Investigation of Genotype-Environment Interaction Effect on Sugar Yield and Determination of Stability of Sugar Beet (Beta Vulgaris L.) Hybrids

Taleghani, D F; Saremirad, Ali

doi:10.52547/jcb.14.44.103

Cited by 5 publications

(6 citation statements)

References 32 publications

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“…The low variance of the GEI indicated the low effect of it on the phenotypic expression of traits in different genotypes, which caused low yield fluctuations from one environment to another. Contrary to the results of this study, Taleghani et al 25 reported high effect of environment (71.50%) on WSY followed by genotype (9.80%), and GEI (7.20%) for autumn-sown sugar beet. In Basafa and Taherian 27 study, the justified variance by GEI was equal to 7.84%.…”

Section: Resultscontrasting

confidence: 99%

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Stability analysis and selection of sugar beet (Beta vulgaris L.) genotypes using AMMI, BLUP, GGE biplot and MTSI

Taleghani

Rajabi

Saremirad

et al. 2023

Sci Rep

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The methods utilized to analyze genotype by environment interaction (GEI) and assess the stability and adaptability of genotypes are constantly changing and developing. In this regard, often instead of depending on a single analysis, it is better to use a combination of several methods to measure the nature of the GEI from various dimensions. In this study, the GEI was investigated using different methods. For this purpose, 18 sugar beet genotypes were evaluated in randomized complete block design in five research stations over 2 years. The additive effects analysis of the additive main effects and multiplicative interaction (AMMI) model showed that the effects of genotype, environment and GEI were significant for root yield (RY), white sugar yield (WSY), sugar content (SC), and extraction coefficient of sugar (ECS). The multiplicative effect's analysis of AMMI into interaction principal components (IPCs) showed that the number of significant components varies from one to four in the studied traits. According to the biplot of the mean yield against the weighted average of absolute scores (WAAS) of the IPCs, G2 and G16 for RY, G16 and G2 for WSY, G6, G4, and G1 for SC and G8, G10 and G15 for ECS were identified as stable genotypes with optimum performance. The likelihood ratio test showed that the effects of genotype and GEI was significant for all studied traits. In terms of RY and WSY, G3 and G4 had high mean values of the best linear unbiased predictions (BLUP), so they were identified as suitable genotypes. However, in terms of SC and ECS, G15 obtained high mean values of the BLUP. The GGE biplot method classified environments into four (RY and ECS) and three (WSY and SC) mega-environments (MEs). Based on the multi-trait stability index (MTSI), G15, G10, G6, and G1 were the most ideal genotypes.

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Section: Resultscontrasting

confidence: 99%

“…It caused variation in genotypes ranking in different environments for all studied traits. Such a result has been reported in almost all similar studies 22 – 25 .…”

Section: Resultssupporting

confidence: 86%

Stability analysis and selection of sugar beet (Beta vulgaris L.) genotypes using AMMI, BLUP, GGE biplot and MTSI

Taleghani

Rajabi

Saremirad

et al. 2023

Sci Rep

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“…This finding is important for breeders in terms of trait selection. The study by Taleghani et al 17 on sugar beet winter cultivation confirmed that the environment has the greatest effect on WSY, explaining 71.50% of the data variations. The genotype accounted for 9.80% of the justified variance, while the GEI accounted for 7.20% of the data variations.…”

Section: Resultsmentioning

confidence: 86%

“…As a result, significant fluctuations in its yield can lead to instability in production 9 ; Therefore, maintaining the yield stability of genotypes has become a critical aspect of the breeding process 12 . The GEI plays a crucial role in determining the yield stability of genotypes across different environments 8 , 13 , 14 . This phenomenon shows that the environment in which a trait is expressed can affect how a given genotype (gene arrangement) affects that trait 15 , 16 .…”

Section: Introductionmentioning

confidence: 99%

“…To address this, breeders can identify genotypes that consistently perform well in various environmental conditions by extensively testing the genotypes in locations with a range of environmental conditions 13 . This approach aids in the selection of genotypes with broad adaptability and stability 11 , 17 , ultimately leading to stability in yield increase.…”

Section: Introductionmentioning

confidence: 99%

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Genotype by environment and genotype by yield*trait interactions in sugar beet: analyzing yield stability and determining key traits association

