Stevia (Stevia Rebaudiana B.) Genotypes Assessment for Leaf Yield Stability Through Genotype by Environment Interactions, Ammi, and Gge Biplot Analyses

Abstract

Multilocation testing plays a vital role in the release of new high-yielding cultivars of stevia (Stevia rebaudiana B.) in Indonesia. In stevia, the leaf yield potential demonstrates an important characteristic for superior genotype selection. The study sought to identify the effects of genotype by environment interactions (GEI) on stevia yield and select the genotypes with stable yield resulting from radiation and hybridization through AMMI and GGE biplot analyses under three growing environments. The experiments took place in three locations, namely, Bandung, Sumedang, and Garut, West Java, Indonesia, consisting of a randomized complete block design (RCBD), with three replications. The combined analysis of variance (ANOVA) attained the genotype by environment interactions measurements. Calculating yield stability used the additive main effects and multiplicative interaction (AMMI), AMMI stability value (ASV), genotype stability index (GSI), and genotype plus genotype by environments (GGE) biplot measurements. The results revealed significant effects of environments (92.38%), followed by GEI (5.20%), and genotype effects (2.43%) of the total variation on stevia yield. The stevia genotypes viz., G11, G27, G2, and G5 gave a higher and more stable yield based on the AMMI-1 biplot. Based on the GGE biplot and the genotypes’ sustainable performances, the stevia genotypes, i.e., G27, G2, G11, G20, and G26, gained selection as stable. The three selected stevia genotypes displayed the highest yields and proved stable in three environments viz., G2 (Tamangwangu EMS mutant number A), G11 (Bogor mutant with gamma ray radiation 5 number C), and G27 (a hybrid from Garut × Bogor-3). These promising genotypes exhibit the potential for further development into new superior stevia genotypes.

Influence of Genotype × Environment Interaction on Yield Stability of Maize Hybrids with AMMI Model and GGE Biplot

“…Similar results were found in Adham et al, Alizadeh et al, and Ansarifard et al that the environment accounted for most of the variance [16,35,36]. Additionally, the GEI effect is largely a response to environmental factors, not genotypic factors [37]; thus, the significant GEI factors demonstrated that the use of AMMI and the GGE biplot was appropriate in yield evaluation [38].…”

Genotype-by-environment interactions (GEIs) and evaluate superior sweet potato (Ipomoea batatas [L.] Lam) using combined analysis and GGE biplot

“…According to Jamshidmoghaddam [ 48 ], the large of variation in the growth environment that affects crop yields causes the appearance of GEIs. Some researchers reported the comparison of the GGE biplot and AMMI measurements were useful for the evaluation of GEIs under multi-locations [ 15 , 21 , [49] , [50] , [51] ].…”

“…Each stability measure has its own advantages and disadvantages in explaining the phenomenon of the GEIs. Therefore, most breeding programs now integrate some of statistical measurements [ 10 , 13 , 20 , 21 ]. Planting the stable and high yield genotype in a diverse environments is one approach to increase sweet potato production in the country.…”

Selection for representative environment to identify high yielding and sweetness levels of sweet potato (Ipomoea batatas L.) in Indonesia

“…The Bandung dry field and Sumedang dry field environments are Type III environments because they have long vectors but large angles to the abscissa. These two environments should not be to select superior genotypes but can be used to select adapted or region-specific genotypes (Mustamu et al 2018;Amien et al 2022). This means that these environments will produce good yields for certain genotypes.…”