Utilizing yield maps to their full potential has been one of the challenges in precision agriculture.  A key objective for understanding patterns of yield variation is to derive management zones, with the expectation that several years of quality yield data will delineate consistent productivity zones.  The anticipated outcome is a map that shows where soil productive potentials differ.  In spite of the widespread usage of yield monitors, commercial agriculture has found it difficult to collect and assemble yield data effectively for this purpose.  A soil-topography-yield analysis approach has been developed and tested on several fields across the US.  It uses soil and topography data that share the dense spatial scale of yield maps. These soil maps are fused into zones delineating the key soil properties that affect yield such as nutrient and water-holding capacity.  Yield datasets are then queried to identify the significant yield drivers on each field. From those relationships the methodology identifies areas that could benefit from soil-specific management and suggests appropriate strategies. Whereas most yield analysis efforts attempt to create soil productivity polygons around yield map patterns, the approach presented here precisely delineates the soil boundaries first and then mines yield data to quantify and explain the actual difference in productive potential.  The objective of this study is to evaluate the fusion results on several US fields. Results show the output can be useful for decision support on drainage and irrigation investments, establishing an appropriate rationale for variable yield goals, and uncovering hidden areas of lost yield potential.