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library(tidyverse)library(tidyverse)
#potatoes
#own
p_s <- c(0, 0, 0, 12, 12, 12, 26, 26, 26, 50, 50, 50, 0 ,30, 0, 30, 100)
p_y <- c(100, 100, 100, 87, 98.63, 47.22, 89.55, 54.69, 45.04, 81.18, 63.48, 30.35, 100, 81.27, 100, 110.75, 0)
df_p <- data.frame(p_s, p_y)
highlight_df_p <- df_p %>%
filter(p_y >100)
p <- ggplot(df_p, aes(x = p_s, y = p_y))+geom_point(color="cornflowerblue", size=4) + stat_smooth(method="lm", se=TRUE, fill=NA,
formula=y ~ poly(x, 3, raw=TRUE),colour="cornflowerblue",linetype = "dotted") + ylim(0, 130) +
geom_point(data=highlight_df_p, aes(x=p_s, y=p_y), color='red', size=4) + ggtitle("Potatoes") +
xlab("Level of shade (%)") + ylab("Relative Yield (%)") + theme(plot.title = element_text(hjust = 0.5)) +
theme_bw(base_size = 16)
p
#winter wheat
w_s <- c(0, 7, 28, 51, 55, 100)
w_y <- c(100, 120, 78, 119, 61, 0)
df_w <- data.frame(w_s, w_y)
highlight_df_w <- df_w %>%
filter(w_y > 100)
pw <- ggplot(df_w, aes(x = w_s, y = w_y)) + geom_point(color="cornflowerblue", size=4) + stat_smooth(method="lm", se=TRUE, fill=NA,
formula=y ~ poly(x, 3, raw=TRUE),colour="cornflowerblue",linetype = "dotted") + ylim(0, 120) +
geom_point(data=highlight_df_w, aes(x=w_s, y=w_y), color='red', size=4) + ggtitle("Winter Wheat") +
xlab("Level of shade (%)") + ylab("Relative Yield (%)") + theme(plot.title = element_text(hjust = 0.5)) +
theme_bw(base_size = 16)
pw
#corn
#own
p_s <- c(0, 12, 26, 50, 0, 5, 10, 100)
p_y <- c(100, 81.9, 81.14, 55.72, 100, 104.9, 96.9, 0)
df_p <- data.frame(p_s, p_y)
highlight_df_p <- df_p %>%
filter(p_y >100)
p <- ggplot(df_p, aes(x = p_s, y = p_y))+geom_point(color="cornflowerblue", size=4) + stat_smooth(method="lm", se=TRUE, fill=NA,
formula=y ~ poly(x, 4, raw=TRUE),colour="cornflowerblue",linetype = "dotted") + ylim(0, 120) +
geom_point(data=highlight_df_p, aes(x=p_s, y=p_y), color='red', size=4) + ggtitle("corn") +
xlab("Level of shade (%)") + ylab("Relative Yield (%)") + theme(plot.title = element_text(hjust = 0.5)) +
theme_bw(base_size = 16)
p
#gras clover
p_s <- c(0, 30, 0, 30, 0, 30, 50, 70, 0, 30, 50, 80, 100)
p_y <- c(100, 94.92, 100, 91.95, 100, 101.36, 81.08, 63.35, 100, 78.96, 80.4187, 37.09575, 0)
df_p <- data.frame(p_s, p_y)
highlight_df_p <- df_p %>%
filter(p_y >100)
p <- ggplot(df_p, aes(x = p_s, y = p_y))+geom_point(color="cornflowerblue", size=4) + stat_smooth(method="lm", se=TRUE, fill=NA,
formula=y ~ poly(x, 3, raw=TRUE),colour="cornflowerblue",linetype = "dotted") + ylim(0, 120) +
geom_point(data=highlight_df_p, aes(x=p_s, y=p_y), color='red', size=4) + ggtitle("grass clover") +
xlab("Level of shade (%)") + ylab("Relative Yield (%)") + theme(plot.title = element_text(hjust = 0.5)) +
theme_bw(base_size = 16)
p
#sugar beets
p_s <- c(0, 24.2129, 39.886, 0, 23.738, 31.9918, 100)
p_y <- c(100, 63.823, 27.021, 100, 82.071, 63.41, 0)
df_p <- data.frame(p_s, p_y)
highlight_df_p <- df_p %>%
filter(p_y >100)
p <- ggplot(df_p, aes(x = p_s, y = p_y))+geom_point(color="cornflowerblue", size=4) + stat_smooth(method="lm", se=TRUE, fill=NA,
formula=y ~ poly(x, 3, raw=TRUE),colour="cornflowerblue",linetype = "dotted") + ylim(0, 120) +
geom_point(data=highlight_df_p, aes(x=p_s, y=p_y), color='red', size=4) + ggtitle("Sugar beets") +
xlab("Level of shade (%)") + ylab("Relative Yield (%)") + theme(plot.title = element_text(hjust = 0.5)) +
theme_bw(base_size = 16)
p
#onions
p_s <- c(0, 30, 0, 30, 100)
p_y <- c(100, 80.58, 100, 90.51, 0)
df_p <- data.frame(p_s, p_y)
highlight_df_p <- df_p %>%
filter(p_y >100)
