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ammi two-dimensional graphical model  (Gauch GmbH)

 
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    Gauch GmbH ammi two-dimensional graphical model
    Ammi Two Dimensional Graphical Model, supplied by Gauch GmbH, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/ammi+biplot/ammi+biplot/pm28671254-89-32-5
    Average 90 stars, based on 1 article reviews
    ammi two-dimensional graphical model - by Bioz Stars, 2026-10
    90/100 stars

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    Related Articles

    Concentration Assay:

    Article Title: Identification of environment types and adaptation zones with self-organizing maps; applications to sunflower multi-environment data in Europe
    Article Snippet: We inspected the predictions of model (5) in an AMMI biplot (Gauch and Zobel ).

    Article Title: Evaluating genetic diversity and seed composition stability within Pan‐African Soybean Variety Trials
    Article Snippet: In the AMMI1 biplot, with PC1 on the Yaxis, cultivars and environments with values near zero for PC1 are considered most stable with low G × E, while those with higher absolute PC1 scores are considered unstable (Gauch et al., 2008).

    Article Title: A Systematic Narration of Some Key Concepts and Procedures in Plant Breeding
    Article Snippet: AMMI1 or AMMI2 (i.e., main effects plus the first one or two principal components of GE) is often found the best AMMI model, and the AMMI1 biplot is often used as a visual tool for genotype evaluation (Gauch, ).

    Article Title: Additive main effects and multiplicative interaction for grain yield of rice (Oryza sativa) genotypes for general and specific adaptation to salt stress locations
    Article Snippet: Context.. Salt stress is one of the major, ever-increasing abiotic stresses that hinders rice production across arable land around the world.. In order to sustain the production of rice (Oryza sativa) in these salt-affected areas, high-yielding stable salt tolerant genotypes must be identified.

    Article Title: STEVIA (STEVIA REBAUDIANA B.) GENOTYPES ASSESSMENT FOR LEAF YIELD STABILITY THROUGH GENOTYPE BY ENVIRONMENT INTERACTIONS, AMMI, AND GGE BIPLOT ANALYSES
    Article Snippet: According to Gauch (2013), the AMMI stability value (ASV) in the AMMI biplot analysis can provide information about the genotype's stability in multilocation testing.

    Article Title: Predictive Power of YREMs and BLUPs for Selecting Superior Genotypes in Perennial Crops: A Black Pepper Case Study
    Article Snippet: Black pepper (Piper nigrum L.), a highly sought-after spice crop with medicinal properties, requires careful evaluation and selection due to its perennial nature and associated resource requirements.. Being a perennial, yield trials across years are feasible and practical in this crop rather than that across locations.. However, parameters to assess the yield trial data and/or derive criteria to select superior cultivars with stable performance are lacking in black pepper.

    Article Title: Multi environmental evaluation for selection of stable and high yielding sugarcane (Saccharum officinarum L.) clones based on AMMI and GGE biplot models
    Article Snippet: There are two widely used biplot models of multivariate approaches: AMMI biplot (Gauch et al. 2006; Gauch et al. 2008) and GGE biplot (Yan et al. 2000; Yan and Tinker 2006).

    Article Title: Ammi Analysis for Yield and Stability in Direct Seeded Rainfed Rice
    Article Snippet: The G x E interaction was analyzed following AMMI biplot (Gauch and Zobel 1989).



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    Principal component analysis, scree plot and biplot of 11 stress indexes calculated using 300 diverse rice genotypes (A) Scatterplots visualize the distribution of sample values in the reduced-dimensional spaces, highlighting clusters and separation patterns based on the principal components of stress indexes. The percentage of total phenotypic variance explained by the first four PCs is 99.17. (B) Scree plot (right panel) showed the % of phenotypic variance explained by the first ten principal components. A sharp decline (angled elbow) in variance after PC2 indicates that the first two components capture most of the data’s variability and the presence of three major clusters within the studied population group. (C) Biplot showed the first two dimensions represent the first (64.2%) and second (30.2%) principal components, capturing most of the variance in the dataset. Arrows represent individual variables, with their direction indicating the correlation with the principal components and their length reflecting the strength of the contribution. The color gradient (ranging from 5.5 to 8.0) indicates the magnitude of each variable’s contribution, with warmer colors (e.g., red) signifying higher contributions.

