Comparison of Spatial Models for Sugar Beet and Barley Trials

Abstract

Several spatial methods exist for the adjustment of local trend in one dimension. The aim of this study was to evaluate and compare the precision of different spatial methods. For this purpose, 293 sugar beet (Beta vulgaris) and 64 multienvironment barley (Hordeum vulgare) trials of two German plant breeding companies were analyzed using a baseline model, which comprised a block and replicate effect, and different one‐dimensional spatial models augmenting the baseline model. Model fit was assessed using the Akaike Information Criterion (AIC), the phenotypic correlation of the adjusted genotype means between two environments, and the relative efficiency. For the sugar beet and barley trials the baseline model outperformed the spatial models in the majority of cases, while in some cases the addition of a spatial component proved beneficial. Based on these results we propose a conservative approach to spatial modeling that starts with a baseline model and then checks whether adding a spatial component improves the fit. Among the alternative models studied, the linear variance and the first‐order autoregressive models were the most promising candidates.