p <- ggplot(df_p, aes(x = p_s, y = p_y))+geom_point(color="cornflowerblue", size=4) + stat_smooth(method="lm", se=TRUE, fill=NA,
formula=y ~ poly(x, 3, raw=TRUE),colour="cornflowerblue",linetype = "dotted") + ylim(0, 120) +
geom_point(data=highlight_df_p, aes(x=p_s, y=p_y), color='red', size=4) + ggtitle("Onions") +
xlab("Level of shade (%)") + ylab("Relative Yield (%)") + theme(plot.title = element_text(hjust = 0.5)) +
theme_bw(base_size = 16)
p
#Barley
p_s <- c(0, 10, 50, 0, 25, 50, 0, 40, 90, 0, 40, 90, 100)
p_y <- c(100, 101.75, 189.84, 100, 101.7786, 118.774, 100, 82.8125, 75.3125, 100, 88.808, 73.2851, 0)
df_p <- data.frame(p_s, p_y)
highlight_df_p <- df_p %>%
filter(p_y >100)
p <- ggplot(df_p, aes(x = p_s, y = p_y))+geom_point(color="cornflowerblue", size=4) + stat_smooth(method="lm", se=TRUE, fill=NA,
formula=y ~ poly(x, 3, raw=TRUE),colour="cornflowerblue",linetype = "dotted") + ylim(0, 120) +
geom_point(data=highlight_df_p, aes(x=p_s, y=p_y), color='red', size=4) + ggtitle("Barley") +
xlab("Level of shade (%)") + ylab("Relative Yield (%)") + theme(plot.title = element_text(hjust = 0.5)) +
theme_bw(base_size = 16)
p
# Peas
p_s <- c(0, 19, 35, 0 , 19, 35, 100)
p_y <- c(100, 75, 23, 100, 99, 53, 0 )
df_p <- data.frame(p_s, p_y)
highlight_df_p <- df_p %>%
filter(p_y >100)
p <- ggplot(df_p, aes(x = p_s, y = p_y))+geom_point(color="cornflowerblue", size=4) + stat_smooth(method="lm", se=TRUE, fill=NA,
formula=y ~ poly(x, 4, raw=TRUE),colour="cornflowerblue",linetype = "dotted") + ylim(0, 120) +
geom_point(data=highlight_df_p, aes(x=p_s, y=p_y), color='red', size=4) + ggtitle("Peas") +
xlab("Level of shade (%)") + ylab("Relative Yield (%)") + theme(plot.title = element_text(hjust = 0.5)) +
theme_bw(base_size = 16)
p
#lettuce
p_s <- c(0, 30, 70, 0, 30, 70, 0, 35, 30, 20, 0, 35, 30, 20, 100 )
p_y <- c(100, 81, 58, 100 ,99, 79, 100, 77 ,78, 77, 100, 86, 69, 82, 0)
df_p <- data.frame(p_s, p_y)
highlight_df_p <- df_p %>%
filter(p_y >100)
p <- ggplot(df_p, aes(x = p_s, y = p_y))+geom_point(color="cornflowerblue", size=4) + stat_smooth(method="lm", se=TRUE, fill=NA,
formula=y ~ poly(x, 4, raw=TRUE),colour="cornflowerblue",linetype = "dotted") + ylim(0, 120) +
geom_point(data=highlight_df_p, aes(x=p_s, y=p_y), color='red', size=4) + ggtitle("Lettuce") +
xlab("Level of shade (%)") + ylab("Relative Yield (%)") + theme(plot.title = element_text(hjust = 0.5)) +
theme_bw(base_size = 16)
p
#spinach
w_s <- c(0, 24, 46, 51, 71, 100)
w_y <- c(100, 99, 76, 73, 48, 0)
df_w <- data.frame(w_s, w_y)
highlight_df_w <- df_w %>%
filter(w_y > 100)
pw <- ggplot(df_w, aes(x = w_s, y = w_y)) + geom_point(color="cornflowerblue", size=4) + stat_smooth(method="lm", se=TRUE, fill=NA,
formula=y ~ poly(x, 3, raw=TRUE),colour="cornflowerblue",linetype = "dotted") + ylim(0, 120) +
geom_point(data=highlight_df_w, aes(x=w_s, y=w_y), color='red', size=4) + ggtitle("Spinach") +
xlab("Level of shade (%)") + ylab("Relative Yield (%)") + theme(plot.title = element_text(hjust = 0.5)) +
theme_bw(base_size = 16)
pw
#green bean
w_s <- c(0, 24, 46, 51, 71, 0, 24, 46, 51, 71, 0, 24, 46, 51, 71, 100)
w_y <- c(100, 84, 77, 60, 45, 100, 85, 72, 61, 50, 100, 82, 55, 54, 50, 0)
df_w <- data.frame(w_s, w_y)
highlight_df_w <- df_w %>%
filter(w_y > 100)
pw <- ggplot(df_w, aes(x = w_s, y = w_y)) + geom_point(color="cornflowerblue", size=4) + stat_smooth(method="lm", se=TRUE, fill=NA,
formula=y ~ poly(x, 3, raw=TRUE),colour="cornflowerblue",linetype = "dotted") + ylim(0, 120) +
geom_point(data=highlight_df_w, aes(x=w_s, y=w_y), color='red', size=4) + ggtitle("Green bean") +
xlab("Level of shade (%)") + ylab("Relative Yield (%)") + theme(plot.title = element_text(hjust = 0.5)) +