    Journal: iScience

    Article Title: Identification of superior rice donors with enhanced nitrogen use efficiency using a comprehensive multivariate genotype selection strategy

    doi: 10.1016/j.isci.2025.113280

    Figure Lengend Snippet: Principal component analysis, scree plot and biplot of 11 stress indexes calculated using 300 diverse rice genotypes (A) Scatterplots visualize the distribution of sample values in the reduced-dimensional spaces, highlighting clusters and separation patterns based on the principal components of stress indexes. The percentage of total phenotypic variance explained by the first four PCs is 99.17. (B) Scree plot (right panel) showed the % of phenotypic variance explained by the first ten principal components. A sharp decline (angled elbow) in variance after PC2 indicates that the first two components capture most of the data’s variability and the presence of three major clusters within the studied population group. (C) Biplot showed the first two dimensions represent the first (64.2%) and second (30.2%) principal components, capturing most of the variance in the dataset. Arrows represent individual variables, with their direction indicating the correlation with the principal components and their length reflecting the strength of the contribution. The color gradient (ranging from 5.5 to 8.0) indicates the magnitude of each variable’s contribution, with warmer colors (e.g., red) signifying higher contributions.

    Article Snippet: AMMI biplot showing the mean yield performance of fifteen rice genotypes in three locations AMMI biplot (Additive Main effects and Multiplicative Interaction model) illustrates the interaction between genotypes and environments based on two principal components, PC1 (56.9%) and PC2 (43.1%) represent the first two interaction principal component axes (IPCA), together explaining 100% of the genotype × environment interaction variance.

    Techniques:

    AMMI biplot showing the mean yield performance of fifteen rice genotypes in three locations AMMI biplot (Additive Main effects and Multiplicative Interaction model) illustrates the interaction between genotypes and environments based on two principal components, PC1 (56.9%) and PC2 (43.1%) represent the first two interaction principal component axes (IPCA), together explaining 100% of the genotype × environment interaction variance. Green points and lines represent environments (E1, E2, E3). Blue points and dashed lines represent genotypes (G1 to G14). The proximity of genotypes to environments indicates their specific adaptability, while genotypes near the origin are considered more stable across environments.

    Journal: iScience

    Article Title: Identification of superior rice donors with enhanced nitrogen use efficiency using a comprehensive multivariate genotype selection strategy

    doi: 10.1016/j.isci.2025.113280

    Figure Lengend Snippet: AMMI biplot showing the mean yield performance of fifteen rice genotypes in three locations AMMI biplot (Additive Main effects and Multiplicative Interaction model) illustrates the interaction between genotypes and environments based on two principal components, PC1 (56.9%) and PC2 (43.1%) represent the first two interaction principal component axes (IPCA), together explaining 100% of the genotype × environment interaction variance. Green points and lines represent environments (E1, E2, E3). Blue points and dashed lines represent genotypes (G1 to G14). The proximity of genotypes to environments indicates their specific adaptability, while genotypes near the origin are considered more stable across environments.

    Article Snippet: AMMI biplot showing the mean yield performance of fifteen rice genotypes in three locations AMMI biplot (Additive Main effects and Multiplicative Interaction model) illustrates the interaction between genotypes and environments based on two principal components, PC1 (56.9%) and PC2 (43.1%) represent the first two interaction principal component axes (IPCA), together explaining 100% of the genotype × environment interaction variance.

