Funded Research Projects: SRM

Climate and Human Dynamics as Amplifiers of Natural Change: A Framework for Vulnerability Assessment and Mitigation Planning

Research Sponsored By: National Science Foundation (NSF)
Principal Investigator: Sergey Rabotyagov
Project Description
In order to understand and accurately predict land use responses to potential climatic and economic policy changes, we propose to develop a field-scale economic model of joint choice of land use and cropping practices for the study area. Available remote sensing data will be used to classify fields by land use and crop choice over time. Availability of this data over time will allow us to incorporate crop sequence dynamics into the economic model of landowner behavior. In addition, we recognize the importance of and account for changes on both the extensive margin (transitions of land in and out of agricultural crop production), as well as the intensive margin (choice of crop rotations and tillage practices) in our modeling strategy. A model which incorporates both the extensive and intensive margin changes will be used to accurately assess water quality impacts in the watershed. Next we outline the model structure, the Simulation-optimization model. While comparisons of the costs and estimated environmental gains amongst various scenarios will be quite informative, a fundamental question of relevance for the design of almost any policy is to be able to characterize the least cost solution that could achieve a particular environmental target. In addition to the desire to compare the efficacy of various policies to such an ideal solution, there is an additional reasons to be interested in such a characterization. The least cost solution to a given environmental target is the predicted outcome associated with a well functioning trading program where all gains from trade are exhausted. While there are numerous obstacles to the implementation of such an idealized trading program for water quality dominated by nonpoint sources, it still provides a target of interest .