theme_bw(base_size = 16)
pw
#Asparagus
w_s <- c(0, 24, 46, 51, 71, 100)
w_y <- c(100, 99, 79, 75, 48, 0)
df_w <- data.frame(w_s, w_y)
highlight_df_w <- df_w %>%
filter(w_y > 100)
pw <- ggplot(df_w, aes(x = w_s, y = w_y)) + geom_point(color="cornflowerblue", size=4) + stat_smooth(method="lm", se=TRUE, fill=NA,
formula=y ~ poly(x, 3, raw=TRUE),colour="cornflowerblue",linetype = "dotted") + ylim(0, 120) +
geom_point(data=highlight_df_w, aes(x=w_s, y=w_y), color='red', size=4) + ggtitle("Asparagus") +
xlab("Level of shade (%)") + ylab("Relative Yield (%)") + theme(plot.title = element_text(hjust = 0.5)) +
theme_bw(base_size = 16)
pw
#Broccoli
w_s <- c(0, 35, 0, 35, 0, 35, 100)
w_y <- c(100, 93.33319333, 100, 106.2500752, 100, 82.25792794, 0)
df_w <- data.frame(w_s, w_y)
highlight_df_w <- df_w %>%
filter(w_y > 100)
pw <- ggplot(df_w, aes(x = w_s, y = w_y)) + geom_point(color="cornflowerblue", size=4) + stat_smooth(method="lm", se=TRUE, fill=NA,
formula=y ~ poly(x, 3, raw=TRUE),colour="cornflowerblue",linetype = "dotted") + ylim(0, 120) +
geom_point(data=highlight_df_w, aes(x=w_s, y=w_y), color='red', size=4) + ggtitle("Broccoli") +
xlab("Level of shade (%)") + ylab("Relative Yield (%)") + theme(plot.title = element_text(hjust = 0.5)) +
theme_bw(base_size = 16)
pw
#Cauliflower
w_s <- c(0, 38, 50, 60, 0, 38, 50, 60, 100)
w_y <- c(100 ,97.64705882, 95.52941176, 90.11764706, 100, 94.91525424, 91.52542373, 88.13559322, 0)
df_w <- data.frame(w_s, w_y)
highlight_df_w <- df_w %>%
filter(w_y > 100)
pw <- ggplot(df_w, aes(x = w_s, y = w_y)) + geom_point(color="cornflowerblue", size=4) + stat_smooth(method="lm", se=TRUE, fill=NA,
formula=y ~ poly(x, 3, raw=TRUE),colour="cornflowerblue",linetype = "dotted") + ylim(0, 120) +
geom_point(data=highlight_df_w, aes(x=w_s, y=w_y), color='red', size=4) + ggtitle("Cauliflower") +
xlab("Level of shade (%)") + ylab("Relative Yield (%)") + theme(plot.title = element_text(hjust = 0.5)) +
theme_bw(base_size = 16)
pw
#Celeriac
w_s <- c(0, 30, 0, 30, 100)
w_y <- c(100 , 81.1836, 100, 111.1416, 0)
df_w <- data.frame(w_s, w_y)
highlight_df_w <- df_w %>%
filter(w_y > 100)
pw <- ggplot(df_w, aes(x = w_s, y = w_y)) + geom_point(color="cornflowerblue", size=4) + stat_smooth(method="lm", se=TRUE, fill=NA,
formula=y ~ poly(x, 3, raw=TRUE),colour="cornflowerblue",linetype = "dotted") + ylim(0, 120) +
geom_point(data=highlight_df_w, aes(x=w_s, y=w_y), color='red', size=4) + ggtitle("Celereiac") +
xlab("Level of shade (%)") + ylab("Relative Yield (%)") + theme(plot.title = element_text(hjust = 0.5)) +
theme_bw(base_size = 16)
pw
#Cabbage
w_s <- c(0, 45, 0, 45, 100)
w_y <- c(100 , 90.3, 100, 98.8, 0)
df_w <- data.frame(w_s, w_y)
highlight_df_w <- df_w %>%
filter(w_y > 100)
pw <- ggplot(df_w, aes(x = w_s, y = w_y)) + geom_point(color="cornflowerblue", size=4) + stat_smooth(method="lm", se=TRUE, fill=NA,
formula=y ~ poly(x, 3, raw=TRUE),colour="cornflowerblue",linetype = "dotted") + ylim(0, 120) +
geom_point(data=highlight_df_w, aes(x=w_s, y=w_y), color='red', size=4) + ggtitle("Cabbage") +
xlab("Level of shade (%)") + ylab("Relative Yield (%)") + theme(plot.title = element_text(hjust = 0.5)) +
theme_bw(base_size = 16)
pw
#Apples
w_s <- c(0, 50, 0, 50, 0, 40, 100)
w_y <- c(100 , 68, 100, 73, 100, 190, 0)
df_w <- data.frame(w_s, w_y)
highlight_df_w <- df_w %>%
filter(w_y > 100)
pw <- ggplot(df_w, aes(x = w_s, y = w_y)) + geom_point(color="cornflowerblue", size=4) + stat_smooth(method="lm", se=TRUE, fill=NA,
formula=y ~ poly(x, 2, raw=TRUE),colour="cornflowerblue",linetype = "dotted") + ylim(0, 200) +
