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Two-Factors Design: Split-Plot in RCBD

Source: vignettes/DoE-2_SPLIT.qmd

Planning an experiment follows a reproducible routine:

  1. Load required libraries: Load inti, knitr, and dplyr packages.
  2. Define factor levels: Set up lists with genotypes, treatments, and management factors.
  3. Dispatch design generator: Choose between CRD, RCBD, Split-plot, or Augmented designs.
  4. Plot the field sketch: Verify spatial layouts and serpentine/zigzag sequences.
  5. Label design: Design the experimental labels to facilitate the data collection.
  6. Export to Field Book app: Generate field-ready sheets with trait parameters.
# Install packages and dependencies

library(inti)
library(dplyr)
library(huito)

Designs with Two Factors

When evaluating two or more factors, four designs become available: CRD, RCBD, Split-plot RCBD, and Augmented.

Split-Plot Design in RCBD

The Split-plot Design is recommended when one factor requires larger experimental units due to management constraints (such as irrigation) assigned to main plots, while a second factor (such as commercial quinoa varieties) is assigned to sub-plots within each main plot.

# 1. Define factors: Irrigation regimes (main plots) and commercial quinoa varieties (sub-plots)
factors_split <- list(
  Irrigation = c("Full", "Deficit"),
  Variety    = c("Var_1", "Var_2", "Var_3")
)

# 2. Generate Split-plot layout: 2 main levels x 3 sub levels x 4 blocks = 24 plots
split_exp <- design_split(
  factors = factors_split,
  type = "split_rcbd",
  rep = 4,
  zigzag = TRUE,
  seed = 2026
)

# Fieldbook preview
split_exp$fieldbook %>% 
  head(10) %>% 
  knitr::kable(caption = "Split-plot Fieldbook preview")
Split-plot Fieldbook preview
qrcode plots ntreat Irrigation Variety wp_sp block sort rows cols design
inkaverse_1001_Full_Var_1 1001 1 Full Var_1 Full_Var_1 1 1 1 1 split-rcbd
inkaverse_1002_Full_Var_2 1002 3 Full Var_2 Full_Var_2 1 2 2 1 split-rcbd
inkaverse_1003_Full_Var_3 1003 5 Full Var_3 Full_Var_3 1 3 3 1 split-rcbd
inkaverse_1004_Deficit_Var_2 1004 4 Deficit Var_2 Deficit_Var_2 1 4 3 2 split-rcbd
inkaverse_1005_Deficit_Var_1 1005 2 Deficit Var_1 Deficit_Var_1 1 5 2 2 split-rcbd
inkaverse_1006_Deficit_Var_3 1006 6 Deficit Var_3 Deficit_Var_3 1 6 1 2 split-rcbd
inkaverse_2001_Deficit_Var_1 2001 2 Deficit Var_1 Deficit_Var_1 2 1 4 1 split-rcbd
inkaverse_2002_Deficit_Var_3 2002 6 Deficit Var_3 Deficit_Var_3 2 2 5 1 split-rcbd
inkaverse_2003_Deficit_Var_2 2003 4 Deficit Var_2 Deficit_Var_2 2 3 6 1 split-rcbd
inkaverse_2004_Full_Var_1 2004 1 Full Var_1 Full_Var_1 2 4 6 2 split-rcbd 

# Field layout visualization
tarpuy_plotdesign(
  data = split_exp,
  factor = "Irrigation",
  fill = c("plots", "Variety")
)

Label

The experimental field book generated by the design is used as the input data for label creation. Each row represents an experimental unit, allowing the automatic generation of individualized labels.

# Experimental fieldbook
fb <- split_exp$fieldbook

Customize the label layout

The label layout can be customized by combining text, images and QR codes. Each layer can use values from the experimental field book, allowing automatic generation of labels for every experimental plot.

Load package and import fonts.

font <- c("Permanent Marker", "Tillana", "Courgette")

huito_fonts(font)

You can find more fonts in https://fonts.google.com/

Label design 

label <- fb %>%  
  label_layout(size = c(10, 2.5)
               , border_color = "blue"
               ) %>%
  include_image(
    value = "https://flavjack.github.io/inti/img/inkaverse.png"
    , size = c(2.1, 2.4)
    , position = c(1.2, 1.25)
    # , opts = list("image_scale(200)", "image_noise()")
    ) %>%
  include_barcode(
     value = "barcode"
     , size = c(2.5, 2.5)
     , position = c(8.2, 1.25)
     ) %>%
  include_text(value = "INKAVERSE"
               , position = c(4.6, 2)
               , size = 20
               , font = font[1]
               , fontface = "bold"
               , color = "red"
               ) %>%
  include_text(value = "Irrigation"
               , position = c(2.4, 1.2)
               , size = 12
               , opts = list(hjust = 0.0, vjust = 0.0)
               , font = font[2]
               , color = "black"
               , prefix = "Irrigation: "
               , fontface = "bold"
               ) %>%
    include_text(value = "Variety"
               , position = c(2.4, 0.5)
               , opts = list(hjust = 0.0, vjust = 0.0) 
               , size = 12
               , color = "#009966"
               , font = font[2]
               , prefix = "Variety: "
               , fontface = "bold"
               ) %>% 
  include_text(value = "plots"
               , position = c(9.7, 1.25)
               , angle = 90
               , size = 12
               , color = "brown"
               , font = font[3]
               , prefix = "Plot: "
               )

Label preview

The preview mode label_print(mode = "preview") generate a example of the label design from a random row of the data set.

Generate the complete labels

If you want generate the complete labels list, change: label_print(mode = "complete").

label %>% 
  label_print(mode = "complete"
              , filename = "horizontal-split"
              , nlabels = 12)