Master of Science / Department of Agricultural Economics / Andrew Barkley / This research identifies and quantifies the impact of biophysical and economic variables on Kansas crop acreage and yields for the period 1977- 2007. Due to long production time requirements, agricultural producers must make vital decisions with imperfect information, based on expectations of future agronomic and economic conditions. This research analyzes the impact of price, climate, and yield expectations on crop acreage allocations and yield responses for the four major commodities produced in Kansas: corn, soybeans, wheat, and grain sorghum (milo). By modeling and analyzing both biophysical and economic variables, total supply response can be estimated for potential future changes in prices, yields, climate, and weather outcomes. The analysis of both biophysical and economic conditions allows for the estimation of supply response in the short and long run. The results provide updated, more precise results than previous research, which has often separated acreage and yield response.

Master of Science / Department of Agricultural Economics / Ted C. Schroeder / Glynn T. Tonsor / Cattle feeders face significant profit risk when placing cattle on feed. Risks arise from both financial and biological sources. To date, few standardized measures exist to measure current risks against historic levels, or to obtain forward looking risk estimates. Those that do exist could benefit from updates and inclusion of additional risk elements. This study measures the risk of expected profits when cattle are placed on feed. This study creates a forward-looking estimate of expected feedlot profits using futures and options market data as price forecasts. Joint probability distributions are created for prices and cattle performance variables affecting feedlot profit margins. Monte Carlo simulation techniques are then employed to generate probability distributions of expected feedlot profits. Results show cattle feeding is a risky business and cattle feeders have been placing cattle on feed facing significantly negative expected returns since June, 2010. This assessment of negative expected profits is consistent with other findings. Over the study’s 2002 to 2013 time frame, the relative risk to cattle feeding profits accounted for by feed costs has been increasing, while the relative risk levels from feeder cattle and fed cattle prices remain steady. Additionally, the probability of realized per-head profits greater than $100 has been decreasing since 2009 and the probability of realized per-head profits less than $-100 has been increasingly rapidly.

Master of Agribusiness / Department of Agricultural Economics / Allen M. Featherstone / Since 1977, the State of Illinois has used a use-value method of assessing farmland for property taxes. The method establishes farmland value by determining a five year average of net income from the land that is capitalized using a five year average interest rate. Other real estate in Illinois follows a different procedure for assessment. For example, residential property is assessed at one-third of its market value. The differences among the methods of assessment for farmland and other types of real estate, along with recent market increases in farmland values and a strong agriculture economy, have led some to question the current method of farmland assessment. The objective of this thesis is to determine the financial impact to farmers resulting from changing from the current use-value assessment of farmland to market-value assessment. This is accomplished with two sub-objectives: determine the potential change in farmland values that could occur and to determine the impact on net farm income that could occur if property tax policy was changed to market-value assessment. To accomplish the first sub-objective, a model was developed to estimate farmland values in Illinois based on the current use-value assessment property tax policy. This model was then adjusted to estimate farmland values under a market-value assessment property tax policy. The models demonstrated that farmland values could fall 53 percent, or an average of $2,548 per acre, in the year immediately following implementation of a tax policy change. Once farmland values stabilize after implementation of the tax policy change, farmland values would be 30 percent less, or an average of $1,875 per acre less, under market-value assessment than under use-value assessment. A simulation of net farm income over a ten year time frame was then conducted to estimate the potential change in net farm income that could occur from a change to market-value assessment. Like farmland values, the greatest impact to net farm incomes occur in the first year market-value assessment is implemented. Farmland values and the resulting property taxes then stabilize during later years. The simulation of net farm income over a ten year time frame estimates that net farm income would be 8 percent lower per year, or a reduction in net farm incomes of an average of $12,721 per year, under market-value assessment. The analysis also showed the potential for an average of a 2 percent increase in the probability that net farm income would fall below zero over the simulation time frame. The analysis demonstrates that a change from use-value assessment to market-value assessment of farmland could reduce farmland values and net farm incomes. Such a change in policy is not in the best interests of farmers or the agriculture industry in Illinois, as the reduced values and incomes would have wide reaching negative consequences that could reach beyond farmers and farmland owners.

