Augmented Randomized Complete Block DesignSource:
This vignette shows how to generate an augmented randomized complete block design using both the FielDHub Shiny App and the scripting function
RCBD_augmented() from the
The augmented randomized complete block design is another option to overcome the problem of limited facilities or lack of seed when researchers want to test many treatments. In this kind of design, the approach is to build augmented blocks and allocate the same amount of controls in every block along with the treatments.
FielDHub includes a function to run such experimental designs, features include options to set the number of entries and the number of checks and augmented blocks for the experiment. Users can also choose to run the same experiment over multiple locations.
For example, we say a project needs to test 120 genotypes of cassava over two locations. In addition, the research includes four checks and six augmented blocks to carry out this experiment. This design setup comes out with 6 blocks of size 24 plots for a total of 144 plots that will be distributed in a field of 12 rows and 12 columns.
To launch the app you need to run either
Once the app is running, go to Unreplicated Designs > RCBD Augmented
Then, follow the following steps where we will show how to generate an RCBD Augmented.
Import entries’ list? Choose whether to import a list with entry numbers and names for genotypes or treatments.
If the selection is
No, that means the app is going to generate synthetic data for entries and names of the treatment/genotypes based on the user inputs.
If the selection is
Yes, the entries list must fulfill a specific format and must be a
.csvfile. The file must have the columns
ENTRYcolumn must have a unique entry integer number for each treatment/genotype. The column
NAMEmust have a unique name that identifies each treatment/genotype. Both
NAMEmust be unique, duplicates are not allowed. In the following table, we show an example of the entries list format. This example has an entry list with four checks and 8 treatments/genotypes. It is crucial to allocate the checks in the top part of the file.
Enter the number of stacked experiments in the Input # of Stacked Expts box. This means the number of times the experiment will be replicated. In our case we will perform just
On the augmented RCBD we have the option to choose whether we randomize the entries or not, with the Randomize Entries toggle button. It is recommended always randomized the treatments/entries but some researchers choose not to randomize treatments, this is often due to logistical issues.
Enter the number of entries/treatments in the Input # of Entries box, which is
120in our example experiment.
Set the number of checks per block with the Checks per Block box. In our case, it is
Set the number of blocks with Input # of Blocks box, which is
6in our example.
The total number of plots in our field will be Input # of Stacked Expts(Input # of Entries + Input # of Blocks * Checks per Block), per location.
Enter the number of locations in Input # of Locations. Set it as
cartesianin the Plot Order Layout. For this example we will set the
To ensure that randomizations are consistent across sessions, we can set a seed number in the box labeled Seed Number. In this example, we will set it to
Enter the starting plot number in the Starting Plot Number box. If the experiment has multiple locations, you must enter a comma separated list of numbers the length of the number of locations for the input to be valid.
Once we have entered the information for our experiment on the left side panel, click the Run! button to run the design.
You will then be prompted to select the dimensions of the field from the list of options in the drop down in the middle of the screen with the box labeled Select dimensions of field. In our case, we will select
12 x 12.
Click the Randomize! button to randomize the experiment with the set field dimensions and to see the output plots. If you change the dimensions again, you must re-randomize.
If you change any of the inputs on the left side panel after running an experiment initially, you have to click the Run and Randomize buttons again, to re-run with the new inputs.
After you run the augmented RCBD in FielDHub and set the dimensions of the field, there are several ways to display the information contained in the field book. The first tab, Get Random, shows the option to change the dimensions of the field and re-randomize, as well as a reference guide for experiment design.
On the second tab, Input Data, you can see all the entries in the randomization in a list, as well as a table of the checks with the number of times they appear in the field. In the list of entries, the reps for each check is included as well.
The Randomized Field tab displays a graphical representation of the randomization of the entries in a field of the specified dimensions. The checks are all colored uniquely, showing the number of times they are distributed throughout the field. The display includes numbered labels for the rows and columns. You can copy the field as a table or save it directly as an Excel file with the Copy and Excel buttons at the top.
On the Plot Number Field tab, there is a table display of the field with the plots numbered according to the Plot Order Layout specified, either serpentine or cartesian. You can see the corresponding entries for each plot number in the field book. Like the Randomized Field tab, you can copy the table or save it as an Excel file with the Copy and Excel buttons.
The Field Book displays all the information on the experimental design in a table format. It contains the specific plot number and the row and column address of each entry, as well as the corresponding treatment on that plot. This table is searchable, and we can filter the data in relevant columns.
