Master of Agribusiness / Department of Agricultural Economics / Daniel M. O'Brien / In recent years, the price volatility in agricultural commodity prices, as well as agricultural input costs, has drastically increased. Today’s famer is faced with difficult decisions concerning when to market their crop, as well as when to secure various inputs. An increase in information availability, coupled with increasing price fluctuations, can make these decisions even more difficult for producers. Although seasonal trends, forecasts, and technical market analysis can be helpful, market efficiency prevents accurate prediction of agricultural prices. Because marketing decisions can be difficult to make, the easiest decision for a producer to make is to not make one at all. However, failure to make sound risk management decisions can be extremely costly to a producer. There are two primary factors that impact a producer’s bottom line: cost of production and grain marketing decisions. Each producer has their own unique cost of production that changes throughout the year. Variable input costs can be volatile within a single growing year, and often the need for certain inputs changes. Marketing decisions and timing can be an even bigger factor in a producer’s gain or loss. Since price prediction is impossible, a producer’s time may be better spent focusing on information they can control. The purpose of this thesis was to test and evaluate a cost of production, crop insurance, and grain marketing calculator with a group of corn and soybean producers in Southeast Nebraska. It is hypothesized that providing customers with a multifaceted, integrated farm management and marketing decision making tool should help them be able to make more profitable risk management and marketing decisions. By knowing how factors as changing expenses impact cost of production and how grain sales impact revenues and profitability per acre, it is hypothesized that users will make more profitable farm management and marketing decisions. In October and November of 2014, twenty corn and soybean farmers were presented with the Grain Marketing Calculator. Grain sales in the 2014 and 2015 crop years were to be entered into the calculator by participating producers as they make their grain sales. Annual production history (APH), revenue protection insurance information, actual or expected yields, and total acres of each crop were entered into the calculator during the initial producer calculator rollout. Generalized costs were entered into the calculator prior to the producer rollouts. Participants were able to change the generalized costs to their actual costs if they chose to do so. Data were gathered from the participants using the Grain Marketing Calculator in March of 2015. Participants weighted average futures sales, weighted average cash sales, percent of APH sold, and percent of total production sold were collected. In March of 2015, the same information from another group of producers who did not use the Grain Marketing Calculator was collected. The two groups average results were compared to each other and regression analyses were done to determine statistical significance of the impact on the test groups’ results. At the end of the experiment, feedback was gathered from participants and improvements were suggested.