Doctor of Philosophy / Department of Agricultural Economics / Ted C. Schroeder / Risk is an inevitable part of agricultural production and all producers face various forms of risk. Output price has been shown to be the major contributor to the risk in cattle feeding, yet few choose to manage this risk. This study used subjective price expectations and price distributions of survey participants to determine how producer's expectations compare with that of the market. In addition, demographic information gathered from survey participants allowed for further examination as to how these factors effect price outlook and variability. Data used for this study were gathered through survey responses from Kansas State University Extension meeting and workshop participants and other meetings targeted to livestock producers. First, data were aggregated and analyzed at a group level. Only two of the twelve price forecast were significantly lower than the futures settlement price. On the other hand, all but one of the aggregated group volatility expectations was different. Typically nearby contract price risk expectation was underestimated and distant contract price risk expectation was overestimated. Individual respondent's discreet stated price and price distribution information was fitted to a continuous distribution and an implied mean and standard deviation were determined. These were compared to market price and price risk data. Respondent's expectation of price was significantly lower than the market for distant months for five of the six groups. Individual volatilities resulting from each fitted distribution were significantly lower from the volatility measure resulting from Black's model. Demographic data were estimated to show the impact of this information on overall error of price forecast and price risk expectations. Those living outside the Northeast and Northern Plains tended to have larger error in their expectation of price volatility. Larger backgrounding operations reported lower price variance error and selling more fed cattle each year increased price risk expectation error. Lastly, prior use of risk management tools for the most part did not have an impact on error in either price expectation or price volatility expectation.

Master of Science / Department of Agricultural Economics / Kevin C. Dhuyvetter / Risk is an inevitable part of production agriculture. Price risk is especially a concern for cattle producers in the Midwest. Producers can curtail profit volatility, to an extent, through the utilization of price risk management strategies such as forward contracting, hedging, using put and call options, Livestock Risk Protection Insurance (LRP), as well as Livestock Gross Insurance (LGM) for feedlot cattle. Learning about such price risk management tools can be a daunting task. Kansas State University Extension created a computer based simulation workshop to assist them in teaching cattle producers about price risk management strategies. The simulation paralleled a lecture where participants learned of the price risk management strategies that are available. The simulation allowed the workshop participants to practice using the management strategies as they assumed the role of a feedlot or ranch manager in charge of marketing the operation's calves. In a cooperative effort with Iowa State University, Kansas State University presented the Cattle Risk Management Workshops across the state of Iowa. Participants were given pre-and posttests to measure the effectiveness of the workshop. The overall post-test scores were 25 percentage points higher than the pre-test scores. This research also discusses the interest and perceptions of cattle producers regarding price risk management strategies. The effectiveness of simulations as a teaching tool in helping producers learn about price risk management strategies is also reviewed. In addition, the various price risk management strategies available to producers, as well as seasonality of prices and basis are analyzed. This research also explains and estimates the LRP Feeder Cattle Basis Model. The LRP Feeder Cattle Basis Model was developed with the objective of assisting producers in forecasting LRP basis. The model was developed using similar methodology applied in the creation of a CME basis forecasting model developed by Kansas State University Extension and Custom Ag Solutions, Inc. The LRP Feeder Cattle Basis Model automatically adjusts for the LRP price adjustment factor applied to beef steer calves weighing less than 600 pounds, and beef heifers weighing 600-900 pounds. The LRP Feeder Cattle Basis Model explains 71.37 percent of the variation of LRP basis.