Master of Agribusiness / Department of Agricultural Economics / Allen M. Featherstone / In the United States, accurately predicting the agricultural industry’s future demand for new farm machinery is a complicated, challenging and ever-changing issue. To compound the matter; as the size of large farm machinery continues to increase, the annualized sales volume is decreasing over time. This thesis also finds that recent mandates applicable to the Environmental Protection Agency (EPA) diesel engine emission compliance and the Internal Revenue Service (IRS) Section 179 tax code may help with forecasting the demand for farm machinery on an annual basis. This thesis evaluates factors that affect the annual unit demand of combines in the United States. Due to the lack of published literature on this specific topic, a survey of John Deere dealership sales professionals who have had recent experience selling new combines to farmers was used. This perspective brings to light factors that impact industry demand for new combines. This study results in an empirical regression model with independent variables based on the survey results. A thorough understanding of the independent variables can aid in predicting the future demand for combines. This work indicates that forty years of historical data proves to provide enough variability such that statistically significant variables are identified to accurately predict future sales. Statistically significant factors that affect the annual unit sales volume of combines in the United States include: Interest Rate, Net Cash Income, IRS Section 179 Tax Code, Planted Acres and Combine Capacity. Future industry demand is predicted by applying forecasted estimates to the model’s applicable independent variables.