Animal waste from animal feeding operations (AFOs) is a significant contributor to nitrate contamination of groundwater. Some animal waste also contains heavy metals and salts that may build up in cropland and underlying aquifers. This thesis focuses on pollution reduction from the largest AFOs, in particular, Concentrated Animal Feeding Operations (CAFOs), which present the greatest potential risk among all AFOs to environmental quality and public health. To find cost-effective policies for controlling pollution at the field level and at the farm level, a dynamic environmental-economic modeling framework for representative CAFOs is developed. The framework incorporates four models (i.e., animal model, crop model, hydrologic model, and economic model) that includes various components such as herd management, manure handling system, crop rotation, water sources, irrigation system, waste disposal options, and pollutant emissions. The operator maximizes discounted total farm profit over multiple periods subject to environmental regulations. Decision rules from the dynamic optimization problem demonstrate best management practices for CAFOs to improve their economic and environmental performance. Results from policy simulations suggest that direct quantity restrictions of emission or incentive-based emission policies are much more cost-effective than the standard approach of limiting the amount of animal waste that may be applied to fields; reason being, policies targeting intermediate pollution and final pollution create incentives for the operator to examine the effects of other management practices to reduce pollution in addition to controlling the polluting inputs. Incentive-based emission policies are shown to have advantages over quantity restrictions over multiple years when seasonal or annual emissions fluctuate either due to inherent operation practices or the accumulation of precursors to the pollution. My approach demonstrates the importance of taking into account the integrated effects of water, nitrogen, and salinity on crop yield and nitrate leaching as well as the spatial heterogeneity of nitrogen/water application. It also suggests that ecosystem services can play an important role in pollution control and thus deserve more attention when designing policies.

This research focuses on addressing methodological issues that impact the performance of spatially explicit discrete choice agent-based land use models that are estimated with remotely sensed data. The empirical setting considers land use transitions between agroforests, perennial crops, grass and corn, and fallow lands during the period 1984 - 2006 in a Mexican coffee growing region in which relatively high deforestation rates were observed. As a starting point, a Mixed Conditional - Multinomial Logit model is implemented to highlight assumptions and limitations associated with this standard modeling approach. The results indicate that this model produces theoretically inconsistent parameter estimates for the revenue variable associated with three out of four land uses considered in the analysis. To investigate whether those counterintuitive marginal effects are generated from misclassified land use data, a Latent Multinomial Logit (LMNL) model is implemented. This approach allows the identification of land use observations that have a high likelihood of being wrongly classified. A reconfiguration of the dataset based on the LMNL model increased the magnitudes of the marginal effects of the analyzed land use drivers in the theoretically expected directions. Next, because static land use models require limiting assumptions that potentially oversimplify the behavioral process followed by landowners, a structural dynamic discrete choice model of land use decisions is implemented under the assumption that land managers are forward-looking and act to maximize their discounted flow of current and future expected utility within a stochastic environment. A comparison between static and dynamic models shows that the directions of the marginal effects corresponding to time-invariant parcel-specific variables generally have the expected directions independent of the selected modeling approach. More importantly, the marginal effect estimates for the revenue variables of the agroforestry and perennial crops categories have the expected direction in the dynamic model. By contrast the myopic modeling approaches generate counter-intuitive results for the revenue variable that corresponds to perennial crops production, which affects the validity of those results for policy design. Finally, a policy simulation exercise shows the sensitivity of welfare estimates to the discount factor selected as representative of the true value used by decision makers.

A nonmarket valuation approach is used in this study to evaluate the recreation values of the San Jacinto Wilderness in southern California. The analysis utilizes survey data from a stated-choice experiment involving backcountry visitors who responded to questions about hypothetical wildfire burn scenarios. Benefits of landscape preservation are derived using a Kuhn-Tucker (KT) demand system. Model results suggest that recreationists are more attracted to sites with recent foreground wildfires. For example, recreational welfare estimates increased for sites that were partially affected by different types of wildfires, with the greatest gains being observed for the most recent wildfires. Welfare analysis indicates that mean individual seasonal CV losses for complete closure of particular sites range from $19 to $169.

Additionally, a latent class approach to the KT model is proposed as a method for incorporating unobserved heterogeneity in preference for recreation behavior using a utility theoretical framework and used to control for endogenous spatial sorting. Using the standard maximization likelihood technique, the latent class KT model suggests that two groups exit in the sample. The groups consist of "hiking enthusiasts" and "casual users." The hiking enthusiasts take twice as much trips as casual users, but their estimated per-trip CV is smaller. However, the results are consistent with Parsons (1991) argument; individuals with stronger preferences for recreation ("enthusiasts") might choose to live closer to recreation sites they frequently visit to reduce their travel costs.