Precision agriculture typically attempts to answer grower questions using an increasingly more fine-grained analysis.  However, some entities, such as cooperatives, can have an interest in answers that are spatially course-grained, such as obtaining an estimate of the overall crop production within a season.  Errors in factors that most influence fine-grained predictions, such as soil quality, may have a smaller impact on overall yield forecasts since their effect is likely to average out.  Systematic errors in observed quantities, such as atmospheric effects on satellite imagery can, in contrast, affect overall estimates much more strongly.  We explore the importance of systematic measurement error in the prediction of crop productivity.