Is it methodologically correct to normalize grain moisture data in maize to mean zero and unit variance and then calculate variance components?
Since grain moisture in full-season maize hybrids is environmentally limited (in environments of interest) and in some years dry-down before autumn frost is slower, while faster in other years, would it be convenient to perform variance components analysis with data that is centered to same mean? In some years (rainy), there is 250-290 g/kg-1 water in grain while in others, dry years, there is no more than 200 g/kg-1.
I am aware that this would shrink environmental variance, and give information only about genotype x environment interaction and genotipic variance, but if goal of selection is only to get drydown faster than in some parental line, information should be preserved since the relations within environments would not change.
Any opinion is much appreciated.
It is advisable to check experiments with a model before the start. In the case you could ask a forum or apply paired t test if it is possible https://stats.stackexchange.com/questions/50226/it-it-legitimate-to-apply-a-one-way-anova-to-data-that-have-been-normalised-to-t
Thank you for your answer, but my issue seems to be somewhat different. Standard deviation is fairly consistent over environments, but the means differ. Normalization is not performed by the control since the experiments were carried out in field conditions, it was performed by setting variance to 1 and mean to zero, a somewhat usual procedure.
https://en.wikipedia.org/wiki/Feature_scaling
Hello Vlatko, I do not like transformations in general, unless strictly necessary. If I understand correctly I'd rather use a model that take the variables that you mention and let the data tell me if this variables are really having an effect on the response.
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