Volume 16, Issue 4 (11-2024)
J Crop Breed 2024, 16(4): 37-50 |
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Ramazi M, Sadeghzadeh Hemayati S, Omidi H, Naji A. (2024). Exploring the Genotype-Environment Interaction in Sugar Beet (Beta vulgaris L.) Genotypes for Optimal Selection. J Crop Breed. 16(4), 37-50. doi:10.61186/jcb.16.4.37
URL: http://jcb.sanru.ac.ir/article-1-1547-en.html
URL: http://jcb.sanru.ac.ir/article-1-1547-en.html
1- Faculty of Agriculture, Shahid University, Tehran, Iran
2- Sugar Beet Seed Institute (SBSI), Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran
3- Department of Agronomy, Faculty of Agricultural Sciences, Shahed University, Tehran, Iran
2- Sugar Beet Seed Institute (SBSI), Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran
3- Department of Agronomy, Faculty of Agricultural Sciences, Shahed University, Tehran, Iran
Abstract: (388 Views)
Extended Abstract
Background: Genotype-environment interaction, reflecting how genotypes respond differently across environments, significantly impacts breeding progress. This phenomenon poses challenges for breeders in selecting and evaluating superior genotypes. Understanding the genotype-environment interaction enables breeders to meticulously assess and select genotypes that align well with target environment conditions. Moreover, it offers opportunities to identify genotypes that exhibit positive interactions with specific environments or demonstrate robust performance in diverse environments. By cultivating genotypes in varied environments, breeders can observe genotypic responses, facilitating the selection of superior and stable genotypes. A thorough exploration of the genotype-environment interaction necessitates the application of robust statistical methods. In this study, a series of sugar beet hybrids were grown in diverse environments to investigate the impact of the genotype-environment interaction on the quantitative production potential of root yield and white sugar yield, assess their adaptability, and employ different statistical approaches to identify successful hybrids.
Methods: This research involved the cultivation of 18 sugar beet genotypes that encompassed 15 hybrids and three control varieties previously developed to enhance resistance against rhizomania, rhizoctonia, and cyst nematode diseases. Phenotypic evaluations were conducted over two consecutive crop years (2022 and 2023) at two agricultural research stations in Karaj, Alborz, Iran, and Kermanshah, Kermanshah, Iran, utilizing a randomized complete block design with four replications. Standard agricultural practices, including weed control, irrigation, fertilizer application, and other farm management activities, were implemented, throughout the cropping season. Statistical analyses were performed on the collected data related to root yield and white sugar yield.
Results: The analysis of genotype main effects revealed a significant difference in both root yield and white sugar yield. The interaction effects of year-location and genotype-year-location at the 1% probability level for both root yield and white sugar yield traits and genotype-year at the 5% probability level on white sugar yield made a significant difference. These findings suggest that fluctuations in environmental conditions and genetic factors can impact genotype performance across diverse environments, leading to marked variations in root yield and white sugar yield among genotypes. Using the GGE biplot method, it was found that the first and second principal components of the genotype-environment interaction explained 93.44 and 3.85%, respectively, and a total of 97.29% of variations in the root yield. Regarding the white sugar yield, the first and second principal components of the genotype-environment interaction explained 95.10 and 2.54%, respectively, and a total of 97.64% of variations. Considering that the first and second principal components played an important role in the variance of the data, the respective biplots can well explain the genotype variations and the genotype-environment interaction. The association biplot illustrated positive correlations between experimental environments, with a strong positive correlation observed between Karaj in 2023 and Kermanshah in 2022 for the root yield. Similarly, a positive correlation between the environments of Karaj and Kermanshah was identified in terms of the white sugar yield. Genotypes 18, 15, 12, 14, and 8 exhibited superior root yields across locations, while genotypes 18, 15, and 14 excelled in the white sugar yield across the four studied environments. A combined assessment of stability and yield indicated that genotypes 18, 15, 12, and 14 excelled in the root yield, and genotypes 18, 15, and 14 performed exceptionally well in the white sugar yield and stability, being recognized as top-performing genotypes. Through the analysis of a hypothetical ideal genotype, genotypes 15, 12, and 14 were identified as the best yields in terms of root and white sugar yields, considering their proximity to the ideal genotype. By employing a multi-trait stability index with a selection pressure of 20%, genotype 16 emerged as the top-ranking genotype, followed by genotypes 10, 9, and 6. A comparison of trait values in the selected genotypes revealed increased root yield and sugar content, alongside decreased levels of potassium, alpha-amino nitrogen, and sodium, showcasing overall improvements across all studied traits with high heritability.
