The genotype × environment (G×E) interaction is considered a stumbling block to plant breeders, since the presence of significant GxE interaction component can complicate the identification of superior genotypes and reduce the usefulness of selection. Seed yields of 26 soybean genotypes were evaluated in three locations i.e. Sakha, Etay ElBaroud and Mallawy, through four successive summer seasons from 2012 to 2015. The used design was a randomized complete block design with three replications. This research is aimed to estimate the stability parameters of seed yield of 26 soybean genotypes over twelve environmental conditions and to examine the usefulness and validity of a new simple stability method comparing with four widely used methods. The four stability methods follow three main statistical models namely; regression, variance, and non-parametric approaches. Results showed highly significant mean squares for genotypes, environments and G×E interaction indicating that the tested genotypes exhibited different responses to environmental conditions giving the justification for running stability analysis. The terms of predictable (linear) and unpredictable (non-linear) interaction components were highly significant indicating that the tested soybean genotypes were different in their relative stability. The two soybean cultivars Giza 111 and Giza 21 in addition to their high mean yields, they met all the rules of stable genotypes. Therefore, both cultivars could be considered a good breeding material stock in any future breeding program. Also, when the simplified stability method was applied, the unstable eighteen genotypes were differentiated into three classes. These classes included three genotypes (L162, H29 L115, and H2 L12) were adapted to the unpredictable low yielding environments, while five others (H15 L273, L163, H3 L4, H4 L24 and DR 101) were adapted to high yielding environments. Whereas, the rest ten genotypes were unstable over the low, medium and high environmental groups. The results proved also that, the proposed stability method of Thillainathan and Fernandez (2002) is very simple and easy to apply, understand and interpret by agronomists and plant breeders than the other popular stability models. Also, it is possible to support the results of this stability method by a scatter plot diagram that enable the researchers to visually, directly and quickly compare the mean yield performance and stability of the tested genotypes.

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The present investigation was conducted in a field experiments at Sakha Agricultural Research Station Farm to evaluate 24 bread wheat genotypes during the two growing seasons 2014/2015 and 2015/2016 under normal and salinity stress conditions. The experimental design used was a randomized complete block design with three replicates. Eleven stress tolerance indices (STI's) were calculated based on average grain yield under normal and stress conditions across the two seasons. Moreover, cluster analysis was performed to identify the similarity/dissimilarity among the tested genotypes for grain yield and salinity tolerance. Results showed large values of broad-sense heritability (h b 2) coupled with high values of genetic advance as a percent of mean (GA%) at 5% selection intensity for number of spikes/m 2 and number of grains/spike in the adequate site. Concerning the salt stressed soil, the grain yield ratio, number of spikes/m 2 and grain yield recorded the highest values of h b 2 and GA%. However, there were crucial differences among tested genotypes in respect to grain yield under non-stress and salt stress sites, which demonstrates high genetic diversity among them that enabled us to screen salt tolerant genotypes. Already, the tested wheat genotypes exhibited different responses for salinity stress tolerance indices (STI's). Perfect and positive correlation coefficients (r = 1) were found between three pairs of indices (STI and GMP), (SSPI and TOL) and (CV and SSI) where each one of the previous three pairs occupied one dot on the biplot graph indicating that the three indices are identical for ranking genotypes for salinity tolerance and they could be interchangeably used as a substitute for each other. Therefore, using these pairs of (STI's) together in the same study is considered a waste of time and effort. The cluster analysis classified the tested genotypes into five main groups (clusters) where each group contained the genotypes that showed similar yield potential and salinity tolerance. The fifth cluster contained two promising genotypes namely; lines 2 and 17 that were characterized by moderate grain yield in each of the normal and salt soils recording the lowest grain yield reduction. Also, they occupied the first and second ranks among the tolerant genotypes for salinity stress. Accordingly, results would give a good chance to achieve genotypic improvement of wheat through the hybridization among genotypes taken from different clusters.

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Two field experiments were conducted at Mallawy Research Station, Menia Governorate, during the two successive seasons of 2009/2010 and 2010/2011 to evaluate some statistical methods that used for estimating the relative contribution of sugar yield components in sugarcane crop. The present work included nine treatments which were the combination between three treatments of growth promoters (Agrispon, Stimulate and control) and three genotypes of sugarcane namely: G.T. 54-9, G. 84-47 and Phil-8013. Five statistical procedures were used to study the relationship between sugar yield and its components using the data over the two seasons. The used methods of analysis were simple correlation coefficient, path analysis, full model regression, stepwise multiple linear regression and factor analysis. Highly significant and positive correlation coefficients were detected between sugar yield and each of number of internodes/stalk, number of millable stalks/m 2 , total soluble solids % and sucrose %. The results of path analysis revealed that the number of millable stalks/m 2 was the most important trait with the highest direct and indirect effects on sugar yield followed by sucrose % and stalk weight. The same three traits were also responsible for the most sugar yield variability using full model regression and stepwise multiple linear regression with goodness of fit of the two models. Factor analysis grouped the studied eight traits as sugar yield components into three main factors accounting for 85.3 % of the total variability in the dependence structure. Factor I was responsible for 34.89 of the total variation and included stalk weight, stalk diameter and number of millable stalks/m 2 . Factor II contained total soluble solids % and sucrose % and contributed to 28.17 % of the total variation. Stalk length, number of internodes/stalk and reducing sugar % were the components of the last factor and explained 22.25 % of the total variation. Based on the previous results, it could be concluded that the high sugar yield of sugarcane crop would be obtained by selecting breeding materials that have heavy weight of stalk, large number of millable stalks/m 2 and high percent of sucrose.