ABSTRACT
A goal of precision farming is to maximize protability, increase energy gains, and minimize the impacts of agriculture on the environment. To achieve this goal, appropriate recommendations must be followed. One precision farming adoption barrier is the “poor” accuracy of many site-specic application rate models. Numerous eld experiments have been conducted across the world with the goal of dening the relationships between inputs and outputs over landscapes. To integrate this information into site-specic recommendations, these data must be analyzed. This chapter provides a case study that demonstrates how data from numerous site years can be used to develop a regional site-specic application recommendation model for corn (Zea mays) plant populations. A similar approach can be used for developing locally derived site-specic recommendations for fertilizers and pesticides. Data collected from multiple on-farm studies are provided with this case study.
