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

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

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Genetic Variation and Genotype by Environment Interaction for Agronomic Traits in Maize (Zea mays L.) Hybrids

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The analysis revealed that the genotype RL-30 was stable for yield building factors in all the environments followed by genotype RB-21 (except in April) and RL-2210 was stableFor root plant ratio, root shoot ratio and quality characters, which revealed that marketable yield had the maximum positive direct effect on root yield followed by plant weight.



Additive main effects and multiplicative interaction analysis of two international Maize cultivar trials

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.

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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.

Statistical Analysis of a Yield Trial

Yield trials frequently have both significant main effects and a significant genotype x environment (GE) interaction. Traditional statistical analyses are not always effective with this data

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

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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.

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Model selection and validation for yield trials with interaction

AMMI analysis of yield trial data is a useful extension of the more familiar ANOVA, PCA, and linear regression procedures, particularly given a large genotype-by-environment interaction.

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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

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