Revealing GE Interactions from Trial Data without Replications ()
Affiliation(s)
1Department of Agronomy, Horticulture, and Plant Science, South Dakota State University, Brookings, SD, USA.
2Crop Science Research Laboratory, USDA-ARS, Mississippi State, MS, USA.
ABSTRACT
Detecting genotype-by-environment (GE) interaction effects or yield
stability is one of the most important components for crop trial data analysis,
especially in historical crop trial data. However, it is statistically
challenging to discover the GE interaction effects because many published data
were just entry means under each environment rather than repeated field plot
data. In this study, we propose a new methodology, which can be used to impute
replicated trial data sets to reveal GE interactions from the original data. As
a demonstration, we used a data set, which includes 28 potato genotypes and six
environments with three replications to numerically evaluate the properties of
this new imputation method. We compared the phenotypic means and predicted
random effects from the imputed data with the results from the original data.
The results from the imputed data were highly consistent with those from the
original data set, indicating that imputed data from the method we proposed in
this study can be used to reveal information including GE interaction effects
harbored in the original data. Therefore, this study could pave a way to detect
the GE interactions and other related information from historical crop trial
reports when replications were not available.
Share and Cite:
Wu, J. , Jenkins, J. and McCarty, J. (2019) Revealing GE Interactions from Trial Data without Replications.
Open Journal of Statistics,
9, 407-419. doi:
10.4236/ojs.2019.93027.
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