88 research outputs found

    Genetic analysis of field and physiological indicators of drought tolerance in bread wheat (Triticum aestivum L.) using diallel mating design

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    In order to study the inheritance of field, physiological and metabolite indicators of drought tolerance in wheat, an eight-parental diallel cross, excluding reciprocals, was grown in a randomized complete block design (RCBD) with three replications under two different water regimes (irrigated and rainfed). Significant differences were found for yield potential (Yp), stress yield (Ys), stress tolerance index (STI), leaf water potential (LWP), relative water content (RWC), water use efficiency (WUE) and evapotranspiration efficiency (ETE). Yp, RWC and evapotranspiration efficiency (ETE) showed highly significant differences for both general combining ability (GCA) and specific combining ability (SCA), indicating the involvement of both additive and non-additive gene action in their inheritance. Ys, STI and WUE revealed highly significant differences for SCA, hence non-additive gene action was predominant for these traits. The best general combiners with positive effects, for improvement of Yp, Ys, STI, LWP, RWC, WUE and ETE under drought conditions were parents 5, 1, 6, 2, 7, 1 and 2, respectively. The best specific combination with heterobeltiosis over the best parents for improvement of Yp, Ys, STI, LWP, RWC, WUE and ETE were crosses 3 × 6, 2 × 4, 2 × 6, 5 × 8, 2 × 6, 2 × 4 and 1 × 7, respectively indicating that parents of these crosses are genetically diverse. High broad-sense heritability observed for all the traits confirmed that all the traits are more genetic, but because of low narrow-sense heritability the rule of additive part was low.Key words: Drought tolerance, physiological indicators, diallel mating design, genetic analysis

    Potential new genes for resistance to Mycosphaerella graminicola identified in Triticum aestivum x Lophopyrum elongatum disomic substitution lines.

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    Lophopyrum species carry many desirable agronomic traits, including disease resistance, which can be transferred towheat by interspecific hybridization. To identify potentially new genes for disease and insect resistance carried by individual Lophopyrum chromosomes, 19 of 21 possible wheat cultivar Chinese Spring 9 Lophopyrum elongatum disomic substitution lines were tested for resistance to barley yellow dwarf virus (BYDV), cereal yellow dwarf virus (CYDV), the Hessian fly Mayetiola destructor, and the fungal pathogens Blumeria graminis and Mycosphaerella graminicola (asexual stage: Septoria tritici). Low resistance to BYDV occurred in some of the disomic substitution lines, but viral titers were significantly higher than those of two Lophopyrum species tested. This suggested that genes on more than one Lophopyrum chromosome are required for complete resistance to this virus. A potentially new gene for resistance to CYDV was detected on wheatgrass chromosome 3E. All of the substitution lines were susceptible to Mayetiola destructor and one strain of B. graminis. Disomic substitution lines containing wheatgrass chromosomes 1E and 6E were significantly more resistant to M. graminicola compared to Chinese Spring. Although neither chromosome by itself conferred resistance as high as that in the wheatgrass parent, they do appear to contain potentially new genes for resistance against this pathogen that could be useful for future plant-improvement programs

    AMMI analysis of the adaptability and yield stability of yellow passion fruit varieties

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    High yield stability and adaptability of yellow passion fruit varieties (Passiflora edulis Sims. f. flavicarpa Deg.) are highly desirable attributes when exploring different environments. This study aimed to evaluate the adaptability and yield stability of yellow passion fruit varieties using AMMI (additive main effects and multiplicative interaction) and other ancillary statistics. Twelve varieties were evaluated in eight environments. Analysis of variance showed effects attributable to the varieties (G), environment (E) and their interaction (G × E). The first two multiplicative components of the interaction accounted for 69% of the sum of squares. The scores of the principal interaction components showed high variability for the environments relative to the variety effects. High varietal phenotypic stability was observed in three environments; which can be used in yellow passion fruit breeding programs for initial selection trials. A biplot-AMMI analysis and yield stability index incorporating the AMMI stability value and yield capacity in a single non-parametric index were useful for discriminating genotypes with superior and stable fruit yield. AMMI analysis also allowed for the identification of more productive varieties in specific environments, leading to significant increase in passion fruit productivity

    Simultaneous selection of yield and yield stability in chickpea genotypes using the GGE biplot technique

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    GGE biplot analysis is an effective method, based on principal component analysis (PCA), to fully explore multi-environment trials (METs). It allows visual examination of the relationships among the test environments, genotypes and the genotype-by-environment interactions (G×E interaction). The objective of this study was to explore the effect of genotype (G) and the genotype × environment interaction (GEI) on the grain yield of 20 chickpea genotypes under two different rainfed and irrigated environments for 4 consecutive growing seasons (2008–2011). The yield data were analysed using the GGE biplot method. The first mega-environment contained environments E1, E3, E4 and E6, with genotype G17 (X96TH41K4) being the winner; the second mega-environment contained environments E5, E7 and E8, with genotype G12 (X96TH46) being the winner. The E2 environment made up another mega-environment, with G19 (FLIP-82-115) the winner. The mean performance and stability of the genotypes indicated that genotypes G4, G16 and G20 were highly stable with high grain yield.</jats:p

    Locating QTLs controlling adaptation in wheat using AMMI model

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