geom_point(data=highlight_df_w, aes(x=w_s, y=w_y), color='red', size=4) + ggtitle("Apples") +
xlab("Level of shade (%)") + ylab("Relative Yield (%)") + theme(plot.title = element_text(hjust = 0.5)) +
theme_bw(base_size = 16)
pwlibrary(tidyverse)
library(broom)
library(GGally)
#creating dataframe with potatoes
dat1 <- data.frame(1:17)
dat1 <- dat1 %>%
rename(index=X1.17)
dat1$Crop <- c("Potatoes")
dat1$shadow_yield <- c(0, 0, 0, 12, 12, 12, 26, 26, 26, 50, 50, 50, 0 ,30, 0, 30, 100)
dat1$crop_yield <- c(100, 100, 100, 87, 98.63, 47.22, 89.55, 54.69, 45.04, 81.18, 63.48, 30.35, 100, 81.27, 100, 110.75, 0)
dat1 <- dat1[,-1]
#winter wheat
dat2 <- data.frame(1:6)
dat2 <- dat2 %>%
rename(index=X1.6)
dat2$Crop <- c("Winter wheat")
dat2$shadow_yield <- c(0, 7, 28, 51, 55, 100)
dat2$crop_yield <- c(100, 120, 78, 119, 61, 0)
dat2 <- dat2[,-1]
dat1 <- rbind(dat1, dat2)
#gras clover
dat2 <- data.frame(1:13)
dat2 <- dat2 %>%
rename(index=X1.13)
dat2$Crop <- c("Grass clover")
dat2$shadow_yield <- c(0, 30, 0, 30, 0, 30, 50, 70, 0, 30, 50, 80, 100)
dat2$crop_yield <- c(100, 94.92, 100, 91.95, 100, 101.36, 81.08, 63.35, 100, 78.96, 80.4187, 37.09575, 0)
dat2 <- dat2[,-1]
dat1 <- rbind(dat1, dat2)
#corn
dat2 <- data.frame(1:8)
dat2 <- dat2 %>%
rename(index=X1.8)
dat2$Crop <- c("Corn")
dat2$shadow_yield <- c(0, 12, 26, 50, 0, 5, 10, 100)
dat2$crop_yield <- c(100, 81.9, 81.14, 55.72, 100, 104.9, 96.9, 0)
dat2 <- dat2[,-1]
dat1 <- rbind(dat1, dat2)
#sugar beets
dat2 <- data.frame(1:7)
dat2 <- dat2 %>%
rename(index=X1.7)
dat2$Crop <- c("Sugar beets")
dat2$shadow_yield <- c(0, 24.2129, 39.886, 0, 23.738, 31.9918, 100)
dat2$crop_yield <- c(100, 63.823, 27.021, 100, 82.071, 63.41, 0)
dat2 <- dat2[,-1]
dat1 <- rbind(dat1, dat2)
#onions
dat2 <- data.frame(1:5)
dat2 <- dat2 %>%
rename(index=X1.5)
dat2$Crop <- c("Onions")
dat2$shadow_yield <- c(0, 30, 0, 30, 100)
dat2$crop_yield <- c(100, 80.58, 100, 90.51, 0)
dat2 <- dat2[,-1]
dat1 <- rbind(dat1, dat2)
#Barley
dat2 <- data.frame(1:13)
dat2 <- dat2 %>%
rename(index=X1.13)
dat2$Crop <- c("Barley")
dat2$shadow_yield <- c(0, 10, 50, 0, 25, 50, 0, 40, 90, 0, 40, 90, 100)
dat2$crop_yield <- c(100, 101.75, 189.84, 100, 101.7786, 118.774, 100, 82.8125, 75.3125, 100, 88.808, 73.2851, 0)
dat2 <- dat2[,-1]
dat1 <- rbind(dat1, dat2)
# Peas
dat2 <- data.frame(1:7)
dat2 <- dat2 %>%
rename(index=X1.7)
dat2$Crop <- c("Peas")
dat2$shadow_yield <- c(0, 19, 35, 0 , 19, 35, 100)
dat2$crop_yield <- c(100, 75, 23, 100, 99, 53, 0 )
dat2 <- dat2[,-1]
dat1 <- rbind(dat1, dat2)
#lettuce
dat2 <- data.frame(1:15)
dat2 <- dat2 %>%
rename(index=X1.15)
dat2$Crop <- c("Lettuce")
dat2$shadow_yield <- c(0, 30, 70, 0, 30, 70, 0, 35, 30, 20, 0, 35, 30, 20, 100 )
dat2$crop_yield <- c(100, 81, 58, 100 ,99, 79, 100, 77 ,78, 77, 100, 86, 69, 82, 0)
dat2 <- dat2[,-1]
dat1 <- rbind(dat1, dat2)
#spinach
dat2 <- data.frame(1:6)
dat2 <- dat2 %>%
rename(index=X1.6)
dat2$Crop <- c("Spinach")
dat2$shadow_yield <- c(0, 24, 46, 51, 71, 100)
dat2$crop_yield <- c(100, 99, 76, 73, 48, 0)
dat2 <- dat2[,-1]
dat1 <- rbind(dat1, dat2)
#green bean
dat2 <- data.frame(1:16)
dat2 <- dat2 %>%
rename(index=X1.16)
dat2$Crop <- c("Green beans")
dat2$shadow_yield <- c(0, 24, 46, 51, 71, 0, 24, 46, 51, 71, 0, 24, 46, 51, 71, 100)
dat2$crop_yield <- c(100, 84, 77, 60, 45, 100, 85, 72, 61, 50, 100, 82, 55, 54, 50, 0)
dat2 <- dat2[,-1]
dat1 <- rbind(dat1, dat2)
#Asparagus
dat2 <- data.frame(1:6)
dat2 <- dat2 %>%
rename(index=X1.6)
dat2$Crop <- c("Asparagus")
dat2$shadow_yield <- c(0, 24, 46, 51, 71, 100)
dat2$crop_yield <- c(100, 99, 79, 75, 48, 0)
dat2 <- dat2[,-1]
