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Selection in Cultivar Trials—Is It Ignorable?
Author(s) -
Piepho HansPeter,
Möhring Jens
Publication year - 2006
Publication title -
crop science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.76
H-Index - 147
eISSN - 1435-0653
pISSN - 0011-183X
DOI - 10.2135/cropsci2005.04-0038
Subject(s) - restricted maximum likelihood , best linear unbiased prediction , selection (genetic algorithm) , biology , cultivar , mixed model , missing data , multivariate statistics , statistics , maximum likelihood , multivariate analysis , microbiology and biotechnology , econometrics , mathematics , agronomy , computer science , machine learning
Crop cultivar registration requires multienvironment trials for assessing the value for cultivation and use (VCU). The series of trials usually extends across 3 yr, with some cultivars being discarded each year. Selection gives rise to a missing‐data or drop‐out pattern that is not completely random. The present paper studies the effect of drop‐out on the validity of mixed model procedures such as REML and BLUP. It is shown on the basis of the pertinent statistical theory and simulations that selection is ignorable providing that all data used in the selection process are included in the analysis. Simulations show that REML is preferable to ML and BLUP is preferable to BLUE. It is suggested that cultivar registration authorities can benefit from multivariate mixed model analyses comprising all traits on which selection is based.