There is a lot of interest in the concept of precision farming, also called precision agriculture or site-specific management. Although the total acreage managed by these concepts is increasing worldwide each year, there are several limitations and constraints that must be resolved to sustain this increase. These include (1) collecting and managing the large amounts of information necessary to accomplish this micromanagement, (2) building and delivering geo-referenced fine-scale (i.e., change every few meters or less) prescriptions in a timely manner, (3) finding or developing agricultural machines capable of quickly and simultaneously altering the rates of one or more agri-chemicals applied to the crop according to a geo-referenced prescription, (4) the need to have personnel stay "current" with advancements in developing technologies and adapting them to agriculture, (5) refining existing and/or creating new analytical theories useful in agriculture within a multidisciplinary, multi-institutional, and multibusiness environment of cooperation, and (6) modification of agricultural practices that enhances environmental conservation and/or stewardship while complying with governmental regulations and facing difficult economic constraints to remain profitable. There are many myths that overshadow the realities and obscure the true possibilities of precision agriculture. Considerations to establish productive linkages between the diverse sources of information and equipment necessary to apply site-specific practices and geographically monitor yield are daunting. It is anticipated that simulation models and other decision support systems will play key roles in integrating tasks involved with precision agriculture. Discovering how to connect models or other software systems to the hardware technologies of precision agriculture, while demonstrating their reliability and managing the flows of information among components, is a major challenge. The close cooperation of the e