dat1 <- rbind(dat1, dat2)
#Broccoli
dat2 <- data.frame(1:7)
dat2 <- dat2 %>%
rename(index=X1.7)
dat2$Crop <- c("Broccoli")
dat2$shadow_yield <- c(0, 35, 0, 35, 0, 35, 100)
dat2$crop_yield <- c(100, 93.33319333, 100, 106.2500752, 100, 82.25792794, 0)
dat2 <- dat2[,-1]
dat1 <- rbind(dat1, dat2)
#Cauliflower
dat2 <- data.frame(1:9)
dat2 <- dat2 %>%
rename(index=X1.9)
dat2$Crop <- c("Cauliflower")
dat2$shadow_yield <- c(0, 38, 50, 60, 0, 38, 50, 60, 100)
dat2$crop_yield <- c(100 ,97.64705882, 95.52941176, 90.11764706, 100, 94.91525424, 91.52542373, 88.13559322, 0)
dat2 <- dat2[,-1]
dat1 <- rbind(dat1, dat2)
#Celeriac
dat2 <- data.frame(1:5)
dat2 <- dat2 %>%
rename(index=X1.5)
dat2$Crop <- c("Celeriac")
dat2$shadow_yield <- c(0, 30, 0, 30, 100)
dat2$crop_yield <- c(100 , 81.1836, 100, 111.1416, 0)
dat2 <- dat2[,-1]
dat1 <- rbind(dat1, dat2)
#Cabbage
dat2 <- data.frame(1:5)
dat2 <- dat2 %>%
rename(index=X1.5)
dat2$Crop <- c("Cabbage")
dat2$shadow_yield <- c(0, 45, 0, 45, 100)
dat2$crop_yield <- c(100 , 90.3, 100, 98.8, 0)
dat2 <- dat2[,-1]
dat1 <- rbind(dat1, dat2)
#Fitting with NLS
#plot results
#show(dat1)
g1 <- ggplot(dat1, aes(x = shadow_yield, y = crop_yield, color = Crop)) + geom_point(size = 3) + ggtitle("Crops yield vs shadow") + xlab("Level of shade (%)") + ylab("Relative Yield (%)") + theme(plot.title = element_text(hjust = 0.5)) + ylim(0, 120)+
theme_bw(base_size = 16)
g1
highlight_df_p <- dat1 %>%
filter(crop_yield >100)
#show(highlight_df_p)
g2 <- ggplot(dat1, aes(x = shadow_yield, y = crop_yield)) + geom_point(color="cornflowerblue", size = 3) +
geom_smooth(method = "lm" ,linetype = "dotted") +
#geom_point(data=highlight_df_p, aes(x = shadow_yield, y = crop_yield, color='red', size=3) +
geom_point(data=highlight_df_p, aes( x = shadow_yield, y = crop_yield), color='red', size=3) +
ggtitle("Crops yield vs shadow") + xlab("Level of shade (%)") + ylab("Relative Yield (%)") +
theme(plot.title = element_text(hjust = 0.5)) + ylim(0, 120) +
theme_bw(base_size = 16)
g2
g3 <- ggplot(dat1, aes(x = shadow_yield, y = crop_yield)) + geom_point(color="cornflowerblue", size = 1.5) +
geom_smooth(method = "lm" ,linetype = "dotted" , se= F) +
#geom_point(data=highlight_df_p, aes(x = shadow_yield, y = crop_yield, color='red', size=3) +
#geom_point(data=highlight_df_p, aes( x = shadow_yield, y = crop_yield), color='red', size=3) +
ggtitle("Crops yield vs shadow") + xlab("Level of shade (%)") + ylab("Relative Yield (%)") +
theme(plot.title = element_text(hjust = 0.5)) + ylim(0, 120) +
theme_bw(base_size = 16) + geom_vline(xintercept = 30, linetype = "dotted" , color = 'red') + facet_wrap(vars(Crop))
g3
#geom_text(data = data.frame(shadow_yield = 30, crop_yield = predict(lm(crop_yield ~ shadow_yield, dat1), newdata = data.frame(shadow_yield = 30))), aes(x = shadow_yield, y = crop_yield, label = paste(round(crop_yield, 2), "%")), color = "red", size = 6) + facet_wrap(vars(Crop))
dat1 <- dat1[-59,]
reg <- lm(crop_yield~shadow_yield, data = dat1)
summary(reg)
res <- reg$residuals
#g4 <- ggplot(data.frame(res)) + geom_qq() + geom_qq_line()
plot(reg)
shapiro.test(res)
fit <- reg$fitted.values
cook <- cooks.distance(reg)
# repeat for the numner of reps
reps <- 5000
n.data <- nrow(dat1)
8
#initialize intercept and slope
interc <- 0
slope <- 0
for (count in 1:reps) {
#Randomly select a vector of row indices (same size as data set)
row_index <- sample(1:n.data, replace = TRUE, size = n.data)
#resample MwH and CDD at the rows defined in row-index
dat1_star <- dat1[row_index, ]
#calculate slope and intercept for the new samples