    Techniques:

    GGE biplot showing the relationship between environment and yield performance of 15 rice genotypes in three locations GGE biplot in the “Which-won-where” view is used to visualize the performance of genotypes across multiple environments, identify mega-environments and the best-performing genotypes within each, supporting genotype selection and recommendation. The x axis (PC1: 65.5%) and y axis (PC2: 22.81%) represent the first two principal components derived from genotype and genotype × environment interaction effects. Points labeled G1 to G16 represent different genotypes. Points labeled E1 to E3 represent different environments. Polygons connect the outermost genotypes, forming sectors that help identify which genotype performed best in which environment. Dotted lines (rays) divide the plot into sectors, each associated with a specific environment. The genotype at the vertex of each sector is considered the “winner” in that environment.

    Journal: iScience

    Article Title: Identification of superior rice donors with enhanced nitrogen use efficiency using a comprehensive multivariate genotype selection strategy

    doi: 10.1016/j.isci.2025.113280

    Figure Lengend Snippet: GGE biplot showing the relationship between environment and yield performance of 15 rice genotypes in three locations GGE biplot in the “Which-won-where” view is used to visualize the performance of genotypes across multiple environments, identify mega-environments and the best-performing genotypes within each, supporting genotype selection and recommendation. The x axis (PC1: 65.5%) and y axis (PC2: 22.81%) represent the first two principal components derived from genotype and genotype × environment interaction effects. Points labeled G1 to G16 represent different genotypes. Points labeled E1 to E3 represent different environments. Polygons connect the outermost genotypes, forming sectors that help identify which genotype performed best in which environment. Dotted lines (rays) divide the plot into sectors, each associated with a specific environment. The genotype at the vertex of each sector is considered the “winner” in that environment.

    Article Snippet: AMMI biplot showing the mean yield performance of fifteen rice genotypes in three locations AMMI biplot (Additive Main effects and Multiplicative Interaction model) illustrates the interaction between genotypes and environments based on two principal components, PC1 (56.9%) and PC2 (43.1%) represent the first two interaction principal component axes (IPCA), together explaining 100% of the genotype × environment interaction variance.

    Techniques: Selection, Derivative Assay, Labeling

    AMMI I biplot of 27 red sorghum genotypes (blue dots) tested in three environments (green dots) from summer–2024: A PW: panicle weight, B YIELD: single-plant yield, C Fe: iron content, D Zn: zinc content. PC: principal component

    Journal: BMC Plant Biology

    Article Title: Delineation of genotype × environment interaction and identifying superior red sorghum [ Sorghum bicolor L. Moench] genotypes via multi-trait-based stability selection methods

    doi: 10.1186/s12870-025-06188-4

    Figure Lengend Snippet: AMMI I biplot of 27 red sorghum genotypes (blue dots) tested in three environments (green dots) from summer–2024: A PW: panicle weight, B YIELD: single-plant yield, C Fe: iron content, D Zn: zinc content. PC: principal component

    Article Snippet: The AMMI I exploratory graph or biplot was designed with the X-axis representing the trait's mean across environments, highlighting the principal effects [ ], whereas the Y-axis displayed the first interactive principal component axis (IPCA 1) score, addressing the multiplicative effects (Fig. ).

    Techniques:

    AMMI II biplot developed using the PC I and PC II values of 27 red sorghum genotypes (blue dots) evaluated in three environments (green dots) from summer–2024: A PW: panicle weight, B YIELD: single-plant yield, C Fe: iron content, D Zn: zinc content

    Journal: BMC Plant Biology

    Article Title: Delineation of genotype × environment interaction and identifying superior red sorghum [ Sorghum bicolor L. Moench] genotypes via multi-trait-based stability selection methods

    doi: 10.1186/s12870-025-06188-4

    Figure Lengend Snippet: AMMI II biplot developed using the PC I and PC II values of 27 red sorghum genotypes (blue dots) evaluated in three environments (green dots) from summer–2024: A PW: panicle weight, B YIELD: single-plant yield, C Fe: iron content, D Zn: zinc content

    Article Snippet: The AMMI I exploratory graph or biplot was designed with the X-axis representing the trait's mean across environments, highlighting the principal effects [ ], whereas the Y-axis displayed the first interactive principal component axis (IPCA 1) score, addressing the multiplicative effects (Fig. ).

    Techniques: