1- Kohgiluyeh and Boyer-Ahmad Agricultural and Natural Resources Research and Education Center, Dryland Agricultural Research Institute, AREEO, Gachsaran, Iran
Abstract: (27 Views)
Introduction and Objectives: Barley (Hordeum vulgare L.) is recognized as one of the world's most significant cereals, playing a crucial role in providing both human food and animal feed. Its importance is particularly pronounced in low-precipitation regions, given its considerable resilience to environmental stresses, notably drought. With ongoing climate change and the constraints on water resources, the identification and development of barley cultivars suited to dryland conditions are considered essential. The objective of this research was to investigate the genetic diversity of 90 selected barley genotypes from germplasm sourced from the International Center for Agricultural Research in the Dry Areas (ICARDA), along with two control cultivars (Fardan and Rasta), under dryland conditions. This investigation was undertaken to identify superior genotypes exhibiting high yield and favorable agronomic traits, for utilization in breeding programs and the development of cultivars adapted to water-scarce conditions. Given the strategic role of barley as a crop in many developing nations, the enhancement of its yield and production stability is seen as potentially contributing significantly to food security in these regions. By focusing on the evaluation of genetic diversity present in barley germplasm, this study seeks to identify valuable genetic resources that may be employed in the development of drought-resistant and high-yielding cultivars. Ultimately, it is hoped that the results of this research can contribute to improving the livelihoods of farmers and enhancing productivity in low-precipitation areas.
Materials and Methods: The study was conducted during the 2023-2024 cropping season at the Gachsaran Agricultural Research and Education Campus (National Dryland Site) in the warm and dry climate of southwestern Iran. Ninety barley genotypes and two control cultivars (Fardan and Rasta) were evaluated using an augmented design with a randomized complete block design (RCBD) base in nine blocks. Each block consisted of 12 experimental plots. Each plot comprised six rows, each seven meters long, with a row spacing of 17.5 cm. Throughout the growing season, morphological and phenological traits, including days to heading emergence, days to grain physiological maturity, plant height, spike length, awn length, peduncle length, number of grains per spike, thousand-grain weight, and grain yield, were measured. Standard methods and precision instruments were employed for data collection to ensure data reliability and validity. Variance analysis for the control cultivars was conducted based on the RCBD. Correction of the mean for each trait in the genotypes was performed utilizing the results of the variance analysis and the augmented design model. Cluster analysis was conducted using the Euclidean distance method and Ward's clustering algorithm, employing R software and associated statistical packages. Trait correlations were calculated using Pearson's method. In addition, the online software jvenn.toulouse.inra.fr was used to construct a Venn diagram to identify genotypes exhibiting superior traits.
Results: The results of the variance analysis revealed that significant differences existed among the control genotypes in the agronomic traits of days to grain physiological maturity, plant height, peduncle length, number of grains per spike, and thousand-grain weight at the 1% probability level. Furthermore, the block effect was significant only for the traits days to grain physiological maturity and plant height at the 1% probability level. Following trait value correction, descriptive statistics analysis indicated that the highest coefficient of variation was observed in the trait number of grains per spike, demonstrating the extensive diversity of this trait among the genotypes. The trait days to grain physiological maturity exhibited the lowest coefficient of variation. Grain yield assessment showed that a considerable coefficient of variation existed among the genotypes. The lowest and highest values for this trait were observed in genotypes G102 and G96, respectively. The mean grain yield among the genotypes was estimated to be 4205.8 kg/ha. Cluster analysis using the measured traits based on the Euclidean distance coefficient and Ward's clustering algorithm revealed that the 92 barley genotypes and cultivars under investigation could be distinguished into four distinct clusters. The highest mean grain yield among the clusters was observed in cluster four. Correlation analysis results demonstrated a significant positive correlation between plant height with peduncle length and spike length. A significant negative correlation between the number of grains per spike and thousand-grain weight was also observed.
