AMMI and GGE biplot analyses of Bambara groundnut [Vigna subterranea (L.) Verdc.] for agronomic performances under three environmental conditions

Abstract

IntroductionThe two most common styles to analyze genotype-by-environment interaction (GEI) and estimate genotypes are additive main effects and multiplicative interaction (AMMI) and genotype + genotype × environment (GGE) biplot. Therefore, the aim of this study was to find the winning genotype(s) under three locations, as well as to investigate the nature and extent of GEI effects on Bambara groundnut production.MethodsThe experiment was carried out in the fields of three environments with 15 Bambara groundnut accessions using the randomized complete block design (RCBD) with three replications each in Ibadan, Osun, and Odeda. Yield per plant, fresh seed weight, total number of pods per plant, hundred seed weight, length of seeds, and width of seeds were estimatedResultsAccording to the combined analysis of variance over environments, genotypes and GEI both had a significant (p < 0.001) impact on Bambara groundnut (BGN) yield. This result revealed that BGN accessions performed differently in the three locations. A two-dimensional GGE biplot was generated using the first two principal component analyses for the pattern of the interaction components with the genotype and GEI. The first two principal component analyses (PCAs) for yield per plant accounted for 59.9% in PCA1 and 40.1% in PCA2. The genotypes that performed best in each environment based on the “which-won-where” polygon were G8, G3, G2, G11, G6, and G4. They were also the vertex genotypes for each environment. Based on the ranking of genotypes, the ideal genotypes were G2 and G6 for YPP, G1 and G5 for FPW, G15 and G13 for TNPP, G3 and GG7 for HSW, G7 and G12 for LOS, and G10 and G7 for WOS. G8 was recorded as the top most-yielding genotype. G8, G4, G7, and G13 were high yielding and the most stable across the environments; G11, G14, and G9 were unstable, but they yielded above-average performance; G14, G12, G15, and G1 were unstable and yielded poorly, as their performances were below average. Bowen was the most discriminating and representative environment and is classified as the superior environment.DiscussionBased on the performance of accessions in each region, we recommend TVSU 455 (G8) and TVSU 458 (G3) in Bowen, TVSU 455 (G8) and TVSU 939 (G6) and TVSU 454 (G1) in Ibadan, and TVSU 158 (G2) and TVSU 2096 (G10) in Odeda. The variety that performed best in the three environments was TVSU 455 (G8). They could also be used as parental lines in breeding programs.

AMMI and GGE biplot analysis of genotype by environment interaction for yield and yield contributing traits in confectionery groundnut

“…Conversely, a deleterious interaction arises when there is a discrepancy in polarity between the genotype and the environment. The outcomes derived from our research are consistent with the findings documented 29 – 33 . The findings of present investigation demonstrate that the AMMI model exhibits a satisfactory level of compatibility with the collected data, thereby providing empirical justification for the application of AMMI 2.…”

Deciphering genotype‐by‐environment interaction in new soybean lines based on multiple traits using different adaptability and stability methods

“…( 2023 ), Esan et al. ( 2023 ), and Kindie et al. ( 2022 ), who reported on dividing testing environments into various mega environments with different sectors and numbers of genotypes.…”

Assessment of yield performances for grain sorghum varieties by AMMI and GGE biplot analyses

“…The AMMI model generates AMMI1 and AMMI2 biplots for evaluating the significant multiplicative interactions of agronomic traits between genotypes and environments in multi-environment field trials. AMMI1 biplot gives visual information on yield potential and stability performance of genotypes under evaluation ( Esan et al., 2023 ). In the AMMI1 biplot the ordinate and abscissa axes stand for the influence of environment effects and the mean yields, respectively ( Olivoto et al., 2019 ).…”

“…In the AMMI1 biplot the ordinate and abscissa axes stand for the influence of environment effects and the mean yields, respectively ( Olivoto et al., 2019 ). Esan et al. (2023) and Habtegebriel (2022) reported that genotypes located on the right side of the origin of the biplot along the abscissa axe had greater yields whereas those on the left side had lower yields, and that the influences of environments are larger when genotypes are arranged farther from the abscissa axe.…”

“…Alternatively, GGE biplot analysis incorporates a series of functionalities that assess genotype effects and GEI effects simultaneously to produce “representativeness and discriminativeness” view for test environments, and “which-won-where” pattern, “stability vs. mean performance” as well as “ranking genotypes” views for genotypes in METs ( Angelini et al., 2019 ). Although GGE biplot analysis provides more sophisticated insights into understanding GEI patterns, the two statistical tools complement each other enabling us to comprehend GEI effects ( Esan et al., 2023 ). Aided by these statistical tools, breeders become able to describe how genotypes interact with environments to impact crop output and effectively select superior genotypes ( Yan and Kang, 2003 ).…”