interc[count] <- lm(crop_yield ~ shadow_yield, data = dat1_star)$coefficients[1]
slope[count] <- lm(crop_yield ~ shadow_yield, data = dat1_star)$coefficients[2]
}
ggplot(data = data.frame(slope), aes(x= slope)) + geom_histogram()
# calculate the standard deviation of all slope and intercepts
se_slope <- sd(slope)
se_interc <- sd(interc)
# Bootstrap 0.025 and 0.975 quantiles for the confidence interval
conf_slope_bs <- quantile(slope, probs = c(0.025,0.975))
conf_int_bs <- quantile(interc, probs = c(0.025,0.975))
# direct calculation of cf
#conf_slope <- 97.988 + qt(c(0.025, 0.972), df = 873)* 1.167
#conf_int <- 372.949 + qt(c(0.025, 0.972), df = 873)* 22.370
# pw <- ggplot(df_w, aes(x = w_s, y = w_y)) + geom_point(color="cornflowerblue", size=4) + stat_smooth(method="lm", se=TRUE, fill=NA,
# formula=y ~ poly(x, 2, raw=TRUE),colour="cornflowerblue",linetype = "dotted") + ylim(0, 200) +
# geom_point(data=highlight_df_w, aes(x=w_s, y=w_y), color='red', size=4) + ggtitle("Apples") +
# xlab("Level of shade (%)") + ylab("Relative Yield (%)") + theme(plot.title = element_text(hjust = 0.5)) +
# theme_bw(base_size = 16)
#pw
library(tidyverse)
#potatoes
#own
p_s <- c(0, 0, 0, 12, 12, 12, 26, 26, 26, 50, 50, 50, 0 ,30, 0, 30, 100)
p_y <- c(100, 100, 100, 87, 98.63, 47.22, 89.55, 54.69, 45.04, 81.18, 63.48, 30.35, 100, 81.27, 100, 110.75, 0)
#Wur
#p_s <- c(0, 23, 24, 25, 50, 100)
#p_y <- c(100, 80, 120, 75, 56, 0)
df_p <- data.frame(p_s, p_y)
highlight_df_p <- df_p %>%
filter(p_y >100)
p <- ggplot(df_p, aes(x = p_s, y = p_y))+geom_point(color="cornflowerblue", size=4) +
geom_smooth(method = "lm" ,linetype = "dotted" , se= F) +
ggtitle("Potatoes") + xlab("Level of shade (%)") + ylab("Relative Yield (%)") +
theme(plot.title = element_text(hjust = 0.5)) +theme_bw(base_size = 16) +
geom_text(data = data.frame(p_s = 30, p_y = predict(lm(p_y ~ p_y, dat1), newdata = data.frame(p_s = 30))), aes(x = p_s, y = p_y, label = paste(round(p_y, 2), "%")), color = "red", size = 6)
p
#winter wheat
w_s <- c(0, 7, 28, 51, 55, 100)
w_y <- c(100, 120, 78, 119, 61, 0)
df_w <- data.frame(w_s, w_y)
highlight_df_w <- df_w %>%
filter(w_y > 100)
pw <- ggplot(df_w, aes(x = w_s, y = w_y)) + geom_point(color="cornflowerblue", size=4) + geom_smooth(method = "lm" ,linetype = "dotted" , se= F) + ylim(0, 120) +
geom_point(data=highlight_df_w, aes(x=w_s, y=w_y), color='red', size=4) + ggtitle("Winter Wheat") +
xlab("Level of shade (%)") + ylab("Relative Yield (%)") + theme(plot.title = element_text(hjust = 0.5)) +
theme_bw(base_size = 16) +
geom_text(data = data.frame(w_s = 30, w_y = predict(lm(w_y ~ w_y, dat1), newdata = data.frame(w_s = 30))), aes(x = w_s, y = w_y, label = paste(round(w_y, 2), "%")), color = "red", size = 6)
pw
#corn
#own
p_s <- c(0, 12, 26, 50, 0, 5, 10, 100)
p_y <- c(100, 81.9, 81.14, 55.72, 100, 104.9, 96.9, 0)
df_p <- data.frame(p_s, p_y)
highlight_df_p <- df_p %>%
filter(p_y >100)
p <- ggplot(df_p, aes(x = p_s, y = p_y))+geom_point(color="cornflowerblue", size=4) + geom_smooth(method = "lm" ,linetype = "dotted" , se= F) + ylim(0, 120) +
geom_point(data=highlight_df_p, aes(x=p_s, y=p_y), color='red', size=4) + ggtitle("Corn") +
xlab("Level of shade (%)") + ylab("Relative Yield (%)") + theme(plot.title = element_text(hjust = 0.5)) +
theme_bw(base_size = 16) +
geom_text(data = data.frame(p_s = 30, p_y = predict(lm(p_y ~ p_y, dat1), newdata = data.frame(p_s = 30))), aes(x = p_s, y = p_y, label = paste(round(p_y, 2), "%")), color = "red", size = 6)
p
#gras clover
p_s <- c(0, 30, 0, 30, 0, 30, 50, 70, 0, 30, 50, 80, 100)
p_y <- c(100, 94.92, 100, 91.95, 100, 101.36, 81.08, 63.35, 100, 78.96, 80.4187, 37.09575, 0)
