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  • Separability  (2)
  • Shifted multiplicative model  (2)
  • AMMI  (1)
  • Adaptation  (1)
  • 1
    Electronic Resource
    Electronic Resource
    A shifted multiplicative model cluster analysis for grouping environments without genotypic rank change (1993)
    Springer
    Theoretical and applied genetics 85 (1993), S. 577-586 
    Details
    ISSN: 1432-2242
    Keywords: Genotype-environment interaction ; Crossover interaction ; Separability ; Shifted multiplicative model ; Distance measure ; Cluster analysis ; Zea mays L
    Source: Springer Online Journal Archives 1860-2000
    Topics: Biology
    Notes: Summary The shifted multiplicative model (SHMM) is used with a cluster method to identify subsets of sites in an international maize (Zea mays L.) trial without genotypic rank-change. For cluster analysis, distance between two sites is defined as the residual sum of squares after fitting SHMM with one multiplicative term (SHMM1) if SHMM1 does not show genotypic rank-change. However, if SHMM1 does show genotypic rank-change, the distance between two sites is defined as the smaller of the sums of squares owing to genotypes within each of the two sites. Calculation of distance between two sites is facilitated by using the site regression model with one multiplicative term (SREG1), which can be reparameterized as SHMM1 when only two sites are considered. The dichotomous splitting procedure, used on the dendrogram obtained from cluster analysis, will first perform SHMM analyses on each of the last two cluster groups to join (end of the dendrogram). If SHMM1 does not give an adequate fit, the next step is to move down the branches of the tree until groups of sites (clusters) are found to which SHMM1 provides an adequate fit and primary effects of sites are all of the same sign. Five final groups of sites to which SHMM1 provides an adequate fit and primary effects of sites are all of the same sign were obtained. The procedure appears to be useful in identifying subsets of sites in which genotypic rank-change interactions are negligible.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00220916
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  • 2
    Electronic Resource
    Electronic Resource
    Using the shifted multiplicative model to search for “separability” in crop cultivar trials (1992)
    Springer
    Theoretical and applied genetics 84 (1992), S. 161-172 
    Details
    ISSN: 1432-2242
    Keywords: Genotype x environment interaction ; Shifted multiplicative model ; Separability ; Concurrent regression model ; Crossover interaction ; Qualitative interaction
    Source: Springer Online Journal Archives 1860-2000
    Topics: Biology
    Notes: Summary The shifted multiplicative model (SHMM) is used in an exploratory step-down method for identifying subsets of environments in which genotypic effects are “separable” from environmental effects. Subsets of environments are chosen on the basis of a SHMM analysis of the entire data set. SHMM analyses of the subsets may indicate a need for further subdivision and/or suggest that a different subdivision at the previous stage should be tried. The process continues until SHMM analysis indicates that a SHMM with only one multiplicative term and its “point of concurrence” outside (left or right) of the cluster of data points adequately fits the data in all subsets. The method is first illustrated with a simple example using a small data set from the statistical literature. Then results obtained in an international maize (Zea mays L.) yield trial with 20 sites and nine cultivars is presented and discussed.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00223996
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  • 3
    Electronic Resource
    Electronic Resource
    The area under the function: an index for selecting desirable genotypes (1993)
    Springer
    Theoretical and applied genetics 87 (1993), S. 409-415 
    Details
    ISSN: 1432-2242
    Keywords: Genotype x environment interaction ; Adaptation ; Stability ; Desirability index
    Source: Springer Online Journal Archives 1860-2000
    Topics: Biology
    Notes: Abstract The linear regression approach has been widely used for selecting high-yielding and stable genotypes targeted to several environments. The genotype mean yield and the regression coefficient of a genotype's performance on an index of environmental productivity are the two main stability parameters. Using both can often complicate the breeder's decision when comparing high-yielding, less-stable genotypes with low-yielding, stable genotypes. This study proposes to combine the mean yield and regression coefficient into a unified desirability index (D i). Thus, D i is defined as the area under the linear regression function divided by the difference between the two extreme environmental indexes. D i is equal to the mean of the i th genotype across all environments plus its slope multiplied by the mean of the environmental indexes of the two extreme environments (symmetry). Desirable genotypes are those with a large D i. For symmetric trials the desirability index depends largely on the mean yield of the genotype and for asymmetric trials the slope has an important influence on the desirability index. The use of D i was illustrated by a 20-environments maize yield trial and a 25-environments wheat yield trial. Three maize genotypes out of nine showed values of D i 's that were significantly larger than a hypothetical, stable genotype. These were considered desirable, even though two of them had slopes significantly greater than 1.0. The results obtained from ranking wheat genotypes on mean yield differ from a ranking based on D i .
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00215085
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  • 4
    Electronic Resource
    Electronic Resource
    Combining genotype, environment and attribute variables in regression models for predicting the cell-means of multi-environment cultivar trials (1998)
    Springer
    Theoretical and applied genetics 96 (1998), S. 803-811 
    Details
    ISSN: 1432-2242
    Keywords: Key words Genotype × environment interaction (GEI) ; AMMI ; GEI variance
    Source: Springer Online Journal Archives 1860-2000
    Topics: Biology
    Notes: Abstract  The main objectives of this study were: (1) to develop models which combine variables of genotype, environment and attribute in regression models (GEAR) for increasing the accuracy of predicted cell-means of the genotype×environment two-way table, and (2) to compare GEAR models with the additive main effects and multiplicative interaction (AMMI) model. GEAR models were developed by regressing the observed values on principal components of genotypes (PCG) and environments (PCE). Genetic and environmental attributes were also added to the GEAR models. GEAR and AMMI models were applied to multi-environment trials of triticale (trial 1), maize (trial 2) and broad beans (trial 3). The random data-splitting and cross-validation procedure was used and the root mean square-predicted difference (RMSPD) was computed to validate each model. GEAR models increased the accuracy of predicted cell-means. Attribute variables, such as soil pH, rainfall, altitude and class of genotype, did not improve the best GEAR model of trial 1, but they increased the predictive value of other models. Two iterations of the computer program further refined the best GEAR model. Based on the RMSPD criterion, GEAR models were as good as, or better than, some AMMI truncated models for predicting cell-means. The approximate accuracy gain factors (GF) of the best GEAR model over the raw data were 2.08, 3.02 and 2.22, for trials 1, 2 and 3, respectively. The GF of the best AMMI model were 1.74, 2.28 and 2.32 for trials 1, 2 and 3, respectively. The analysis of variance of the predicted cell means showed that the genotype×environment interaction (GEI) variance was reduced by about 20% in trial 1 and 81% in trial 2. A bias associated with the predicted cell reduced the GEI variability. Advantages of using GEAR models in muti-environment cultivar trials are that they: (1) increase the precision of cell-mean estimates and (2) reduce the GEI variance and increase trait heritability.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/s001220050806
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