Computer simulation of a selection strategy to accommodate genotype-environment interactions in a wheat recurrent selection programme

Multi-environment trials (METs) are used in plant breeding programmes to ev aluate genotypes (lines/families) as a basis for selection on expected perf ormance (yield and/or quality) in a target population of environments (TPE) . When a large component of the genotype-environment (G x E) interactions r esults from crossover interactions, samples of environments in METs that de viate From the TPE provide a suboptimal basis for selection of genotypes on performance expected in the TPE. To adjust for the negative effects of the se deviations, a selection strategy that weights the data from the MET acco rding to their expected frequency of occurrence in the TPE (i.e. a weighted selection strategy)was investigated. Computer simulation methodology was u sed to obtain preliminary information on the weighted selection strategy an d compare it to the traditional unweighted selection strategy For a range o f MET scenarios and G x E interaction models. The evaluation of the weighte d selection strategy was conducted in context with the germplasm enhancemen t programme (GEP) of the Northern Wheat Improvement Programme in Australia. The results indicated that when the environments sampled in the MET matche d those expected in the TPE, the unweighted and weighted selection strategi es achieved a similar response to selection in the TPE. However. when the e nvironments sampled in the MET did not match the expectations in the TPE an d a large component of the G x E interactions resulted from crossover inter actions, the weighted selection strategy achieved a greater response to sel ection in the TPE. The advantage of the weighted strategy increased as the amount of crossover G x E interaction increased or fewer environments were sampled in the METs.