Hassani,

Mahmoudi,

Saremirad

et al. 2024

Sci Rep

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The genotype by environment interaction significantly influences plant yield, making it imperative to understand its nature for the creation of breeding programs to enhance crop production. However, this is not the only obstacle in the yield improvement process. Breeders also face the significant challenge of unfavorable and negative correlations among key traits. In this study, the stability of root yield and white sugar yield, and the association between the key traits of root yield, sugar content, nitrogen, sodium, and potassium were examined in 20 sugar beet genotypes. The study was conducted using a randomized complete block design with four replications over two consecutive years across five locations. The combined analysis of variance results revealed significant main effects of year, location, and genotype on both root yield and white sugar yield. Notably, two-way and three-way interactions between these main effects on root yield and white sugar yield resulted in a significant difference. The additive main effect and multiplicative interaction analysis revealed that the first five interaction principal components significantly impacted both the root yield and white sugar yield. The linear mixed model results for root yield and white sugar yield indicated that the genotype effect and the genotype by environment interaction were significant. The weighted average absolute scores of the best linear unbiased predictions biplot demonstrated that genotypes 20, 4, 7, 2, 16, 3, 6, 1, 14, and 15 were superior in terms of root yield. For white sugar yield, genotypes 4, 16, 3, 7, 5, 1, 10, 20, 2, and 6 stood out. These genotypes were not only stable but also had a yield value higher than the total average. All key traits, which include sugar content, sodium, potassium, and alpha amino nitrogen, demonstrated a negative correlation with root yield. Based on the genotype by yield*trait analysis results, genotypes 20, 19, and 16 demonstrated optimal performance when considering the combination of root yield with sugar content, sodium, alpha amino nitrogen, and potassium. The multi-trait stability study, genotype 13 ranked first, and genotypes 10, 8, and 9 were identified as the most ideal stable genotypes across all traits. According to the multi-trait stability index, genotype 13 emerged as the top-ranking genotype. Additionally, genotypes 10, 8, and 9 were recognized as the most stable genotypes.

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Genotype by environment and genotype by yield*trait interactions in sugar beet: Analyzing yield stability and determining key traits association

Hassani,

Mahmoudi,

Saremirad

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

The GEI significantly influences plant yield, making it imperative to understand its nature for the creation of breeding programs to enhance crop production. However, this is not the only obstacle in the yield improvement process. Breeders also face the significant challenge of unfavorable and negative correlations among key traits. In this study, the stability of RY and WSY, and the association between the key traits of RY, SC, N, Na+, and K+ were examined in 20 sugar beet genotypes. The study was conducted using a randomized complete block design with four replications over two consecutive years across five locations. The combined analysis of variance results revealed significant main effects of year, location, and genotype on both RY and WSY. Notably, two-way and three-way interactions between these main effects on RY and WSY resulted in a significant difference. The AMMI method was used to analyze the GEI. The analysis revealed that the first five IPCs significantly impacted both the RY and WSY. The LMM results for RY and WSY indicated that the genotype effect and the GEI were significant. The WAASB biplot demonstrated that genotypes 20, 4, 7, 2, 16, 3, 6, 1, 14, and 15 were superior in terms of RY. For WSY, genotypes 4, 16, 3, 7, 5, 1, 10, 20, 2, and 6 stood out. These genotypes were not only stable but also had a yield value higher than the total average. According to the WAASBY, genotypes 16, 20, 2, and 14 exhibited high stability and RY. In terms of WSY and stability, genotypes 20, 3, 16, 4, and 10 were identified as the most suitable.

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Investigation of Genotype-Environment Interaction Effect on Sugar Yield and Determination of Stability of Sugar Beet (Beta Vulgaris L.) Hybrids

Cited by 5 publications

References 32 publications

Stability analysis and selection of sugar beet (Beta vulgaris L.) genotypes using AMMI, BLUP, GGE biplot and MTSI

Stability analysis and selection of sugar beet (Beta vulgaris L.) genotypes using AMMI, BLUP, GGE biplot and MTSI

Genotype by environment and genotype by yield*trait interactions in sugar beet: analyzing yield stability and determining key traits association

Genotype by environment and genotype by yield*trait interactions in sugar beet: Analyzing yield stability and determining key traits association

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