ALBERT

Notes: Abstract  CIMMYT (the International Maize and Wheat Improvement Center) has routinely conducted international wheat yield trials to study the adaptation of spring bread wheat. The first of these, the International Spring Wheat Yield Nursery (ISWYN), was conducted for 31 years from 1964 to 1994 inclusive (30 cycles were conducted as no nursery was distributed in 1993 because of Karnal Bunt). Recently, pattern analysis methods have been developed and a set of computer programs written, which enable retrospective analyses of such historical databases to appraise the relationships among test environments in a way that discriminates among genotypes. Such an analysis was conducted on the 30 years of yield data from ISWYN and the classification derived from these analyses was compared with an agroecological classification of spring wheat test environments derived by CIMMYT. The incidence of foliar diseases (stem rust, leaf rust, yellow rust, Septoria spp. and Fusarium spp.) was important in the distinction between the high-rainfall low-latitude (mega-environment 2) and the high-input-irrigated low-latitude (mega-environment 1) environment types. The accumulation of resistance genes for these diseases has been an objective of the CIMMYT wheat breeding program. It was hypothesized that, as the relevant resistance genes were successfully pyramided into the germplasm, the distinction between these two mega-environment types would disappear. The results of the retrospective analyses support this hypothesis.

Notes: Summary Several subjective choices must be made when classifying genotypes based on data from plant breeding trials. One choice involves the method used to weight the contribution each environment makes to the classification. A second involves the use of either genotype-means for each environment or genotypevalues for each block, i.e., considering each block to be a different environment. Another involves whether environments (or blocks) in which genotypes are nonsignificantly different should be included or excluded from such classifications. An alternative to the use of raw or standardized data, is proposed in which each environment is weighted by a discrimination index (DI) that is based on the concept of repeatability. In this study the effect of three weighting methods (raw, standardized and DI), the choice of using environments or blocks, and the choice of including or excluding environments or blocks in which genotypic effects were not significant, were considered in factorial combination to give 12 options. A data set comprised of five check cultivars each repeated six times in each of three blocks at six environments was used. The effect of these options on the ability of a hierarchical clustering technique to correctly classify the repeats into five groups, each consisting of all the six repeats of a particular check cultivar, was investigated. It was found that the DI weighting method generally led to better recovery of the known structure. Using block data rather than environmental data also improved structure recovery for each of the three weighting methods. The exclusive use of environments in which genotypic effects were significant decreased structure recovery while the contrary generally occurred for blocks. The best structure recovery was obtained from the DI weighting applied to blocks (whether genotypes were significant or not).

Notes: Abstract An objective of the CSR sugarcane breeding programme in Australia was to assess the scope for broadening the genetic base of the commercial sugarcane germ plasm through interspecific hybridization with Saccharum spontaneum clones. The contribution of both selection history and S. spontaneum to sugar yield and its components was investigated in the germ plasm pool assembled. The analysis was conducted on a data-set of 256 clones, consisting of parents and full-sib families generated from 32 biparental crosses, tested in six environments. The minimum number of generations back to S. spontaneum ancestor in the clone's pedigree was used as a germ plasm score. The geographical origin and selection history of each parent and their use in the biparental crosses were used to develop a selection history score for parents and offspring. The variation for seven attributes, cane yield, commercial cane sugar %, sugar yield, stalk number per stool, stalk weight, fibre % and ash % juice was partitioned according to the germ plasm and selection history scores. Significant (P〈0.05) clone variation and clone x environment interaction for all attributes was present. The germ plasm scores accounted for a significant (P〈0.05) component of the clone variation for all of the attributes except cane yield. There was an increase in sugar yield with an increase in the minimum number of generations back to a S. spontaneum clone. The selection history groups accounted for a high proportion of the variation among parental clones for all of the attributes except cane yield. This suggested that parents were the outcome of strong selection pressure for the commercial cane attributes. However, the selection history groups for the offspring produced by random mating of parents did not account for a high proportion of the variation for the attributes. Using the mixture method of classification we partitioned the 256 clones into five groups for patterns of performance for the seven attributes across the six environments. The five groups emphasized major differences in the patterns of performance for the seven attributes across environments. The distribution of germ plasm and selection history scores in each of the five groups indicated that their patterns of performance were associated with selection history and minimum generations to S. spontaneum. Therefore, both the analysis on selection history and germ plasm scores (extrinsic classification) and the analysis on the mixture method of classification (intrinsic classification) emphasized the influence of selection history on the sugar yield of sugarcane.

Notes: Abstract Following the recognition of the importance of dealing with the effects of genotype-by-environment (G ×E) interaction in multi-environment testing of genotypes in plant breeding programs, there has been substantial development in the area of analytical methodology to quantify and describe these interactions. Three major areas where there have been developments are the analysis of variance, indirect selection, and pattern analysis methodologies. This has resulted in a wide range of analytical methods each with their own advocates. There is little doubt that the development of these methodologies has greatly contributed to an enhanced understanding of the magnitude and form ofG ×E interactions and our ability to quantify their presence in a multi-environment experiment. However, our understanding of the environmental and physiological bases of the nature ofG ×E interactions in plant breeding has not improved commensurably with the availability of these methodologies. This may in part be due to concentration on the statistical aspects of the analytical methodologies rather than on the complementary resolution of the biological basis of the differences in genotypic adaptation observed in plant breeding experiments. There are clear relationships between many of the analytical methodologies used for studying genotypic variation andG ×E interaction in plant breeding experiments. However, from the numerous discussions on the relative merits of alternative ways of analysingG ×E interactions which can be found in the literature, these relationships do not appear to be widely appreciated. This paper outlines the relevant theoretical relationships between the analysis of variance, indirect selection and pattern analysis methodologies, and their practical implications for the plant breeder interested in assessing the effects ofG ×E interaction on the response to selection. The variance components estimated from the combined analysis of variance can be used to judge the relative magnitude of genotypic andG ×E interaction variance. Where concern is on the effect of lack of correlation among environments, theG ×E interaction component can be partitioned into a component due to heterogeneity of genotypic variance among environments and another due to the lack of correlation among environments. In addition, the pooled genetic correlation among all environments can be estimated as the intraclass correlation from the variance components of the combined analysis of variance. WhereG ×E interaction accounts for a large proportion of the variation among genotypes, the individual genetic correlations between environments could be investigated rather than the pooled genetic correlation. Indirect selection theory can be applied to the case where the same character is measured on the same genotypes in different environments. Where there are no correlations of error effects among environments, the phenotypic correlation between environments may be used to investigate indirect response to selection. Pattern analysis (classification and ordination) methods based on standardised data can be used to summarise the relationships among environments in terms of the scope to exploit indirect selection. With the availability of this range of analytical methodology, it is now possible to investigate the results of more comprehensive experiments which attempt to understand the nature of differences in genotypic adaptation. Hence a greater focus of interest on understanding the causes of the interaction can be achieved.