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Teaching Farm Financial Management by Interactive Simulation William M. Edwards College courses that include farm financial management topics typically rely heavily on quantitative techniques to reinforce concepts presented in the classroom. These include numerous budgeting and analysis exercises which students are asked to solve out of class. While useful, there are several drawbacks to this technique. Student understanding of quantitative techniques in farm financial management can be enhanced by the use of simulation models. One such model, called Farmsim, has been used successfully in both classroom and extension teaching. It features enterprise selection, input choices, renting and contracting of resources, expansion decisions, short- and long-term financing options, risk management strategies, and cash flow control. By simulating 10 consecutive farming years, students learn whole-farm, long-term financial management concepts. Interactive input screens and complete printed financial reports minimize instructor time, give immediate feedback to students, and provide individualized results for use in class exercises. Key words: simulation, teaching, experiential learning, financial management, farm management. Article <top> Each example exercise must be accompanied by a complete, logical, and consistent set of facts which allows students to perform the desired analysis. Developing good exercises is a time-consuming task for the instructor. In addition, when all students begin with identical information and exercises are to be completed outside the classroom, the potential for collusion or copying among students always exists. Each exercise usually illustrates a different principle. Students rarely have the opportunity to integrate all the individual problem situations into a complete farm business situation (as a real-life farm manager is required to do). Comprehensive paper exercises that do allow integrative decision making become very time consuming to complete and grade. Even then, students are not able to implement the decisions they have made, or to see the results and learn from them. In particular, the long-term consequences of investment and financing decisions on the total business are difficult to show in a static, one-decision analysis. Farm Financial Management Simulators and Experiential Learning <top> As early as the mid-1960s, computerized farm management simulation models were developed to attempt to resolve some of the limitations of stand-alone decision exercises, and to more nearly emulate the decision-making environment of an actual farm manager. Conner compared them to the laboratory facilities used in physical and biological science courses for student experiments. One important difference (as noted by Schroeder, Tierney, and Kiser) is that economics laboratories must rely on simulated environments without tangible subjects. Many teachers of agricultural economics have relied on hypothetical business situations and computers to bring experiential learning situations to students. Koontz, Peel, Trapp, and Ward argue that these exercises are highly complementary to lectures and other inductive learning methods. In their recent study, Dobbins, Boehlje, Erickson, and Taylor reported that farm management simulators (a) improve student motivation, (b) increase realism, (c) integrate principles and methods of analysis, (d) improve dynamic decision making, (e) leverage instructor time, (f) sharpen interpersonal and communication skills, (g) provide practice in dealing with decision risks, and (h) provide experience using computer software. Major drawbacks to management simulation models have been the high initial development time and cost, the amount of time required to utilize and maintain the software, the need for students to have an adequate technical background in the industry being simulated, and the difficulty of evaluating students’ performance (Dobbins et al.). Boehlje and Eidman also stressed the large amount of required administrative time as an obstacle to using simulation models for teaching. The objective of this article is to describe how one computerized farm financial management simulation model has successfully addressed the above problems in both a classroom setting and an extension environment for over 15 years. The Farmsim Model <top> The simulation model discussed here has been used with over 3,000 undergraduate farm management students and over 800 agricultural lenders. It was developed at Iowa State University and modified by the Center for Farm Financial Management at the University of Minnesota. The model originally was named Farm-Man, but the current personal computer version is called Farmsim. The specific objectives of the Farmsim program are (a) to provide students with a whole-farm case study framework in which to analyze typical farm and financial management decisions, (b) to give students experience in interpreting farm record summaries and financial statements, and (c) to increase students’ interest by providing quick feedback from their own decisions and those of the rest of the class. In Farmsim, each student is given a hypothetical grain and livestock farm to manage for 10 years. Each year the exercise builds on information and results from previous years. New management decisions are introduced each week, and are coordinated with lecture topics and laboratory exercises. Default prices and potential yields vary from year to year, but are the same for all students. Typical price cycles are reflected, although no specific sequence of actual years is used. Due to different management and marketing decisions, however, every student achieves different results. Therefore, after the first few simulated years, each student has a unique set of data from which to work. Every student receives a detailed production and financial summary for each simulated year. This provides a whole-farm context into which the separate decisions that are analyzed can be integrated—and is particularly