Genotype x environment interaction and yield stability of soybean (Glycine max l.) genotypes in multi-environment trials (METs) in Nigeria.

Genotype × environment interaction (GEI) poses a critical challenge to plant breeders by complicating the identification of stable variety (ies) for performance across diverse environments. GGE biplot and AMMI analyses have been identified as the most effective and appropriate statistical techniques for identifying stable and high-performing genotypes across diverse environments. The objective of this study was to identify widely adapted and high-yielding soybean genotypes from Multi-Locational Trials (MLTs) using GGE and AMMI biplot analyses. Fifteen IITA-bred elite soybean lines and three standard checks were evaluated for stability of performance in a 3 × 6 alpha lattice design with three replications across seven locations in Nigeria. Significant (p < 0.001) differences were detected among genotypes, environments, and GEI for grain yield, which ranged between 979.8 kg ha-1 and 3645 kg ha-1 with a mean of 2324 kg ha-1. To assess the stability of genotypes, analyses were conducted using the general linear method, GGE, and the Additive Main Effect and Multiplicative Interaction (AMMI) approach, as well as WAAS and ASV rank indices. In the GGE biplot model, the first two principal components accounted for 67.4 % of the total variation, while in the AMMI model, the first two Interaction Principal Component Axes (IPCA1 and IPCA2) explained 73.20 % and 11.40 % of the variation attributed to genotype by environment interaction, respectively. GGE biplot identified G10 and G16 as the most stable and productive genotypes, while WAASB index revealed G16, G10, G9, G4 and G2 as the most adaptive, stable and productive genotypes across locations, and ASV identified G9, G13, G4, G14 and G10 as the most stable and productive. Consequently, genotypes G2, G4, G9, G10 and G16 displayed outstanding and stable grain yield performance across the test locations and are, therefore, recommended for release as new soybean varieties suitable for cultivation in the respective mega environment where they performed best. More importantly, the two genotypes are recommended for recycling as sources of high-yield and yield stability genes, and as parental lines for high-yield and stable performance for future breeding and genomic selection.

Abebe AT ，Adewumi AS ，Adebayo MA ，Shaahu A ，Mushoriwa H ，Alabi T ，Derera J ，Agbona A ，Chigeza G ... -  《Heliyon》

Analysis of genotype-by-environment interaction effect in barely genotypes using AMMI and GGE biplot methods.

Genotype-by-environment interaction (GEI) analysis play a key role in any breeding program involving the development of new varieties for cultivation across various environments or in a specific region. The additive main effects and multiplicative interaction (AMMI) method and the GGE biplot are the two main statistical tools that have emerged to analyze GEI in multi-environment trials (METs). The main goal of the present study was to identify the best-performing and stable barley genotypes for the warm regions of Iran. For this purpose, 18 new advanced barley genotypes were investigated in five warm locations in Iran during two cropping seasons (2021-2023). In all experiments, test genotypes were evaluated in a randomized complete block design (RCBD) with three replications. Based on results, grain yield was significantly dependent on environments (E), genotypes (G), and GEI. The GEI effect was further divided into three principal component axes (IPCAs). The AMMI method identified genotypes G3, G9, G10, and G14 as ideal genotypes due to their low IPCA scores and high performances. In the GGE biplot analysis, the initial two PCAs accounted for 49.36 % of the total variation of grain yield, including both G and GEI effects. Based on averaged two-year data, genotypes G3, G4, G10, and G14 showed particular adaptability in the Zabol and Moghan regions. Moreover, the ranking of test environments showed good discriminatory and representative abilities for the Zabol and Moghan regions, so these environments constituted a mega-environment in Iran's warm climate. The genotype ranking indicated G3, G10 and G14 genotypes as the superior genotypes with the highest grain yield and stability in different test environments. Moreover, these results were confirmed by the results obtained by WAASB and WAASBY biplots. In conclusion, genotypes G3, G10 and G14 can be suggested for commercial usage and cultivation in various regions in Iran's warm climate.

Rahmati S ，Azizi-Nezhad R ，Pour-Aboughadareh A ，Etminan A ，Shooshtari L ... -  《-》

Genotype-by-environment interaction and stability analysis of grain yield of bread wheat (Triticum aestivum L.) genotypes using AMMI and GGE biplot analyses.

