![]() ![]() ![]() It is important to highlight that the analytical criteria of BIC (Bayesian Information Criteria) was chosen to assist on model selection. The models use were a baseline randomized complete block design (RCB), which is widely used in this type of studies, and four variants considering different spatial-structured residual terms.ĭespite the slightly greater computational needs in fitting the analysis, the spatial approaches resulted in greater genetic gains, heritability and accuracy than the baseline model (RCB), and this can be verified in the table below (adapted from Bernardeli et al., 2021). The authors evaluated seed composition traits (protein, oil, and storage protein) in a set of soybean field trials and compared several statistical models within and across trials. (2021) showed the benefits of performing spatial analysis in plant breeding studies. This specific random effect can be defined asĪnd another effect, such as an independent error or local error can be added as another residual term.Ī recent study elaborated by Bernardeli et al. This type of spatial analysis can be performed in the ASReml-R package version 4 (Butler et al., 2017), and it is particularly directed at modeling the residual effect of a genetic/statistical model, by estimating the autoregressive correlation of residuals between columns and rows in a field. ![]() Some statistical approaches can cope with spatial heterogeneity in different ways, but special attention must be given to the AR1 x AR1 error modeling. ![]()
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