Statistical models for evaluating the genotype-environment interaction in maize (Zea mays L.) Modelos estadísticos para evaluar la interacción genotipo-ambiente en maíz (Zea mays L.)

  title={Statistical models for evaluating the genotype-environment interaction in maize (Zea mays L.) Modelos estad{\'i}sticos para evaluar la interacci{\'o}n genotipo-ambiente en ma{\'i}z (Zea mays L.)},
  author={David Almorza and R Boggio Ronceros and Juan Carlos Salerno},
Our objective was to determine the genotype-environ- ment interaction (GxE) in a hybrid integrated by maize lines either carrying or not balanced lethal systems. Experiments were conducted in three locations over a period of two years considering each year- location combination as a different environment. Yield data were analysed using the Additive Main Effects and Multiplicative Inter- action (AMMI) model and the Sites Regression Analysis (SREG). Results were represented by biplots. The AMMI… 

Genotype and genotype × environment interaction effects on the grain yield performance of cowpea genotypes in dryland farming system in South Africa

This study identified elite cowpea lines and testing environments using the Additive main effects multiplicative interaction (AMMI) and the genotype, genotype by environment biplot analysis to inform future cultivar development strategies.


The comprehensive utilization of the AMMI model and GGE biplot can enable the scientific and objective judgment of the high yield, stability, and adaptability of tested maize hybrids and provides theoretical support for the rational layout of maize hybrids in the environments of Hebei Province.

Genotype by environment interaction and grain yield stability of extra-early maize (Zea mays L.) Hybrids Evaluated at Three Locations in Ghana

In Ghana, genotype by environment interaction effect on maize grain yield is usually significant due to considerable variation in soil and weather conditions at growing sites. A proper understanding

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No single variety showed a superior performance in all the environments but genotype EH02-036-2, followed by Coll.026/01-4, demonstrated top ranking at five of the sixteen environments, thereby supporting decisions on variety selection and recommendation in different environments.

Determining behaviour of maize genotypes and growing environments using AMMI statistics

Analysis of data across the environments using additive main effect and multiplicative interaction (AMMI) statistics revealed significant variance for genotypes, environment and genotype x environment (GxE) interaction.

Multi-environmental evaluation of maize hybrids developed from tropical and temperate lines

The study reveals that hybrids generated from temperate × tropical inbred lines could be useful for replacing currently used poor performing commercial hybrids.

Genotype-by-environment interaction and yield stability of quality protein maize hybrids developed from tropical-highland adapted inbred lines

The objectives of this study were to determine G × E interaction and yield stability of quality protein maize (QPM) single-cross hybrids recently developed from tropical-highland adapted inbred lines, and to identify promising genotypes and representative test and seed production environments.

Genotype and environment interaction in radish (Raphanus sativus L.) for growth, yield and quality traits

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GGE and Ammi Biplot Analysis for Field PEA Yield Stability in Snnpr State, Ethiopia

The experiment was conducted for two consecutive years across four locations using 16 field pea genotypes. The objective of this paper is to determine the magnitude of genotype by environment



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The objective of this study was to use the Additive Main effects and Multiplicative Interaction (AMMmI) ethod,w ith additive effects for genotypesa nd environment as well as ultiplicative terms for genotype-environmenitn teractions, for analyzing data from two international maize caltivar trials.

Interpretation of Genotype × Environment Interactions for Early Maize Hybrids over 12 Years

Flowering earliness of hybrids, water balance around flowering, and mean temperature from the 12 leaf stage to the end of the grain filling phase were determinants of genotype x environment interaction for grain yield in the considered area.

Identifying mega-environments and targeting genotypes

Preliminary results indicate that a small and workable number of mega-environments often suffices to exploit interactions and increase yields.

Yield Stability of Maize Hybrids Evaluated in Multi‐Environment Trials in Yunnan, China

The objectives of this investigation were to evaluate grain yield stability of 13 Chinese hybrids tested across 10 locations in 2002 and 2003 via GGE biplot analysis and Kang's yield-stability statistic and to identify nonrepresentative and/or nondiscriminating locations.

Biplot Analysis of Diallel Data.

A biplot approach for graphical diallel analysis that allows hypotheses to be formulated concerning the genetics of the genotypes is formulated.

Genetic Analysis of Inbred and Hybrid Grain Yield under Stress and Nonstress Environments in Tropical Maize

The need for drought tolerance in both parental lines to achieve acceptable hybrid performance under severe drought suggests good performance across stress levels can be achieved in tropical maize hybrids.

Cultivar Evaluation and Mega‐Environment Investigation Based on the GGE Biplot

This paper presents a GGE (i.e., G + GE) biplot, which is constructed by the first two symmetrically scaled principal components (PC1 and PC2) derived from singular value decomposition of environment-centered MET data.

Statistical Analysis of Yield Trials by AMMI and GGE

A systematic comparison of the Additive Main effects and Multiplicative Interaction model, GGE, and other SVD-based model families is presented, using both statistical theory and empirical investigations, while considering both current practices and best practices.

Predictive and postdictive success of statistical analyses of yield trials

SummaryThe accuracy of a yield trial can be increased by improved experimental techniques, more replicates, or more efficient statistical analyses. The third option involves nominal fixed costs, and

Statistical analyses of multilocation trials