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Corn_Stats_manuscript.R
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Github_loc<-"F:/My Drive/Tidewater/Tidewater Data Analysis"
dat_loc<-paste0(Github_loc,"/Trial_2021/")
plot_loc<-paste0(Github_loc,"/Plot_2021/")
options(scipen = 9999)
setwd(Github_loc)
library(stargazer)
library(tidyverse)
library(Rmisc)
options(scipen=999)
library(predictmeans)
# library(agricolae)
# library(doebioresearch)
library(lmerTest)
# library(AICcmodavg)
###################################################################################
#### Data Preparation ##############################################################
###################################################################################
corn_stats_21 <- read.csv(paste0(dat_loc, "corn_stats.csv"))
colnames(corn_stats_21)[1]<- "Rep"
corn_stats_21 <- corn_stats_21[-c(2)]
corn_stats_21<-dplyr::mutate(corn_stats_21, Rep = case_when(
Rep ==1 ~ 5,
Rep ==2 ~ 6,
Rep ==3 ~ 7,
Rep ==4 ~ 8,
))
corn_stats_21 <- corn_stats_21[,-c(5,6)]
# corn_stats_21[,c(5:ncol(corn_stats_21))] <- sapply(corn_stats_21[,c(5:ncol(corn_stats_21))], function(x) as.numeric(as.character(x)))
dat_loc2<-paste0(Github_loc,"/Data_2020/")
corn_stats_20 <- read.csv(paste0(dat_loc2, "Corn2020_dat.csv"))
corn_stats_20 <- corn_stats_20[-c(5:7)]
source('F:/My Drive/Tidewater/Tidewater Data Analysis/NUE_WUE_FUNCTION.R', echo=TRUE)
Corn_data_2020 <- Corn_data(2020)
Corn_data_2021 <- Corn_data(2021)
Corn_data_2020$Year = 2020
Corn_data_2021$Year = 2021
Corn_data_2021 <- filter(Corn_data_2021, Nuptake < 390) ##########Remove outliers
rm(corn_stats_21,corn_stats_20)
#############################################################################################################################
# Summary Tables
Yield_stats_20 <- summarySE(Corn_data_2020, measurevar="Yield", groupvars=c("Nrate","Irrigation","Treatment"))
Yield_stats_21 <- summarySE(Corn_data_2021, measurevar="Yield", groupvars=c("Nrate","Irrigation","Treatment"))
NUE_stats_20 <- summarySE(Corn_data_2020_NUE, measurevar="NUE", groupvars=c("Nrate","Irrigation","Treatment"))
NUE_stats_21 <- summarySE(Corn_data_2021, measurevar="NUE", groupvars=c("Nrate","Irrigation","Treatment"))
irrWUE_stats_20 <- summarySE(Corn_data_2020, measurevar="irrWUE", groupvars=c("Nrate","Irrigation","Treatment"))
irrWUE_stats_21 <- summarySE(Corn_data_2021, measurevar="irrWUE", groupvars=c("Nrate","Irrigation","Treatment"))
############################################################################################################################
#Generate Models for both years
Corn_data_2020$Nrate <- factor(Corn_data_2020$Nrate)
Corn_data_2020$Irrigation <- factor(Corn_data_2020$Irrigation)
Corn_data_2021$Nrate <- factor(Corn_data_2021$Nrate)
Corn_data_2021$Irrigation <- factor(Corn_data_2021$Irrigation)
# Corn_data_2020$Year =1
#########################################################################################
# Get all the models
# My thoughts here:
# 1. Don't merge the years Results between the years varies a lot for yield
# 2. Removed outliers by combining the years. In this way we are less aggressive in removing them
m1 = lmer(Yield ~ Nrate * Irrigation + (1 | Rep), data = Corn_data_2020)
m2 = lmer(Yield ~ Nrate * Irrigation + (1 | Rep), data = Corn_data_2021)
Corn_data_2020_NUE <- Corn_data_2020 %>%
filter(Nrate != "0")
