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Abstract. Data from different multi-environmental trails (MET) were analysed, including different number of varieties, number of locations and different research periods. The first experiment (24 PhD) included 24 wheat varieties that were studied in five locations of the country over a period of four years (2009-2012). The second field experiment (40 ABC) consists of 40 new advanced wheat lines and cultivars, which were studied in three locations over a three-year period (2017-2019). The grain yield datasets from the two experiments were used to make a direct comparison of various statistical parameters to assess the genotype stability against the background of significant growing conditions. The study involves the use of several statistical packages that are specialized for this purpose. Based on the ranking assessment of the values of each statistical parameter, a critical analysis was made of its relationship with the yield, for each dataset separately. For this purpose, the possibilities of correlation, principal component and cluster analyses were used. Parameters for which information differs between datasets or between statistical packages are removed from the analysis list. The final set of 31 parameters was analysed according to the set goal, after a statistically justified possibility to merge the two datasets. Most of the rank parameters do not show correlation with grain yield. The units are the parameters, the correlation of which is either positive (Pi, Ysi, TOP, λ) or, respectively, negative (DJi, NP(1), CVi]). The analysis of the data through different statistical approaches shows that the parameters correspond to the dynamic concept of stability assessment. Only one of the parameters (θi) is related to static stability assessment. In the presence of many more effective than it, it should not be applied because it is an exception from the analysed group. The groups of parameters of the regression coefficient (bi), the deviation from the regression line (s2di), ecovalence (W2i) and the stability variance (σ²i), give objective information about the behaviour of the variety in environmental conditions and it is not influenced by software. Some of the non-parametric [S(i) NP(i)] assessment methods provide diametrically opposed information for stability because of differences arising from either the dataset or the software used. Suitable for stability assessment are non-parametric approaches - [S(1) and S(2)], which is fully confirmed by the three software packages. Each of the used software packages contains a set of parameters, the application of which as a set gives correct information about all aspects of the wheat stability

Abstract. Wheat is a crop with a very long growing season, during which it is subjected to prolonged exposure to many environmental factors. For this reason, the interaction of genotype with conditions is very common for any character of wheat. This study aims to determine whether the grain yield is affected by the change of the ear emergence date (EED) in various environments. In a four-year period, 30 current for national real grain production winter wheat varieties were studied. The EED and grain yield (GY) were studied as quantitative traits within five locations of the country having various soil and climatic conditions. Using several statistical programs, genotype x environment interaction of two traits was analyzed. The emphasis on data analysis was whether changes of traits due to the conditions were related and that the optimization of the ear emergence date could serve as a breeding tool for increasing grain yield. The date of ear emergence and grain yield are traits that are reliably influenced by growing conditions. The change in the date of emergence is mainly of the linear type, while the grain yield shows linear and nonlinear type changes in the same environmental conditions. It was found that the key roles in the change of characteristics are the conditions of the year, with the relatively weakest impact of the genotype on them. There is a positive relationship between the two traits, although their change depends on environmental factors. Although they change to different degrees and in relation to each other, there is a positive correlation between them. The more favorable the environmental conditions, the weaker the relationship between these two traits and vice versa. Under changing climatic conditions, the change in the relationship between the two traits is a signal of the need to create different varieties by date of ear emergence in order to obtain higher yields in the future.

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Data from the Multi Environmental Field Trail (MET), which examined 24 varieties of common wheat, were divided into three "datasets" related to three of the five study locations. Using meta-analysis, these three data sets were compared with those from the whole experiment. The aim of the study is to determine whether a 4-year growing period in three country-specific locations, it is possible to establish a significant impact of the environment on the stability of a group of varieties. The analysis of the genotype x environment interaction (GEI) was performed in parallel in the three groups of data, which were compared with the entire MET database. A direct comparison was made on them regarding the possibilities of non-parametric methods to assess the stability of the variety. The analysis of the results of the four "datasets" is done through a number of statistical approaches, allowing them to be correctly compared at different levels. Genotype x environment interaction was found in each of the studied locations. The variation in yield in them is a result of the direct effect of the "year" and the combined effect of the genotype x year. At all three locations, the GEI is broken down into four main components, which is evidence of the strong linear and non-linear nature of the dispersion of grain yield. These results are a prerequisite for an objective assessment of genotype stability. All applied parameters give completely similar stability information for each of them, regardless of the test location. Data from one location are sufficient to assess the stability of each variety in a group. This may be the case if significant differences between the seasons of the trial, are found. The applied non-parametric methods for stability assessment give correct information about the varieties, in the presence of GEI, regardless of the conditions from which the data for analysis are selected.