Removing Spatial Variation from Wheat Yield Trials: A Comparison of Methods

Abstract

Most cultivar evaluation trials use blocked designs and are analyzed using classical analysis of variance; however, a standard analysis for blocked designs often does not adequately account for spatial variability. Recent advances in spatial statistics suggest that there are better alternatives. The primary objective of this research was to compare randomized complete block (RCB) analysis with two nearest neighbor adjustment (NNA) methods; a random field procedure was also examined, as another way to remove spatial variability. Yield data from three replicated breeding nurseries involving diverse adapted and unadapted germplasm, each grown at four locations in Nebraska during 1988–1989, were used for the comparisons. The NNA approach was superior for all nurseries at all locations, on the basis of lower coefficient of variation and greater ability to distinguish cultivar differences. For 9 of 12 trials, RCB cultivar means were highly correlated with NNA cultivar means, indicating that both procedures would identify similar elite lines. For three trials, however, the estimated cultivar yield and cultivar rank from RCB and NNA procedures were different, and so different lines would be selected. The random field analysis was applied to the trial for which the discrepancy between the RCB and NNA analysis was greatest; this yielded results similar to the nearest neighbor analysis. Our results suggest that spatial trends are common in Nebraska and compromise the accuracy and precision of standard analysis of blocked designs. Therefore, NNA or random field analysis should be used to improve the analysis of breeding trials.