You can run the same design with the function
RCBD_augmented() in the
First, you need to load the
FielDHub package typing,
Then, you can enter the information describing the above design like this:
aug_RCBD <- RCBD_augmented( lines = 120, checks = 4, b = 6, repsExpt = 1, l = 2, random = TRUE, exptName = "Cassava_2022", plotNumber = c(1001, 2001), locationNames = c("FARGO", "CASSELTON"), nrows = 12, ncols = 12, seed = 1987 )
The description for the inputs that we used to generate the design,
lines = 120is the number of entries
checks = 4is the number of checks in each augmented block.
b = 6is the number of augmented blocks.
repsExpt = 1is the number of reps for the experiment.
l = 2is the number of locations.
random = TRUEit means both treatment/entries and checks will be randomized.
exptName = "Cassava_2022"is an optional name for the experiment.
plotNumber = c(1001,2001)are the starting plot number for each location respectively, or a single number for 1 location.
locationNames = c("FARGO", "CASSELTON")are the values for representing respective name for each location.
nrows = 12is the number of rows in the field. It is optional
ncols = 12is the number of columns in the field. It is optional.
seed = 1987is the seed number to replicate identical randomizations.
To print a summary of the information that is in the object
aug_RCBD, we can use the generic function
Augmented Randomized Complete Block Design: Information on the design parameters: List of 11 $ rows : num 12 $ columns : num 12 $ rows_within_blocks : num 2 $ columns_within_blocks: num 12 $ treatments : num 120 $ checks : num 4 $ blocks : num 6 $ plots_per_block : num [1:6] 24 24 24 24 24 24 $ locations : num 2 $ fillers : num 0 $ seed : num 1987 10 First observations of the data frame with the RCBD_augmented field book: ID EXPT LOCATION YEAR PLOT ROW COLUMN CHECKS BLOCK ENTRY TREATMENT 1 1 Cassava_2022 FARGO 2022 1001 1 1 0 1 98 G98 2 2 Cassava_2022 FARGO 2022 1002 1 2 0 1 103 G103 3 3 Cassava_2022 FARGO 2022 1003 1 3 0 1 87 G87 4 4 Cassava_2022 FARGO 2022 1004 1 4 1 1 2 CH2 5 5 Cassava_2022 FARGO 2022 1005 1 5 0 1 21 G21 6 6 Cassava_2022 FARGO 2022 1006 1 6 0 1 122 G122 7 7 Cassava_2022 FARGO 2022 1007 1 7 1 1 4 CH4 8 8 Cassava_2022 FARGO 2022 1008 1 8 0 1 44 G44 9 9 Cassava_2022 FARGO 2022 1009 1 9 0 1 23 G23 10 10 Cassava_2022 FARGO 2022 1010 1 10 0 1 113 G113
RCBD_augmented() returns a list consisting of all the information displayed in the output tabs in the FielDHub app: design information, plot layout, plot numbering, entries list, and field book. These are accessible by the
$ operator, i.e.
aug_RCBD$fieldBook is a list containing information about every plot in the field, with information about the location of the plot and the treatment in each plot. As seen in the output below, the field book has columns for
Let us see the first 10 rows of the field book for this experiment.
field_book <- aug_RCBD$fieldBook head(field_book, 10)
ID EXPT LOCATION YEAR PLOT ROW COLUMN CHECKS BLOCK ENTRY TREATMENT 1 1 Cassava_2022 FARGO 2022 1001 1 1 0 1 98 G98 2 2 Cassava_2022 FARGO 2022 1002 1 2 0 1 103 G103 3 3 Cassava_2022 FARGO 2022 1003 1 3 0 1 87 G87 4 4 Cassava_2022 FARGO 2022 1004 1 4 1 1 2 CH2 5 5 Cassava_2022 FARGO 2022 1005 1 5 0 1 21 G21 6 6 Cassava_2022 FARGO 2022 1006 1 6 0 1 122 G122 7 7 Cassava_2022 FARGO 2022 1007 1 7 1 1 4 CH4 8 8 Cassava_2022 FARGO 2022 1008 1 8 0 1 44 G44 9 9 Cassava_2022 FARGO 2022 1009 1 9 0 1 23 G23 10 10 Cassava_2022 FARGO 2022 1010 1 10 0 1 113 G113
For plotting the layout in function of the coordinates
COLUMN in the field book object we can use the generic function
plot() as follow,
It is possible to pass more arguments to
plot() such as the specific location. For example, you can plot the layout for location 2.
plot(aug_RCBD, l = 2)