Strategic Identification of New Genetic Diversity to Expand Lentil (Lens culinaris Medik.) Production (Using Nepal as an Example)

Agronomy. (2021) 11(10): 1933. doi.org/10.3390/agronomy11101933

Data Preparation

Load the nessesary R packages and prepare the data for analysis.

#install.packages(c("tidyverse","ggbeeswarm","plotly","htmlwidgets"))
# Load libraries
library(tidyverse)   # data wrangling
library(ggbeeswarm)  # geom_quasirandom()
library(plotly)      # plot_ly()
library(htmlwidgets) # saveWidget()
# ggplot theme
theme_AGL <- theme_bw() + 
  theme(strip.background   = element_rect(colour = "black", fill = NA, size = 0.5),
        panel.background   = element_rect(colour = "black", fill = NA, size = 0.5),
        panel.border       = element_rect(colour = "black", size = 0.5),
        panel.grid         = element_line(color  = alpha("black", 0.1), size = 0.5),
        panel.grid.minor.x = element_blank(), 
        panel.grid.major.x = element_blank())
# General color palettes 
myColors_Cluster <- c("darkred",   "darkorange3", "darkgoldenrod2", "deeppink3", 
                 "steelblue", "darkorchid4", "cornsilk4",      "darkgreen")
myColors_Expt <- c("darkred", "purple4", "steelblue", "darkblue")
# Country info
myCountries <- read.csv("https://raw.githubusercontent.com/derekmichaelwright/AGILE_LDP_Phenology/master/data/data_countries.csv") %>%
  select(Origin=Country, Region, Area)
# Lentil Diversity Panel metadata
myPCA <- read.csv("https://raw.githubusercontent.com/derekmichaelwright/AGILE_LDP_Phenology/master/data/data_pca_results.csv") %>%
  select(Entry, Name, Origin, Cluster)
# Photothermal model coefficients
myCoefs <- read.csv("https://raw.githubusercontent.com/derekmichaelwright/AGILE_LDP_Phenology/master/data/model_t%2Bp_coefs.csv") %>%
  select(Entry, a, b, c)
#
myExpts <- c("Banke", "Kathmandu", "Jumla2", "Jumla1")
myLabels <- c("lab_Banke", "lab_Kathmandu", "lab_Jumla2", "lab_Jumla1")
myAreas <- c("", "S. Asian", "Mediterranean", "Latin America", "Temperate")
myRegions <- c("", "Asia", "Africa", "Americas", "Europe")
#
write.csv(myPCA, "data_countries.csv", row.names = F)
write.csv(myPCA, "data_pca_results.csv", row.names = F)
write.csv(myCoefs, "model_t+p_coefs.csv", row.names = F)

Predict DTF

\[DTF = 1 / (a + b * T + c * P)\]

where:

  • \(T\): Mean temperature
  • \(P\): Mean photoperiod


  • Jumla1: \(T\) = 3.72 | \(P\) = 11.88
  • Jumla2: \(T\) = 8.12 | \(P\) = 13.06
  • Banke: \(T\) = 23.52 | \(P\) = 11.18
  • Kathmandu: \(T\) = 13.8 | \(P\) = 11.2
# Predict DTF
dd <- myPCA %>%
  left_join(myCountries, by = "Origin") %>%
  left_join(myCoefs, by = "Entry") %>%
  mutate(Cluster = factor(Cluster),
         Jumla1 = 1 / (a + b * 3.72 + c * 11.88),
         Jumla2 = 1 / (a + b * 8.12 + c * 13.06),
         Banke = 1 / (a + b * 23.52 + c * 11.18),
         Kathmandu = 1 / (a + b * 13.8 + c * 11.2)) %>%
  gather(Expt, Value, Jumla1, Jumla2, Kathmandu, Banke) %>%
  mutate(Value = ifelse(Value > 225, 225, Value),
         Expt = factor(Expt, levels = myExpts),
         Area = factor(Area, levels = myAreas),
         Region = factor(Region, levels = myRegions))

Selections

For prediction, the maximum DTF from cluster 2 of the South Asian genotypes was considered a cut-off for Banke and Kathmandu and the maximum from cluster 5 as the cut-off for Jumla.

