1- Urmia University
Abstract: (8 Views)
Introduction and Objective: Stability and high performance under various environmental conditions are key characteristics for selecting tolerant genotypes in wheat breeding programs. One of the main challenges in wheat seed quality management is the phenomenon of seed aging, which leads to a decrease in germination, vitality, and ultimately seed performance over time. This phenomenon, especially under long-term storage conditions and influenced by unfavorable storage conditions such as high temperature and relative humidity, has negative effects on seed quality and future crop performance. Furthermore, genotype × environment (GEI) interactions play an important role in identifying genotypes resistant to seed aging, as these interactions can lead to significant changes in the genotype's response to different environmental conditions. This study aimed to identify high-performance (germination percentage) and stable genotypes under accelerated seed aging stress. To achieve this goal, a set of parametric and non-parametric stability indices was used to analyze the stability of germination percentage, and advanced selection models such as MGIDI and FAI-BLUP were employed.
Materials and Methods: In this study, 228 wheat genotypes, including 161 landraces and 67 cultivated varieties, were evaluated for seed aging tolerance during the germination stage in the seed laboratory of Urmia University in the 2022-2023 growing season. The experiment was conducted using a factorial design in a completely randomized layout with 3 replications. The accelerated aging treatments were applied at four levels: 0, 48, 72, and 96 hours at 45°C with near-saturation humidity. Germination percentage (GP) was then measured using the International Seed Testing Association (ISTA) guidelines by the Between Paper (BP) method. Subsequently, a combined analysis of variance (ANOVA) was performed to evaluate the effects of environment, genotype, and their interaction on germination percentage. Following that, parametric and non-parametric stability parameters based on the AMMI and BLUP models were calculated using the STABILITYSOFT software and the metan package in RStudio. The GGE biplot analysis was also conducted using the GGEBiplotGUI package in RStudio. To identify superior genotypes in multi-environment trials (METs), modern selection models such as MGIDI and FAI-BLUP were used. These models were calculated using the metan package in RStudio, with a selection intensity (SI) of 10% for genotype selection. Additionally, to combine results and identify stable genotypes with high performance (germination percentage), the Average Sum of Ranking (ASR) was calculated. Finally, the selected genotypes were identified based on MGIDI, FAI-BLUP, and the ASR ranking method using a Venn diagram in RStudio with the VennDiagram and grid packages.
Results: The results of the combined analysis of variance (ANOVA) showed that the effects of environment, genotype, and genotype × environment interaction on wheat germination percentage under accelerated aging stress were significant at the 1% probability level. The significant interaction between genotypes and environments, especially under accelerated aging stress, indicated that the response of germination to environmental effects varies across genotypes. Furthermore, the results showed that the first three principal components accounted for 100% of the variation in genotype × environment interaction. The first principal component accounted for 59.2% and the second principal component accounted for 30.3% of the variance in the data, indicating the importance of interaction effects in determining genotype responses to environmental stresses. Additionally, AMMI1 and AMMI2 biplots were used to identify stable genotypes and analyze genotype × environment interactions under different conditions. According to the GGE biplot analysis, genotypes 55 (623379), 207 (624580), 19 (627359), 29 (624911), 227 (624894), and 73 (626261) were identified as the most stable genotypes. Furthermore, the combination of stability parameters and multi-trait selection models such as FAI-BLUP and MGIDI successfully identified superior genotypes. These selected genotypes not only demonstrated stability but also had higher performance compared to other genotypes. Accordingly, genotypes 138 (AZAR2), 64 (624900), 67 (627189), 151 (623162), and 14 (623508) were identified as high-performance (germination percentage) and stable genotypes under seed aging conditions. In contrast, the ASR ranking method was mainly used for selecting stable genotypes, but this method was not able to identify genotypes with high germination percentage.
