On the design of early generation variety trials with correlated data

By Brian R. Cullis, Alison B. Smith, Neil Coombes. 2006

792 citations recorded by the source corpus.

Subfields: Genetics, Management Science and Operations Research, Plant Science

View Paper: https://www.semanticscholar.org/paper/7e0c72d0933852486065834432a9fd3b389f9fc5

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In Genetics

In Management Science and Operations Research

In Plant Science

Citations in the corpus

The accuracy of varietal selection using factor analytic models for multi-environment plant breeding trials (2007, methodology): “The South Australia barley and Victoria lentil series were designed using grid-plot designs (e.g., see Cullis et al., 2006 ), which, historically have been the most common type of design for this stage of testing.”
BLUP for phenotypic selection in plant breeding and variety testing (2008, background): “In general, the same types of search algorithms as used for designs having a fixed effects analysis in mind can be used (John and Williams 1995; Cullis et al. 2006).”
Statistical Analysis of Yield Trials by AMMI and GGE: Further Considerations (2008, methodology): “By contrast, better experimental designs can make adjustments that increase accuracy, including lattice and row-column designs (John and Williams, 1995), designs optimized with a spatial analysis in mind ( Cullis et al., 2006; Williams et al., 2006), improved methods of analysis for individual trai”
Perspectives for Genomic Selection Applications and Research in Plants (2015, methodology): “Mixed model derived criteria were proposed to optimize unreplicated field trials, taking into account the spatial correlation between the residuals (Cullis et al., 2006).”
Correcting for spatial heterogeneity in plant breeding experiments with P-splines (2018, methodology): “…that C∗gg corresponds to Cov (ĉg − cg) (i.e., the prediction error covariance matrix for the genetic effects), the generalised heritability measure proposed by Cullis et al. (2006) can also be expressed in terms of the genetic effective dimension H2c = 1− 1 σ2g mg∑ i=1 pev(cgi) mg = trace(Img…”
Predicting the future of plant breeding: complementing empirical evaluation with genetic prediction (2014, background): “Cullis et al. (2006) extended the use of the augmented design to incorporate replication on a specified percentage of the test genotypes.”
Efficiency of Plant Breeding (2015, methodology): “In the first-year trials, partially replicated row and column designs (Cullis et al., 2006) combined with optimized randomization are used, while in the second-, third-, and fourth-year trials, a randomized complete block design, with two replications in rows and columns with optimized randomization”
Genetic Analysis of Visually Scored Orange Kernel Color in Maize (2013, methodology): “The heritability across the entire experiment ( 2 Ĉ h ) (Cullis et al., 2006) was estimated as follows:”