Search

Genotype × environment interaction (GE) and unfavorable associations among breeding objectives are the two key challenges in genotype evaluation and selection. Dealing with GE includes utilizing repeatable GE and accommodating nonrepeatable GE, and analytical tools for both steps have been developed in recent years. The genotype by yield × trait (GYT) analysis was also developed to address the issue of genotype selection based on multiple traits. However, a method to integrate both multi‐trial and multi‐trait information has been lacking. The purpose of this study was to fill the gap. Two types of biplots were described and demonstrated, using an oat (Avena sativa L.) dataset as an example. The G + GE biplot of GYT index graphically displays the mean and stability of the genotypes, considering all breeding objectives. The GYT biplot across trials graphically displays the overall superiority and the strengths and weaknesses of the genotypes, after accommodating the nonrepeatable GE. The two types of biplots rank the genotypes in the same order of superiority; they complementarily provide a complete picture of the genotypes and allow confident genotype evaluation, selection, and recommendation. Core Ideas: Reliable genotype evaluation must be based on multi‐trial and multi‐trait information.Two types of biplots to integrate both multi‐trial and multi‐trait information are described.The G + GE biplot of genotype by yield × trait (GYT) index displays the mean and stability of the genotypes considering all key traits.The GYT biplot across trials displays the superiority and trait profiles of the genotypes.Together, the two biplots provide full understanding of the genotypes. [ABSTRACT FROM AUTHOR]

This study was conducted to develop and test new markers for use in molecular characterization of oat (Avena spp.). Results are presented for two polymerase chain reaction (PCR)-based markers targeting multiple avenin protein loci and 16 new m crosatellite or simple sequence repeat (SSR) markers. The markers were mapped in two segregating oat (Avena sativa L.) populations, 'Kanota' x 'Ogle' (KxO) and 'Terra' x 'Marion' (TxM), and were also tested across a diversity panel consisting of 35 cultivated varieties of oat and representatives of five other Avena species. All markers were robust, multiallelic, and highly polymorphic. The SSR markers, some highlighting multiple loci, mapped throughout the genome, and the avenin markers mapped to their expected locations in KxO and TxM. Also as expected, the avenin markers were shown to be linked to the stem rust resistance gene Pg9 in the population 'OT328' x 'Dumont' (OTxDu). Practical applications in variety identification and germplasm diagnostic assays were explored. The markers were able to distinguish closely related lines for fingerprinting purposes and were also useful for determining whether variation within a line was the result of residual heterozygosity or contamination. In addition, the markers were found to be useful for forensic purposes and for determining whether crossing and backcrossing had been successful. doi: 10.2135/cropsci2009.09.0474

The oat (Avena sativa L.) breeding program at the Eastern Cereal and Oilseed Research Centre of Agriculture & Agri-Food Canada has the responsibility to breed new oat cultivars for producers in eastern Canada, which includes Ontario, Quebec, and the Atlantic provinces. A 3-yr multilocation test was conducted to understand the genotype x location interaction patterns and the relationships among test locations in eastern Canada. A genotype + genotype x environment interaction biplot analysis of yield data revealed three distinct oat mega-environments in eastern Canada: (i) northern Ontario, (ii) southern and eastern Ontario, and (iii) Quebec and Atlantic Canada. To breed for all mega-environments, initial yield screening must be conducted at locations representing each of these mega-environments. Based on the relationships among test locations, six essential test locations were identified: three in Ontario, two in Quebec, and one in Atlantic Canada. Testing at all six locations appeared to provide a good coverage of the whole oat-growing area in eastern Canada. Based on these findings, a breeding and test strategy was developed. This includes conducting initial yield screening at three locations in Ontario, Quebec, and Atlantic Canada, followed by a formal yield test at all six essential test locations. Specifically adapted genotypes selected from this test will then be tested in the Registration Tests in their respectively adapted subregions. doi: 10.2135/cropsci2009.03.0133

