Analysis of line x environment interactions for yield in navy beans. 3. Pattern analysis of environments over years

Yield trials of navy bean (Phaseolus vulgaris L.) lines were grown over a diverse range of locations for 7 years in Queensland, with changes in entries and locations in each year. The yield data were analysed over years using 3 recently developed pattern analysis techniques for the integration of historical, severely unbalanced data from plant breeding programs to derive relationships among environments in the way they discriminate among the entries grown in them. These techniques have been named as cumulative analysis, sequential analysis, and status analysis. The relationships among the locations for testing navy bean lines, although sensitive to the addition of new locations, quickly stabilised. These relationships were related to management (irrigation and row width) and latitude (north v. central v. Kingaroy v. southern Queensland)

A procedure to assess the relative merit of classification strategies for grouping environments to assist selection in plant breeding regional evaluation trials

Classification methodology is widely used by plant breeders to group environments on the results of regional evaluation trials to assist in selection among genotypes. To be effective, this strategy must be integrated with the theory of indirect selection. Environments which group together should reflect commonality of genotypic discrimination and therefore give rise to similar selection among genotypes. Four strategies for classifying environments were compared. These were based on untransformed and three forms of transformed data (coded, standardised and rank). The comparison assessed how effectively the groups of environments formed by using each transformation maximised the opportunity for exploiting indirect selection between environments within the same group relative to environments in other groups. The objective in this study was to identify groups of international environments, used by CIMMYT in its international nursery program, which gave high indirect response to selection for grain yield in six Australian environments. Generally the four classification strategies identified subsets of international environments for which selection gave a greater indirect response than that for selection on average performance across all of the international environments (35% to 94% on average over all Australian environments). Environmental classifications based on the standardised and rank transformations were generally superior to those based on the untransformed and coded transformations (46% on average over all Australian environments). The magnitude of this advantage differed between the Australian environments but was substantial for the two environments which expressed the greatest opportunity for exploiting indirect selection. These results have obvious and large implications for the use of classification methodology to structure regional testing regimes for plant breeding programs

Utilization of Multiyear Plant Breeding Data to Better Predict Genotype Performance

Despite the availability of multiyear, multicycle, and multiphase data in plant breeding programs for annual crops, selection is often based on single-year, single-cycle, and single-phase data. As genotypes in the same fields are usually grown under the same management practice, data from these fields can and should be analyzed together. In Monsanto’s North American maize (Zea mays L.) breeding program, this approach enables a spatial model to be fitted in each field, providing an estimate of spatial trend and a better estimate of residual variance in each field. Multiyear, multicycle analysis showed that the estimates of genotype × year variance (VGY) and genotype × year × location variance (VGYL) were still the largest components of the estimated phenotypic variance. Analysis of any single-year subset of the data inflated the estimate of genotypic variance (VG) by the size of the estimate of VGY, resulting in potential bias in the estimates of genotype performance. These results demonstrate the advantage of a combined analysis of data across years and cycles to make selection decisions for genotype advancement

Theoretical Criteria for Scattering Dark States in Nanostructured Particles

Nanostructures with multiple resonances can exhibit a suppressed or even completely eliminated scattering of light, called a scattering dark state. We describe this phenomenon with a general treatment of light scattering from a multiresonant nanostructure that is spherical or nonspherical but subwavelength in size. With multiple resonances in the same channel (i.e., same angular momentum and polarization), coherent interference always leads to scattering dark states in the low-absorption limit, regardless of the system details. The coupling between resonances is inevitable and can be interpreted as arising from far-field or near-field. This is a realization of coupled-resonator-induced transparency in the context of light scattering, which is related to but different from Fano resonances. Explicit examples are given to illustrate these concepts.Massachusetts Institute of Technology. Institute for Soldier Nanotechnologies (Contract W911NF-13-D-0001)National Science Foundation (U.S.). Materials Research Science and Engineering Centers (Program) (Grant DMR-0819762

Classification of locations in regional cotton variety trials where trial entries change over years

Cultivars and advanced breeding lines evaluated for performance in regional trials usually change over successive years. Cluster analysis procedures are presented for analysing these unbalanced genotype×location×year data-sets to help study the effectiveness of locations to discriminate amongst genotypes (lines). To illustrate the use of these methods, yield data from 12 years of cotton evaluation trials conducted at eleven locations in eastern Australia were analysed. The eleven locations were classified according to the way they discriminated amongst lines. The combined classification analysis provides the plant breeder with a better understanding of the relationships amongst the locations used for regional cultivar trials and provides a rational method for choosing a few key locations to be used for early generation testing of lines from a breeding program

Correlated Genetic Advance from Multi-Environment Trials to the Target Population of Environments

The relative performance of genotypes for yield and agronomic traits is measured in multi-environment trials (METs) in order to predict their performance in a target population of environments (TPE). It is shown that it is the correlation of genotype performance between environments that allows such a prediction. Thus, efficiency of selection and size of realised genetic advance in the TPE depend on the genetic correlation between the test and target environments, the heritability in the test and target environments and the phenotypic variance in the target environments. The phenotypic correlation between genotype performance in test and target environments estimates the combined effects of the genetic correlation and heritability parameters. These relationships from correlated genetic advance theory can be applied to various forms of retrospective analysis (cumulative analysis over years, sequential analysis by adding one year’s data at a time, and status analysis by embedding this year’s data in a long term discrimination space) used to analyse MET data. The best estimate of genotype performance to use in these analyses is obtained using the approach of Gilmour, Cullis and Verbyla (JABES 2:269-293, 1997). One should fit a model combining design information and spatial adjustment within the trials, either in a one-stage analysis or, if that is not possible, a two-stage analysis where each individual trial is weighted according to the inverse of its estimated error variance and number of replicates