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

Abstract

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.

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

“…Sugar beet, a critical sugar plant that provides about 30% (approximately 42 million tons) of the world sugar needs (FAO, 2018), is of considerable importance, especially in light of climate change concerns and the need to transition from fossil fuel consumption to renewable fuels. Developing genotypes with high and stable performance under varying environmental conditions is a signi cant solution 8,9,11 . In light of this, the WAASBY index, a quantitative stability measure, is used to balance yield average and stability.…”

Screening of salt tolerance of maize (Zea mays L.) lines using membership function value and GGE biplot analysis

“…6 , it was deduced that the 120 mMol/LNaCl concentration, which was represented by the smallest circle, indicated the highest level of the respective environment ( Habtegebriel, 2022 ). It is worth mentioning that in GGE biplots analysis, the angle among environment vectors reflects their correlation, and a smaller angle indicates a higher level of correlation ( Taleghani et al, 2023 ). In contrast, the angles of the lines representing the environments at the four stress concentrations were less than 90° ( Fig.…”

AMMI Analysis of Genotype × Environment Interaction on Sugar Beet (Beta vulgaris L.) Yield, Sugar Content and Production in Romania

“…PC1 primarily measures the positive influence of Environments 1 and 3 on 12 genotypes, emphasizing the best results obtained as a consequence of the G × E interaction (Figure 4b). The recognition of mega-environments holds significant value in practice because it allows for the identification of distinctive and representative environments [32]. These environments can serve as effective test locations to evaluate genotypes that are adaptable to various conditions.…”

“…PC1 explains 47.70% of the variability in the data, whereas PC2 explains 34.10% (Figure 4b). The recognition of mega-environments holds significant value in practice because it allows for the identification of distinctive and representative environments [32]. These environments can serve as effective test locations to evaluate genotypes that are adaptable to various conditions.…”