Conclusion: The results indicate that interactions between environmental factors and genetic makeup can influence the performance of different genotypes across varying conditions, leading to substantial variations in their outcomes. Overall, genotypes 18, 15, 14, and 10 were identified as the most stable genotypes.
Background: Genotype-environment interaction, reflecting how genotypes respond differently across environments, significantly impacts breeding progress. This phenomenon poses challenges for breeders in selecting and evaluating superior genotypes. Understanding the genotype-environment interaction enables breeders to meticulously assess and select genotypes that align well with target environment conditions. Moreover, it offers opportunities to identify genotypes that exhibit positive interactions with specific environments or demonstrate robust performance in diverse environments. By cultivating genotypes in varied environments, breeders can observe genotypic responses, facilitating the selection of superior and stable genotypes. A thorough exploration of the genotype-environment interaction necessitates the application of robust statistical methods. In this study, a series of sugar beet hybrids were grown in diverse environments to investigate the impact of the genotype-environment interaction on the quantitative production potential of root yield and white sugar yield, assess their adaptability, and employ different statistical approaches to identify successful hybrids.
Methods: This research involved the cultivation of 18 sugar beet genotypes that encompassed 15 hybrids and three control varieties previously developed to enhance resistance against rhizomania, rhizoctonia, and cyst nematode diseases. Phenotypic evaluations were conducted over two consecutive crop years (2022 and 2023) at two agricultural research stations in Karaj, Alborz, Iran, and Kermanshah, Kermanshah, Iran, utilizing a randomized complete block design with four replications. Standard agricultural practices, including weed control, irrigation, fertilizer application, and other farm management activities, were implemented, throughout the cropping season. Statistical analyses were performed on the collected data related to root yield and white sugar yield.
Results: The analysis of genotype main effects revealed a significant difference in both root yield and white sugar yield. The interaction effects of year-location and genotype-year-location at the 1% probability level for both root yield and white sugar yield traits and genotype-year at the 5% probability level on white sugar yield made a significant difference. These findings suggest that fluctuations in environmental conditions and genetic factors can impact genotype performance across diverse environments, leading to marked variations in root yield and white sugar yield among genotypes. Using the GGE biplot method, it was found that the first and second principal components of the genotype-environment interaction explained 93.44 and 3.85%, respectively, and a total of 97.29% of variations in the root yield. Regarding the white sugar yield, the first and second principal components of the genotype-environment interaction explained 95.10 and 2.54%, respectively, and a total of 97.64% of variations. Considering that the first and second principal components played an important role in the variance of the data, the respective biplots can well explain the genotype variations and the genotype-environment interaction. The association biplot illustrated positive correlations between experimental environments, with a strong positive correlation observed between Karaj in 2023 and Kermanshah in 2022 for the root yield. Similarly, a positive correlation between the environments of Karaj and Kermanshah was identified in terms of the white sugar yield. Genotypes 18, 15, 12, 14, and 8 exhibited superior root yields across locations, while genotypes 18, 15, and 14 excelled in the white sugar yield across the four studied environments. A combined assessment of stability and yield indicated that genotypes 18, 15, 12, and 14 excelled in the root yield, and genotypes 18, 15, and 14 performed exceptionally well in the white sugar yield and stability, being recognized as top-performing genotypes. Through the analysis of a hypothetical ideal genotype, genotypes 15, 12, and 14 were identified as the best yields in terms of root and white sugar yields, considering their proximity to the ideal genotype. By employing a multi-trait stability index with a selection pressure of 20%, genotype 16 emerged as the top-ranking genotype, followed by genotypes 10, 9, and 6. A comparison of trait values in the selected genotypes revealed increased root yield and sugar content, alongside decreased levels of potassium, alpha-amino nitrogen, and sodium, showcasing overall improvements across all studied traits with high heritability.
Conclusion: The results indicate that interactions between environmental factors and genetic makeup can influence the performance of different genotypes across varying conditions, leading to substantial variations in their outcomes. Overall, genotypes 18, 15, 14, and 10 were identified as the most stable genotypes.
Type of Study: Research |
Subject:
اصلاح نباتات، بیومتری
Received: 2024/04/13 | Accepted: 2024/07/25
Received: 2024/04/13 | Accepted: 2024/07/25
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