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Playing marketing games
When this week is over, the University of Nebraska teaching team will have completed four of the marketing workshops we named “Marketing in a New Era”.
I was on the team for two of the meetings, at Geneva and West Point. In these meetings, we test marketing theories using a computer spreadsheet program to compare the timing of sales made during the pre-harvest time period.
Farmers are assigned to write and implement a marketing plan for 600 acres of irrigated corn. They are allowed to sell at as many as 12 different times during the period between April 1 and October 1. The farm has an APH of 200 bushels per acre. Prices and production numbers are from an actual year. Participants are not told until the end of the game what year it was or how much corn was produced during that year. Sales decisions are turned in to a record keeper at the end of each two week time period, during the marketing year, and then entered into the computer.
Sales are totaled for each participant at the end of the game and compared to benchmark strategies, which were designed to represent ways average farmers might sell their grain. Comparisons between contestants and the benchmarks give the participants a chance to see how their strategies would have performed under real production and price conditions.
In the games we have played so far this year, selling everything at harvest with no forward-pricing resulted in a net return over production costs of $112,572. Forward-pricing 100% of the production in equal increments during the six month period resulted in a return over cost of $180,400. Selling all of the production at the highest price of the season resulted in a net return of $369,820. The selling price of that strategy was $7.26 per bushel.
At some point, the software we use for the game will become available to the public. It is not developed to the point where that is feasible yet. Workshops this year are giving it a good test. The fun part of using this spreadsheet is to compare the sales strategies in a variety of years. Price patterns and yields vary from year-to-year. Clearly, the year we used for the target year for this round of meetings offered some excellent prices and good profit levels. It is not realistic to expect that to happen very often.
At the end of the meeting, I ask the question “Why not forward price everything”? Those who forward priced 100% of their APH had the highest net return in the games played, so far. The first excuse that comes to mind is that you might not raise that much. Using revenue crop insurance offsets part of that risk. A second and more important reason is that the market and growing conditions do not always make forward-pricing the best choice. Procrastination is the most profitable choice, occasionally. Forward-pricing, using futures or options followed by a storage strategy after harvest, is a possibility that is often a profitable combination.
Every time I run one of these simulations I learn something. There are more workshops coming up in March. I hope to see you there.