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  • 1
    ISSN: 1573-5060
    Keywords: pathotype ; virulence group ; partial resistance ; Globodera rostochiensis ; Globodera pallida ; Solanum tuberosum ; potato ; potato cyst nematode
    Source: Springer Online Journal Archives 1860-2000
    Topics: Agriculture, Forestry, Horticulture, Fishery, Domestic Science, Nutrition
    Notes: Summary In two experiments, using different testing methods, the number of newly formed cysts was determined on nine potato genotypes with resistance from various sources. Ten potato cyst nematode (PCN) populations were used in these experiments. Rank correlation between numbers of cysts over potato genotype-PCN population combinations for both experiments was high (rs = 0.90). Dendrograms for PCN populations and potato genotypes were constructed, based on a simultaneous hierarchical clustering procedure for potato genotype-PCN population interaction terms. Several virulence groups could be identified within Globodera rostochiensis as well as within G. pallida. Host genotypes, derived from the same sources of resistance, were clustered in different resistance groups.
    Type of Medium: Electronic Resource
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  • 2
    ISSN: 1573-5060
    Keywords: AMMI ; biadditive model ; factorial regression ; multiplicative interaction ; potato ; variety trials ; Solanum tuberosum
    Source: Springer Online Journal Archives 1860-2000
    Topics: Agriculture, Forestry, Horticulture, Fishery, Domestic Science, Nutrition
    Notes: Summary Genotype by environment interaction was investigated for yield data from the official Dutch Variety List trials for potato. The data set included 64 genotypes by 26 environments, where environments consisted of year by soil type combinations. Factorial regression models incorporating genotypic and environmental covariates in the interaction were used to analyse the data. The merits of factorial regression models were compared with those of biadditive models. Factorial regression models and biadditive models described comparable amounts of interaction, but factorial regression models provided a better basis for biological interpretation of the interaction.
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  • 3
    ISSN: 1573-5060
    Keywords: Additive Main effects and Multiplicative Interaction effects model (AMMI) ; Beta vulgaris ; beet necrotic yellow vein virus ; cultivar by location interaction ; factorial regression ; sugar beet ; virus resistance
    Source: Springer Online Journal Archives 1860-2000
    Topics: Agriculture, Forestry, Horticulture, Fishery, Domestic Science, Nutrition
    Notes: Summary Sugar beet cultivars were evaluated for resistance to beet necrotic yellow vein virus (BNYVV) on various locations in two consecutive years. Resistance levels of cultivars were measured by virus assays of plants from the field and the greenhouse. Infection levels in the fields were characterised by sampling plants of a susceptible indicator cultivar. For each year, statistical analyses were performed on two-way tables of cultivar by location for yield and quality parameters. In analysis of variance (ANOVA) significant main effects and significant cultivar by location interaction were found for all parameters (P〈0.05). Interactions were further investigated by multiplicative models. In the Additive Main effects and Multiplicative Interaction effects (AMMI) model, interaction was written as the product of a cultivar score and a location score. Cultivar interaction scores were highly correlated to virus concentrations of the cultivars, and location interaction scores to virus concentrations of the susceptible indicator cultivar. Main effects of cultivars and locations were less clearly related to virus concentrations than interaction effects. In general, virus concentrations of plants from a greenhouse test gave higher correlations than virus concentrations of plants from the field. In the factorial regression model, virus concentrations were incorporated in the model. The model can be understood as a two-way ANOVA, with greenhouse virus concentrations and virus concentration of the indicator cultivar as concomitant variables on the cultivar and location factor. Results of analyses with both multiplicative interaction models showed that interactions of all yield and quality parameters can be described in terms of virus concentrations. Therefore, the relative performance of susceptible and partially resistant cultivars in infested fields can be estimated by means of three independent parameters, (i) the level of resistance determined in a greenhouse experiment, (ii) the yield and quality in non-infested fields, and (iii) the level of infection in the field.
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  • 4
    Electronic Resource
    Electronic Resource
    Springer
    Euphytica 84 (1995), S. 1-7 
    ISSN: 1573-5060
    Keywords: AMMI ; best linear unbiassed prediction ; factorial regression ; genotype by environment interaction ; multiplicative interaction ; reduced rank regression ; two-way table ; variance components ; variety trials
    Source: Springer Online Journal Archives 1860-2000
    Topics: Agriculture, Forestry, Horticulture, Fishery, Domestic Science, Nutrition
    Notes: Summary The multi-environment trial, in which a number of genotypes is evaluated over a range of environmental conditions, is a standard experiment in plant breeding in general, and variety testing in particular. Useful statistical models for the analysis of multi-environment trials, with emphasis on the analysis of genotype by environment interaction, can be found in the classes of linear and bilinear models. Statistical properties of the most important representatives of these model classes are shortly reviewed. Structural differences between the models stem from: (1) the inclusion of random model terms in addition to fixed model terms; (2) the representation of the interaction by additive or multiplicative parameters; (3) the incorporation of concomitant variables on the levels of the environmental factor. For models with bilinear multiplicative structure for the interaction it is described how the interaction can be visualized by biplots. An illustration of the application of the models and biplots is given in a companion paper.
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  • 5
    ISSN: 1573-5060
    Keywords: AMMI ; biadditive model ; factorial regression ; multiplicative interaction ; potato ; variety trials ; Solanum tuberosum
    Source: Springer Online Journal Archives 1860-2000
    Topics: Agriculture, Forestry, Horticulture, Fishery, Domestic Science, Nutrition
    Notes: Summary Genotype by environment interaction was investigated for yield data from the official Dutch Variety List trials for potato. The data set included 64 genotypes by 26 environments, where environments consisted of year by soil type combinations. Factorial regression models incorporating genotypic and environmental covariates in the interaction were used to analyse the data. The merits of factorial regression models were compared with those of biadditive models. Factorial regression models and biadditive models described comparable amounts of interaction, but factorial regression models provided a better basis for biological interpreration of the interaction.
    Type of Medium: Electronic Resource
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  • 6
    ISSN: 1573-5060
    Keywords: AMMI ; best linear unbiassed prediction ; factorial regression ; genotype by environment interaction ; maize ; missing values ; multiplicative interaction ; reduced rank regression ; two-way table
    Source: Springer Online Journal Archives 1860-2000
    Topics: Agriculture, Forestry, Horticulture, Fishery, Domestic Science, Nutrition
    Notes: Summary As the sequel to a paper that dealt with the theoretical aspects of linear and bilinear models for the analysis of genotype by environment interaction in multi-environment trials, this paper presents an illustration of the application of these models to real life data. The data come from maize trials that were conducted within the ongoing evaluation programme for the Dutch Descriptive Variety List of Field Crops. The variable that is analyzed is dry matter content. It is shown how linear and bilinear models can be used supplementary to each other within a general strategy for dealing with genotype by environment interaction.
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