Intrablock variance among duplicate treatments for nearest-neighbor analyses

Nearest-neighbor analysis (NNA) adjusts for spatially correlated residuals, with the goal of increasing precision. The magnitude of the block x treatm ent interaction mean square is commonly used to evaluate the precision of t he NNA model. An alternative method of evaluating the precision of the NNA and classical unadjusted (UNADJ) randomized complete block (RCB) analysis w ould be to use the pooled variance between duplicate treatments within each block. We defined pare error as variation between plots that are treated a like within a block. Within each location, each genotype was randomly assig ned to two plots within each block of an RCB design. The pure error of soyb ean [Glycine max (L.) Merr.] genotypes was evaluated at eight locations. Ou r objective was to compare the block X treatment and pure error mean square s for yield, physiological maturity, and plant height to determine whether the NNA or UNADJ analysis reduces intrablock variation. The NNA analysis al ways decreased the magnitude of the block x treatment interaction mean squa res, compared with the UNADJ analysis. In some comparisons, the pure error mean square of the NNA analysis was significantly smaller than the pure err or of the UNADJ analysis. The magnitude of the block x treatment mean squar e is not useful for comparing the relative precision of these two analyses. When the pure error mean square was used to measure precision, the NNA was at least as precise as the UNADJ analysis.