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Three hulless barley varieties were grown under normal conditions during 2017/2018 and 2018/2019, to improve their agronomic yield, and to assess how the genotype influences β‐glucan contents, and its structural, thermal, rheological, and functional properties, as intended to be used in food applications. The extracted gums with hot water at 55 °C and pH 8.0, showed contents from 5.75% to 6.41% (w/w), and concentrations from 68.55% to 79.29% of β‐glucan, with some starch and protein impurities. The results of the agronomic trail indicated the highly significant (P ≤ 0.01) influence of the genotype on all studied characteristics, and on the β‐glucan contents (0.28** and 0.33**) at both seasons. The morphology of the three gums was significantly different in the distribution and structure of networks. Peak intensities of the –OH and –CH groups and CH2 stretching were higher and wider in Giza129 and Giza131. β‐Glucan networks melt from 71.5 to 87.18 °C, and Giza131 exhibited the highest thermal stability. The aqueous dispersions (1%) of β‐glucan gums exhibited a non‐Newtonian behavior, and Giza130 presented the highest significant (P ≤ 0.05) apparent viscosity (η) and foaming stability. Giza129 showed the highest significant water and fat binding capacities, whereas Giza131 showed the highest significant foaming capacity. β‐Glucan gums showed different potentials in food applications as fat replacers, stabilizers, thickeners, and foaming agents in food systems. This study suggests planting the proper barley variety in breeding and genetic improvement programs to supply the food industry with the expected β‐glucan content with consistent structural, thermal, rheological, and functional properties. Practical Application β‐Glucans play an important technological role in processed foods. Little current information is available on β‐glucan contents, and its potentiality on food applications, as influenced by variability among hulless barley genotypes. Accordingly, knowledge of β‐glucan levels in barley varieties is a valuable attribute for both consumers and food processors, and it will create an opportunity for scientific cooperation between food technologist and breeders to identify the suitable barley varieties to be used in breeding programs, to obtain barley with required β‐glucan contents, targeted for specific end uses.

High and stable yield is very desirable in barley (Hordeum vulgare L.) genotypes .The experiment of this study was conducted in eighteen environments (three locations , three K rates and two seasons). The three locations used are El-Serw , Noubaria and Hossienia all of them have salty soils. Two season; 2009/ 2010 and 2010/2011 using eight genotypes of barley. The experiments were grown in a split-split blocks design with three replications in each location. The objectives of this study were to increase barley productivity and adaptability under different conditions by identifying and developing genotypes that are more adapted and more stable in production under these harsh environments. The combined analysis of variance for environment (E), genotypes (G) and GE interaction was significant suggesting differential response of the genotypes and the need for stability analysis. The stability measures are useful in characterizing genotypes by showing their relation performance in various environments. Results revealed that high yielding cultivars can also be stable cultivars. The genotype 4 followed by genotype 1 and genotype 2 were the most stable for grain yield because their regression coefficients were the highest, bi value almost near unity and they had lower deviations from regression; these would be recommended for 18 environmental conditions.. These genotypes could be considered as wide adaptive genotypes. The regression coefficient (bi) and deviation of regression (S 2 d) displayed highly significant positive correlation with phenotypic variance and coefficient of variation. In contrast, the insignificant correlation coefficients among the other stability parameters were found. Moreover, the mean grain yield displayed positive correlation with phenotypic variance and regression coefficient, but there was negative correlation with coefficient of variation and deviation of regression.