Master of Agribusiness / Department of Agricultural Economics / Allen M. Featherstone / During the past 10 years, ethanol production in the United States has grown exponentially. From 2000 to 2009 U.S. ethanol production increased from 1.6 billion gallons annually to 10.8 billion gallons annually. In 2010, U.S ethanol production increased by 23 percent from 2009 to 13.23 billion gallons. The increase in ethanol production was due to lawmakers reacting to skyrocketing oil prices by implementing a Renewable Fuels Standard (RFS) in 2005 and expanding the RFS in 2007. The RFS requires the use of specified amounts of biofuels, such as ethanol, through the year 2022. The creation of the RFS represented a step beyond lawmakers’ usual policy of using the tax code to promote ethanol production. There is a long history of encouraging ethanol production by using the tax code, but the implementation of a biofuels mandate is new and therefore there is not a great deal of research on the effects of such a policy. This study analyzes U.S. oil, unleaded gasoline, corn and ethanol prices dating back to 1985 to determine the impact that the RFS has had on corn prices. The key question answered is whether the creation and expansion of the RFS has brought the instability of the oil market into the corn market. The prices that an ethanol plant in western Kansas paid for the grain it used to produce ethanol and the price that the plant received for the ethanol that it produced are also analyzed. The plant began operation in January 2004, so it is possible to analyze the grain and ethanol prices both before and after the implementation and expansion of the RFS. To study the impact of the RFS creation and expansion, the prices were analyzed to see if there was an increase in the correlation after the creation and expansion of the RFS. Regression analysis of the national corn prices and the prices that Western Plains Energy paid for the grain that it used to produce ethanol; and regression analysis of the national price of ethanol and the price that Western Plains Energy sold its ethanol for were also used to study the impact of the RFS. Finally, the vector autoregression (VAR) model is used to analyze the dynamic relationships between the variables in the system: corn price, oil price, ethanol price and unleaded gasoline price. The analysis of the correlation reveals that both at the national and plant level grain and oil prices track much more closely together after the creation and then expansion of the RFS. The VAR reveals that there is some relationship between corn and oil prices contemporaneously. The correlation matrix of residuals reveals that there is not a strong correlation between national corn and oil prices. The results suggest the need for greater research in this area. The creation and expansion of the RFS represented a step into uncharted territory and the consequences are still not known.

Master of Agribusiness / Department of Agricultural Economics / Michael R. Langemeier / The cattle market has drastically changed over the last half century. Today, ranchers and farmers are faced with various governmental regulations as well as fluctuating grain and fuel prices. While beef may still be a commodity, it can be sold in specialized markets, in markets that have enhanced consumer demand. It is nearly impossible for a producer with a small herd to compete with a larger ranch if they are selling their cattle as a commodity. The primary economic objective of the producer is to generate revenue. Producers must be profitable to remain in business as well as provide a livelihood for their family. Providing a quality product is part of the business model. Choice within a marketplace is beneficial for producers and consumers. That said forage-fed beef will prosper in marketplaces where consumers desire their product more than alternate products. If producers are intent on progressively growing their market share, Georgia-grown, forage-fed beef must be marketed correctly. This includes promoting it on a basis of locally grown, pasture raised, and other attributes consumer’s desire. The results of the marketing survey present data that may be helpful when marketing Georgia-grown, forage-fed beef. The weekly consumption of beef products provided insight into the potential scope of the market. Approximately 39 percent of respondents consumed beef products three or more times per week. Additionally, 43 percent of the respondents were not familiar with forage-fed beef. This is a significant share of the market that is possibly open to a new product like Georgia-grown, forage-fed beef. Similarly, traceability of the product was an important feature that customers preferred. In addition to a larger selection of naturally produced beef products, respondents also indicated they were willing to pay more for the product. Approximately 49 percent of the respondents indicated that they would be willing to pay up to a dollar more per pound for Georgia-grown, forage-fed beef. Another 30 percent of the respondents indicated that they would be willing to pay more than $1 per pound for Georgia-grown, forage-fed beef. The results of the survey appear to offer opportunities for producers of Georgia-grown, forage-fed beef to expand their market share.