Corn_data_2021_NUE <- Corn_data_2021 %>%
filter(Nrate != "0")
m3 = lmer(NUE ~ Nrate * Irrigation +(1 | Rep), data = Corn_data_2020_NUE)
m4 = lmer(NUE ~ Nrate * Irrigation +(1 | Rep), data = Corn_data_2021_NUE)
Corn_data_2020_WUE <- Corn_data_2020 %>%
filter(Irrigation != "None")
Corn_data_2021_WUE <- Corn_data_2021 %>%
filter(Irrigation != "None")
m5 = lmer(irrWUE ~ Nrate * Irrigation +(1 | Rep), data = Corn_data_2020_WUE)
m6 = lmer(irrWUE ~ Nrate * Irrigation +(1 | Rep), data = Corn_data_2021_WUE)
##########################
# Analysis of models
#
# 1. Check ANOVA to see if the interaction is significant else process to main effects
# 2. Apply predictmeans function for lsd and other summary
a1 <- anova(m1) # Nrate significant
a2 <- anova(m2) # Nrate significant
a3 <- anova(m3) # Interaction significant
a4 <- anova(m4) # None
a5 <- anova(m5) # None
a6 <- anova(m6) # None
library(emmeans)
TK1 <- emmeans(m1, list(pairwise ~ Nrate), adjust = "tukey")
TK2 <- emmeans(m2, list(pairwise ~ Nrate), adjust = "tukey")
TK3 <- emmeans(m3, list(pairwise ~ Nrate * Irrigation), adjust = "tukey")
# TK4 <- emmeans(m4, list(pairwise ~ Nrate), adjust = "tukey")
# TK5 <- emmeans(m5, list(pairwise ~ Irrigation), adjust = "tukey")
# TK6 <- emmeans(m6, list(pairwise ~ Irrigation), adjust = "tukey")
library(multcomp)
cld(TK1)
p_m1 <- predictmeans(m1, "Nrate", adj="tukey",pairwise=TRUE, barplot=FALSE, plot = FALSE)
p_m1
p_m1$mean_table
# ______________________________________________________________________________________________
cld(TK2)
p_m2 <- predictmeans(m2, "Nrate", adj="tukey",pairwise=TRUE, barplot=FALSE, plot = FALSE)
p_m2
p_m2$`Pairwise p-value`
# ______________________________________________________________________________________________
cld(TK3)
p_m3 <- predictmeans(m3, "Nrate:Irrigation", adj="tukey", barplot=FALSE, plot = FALSE)
p_m3
p_m3$mean_table
# # ______________________________________________________________________________________________
# cld(TK4)
#
# p_m4 <- predictmeans(m4, "Nrate", adj="tukey", barplot=FALSE, plot = FALSE)
# p_m4
#
# p_m4$mean_table
#
# # ______________________________________________________________________________________________
# cld(TK5)
#
# p_m5 <- predictmeans(m5, "Irrigation", adj="tukey", barplot=FALSE, plot = FALSE)
# p_m5
#
# p_m5$mean_table
#
# # ______________________________________________________________________________________________
# cld(TK6)
#
# p_m6 <- predictmeans(m6, "Irrigation", adj="tukey", barplot=FALSE, plot = FALSE)
# p_m6
#
# p_m6$mean_table
#
# __________________________________________________________________________________________________
Yield_20_sign <- cld(TK1)
Yield_20_sign$.group<- gsub("3", "a",Yield_20_sign$.group)
Yield_20_sign$.group<- gsub("2", "b",Yield_20_sign$.group)
Yield_20_sign$.group<- gsub("1", "c",Yield_20_sign$.group)
Yield_21_sign <- cld(TK2)
Yield_21_sign$.group<- gsub("4", "a",Yield_21_sign$.group)
Yield_21_sign$.group<- gsub("3", "b",Yield_21_sign$.group)
Yield_21_sign$.group<- gsub("2", "c",Yield_21_sign$.group)
Yield_21_sign$.group<- gsub("1", "d",Yield_21_sign$.group)
NUE_20_sign <- cld(TK3)
NUE_20_sign$.group<- gsub("2", "a",NUE_20_sign$.group)
NUE_20_sign$.group<- gsub("1", "b",NUE_20_sign$.group)
# save(Yield_20_sign,Yield_21_sign,NUE_20_sign, file = "Corn_signs.Rdata")
#######################################