Conclusion: Overall, the results of this study could help improve the selection of resistant and high-performance genotypes in wheat breeding and seed production programs. In particular, this research emphasizes the importance of using advanced models such as MGIDI and FAI-BLUP along with classical analyses for accurate evaluation and selection of optimal genotypes in multi-environment trials. These models, with their ability to analyze complex genotype × environment interactions and assess genotype stability, are effective tools for identifying genotypes resistant to various environmental stresses, including seed aging. The genotypes identified in this study can be used in breeding programs to enhance resistance to environmental stresses and improve seed quality. These genotypes, especially in conditions where seeds are rapidly affected by environmental factors, can be used as valuable genetic resources for the production of resistant and high-quality seeds.
Materials and Methods: In this study, 228 wheat genotypes, including 161 landraces and 67 cultivated varieties, were evaluated for seed aging tolerance during the germination stage in the seed laboratory of Urmia University in the 2022-2023 growing season. The experiment was conducted using a factorial design in a completely randomized layout with 3 replications. The accelerated aging treatments were applied at four levels: 0, 48, 72, and 96 hours at 45°C with near-saturation humidity. Germination percentage (GP) was then measured using the International Seed Testing Association (ISTA) guidelines by the Between Paper (BP) method. Subsequently, a combined analysis of variance (ANOVA) was performed to evaluate the effects of environment, genotype, and their interaction on germination percentage. Following that, parametric and non-parametric stability parameters based on the AMMI and BLUP models were calculated using the STABILITYSOFT software and the metan package in RStudio. The GGE biplot analysis was also conducted using the GGEBiplotGUI package in RStudio. To identify superior genotypes in multi-environment trials (METs), modern selection models such as MGIDI and FAI-BLUP were used. These models were calculated using the metan package in RStudio, with a selection intensity (SI) of 10% for genotype selection. Additionally, to combine results and identify stable genotypes with high performance (germination percentage), the Average Sum of Ranking (ASR) was calculated. Finally, the selected genotypes were identified based on MGIDI, FAI-BLUP, and the ASR ranking method using a Venn diagram in RStudio with the VennDiagram and grid packages.
Results: The results of the combined analysis of variance (ANOVA) showed that the effects of environment, genotype, and genotype × environment interaction on wheat germination percentage under accelerated aging stress were significant at the 1% probability level. The significant interaction between genotypes and environments, especially under accelerated aging stress, indicated that the response of germination to environmental effects varies across genotypes. Furthermore, the results showed that the first three principal components accounted for 100% of the variation in genotype × environment interaction. The first principal component accounted for 59.2% and the second principal component accounted for 30.3% of the variance in the data, indicating the importance of interaction effects in determining genotype responses to environmental stresses. Additionally, AMMI1 and AMMI2 biplots were used to identify stable genotypes and analyze genotype × environment interactions under different conditions. According to the GGE biplot analysis, genotypes 55 (623379), 207 (624580), 19 (627359), 29 (624911), 227 (624894), and 73 (626261) were identified as the most stable genotypes. Furthermore, the combination of stability parameters and multi-trait selection models such as FAI-BLUP and MGIDI successfully identified superior genotypes. These selected genotypes not only demonstrated stability but also had higher performance compared to other genotypes. Accordingly, genotypes 138 (AZAR2), 64 (624900), 67 (627189), 151 (623162), and 14 (623508) were identified as high-performance (germination percentage) and stable genotypes under seed aging conditions. In contrast, the ASR ranking method was mainly used for selecting stable genotypes, but this method was not able to identify genotypes with high germination percentage.
Conclusion: Overall, the results of this study could help improve the selection of resistant and high-performance genotypes in wheat breeding and seed production programs. In particular, this research emphasizes the importance of using advanced models such as MGIDI and FAI-BLUP along with classical analyses for accurate evaluation and selection of optimal genotypes in multi-environment trials. These models, with their ability to analyze complex genotype × environment interactions and assess genotype stability, are effective tools for identifying genotypes resistant to various environmental stresses, including seed aging. The genotypes identified in this study can be used in breeding programs to enhance resistance to environmental stresses and improve seed quality. These genotypes, especially in conditions where seeds are rapidly affected by environmental factors, can be used as valuable genetic resources for the production of resistant and high-quality seeds.
Keywords: Germination, Seed Performance, Seed Aging, Stability Indices, Multi-environment Trials (METs).
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