Roohallah Jafari Solhdar, Saeed Sadeghzadeh Hemayati *1, Khodadad Mostafavi, Ali Saremirad, Abdollah Mohammadi
Abstract: (8 Views)
Introduction and Objective: Sugar beet, as one of the two main sources of sugar production in the world, plays a vital role in food security and the sustainability of the agricultural economy in various countries. Given the growing global population and increasing demand for sugar, the continuous improvement of this plant's genetic potential to achieve higher yield, desirable quality, and adaptation to changing climatic conditions is an undeniable necessity. The primary goal in sugar beet breeding programs is the simultaneous improvement of root yield and technological quality (high sugar content and low impurities). However, achieving this goal has always been a fundamental challenge in breeding programs due to the physiological negative correlation between these two traits. This physiological trade-off necessitates the use of advanced statistical and selection methods to identify genotypes with an optimal balance between traits. Nevertheless, the cornerstone of any successful breeding program and the efficiency of selection methods depend on the existence of sufficient genetic diversity in the base population. Accordingly, the accurate evaluation of germplasm and the estimation of genetic parameters provide valuable information for breeders to formulate the most effective selection program. In this context, the present study was designed and conducted to evaluate the genetic potential and select superior sugar beet hybrids based on the analysis of quantitative and qualitative traits, in order to identify superior hybrids for introduction into advanced trials and to select promising parents for future crosses and the development of new genotypes.
Materials and Methods: This study was conducted during the 2023 growing season at the Motahari Sugar Beet Research Station in Karaj, affiliated with the Sugar Beet Seed Institute (SBSI). The plant materials consisted of 140 experimental hybrids along with four foreign check varieties. To accurately evaluate this large number of genotypes, a preliminary yield trial using an augmented design with five incomplete blocks was employed. This design, by replicating the check varieties in all blocks, allowed for the control of environmental error and the estimation of adjusted trait values for the unreplicated hybrids. All agronomic practices were carried out according to the standard protocols for the region. At the harvest stage, key traits including root yield, sugar content, and sugar yield were evaluated. Statistical analyses were performed using R software. The data were first adjusted based on the statistical model of the augmented design. Then, an analysis of variance was conducted to assess the significance of genetic differences. Genetic parameters, including phenotypic and genotypic variances, phenotypic and genotypic coefficients of variation, broad-sense heritability, and expected genetic advance from selection, were estimated. For simultaneous selection based on multiple traits and to identify genotypes close to the ideal genotype, the selection index of ideal genotype (SIIG) was applied. Finally, to classify the genotypes and investigate the genetic structure of the population, cluster analysis was performed using Ward's method based on Euclidean distance.
Results: The results of the analysis of variance indicated significant genetic variation for root yield and sugar yield among the studied genotypes, which is the primary prerequisite for successful genotype selection.
In contrast, no significant difference was observed for sugar content, neither among the entire set of genotypes nor within the hybrid and check groups, which could be attributed to the limited range of genetic diversity for this trait in the experimental materials or the predominant effect of the environment during the experimental year. The estimation of genetic parameters provided deeper insights into the genetic control of the traits. Root yield and sugar yield exhibited very high broad-sense heritability along with considerable expected genetic advance. This finding, particularly when combined with the considerable expected genetic advance, indicates the predominance of additive genetic effects in the control of these traits and implies that direct phenotypic selection can lead to their genetic improvement with high efficiency. Conversely, the low heritability estimated for sugar content demonstrated its greater susceptibility to environmental factors and the complexity of its genetic control in this set of plant materials. In the mean comparison, hybrids such as 35458 and 37350 exhibited a significant superiority over the checks, with root yields exceeding 114 t.ha⁻¹. In terms of sugar yield, genotype 35458 recorded 17.61 t.ha⁻¹, a performance on par with the best commercial check. For multi-trait selection, the SIIG index was employed. The check variety BTS6975N (with a score of 0.88) and the hybrid 35458 (with a score of 0.85) were identified as the closest genotypes to the ideal genotype. The high consistency between the results of this index and the mean comparison analysis further validated the superiority of the identified elite genotypes. Finally, cluster analysis classified the genotypes into five distinct groups. The superior genotypes were mainly placed in the high root yield and balanced and high-quality clusters. The significant genetic distance between these clusters promises a high potential for heterosis in future crosses utilizing the parental lines of these hybrids.