Bread wheat is a vital staple crop worldwide; including in Ethiopia, but its production is prone to various environmental constraints and yield reduction associated with adaptation. To identify adaptable genotypes, a total of 12 bread wheat genotypes (G1 to G12) were evaluated for their genotype-environment interaction (GEI) and stability across three different environments for two years using Additive Main Effect and Multiplicative Interaction (AMMI) and genotype main effect plus genotype-by-environment interaction (GGE) biplots analysis. GEI is a common phenomenon in crop improvement and is of significant importance in genotype assessment and recommendation. According to combined analysis of variance, grain yield was considerably impacted by environments, genotypes, and GEI. AMMI and GGE biplots analysis also provided insights into the performance and stability of the genotypes across diverse environmental conditions. Among the 12 genotypes, G6 was selected by AMMI biplot analysis as adaptive and high-yielding genotype; G5 and G7 demonstrated high stability and minimal interaction with the environment, as evidenced by their IPCA1 values. G7 was identified as the most stable and high-yielding genotype. The GGE biplot's polygon view revealed that the highest grain yield was obtained from G6 in environment three (E3). E3 was selected as the ideal environment by the GGE biplot. The top three stable genotypes identified by AMMI stability value (ASV) were G5, G7, and G10, while the most stable genotype determined by Genotype Selection Index (GSI) was G7. Even though G6 was a high yielder, it was found to be unstable according to ASV and ranked third in stability according to GSI. Based on the study's findings, the GGE biplot genotype view for grain yield identified Tay genotype (G6) to be the most ideal genotype due to its high grain yield and stability in diverse environments. G7 showed similar characteristics and was also stable. These findings provide valuable insights to breeders and researchers for selecting high-yielding and stable, as well as high-yielding specifically adapted genotypes.

Mullualem D ，Tsega A ，Mengie T ，Fentie D ，Kassa Z ，Fassil A ，Wondaferew D ，Gelaw TA ，Astatkie T ... -  《Heliyon》

Adaptability and stability for soybean yield by AMMI and GGE models in Ethiopia.

Genotype by environment interaction (GEI) is a phenomenon that occurs in heterogeneous environments that slows breeding progress by preventing the selection of superior cultivars for breeding and commercialization. Therefore, the objectives of this study were to find out how GEI impacts soybean output and to identify the most adapted and stable genotypes. Moreover, to look at the possibility of other mega environments for testing in the future. The experiments were grown for two years in a four-replicated randomized block design at each environment. Over the course of several harvests, yield components, days to flowering, days to maturity, plant height, the number of pods per plants, the number of seeds per plant, hundred seed weight and grain yield per hectare were evaluated in the main for 2018 and 2019.To analyze the stability performance of the genotypes, general linear method, GGE and Additive main effect and multiplicative interaction effects analysis (AMMI) and ASV rank analysis were applied. The GGE biplot revealed that the GGE biplots explained 74.29% of the total variation distributed as,56.69% and 17.62% of sum of squares between principal component PC1 and PC2, respectively whereas, AMMI model, the first two interaction principal component axes (IPCA1 and IPCA2) explained 47.74% and 26.62% of the variation due to GEI, respectively, exposed genotypes identified the five as best performer. The results from the four distinct stability statistics AMMI biplot (G8, G2, G1, G11), ASV (G1, G11; (GSI; G9, G1, G11) and (GGE: G2, G8, G9) are taken into account together with the genotypes` grand mean. The genotypes JM-CLK/CRFD-15-SD (G8) and 5002T (G1), which rank among the best and have the highest seed output, are suitable for hybridization as a parent and commercial production. Therefore, genotypes JM-CLK/CRFD-15-SD (G8) and 5002T(G1) have the highest seed output were among the best and thus could be recommended for release as a new soybean varieties cultivation across.

Hailemariam Habtegebriel M 《-》

Integrating BLUP, AMMI, and GGE Models to Explore GE Interactions for Adaptability and Stability of Winter Lentils (Lens culinaris Medik.).

Hossain MA ，Sarker U ，Azam MG ，Kobir MS ，Roychowdhury R ，Ercisli S ，Ali D ，Oba S ，Golokhvast KS ... -  《-》