Three Essays on Applied Environmental Economics.

This dissertation studies economic and ecological outcomes of large-scale weather risk and land-use change. Three independent studies are completed all of which apply microeconometric techniques, unique panel datasets, and, in two cases, fine-scale spatial data. Chapter 1 addresses the relationship among government agricultural programs, moral hazard, and land-use adaptation to weather risk. Using fine-scale spatial data, we identify farmers’ cropping pattern adaptation to weather risk, and whether a federal disaster assistance policy shock in 2008 from the Supplemental Revenue Assistance Payments (SURE) program distorts this adaptation. Our results show that farmers’ land-use decisions on corn, grassland, soybeans, and wheat are sensitive to pre-plant precipitation in North Dakota, but not in Iowa. Moreover, the SURE program gives farmers in North Dakota a disincentive to adjust cropping pattern to pre-plant precipitation. Limited adaptation implies substantial losses as climate change will increase the frequency of extreme weather events. Chapter 2 introduces microeconometric techniques into ecological research through its analysis of the effect of land-use change on grassland bird species richness. Using fine-scale weather, land cover, and soil data and dynamic panel data models, the causal impact of large-scale land-use change on grassland bird species richness is identified. Based on the estimation model, our projections show that that under the US biofuel mandate (our baseline scenario), the average grassland bird species richness in 2030 will decrease by 22% from 2013 levels. We also identify potential conservation hotspots by projecting heterogeneous county-level outcomes in a spatially-explicit setting under the baseline scenario. Chapter 3 assesses the economic impacts of extremely low Great Lakes water levels. We apply a difference-in-difference-in-differences estimator to generate causal evidence on the economic impacts of an episode of extremely low levels of Lakes Michigan and Huron in 2000-06. We find no evidence that economic outcomes were sensitive to extremely low lake levels. However, our statistically insignificant estimated results suggest that we cannot rule out substantial economic effects of the extremely low levels in the recreation and tourism sector.PHDNatural Resources and EnvironmentUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120754/1/hsihuang_1.pd

Assessing Cellulosic Biofuel Feedstock Production Across a Gradient of Agricultural Management Systems in the U.S. Midwest

While biofuels are widely considered to be a part of the solution to high oil prices, a comprehensive assessment of the environmental sustainability of existing and future biofuel systems is needed to assess their utility in meeting U.S. energy and food needs without exacerbating environmental harm. The following questions guide this research: 1. What is the spatial extent and composition of agricultural management systems that exist in the U.S. Midwest? 2. How does sub-grid scale edaphic variation impact our estimation of poplar biomass productivity across a gradient of spatial scales in the U.S. Midwest? 3. How do location and management interactions impact yield gap analysis of cellulosic ethanol production in U.S. Midwest? In the first chapter, I developed an algorithm to identify representative crop rotations in the U.S. Midwest, using remotely sensed data; and used this information to detect pronounced shifts from grassland to monoculture cultivation in the U.S. Midwest. In the second chapter, a new algorithm is developed to reduce the computational burden of high resolution ecosystem modeling of poplar plantations in U.S. Midwest, with the results from the high resolution modeling being used to estimate the impact of averaging and discretization of soil properties on poplar yield estimates. In the third chapter, a novel yield gap analysis of cellulosic feedstocks on marginal lands in the U.S. Midwest is conducted to determine the management inputs needed to reach their yield potential in a sustainable manner. The significance of this research lies in providing a spatially explicit regional scale analysis of the tradeoffs between food and fuel production and providing an understanding of which biofuel crops should be grown where to maximize production while mitigating environmental damage

Remote sensing and GIS in support of sustainable agricultural development

Over the coming decades it is expected that the vast amounts of area currently in agricultural production will face growing pressure to intensify as world populations continue to grow, and the demand for a more Western-based diet increases. Coupled with the potential consequences of climate change, and the increasing costs involved with current energy-intensive agricultural production methods, meeting goals of environmental and socioeconomic sustainability will become ever more challenging. At a minimum, meeting such goals will require a greater understanding of rates of change, both over time and space, to properly assess how present demand may affect the needs of future generations. As agriculture represents a fundamental component of modern society, and the most ubiquitous form of human induced landscape change on the planet, it follows that mapping and tracking changes in such environments represents a crucial first step towards meeting the goal of sustainability. In anticipation of the mounting need for consistent and timely information related to agricultural development, this thesis proposes several advances in the field of geomatics, with specific contributions in the areas of remote sensing and spatial analysis: First, the relative strengths of several supervised machine learning algorithms used to classify remotely sensed imagery were assessed using two image analysis approaches: pixel-based and object-based. Second, a feature selection process, based on a Random Forest classifier, was applied to a large data set to reduce the overall number of object-based predictor variables used by a classification model without sacrificing overall classification accuracy. Third, a hybrid object-based change detection method was introduced with the ability to handle disparate image sources, generate per-class change thresholds, and minimize map updating errors. Fourth, a spatial disaggregation procedure was performed on coarse scale agricultural census data to render an indicator of agricultural development in a spatially explicit manner across a 9,000 km2 watershed in southwest Saskatchewan for three time periods spanning several decades. The combination of methodologies introduced represents an overall analytical framework suitable for supporting the sustainable development of agricultural environments