MET datasets are then formed by combining bands of data in such a way as to trace the selection histories of lines within contemporary groups. The second is that of “data bands,” which are sequences of trials that correspond to the progression through stages of testing from year to year. The first is that of “contemporary groups,” which are defined to be groups of lines that enter the initial testing stage of the breeding program in the same year.
This is based on two new concepts that characterize the structure of a breeding program. In this paper we present an approach for constructing MET datasets that optimizes the information available for selection decisions. The gains will only be achieved, however, if the methods are applied to suitable MET datasets. Selection accuracy can be improved with the use of advanced statistical analysis methods that employ informative models for genotype by environment interaction, include information on genetic relatedness and appropriately accommodate within-trial error variation. Plant breeding programs use multi-environment trial (MET) data to select superior lines, with the ultimate aim of increasing genetic gain.
