Position-Balanced Designs Reduce Negative Effects of Randomization in Field Experiments.

Spatial heterogeneity in fields affects the outcome of experiments. The conventional randomized allocation of treatments to plots may cause bias and variable precision in the presence of trends and spatial autocorrelation. Agricultural scientists mostly use conventional randomized complete block designs that are susceptible to adverse effects from field variability. The method of simulated annealing was used to develop Spatially-Balanced Complete Block (SBCB) designs based on two objective functions: promoting spatial balance among treatment contrasts, and disallowing treatments to occur in the same position in different blocks, when possible. Square SBCB designs were realized as Latin Squares, and perfect spatial balance was obtained when feasible. SBCB designs are simple to implement, are analyzed through conventional ANOVAs, and provide protection against the adverse effects of spatial heterogeneity, while randomized allocation of treatments still insures against user bias. The designs are implemented through the ARM software by Gylling Data Management, and are also available in Geoderma 140:346–352 and on a Cornell University website. Recently, the term Position-Balanced Complete Block Designs has been adopted as they are now used in a broader set of studies.