#Split_plot

Multi-environment split-plot experiments

Published at September 13, 2022 ·  7 min read

Have you made a split-plot field experiment? Have you repeated such an experiment in two (or more) years/locations? Have you run into troubles, because the reviewer told you that your ANOVA model was invalid? If so, please, stop for awhile and read: this post might help you understand what was wrong with your analyses. Motivating example Let’s think of a field experiment, where 6 genotypes of faba bean were compared under two different sowing times (autumn and spring)....


Split-plot designs: the transition to mixed models for a dinosaur

Published at February 11, 2021 ·  15 min read

Those who long ago took courses in ‘analysis of variance’ or ‘experimental design’ … would have learned methods … based on observed and expected mean squares and methods of testing based on ‘error strata’ (if you weren’t forced to learn this, consider yourself lucky). (Douglas Bates, 2006). In a previous post, I already mentioned that, due to my age, I see myself as a dinosaur within the R-users community. I already mentioned how difficult it is, for a dinosaur, to adjust to new concepts and paradigms in data analysis, after having done things differently for a long time ( see this post here )....


Accounting for the experimental design in linear/nonlinear regression analyses

Published at December 4, 2020 ·  11 min read

In this post, I am going to talk about an issue that is often overlooked by agronomists and biologists. The point is that field experiments are very often laid down in blocks, using split-plot designs, strip-plot designs or other types of designs with grouping factors (blocks, main-plots, sub-plots). We know that these grouping factors should be appropriately accounted for in data analyses: ‘analyze them as you have randomized them’ is a common saying attributed to Ronald Fisher....


Fitting 'complex' mixed models with 'nlme': Example #2

Published at September 13, 2019 ·  9 min read

A repeated split-plot experiment with heteroscedastic errors Let’s imagine a field experiment, where different genotypes of khorasan wheat are to be compared under different nitrogen (N) fertilisation systems. Genotypes require bigger plots, with respect to fertilisation treatments and, therefore, the most convenient choice would be to lay-out the experiment as a split-plot, in a randomised complete block design. Genotypes would be randomly allocated to main plots, while fertilisation systems would be randomly allocated to sub-plots....