On-farm experiments are used to evaluate a wide variety of products ranging from pesticide and fertilizer rates to the installation of tile drainage. The experimental design for these experiments is usually replicated strip trials.  Replication of strip trials is used to estimate experimental error, which is the basis for judging statistical significance of treatment effects. Another consideration for using strip trials is greater within-field variability than smaller fields used for small-plot research. Data from strip trials also differ in their statistical properties from data collected in small-plot trials.  The larger scale experiments often result in thousands of correlated observations, violating the assumption of independence. When conducting on-farm experiments, it is not always possible to replicate strip trials. Since there is no replication, there is no estimate of error and it is not possible to determine statistically whether or not there is a real treatment effect.  Spatial statistical modeling is used to describe spatial heterogeneity and the relationship of neighboring observations.  In spatial analysis, characteristics of neighboring observations are incorporated into the statistical model.  A method is proposed to exploit the underlying spatial relationship of a strip trial with adjacent control strips to estimate a treatment effect for unreplicated strip trials and improve management decisions. Specifically, ordinary kriging based on a spherical semivariogram model with nugget is used to predict yield for treatment points.  These predicted yields are matched with the corresponding observed yields for the treatment points and a paired   test is performed.  The proposed method is applicable to many on-farm experiments to improve the quality and value of farmer’s site specific management decisions.