Additive Main Effects and Multiplicative Interactions (AMMI) Analysis of Grain Yield in Barley Genotypes Across Environments

Kılıç, Hasan

doi:10.15832/tbd.44431

Cited by 17 publications

(6 citation statements)

References 14 publications

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“…The high value in IPCA 1 was sufficient to study the total G×E interaction that was in accordance with the findings of Win et al (2017). Based on the performance of genotypes across different environments, AMMI 1 classified the genotypes as most stable and high yielding genotypes (G52, G29, and G43), less stable and high yielding genotypes (G37, G30, G14, and G11) and most stable and low yielding genotypes viz., G42, G22 and G4 as reported by Kilic (2014) in barley. From AMMI 2 biplot the highly interactive environment (E3) and genotypes (G34 and G14) were identified.…”

Section: Discussionsupporting

confidence: 51%

Delineation of Genotype × Environment Interaction for Identification of Stable Genotypes to Grain Yield in Mungbean

et al. 2020

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In the present study, fifty-two mungbean (Vigna radiata) genotypes were evaluated for seven morphological traits at three different environments in South Indian state Tamil Nadu, namely Virinjipuram (E1), Eachangkottai (E2), and Bhavanisagar (E3) during Kharif 2017, 2018, and 2019, respectively. The data collected were subjected to variability and correlation analyses, followed by stability analysis using additive main effects and multiplicative interaction (AMMI) model, genotype and genotype × environment interaction effects (GGE) biplot. Variablility was observed among the genotypes for the following traits viz., plant height, days to fifty per cent flowering, number of pods per plant, pod length, number of seeds per pod, hundred seed weight and grain yield. Correlation analysis showed that the trait number of pods per plant was significantly associated with grain yield. The G × E was smaller than the genetic variation of grain yield as it portrayed the maximum contribution of genotypic effects (61.07%). GGE biplot showed E3 as a highly discriminating and representative environment. It also identified environment-specific genotypes viz., EC 396111 for E1, EC 396125 for E2 and EC 396101 for E3 environments. The genotypes with minimum genotype stability index (GSI) viz., V2802BG (7), HG 22 (13), and EC 396098 (13) were observed with wide adaptation and high yields across all the three environments. In summary, we identified stable genotypes adapted across environments for grain yield. These genotypes can be used as parent/pre-breeding materials in future mungbean breeding programs.

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Section: Discussionsupporting

confidence: 51%

Delineation of Genotype × Environment Interaction for Identification of Stable Genotypes to Grain Yield in Mungbean

et al. 2020

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“…AMMI model 1 biplot is one of the many versions of AMMIs, basically for identifying high potential yield and stability (Olivoto et al, 2019). Kılıç(2014) reported that in AMMI 1 when the mean environments and genotypes are placed on the same lines that are parallel to the ordinate, it means that they have almost the same performance, but when the genotypes are placed on the right side of the center of the biplot, they show higher yield than those genotype at the left-hand side. These results are similar to our findings where all the genotypes like G8, and G13 located on the right side were the best genotypes for YPP, TNPP, HSW, FPW, LOS, and WOS.…”

Section: Ammi 1 Biplotsmentioning

confidence: 99%

“…The AMMI model 2 biplot is produced to make use of the environmental and genotypic scores of the first two AMMI components as reported by Vargas and Crossa (2000) and Kılıc( 2014). The IPCA1 and IPCA2 scores help to understand the function of genotype-by-environment interaction and the adaptability of the genotypes in the test environments.…”

Section: Ammi Modelmentioning

confidence: 99%

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

et al. 2023

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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.

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“…(2019) . While analyzing AMMI 1, Kılıç (2014) reported that the genotype and environment mean when positioned parallel to the ordinate indicates nearly equal performance. However, a higher yield is displayed by the genotypes positioned on the right side of the biplot’s center than by those on the left.…”

Section: Discussionmentioning

confidence: 99%

Multi-environment testing for G×E interactions and identification of high-yielding, stable, medium-duration pigeonpea genotypes employing AMMI, GGE biplot, and YREM analyses

Bomma,

Shruthi,

Soregaon

et al. 2024

Front. Plant Sci.

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Pigeonpea [Cajanus cajan (L.) Millspaugh] is a widely grown pulse with high seed protein content that contributes to food and nutritional security in the Indian subcontinent. The majority of pigeonpea varieties cultivated in India are of medium duration (<180 days to maturity), which makes it essential for breeders to focus on the development of stable high-yielding varieties. The diverse agroecological regime in the Indian subcontinent necessitates an efficient multi-environment study by taking into consideration genotype (G) × environment (E) interaction (GEI) that has a significant impact on traits like grain yield (GY) in developing high-yielding and widely adaptable varieties. In the present study, 37 pigeonpea genotypes were evaluated during the 2021 rainy season at ARS Badnapur, ARS Tandur, BAU Ranchi, GKVK Bengaluru, and ICRISAT Patancheru. The GEI was significant on the grain yield (p < 0.01), and hence, genotype + genotype × environment (GGE) and additive main effects and multiplicative interaction (AMMI) biplots along with AMMI stability value (ASV) and yield relative to environmental maximum (YREM) statistics were used to identify stable high-yielding genotypes. The interaction principal component analysis 1 and 2 (IPC1 and IPC2) explained 40.6% and 23.3% variations, respectively. Based on the rankings of genotypes, G37 (ICPL 20205), G35 (ICPL 20203), G8 (ICPL 19404), G17 (ICPL 19415), and G9 (ICPL 19405) were identified as ideal genotypes. Discriminativeness vs. representativeness identified GKVK Bengaluru as an ideal environment for comprehensive evaluation of test genotypes. However, ICPL 19405 was identified as the potentially stable high-yielding genotype for further testing and release across the test environments based on its mean grain yield (1,469.30 kg/ha), least ASV (3.82), and low yield stability index (YSI) of 13.

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Additive Main Effects and Multiplicative Interactions (AMMI) Analysis of Grain Yield in Barley Genotypes Across Environments

Cited by 17 publications

References 14 publications

Delineation of Genotype × Environment Interaction for Identification of Stable Genotypes to Grain Yield in Mungbean

Delineation of Genotype × Environment Interaction for Identification of Stable Genotypes to Grain Yield in Mungbean

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

Multi-environment testing for G×E interactions and identification of high-yielding, stable, medium-duration pigeonpea genotypes employing AMMI, GGE biplot, and YREM analyses

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