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CROP ECOLOGY, MANAGEMENT & QUALITY

Effect of Environmental Variates on Genotype x Environment Interaction of Winter Wheat

A Comparison of Biadditive Factorial Regression to AMMI

^a INRA, Unité de Génétique et d'Amélioration des Plantes, 80200 Estrées-Mons, France
^b INRA, Station de Génétique et d'Amélioration des Plantes, 17 rue Sully, BP 86510, 21065 Dijon Cedex, France

^* Corresponding author (hulmel{at}mons.inra.fr)

Genotype x environment interaction is a commonly observed phenomenon in experiments in plant breeding and genetics. This interaction can be modeled by different statistical models which can include covariates, such as in biadditive factorial regression, or in other ways, such as in the classical additive main effect and multiplicative interaction (AMMI) model. The aim of this paper was to explain genotype x environment interaction in multienvironment trials of winter wheat (Triticum aestivum L.) by environmental variates to assess the genotype sensitivities to the environmental conditions and to compare the results from biadditive factorial regression (BIAREG) and AMMI. Data consisted of 13 lines grown in France in 14 environments (combinations of two years, four locations and two treatments). Grain yield and heading date were measured and the environments were characterized by climatic data (water deficit, radiation, temperature above 25°C) and by observations of powdery mildew infection (caused by Erysiphe graminis DC F. sp. tritici), lodging, and nitrogen status. AMMI model explained most of the genotype x environment interaction (77.4%) but did not provide a direct biological explanation. BIAREG explained a slightly smaller part (74.0%) but with fewer degrees of freedom. The contributions of the environmental variates to the synthetic variates revealed two important subsets of initial covariates—biotic variates and nitrogen status in contrast to climatic ones—associated with the interaction. The interactive pattern of the genotypes and the environments was similar for both models. BIAREG is more powerful than AMMI because it provides a description of the sensitivities of the genotypes in regard to the observed environmental covariates. When environments can be characterized by variates, we suggest that BIAREG can complement AMMI because it provides direct biological explanation of the interaction.

Abbreviations: AMMI, additive main effect and multiplicative interaction • BIAREG, biadditive factorial regression • BK, ratio between nitrogen absorbed during the whole cycle and kernel number • E, environment • ETa, actual evapotranspiration • ETm, maximal evapotranspiration • -F, medium sowing date without fungicides • G, genotype • GY, grain yield • HTT, high temperature during grain-filling • IN, medium sowing date with fungicides • LodgT, lodging during grain-filling • PMK, powdery mildew during grain number formation • PMT, powdery mildew during grain-filling • RK, radiation during grain number formation • RKm, radiation ± 3 d at meiosis • RT, radiation during grain-filling • LS, late sowing date with fungicides • S, site • SSI, sum of squares of interaction • WDK, water deficit during grain number formation • WDT, water deficit during grain-filling • Y, year

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