df_p <- data.frame(p_s, p_y)
highlight_df_p <- df_p %>%
filter(p_y >100)
p <- ggplot(df_p, aes(x = p_s, y = p_y))+geom_point(color="cornflowerblue", size=4) + geom_smooth(method = "lm" ,linetype = "dotted" , se= F) + ylim(0, 120) +
geom_point(data=highlight_df_p, aes(x=p_s, y=p_y), color='red', size=4) + ggtitle("Grass") +
xlab("Level of shade (%)") + ylab("Relative Yield (%)") + theme(plot.title = element_text(hjust = 0.5)) +
theme_bw(base_size = 16) +
geom_text(data = data.frame(p_s = 30, p_y = predict(lm(p_y ~ p_y, dat1), newdata = data.frame(p_s = 30))), aes(x = p_s, y = p_y, label = paste(round(p_y, 2), "%")), color = "red", size = 6)
p
#sugar beets
p_s <- c(0, 24.2129, 39.886, 0, 23.738, 31.9918, 100)
p_y <- c(100, 63.823, 27.021, 100, 82.071, 63.41, 0)
df_p <- data.frame(p_s, p_y)
highlight_df_p <- df_p %>%
filter(p_y >100)
p <- ggplot(df_p, aes(x = p_s, y = p_y))+geom_point(color="cornflowerblue", size=4) + geom_smooth(method = "lm" ,linetype = "dotted" , se= F) + ylim(0, 120) +
geom_point(data=highlight_df_p, aes(x=p_s, y=p_y), color='red', size=4) + ggtitle("Sugar beets") +
xlab("Level of shade (%)") + ylab("Relative Yield (%)") + theme(plot.title = element_text(hjust = 0.5)) +
theme_bw(base_size = 16) +
geom_text(data = data.frame(p_s = 30, p_y = predict(lm(p_y ~ p_y, dat1), newdata = data.frame(p_s = 30))), aes(x = p_s, y = p_y, label = paste(round(p_y, 2), "%")), color = "red", size = 6)
p
#onions
p_s <- c(0, 30, 0, 30, 100)
p_y <- c(100, 80.58, 100, 90.51, 0)
df_p <- data.frame(p_s, p_y)
highlight_df_p <- df_p %>%
filter(p_y >100)
p <- ggplot(df_p, aes(x = p_s, y = p_y))+geom_point(color="cornflowerblue", size=4) + geom_smooth(method = "lm" ,linetype = "dotted" , se= F) + ylim(0, 120) +
geom_point(data=highlight_df_p, aes(x=p_s, y=p_y), color='red', size=4) + ggtitle("Onions") +
xlab("Level of shade (%)") + ylab("Relative Yield (%)") + theme(plot.title = element_text(hjust = 0.5)) +
theme_bw(base_size = 16) +
geom_text(data = data.frame(p_s = 30, p_y = predict(lm(p_y ~ p_y, dat1), newdata = data.frame(p_s = 30))), aes(x = p_s, y = p_y, label = paste(round(p_y, 2), "%")), color = "red", size = 6)
p
#Barley
p_s <- c(0, 10, 50, 0, 25, 50, 0, 40, 90, 0, 40, 90, 100)
p_y <- c(100, 101.75, 189.84, 100, 101.7786, 118.774, 100, 82.8125, 75.3125, 100, 88.808, 73.2851, 0)
df_p <- data.frame(p_s, p_y)
highlight_df_p <- df_p %>%
filter(p_y >100)
p <- ggplot(df_p, aes(x = p_s, y = p_y))+geom_point(color="cornflowerblue", size=4) + geom_smooth(method = "lm" ,linetype = "dotted" , se= F) + ylim(0, 120) +
geom_point(data=highlight_df_p, aes(x=p_s, y=p_y), color='red', size=4) + ggtitle("Barley") +
xlab("Level of shade (%)") + ylab("Relative Yield (%)") + theme(plot.title = element_text(hjust = 0.5)) +
theme_bw(base_size = 16) +
geom_text(data = data.frame(p_s = 30, p_y = predict(lm(p_y ~ p_y, dat1), newdata = data.frame(p_s = 30))), aes(x = p_s, y = p_y, label = paste(round(p_y, 2), "%")), color = "red", size = 6)
p
# Peas
p_s <- c(0, 19, 35, 0 , 19, 35, 100)
p_y <- c(100, 75, 23, 100, 99, 53, 0 )
df_p <- data.frame(p_s, p_y)
highlight_df_p <- df_p %>%
filter(p_y >100)
p <- ggplot(df_p, aes(x = p_s, y = p_y))+geom_point(color="cornflowerblue", size=4) + geom_smooth(method = "lm" ,linetype = "dotted" , se= F) + ylim(0, 120) +
geom_point(data=highlight_df_p, aes(x=p_s, y=p_y), color='red', size=4) + ggtitle("Peas") +
xlab("Level of shade (%)") + ylab("Relative Yield (%)") + theme(plot.title = element_text(hjust = 0.5)) +
theme_bw(base_size = 16) +
