EPIC is able to simulate
important natural processes in agricultural land management such as photosynthesis,
evapotranspiration, runoff, erosion, mineralization, nitrification and respiration. In this
context, EPIC has been applied to analyze its sensitivity to climate change and more
specifically to increased drought occurrences by applying climate datasets from
different sources. Such analyses are of particular interest and provide information
about the usefulness and appropriateness of deterministic process models for large
scale applications as well as for model inter-comparisons. In addition, EPIC has been
applied to provide biophysical data for several economic impact analyses. Economic
optimization models are well suited to help finding optimal land use and crop
management options as well as investment strategies in the context of production
risks and uncertainties due to changing climatic conditions. The integration of
biophysical data in economic optimization models allows a better representation of
the biophysical heterogeneity and interrelationships in spatial and temporal contexts.
In the course of this thesis, optimal crop management portfolios as well as optimal
investment strategies for irrigation under climate change have been detected for the
Marchfeld region in Austria. In addition, the biophysical and economic potentials of
large scale poplar plantations for bioenergy production have been analyzed on
Austrian croplands. These case study analyses should exemplify the capability of an
integrated modeling framework for climate change impact analyses in agriculture as
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well as for developing cost-effective adaptation strategies (i.e. management and
investment) to better cope with the adverse effects of climate change.