useful for analyzing effects on the long-term financial structure of the business. Characteristics of the Farmsim Model <top> All students begin with identical resources: 300 acres of land, small-scale swine raising and cattle feeding facilities, a set of used machinery, and one full-time operator. The initial debt load is roughly 30% of total asset value, and consists of a machinery loan and an installment land contract. Farm enterprises allowed the first year are deliberately limited to one feed grain (corn), one cash crop (soybeans), one annual livestock feeding activity (cattle), and one long-term breeding livestock activity (farrow-to-finish swine). While limiting the number of enterprises may detract from the realism of the model for some students, it makes the results easier to interpret. Allowing only a small number of possible enterprises also decreases the chances of biasing the rank order of the students by inadvertently assigning a particularly favorable set of prices or yields to one enterprise which only some of the students choose. The first year’s decisions are limited to a simple product-product choice between two alternative crops. The tool of enterprise budgets and the concept of gross margins are introduced to help analyze the decision. The first year is deliberately kept simple so that students can learn the mechanics of using the program without being overwhelmed by many decisions. In the following years, the decisions become more numerous and complex. In year two, students are allowed to purchase feeder cattle, which helps illustrate fixed and variable cost concepts, and requires a factor-factor decision, choosing the least-cost ration. Livestock enterprise budgets also are introduced at this time. In the third year, students are allowed to expand the farm by renting additional cropland and hiring extra labor. They also are given the opportunity to trade the initial machinery set for one of four larger sets. The model contains functions which decrease crop yield levels when planting and harvesting dates are delayed due to a lack of labor or machinery capacity. This creates diminishing marginal returns to rented land, an illustration of the factor-product principle. Livestock production efficiency also suffers if the labor supply is insufficient. The student-managers have the opportunity to purchase a combine instead of hiring a custom operator for harvesting their crops—a classic example of substituting a fixed capital investment for an annual operating cost. Beginning in year five, the swine production facilities can be expanded. Three types of facilities are available, each with increasing capital intensity. These illustrate capital-labor substitution considerations, as well as the effect that expansion has on farm cash flow needs and financial security. At this time, the students also can discontinue the swine enterprise and specialize in crop production, or crops and cattle feeding. One risk management tool is illustrated by allowing additional land to be rented under either a fixed cash rent lease or a crop share lease each year. Currently, the cash rent level is calculated as 36% of the average gross revenue per acre from corn and soybeans for the previous two years, a realistic relationship for Corn Belt rental markets. Students are allowed to rent as many acres as they wish. A new, more realistic routine is under development in which a fixed quantity of land is available to rent, and students must bid against each other to obtain it each year. Other risk-reduction options that are available include purchasing multiple-peril crop insurance, participating in an acreage set-aside program in return for commodity loan eligibility and a price deficiency payment (this option was deactivated in 1996), forward contracting grain sales for year-end delivery, hedging the sale of feedlot cattle with a futures contract, and diversifying enterprises. Between the sixth and seventh years of the simulation, a land auction is held. A fixed amount of land is sold in 80-acre parcels to the highest bidders. Bids are posted on a bulletin board for a 10-day period. Students are required to analyze both the profitability and the financial feasibility of purchasing land before they bid. They observe how those who have greater liquidity after the first six years are able to bid more for the limited amount of land for sale. Since land values are allowed to fluctuate, students subsequently learn how leveraged land purchases can affect market value net worth. Regardless of whether they purchase land or not, students can continue to rent additional land acres each year. Years 8, 9, and 10 do not introduce any new decisions, but allow students to refine their operating plans and observe the longer-run consequences of their past investment and financing decisions. The dynamic linkages among financial leverage, risk-bearing ability, and changes in net worth are similar to those described by Oltmans in a more narrowly focused exercise in which students invest a certain amount of equity funds in several risky alternatives for a series of time periods. Financial Management Options <top> Students have considerable flexibility in their use of credit. Short-term credit for operating expenses is borrowed in the spring of each year, and repaid at the end of the year. In year one, the amount of operating capital that is borrowed in the spring is determined within the program. In subsequent years, the student specifies the size of the loan. Interest rates on operating loans fluctuate from year to year. Students with a cash surplus earn interest on it. Funds also can be borrowed for feeder cattle purchases, land investment, combine and machinery purchases, and swine facilities expansion. Machinery loans are amortized for five years and swine facility loans for seven years, at a fixed interest rate. Land purchases can be financed by a long-term, variable rate mortgage or a lower interest, shorter-term contract. Minimum downpayments are established for all amortized loans. For simplicity, all loan