One major goal of prescription farming is to optimize application rates for seed fertilizer, and other agricultural inputs as a function of location within a field. There are four main components of a variable rate application system: (1) A method of determining the location of farm equipment (e.g. combines, tractors) within a field. (2) A system for controlling the application rate of materials (seeds, fertilizers, pesticides). (3) The capability to measure the results using a real time yield monitor. (4) An algorithm or prescription for determining what rates of materials to apply at each location. In this dissertation it is the fourth component that is the focus of the research. The main objective of this research was to develop a method of predicting yields as a function of position in the field that can be used to make fertilizer application rate decisions in a prescription farming system;The dissertation is organized into three parts: a discussion of general strategies for determining application rates and a review of the literature on research into in-field variability; development of a fuzzy logic yield model for predicting yields as a function of location; and discussion on implementation of a variable rate application system;In the yield model, rules combined yield data, weather data and estimated physical and chemical properties of the soil. The rules were adjusted manually for two transects and then applied to a third transect. Reasonably good agreement between measured and predicted yields at each position was obtained. The results indicated that this type of model could be combined with variable rate fertilizer application results and used as a decision support system for variable rate application. The results also indicated that it may be possible to develop a yield modeling program that operates only on yield history, weather data and application rates. However, the specific model described in this dissertation should not be used without incorporating some method of automatically optimizing the rules.