# Do manual
# WUE_20_sign <- cld(TK5)
# WUE_20_sign$.group<- gsub("2", "a",WUE_20_sign$.group)
# WUE_20_sign$.group<- gsub("1", "b",WUE_20_sign$.group)
#
# WUE_21_sign <- cld(TK6)
# WUE_21_sign$.group<- gsub("2", "a",WUE_21_sign$.group)
# WUE_21_sign$.group<- gsub("1", "b",WUE_21_sign$.group)
#________________________________________________________________________________________________
#############################################################################################################################
# Summary Tables
Yield_stats_20 <- summarySE(Corn_data_2020, measurevar="Yield", groupvars=c("Nrate"))
Yield_stats_20 <-left_join(Yield_stats_20,Yield_20_sign[,c(1,7)], by = "Nrate")
Yield_stats_21 <- summarySE(Corn_data_2021, measurevar="Yield", groupvars=c("Nrate"))
Yield_stats_21 <-left_join(Yield_stats_21,Yield_21_sign[,c(1,7)], by = "Nrate")
NUE_stats_20 <- summarySE(Corn_data_2020_NUE, measurevar="NUE", groupvars=c("Nrate","Irrigation"))
NUE_stats_20 <-left_join(NUE_stats_20,NUE_20_sign[,c(1,2,8)], by = c("Nrate"= "Nrate","Irrigation"= "Irrigation"))
# NUE_stats_21 <- summarySE(Corn_data_2021, measurevar="NUE", groupvars=c("Nrate"))
# NUE_stats_21 <-left_join(NUE_stats_21,NUE_21_sign[,c(1,7)], by = "Nrate")
#
#
# irrWUE_stats_20 <- summarySE(Corn_data_2020, measurevar="irrWUE", groupvars=c("Irrigation"))
# irrWUE_stats_20 <-left_join(irrWUE_stats_20,WUE_20_sign[,c(1,7)], by = "Irrigation")
#
# irrWUE_stats_21 <- summarySE(Corn_data_2021, measurevar="irrWUE", groupvars=c("Irrigation"))
# irrWUE_stats_21 <-left_join(irrWUE_stats_21,WUE_21_sign[,c(1,7)], by = "Irrigation")
###############################################################################
# Significance plot
# Yield plots
# ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
# Yield
Yield_plot_CORN_2021<-Yield_stats_21 %>%
# filter(Irrigation != "3No Irrigation") %>%
ggplot(aes(x=as.factor(Nrate), y=Yield))+
geom_bar(stat = "identity", position=position_dodge(), colour="black", width = 0.75, alpha = 0.5, fill = "#9ecae1")+
geom_errorbar(aes(ymin=Yield-se, ymax=Yield+se),
size=0.5,
width=0.15, # Width of the error bars
position=position_dodge(.9))+
# geom_line(size=1)+
theme_bw()+
# scale_x_continuous(breaks=seq(80, 240, 80))+
scale_y_continuous(breaks=seq(0, 250, 50))+
theme(plot.title = element_text(size=12),
legend.position="top",
legend.title=element_blank(),
legend.box = "horizontal",
legend.background = element_rect(fill="white",
size=0.5, linetype="solid",
colour ="white"),
legend.text=element_text(size=12),
axis.text=element_text(size=12, colour="black"),
axis.title=element_text(size=12, colour="black"),
axis.line = element_line(colour = "black",
size = 0.5, linetype = "solid"),
axis.ticks = element_line(colour="black"),
panel.grid.major=element_line(colour = "light grey"),
panel.grid.minor=element_blank())+
scale_fill_manual(values=c("#bdbdbd", "#a6bddb", "#fee0d2"),
breaks=c("1Full Irrigation", "2Precision Irrigation", "3No Irrigation"),
labels=c("Full Irrigation", "Precision Irrigation", "No Irrigation"))+
guides(colour = guide_legend(override.aes = list(size=5)))+
labs(title="Corn Yield (2021)",
x="N Application (lb/acre)", y = "Yield (bu/ac)",
color=('Irrigation Treatment'),
# shape=('Irrigation Treatment'),
# linetype=('Irrigation Treatment')
)+
geom_text(aes(label=.group), position = position_dodge(0.90), size = 5,
vjust=-0.8, hjust=-0.5, colour = "gray25")
Yield_plot_CORN_2021