payments are due at the end of each year. Students who have surplus cash at the end of the year are allowed to pay ahead on any of their amortized loans. Cash operating expenses, family living costs, and income taxes are paid automatically. If the cash balance at the end of the year is negative, the student has five choices: (a) sell more grain, (b) sell hogs as feeder pigs, (c) sell the combine if one is owned, (d) carry over short-term debt to the following year, or (e) refinance by borrowing against equity in land. Limits are placed on the amount of debt that can be carried over or refinanced. If the student exhausts all five options and still has a negative cash balance, additional operating credit is extended and a warning is given. One of the greatest benefits of using a whole-farm simulation model is that it forces students to integrate economic decisions with financial management constraints. For example, both expanding the hog enterprise and buying additional cropland may appear profitable when analyzed in isolation, but the equity position and repayment capacity of the business may make it financially infeasible to do both. Likewise, students who buy land can choose between a long-term loan with a higher interest rate, or a lower interest contract with larger principal payments. The long-term impacts of these decisions are observed in the later years. When a whole class completes an economic analysis of a decision about investing in land, machinery, or livestock, students tend to come up with similar results. However, when they assess the financial feasibility, generally with a long-term cash flow projection, their results can differ markedly, depending on their own debt-to-asset ratios, repayment schedules, use of hired labor, and so on. Likewise, when they are asked to develop a bimonthly cash flow projection for one year, the budgets are vastly different for each student. They can relate these differences to the specific management decisions they have made. Operating Procedures <top> The Farmsim model is written in Turbo Pascal programming language. It was originally run on a mainframe computer at Iowa State University which was accessed from time-sharing terminals located in campus buildings and dormitories. In 1995, this system was eliminated and the model was moved to a networked set of personal computers in the departmental computer lab. Although students can run the model only in the lab now, it is hoped that access can be extended throughout the university’s computer network in the future. Simulations are conducted over 10 weeks, with each week representing one production year. Each simulation requires an average of 15 to 20 minutes on the computer. Students’ responses to the input prompts are shown in tabular form on the screen, and they are able to change any response until they are satisfied with all of them. The flow of input screens and reports is summarized in Figure 1. Students are first prompted for information about their investment decisions for land, machinery, and livestock facilities. Next, they can rent additional cropland, input their annual crop production plans, and specify the amount of labor they wish to hire. Finally, they are given the opportunity to sell old crop grain in the spring, and contract new crop grain for fall delivery. When all the information about these decisions has been entered, the students’ results for the year are displayed in a series of tables which provide immediate feedback about their decisions. The first reports summarize swine, crop, and cattle production. Next, the student’s cash flow statement for the year is shown. If the ending cash balance is negative, he/she must continue taking the actions described earlier until a positive balance is achieved. After this, prepayment of principal on amortized loans is allowed. Before they make year-end marketing decisions, students are informed of their estimated taxable income. Once all decisions are completed, hard copies of the reports are printed. Financial statements that summarize net income, income taxes, net worth, and business analysis ratios also are included, as well as a trend sheet showing key results for each year completed. A final table contains outlook information for the next year. The data from these reports then are incorporated into future class exercises. Students can print extra copies of reports from past years at any time. Each week a composite summary showing the average results for the entire class is generated and made available to students. By comparing their own performance to the class averages, students can better evaluate their decisions. A complete class ranking, based on ending net worth, is compiled and posted each week. This element of competition with their peers motivates students to analyze their decisions carefully before each weekly assignment, and creates additional interest in the exercise. Figure 1. Farmsim Program Flow
Students are allowed to rerun each simulated year as many times as they wish. Although this does not reflect real-life decision making, it stimulates students to do further analysis and comparison of their results. Inductive learning takes place when students compare results from performing successive simulations of the same year or a series of years. This "trial-and-error" approach is useful as long as students make only one or two changes each time, and can relate the differences in their results to the particular management choices that they made. Results from Farmsim <top> The large number of decisions made in the later years of Farmsim and the stochastic nature of the year-to-year prices and crop yields make it difficult for some students to correctly discern the cause-and-effect relationships between their actions and their results. Therefore, it is extremely important that the lab instructor take time each week to discuss the key decisions that were made and explain how they affected the range of results observed among students. The instructor should be thoroughly familiar with the program, and willing