keith
Current Investigators: Antle, John M. , Capalbo, Susan M. , Hoagland, Kyle D. , Hunt, William , Mooney, Sian , Paustian, Keith
Institution: Montana State University - Bozeman , Colorado State University , University of Nebraska at Omaha
Current Institution: Montana State University - Bozeman
EPA Project Officer: Hahn, Intaek
Project Period: October 1, 2000 through September 1, 2003
Project Amount: $1,420,860
RFA: Assessing the Consequences of Interactions between Human Activities and a Changing Climate (2000) RFA Text | Recipients Lists
Research Category: Global Climate Change , Climate Change , Air
The overall 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. Specific objectives are:
- 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.
- Simulate the ecological and economic impacts of climate change on agriculture in the central U.S., 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.
- 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.
In this research we will develop a conceptual framework for closer model coupling, and 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 will to do this for one of the most important agroecosystems, the crop-based system of the central United States.
To meet our first objective, we ill link 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.
To meet our second objective, to simulate the ecological and economic models, we will derive climate scenarios 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.
Our third objective is to investigate the dynamic and spatial properties of agricultural ecosystems and to assess how they are affected by 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.
We expect that the possibility of successfully coupling ecosystem and economic models will depend on the level of data aggregation and spatial scale. Such coupling is expected to be most successful on a site-specific basis, and less successful as data are spatially and temporally aggregated.
We hypothesize that coupling ecosystem and economic models will, at least in some important cases, lead to significantly different estimates of climate change impacts on agriculture than is obtained from uncoupled models. Likewise, we expect to find significant effects of spatial and temporal aggregation on impacts of climate change.