The main objectives of multi-environmental trials (METs) are to assess cultivar adaptation patterns under different environmental conditions and to investigate genotype by environment (G×E) interactions. Linear mixed models (LMMs) with more complex variance-covariance structures have become recognized and widely used for analyzing METs data. Best practice in METs analysis is to carry out a comparison of competing models with different variance-covariance structures. Improperly chosen variance-covariance structures may lead to biased estimation of means resulting in incorrect conclusions. In this work we focused on adaptive response of cultivars on the environments modeled by the LMMs with different variance-covariance structures. We identified possible limitations of inference when using an inadequate variance-covariance structure. In the presented study we used the dataset on grain yield for 63 winter wheat cultivars, evaluated across 18 locations, during three growing seasons (2008/2009-2010/2011) from the Polish Post-registration Variety Testing System. For the evaluation of variance-covariance structures and the description of cultivars adaptation to environments, we calculated adjusted means for the combination of cultivar and location in models with different variance-covariance structures. We concluded that in order to fully describe cultivars adaptive patterns modelers should use the unrestricted variance-covariance structure. The restricted compound symmetry structure may interfere with proper interpretation of cultivars adaptive patterns. We found, that the factor-analytic structure is also a good tool to describe cultivars reaction on environments, and it can be successfully used in METs data after determining the optimal component number for each dataset.

Multi-environment trials are commonly used to assess cultivar adaptation patterns under different environmental conditions and to help make effective cultivar recommendations for growers. An example of a multi-environment trial system used for cultivar recommendations is the Polish Post-registration Variety Testing System. A common approach in cultivar recommendations is to evaluate the adaptability of cultivars across, or for, specific trial locations. However, the locations of the trials and the fields where a farmer will grow a crop are hardly ever in the same place. Therefore, it would be better to group the trial locations into regions and give recommendations for the whole region. The aim of this study is to evaluate the grain yield adaptation patterns of 62 modern winter wheat cultivars in six agro-ecological regions of Poland for two crop management intensities over five growing seasons. The analysis of the grain yield data was performed separately for each intensity using single-stage approaches in linear mixed models. We ascertained that winter wheat yield variability was in the main determined by agro-ecological region and their interactions, and to a small extent by the cultivar effect. Cultivars Sailor and Linus were widely adapted to all agro-ecological regions studied for both crop management intensities. It is highly probable that these two cultivars will obtain high yield in all agro-ecological regions as well as with both crop management intensities studied. We observed high compatibility rankings between locations for both crop management intensities. High compatibility of the cultivar rankings in the trial locations also provides high precision when determining regions.

The recommendation of cultivars for a larger number of locations relies on similar agricultural environment or similar crop yield obtained in these locations. There are many studies on the impact of environmental conditions on the yield of cultivars of agricultural crops and recommendation for their cultivation. However, there is little research on triticale in comparison to other cereals. We presented the influence of the cultivar, location nested in region, year, and their interactions on triticale grain yield separately for two levels of crop management intensity. In this work, we checked compatibility in the rankings of cultivars between six regions and compatibility in the rankings of cultivars between locations belonging to the same region. The results indicated a large variation in the rankings of cultivars between locations in the regions, i.e., the ranking of cultivars in locations belonging to the same region was different. We observed low compatibility in rankings between locations for both crop management intensities. The low compatibility of cultivar rankings in trial locations also translates into low precision when determining regions. This means that the process of recommending cultivars to environments should be constantly checked and improved. In addition, using GGE biplot and measure of yield superiority (Pi) we presented an adaptive response of 12 cultivars in six regions at two levels of crop management intensity and their stability during five growing seasons.

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The yield and yield quality of sugar from the sugar beet (Beta vulgaris L.) and are determined by genotype, environment and crop management. This study was aimed at analyzing the stability of white sugar yield and the adaptation of cultivars based on 36 modern sugar beet cultivars under different environmental conditions. The compatibility of sugar beet cultivars’ rankings between the three growing seasons and between the 11 examined locations was assessed. In addition, an attempt was made to group environments to create mega-environments. From among the 11 examined locations, four mega-environments were distinguished on the basis of the compatibility of the white sugar yield rankings. The assessment of the adaptation of cultivars and the determination of mega-environments was carried out using GGE (genotype main effects plus genotype environment interaction effects) biplots and confirmed by the Spearman rank correlation test performed for cultivars between locations. The cultivars studied were characterized by a high stability of white sugar yield in the considered growing seasons. The high compliance of the sugar yield rankings between the years contributes to a more effective recommendation of cultivars.