Joint regression vs AMMI analysis of genotype-environment interactions for cereals in Italy

Joint regression and Additive Main effects and Multiplicative Interaction (AMMI) models were compared for i) capacity of describing genotype-location (GL) and genotype-environment (GE) interaction effects (environments = location-season combinations), assessed in terms of estimated variance of heterogeneity of genotype regressions and of the sum of the variances of significant interaction principal component (PC) axes, and ii) repeatability between cropping seasons of measures of genotype stability across locations. These measures were Finlay and Wilkinson's regression coefficient for joint regression, and the Euclidean distance from the origin of significant interaction PC axes (D) and the absolute value of PC 1 score (| PC 1 |) for AMMI. Shukla's stability variance (σsup2;) was considered in addition. The study included three data sets for durum wheat, two for maize and one each for bread wheat and oat. Relationships between climatic variables and GL interaction occurrence were also assessed. AMMI proved distinctly more valuable in six data sets for description of GE effects and in four for description of GL effects over seasons. Its superiority was not crop-specific and tended to occur when more, distinct environmental constraints affected genotype responses. When both methods were appropriate, they provided a similar ordination of sites and genotypes for GL effects. The models that adequately described GL interaction over seasons generally provided also stability measures that were moderately repeatable between seasons. D was better repeatable than | PC 1 | and σ& 2; in a few cases. Ordination of locations on GL interaction PC 1 tended to be consistent both between wheat and between maize data sets having either no seasons or no genotypes in common.