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

100%
Hongyu K., García-Peña M., Araújo L. B. d., Santos Dias C. T. d.
Biometrical Letters
|
2014
|
tom 51
|
nr 2
89-102
EN
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.
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Evaluation of spring barley breeding lines in a two-year multi-location experiment using some statistical methods

88%
Zawieja B., Bakinowska E., Bichoński A.
Biometrical Letters
|
2016
|
tom 53
|
nr 2
149-162
EN
In breeding experiments conducted prior to tests connected with the registration of new breeding lines of crops, pre-preliminary and preliminary trials are carried out. In this study a comparison was made among some models of analysis of variance, in relation to the selection of new breeding lines of spring barley (Hordeum vulgare L.). The aim is to determine whether the choice of model of analysis of variance may influence the choice of tested breeding lines. The trait considered was the yield in two years of trials. A more comprehensive analysis of variance model was found to be superior. It was also found that the results of analyses performed using average measurements for lines significantly differ from those obtained on the basis of all measurements. It was concluded that the type of ANOVA model used may have an impact on inferences about breeding lines. Moreover, a lack of stability in the yields of tested lines was revealed, implying the necessity of several years of trials.
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