Master of Agribusiness / Department of Agricultural Economics / Jason Bergtold / Company managers continually face challenges in the market, such as increased demand for their services and variability in the types of service requested. In addition, managers may face internal challenges during periods adjustment such as moving the company forward through a hiring freeze. In these situations, a manager must be able to allocate their scarce resources in a way to continue to perform. For employees, this could mean specializing in tasks or increasing crosstraining to improve work schedule flexibility. The objective of this research is to determine the optimal allocation of employees to tasks, given resource constraints and the need for staff flexibility, to satisfy alternative management strategies. The setting is the service industry, in particular a laboratory setting providing testing and consulting services. An optimization model was developed to incorporate key aspects of a company’s operation, and determine labor allocation among tasks, and for how many hours, to satisfy the manager’s objective. The model estimates the optimal allocation of labor and how much production and net revenues would be generated, with more specialized employees. A sensitivity analysis was employed to determine the impact of cross-training current staff. Results indicate that cross-training affords flexibility; however, the impact on overall production varies depending on the employee trained. The highest benefit is derived from training a lower-producing employee into a high value task at a high productivity rate. Specialization can help to improve productivity in net returns for higher valued tasks, but may limit flexibility, as employees cannot switch between tasks as readily.

Master of Agribusiness / Department of Agricultural Economics / Allen M. Featherstone / Institutions of the Farm Credit System (FCS) focus on risk-based lending in accordance with regulatory direction. The rating of risk also assists retail staff in loan approval, risk-based pricing, and allowance decisions. FCS institutions have developed models to analyze financial and related customer information in determining qualitative and quantitative risk measures. The objective of this thesis is to examine empirical account data from 2006-2012 to review the probability of default (PD) rating methodology within the overall risk rating system implemented by a Farm Credit System association. This analysis provides insight into the effectiveness of this methodology in predicting the migration of accounts across the association’s currently-established PD ratings where negative migration may be an apparent precursor to actual loan default. The analysis indicates that average PD ratings hold relatively consistent over the years, though the distribution of the majority of PD ratings shifted to higher quality by two rating categories over the time period. Various regressions run in the analysis indicate that the debt to asset ratio is most consistently statistically significant in estimating future PD ratings. The current ratio appears to be superior to working capital to gross profit as a liquidity measure in predicting PD rating migration. Funded debt to EBITDA is more effective in predicting PD rating movement as a measure of earnings to debt than gross profit to total liabilities, although the change of these ratios over time appear to be weaker indicators of the change in PD rating potentially due to the variable nature of annual earnings of production agriculture operations due to commodity price volatility. The debt coverage ratio is important as it relates to future PD migration, though the same variability in commodity price volatility suggests the need implement multi-year averaging for calculation of earnings-based ratios. These ratios were important in predicting the PD rating of observations one year into the future for production agriculture operations. To further test the predictive ability of the PD ratings, similar regression analyses were completed comparing current year rating and ratios to future PD ratings beyond one year, specifically for three and five years. Results from these regression models indicate that current year PD rating and ratios are less effective in predicting future PD ratings beyond one year. Furthermore, because of the variation in regression results between the analyses completed for one, three and five years into the future, it is important to regularly capture ratio and rating information, at least annually.

Master of Agribusiness / Department of Agricultural Economics / Bryan Schurle / Quantifying the impact of various economic events is essential for risk management in community banks. Interest rate shocks of either rapidly increasing or decreasing rates, in magnitudes of at least 200 basis points, is one of the more common risks modeled. Liquidity crises that impact deposits or loan demand can arise from either local or national economic events is another risk factor that regulators are requiring banks to quantify and plan for. Excel spreadsheets can be used to develop models to measure and quantify these risks. Simulation tools and what-if analysis using data table and scenario manager identify possible outcomes for differing interest rate scenarios, interest rate shocks and liquidity stresses. Data table was used for simulation of a stochastic model to produce a cumulative distribution function of two hundred results each on three different interest rate environments. Scenario manager was used to narrow the simulation to a certain set of expectations affecting the balance sheet of the bank and another set of expectations from an interest rate shock. Changes in the bank’s balance sheet resulting from three different commodity price expectations were modeled. An interest rate shock of four hundred basis points over a two year period was also modeled. These models are simple and cost effective. Once data are captured, the time required to develop and generate scenarios is manageable. The model can be used for a wide range of what-if alternatives as an individual bank may see fit. These models are adequate to meet present regulatory requirements for a community bank of smaller size that is not complex and does not possess a high risk profile.

