Adaptive yield response of winter wheat cultivars across environments in Poland using combined AMMI and

The objective of the paper was to illustrate using and usefulness of a joint AMMI and cluster analyses to assess the grain yield adaptive response of Polish and foreign 31 winter wheat cultivars in a range of 20 environments (locations) and across 3 years (2005-2007) under integrated crop management, using data obtained in the post-registration variety testing trials (called PDO trials), to identify those entries with specific and wide adaptation. Two-stage combined analysis of variance for data in the three-way GLY classification was carried out according to a mixed model (cultivar and location as fixed factors and years as random factor). GL repeated (across years) interaction effects were modeled by (a) joint regression and (b) additive main effects and multiplicative interaction (AMMI). The thirty one cultivar adaptive responses, expressed by nominal yields based on significant AMMI-1 model, accounting for 27.8% of SS for GL interactions, were divided into six homogenous groups by Ward’s method of cluster analysis. Group-mean cultivar adaptive responses indicated clearly the wide adaptation of cultivars in groups 1 and 2 including mostly German and United Kingdom entries and also two Polish ones. Cultivars from group 6, including three Polish cultivars and three foreign ones, were among at most four top-ranking entries at all locations excluding one environment (Wyczechy at Pomerania region). Cultivars from group 3, including seven Polish cultivars and one from United Kingdom and France, showed extremely specific adaptation characterized by nominal yield responses being positively related to GL interaction PC 1 scores of the locations. However, cultivars from group 5, including five Polish ones and a French one were poor adapted to the growing area. Presented the joint AMMI and cluster analyses were effective to distinguish adaptive responses of studied cultivars on the basis of data from PDO trials and could be seen as a better alternative, based more on probability-approached methodology, to common pattern analysis.