Application of Linear Mixed Model to Incomplete Block Designs
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Date
2010-06
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Addis Ababa University
Abstract
The study was designed to examine th e application of linear mixed model to incomplete
block designs. In addition to thi s, it is planned to compare VARCOMP, ML and REML
estimation methods for variance components of linear mixed models. Sixty three promising
bar ley lines and one standard check cultivar which were obtained from EIAR have been
evaluated for grain yield performance and adaptation across eight environments
(combination offour lo cations by two ferti lizers).
The mean grain yields for individual line ranged from 17.06 to 33.21 quintals per hectare
and the mean grain yields for in dividual environment ranged from 16.80 to 44.214 quintals
per hectare. The highest mean grain yield was observed at BEKOjl, while the lowest mean
grain yield was registe red at SHENO with both fertilizers doses (100 and 150 kg). When we
compare each variety with specific environment, lin e 8 and 55, Variety 20 and 14, Variety
49 and 54 and Variety 48 and the Local check to be ada pted and have best mean effects to
BEKOJI, SHENO, HOLETTA and NORTH GONDAR respectively.
KEML estimates for vari a nce components are in distinguishable from classical techniques in
case of balanced data. This im pl ies optimal minimum variance properties and REML
estimates do not rely on normality assumption. But, for unbalanced data, the REML
estimation for variance components is different from classical estimates. We have seen
from the diffe rences that estimation of variance components benefits from REML bu t
VARCOMP does not. Therefore, the REML approach is appropriate to estimate va riance
components in SLD and SLD with missing plots.
Varieties/lines listed above were recommended to release in Ethiopia that have similar
ecologic zones of respective location s and REML techniques was recommended to be used
for va riance components esti mates of linear mixed model for SLD and SLD with missing
plots. It was found that diagnostics for Linear Mixed Model applied to estim ate variance
components are perh aps an area that needs exploration in the future.
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Keywords
Application of Linear Mixed Model