Master of Agribusiness / Department of Agricultural Economics / Vincent Amanor-Boadu / As the dairy industry faces the future, consumers’ demand for better milk quality and safety is increasing. Milk quality is of major interest to both consumers and dairy farmers alike. However, scientific data on milk quality in terms of somatic cell count (SCC) in Uganda and most developing countries has been lacking. This study used SCC to compare Southwestern Uganda’s milk quality against international standards. The study also sought to assess dairy farmers’ perceptions about milk quality. Milk samples were obtained from 100 farms in Mbarara and Kiruhura districts, the major cattle corridor in Uganda. The milk’s SCC was analyzed using a DeLaval DCC. A structured questionnaire surveyed farmers on milking procedures and milk-quality perception. Descriptive statistics and qualitative analysis was used to characterize and compare milk quality against the international benchmark. The study found that the 100 farms had an average SCC of 507,000 cells/ml. About 34% of farms in the study had SCC under 200,000 cells/ml, an indication of high-quality milk. Excluding 7% of the farms with SCC over 1,000,000 cells/ml, the remaining 93% had an average SCC of 276,000 cells/ml, a level comparable to international standards, well below the EU threshold of 400,000. The study also revealed that 98% of farmers considered milk quality as important or very important both to them and to the milk buyers. However, all farmers reported that they currently do not receive a milk-quality premium and are not penalized for poor quality. Seventy-nine percent of farmers reported the cooperative they belong to as their main source of information on management practices. An improved perception of milk quality both domestically and internationally will benefit Uganda’s dairy farmers and its dairy industry at large. Consumers must be assured that Uganda’s dairy industry, its government, industry stakeholders such as the Dairy Development Authority, the Uganda National Bureau of Standards, and the private sector place the utmost importance on the quality and safety of milk and other dairy products. New technologies to measure for SCC and strict food safety regulations will help improve the country’s milk-quality image, allowing Uganda’s dairy industry to tap into major milk export markets. Most developed countries have seen increased raw-milk quality or reduced SCC as a result of strong regulatory pressure.

Master of Agribusiness / Department of Agricultural Economics / Gregory Ibendahl / One of the latest buzzes amongst agriculture is the storage and analysis of “Big Data.” There are a number of questions surrounding the quality, quantity, and capacity of big data to form real-world decisions based upon past information. Much like the teachings of history, the storybook that big data can reveal about a grower’s operation may hold the answers to the question of: “what is necessary to increase food production which will be required to feed an ever-growing world?” With the increase in interest in precision agriculture, sustainability practices, and the processing of the immense spatial dataset generated on the farm, the next challenge at hand will be in determining how to make technology not only streamlined, but also profitable. Over the past few years, precision agriculture technology has become widely adopted as an agronomic decision making tool. Much like a scientific experiment, the greater the number of similar observations, the greater the degree of confidence can be placed upon a decision. As a means of increasing the number of observations that a farmer can use to base a decision upon, there is becoming increasing demand in being able to combine the data of similar farming operations in order to increase the size and scope of the dataset to generate better decisions benefitting many farms instead of just one. The growing interest in forming community data pools for farm data demonstrates the need for a study for determining how farming practices can be properly benchmarked. The goal was be to evaluate how to use farm data to make economic decisions in a similar manner as one would make agronomic decisions using similar observations. The objective was to design the proper protocol for benchmarking the farm’s potential, and evaluating potential increases in technical efficiency by adopting precision agriculture technology. To accomplish this, a data envelopment analysis was conducted using scale efficiency as a means of determining the frontier of efficient farms. The resounding goal for this study in the future will be to use the model as a means of implementing the secondary process of pooling precision agriculture data to analyze efficiencies gained by the adoption of technology. By demonstrating the value of generating peer groups to increase observations and refine farming practices, farmers can find increased profitability and efficiency by using resources that may already be held within the operation.

