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Geospatial strategies to optimize placement of advanced bioenergy crops in marginal landscapes
- Lewis, Sarah Marie
- Advisor(s): Kelly, Maggi
Abstract
As energy policies around the world progressively mandate increases in bioenergy production, the amount of land required to meet these demands might come at the cost of food production or environmental protection. To ensure sustainable practices, planting energy crops on marginal land is frequently proposed as a solution to this land use dilemma in order to meet mandates while avoiding negative externalities. Yet even though the term "marginal land" has become so entwined with discussions surrounding bioenergy, its definition is in fact malleable - easily shaped to fit research needs. This research investigates how marginal lands are commonly viewed in a Geographic Information Systems (GIS) framework and presents an innovative and flexible model that optimizes the placement of drought-tolerant bioenergy crops in marginal landscapes in the United States. In my first chapter, I present the first work that evaluates a suite of studies that use GIS to map marginal lands available for bioenergy production. In my second two chapters, I focus on the development of a site suitability model for several types of drought-tolerant bioenergy crops, which are of particular interest in the US because of their ability to grow on land not suitable for conventional agricultural production. In this work I demonstrate that incorporating fuzzy logic into the suitability model provides a more progressive suitability index that best synthesizes tradeoffs between multiple criteria. Using this model I present the first evidence to demonstrate that the area suitable for growing Agave as a bioenergy feedstock in the Southwestern US is sufficient to contribute to domestic renewable energy needs. I conclude that Agave would not be a major competitor to other energy feedstocks, but rather serve a parallel benefit as a renewable energy crop on lands unsuitable for conventional bioenergy production.
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