Recent investigations in northern Europe and western Canada suggested that mycotoxins caused by Fusarium head blight (FHB) could be a potential problem for oat (Avena sativa L.) production and oat food safety. Here we report studies conducted in eastern Canada to address this issue. In one study, oat genotypes of diverse origin were evaluated for grain and groat deoxynivalenol (DON) contamination owing to artificial FHB inoculation at Ottawa, ON, in 2006 and 2007. In a separate study, oat genotypes tested in the yearly Quebec Oat Registration and Recommendation Trials were evaluated for DON contamination due to artificial inoculation in Quebec from 2003 to 2008. Deoxynivalenol was detected in all tested genotypes, and up to 43 [micro]g [g.sup.-1] of DON was observed in the oat grain. Up to 86% of the DON in the oat grain was removed by dehulling. However, some genotypes still retained up to 9 [micro]g [g.sup.-1] of DON in the groat. Genotype ranking in DON content was relatively consistent across years, planting dates, and experiments, and genotypes with consistently low and high DON levels were identified. It was concluded that FHB is a potential problem for oat production and oat food safety under high FHB pressures; however, the severity of this problem needs to be further assessed by extensive monitoring of the DON level under natural conditions. The most susceptible genotypes identified in this study can be useful for this purpose. doi: 10.2135/cropsci2009.03.0129

Breeding line selection, either for potential varieties or for useful parents, must be based on multiple breeding objectives (or traits). Varieties cannot have any major defects, while parents must have outstanding levels in at least one trait. Due to undesirable associations among breeding objectives, it is difficult to accomplish both tasks (variety selection and parent selection) through a single selection strategy. Additional complication results when a program is breeding for different end-uses such that both high and low levels of a trait are desirable. The first purpose of this paper was to propose a comprehensive multitrait selection procedure that coherently combines independent selection, independent culling, and index selection so that all the aspects in breeding line selection are taken into consideration. A dataset of 150 oat (Avena sativa L.) breeding lines with values evaluated for four quality traits (groat, oil, protein, and beta-glucan concentrations) was used for illustration. A genotype by trait biplot is a useful tool for exploring multiple trait data and can aid in multitrait selection because it graphically displays the trait associations across, and the trait profiles of, the genotypes. Procedures are outlined to avoid possible misinterpretation of such a biplot when the biplot does not fully display the patterns.

The use of genotype main effect (G) plus genotype-by-environment (GE) interaction (G+GE) biplot analysis by plant breeders and other agricultural researchers has increased dramatically during the past 5 yr for analyzing multi-environment trial (MET) data. Recently, however, its legitimacy was questioned by a proponent of Additive Main Effect and Multiplicative Interaction (AMMI) analysis. The objectives of this review are: (i) to compare GGE biplot analysis and AMMI analysis on three aspects of genotype-by-environment data (GED) analysis, namely mega-environment analysis, genotype evaluation, and test-environment evaluation; (ii) to discuss whether G and GE should be combined or separated in these three aspects of GED analysis; and (iii) to discuss the role and importance of model diagnosis in biplot analysis of GED. Our main conclusions are: (i) both GGE biplot analysis and AMMI analysis combine rather than separate G and GE in mega-environment analysis and genotype evaluation, (ii) the GGE biplot is superior to the AMMI1 graph in mega-environment analysis and genotype evaluation because it explains more G+GE and has the inner-product property of the biplot, (iii) the discriminating power vs. representativeness view of the GGE biplot is effective in evaluating test environments, which is not possible in AMMI analysis, and (iv) model diagnosis for each dataset is useful, but accuracy gain from model diagnosis should not be overstated.

