4/10/2023 0 Comments Asreml r package caretAdaptation zones can subsequently be identified by grouping trial locations with similar distributions of environment types across years. An aggregation of the units in the SOM by hierarchical clustering then produces environment types, i.e. In an iterative process of reweighting trial contributions to units, the grid configuration is learnt simultaneously with the trial assignment to units. Units that are further apart contain more distinct trials. In the SOM, trials across locations and years are assigned to groups, called units, that are organized on a two-dimensional grid. In this paper, we present a two-step procedure to identify adaptation zones that starts from a self-organizing map (SOM). By selecting for good performance inside those zones, response to selection is increased. A possible solution is to group trial locations into adaptation zones with G × E occurring mainly between zones. Genotype-by-environment interactions (G × E) complicate the selection of well-adapted varieties. We apply SOM to multiple traits and crop growth model output of large-scale European sunflower data. We evaluate self-organizing maps (SOM) to identify adaptation zones and visualize multi-environment genotypic responses.
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