Advantage of single-trial models for response to selection in wheat breeding multi-environment trials.

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.

A selection strategy to accommodate genotype-by-environment interaction for grain yield of wheat: managed-environments for selection among genotypes.

Selection for grain yield among wheat lines is complicated by large line-by-environment (L × E) interactions in Queensland, Australia. Early generation selection is based on an evaluation of many lines in a few environments. The small sample of environments, together with the large L × E interaction, reduces the realised response to selection. Definition of a series of managed-environments which provides discrimination among lines, which is relevant to the target production-environments, and can be repeated over years, would facilitate early generation selection. Two series of managed-environments were conducted. Eighteen managed-environments were generated in Series-1 by manipulating nitrogen and water availability, together with the sowing date, at three locations. Nine managed-environments based on those from Series-1 were generated in Series-2. Line discrimination for grain yield in the managed-environments was compared to that in a series of 16 random production-environments. The genetic correlation between line discrimination in the managed-environments and that in the production-environments was influenced by the number and combination of managed-environments. Two managed-environment selection regimes, which gave a high genetic correlation in both Series-1 and 2, were identified. The first used three managed-environments, a high input (low water and nitrogen stress) environment with early sowing at three locations. The second used six managed-environments, a combination of a high input (low water and nitrogen stress) and medium input (water and nitrogen stress) with early sowing at three locations. The opportunities for using managed-environments to provide more reliable selection among lines in the Queensland wheat breeding programme and its potential limitations are discussed.

Interpretation of randomly amplified polymorphic DNA marker data for fingerprinting sweet potato (Ipomoea batatas L.) genotypes.

In this paper we present a method for the generation of randomly amplified polymorphic DNA (RAPD) markers for sweet potato. These were applied to produce genetic fingerprints of six clonal cultivars and to estimate genetic distances between these cultivars. The level of polymorphism within the species was extremely high. From the 36-decamer random primers used, 170 fragments were amplified, of which 132 (77.6%) were polymorphic. Ten primers resulted in no detected amplification. Of the remaining 26 primers for which amplification was achieved, only one did not reveal polymorphism. Six primers used alone enabled the discrimination of all six genotypes. Pattern analysis, which employed both a classification and ordination method, enabled the grouping of cultivars and the identification of primers which gave greatest discrimination among the cultivars.

Relationships among analytical methods used to study genotypic variation and genotype-by-environment interaction in plant breeding multi-environment experiments.

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.

Retrospective analysis of the relationships among the test environments of the Southern Queensland sugarcane breeding programme.

Repeatability of aspects of genotype by environment (GxE) interactions is an important factor to be assessed in designing more efficient selection programmes. Sugar yield data from multi environment trials (METs) which were part of the sugarcane breeding programme in southern Queensland were analysed. Data were obtained from 71 environments consisting of trials planted from 1986 to 1989. Retrospective analysis on these data was conducted to assess the repeatability of the clone by environment (CxE) interactions over locations and years. This analysis focussed on identifying similarities among test environments in the way they discriminated among clones for sugar yield. Analyses of variance and pattern analyses on environments over years based on standardised data were conducted. The pattern analyses were done sequentially according to the accumulated data sets over years. Squared Euclidean distances among environments were averaged over data sets and years before pattern analyses across the data sets were conducted. A graphical methodology was developed to present the results of the cumulative historical analysis. CxE interactions of a magnitude which affected selection decisions were present in each data set studied. Pattern analyses on cumulative data sets identified environmental groupings that were based on geographical positions. Each location generated a different pattern of discrimination among the clones. These results emphasised the importance of clone by location (CxL) interactions in southern Queensland and the need to concentrate more on testing across locations than on ratooning ability within a location. The classifications identified similarities among ratoon crops within a location, differences among locations and differences between ratoon crops and their plant crop (PC). This suggested that some aspects of CxL and clone by crop-year (CxY) interactions were repeatable across years. The potential applications of these results to increase efficiency of the sugarcane breeding programme, such as the possibility of applying indirect selection among environments generating similar discrimination among clones, are discussed.

Classifying genotypic data from plant breeding trials: a preliminary investigation using repeated checks.

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).

Multiattribute evaluation of regional cotton variety trials.

The Australian Cotton Cultivar Trials (ACCT) are designed to investigate various cotton [Gossypium hirsutum (L.)] lines in several locations in New South Wales and Queensland each year. If these lines are to be assessed by the simultaneous use of yield and lint quality data, then a multivariate technique applicable to three-way data is desirable. Two such techniques, the mixture maximum likelihood method of clustering and three-mode principal component analysis, are described and used to analyze these data. Applied together, the methods enhance each other's usefulness in interpreting the information on the line response patterns across the locations. The methods provide a good integration of the responses across environments of the entries for the different attributes in the trials. For instance, using yield as the sole criterion, the excellence of the namcala and coker group for quality is overlooked. The analyses point to a decision in favor of either high yields of moderate to good quality lint or moderate yield but superior lint quality. The decisions indicated by the methods confirmed the selections made by the plant breeders. The procedures provide a less subjective, relatively easy to apply and interpret analytical method of describing the patterns of performance and associations in complex multiattribute and multilocation trials. This should lead to more efficient selection among lines in such trials.