Master of Science / Department of Agricultural Economics / Kevin C. Dhuyvetter / The 2008 National Stocker Survey defines the backgrounding/stocking of cattle as ―operations where calves are grown after weaning and/or preconditioning but before the feedlot. This includes calves purchased for this purpose as well as those retained by cow-calf producers post-weaning, but before marketing or retention through the feedlot. Backgrounding offers many benefits to farmers including, but not limited to, adding value to their feedstuffs—hay, grain, etc.—by feeding it to their cattle and potentially spreading risk by increasing marketing time or engaging in contracts with feedlots. However, producers also take on increased costs as it takes more time to wean, bunk-train, vaccinate, etc. compared to other operations in the cattle industry. This thesis attempts to analyze two studies using the 2008 National Stocker Survey. The first is how producer and operation characteristics—producer age, type of operation, income derived from backgrounding—relate to why producers find variables such as cattle prices, animal health management, marketing practices, and nutrition important. The second is how producer and operation characteristics relate to producers that use futures market contracts and options on futures. Binary and ordered logit models were used to find the statistical significance of the aforementioned studies.Since this survey was specifically designed to profile the stocking/backgrounding industry, some of the estimated models did not add a lot of value beyond the summary statistics for the various dependent variables. That is, the ordered logit models did not identify any strong relationships given that almost all of the producers that responded to these questions found feeder cattle prices, animal health management, marketing practices, and nutrition very important, which can be seen by analyzing the summary statistics. In addition, the binary logit models that were used for the futures market contract and options on futures models, found that the best way to pinpoint producers using either futures contracts or options was if producers were already using risk management strategies. Therefore, the survey’s purpose of profiling the stocker industry may be its best use.

Master of Science / Department of Agricultural Economics / Kevin C. Dhuyvetter / Structural change within the industry, improved management, and volatility in commodity markets are reasons to evaluate and monitor the dairy industry in the future. The dairy industry has shifted concentration of production between regions over time. The Southern High Plains region, including the states of Colorado, Kansas, Oklahoma, New Mexico, and Texas, has undergone cow inventory growth in the past ten years. Dairies have become more concentrated, management has become more refined, and the commodity markets have become more volatile. Education and tools are readily available to producers with issues on reducing production, animal health, and feed losses. Financial risk is a key area producers have limited knowledge and resources. Mitigating this risk is essential in today‟s marketplace to maximize gains and margins as well as create opportunities for the operation to succeed and be financially sound. There are several resources which approximate returns based on either a point in time reference or complete user input. This study allows users to reflect on 21 years of historical data, 1990-2010, as well as plug in their own data or use default market data to estimate projected returns over the next 12 months. This study also builds a modeling framework that will allow historical dairy returns to be estimated and future returns projected on a regular basis. Over time average herd size has grown to reduce cost per head and producers are more efficient, milk production per cow has increased to over 70 pounds per day. Historically prices have increased over time, but the spread between highs and lows has escalated. This model solidifies that milk price and production are key revenue drivers while feed, replacement costs, and labor are large cost components at 39, 17, and 6 percent, respectively. Additionally, changing market prices can intensify the gain or loss an operation will incur over the short term, the projection model shows 2011 just below breakeven due to strong commodity markets. Dairy operations in the Southern High Plains region have shown positive returns in 108 of 252, 43 percent, months with greatest negative annual returns 2006 and 2009.

Doctor of Philosophy / Department of Agricultural Economics / Ted Schroeder / This dissertation empirically examines how demand-enhancing activities conducted by food industry participants affect retail beef steak pricing, consumer demand for ground beef, and industry concentration. It follows the journal article style and includes three self-contained chapters. Chapter 1 uses a two step hedonic model with retail scanner data of consumer beef steak purchases to determine if there are incentives to identify certain attributes and to determine what types of attributes entertain price premiums and at what levels these premiums exists. Results indicate that most branded beef steak products garnered premiums along with organic claim, religious processing claim, and premium steak cuts. Factors influencing brand equity are new brands targeting emerging consumer trends, brands with regional prominence, and those positioned as special-labels, program/breed specific, and store brands. Chapter 2 reports tests of aggregation over elementary ground beef products and estimates composite demand elasticities. Results suggest consumers differentiate ground beef according to lean percentage (70-77%, 78-84%, 85-89%, 90-95%, and 96-100%) and brand type (local/regional, national, store, and unbranded). The range in composite elasticity estimates shows the value of analyzing demand elasticity based on differentiation and not simply considering ground beef as being homogeneous. Composite elasticity estimates provide improved understanding of how consumers make decisions concerning ground beef purchases. Chapter 3 examines industry concentration for the U.S. food manufacturing sector. This study is the first to examine whether particular subsectors within the food manufacturing industry, which operate in the presence of industry-funded check-off programs such as marketing orders, are more or less concentrated than industries without such research and marketing programs. Results provide evidence to support the hypothesis that industries with demand-enhancing check-off programs have lower concentration relative to industries without these programs.

