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.

Predicting the future of plant breeding: complementing empirical evaluation with genetic prediction

“…6a), based on all pairs of environments from 1126 METs, the average genetic correlation coefficient based on the raw data was 0.25, whereas for the spatially adjusted data, the average correlation coefficient was increased to 0.34. Consistent with the study by Qiao et al (2000Qiao et al ( , 2004 for wheat in Australia, applying the framework of Gilmour et al (1997) to the maize experiments in the US cornbelt, we interpret the positive impact of the statistical adjustment procedures on the genetic correlation coefficient estimates as an improved characterisation of the relative yield performance of the genotypes within the experiments.…”

“…5. Qiao et al (2000Qiao et al ( , 2004 demonstrated the positive impact of combining incomplete-block experimental designs (Williams et al 2002 and spatial adjustment procedures (Gilmour et al 1997) for comparing and selecting genotypes in a wheat breeding program. They demonstrated that the statistical Yield values were obtained from small-plot, combine-harvesting equipment.…”

Modelling spatial trends in sorghum breeding field trials using a two-dimensional P-spline mixed model

“…Efficient phenotypic and genomic selection schemes in plant breeding programs rely on accurate assessment of the phenotypic performance of genotypes in field experiments (Qiao et al 2004; Lado et al 2013; Bernal-Vasquez et al 2014; Sarker and Singh 2015). Plant breeding trials usually involve a large number of test entries covering large areas where spatial variation is likely to be an obstacle to reliable prediction of genetic values.…”

Iterative kriging for removing spatial autocorrelation in analysis of forest genetic trials

“…Spatial analysis, such as trend analysis, neighbor analysis, or nearest neighbor analysis, have shown to enhance the analysis of agricultural field trials, and are becoming quite popular in agronomy (e.g., Gilmour et al 1997;Grondona et al 1996;Qiao et al 2000Qiao et al , 2004. Although not so commonly used, spatial analytical methods have also given successful results in forest trials (Anekonda and Libby 1996;Costa-Silva et al 2001;Dutkowski et al 2002;Hamann et al 2002;Joyce et al 2002;Magnussen 1993aMagnussen , 1994.…”