Volume 18, Issue 2 (4-2026)
J Crop Breed 2026, 18(2): 3-15 |
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Haghpanah M, Vaezi B, Namdari A. (2026). Assessment of Genetic Diversity in Barley Germplasm and Selection of Superior Genotypes under Rainfed Conditions Based on Agronomic Traits. J Crop Breed. 18(2), 3-15. doi:10.61882/jcb.2026.1632
URL: http://jcb.sanru.ac.ir/article-1-1632-en.html
URL: http://jcb.sanru.ac.ir/article-1-1632-en.html
1- Kohgiluyeh and Boyerahmad Agricultural and Natural Resources Research and Education Center, Dryland Agricultural Research Institute (DARI), Agriculture Research, Education and Extension Organization (AREEO), Gachsaran, Iran
Abstract: (489 Views)
Extended Abstract
Background: Understanding and utilizing genetic diversity are the main pillars of success in plant breeding programs and can be an effective approach to produce suitable varieties to improve grain yield and increase production efficiency in different climatic conditions. Multivariate statistical methods are widely used to evaluate genetic diversity and analyze relationships between different traits. Given the urgent need to produce varieties suitable for rainfed cultivation, this study aimed to evaluate the genetic diversity of barley genotypes based on agronomic traits. The findings of this study can provide valuable data for improving and cultivating barley in low-rainfall areas of southern Iran, especially the tropical areas of Kohgiluyeh and Boyer-Ahmad.
Methods: This study aimed to investigate the genetic diversity and agronomic traits of 310 barley genotypes, along with four control cultivars (Behdad, Khoram, Rasta, and Fardan) in the 2023-2024 crop year at the Gachsaran Agricultural Research Campus and National Rainfed Site. The experiment was conducted in a large block design with a randomized complete block design in 23 blocks. Initially, analysis of variance was performed for the control cultivars based on a randomized complete block design using InfoStat statistical software. Cluster analysis was performed based on Euclidean distance and the Ward clustering algorithm using R software and dynamicTreeCut, colorspace, and dendextend statistical packages. Correlation between traits was examined using the Pearson method and the R statistical software. Online software (jvenn.toulouse.inra.fr) was used to draw the Venn Diagram to identify genotypes with overlaps in important traits.
Results: MGIDI analysis was performed for the 13 best genotypes selected from the Venn diagram. Four factors (FA1 to FA4) explained 86.6% of the total variance in the following order: FA1 Phenological factor, including days to flowering, spike length, and plant height (31.1% variance), FA2 Morphological factor, including panicle length, peduncle length, and 1000-seed weight (24.3% variance), FA3 Yield factor, including grain yield, days to physiological maturity, and the number of seeds per spike (18.9% variance), and FA4 Early emergence factor, including early emergence vigor (12.3% variance). Genotypes 184, 79, and 28 were identified as having the least distance from the ideal genotype and the best multi-trait performance. Genotype 79 performed better in traits related to the first factor (phenological), and Genotype 28 in traits related to the second and third factors (morphological and yield). The condition of all three superior genotypes was more favorable than the theoretical average regarding the fourth factor (initial germination strength). The grain yields of genotypes 79, 184, 25, 172, and 59 were higher than the average of the controls (3995 kg/ha). Despite a slightly lower yield than the average of the controls, Genotype 28 produced a higher yield than the cultivar Rosta, and the heat diagram showed that Genotype 28 was favorable for almost all the studied traits. With the highest yield among the selected genotypes, Genotype 184 was less favorable than the others in some traits (such as the number of grains per spike and days to spike emergence).
Conclusion: The results show significant genetic diversity among the studied genotypes. These genotypes can serve as valuable sources of germplasm in future breeding programs. Traits evaluated included initial vigor (VIG), date of spike emergence (DHE), time of physiological maturity (DMA), plant height (PLH), spike length (SP), spike length (AWN), peduncle length (PL), number of grains per spike (GS), thousand grain weight (TGW), and grain yield (YLD). Thirteen genotypes with desirable values in several key traits were identified among the high-yielding genotypes (more than 2900 kg/ha). Nine and genotypes were from the gene bank germplasm and control cultivars, respectively. Through multivariate analysis of the mentioned traits, three superior genotypes (28, 79, and 184) were identified as having the highest combined advantage between yield and major agronomic traits. These genotypes can be utilized in future breeding programs to develop varieties tolerant to dryland and low-rainfall conditions.
