This gives us a feel for what happened in all experiments. It is always useful to start by separately considering the results for each year. dataset$YearBlock <- with(dataset, factor(Year:Block))ĭataset$MainPlot <- with(dataset, factor(Year:Block:Genotype))įor the analyses, we will make use of the ‘plyr’ (Wickham, 2011), ‘car’ (Fox and Weisberg, 2011) and ‘nlme’ (Pinheiro et al., 2018) packages, which we load now. The first factors (‘YearBlock’) has 12 levels (4 years and 3 blocks per year) and the second factor (‘MainPlot’) has 180 levels (4 years, 3 blocks per year and 15 genotypes per block).
#Split split plot design r program asreml code#
rm(list=ls())Īdditionally, it may be useful to code some ‘helper’ factors, to represent the blocks (within years) and the main-plots. The following snippets loads the file and recodes the numerical independent variables into factors. The fifteen genotypes are coded by using the letters from A to O, while the levels of the other independent variables are coded by using numbers. The dataset has five columns, the ‘Year’, the ‘Genotype’, the fertilisation level (‘N’), the ‘Block’ and the response variable, i.e. It provides the results for a split-plot experiment with 15 genotypes and 2 N fertilisation treatments, laid-out in three blocks and repeated in four years (360 observations, in all). Please, look at the dataset ‘kamut.csv’, which is available on github. What could we expect from such an experiment? As usual in agricultural research, the experiment should be repeated in different years, in order to explore the environmental variability of results. Genotypes would be randomly allocated to main plots, while fertilisation systems would be randomly allocated to sub-plots. 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. Let’s imagine a field experiment, where different genotypes of khorasan wheat are to be compared under different nitrogen (N) fertilisation systems. A repeated split-plot experiment with heteroscedastic errors
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