Master of Agribusiness / Agricultural Economics / Vincent R. Amanor-Boadu / The United States was the world’s third largest producer of raspberries (by pounds) in 2013, behind Russia and Poland. Raspberries are the third most popular berry in the United States behind strawberries and blueberries. Most U.S. production of red raspberries occurs in the states of Washington and Oregon during July and August depending on variety. Harvest and production for industrial pack typically runs for five weeks. Sieved red raspberries or single strength red raspberry puree is one of many industrial packs produced in the Pacific Northwest of the United States. Sieved red raspberries are produced by forcing fresh, cleaned and sorted red raspberries and red raspberry crumbles and pieces through a mesh screen, collected in drums or pails and stored for use in further processed products such as pies, confectioneries and other consumer food products. For this thesis, sieved berries are packed in 55-gallon steel drums lined with food grade plastic bags. They are shipped from the processing plant to a third party warehouse to be frozen and stored. The final processing plant draws on these stored frozen products for use in the production of the Company’s consumer food products. The purpose of this thesis is to review the Company’s current procurement practices of sieved red raspberries and determine how these practices may be improved to reduce its total delivered cost. We use an optimization modelling approach to assess the procurement process used by the Company. The results indicate that it is possible to reduce procurement costs and improve efficiencies by making changes to the current procurement strategy. By implementing the procurement strategy developed in this study, we show that the Company can save as much as $1.69 million per year, which is equivalent to about 20.3% of the current spend. This would suggest that adopting the optimization strategy could allow the Company to increase its total sieved raspberry utilization by as much as 0.9 million pounds per annum, all other things remaining unchanged.

Master of Agribusiness / Department of Agricultural Economics / Rodney D. Jones / After remaining stable for several decades, corn price has recently had unprecedented price increases and volatility. United States Department of Agriculture (USDA) predicts an average corn price of $5.80 per bushel for 2008, which is 232% of its 28-year (1980-2007) average price of $2.50. The record increase in corn price was the result of increased starch-based ethanol production associated with increased energy costs, and other factors such as a declining value in the United States dollar, and increased global commodity demand. High corn prices have impacted the profitability of the livestock feeding industry. It was less clear how the record high corn prices would affect the cow-calf industry since corn is not a significant input for cow-calf enterprises. This study quantified the relationship between cow-calf profitability and corn price. Because feed costs for a cow-calf producer are among the highest variable costs for the operation, both grazing and non-grazing feed costs were estimated as a function of corn price. Models were estimated to determine if a relationship between corn price and Returns Over Variable Costs (ROVC) at the cow-calf level could be identified. Corn price from 1978-2007 explained none of the variability in grass grazing rental rate, however when the projected 2008 corn price was included in the analysis, corn price explained 10% of the variation in grass grazing rates. Year (linear time trend) and corn price from 1978-2008 explained 88% of historical grass grazing rental rate variability, 71% of alfalfa price variability, and 63% of other hay price variability in Kansas. These results suggest that the new corn market paradigm likely will increase the relationship between corn price and feed costs at the cow-calf level. Several models were evaluated using bulk diesel fuel price, feeder calf price, corn price, alfalfa price, other hay price, and grass grazing rental rate to estimate Kansas cow-calf producer ROVC. Models that included diesel fuel price, feeder calf price, grazing rent, and one of the harvested feeds (corn, alfalfa, or other hay) price explained 90-91% of the variability in ROVC. Models that included diesel fuel price, feeder calf price, and either grazing rent or corn price explained less of the variability in ROVC; using grazing rent explained 89% and using corn price explained 79%. Including grass grazing rental rate along with corn price, feeder calf price, and bulk diesel fuel price improved the model's ability to predict ROVC, explaining 91% of the variability. While cow-calf producers might use very little corn directly in their operations, this research shows that corn price is an important determinant of cow-calf production returns, and corn price can be used by producers to plan for future rising costs in order to maximize returns.