Agricultural systems are spatially variable due to inherent variability of soil properties and landscape. Determining the optimum prescription for a location within a field is challenging. The biggest challenge is that the plant response to variable management levels is often highly dependent upon the weather that occurs during the season. Simple statistical functions that relate yield response to environmental variables or nitrogen rate for example do not sufficiently account for temporal interactions of weather and stress on yield response to management. Process oriented crop simulation integrate the effects of temporal and multiple stress interactions on crop growth processes under different environmental and management conditions.  It is rather obvious that crop simulations cannot be performed everywhere in a field given that the cost and the availability of detailed inputs would be prohibitive. A more balanced approach to the application of crop simulation models to precision agriculture would be to delineate zones within the field of similar crop performance.

This paper presents case studies of crop management and spatial variability impact on yield and environment. The role of crop modeling in understanding yield variability will be illustrated.