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2001 Progress Report: Close-coupling of Ecosystem and Economic Models: Adaptation of Central U.S. Agriculture to Climate Change
EPA Grant Number: R828745Title: 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: Smith, Bernice
Project Period: October 1, 2000 through September 1, 2003
Project Period Covered by this Report: October 1, 2000 through September 1, 2001
Project Amount: $1,420,860
RFA: Assessing the Consequences of Interactions between Human Activities and a Changing Climate (2000)
Research Category: Global Climate Change
Description:
Objective:The objective of this research project is to significantly advance the status of modeling impacts of climate change in agroecosystems, by moving beyond the loose-coupling of unrelated and independent disciplinary models. With this research project, 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 complete this for one of the most important agroecosystems, the crop-based system of the central United States.
Progress Summary:Agricultural systems are complex and involve feedbacks among system components. Existing models of agricultural systems have a limited capability to represent these complexities. We plan to utilize more closely coupled systems that should provide a more accurate representation of how agricultural systems respond to climate change. Analysis of annual time series of agricultural commodity acres and prices showed evidence of chaos; notably the prices of wheat, oats, hay and barley. However, the available methods were not found to produce statistically reliable estimates of Lyapunov coefficients.
Vulnerability to climate change can be characterized using economic measures of the performance of an agricultural production system. These measures can be used to analyze vulnerability and the role that adaptation can play in mitigating it. Analysis of agriculture in the northern Great Plains shows that the spatial distributions of net returns vary systematically with assumptions about climate impacts, CO2 fertilization, and adaptation. The results support the hypothesis that the most adverse changes occur in the areas with the poorest resource endowments and when mitigating effects of CO2 fertilization or adaptation are absent.
Measured vulnerability of agriculture to climate change depends on both the type of vulnerability measure that is used, and on complex interactions between climate change, CO2 fertilization, adaptation, and economic conditions. When we subject our coupled models to a sensitivity analysis of our economic assumptions, the degree of vulnerability of wealthier and poorer regions can change substantially. The results do not generally support the hypothesis that the regions with poorer resource endowments are most vulnerable to climate change when vulnerability is measured in relative terms. However, when vulnerability is measured in relation to an absolute threshold, we do find a strong negative relationship between resource endowments and vulnerability. We also find that there is a positive relationship between gains from adaptation and the resource endowment of a region. This finding underlines the particularly important role that adaptation plays in mitigating climate change impacts in poorer regions.
Our finding that measures of vulnerability interact strongly with economic conditions and the degree of adaptation demonstrates the importance of subjecting integrated assessments of climate change impacts to sensitivity analysis. Our findings strongly support the Intergovernmental Panel on Climate Change (IPCC) conclusion that without adequate sensitivity analysis, results of integrated assessments should be given low confidence. Our findings on the importance of spatial heterogeneity also show that the concept of sensitivity analysis should be extended to spatial variation. Results from studies based on "representative" land units of farm units should not be extrapolated to larger populations without careful assessment of spatial heterogeneity of those populations.
Future Activities:We will continue the development of both a conceptual framework for closer model coupling, and will implement the close-coupling of an ecological model with an economic decision model. We will investigate how our ability to stimulate behavior in response to climate change is affected by the temporal and spatial scales of analysis, the coupling degree of the models, and the dynamic properties of the models, for the crop-based system of the central United States.
Journal Articles:No journal articles submitted with this report: View all 35 publications for this project
Supplemental Keywords:close-coupling, ecological models, economic models, climate change, agriculture, agroecosystems. , Ecosystem Protection/Environmental Exposure & Risk, Air, Scientific Discipline, RFA, Ecosystem/Assessment/Indicators, Social Science, climate change, Ecology, Ecological Risk Assessment, Ecological Effects - Environmental Exposure & Risk, Ecosystem Protection, Environmental Monitoring, Agronomy, Economics, agriculture ecosystems, ecosystem sustainability, meteorology, human activities, Global Climate Change, socioeconomic indicators, agro ecosystems, farming, climate change impact, sensitivity, agriculture, economic models, climate models, ecological exposure, modeling ecological risk, circulation model, anthropogenic stress, ecological models, environmental stressors, global warming
Progress and Final Reports:
Original Abstract
2002 Progress Report
2003 Progress Report
Final Report