#ggsave(paste0(plot_loc,"Sig_Yield_plot_CORN_2021.png"),Yield_plot_CORN_2021, width = 7, height = 5, units="in", dpi = 600)
# ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
#
Yield_plot_CORN_2020<-Yield_stats_20 %>%
# filter(Irrigation != "3No Irrigation") %>%
ggplot(aes(x=as.factor(Nrate), y=Yield))+
geom_bar(stat = "identity", position=position_dodge(), colour="black",width = 0.75, alpha = 0.5, fill = "#9ecae1")+
geom_errorbar(aes(ymin=Yield-se, ymax=Yield+se),
size=0.5,
width=0.15, # Width of the error bars
position=position_dodge(.9))+
# geom_line(size=1)+
theme_bw()+
# scale_x_continuous(breaks=seq(80, 240, 80))+
scale_y_continuous(limits = c(0,250),breaks=seq(0, 250, 50))+
theme(plot.title = element_text(size=12),
legend.position="top",
legend.title=element_blank(),
legend.box = "horizontal",
legend.background = element_rect(fill="white",
size=0.5, linetype="solid",
colour ="white"),
legend.text=element_text(size=12),
axis.text=element_text(size=12, colour="black"),
axis.title=element_text(size=12, colour="black"),
axis.line = element_line(colour = "black",
size = 0.5, linetype = "solid"),
axis.ticks = element_line(colour="black"),
panel.grid.major=element_line(colour = "light grey"),
panel.grid.minor=element_blank())+
scale_fill_manual(values=c("#bdbdbd", "#a6bddb", "#fee0d2"),
breaks=c("1Full Irrigation", "2Precision Irrigation", "3No Irrigation"),
labels=c("Full Irrigation", "Precision Irrigation", "No Irrigation"))+
guides(colour = guide_legend(override.aes = list(size=5)))+
labs(title="Corn Yield (2020)",
x="N Application (lb/acre)", y = "Yield (bu/ac)",
color=('Irrigation Treatment'),
# shape=('Irrigation Treatment'),
# linetype=('Irrigation Treatment')
)+
geom_text(aes(label=.group), position = position_dodge(0.90), size = 5,
vjust=-0.8, hjust=-0.5, colour = "gray25")
Yield_plot_CORN_2020
#ggsave(paste0(plot_loc,"Sig_Yield_plot_CORN_2020.png"),Yield_plot_CORN_2021, width = 7, height = 5, units="in", dpi = 600)
# ========================================================================================================================================
NUE_plot_dat<-dplyr::mutate(NUE_stats_20, Irrigation = case_when(
Irrigation == "None" ~ "3No Irrigation",
Irrigation == "Full" ~ "1Full Irrigation",
Irrigation == "Environmental " ~ "2Precision Irrigation",
))
nue_plot_CORN_2020<-NUE_plot_dat %>%
filter(Nrate != 0) %>%
ggplot(aes(x=as.factor(Nrate), y=NUE*100, fill= Irrigation))+
geom_bar(stat = "identity", position=position_dodge(), colour="black",width = 0.75, alpha = 0.5)+
geom_errorbar(aes(ymin=NUE*100-se*100, ymax=NUE*100+se*100),
size=0.5,
width=0.15, # Width of the error bars
position=position_dodge(.9))+
# geom_line(size=1)+
theme_bw()+
scale_y_continuous(limits = c(0,100),breaks=seq(0, 100, 25))+
theme(plot.title = element_text(size=12),
legend.position="top",
legend.title=element_blank(),
legend.box = "horizontal",
legend.background = element_rect(fill="white",
size=0.5, linetype="solid",
colour ="white"),
legend.text=element_text(size=12),
axis.text=element_text(size=12, colour="black"),
axis.title=element_text(size=12, colour="black"),
axis.line = element_line(colour = "black",
size = 0.5, linetype = "solid"),
axis.ticks = element_line(colour="black"),
panel.grid.major=element_line(colour = "light grey"),
panel.grid.minor=element_blank())+
scale_fill_manual(values=c("#bdbdbd", "#a6bddb", "#fee0d2"),
breaks=c("1Full Irrigation", "2Precision Irrigation", "3No Irrigation"),
