Best Linear Unbiased Prediction of Cultivar Effects for Subdivided Target Regions

By Hans-Peter Piepho, Jens Möhring. 2005

94 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/86e5afbf1b8f7b683a75d85b567daac2f77d4306

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In Genetics

In Plant Science

Citations in the corpus

Biplot Analysis of Genotype × Environment Interaction: Proceed with Caution (2009, background): “…GE literature and the recent overemphasis on visual description and characterization of GE interaction by biplot proponents has sidetracked other important perspectives centering around impacts of GE on selection response and breeding strategy (e.g., Atlin et al., 2000; Piepho and Mohring, 2005).”
Comparing the performance of cereal varieties in organic and non-organic cropping systems in different European countries (2008, methodology): “Following, the work of Piepho and Möhring (2005) we have shown how to use the estimated variances to obtain predicted responses to selection and to index selection.”
BLUP for phenotypic selection in plant breeding and variety testing (2008, background): “Piepho and Möhring (2005) put this approach in a BLUP framework and showed that fitting a genetic correlation structure across sub-regions can improve predictions for target regions by exploiting information from adjacent sub-regions.”
Statistical Analysis of Yield Trials by AMMI and GGE: Further Considerations (2008, background): “In addition, a preference for simple models and fewer mega-environments may arise from the tradeoff between the advantage of greater gains from specifi c adaptation and the disadvantage of less data and hence less accuracy within each mega-environment that derive from increasing the number of mega-e”
Estimating Genotypic Correlations and Their Standard Errors Using Multivariate Restricted Maximum Likelihood Estimation with SAS Proc MIXED (2006, background): “The differences in estimation of the key variance and covariance components of interest (genotype, genotype 3 environment, and error), however, are expected to be small (Piepho and Mohring, 2005).”
Perspectives for Genomic Selection Applications and Research in Plants (2015, methodology): “Genetic correlations can then be used to increase the accuracy in different target regions (Piepho and Möhring, 2005; Cullis et al., 2010; Burgueño et al., 2012).”