# Selection values
max_jumla1 <- dd %>% 
  filter(Expt == "Jumla1", Cluster == 5) %>%
  pull(Value) %>% max()
max_jumla2 <- dd %>% 
  filter(Expt == "Jumla2", Cluster == 5) %>%
  pull(Value) %>% max()
max_banke <- dd %>% 
  filter(Expt == "Banke", Cluster == 2) %>%
  pull(Value) %>% max()
max_kathmandu <- dd %>% 
  filter(Expt == "Kathmandu", Cluster == 2) %>%
  pull(Value) %>% max()
# Make selections
dd <- dd %>% 
  mutate(Selection = ifelse(Expt == "Jumla1" & Value <= max_jumla1, "Yes", "No"),
         Selection = ifelse(Expt == "Jumla2" & Value <= max_jumla2, "Yes", Selection),
         Selection = ifelse(Expt == "Banke" & Value <= max_banke, "Yes", Selection),
         Selection = ifelse(Expt == "Kathmandu" & Value <= max_kathmandu, "Yes", Selection))
write.csv(dd, "model_nepal.csv", row.names = F)
# Prep data
myThresholds <- data.frame(Expt = c("Jumla1", "Jumla2", "Banke", "Kathmandu"),
                           Cutoff = c(max_jumla1, max_jumla2, 
                                      max_banke, max_kathmandu)) %>%
  mutate(Expt = factor(Expt, levels = myExpts),
         Label = paste0("lab_", Expt),
         Label = factor(Label, levels = myLabels))
# Plot labeling prep
dd <- dd %>% 
  mutate(Label = paste0("lab_", Expt),
         Label = factor(Label, levels = myLabels))
new.lab <- as_labeller(c(lab_Jumla1 = "italic(T)==3.72~degree*C~+~italic(P)==11.88~h", 
                         lab_Jumla2 = "italic(T)==8.12~degree*C~+~italic(P)==13.06~h", 
                         lab_Banke = "italic(T)==23.52~degree*C~+~italic(P)==11.18~h", 
                         lab_Kathmandu = "italic(T)==13.8~degree*C~+~italic(P)==11.2~h"), 
                       label_parsed)

Predicted DTF

All Data

# Plot
mp <- ggplot(dd, aes(x = Expt, y = Value, group = Expt,
                     color = Cluster, alpha = Selection)) +
  geom_quasirandom(size = 0.75, pch = 16) +
  scale_color_manual(values = myColors_Cluster) +
  scale_alpha_manual(values = c(0.4, 0.8)) +
  guides(colour = guide_legend(override.aes = list(size = 2)),
         alpha = guide_legend(override.aes = list(size = 2))) +
  theme_AGL +
  labs(title = "Predicted DTF in Nepal",
       y = "Days from sowing to flowering", x = NULL)
ggsave("Figure_01.png", mp, width = 6, height = 4)

By DTF Cluster Group

# Plot
mp <- ggplot(dd, aes(x = Cluster, y = Value, 
                     color = Cluster, alpha = Selection)) +
  geom_quasirandom(size = 0.75, pch = 16) +
  geom_hline(data = myThresholds, aes(yintercept = Cutoff), alpha = 0.7) +
  scale_color_manual(name = NULL, values = myColors_Cluster) +
  scale_alpha_manual(values = c(0.4, 0.7)) +
  facet_grid(. ~ Expt + Label, labeller = new.lab) +
  theme_AGL +
  theme(legend.position = "none") +
  labs(title = "Predicted DTF in Nepal",
       y = "Days from sowing to flowering")
ggsave("Figure_02.png", mp, width = 8, height = 4)

By Origin

Area

# Plot
mp <- ggplot(dd) +
  geom_hline(data = myThresholds, aes(yintercept = Cutoff), alpha = 0.7) +
  geom_quasirandom(aes(x = Origin, y = Value, group = Origin,
                       alpha = Selection, color = Cluster)) +
  facet_grid(Expt + Label ~ Area, scales = "free", space = "free_x", 
             labeller = labeller(Area = label_value, Expt = label_value,
                                 Label = new.lab)) +
  scale_color_manual(name = NULL, values = myColors_Cluster) +
  scale_alpha_manual(values = c(0.3, 0.9)) +
  theme_AGL +
  theme(legend.position = "none",
        axis.text.x = element_text(angle = 45, hjust = 1)) +
  labs(title = "Predicted DTF in Nepal",
       y = "Days from sowing to flowering")
ggsave("Figure_03.png", mp, width = 10, height = 8)

Region

# Plot
mp <- ggplot(dd) +
  geom_hline(data = myThresholds, aes(yintercept = Cutoff), alpha = 0.7) +
  geom_quasirandom(aes(x = Origin, y = Value, group = Origin,
                       alpha = Selection, color = Cluster,
                       key1 = Entry, key2 = Name)) +
  facet_grid(Expt + Label ~ Region, scales = "free", space = "free_x", drop = T,
             labeller = labeller(Area = label_value, Expt = label_value,
                                 Label = new.lab)) +
  scale_color_manual(name = NULL, values = myColors_Cluster) +
  scale_alpha_manual(values = c(0.3, 0.9)) +
  theme_AGL +
  theme(legend.position = "none",
        axis.text.x = element_text(angle = 45, hjust = 1)) +
  labs(title = "Predicted DTF in Nepal",
       y = "Days from sowing to flowering", x = NULL)
ggsave("Figure_04.png", mp, width = 10, height = 8)
mp <- ggplotly(mp)
saveWidget(mp, file="Figure_04.html")

model_nepal.csv

Banke

Kathmandu

Jumla2

Jumla1

© Derek Michael Wright