Determining environmental covariates which explain genotype environment interaction in winter wheat through probe genotypes and biadditive factorial regression

Abstract

sured environmental variates (van Eeuwijk, 1995). Just as with AMMI, BIAREG provides axes or synthetic Genotype ϫ environment interaction is a commonly observed pheenvironmental variates to which genotypes differ maxinomenon in experiments in plant breeding and genetics. This interaction can be modeled by different statistical models which can include mally in sensitivity, but under the restriction of being covariates, such as in biadditive factorial regression, or in other ways, linear combinations of the initial environmental varisuch as in the classical additive main effect and multiplicative interacates. BIAREG can be considered as a generalization of tion (AMMI) model. The aim of this paper was to explain genotype ϫ both AMMI and factorial regression. environment interaction in multienvironment trials of winter wheat Biadditive factorial regression can be interpreted from (Triticum aestivum L.) by environmental variates to assess the genothe environmental viewpoint via the correlations betype sensitivities to the environmental conditions and to compare the tween the synthetic variates and the initial environmenresults from biadditive factorial regression (BIAREG) and AMMI. tal covariates. These correlations can be displayed in Car-Data consisted of 13 lines grown in France in 14 environments (combitesian diagrams as it is done in principal component nations of two years, four locations and two treatments). Grain yield analysis. The biplot can contain three types of vectors and heading date were measured and the environments were characterized by climatic data (water deficit, radiation, temperature above whose coordinates are determined by the genotype sen-25؇C) and by observations of powdery mildew infection (caused by sitivities, the environmental characterizations, and co-Erysiphe graminis DC F. sp. tritici), lodging, and nitrogen status. efficients for the environmental covariates within the AMMI model explained most of the genotype ϫ environment intersynthetic axes (van Eeuwijk, 1995). Environmental charaction (77.4%) but did not provide a direct biological explanation. acterizations come from the linear combinations of the BIAREG explained a slightly smaller part (74.0%) but with fewer initial environmental covariates. To clarify such a biplot, degrees of freedom. The contributions of the environmental variates these three types of vectors can be considered in two to the synthetic variates revealed two important subsets of initial separate plots (Brancourt-Hulmel et al., 2000): one incovariates-biotic variates and nitrogen status in contrast to climatic cluding the genotype sensitivities and the coefficients ones-associated with the interaction. The interactive pattern of the of the environmental covariates and the other congenotypes and the environments was similar for both models. BIA-REG is more powerful than AMMI because it provides a description taining the same genotype sensitivities and the environof the sensitivities of the genotypes in regard to the observed environ-mental characterizations. Th...

Broccoli yield response to environmental factors in sustainable agriculture

“…Higher percentages were found for potato, soybean [Glycine max (L.) Merr. ], pea, wheat, maize and sorghum (Baril et al, 1995;Sneller et al, 1997;Brancourt-Hulmel et al, 2000;Abush, 2001;Alberts, 2004;Das et al, 2012), while similar percentages were reported for maize (Ajibade et al, 2003) and winter/spring wheat (Voltas et al, 2005).…”

“…Indeed, the observed percentage was higher than that found in other crops, such as potato, winter wheat, pea (Pisum sativum L.), maize and sugarcane (Baril et al, 1995;Brancourt-Hulmel et al, 2000;Taye et al, 2000;Admassu et al, 2008;Ramburan et al, 2011). Similar percentages were otherwise reported for maize [Zea mays L.], sorghum [Sorghum bicolor L. Moench] and winter/spring wheat (Ajibade et al, 2003;Alberts, 2004;Voltas et al, 2005;Das et al, 2012).…”

Evaluation of Genotype × Environment Interactions in Chinese Spring Wheat by the AMMI Model, Correlation and Path Analysis

“…It has been proposed that the AMMI model and biplot were useful methods for the analysis of GE interactions (Ebdon andGauch 2002, Brancourt-Hulmel andLecomte 2003). The AMMI model had been exploited in the variety evaluation of soya bean (Zobel et al 1988), barley (van Oosterom et al 1993, wheat (Vargas et al 1999), rice and pigeonpea (Wamatu and Thomas 2002).…”

Biplot Analysis of Genotype × Environment Interaction: Proceed with Caution

“…In an attempt to address this defi ciency, Yan and Tinker (2005) recently suggested a covariate-eff ect biplot which is generated from the fi rst two PCs of SVD of correlations of target trait (say yield) with every other trait in each of the test environments. To examine eff ects of both genetic and environmental covariables and to develop functional relationships and predictability with explanatory covariables, factorial regression or partial least squares analysis (e.g., Vargas et al, 1999;Brancourt-Hulmel and Lecomte, 2003;van Eeuwijk et al, 2005) may be more useful. These other explanatory traits may be replaced or augmented by other genetic covariables, such as quantifi cations of pedigree information, presence/absence of QTLs, or gene expression profi les in a microarray.…”