title: Advantage of single-trial models for response to selection in wheat breeding multi-environment trials.
abstract: An investigation was conducted to evaluate the impact of experimental designs and spatial analyses (single-trial models) of the response to selection for grain yield in the northern grains region of Australia (Queensland and northern New South Wales). Two sets of multi-environment experiments were considered. One set, based on 33 trials conducted from 1994 to 1996, was used to represent the testing system of the wheat breeding program and is referred to as the multi-environment trial (MET). The second set, based on 47 trials conducted from 1986 to 1993, sampled a more diverse set of years and management regimes and was used to represent the target population of environments (TPE). There were 18 genotypes in common between the MET and TPE sets of trials. From indirect selection theory, the phenotypic correlation coefficient between the MET and TPE single-trial adjusted genotype means [ r(p(MT))] was used to determine the effect of the single-trial model on the expected indirect response to selection for grain yield in the TPE based on selection in the MET. Five single-trial models were considered: randomised complete block (RCB), incomplete block (IB), spatial analysis (SS), spatial analysis with a measurement error (SSM) and a combination of spatial analysis and experimental design information to identify the preferred (PF) model. Bootstrap-resampling methodology was used to construct multiple MET data sets, ranging in size from 2 to 20 environments per MET sample. The size and environmental composition of the MET and the single-trial model influenced the r(p(MT)). On average, the PF model resulted in a higher r(p(MT)) than the IB, SS and SSM models, which were in turn superior to the RCB model for MET sizes based on fewer than ten environments. For METs based on ten or more environments, the r(p(MT)) was similar for all single-trial models.
authors.affiliation: School of Land and Food Sciences, The University of Queensland, 4072 Brisbane, Queensland, Australia.
authors.forename: C G
authors.initials: CG
authors.lastname: Qiao
authors.forename: K E
authors.initials: KE
authors.lastname: Basford
authors.forename: I H
authors.initials: IH
authors.lastname: DeLacy
authors.forename: M
authors.initials: M
authors.lastname: Cooper
mesh_terms: D000383:Agriculture / Q000379:methods; D000704:Analysis of Variance; D001315:Australia; D001947:Breeding; D004777:Environment; D005838:Genotype; D010641:Phenotype; D012107:Research Design; D012641:Selection, Genetic; D014908:Triticum / Q000235:genetics*