ArticleScientific reports2024
Evaluation of yield and stability of sugar beet (beta vulgaris L.) genotypes using GGE biplot and AMMI analysis.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Comprehensive evaluation of capsicum genotype-environment interactions utilizing AMMI and GGE biplot models across staggered transplanting environments.Scientific reports · 2026Article
- Investigating the spatial adaptation for iron and zinc content using AMMI and GGE biplot model in lentil (Lens culinaris L.).BMC plant biology · 2026Article
- Genotype-by-environment interactions and seed yield stability of narrow-leaf lupin sweet varieties (Lupinus angustifolius L.) based on AMMI and GGE biplot analyses.Scientific reports · 2026Article
- AMMI and GGE biplot analysis for seed yield and stability performance of selected buckwheat genotypes under multi-environmental trials.Scientific reports · 2025Article
- Adaptability and Stability of Proso Millet Grain Yield: A Multi-Environment Evaluation Using AMMI, GGE, and GYT Biplots.Plants (Basel, Switzerland) · 2025Article
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7 authors.
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Abstract
Rhizomania is the most destructive sugar beet disease in the world, and in recent years, it has widespread in most of the sugar beet growing areas in Iran. Since the control of this soil-borne disease is a difficult task, the use of resistant genotypes is known as the best measure against the disease. The ultimate goal of sugar beet breeders is to produce genotypes that can be used in both infected and non-infected fields without any reduction in terms of yield and quality. Twenty-one sugar beet genotypes along with four controls were evaluated in randomized complete block design with four replications in fields with natural infection to rhizomania in five research stations. Important sugar beet traits including root yield, sugar yield, sugar content, and white sugar yield were evaluated for two years (2021 and 2022). For all traits, location was the main source of variation that spanned 33 to 55% of the total sum of the square followed by the location×year×genotype accounted for 3-40% of the variation. Based on the results of analysis of variance, multivariate stability parameters were computed to evaluate the genotypes' stability. The first two principal components (IPCA1 and IPCA2) generated by GGE biplot contributed for 31.3 and 17.5% difference in genotype×environment interaction for root yield, respectively. According to the GGE biplot, genotypes RM-11 and RM-12 were identified as the winning genotypes across environments for both root yield and white sugar yield traits whereas AMMI model identified RM-14 and RM-9 (for root yield) and RM-1 (for white sugar yield) as best genotypes. Based on the ideal genotype ranking, RM-11 and RM-10 were the best performer with a high mean yield as well as stability in the studied environments. The biplot rendered using the weighted average of absolute scores (WAASB) and root yield and white sugar yield identified RM-11 and RM-9 as superior genotypes in terms of yield and stability. The selected genotypes can be used in breeding programs to transfer the disease resistance and cultivar development.
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