Multienvironment trials (MET) generate two types of two-way data: genotype x environment data for a target trait and genotype x trait data in individual or across environments. These data can be visually analyzed by a GGE biplot and a genotype x trait biplot, respectively. This paper describes a third type of biplot, the covariate-effect biplot, and illustrates its tandem use with the other biplots to achieve a fuller understanding of MET data. The covariate-effect biplot is generated on the basis of an explanatory trait x environment two-way table consisting of correlation coefficients between the target trait (e.g., yield) and each of the other traits in each of the environments. This biplot displays the yield-trait relations in individual environments and addresses whether and how the genotype x environment interactions (GE) for yield can be explored by indirect selection for the other traits. These other traits are treated as genetic covariables and can be replaced by other genetic covariables such as genetic markers, QTL, or genes. The biplot methodology was demonstrated by MET data of barley (Hordeum vulgare L.) conducted across North America. Both the GGE biplot and the covariate-effect biplot showed that the environments fell into two (eastern vs. western) megaenvironments. The covariate-effect pattern explained 81% of the GGE pattern, suggesting that the GE pattern for yield can be effectively explored by indirect selection for these traits. Specifically, barley yield can be improved by selecting for larger kernel weight, earlier heading, and better lodging resistance in the eastern megaenvironment. In contrast, the yield-trait relationship in the western megaenvironment was highly variable, and yield improvement can be achieved only by selecting for yield per se across environments. We suggest that the GGE biplot, the genotype x trait biplot, and the covariate-effect biplot be used jointly to better understand and more fully explore MET data., FOLLOWING the proposal of Gabriel (1971), biplots have been increasingly used in the analysis of MET (Bradu and Gabriel, 1978; Kempton, 1984; Zobel et al., 1988; Gauch, 1992; Cooper et [...]

Because of the omnipresent genotype x year or genotype x location x year interactions in crop performance trials, it is commonly believed that multiple-year data should be used in selecting cultivars for the next year. An implicated but rarely tested hypothesis is that multiple-year data are more predictive than single-year data of cultivar performance in the next year. Yield data of the 1991 to 2000 Ontario Soybean Variety Trials in the 2800 Crop Heat Unit (CHU) area were used to study the power of single-year, multiple-location trials in predicting cultivar performances in the following year, and to see if data from multiple-year trials are more predictive. Mixed models were used to estimate best linear unbiased predictions (BLUP) of tested genotypes on the basis of single- or multiple-year trials, and the t-statistic of BLUP (tBLUP) was used as a measure of cultivar performance. Results indicated that a single-year, multiple-location trial had sufficient power for identifying genotypes that would perform well or poorly in the next year. Two to four years' data gave only slightly better predictions of next-year performances than single-year data but allowed more genotypes to be evaluated conclusively. The tBLUP of genotype effects based on 2 yr of multiple-location trials should be used as a basis for soybean cultivar selection and recommendation in the 2800 CHU area of Ontario., REGIONAL PERFORMANCE TRIALS are conducted annually for all major crops throughout the world to help growers select cultivars for the next year. Such a decision would be straightforward were there [...]

Linear-bilinear models, such as the Shifted Multiplicative Model (SHMM) and Sites Regression Model (SREG), have been used to develop clustering procedures for finding subsets of sites (or cultivars) without cultivar crossover interaction (non-COI). Biplots of these models are useful for visual evaluation of cultivar responses across environments. The main purposes of this study were to investigate (i) SRE[G.sub.2] and SHM[M.sub.2] biplots with the first multiplicative components constrained to be non-COI SRE[G.sub.1] and SHM[M.sub.1] solutions, (ii) how the biplots can be used for identifying subsets of sites and cultivars with different levels of COI and with non-COI, and (iii) how these biplots compare with results obtained when clustering only sites or cultivars without cultivar rank change. Transformed and untransformed data from two multienvironment cultivar trials were used for illustration. Biplots from SHM[M.sub.2] and SRE[G.sub.2] models graphically display the interaction variation due to low level COI or non-COI (first multiplicative term) versus the interaction variation due to COI (second multiplicative term). The biplots obtained by means of the non-COI first term constrained solution of the SRE[G.sub.2] and SHM[M.sub.2] models have the same interpretability properties as the standard biplots obtained by means of the unconstrained solution. With the unconstrained and constrained solutions, it is possible to identify subsets of sites and cultivars with low level COI and non-COI. Biplots based on unscaled or scaled data produced similar results. Groups of sites and cultivars with low level COI and non-COI were similar to those found when only sites (or cultivars) were clustered into non-COI groups using the SHMM and SREG clustering approach., MULTIPLICATIVE MODELS for multisite cultivar trials have been used for studying genotype x environment interaction (GEI) and for developing methods for clustering sites or cultivars into groups with statistically negligible [...]