Background: Understanding and utilizing genetic diversity are the main pillars of success in plant breeding programs and can be an effective approach to produce suitable varieties to improve grain yield and increase production efficiency in different climatic conditions. Multivariate statistical methods are widely used to evaluate genetic diversity and analyze relationships between different traits. Given the urgent need to produce varieties suitable for rainfed cultivation, this study aimed to evaluate the genetic diversity of barley genotypes based on agronomic traits. The findings of this study can provide valuable data for improving and cultivating barley in low-rainfall areas of southern Iran, especially the tropical areas of Kohgiluyeh and Boyer-Ahmad.
Methods: This study aimed to investigate the genetic diversity and agronomic traits of 310 barley genotypes, along with four control cultivars (Behdad, Khoram, Rasta, and Fardan) in the 2023-2024 crop year at the Gachsaran Agricultural Research Campus and National Rainfed Site. The experiment was conducted in a large block design with a randomized complete block design in 23 blocks. Initially, analysis of variance was performed for the control cultivars based on a randomized complete block design using InfoStat statistical software. Cluster analysis was performed based on Euclidean distance and the Ward clustering algorithm using R software and dynamicTreeCut, colorspace, and dendextend statistical packages. Correlation between traits was examined using the Pearson method and the R statistical software. Online software (jvenn.toulouse.inra.fr) was used to draw the Venn Diagram to identify genotypes with overlaps in important traits.
Results: MGIDI analysis was performed for the 13 best genotypes selected from the Venn diagram. Four factors (FA1 to FA4) explained 86.6% of the total variance in the following order: FA1 Phenological factor, including days to flowering, spike length, and plant height (31.1% variance), FA2 Morphological factor, including panicle length, peduncle length, and 1000-seed weight (24.3% variance), FA3 Yield factor, including grain yield, days to physiological maturity, and the number of seeds per spike (18.9% variance), and FA4 Early emergence factor, including early emergence vigor (12.3% variance). Genotypes 184, 79, and 28 were identified as having the least distance from the ideal genotype and the best multi-trait performance. Genotype 79 performed better in traits related to the first factor (phenological), and Genotype 28 in traits related to the second and third factors (morphological and yield). The condition of all three superior genotypes was more favorable than the theoretical average regarding the fourth factor (initial germination strength). The grain yields of genotypes 79, 184, 25, 172, and 59 were higher than the average of the controls (3995 kg/ha). Despite a slightly lower yield than the average of the controls, Genotype 28 produced a higher yield than the cultivar Rosta, and the heat diagram showed that Genotype 28 was favorable for almost all the studied traits. With the highest yield among the selected genotypes, Genotype 184 was less favorable than the others in some traits (such as the number of grains per spike and days to spike emergence).
Conclusion: The results show significant genetic diversity among the studied genotypes. These genotypes can serve as valuable sources of germplasm in future breeding programs. Traits evaluated included initial vigor (VIG), date of spike emergence (DHE), time of physiological maturity (DMA), plant height (PLH), spike length (SP), spike length (AWN), peduncle length (PL), number of grains per spike (GS), thousand grain weight (TGW), and grain yield (YLD). Thirteen genotypes with desirable values in several key traits were identified among the high-yielding genotypes (more than 2900 kg/ha). Nine and genotypes were from the gene bank germplasm and control cultivars, respectively. Through multivariate analysis of the mentioned traits, three superior genotypes (28, 79, and 184) were identified as having the highest combined advantage between yield and major agronomic traits. These genotypes can be utilized in future breeding programs to develop varieties tolerant to dryland and low-rainfall conditions.
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