Kelly, A.M. and Smith, A.B. and Eccleston, J.A. and Cullis, B.R. (2008) The accuracy of varietal selection using factor analytic models for multi-environment plant breeding trials. Crop Science, 47 (3). pp. 1063-1070.
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Article Link(s): http://dx.doi.org/10.2135/cropsci2006.08.0540
Publisher URL: https://www.crops.org
Modeling of cultivar x trial effects for multienvironment trials (METs) within a mixed model framework is now common practice in many plant breeding programs. The factor analytic (FA) model is a parsimonious form used to approximate the fully unstructured form of the genetic variance-covariance matrix in the model for MET data. In this study, we demonstrate that the FA model is generally the model of best fit across a range of data sets taken from early generation trials in a breeding program. In addition, we demonstrate the superiority of the FA model in achieving the most common aim of METs, namely the selection of superior genotypes. Selection is achieved using best linear unbiased predictions (BLUPs) of cultivar effects at each environment, considered either individually or as a weighted average across environments. In practice, empirical BLUPs (E-BLUPs) of cultivar effects must be used instead of BLUPs since variance parameters in the model must be estimated rather than assumed known. While the optimal properties of minimum mean squared error of prediction (MSEP) and maximum correlation between true and predicted effects possessed by BLUPs do not hold for E-BLUPs, a simulation study shows that E-BLUPs perform well in terms of MSEP.
|Business groups:||Agri-Science, Crop and Food Science|
|Additional Information:||© Crop Science Society of America.|
|Keywords:||Modeling; Cultivar x trial effects; multienvironment trials (METs); mixed model; plant breeding programs; factor analytic (FA) model; genetic variance-covariance; best linear unbiased predictions (BLUPs); empirical BLUPs (E-BLUPs); mean squared error of prediction (MSEP).|
|Subjects:||Plant culture > Propagation|
Science > Statistics > Statistical data analysis
Science > Statistics > Simulation modelling
|Deposited On:||05 Feb 2009 04:44|
|Last Modified:||25 Oct 2011 04:40|
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