labels=c("Full Irrigation", "Precision Irrigation", "No Irrigation"))+
guides(colour = guide_legend(override.aes = list(size=5)))+
labs(title="Corn Nitrogen Use Efficiency (2020)",
x="N Application (lb/acre)", y = "NUE (%)",
color=('Irrigation Treatment'),
# shape=('Irrigation Treatment'),
# linetype=('Irrigation Treatment')
)+
geom_text(aes(label=.group), position = position_dodge(0.9), size = 5,
vjust=-2.75, hjust=0.75, colour = "gray25")
nue_plot_CORN_2020
#ggsave(paste0(plot_loc,"Sig_nue_plot_CORN_2020_Updated.png"),nue_plot_CORN_2020, width = 7, height = 5, units="in", dpi = 600)
library(ggpubr)
p<- ggarrange(nue_plot_CORN_2020,
ggarrange(Yield_plot_CORN_2020, Yield_plot_CORN_2021),
# labels = c("A", "B", "C"),
ncol = 1, nrow = 2)
p
#####################################################################################################
# ANOVA TABLES
##################################
# All ANOVA tables
final <- list(a1,a2,a3,a4,a5,a6)
aov.table = rlist::list.rbind(final)
aov.table <- aov.table[,-c(3,5,7,9,11)]
aov.table$sign[aov.table$`Pr(>F)` < 0.05] <- "*"
aov.table$sign[aov.table$`Pr(>F)` < 0.01] <- "**"
aov.table$sign[aov.table$`Pr(>F)` < 0.001] <- "***"
aov.table$sign[aov.table$`Pr(>F)` > 0.05] <- ""
aov.table$`Pr(>F)` <- round(aov.table$`Pr(>F)` ,3)
aov.table[[4]] = paste(aov.table[[4]],
ifelse(is.na(aov.table[[4]]), " ", aov.table[[5]]))
# colnames(aov.table)[2:7] <- m_names
colnames(aov.table)[1] <- "DF"
write.csv(aov.table, "All_Anova_Corn.csv")
#############################################################################################
# Based on significance proceed for post ad hoc
# p_m1 <- predictmeans(m1, "Nrate", adj="bonferroni",pairwise=TRUE, barplot=FALSE, plot = FALSE) #
#
# p_m2 <- predictmeans(m2, "Nrate", adj="bonferroni",pairwise=TRUE, barplot=FALSE, plot = FALSE)
#
# p_m3 <- predictmeans(m3, "Nrate:Irrigation", adj="bonferroni",pairwise=TRUE, barplot=FALSE, plot = FALSE)
#
# p_m4 <- predictmeans(m4, "Nrate", adj="bonferroni",pairwise=TRUE, barplot=FALSE, plot = FALSE)
#
# p_m5 <- predictmeans(m5, "Irrigation", adj="bonferroni",pairwise=TRUE, barplot=FALSE, plot = FALSE)
#
# p_m6 <- predictmeans(m6, "Irrigation", adj="bonferroni",pairwise=TRUE, barplot=FALSE, plot = FALSE)
#################################################################################################
residplot(m1, group = "none", level = 1, slope = FALSE, id = FALSE, newwd=TRUE,ask=FALSE)
residplot(m2, group = "none", level = 1, slope = FALSE, id = FALSE, newwd=TRUE, ask=FALSE)
residplot(m3, group = "none", level = 1, slope = FALSE, id = FALSE, newwd=TRUE, ask=FALSE)
residplot(m4, group = "none", level = 1, slope = FALSE, id = FALSE, newwd=TRUE, ask=FALSE)
residplot(m5, group = "none", level = 1, slope = FALSE, id = FALSE, newwd=TRUE, ask=FALSE)
residplot(m6, group = "none", level = 1, slope = FALSE, id = FALSE, newwd=TRUE, ask=FALSE)
###################################################################################################
# Cooks distance
# CookD(m1, group=NULL, plot=TRUE, idn=5, newwd=TRUE)
#
# cooksd <- cooks.distance(m1)
# sample_size <- nrow(Corn_data_2020)
# plot(cooksd, pch="*", cex=2, main="Influential Obs by Cooks distance") # plot cook's distance
# abline(h = 4/sample_size, col="red") # add cutoff line
# text(x=1:length(cooksd)+1, y=cooksd, labels=ifelse(cooksd>4/sample_size, names(cooksd),""), col="red") # add labels