IDENTIFYING MEGA-ENVIRONMENTS AND TARGETING GENOTYPES

To maximize yield throughout a crop's heterogeneous growing region, de spite differences in cultivar rankings from place to place due to geno type-environment interactions, frequently it is necessary to subdivide a growing region into several relatively homogeneous mega-environment s and to breed and target adapted genotypes for each mega-environment. The objectives of this study are to identify relevant criteria for ev aluating mega-environment analyses and to apply the Additive Main Effe cts and Multiplicative Interaction (AMMI) model to mega-environment an alysis. The proposed analysis is illustrated using a Louisiana corn (Z ea mays L.) trial. Statistical strategies for identifying mega-environ ments should meet four criteria: flexibility in handling yield trials with various designs, focus on that fraction of the total variation th at is relevant for identifying mega-environments, duality in giving in tegrated information on both genotypes and environments, and relevance for the primary objective of showing which genotypes win where. The A MMI model meets these criteria effectively when the usual biplots are supplemented with several new types of graphs designed to address ques tions about mega-environments. Preliminary results indicate that a sma ll and workable number of mega-environments often suffices to exploit interactions and increase yields.