Master of Agribusiness / Department of Agricultural Economics / Vincent Amanor-Boadu / Beans and cowpeas are considered nutritionally dense and good sources of protein. In this sense, they are considered excellent food in poor households, especially in those that exhibit high levels of malnutrition or under-nutrition. To address food security and nutrition security in poor countries, there has been an increasing interest in encouraging farmers to grow beans and cowpeas. This has spurred research in value chains for these crops in many countries, especially those that do not traditionally grow them as primary staples. Most of these research efforts have focused on the producer and consumer issues, with little or no attention paid to traders who operated between these two players in the value chain. The objective of this study, therefore, is to contribute to the literature on the bean and cowpea value chain research by identifying the factors influencing the participation decisions of traders in this segment of the agricultural economy in Zambia. Using data collected by the Pulse Value Chain Initiative – Zambia in 2011, a probit model was used to analyze data. The dependent variable trader participation in wholesale marketing of beans and cowpeas in Lusaka and its principal food markets. The explanatory variables encompass trader demographic characteristics and available assets or resources. The research explored the effect of the assets or resources on the choice to trade cowpeas or beans at the wholesale level in Lusaka with and without controlling for traders’ demographic characteristics. Three procurement sources are identified in the study: the local market within which the traders operate; producers/suppliers within Lusaka District; and producers/suppliers outside Lusaka District. The results indicate that the procurement source for beans and cowpeas influenced trader decision to operate at the wholesale level. For example, traders who purchased their produce from locations outside Lusaka District were about 37% more likely to participate in wholesale trade compared to those sourcing their produce within the market in which they operate when demographic characteristics of traders are not controlled for in the model. When the demographic factors are controlled, the likelihood of those procuring from outside Lusaka District participating in the wholesale trade declines slightly to about 34%. These coefficients were both statistically significant at the 1 percent level. The results also showed that traders using credit from friends and family were nearly 18% less likely to participate in wholesale trade than those borrowing from other traders, significant at the 5% level. Controlling for demographic characteristics led to a reduction of this likelihood to about 16.7%, significant only at the 10% level. There were no statistical differences between traders for all education levels and those without any education except for respondents with lower primary and lower secondary education. Traders with lower primary and lower secondary education had a 31% higher likelihood of operating at the wholesale level compared to those without any formal education while those with upper secondary education had about 26.7% higher likelihood of operating at the wholesale level compared to those without any formal education. Marital status was not a discriminant in the decision to operate at the wholesale level. However, males had about a 9% higher probability than females in operating at the wholesale level. Wholesalers tend to move larger volumes of produce and, hence, create wealth much quickly than retailers. Wholesalers are also more likely to be engaging processors when these exist in the supply chain. Given that traders sourcing their produce from outside Lusaka District are more likely to engage in wholesale trading, it recommended that further research into the intricate characteristics of these traders are explored. This future research will do well to explore the factors that specifically differentiate these traders from the others. Understanding these and their potential effects could allow policymakers to provide support and services to this class of traders to engage in structured relationships with larger organizations such as processors and exporters.

Master of Science / Department of Agricultural Economics / John A. Fox / The United States has been inspecting commercial meat processing for over 100 years. Currently there is a push to increase the number of bacteria that meat processors are responsible to test for, which would lead to an increase in the costs of production. The goal of this thesis was to investigate antimicrobial practices used in small beef processing facilities across the Great Plains and the costs associated with those practices. A survey instrument was constructed and administered resulting in a total of 39 usable surveys for analysis. Preliminary analysis of the data was followed by an ordinary least squares regression to determine cost increasing or decreasing practices and attributes of the small processors. It was determined that on average small meat processors face a per ton variable cost of $914.71 or $0.46 per pound. Regression analysis indicated that plants can benefit from economies of scale. It was also determined there are no cost savings from being a state inspected as opposed to a federally inspected plant. Certain practices such as dry aging beef to increase quality and decrease bacterial load were found to increase the variable cost per pound. Microbial testing accounted for only 0.5% of the total variable cost of production.