to help students who achieve below-average results understand how they could have improved their performance. To encourage more thorough analysis, very few limits have been placed on the amount of credit that can be used, or on the amount of land that can be leased or purchased. The only real limiting factors on farm size are machinery capacity and the amount of extra labor that can be hired. As a result, a few students usually rent too much land and experience a very large "disaster" year which drives them so deeply in debt that they can never recover financially. They quickly appreciate the concepts of leverage and increasing risk. The opportunity to repeat each year allows them to correct their mistakes and continue the simulation. Students who achieve the best results are generally those who take extra time to analyze decisions over and above that required to complete the lab exercises. Typically, about 5% of the students finish with a negative net worth, while those at the top of the rankings roughly double their initial net worth after 10 years. Students sometimes want to know what the "correct" decisions are. They are surprised to learn that no one strategy guarantees success. The common denominator for success seems to be consistently paying attention to details for each decision and each year. Achieving the proper balance of land, labor, and machinery is also important, as is careful cash flow management. Students who expand rapidly and are highly leveraged usually finish either near the top or near the bottom of the rankings. They earn higher profits if their other decisions are favorable, but suffer larger losses if they are not. Those who are more conservative generally finish near the middle. For the most recent semester, the correlation coefficient between students’ final ranking in Farmsim, based on ending net worth, and their total points for the course (excluding points awarded for Farmsim) was 0.47, significant at the 1% level for a class of 142. For an earlier semester, the correlation coefficient was 0.35, also significant at the 1% level, for a class of 101 students. Apparently those factors that allow some students to outperform others on exams and lab exercises also help them achieve better results in Farmsim. At the end of the semester, students are asked to evaluate their results in a written report, and selected students are asked to give short oral reports. Student evaluations of Farmsim have been very positive. Students generally have found the exercises to be stimulating, enjoyable, realistic, and educational. Suggestions for improvement have been directed mostly at details of the program rather than at its general structure or the overall concept of a farm management simulation. Using Farmsim in Extension Education <top> Although most farm management simulation models have been utilized for classroom teaching, Farmsim also has been used successfully in agricultural lender schools sponsored by Iowa State University and the University of Minnesota. Both schools emphasize farm financial decision making, and Farmsim allows the lenders and examiners to play the role of a farm operator throughout the simulation. Compared to the classroom scenario, there are a few differences in operating procedures for the banking schools. For example, two-person teams are designated, to facilitate interaction and to help those participants who have a limited knowledge of production agriculture. Two years are simulated each day of the school, so less time is available to analyze decisions and evaluate results. However, the banking school participants are more familiar with farm financial statements and budgets than college students are, and they usually can assimilate and analyze the information in less time. Rankings and averages are computed each day. At the end of the week, each team completes a summary of its strategies and results, and selected teams present oral reports to the group. Issues and Conclusions <top> The following issues regarding management simulations were raised at the beginning of this article: initial development costs, administrative and maintenance time, technical knowledge required of students, and evaluation of student performance. All of these concerns have been dealt with, to some extent, by Farmsim. Initial Development <top> The development of a management simulation model is similar to that for other educational tools such as a textbook or a case study. The initial investment is high, but the long-term payoff is large. In particular, if extensive reports are produced and results are carried over from one period to the next, a detailed set of accounts and data files must be incorporated. The initial 1982 version of Farmsim was written and placed into service in a little less than a year, using undergraduate students as paid programmers. Improvements in computer hardware and software since 1982 probably would reduce this time significantly today. Over the years, new options have been added and many procedures have been streamlined. The Center for Farm Financial Management at the University of Minnesota made major improvements in the input routines when Farmsim was converted from a mainframe to a microcomputer program. One obvious way to avoid the initial development costs of a simulation model is to adopt one that is already in use. Farmsim has been used only at Iowa State University and (for extension purposes) at the University of Minnesota. Whether Farmsim could be transferred to another institution depends on the extent to which the user would be willing to accept the activities and conventions that the model contains. The program contains a simple instructor utility for changing crop yields, commodity prices, and interest rates each semester. Other parameter values, such as machinery field capacities, investment costs for machinery and swine facilities, crop and livestock operating costs, labor requirements, tax rates and brackets, and livestock rations, are embedded in the source code. However, these values are clearly identified and could