Conclusion: This research successfully demonstrated that the studied germplasm is a rich source of genetic diversity for sugar beet improvement. Elite hybrids such as 35458, 37412, and 37352 were identified as superior candidates for advanced stages of breeding programs or for introduction to farmers. The results clearly indicated that, within this genetic collection, focusing on selection for high root yield is the most effective and efficient strategy for achieving maximum sugar yield. The genetic grouping obtained can also serve as a practical roadmap for designing targeted crosses and exploiting the existing genetic potential to produce the next generation of superior hybrids.
Materials and Methods: This study was conducted during the 2023 growing season at the Motahari Sugar Beet Research Station in Karaj, affiliated with the Sugar Beet Seed Institute (SBSI). The plant materials consisted of 140 experimental hybrids along with four foreign check varieties. To accurately evaluate this large number of genotypes, a preliminary yield trial using an augmented design with five incomplete blocks was employed. This design, by replicating the check varieties in all blocks, allowed for the control of environmental error and the estimation of adjusted trait values for the unreplicated hybrids. All agronomic practices were carried out according to the standard protocols for the region. At the harvest stage, key traits including root yield, sugar content, and sugar yield were evaluated. Statistical analyses were performed using R software. The data were first adjusted based on the statistical model of the augmented design. Then, an analysis of variance was conducted to assess the significance of genetic differences. Genetic parameters, including phenotypic and genotypic variances, phenotypic and genotypic coefficients of variation, broad-sense heritability, and expected genetic advance from selection, were estimated. For simultaneous selection based on multiple traits and to identify genotypes close to the ideal genotype, the selection index of ideal genotype (SIIG) was applied. Finally, to classify the genotypes and investigate the genetic structure of the population, cluster analysis was performed using Ward's method based on Euclidean distance.
Results: The results of the analysis of variance indicated significant genetic variation for root yield and sugar yield among the studied genotypes, which is the primary prerequisite for successful genotype selection.
In contrast, no significant difference was observed for sugar content, neither among the entire set of genotypes nor within the hybrid and check groups, which could be attributed to the limited range of genetic diversity for this trait in the experimental materials or the predominant effect of the environment during the experimental year. The estimation of genetic parameters provided deeper insights into the genetic control of the traits. Root yield and sugar yield exhibited very high broad-sense heritability along with considerable expected genetic advance. This finding, particularly when combined with the considerable expected genetic advance, indicates the predominance of additive genetic effects in the control of these traits and implies that direct phenotypic selection can lead to their genetic improvement with high efficiency. Conversely, the low heritability estimated for sugar content demonstrated its greater susceptibility to environmental factors and the complexity of its genetic control in this set of plant materials. In the mean comparison, hybrids such as 35458 and 37350 exhibited a significant superiority over the checks, with root yields exceeding 114 t.ha⁻¹. In terms of sugar yield, genotype 35458 recorded 17.61 t.ha⁻¹, a performance on par with the best commercial check. For multi-trait selection, the SIIG index was employed. The check variety BTS6975N (with a score of 0.88) and the hybrid 35458 (with a score of 0.85) were identified as the closest genotypes to the ideal genotype. The high consistency between the results of this index and the mean comparison analysis further validated the superiority of the identified elite genotypes. Finally, cluster analysis classified the genotypes into five distinct groups. The superior genotypes were mainly placed in the high root yield and balanced and high-quality clusters. The significant genetic distance between these clusters promises a high potential for heterosis in future crosses utilizing the parental lines of these hybrids.
Conclusion: This research successfully demonstrated that the studied germplasm is a rich source of genetic diversity for sugar beet improvement. Elite hybrids such as 35458, 37412, and 37352 were identified as superior candidates for advanced stages of breeding programs or for introduction to farmers. The results clearly indicated that, within this genetic collection, focusing on selection for high root yield is the most effective and efficient strategy for achieving maximum sugar yield. The genetic grouping obtained can also serve as a practical roadmap for designing targeted crosses and exploiting the existing genetic potential to produce the next generation of superior hybrids.
Keywords: Sugar beet, Genotype selection, Genetic parameters, Cluster analysis, Selection index, Root yield.
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