Conclusion: The results of this study indicate that considerable genetic diversity exists among the barley genotypes, and these genotypes may be used as valuable germplasm in subsequent breeding studies. However, among the high-yielding genotypes, four genotypes (G99, G63, G103, and G19) were identified as possessing four desirable traits (plant height, number of grains per spike, spike length, and peduncle length), and genotype G33 was identified as possessing all five desirable traits, making them the most suitable genotypes for further study. Therefore, these genotypes may be used in the development of high-yielding cultivars adapted to dryland conditions. These findings may contribute to improving barley yield in low-precipitation areas and increasing production stability in adverse environmental conditions. Finally, this research demonstrated that the use of multivariate statistical methods and rigorous analyses can aid in identifying superior genotypes and selecting suitable parents for breeding programs. This may lead to accelerating the production of new cultivars adapted to various environmental conditions.
Materials and Methods: The study was conducted during the 2023-2024 cropping season at the Gachsaran Agricultural Research and Education Campus (National Dryland Site) in the warm and dry climate of southwestern Iran. Ninety barley genotypes and two control cultivars (Fardan and Rasta) were evaluated using an augmented design with a randomized complete block design (RCBD) base in nine blocks. Each block consisted of 12 experimental plots. Each plot comprised six rows, each seven meters long, with a row spacing of 17.5 cm. Throughout the growing season, morphological and phenological traits, including days to heading emergence, days to grain physiological maturity, plant height, spike length, awn length, peduncle length, number of grains per spike, thousand-grain weight, and grain yield, were measured. Standard methods and precision instruments were employed for data collection to ensure data reliability and validity. Variance analysis for the control cultivars was conducted based on the RCBD. Correction of the mean for each trait in the genotypes was performed utilizing the results of the variance analysis and the augmented design model. Cluster analysis was conducted using the Euclidean distance method and Ward's clustering algorithm, employing R software and associated statistical packages. Trait correlations were calculated using Pearson's method. In addition, the online software jvenn.toulouse.inra.fr was used to construct a Venn diagram to identify genotypes exhibiting superior traits.
Results: The results of the variance analysis revealed that significant differences existed among the control genotypes in the agronomic traits of days to grain physiological maturity, plant height, peduncle length, number of grains per spike, and thousand-grain weight at the 1% probability level. Furthermore, the block effect was significant only for the traits days to grain physiological maturity and plant height at the 1% probability level. Following trait value correction, descriptive statistics analysis indicated that the highest coefficient of variation was observed in the trait number of grains per spike, demonstrating the extensive diversity of this trait among the genotypes. The trait days to grain physiological maturity exhibited the lowest coefficient of variation. Grain yield assessment showed that a considerable coefficient of variation existed among the genotypes. The lowest and highest values for this trait were observed in genotypes G102 and G96, respectively. The mean grain yield among the genotypes was estimated to be 4205.8 kg/ha. Cluster analysis using the measured traits based on the Euclidean distance coefficient and Ward's clustering algorithm revealed that the 92 barley genotypes and cultivars under investigation could be distinguished into four distinct clusters. The highest mean grain yield among the clusters was observed in cluster four. Correlation analysis results demonstrated a significant positive correlation between plant height with peduncle length and spike length. A significant negative correlation between the number of grains per spike and thousand-grain weight was also observed.
Conclusion: The results of this study indicate that considerable genetic diversity exists among the barley genotypes, and these genotypes may be used as valuable germplasm in subsequent breeding studies. However, among the high-yielding genotypes, four genotypes (G99, G63, G103, and G19) were identified as possessing four desirable traits (plant height, number of grains per spike, spike length, and peduncle length), and genotype G33 was identified as possessing all five desirable traits, making them the most suitable genotypes for further study. Therefore, these genotypes may be used in the development of high-yielding cultivars adapted to dryland conditions. These findings may contribute to improving barley yield in low-precipitation areas and increasing production stability in adverse environmental conditions. Finally, this research demonstrated that the use of multivariate statistical methods and rigorous analyses can aid in identifying superior genotypes and selecting suitable parents for breeding programs. This may lead to accelerating the production of new cultivars adapted to various environmental conditions.
Keywords: Augmented Design, Cluster Analysis, Correlation Analysis, Multivariate Analysis, and Venn Diagram
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