Comparison of Spatial Variables over Subregions Using a Block Bootstrap

In environmental and agricultural studies, it is often of interest to compare spatial variables across different regions. Traditional statistical tools that assume independent samples are inadequate, because of potential spatial correlations. In this paper, such spatial dependence is accounted for by a random field model, and a non-parametric test is developed to compare the overall distributions of variables in two neighboring regions. The sampling distribution of the test statistic is estimated by a spatial block bootstrap procedure. For illustration, this procedure is applied to a study of root-lesion nematode populations on a production field in Wisconsin. Choices of the bootstrap block sizes are investigated via a simulation study and the results of this test are compared to traditional approaches.