Doctor of Philosophy / Department of Agricultural Economics / Ted Schroeder / Food recalls have been an issue of great concern in the food industry. Stakeholder responses to food safety scares can cause significant economic losses for food firms. Assessing the overall impact that may result from a food recall requires a thorough understanding of the costs incurred by firms. However, quantifying these costs is daunting if not impossible. A direct measurement of a firm’s total costs and losses of revenue associated with a food recall requires firm-level data that is not available. The method utilized in this study overcomes this severe limitation. Using an event study, the impact of meat and poultry recalls is quantified by analyzing price reactions in financial markets, where it is expected that stock prices would reflect the overall economic impact of a recall. A unique contribution of this study is evaluating whether recall and firm specific characteristics are economic drivers of the magnitude of impact of meat and poultry recalls on stock prices. Results indicate that on average shareholders’ wealth is reduced by 1.15% within 5 days after a firm is implicated in a recall involving serious food safety hazards. However, when recalls involve less severe hazards, stock markets do not react negatively. Also, reductions in company valuations return to pre-recall levels after day 20. Firm size, firm’s experience, media information and recall size are drivers of the economic impact of meat and poultry recalls. That is, firms recalling a larger amount of product perceive greater reductions in company valuations. Additionally, recalls issued by larger firms are less likely to present negative effects on stock prices, compared to smaller firms. Moreover, firms that have recently issued a recall are less harmed by a new recall compared to those firms issuing a recall for first time. Thus, suggesting that investors take into consideration the past performance of a company when dealing with food recalls. Furthermore, media information has a negative impact on shareholder’s wealth. Findings from this study provide essential information to the meat industry. In particular, understanding the likely impact of such “black swan” events is critical for firm’s investing in food safety technologies and protocols.

Master of Science / Department of Agricultural Economics / Glynn T. Tonsor / The purpose of this analysis is to examine two aspects of price analysis in the stocker industry in order to better assist producers making purchasing decisions. One analysis looks at forecasting value of gain, while the second looks at drivers of price differentials between calves and yearlings. When analyzing forecasts on value of gain, weekly data was collected to compare a naïve approach and futures market implied basis-adjusted approaches that include one to five years of historical average basis. This allowed for the assessment of five different models for nine scenarios. The conclusions from this were inconsistent with what was hypothesized and the naïve approach was either worse or no better when compared to using the futures market implied basis-adjusted approaches to forecast value of gain. The drawback to this analysis was that it was solely influenced by error on forecasting the selling price and in future work a forecasting horizon will be incorporated on the buying price. In order to analyze the price premiums and discounts between calves and yearlings, a confirmation, update and expansion were completed following monthly models by Marsh (1985). Three elements are considered when predicting price premiums and discounts between two weight classes; cost of gain (proxied by corn price), slaughter price, and seasonality. Estimated models in the confirmation for years 1972 to 1982 and the update for years 1973 to 2013, show that premiums and discounts are influenced by expected changes in corn price and/or slaughter price, but not highly affected by seasonality. However, in the expansion for years 1993 to 2013, corn price, slaughter price, and seasonality were all significant to the models and in higher magnitude when compared to those results in the confirmation and update. Understanding the relationships between all variables in these models allows producers in the cattle-feeding industry to make management decisions based on current marketing conditions and trends.

Master of Science / Department of Agricultural Economics / Glynn Tonsor / This decade has seen movements in commodity futures markets never seen before. There are many factors that have intensified price movements and volatility behavior. Those factors likely altering supply and demand include governmental policy within and outside of the U.S, weather shocks, geopolitical conflicts, food safety concerns etc. Whatever the reasons are for price movements it is clear that the volatility behavior in commodity markets constantly change, and risk managers need to use current and efficient tools to mitigate price risk. This study identified market structural breaks of realized volatility in corn, wheat, soybeans, live cattle, feeder cattle and lean hogs futures markets. Furthermore, this study analyzes the forecasting performance of implied volatility, historical volatility, a composite approach and a naïve approach as forecasters of realized volatility. The forecasting performance of these methods was analyzed in the full period of time of our weekly data from January 1995 to April 2014 and in each identified market regime for each commodity. Previous research has analyzed forecasting performance of implied volatility, a time series alternative and a composite method. However, to the best of my knowledge, they have not worried about market structural breaks in the data that might influence the performance of the mentioned forecasting methods in different periods of time. Overall, results indicate that indeed there are multiple market structural breaks present in the volatility datasets across all six commodities. We found differences in the forecasting performance of the analyzed methods when individual market regimes were analyzed. There seems to be evidence that corroborates the idea in the literature about the superiority of implied volatility over a historical volatility, a composite approach and a naïve approach. Additionally, implied volatility encompassed all the information contained in the historical volatility and the naïve measure across each identified market regime in all six commodities. Our results show that when both implied volatility and historical volatility are available, the benefit of combining those measures into a composite forecasting approach is very limited. Our results hold true for a short term 1 week ahead realized volatility forecast. It would be of interest to see how results vary for longer forecasting time horizons.