Posts Tagged ‘crop science’

Generalizing the Sites Regression Model to Three-Way Interaction Including Multi-Attributes

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Published in Crop Science 49(6): 2043-2057, 2009

Generalizing the sites regression model to three-way interaction including multi-attributes

Mario Varela, Jose Crossa, Arun Kumar Joshi, Paul L. Cornelius and Yann Manes

When a multienvironment trial (MET) is established across several locations and years, the interaction is referred to as a three-way array. Three-way interaction can be studied by means of three-way principal components analysis. In this study, the three-way principal components analysis is adapted to the sites regression model (three-way SREG). The three-way SREG with location and year combines the effects of genotype, genotype x location, genotype x year, and genotype x location x year. The objective of this study is to show how the three-way SREG can be put to practical use in agriculture and breeding. We utilized two wheat (Triticum aestivum L) data sets that have already been used for fitting a three-way additive main effects and multiplicative interaction model. One data set had genotype (25) x location (4) x sowing times (4) and eight attributes, and the other data set included genotype (20) x irrigation level x year on grain yield. The three-way SREG applied simultaneously to eight attributes facilitates the interpretation of genotypic performance for all traits in specific locations and across locations for a selected sowing time. Results of the three-way SREG for both data sets show the different response patterns of genotypes for locations and sowing dates (Data Set 1), as well as genotypic responses across irrigation levels in different years (Data Set 2). Using Data Set 1, we show that fitting a three-way data structure to a three-way SREG model is more effective for detecting important interaction patterns than using the two-way SREG.

Allelic Variants at the Psy-A1 and Psy-B1 Loci in Durum Wheat and Their Associations with Grain Yellowness

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Published in Crop Science 49(6): 2058-2064, 2009

Allelic Variants at the Psy-A1 and Psy-B1 Loci in Durum Wheat and Their Associations with Grain Yellowness

Xinyao He, Jianwu Wang, Karim Ammar, Roberto Javier Peña, Xianchun Xia  and Zhonghu He

Phytoene synthase (PSY) genes are involved in the biosynthesis of carotenoid pigments in durum wheat [Triticum turgidum L. subsp. durum (Desf.) Husn.], significantly influencing grain yellowness. This study was conducted to identify new allelic variants at the Psy-A1 and Psy-B1 loci in durum wheat, and to evaluate the applicability of functional markers developed from common wheat (Triticum aestivum L.) for durum wheat breeding. Two new allelic variants, Psy-A1d and Psy-A1e, were identified at the Psy-A1 locus, and both the codominant markers YP7A and YP7A-2 can be used to discriminate the two haplotypes, yielding 194- and 231-bp polymerase chain reaction (PCR) products with YP7A and 1001- and 1684-bp fragments with YP7A-2, respectively. At the Psy-B1 locus, three allelic variants were identified. Psy-B1e was also found in common wheat, whereas Psy-B1f and Psy-B1g were detected only in durum wheat. The codominant marker YP7B-1 can be used to distinguish Psy-B1f and Psy-B1g, generating 151- and 153-bp PCR fragments, respectively, and the dominant marker YP7B-4 was specific to haplotype Psy-B1e, producing a 717-bp PCR product. In a set of 100 CIMMYT durum wheat lines with widely variable grain yellowness, the frequencies of Psy-A1d, Psy-A1e, Psy-B1e, Psy-B1f, and Psy-B1g were 99, 1, 0, 67, and 33%, respectively, and the genotype Psy-B1f showed a significant association with higher grain yellowness, whereas the presence of Psy-B1g led to lower yellowness. A phylogenetic tree generated from the gene sequences of the allelic variants at Psy-A1 and Psy-B1 loci indicated two parallel lineages of durum–common wheat, suggesting that more than one tetraploid T. turgidum L. subsp. dicoccon (Schrank) Thell. genotypes were involved in the origin of common wheat. Our results suggested that Psy-B1f should be paid more attention in durum breeding programs for its association with elevated grain yellowness

Mega-environment identification for barley based on twenty-seven years of global grain yield data

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Published in Crop Science 49(5): 1705-1718

Mega-environment identification for barley based on twenty-seven years of global grain yield data

Eduardo Hernandez-Segundo, Flavio Capettini, Richard Trethowan, Maarten van Ginkel, Apolinar Mejia, Aquiles Carballo, Jose Crossa, Mateo Vargas and Artemio Balbuena-Melgarejo

Knowledge of target environments in breeding programs is important to better direct the development of germplasm. The objectives of this study were to identify associations among barley (Hordeum vulgare L.) growing environments to identify mega-environments to select the best locations to breed barley. Twenty-seven years of grain yield data from the International Barley Yield Trial (IBYT) conducted by the ICARDA-CIMMYT Barley Breeding Program, consisting of 750 grain yield trials of two replications representing 235 locations in 75 countries, were analyzed using pattern analysis to group sites across years that represent similar selection environments. The shifted multiplicative model (SHMM) was employed to group sites within each year. Environments clustered into three main groups and squared Euclidean distances were used to identify a representative location within each cluster. Group 1 locations were characterized as being cool with intermediate precipitation; Group 2 locations were warmer and drier; and Group 3 sites were generally cool and had the highest average precipitation. The respective representative key locations for each of the three groups were Leida, Spain; Boulifa, Tunisia; and Setif, Algeria. All three key locations are located in the Northern Hemisphere between 36° and 41° latitude. The results of this study show that the global adaptation of barley is possible and can be improved by breeding and selection for adaptation within the three main mega-environments identified.

New and diverse sources of multiple disease resistance in wheat

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Published in Crop Science 49(4): 1655-1666

New and Diverse Sources of Multiple Disease Resistance in Wheat

Suraj Gurung, J. Michael Bonman, Shaukat Ali, Jaimin Patel, Mary Myrfield, Mohamed Mergoum, Pawan K. Singh and Tika B. Adhikari

Tan spot (caused by Pyrenophora tritici-repentis) and Stagonospora nodorum blotch (SNB), (caused by Phaeosphaeria nodorum) are destructive diseases of wheat (Triticum aestivum L.). The majority of currently grown wheat varieties are susceptible to both diseases, presumably because of high pathogenic variability occurring in these fungi or narrow genetic background for resistance in wheat varieties. Therefore, identifying new sources of tan spot and SNB resistance in wheat is imperative. A subset of 825 wheat accessions from the core collection of the National Small Grains Collection (NSGC) of the United States Department of Agriculture, National Plant Germplasm System (NPGS) was evaluated for resistance to tan spot and SNB at seedling stage in a growth chamber. On the basis of disease reactions, 88 wheat accessions exhibited resistance to both diseases. Data from the Germplasm Resources Information Network (GRIN) were examined for the 88 accessions to identify those that also have resistance to other key diseases and on this basis 28 accessions with multiple resistances were identified. The genetic relationship among the 88 accessions was assessed using resistance gene analog polymorphism (RGAP) primers. Wheat accessions with similar growth habit were grouped together despite differences in country of origin. Associations between agronomic traits and host resistance indicated that winter wheat habit in the studied collection was strongly associated with both SNB and tan spot resistance. This study identified genetically diverse wheat accessions with broad-spectrum resistance that can be used in developing cultivars with high levels of resistance to multiple diseases in wheat breeding programs.