I. H. DeLacy

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.

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.

Evaluation of experimental designs and spatial analyses in wheat breeding trials

Abstract Thirty-three wheat breeding trials were conducted from 1994 to 1996 in the Northern Grains Region (QLD and Northern NSW) of Australia to evaluate the influence of experimental designs and spatial analyses on the estimation of genotype effects for yield and their impact on selection decisions. The relative efficiency of the alternative designs and analyses was best measured by the average standard error of difference between line means. Both more effective designs and spatial analyses significantly improved the efficiency relative to the randomised complete block model, with the preferred model (which combined the design information and spatial trends) giving an average relative efficiency of 138% over all 33 trials. When the Czekanowski similarity coefficient was used, none of the studied models were in full agreement with the randomised complete block model in the selection of the top lines. The agreement was influenced by selection proportions. Hence, the use of these methodologies can impact on the selection decisions in plant breeding.

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.

Some vocabulary and grammar for the analysis of multi-environment trials, as applied to the analysis of FPB and PPB trials

For the improvement of genetic material suitable for on farm use under low-input conditions, participatory and formal plant breeding strategies are frequently presented as competing options. A common frame of reference to phrase mechanisms and purposes related to breeding strategies will facilitate clearer descriptions of similarities and differences between participatory plant breeding and formal plant breeding. In this paper an attempt is made to develop such a common framework by means of a statistically inspired language that acknowledges the importance of both on farm trials and research centre trials as sources of information for on farm genetic improvement. Key concepts are the genetic correlation between environments, and the heterogeneity of phenotypic and genetic variance over environments. Classic selection response theory is taken as the starting point for the comparison of selection trials (on farm and research centre) with respect to the expected genetic improvement in a target environment (low-input farms). The variance-covariance parameters that form the input for selection response comparisons traditionally come from a mixed model fit to multi-environment trial data. In this paper we propose a recently developed class of mixed models, namely multiplicative mixed models, also called factor-analytic models, for modelling genetic variances and covariances (correlations). Mixed multiplicative models allow genetic variances and covariances to be dependent on quantitative descriptors of the environment, and confer a high flexibility in the choice of variance-covariance structure, without requiring the estimation of a prohibitively high number of parameters. As a result detailed considerations regarding selection response comparisons are facilitated. The statistical machinery involved is illustrated on an example data set consisting of barley trials from the International Center for Agricultural Research in the Dry Areas (ICARDA). Analysis of the example data showed that participatory plant breeding and formal plant breeding are better interpreted as providing complementary rather than competing information.

Long-term association of locations for testing spring bread wheat

SummaryThe International Spring Wheat Yield Nursery (ISWYN) has been distributed annually since 1964 and the results provide a base for investigating relationships among locations. Ordination and clustering of locations was conducted using 26 years of grain yield data. Ordination and clusters based on the discrimination of germplasm were compared with ‘mega-environments’, which are groupings of locations defined by CIMMYT on the basis of climatic factors and perceptions of major biotic and abiotic stresses. Discrepancies among mega-environmental groupings, clusters and ordinations may identify locations for which major stresses affecting wheat yield are yet unidentified.Major environmental discriminators were latitude and the presence or absence of stress, although there was little association of locations due to limited moisture availability. We identified two major spring wheat environments, typified as Asian and European, and suggest the mega-environmental classification does not explain all significant associations among locations. Location groupings based on discrimination of germplasm should be considered in parallel to mega-environments on a regular basis and we propose breeding for a base of broadly adapted germplasm to which specific stress tolerances are incorporated.

Speed breeding is a powerful tool to accelerate crop research and breeding

The growing human population and a changing environment have raised significant concern for global food security, with the current improvement rate of several important crops inadequate to meet future demand1. This slow improvement rate is attributed partly to the long generation times of crop plants. Here, we present a method called ‘speed breeding’, which greatly shortens generation time and accelerates breeding and research programmes. Speed breeding can be used to achieve up to 6 generations per year for spring wheat (Triticum aestivum), durum wheat (T. durum), barley (Hordeum vulgare), chickpea (Cicer arietinum) and pea (Pisum sativum), and 4 generations for canola (Brassica napus), instead of 2–3 under normal glasshouse conditions. We demonstrate that speed breeding in fully enclosed, controlled-environment growth chambers can accelerate plant development for research purposes, including phenotyping of adult plant traits, mutant studies and transformation. The use of supplemental lighting in a glasshouse environment allows rapid generation cycling through single seed descent (SSD) and potential for adaptation to larger-scale crop improvement programs. Cost saving through light-emitting diode (LED) supplemental lighting is also outlined. We envisage great potential for integrating speed breeding with other modern crop breeding technologies, including high-throughput genotyping, genome editing and genomic selection, accelerating the rate of crop improvement.Fully enclosed, controlled-environment growth chambers can accelerate plant development. Such ‘speed breeding’ reduces generation times to accelerate crop breeding and research programmes, and can integrate with other modern crop breeding technologies.