be changed by anyone who knows how to edit and recompile Pascal files. Adding or changing enterprises would involve more extensive reprogramming. Farmsim is written for an MS-DOS operating system. The entire program can be saved on one 3.5-inch diskette, and can be executed with a simple DOS command. To modify the program requires a current version of Turbo Pascal programming language and some knowledge of how to edit and recompile the files. Hardware compatibility is probably less of a problem today than it was before desktop computers with standard operating systems became common. In its current version, Farmsim contains no graphics, and data are entered by using keyboard commands. The introduction of new operating systems that utilize a mouse, icons, high-quality graphics, and even sound make generic screens with text and tables seem dull by comparison. On the other hand, high-quality software development can be very expensive, and does not necessarily enhance the educational value of the program. Rewriting Farmsim to take advantage of the capabilities of more advanced programming languages and operating systems likely would increase student interest and satisfaction. Whether it would also increase student learning is a question that needs to be tested. Administrative Time <top> Hardware improvements have reduced the time needed for maintaining simulation modules. Although shortcomings and failures of the hardware must be endured, having students input data and receive their own results is still preferable to using instructor or clerical time to perform these tasks. About three hours are required to set up the model at the beginning of the semester, and one hour or less per week thereafter is needed for servicing. The instructor utilities make setting up the student files, changing parameter values, and monitoring student results relatively easy. The instructor also controls the last simulation year students can run, to prevent some students from working ahead of others. A summary of each individual student’s decisions and results for each year can be generated, which allows close observation of class performance. The most time-consuming activity for the instructor is discussing and interpreting the simulation results with students, both individually and as a group. Fortunately, the educational payoff of this activity can be quite high. Careful coordination of the lecture schedule, the laboratory exercises, and the introduction of new decisions into the simulation is highly desirable, but sometimes difficult to achieve. For some simulation years, students may have to make decisions with very little analysis simply because there hasn’t been sufficient lecture time to cover the topic. Technical Knowledge <top> The problem of students who lack knowledge about the technical aspects of the simulated farming activities is addressed in two ways by Farmsim. First, by keeping activities limited in number and simple in nature, even at the expense of realism, students can concentrate on principles and avoid getting bogged down in an excessive amount of detail. Examples of this from Farmsim include allowing only two different crops, and grouping all machinery except the combine into a single set without identifying specific machines. Secondly, the technical information that is required is clearly laid out in the student manual, which is discussed with the students at the beginning of the semester. There are some differences of opinion about the degree of realism needed for management simulators. Conklin and Becker argue that prices, yields, and production costs that do not closely mirror reality force students to analyze the information given to them rather than make decisions based on preconceived notions or previous experience. The counter-argument is that familiar, realistic problem settings are necessary to create credibility in the model. Combining students into teams of two, three, or four persons could increase student interaction and help students with limited technical knowledge benefit from the experience of others. However, this practice makes it more difficult to fairly evaluate each student’s contribution. Students also may find it difficult to coordinate their schedules for participating in joint activities. Evaluation of Students <top> How to evaluate students’ performance in Farmsim or any other simulation model is very much an individual question. The results generated by the simulations provide a very complete database from which various quantitative measures of achievement could be derived. Basing at least part of their grades on the level of profitability achieved (or some other economic criteria) stimulates students to take the exercises seriously and enhances their level of interest. Currently, students can earn a total of 30 points for completing Farmsim on time each week, and from 130 points based on their final net worth ranking. This amounts to about 8% of the total points possible in the course. For several years, points were awarded for completion only and not for relative net worth position, but it was observed that the level of student effort devoted to Farmsim was less than when ranking points were awarded. Some students have suggested that points should be awarded for other factors such as improved solvency (e.g., low debt-to-asset ratio). This argument has merit. Farm families usually have multiple goals—such as building net worth, improving cash income, and controlling risk exposure. A student evaluation scheme that considers all of these factors might be more equitable. The Farmsim model has achieved some success in addressing all four of the above concerns. No doubt it will be superseded by future models that will incorporate new generations of computer software and hardware, and more imaginative design characteristics. Nevertheless, the basic elements described in this article will still need to be incorporated for effective learning about farm financial management to take place. References <top>
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