A systematic comparison between two cultivar evaluation and recommendation systems, i.e., the balanced and replicated performance trials conducted in small plots at a small number of locations, and the unbalanced and non-replicated on-farm trials conducted in large strips on many farms, is lacking. This study was initiated to investigate the usefulness of the two contrasting systems in cultivar evaluation and the relationships between them. Yield data from Ontario winter wheat (Triticum aestivum L.) strip trials and performance trials for 1998 to 2000 were analyzed by mixed models. For all 3 yr, results from the two systems were highly correlated, both in terms of the best linear unbiased predictors (BLUP) and for the t-values of BLUP. Cultivars judged to be superior (or inferior) by one system were never judged to be inferior (or superior) by the other. Thus, both on-farm strip trials and replicated small-plot trials provide valid data for effective cultivar evaluation. On the basis of t-statistics, which measure cultivar reliability, cultivars can be classified into superior (t ≥ 2), inferior (t ≤ -2), and intermediate or inadequately tested (-2 < t < 2). Two cultivars can be regarded as different in reliability if their t-values differ by ≥ 3. The evaluation power of strip trials for a cultivar depends on the number of trials in which the cultivar is tested; a cultivar may not be adequately evaluated if it is tested in fewer than 20 trials., CROP CULTIVARS are routinely evaluated in regional performance trials organized by officially recognized institutions or organizations, with data from such trials being used as the basis for cultivar recommendation. The [...]

Diallel crosses have been used in genetic research to determine the inheritance of important traits among a set of genotypes and to identify superior parents for hybrid or cultivar development. Conventional diallel analysis is limited to partitioning the total variation of the data into general combining ability (GCA) of each genotype and specific combining ability (SCA) of each cross. In this paper we formulate a biplot approach for graphical diallel analysis. The biplot is constructed by the first two principal components (PCs) derived from subjecting the tester-centered diallel data to singular value decomposition. It displays the most important entry by tester patterns of the data and allows the following information to be extracted visually: (i) GCA of each genotype; (ii) SCA of each genotype; (iii) groups of parents with similar genetics; and (iv) superior hybrids. In addition, the biplot allows hypotheses to be formulated concerning the genetics of the genotypes. Three published diallel data sets of wheat (Triticum aestivum L.) and maize (Zea mays L.) were used to demonstrate the biplot approach and detailed procedures were provided for constructing and interpreting a biplot., DIALLEL CROSSES have been used in genetic research to investigate the inheritance of important traits among a set of genotypes. Specifically, diallel crosses were devised to investigate GCA of the [...]

Superior crop cultivars must be identified through multi-environment trials (MET) and on the basis of multiple traits. The objectives of this paper were to describe two types of biplots, the GGE biplot and the GT biplot, which graphically display genotype by environment data and genotype by trait data, respectively, and hence facilitate cultivar evaluation on the basis of MET data and multiple traits. Genotype main effect plus genotype by environment interaction effect (GGE) biplot analysis of the soybean [Glycine max (L.) Merr.] yield data for the 2800 crop heat unit area of Ontario for MET in the period 1994-1999 revealed yearly crossover genotype by site interactions. The eastern Ontario site Winchester showed a different genotype response pattern from the three southwestern Ontario sites in four of the six years. The interactions were not large enough to divide the area into different mega-environments as when analyzed over years, a single cultivar yielded the best in all four sites. The southwestern site, St. Pauls, was found to always group together with at least one of the other three sites; it did not provide unique information on genotype performance. Therefore, in future cultivar evaluations, Winchester should always be used but St. Pauls can be dismissed. Applying GT biplot to the 1994-1999 multiple trait data illustrated that GT biplots graphically displayed the interrelationships among seed yield, oil content, protein content, plant height, and days to maturity, among other traits, and facilitated visual cultivar comparisons and selection. It was found that selection for seed yield alone was not only the simplest, but also the most effective strategy in the early stages of soybean breeding., SUPERIOR CULTIVARS must be evaluated on the basis of multi-environment trials (MET) and multiple traits to ensure that the selected cultivars have acceptable performance in variable environments within the target [...]