Statistical and Economic Approaches for Understanding Yield.

Geospatial agricultural technologies such as instantaneous yield monitors, GPS-enabled navigation, and automated application controllers have empowered farmers to conduct field-scale on-farm research.  However, the numerous spatially autocorrected observations across landscape scales caused a paradigm shift in the way experiments are designed and ultimately how data were collected, handled and analyzed. Although the premise of experimentation has not changed, the logical thought processes have including designing landscape-scale experiments and analyzing site-specific data. We present the appropriate spatial statistical methods for analyzing field-scale on-farm trials under a range of designs and offer proper economic analyses. 

Spatial regression methods have been adapted to analyze site-specific on-farm trials. Data usable for farm management decision making can be gathered from limited replication landscape-scale experimental designs if that data were analyzed with the appropriate spatial statistical model. 

Our overall objective is to provide farmers and researchers with appropriate analysis techniques for their field-scale experiments.  The original intent of this research was to assist farmers in making better farm management decisions, however many researchers are interested in landscape-scale experimentation due to field-scale sized equipment, evaluating treatments under farmer conditions, and improve communication with their farmer-clients.