The most commonly used spatial models consider the correlation between residuals from neighboring plots to adjust for local trend or small-scale variation. Several spatial methods have been suggested to improve the precision of phenotyping. However, efficient approaches to account for more complex environmental variation require complementing experimental designs with appropriate models of analysis (Basford et al. 2014), have been developed to correct for part of the field trend. 1978 John and Williams 1995) or partially replicated designs (Cullis et al. This is particularly challenging in early generation variety trials conditioned by the use of limited replication of genetic material.Ī number of sophisticated experimental designs, such as those enabling the recovery of inter-block information (Yates 1940 Patterson et al. Plant breeding trials usually involve a large number of test entries covering large areas where spatial variation is likely to be an obstacle to reliable prediction of genetic values. Therefore, the new method should be considered as an efficient and easy-to-use alternative for routine analyses of plant breeding trials.Įfficient phenotypic and genomic selection schemes in plant breeding programs rely on accurate assessment of the phenotypic performance of genotypes in field experiments (Qiao et al. This strategy reduces potential parameter identification problems and simplifies the model selection process. Furthermore, we used a flexible model to adequately adjust for field trends.
One advantage of the approach with SpATS is that all patterns of spatial trend and genetic effects were modelled simultaneously by fitting a single model. The improvements in precision and the predictions of genotypic values produced by the SpATS model were equivalent to those obtained using the best fitting standard spatial models for each trial. The new model was assessed in comparison with the more elaborate standard spatial models that use autoregressive correlation of residuals. We applied this methodology to a series of large and partially replicated sorghum breeding trials. The method uses two-dimensional P-splines with anisotropic smoothing formulated in the mixed model framework, referred to as SpATS model. This paper reports the application of a novel spatial method that accounts for all types of continuous field variation in a single modelling step by fitting a smooth surface. Current mixed model methods of spatial analysis are based on a multi-step modelling process where global and local trends are fitted after trying several candidate spatial models. AbstractĪdjustment for spatial trends in plant breeding field trials is essential for efficient evaluation and selection of genotypes. A flexible and user-friendly spatial method called SpATS performed comparably to more elaborate and trial-specific spatial models in a series of sorghum breeding trials.
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