Abstract
AbstractEvaluating stability performance and range of adaptation is becoming increasingly important. If cultivars are to be selected for a large group of environments, stability and mean yield across all environments are more important than yield for specific environments. This study was conducted to assess the stability of 3 extra early maize under high and low nitrogen environments. The experiments were conducted at two locations in 2009 and 2010 wet season under high and low N applications. Additive Main Effect and Multiplicative Interaction (AMMI) and GGE biplot were used to assess the genotype-by-environment interactions. The mean square analysis reveals a significant difference for environments effect and a lack of significant mean squares for varietal effects under high and low N and across the research environments. 2004-SYNEEW gave the highest grain yield under combined analysis. Environment sum of squares under AMMI model accounted for about 98.79% of the treatment sum of squares. The first PCA ...
Highlights
Conduct of multi-environment trials (MET) for all major crops throughout the world is important in achieving high yielding and stable cultivars (Fikere, Tadesse, & Letta, 2008; Yan, Cornelius, Crossa, & Hunt, 2001)
Several statistical methods have been developed for anlaysing genotype-by-environment interaction (GEI) or phenotypic stability (Becker & Leon, 1988; Crossa, 1990; Lin, Binns, & Lefkovitch, 1986; Piepho, 1998; Westcott, 1986)
Joint regression is the most popular among univariate methods because of its simplicity of calculation and application (Aremu, 2005; Ariyo, 1987; Ariyo & Ayo-Vaughan, 2000; Eberhart & Russell, 1996), whereas Additive Main Effect and Multiplicative Interaction (AMMI) and Genotype and genotype-byenvironment (GGE) biplot methodology have recently being used as the main alternative multivariate approach to the joint regression analysis in many breeding programs (Annicchiarico, 1997; Badu-Apraku et al, 2013, 2015; Yan & Kang, 2003)
Summary
Conduct of multi-environment trials (MET) for all major crops throughout the world is important in achieving high yielding and stable cultivars (Fikere, Tadesse, & Letta, 2008; Yan, Cornelius, Crossa, & Hunt, 2001). Several statistical methods have been developed for anlaysing GEI or phenotypic stability (Becker & Leon, 1988; Crossa, 1990; Lin, Binns, & Lefkovitch, 1986; Piepho, 1998; Westcott, 1986). These methods can be divided into two major groups, univariate and multivariate stability (Lin et al, 1986). Joint regression is the most popular among univariate methods because of its simplicity of calculation and application (Aremu, 2005; Ariyo, 1987; Ariyo & Ayo-Vaughan, 2000; Eberhart & Russell, 1996), whereas Additive Main Effect and Multiplicative Interaction (AMMI) and Genotype and genotype-byenvironment (GGE) biplot methodology have recently being used as the main alternative multivariate approach to the joint regression analysis in many breeding programs (Annicchiarico, 1997; Badu-Apraku et al, 2013, 2015; Yan & Kang, 2003)
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