Integrating univariate and multivariate statistical models to investigate genotype × environment interaction in durum wheat

• Published in: Annals of applied biology 2021-05, Vol.178 (3), p.450-465
• Main Authors: Mohammadi, Reza, Sadeghzadeh, Behzad, Poursiahbidi, Mohammad Mehdi, Ahmadi, Malak Masoud
• Format: Article
• Language: English
• Subjects: AMMI; Breeding; Climate change; Climate effects; Climatic data; Clustering; Crop yield; durum wheat; Dynamic stability; Environment models; Environmental effects; Freezing; Genotype & phenotype; Genotype-environment interactions; Genotypes; GGE biplot; grain yield; heat map; Least squares method; Mathematical models; Mean; Multivariate analysis; partial least square regression; Pattern analysis; Rainfall; Regression; Relative humidity; Stability analysis; stability methods; Statistical analysis; Statistical models; Triticum durum; Wheat
• ISSN: 0003-4746; 1744-7348
• DOI: 10.1111/aab.12648

There has been a significant trend in the use of different statistical tools to analyse genotype × environment (GE) interaction for grain yield in multi‐environment trials. Several statistical models including 16 univariate stability methods and four multivariate models such as the additive main effects and multiplicative interaction (AMMI), GGE biplot (G+GE biplot), and factorial regression and partial least squares regression were applied to investigate the GE interaction for grain‐yield data of 18 durum wheat genotypes grown in 14 environments (location‐year combinations). The main objectives were to use the different statistical models to evaluate GE interaction for grain yield in durum wheat and to investigate the effect of some climatic variables on the interactions. The main effect of environment, genotype and GE interactions were significant (p