Statistical analysis of yield trials by AMMI analysis of genotype × environment interaction

By Kuang Hongyu, Marisol García-Peña, L. B. D. Araújo, Carlos Tadeu dos Santos Dias. 2014

114 citations recorded by the source corpus.

Abstract The genotype by environment interaction (GEI)) has an influence on the selection and recommendation of cultivars. The aim of this work is to study the effect of GEI and evaluate the adaptability and stability of productivity (kg/ha) of nine maize genotypes using AMMI model (Additive Main effects and Multiplicative Interaction). The AMMI model is one of the most widely used statistical tools in the analysis of multiple-environment trials. It has two purposes, namely understanding complex GEI and increasing accuracy. Nevertheless, the AMMI model is a widely used tool for the analysis of multiple-environment trials, where the data are represented by a two-way table of GEI means. In the complete tables, least squares estimation for the AMMI model is equivalent to fitting an additive two-way ANOVA model for the main effects and applying a singular value decomposition to the interaction residuals. It assumes equal weights for all GEI means implicitly. The experiments were conducted in twenty environments, and the experimental design was a randomized complete block design with four repetitions. The AMMI model identified the best combinations of genotypes and environments with respect to the response variable. This paper concerns a basic and a common application of AMMI: yield-trial analysis without consideration of special structure or additional data for either genotypes or environments.

Subfields: Genetics, Management Science and Operations Research, Plant Science

View Paper: https://www.semanticscholar.org/paper/0bd9e8716c29d34b21b5b967bf34cb04a7ff4320

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Genetics

1. Kuang Hongyu
2. Anderson Cristiano Neisse
3. Jhessica Leticia Kirch

In Management Science and Operations Research

1. Kuang Hongyu
2. Anderson Cristiano Neisse
3. Jhessica Leticia Kirch

In Plant Science

1. Kuang Hongyu
2. Anderson Cristiano Neisse
3. Jhessica Leticia Kirch

Citations in the corpus

• AMMI and GGE Biplot for genotype × environment interaction: a medoid–based hierarchical cluster analysis approach for high–dimensional data (2018, methodology): “In terms of model fitting, the work of Hongyu et al. (2014) led to similar results: two principal components were able to retain 71.2% of the original variability from 9 genotypes in 20 different environments with the AMMI2 model.”