Different open space types are assumed to be valued in different ways by the public. This thesis analyzes four spatially explicit hedonic models of Baltimore County, Maryland to examine the effect of six different open spaces types on house value using 2007 sales data. The first model analyzes open space value using proximity measures of open spaces, while the other three models use percent area measures of open space at different neighborhood distance. Marginal monetary values of the open spaces are estimated. Additional eight hedonic models, four urban and four rural, are used to analyze the differences and similarities between the value placed on open space by urban dwellers and rural dwellers. Among the open space types under study, storm water retention area is found to have the most prevalent influence on house value and in most instants this influence is found to be negative. Differences and similarities in urban and rural perspective on open space value are also discussed. Proximity to lakes without improvements has positive effect on house prices for both rural and urban area. Golf course area in urban neighborhood has negative influence on house prices, whereas in rural area its influence is seen to be positive.

The concept of quality of life (QOL) has been addressed for decades. It was not until recent times when geographical information systems (GIS) have become available that a locale-specific approach could be enabled. Even then, analysis to date has been conducted mostly at the resolution of city or county level. The study presented describes an innovative methodology that may appraise QOL at finer resolutions, i.e. more localespecific. The new approach is called Spatial Appraisal and Valuation of Environment and Ecosystems, or SAVEE. This thesis research is a proof-of-concept study as the first account of the SAVEE methodology. It is to set the stage for future studies toward a more comprehensive framework. In this preliminary study of locale-specific QOL, the SAVEE methodology was used to illustrate the possibility of handling QOL factors in a dynamic manner. The assessment includes three major steps: 1) data preparation, 2) data conversion and normalization, and 3) combining contributions of factors being considered. In the first step, the geospatial data layer of a factor in consideration was input into GIS to plot a proximity map of the feature, e.g. parks or fire stations. In Step Two, each factor was first assigned a range of weight according to the location of a site on a proximity map in terms of the factor’s favorability-unfavorability. In the third step, weights from each factor were combined in a pair-wise manner, e.g. park and fire station proximities, or two factors at a time. The weight combining is done by deploying map algebra formula derived from the expert system algorithm EMYCIN. The computation was done iteratively until all factors were exhausted. The final results were coded as a gradient map of an integrated and locale-specific QOL index in the range of (-1, 1). In this preliminary study, the City of College Station, Texas was used as the study site. A set of factors and their respective ranges of weight were used in the study. By adjusting the incorporation of various factors and their ranges, a series of QOL maps for the city was generated. The resulting QOL maps indicate what factors and ranges may or may not have contributions toward a holistic overall picture of the QOL of a city in the locale-specific context. The SAVEE methodology proved to be successful in handling qualitative hedonic factors in a locale-specific quantitative manner through the GIS interface.

Master of Science / Department of Agricultural Economics / Ted C. Schroeder / It is clear that consumers rely on certain experience and credence attributes when purchasing beef products from the retail meat case. It is essential for all beef industry sectors to recognize the complexity of consumers buying behavior. The objective of this research is to determine if there are incentives to brand beef products and to determine what types of brands entertain price premiums as well as what levels these premiums exists. Retail scanner data, collected from 2004 through March 2009, was used for the evaluation of branded beef and also to determine what other product attributes benefit with a premium to six specific cuts of beef. Hedonic models were estimated using Ordinary Least Squares regressions to determine which variables affected the overall price per pound of each of the six cuts of beef chosen to analyze. Results indicate that there is an incentive to brand beef products at the retail level. Local, regional, national, and store brands all garnered premiums across the six models for the beef cuts, steak, roast, ground, strip, cube, and ribs in relation to products with no brand. Other variables that garnered premiums across all models include organic, Prime quality grade, and Kosher and Kosher-Glatt religious labels. Steak exhibited the highest mean price per pound followed by cube, roast, strip, ribs and ground. In all of the models estimated explaining price variation, there were few coefficients that were statistically insignificant. Additional modeling was done to determine if outlier observations were influencing the regression results. The sensitivity analyses resulted in small changes in parameter estimates indicating the identified influential observations did not have undue impact on the parameter estimates.

The thesis presented consists of two essays that analyze input substitution and decision making in crop and livestock production systems. The first essay consists of a whole-farm analysis that sought to optimize feed mixes and enterprise combinations for an organic dairy operation in the Southeastern United States. This was accomplished through mathematical programming where whole-farm net returns were maximized, and total feed costs were minimized simultaneously for four milk production level cases. Additionally, the sensitivity of the system and break-even milk price were explored. Results suggest substitutability in ration components where an increase in supplemental feeds is justified by additional milk output and sales. The second essay utilizes econometric methods and hedonic modeling to explore factors that drive the price of row crop planters on the used machinery market. Factors relating to make, age, condition, planter specifications, sale type, spatial aspects, seasonality, and year of the sale were analyzed. Results suggest non-linear relationships for row number and age relative to price and interactions between variables make and age that imply varying depreciation depending on the manufacturer. An additional break-even analysis relating to pasture yields and planter purchase price was conducted to explore these primary concepts in further detail.