Production agriculture has continuously been in a state of technology adoption. New technologies emerge, become adopted by farmers, and ultimately become status quo. There is substantial faith by industry participants in historical University-based yield response and fertilizer recommendation models. That faith is despite the fact that University-based fertilizer recommendations are often statewide and rarely depend on site-specific information, other than soil fertility information. Better methods are needed for individual producers to test, evaluate, and fine-tune University-based fertilizer recommendations to their production field. Precision agriculture technologies provide these necessary methodologies. Broadly speaking, precision agriculture evolves data issues (improving the type, quality, and/or cost of information gathered) and yield relationships (improving the crop production decisions made from the data collected). Data issues include topics such as; 1) proxy variables – using more-stable or less expensive variables as proxies for other variables (e.g., soil conductivity and remote sensing), 2) management zones, 3) optimal grid sizes, 4) improved informational sensors, and 5) alternative statistical analysis techniques.

This study was a demonstration project designed to illustrate many of precision agriculture’s technologies to local producers. A University of Delaware research field was given four nitrogen fertilizer rates; 75, 90, 105, and 120 lbs N/ac. GPS- registered sampling points were established and tissue and remote sensing data were collected from these points at various times through out the season. Ground level remote sensing data was collected with two multispectral sensor (Cropscan MSR87 and handheld Greenseeker^®), and a hyperspectral sensor (GER 3700). Aerial remote sensing was collected using an unmanned vehicle acquiring four bands of data. Remote sensing is appealing because; 1) it is spatially dense, 2) it has a low-cost per data point, and 3) multiple images can be gathered during the growing season. Yield data was collected using a GPS equipped combine with a yield monitor.