Two-Factors Design: Split-Plot in RCBD

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 <- list(
  Irrigation = c("Full", "Deficit"),
  Variety    = c("chulpi", "kancolla", "choclito")
)

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

# Fieldbook preview
fb <- design$fieldbook 

fb %>% 
  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_chulpi 1001 1 Full chulpi Full_chulpi 1 1 1 1 split-rcbd
inkaverse_1002_Full_kancolla 1002 3 Full kancolla Full_kancolla 1 2 2 1 split-rcbd
inkaverse_1003_Full_choclito 1003 5 Full choclito Full_choclito 1 3 3 1 split-rcbd
inkaverse_1004_Deficit_kancolla 1004 4 Deficit kancolla Deficit_kancolla 1 4 3 2 split-rcbd
inkaverse_1005_Deficit_chulpi 1005 2 Deficit chulpi Deficit_chulpi 1 5 2 2 split-rcbd
inkaverse_1006_Deficit_choclito 1006 6 Deficit choclito Deficit_choclito 1 6 1 2 split-rcbd
inkaverse_2001_Deficit_chulpi 2001 2 Deficit chulpi Deficit_chulpi 2 1 4 1 split-rcbd
inkaverse_2002_Deficit_choclito 2002 6 Deficit choclito Deficit_choclito 2 2 5 1 split-rcbd
inkaverse_2003_Deficit_kancolla 2003 4 Deficit kancolla Deficit_kancolla 2 3 6 1 split-rcbd
inkaverse_2004_Full_chulpi 2004 1 Full chulpi Full_chulpi 2 4 6 2 split-rcbd
inkaverse_2005_Full_choclito 2005 5 Full choclito Full_choclito 2 5 5 2 split-rcbd
inkaverse_2006_Full_kancolla 2006 3 Full kancolla Full_kancolla 2 6 4 2 split-rcbd
inkaverse_3001_Full_chulpi 3001 1 Full chulpi Full_chulpi 3 1 7 1 split-rcbd
inkaverse_3002_Full_kancolla 3002 3 Full kancolla Full_kancolla 3 2 8 1 split-rcbd
inkaverse_3003_Full_choclito 3003 5 Full choclito Full_choclito 3 3 9 1 split-rcbd
inkaverse_3004_Deficit_chulpi 3004 2 Deficit chulpi Deficit_chulpi 3 4 9 2 split-rcbd
inkaverse_3005_Deficit_choclito 3005 6 Deficit choclito Deficit_choclito 3 5 8 2 split-rcbd
inkaverse_3006_Deficit_kancolla 3006 4 Deficit kancolla Deficit_kancolla 3 6 7 2 split-rcbd
inkaverse_4001_Deficit_chulpi 4001 2 Deficit chulpi Deficit_chulpi 4 1 10 1 split-rcbd
inkaverse_4002_Deficit_choclito 4002 6 Deficit choclito Deficit_choclito 4 2 11 1 split-rcbd
inkaverse_4003_Deficit_kancolla 4003 4 Deficit kancolla Deficit_kancolla 4 3 12 1 split-rcbd
inkaverse_4004_Full_choclito 4004 5 Full choclito Full_choclito 4 4 12 2 split-rcbd
inkaverse_4005_Full_kancolla 4005 3 Full kancolla Full_kancolla 4 5 11 2 split-rcbd
inkaverse_4006_Full_chulpi 4006 1 Full chulpi Full_chulpi 4 6 10 2 split-rcbd 

# Field layout visualization
tarpuy_plotdesign(
  data = design,
  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.

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 = "qrcode"
     , 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.

label %>% 
  label_print(mode = "preview")

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)