Mining Wheat Germplasm Collections for Yield Enhancing Traits

The material in genebanks includes valuable traditional varieties andlandraces, non-domesticated species, advanced and obsolete cultivars,breeding lines and genetic stock. It is the wide variety of potentially usefulgenetic diversity that makes collections valuable. While most of the yieldincreases to date have resulted from manipulation of a few major traits(such as height, photoperiodism, and vernalization), meeting future demandfor increased yields will require exploitation of novel genetic resources.Many traits have been reported to have potential to enhance yield, andhigh expression of these can be found in germplasm collections. To boostyield in irrigated situations, spike fertility must be improved simultaneouslywith photosynthetic capacity. CIMMYTs Wheat Genetic Resourcesprogram has identified a source of multi-ovary florets, with up to 6 kernelsper floret. Lines from landrace collections have been identified that havevery high chlorophyll concentration, which may increase leaf photosyntheticrate. High chlorophyll concentration and high stomatal conductance areassociated with heat tolerance. Recent studies, through augmented use ofseed multiplication nurseries, identified high expression of these traits inbank accessions, and both traits were heritable. Searches are underway fordrought tolerance traits related to remobilization of stem fructans, awnphotosynthesis, osmotic adjustment, and pubescence. Genetic diversityfrom wild relatives through the production of synthetic wheats hasproduced novel genetic diversity.

Characterization of Mexican wheat landraces using agronomically useful attributes

In 1992, 465 individual spikes of bread wheat were collected from 24 sites in three states of Mexico. They were examined for 15 morphological, agronomic and grain quality attributes as part of the routine regeneration process conducted by the CIMMYT Wheat Genetic Resources Program in unreplicated hill plots in a screen house. A pattern analysis (combined use of classification and ordination methods) of the data provided a good description of the accessions and the collection sites. Since economically useful attributes were used the analysis provided relevant information for both potential users and the germplasm curators. Potential users have a description of the accessions from which to choose relevant breeding material and curators can assess how well the accessions represent the diversity in the collection sites. The analysis would not have been possible if the individual spikes from collection sites were bulked as is the common practice.

Global adaptation patterns of Australian and CIMMYT spring bread wheat

The International Adaptation Trial (IAT) is a special purpose nursery designed to investigate the genotype-by-environment interactions and worldwide adaptation for grain yield of Australian and CIMMYT spring bread wheat (Triticum aestivum L.) and durum wheat (T. turgidum L. var. durum). The IAT contains lines representing Australian and CIMMYT wheat breeding programs and was distributed to 91 countries between 2000 and 2004. Yield data of 41 reference lines from 106 trials were analysed. A multiplicative mixed model accounted for trial variance heterogeneity and inter-trial correlations characteristic of multi-environment trials. A factor analytic model explained 48% of the genetic variance for the reference lines. Pedigree information was then incorporated to partition the genetic line effects into additive and non-additive components. This model explained 67 and 56% of the additive by environment and non-additive by environment genetic variances, respectively. Australian and CIMMYT germplasm showed good adaptation to their respective target production environments. In general, Australian lines performed well in south and west Australia, South America, southern Africa, Iran and high latitude European and Canadian locations. CIMMYT lines performed well at CIMMYT’s key yield testing location in Mexico (CIANO), north-eastern Australia, the Indo-Gangetic plains, West Asia North Africa and locations in Europe and Canada. Maturity explained some of the global adaptation patterns. In general, southern Australian germplasm were later maturing than CIMMYT material. While CIANO continues to provide adapted lines to northern Australia, selecting for yield among later maturing CIMMYT material in CIANO may identify lines adapted to southern and western Australian environments.