geom_text(data = data.frame(p_s = 30, p_y = predict(lm(p_y ~ p_y, dat1), newdata = data.frame(p_s = 30))), aes(x = p_s, y = p_y, label = paste(round(p_y, 2), "%")), color = "red", size = 6)
p
#lettuce
p_s <- c(0, 30, 70, 0, 30, 70, 0, 35, 30, 20, 0, 35, 30, 20, 100 )
p_y <- c(100, 81, 58, 100 ,99, 79, 100, 77 ,78, 77, 100, 86, 69, 82, 0)
df_p <- data.frame(p_s, p_y)
highlight_df_p <- df_p %>%
filter(p_y >100)
p <- ggplot(df_p, aes(x = p_s, y = p_y))+geom_point(color="cornflowerblue", size=4) + geom_smooth(method = "lm" ,linetype = "dotted" , se= F) + ylim(0, 120) +
geom_point(data=highlight_df_p, aes(x=p_s, y=p_y), color='red', size=4) + ggtitle("Lettuce") +
xlab("Level of shade (%)") + ylab("Relative Yield (%)") + theme(plot.title = element_text(hjust = 0.5)) +
theme_bw(base_size = 16) +
geom_text(data = data.frame(p_s = 30, p_y = predict(lm(p_y ~ p_y, dat1), newdata = data.frame(p_s = 30))), aes(x = p_s, y = p_y, label = paste(round(p_y, 2), "%")), color = "red", size = 6)
p
#spinach
w_s <- c(0, 24, 46, 51, 71, 100)
w_y <- c(100, 99, 76, 73, 48, 0)
df_w <- data.frame(w_s, w_y)
highlight_df_w <- df_w %>%
filter(w_y > 100)
pw <- ggplot(df_w, aes(x = w_s, y = w_y)) + geom_point(color="cornflowerblue", size=4) +geom_smooth(method = "lm" ,linetype = "dotted" , se= F) + ylim(0, 120) +
geom_point(data=highlight_df_w, aes(x=w_s, y=w_y), color='red', size=4) + ggtitle("Spinach") +
xlab("Level of shade (%)") + ylab("Relative Yield (%)") + theme(plot.title = element_text(hjust = 0.5)) +
theme_bw(base_size = 16) +
geom_text(data = data.frame(w_s = 30, w_y = predict(lm(w_y ~ w_y, dat1), newdata = data.frame(w_s = 30))), aes(x = w_s, y = w_y, label = paste(round(w_y, 2), "%")), color = "red", size = 6)
pw
#green bean
w_s <- c(0, 24, 46, 51, 71, 0, 24, 46, 51, 71, 0, 24, 46, 51, 71, 100)
w_y <- c(100, 84, 77, 60, 45, 100, 85, 72, 61, 50, 100, 82, 55, 54, 50, 0)
df_w <- data.frame(w_s, w_y)
highlight_df_w <- df_w %>%
filter(w_y > 100)
pw <- ggplot(df_w, aes(x = w_s, y = w_y)) + geom_point(color="cornflowerblue", size=4) + geom_smooth(method = "lm" ,linetype = "dotted" , se= F) + ylim(0, 120) +
geom_point(data=highlight_df_w, aes(x=w_s, y=w_y), color='red', size=4) + ggtitle("Green bean") +
xlab("Level of shade (%)") + ylab("Relative Yield (%)") + theme(plot.title = element_text(hjust = 0.5)) +
theme_bw(base_size = 16) +
geom_text(data = data.frame(w_s = 30, w_y = predict(lm(w_y ~ w_y, dat1), newdata = data.frame(w_s = 30))), aes(x = w_s, y = w_y, label = paste(round(w_y, 2), "%")), color = "red", size = 6)
pw
#Asparagus
w_s <- c(0, 24, 46, 51, 71, 100)
w_y <- c(100, 99, 79, 75, 48, 0)
df_w <- data.frame(w_s, w_y)
highlight_df_w <- df_w %>%
filter(w_y > 100)
pw <- ggplot(df_w, aes(x = w_s, y = w_y)) + geom_point(color="cornflowerblue", size=4) + geom_smooth(method = "lm" ,linetype = "dotted" , se= F) + ylim(0, 120) +
geom_point(data=highlight_df_w, aes(x=w_s, y=w_y), color='red', size=4) + ggtitle("Asparagus") +
xlab("Level of shade (%)") + ylab("Relative Yield (%)") + theme(plot.title = element_text(hjust = 0.5)) +
theme_bw(base_size = 16) +
geom_text(data = data.frame(w_s = 30, w_y = predict(lm(w_y ~ w_y, dat1), newdata = data.frame(w_s = 30))), aes(x = w_s, y = w_y, label = paste(round(w_y, 2), "%")), color = "red", size = 6)
pw
#Broccoli
w_s <- c(0, 35, 0, 35, 0, 35, 100)
w_y <- c(100, 93.33319333, 100, 106.2500752, 100, 82.25792794, 0)
df_w <- data.frame(w_s, w_y)
highlight_df_w <- df_w %>%
filter(w_y > 100)
pw <- ggplot(df_w, aes(x = w_s, y = w_y)) + geom_point(color="cornflowerblue", size=4) + geom_smooth(method = "lm" ,linetype = "dotted" , se= F) + ylim(0, 120) +
geom_point(data=highlight_df_w, aes(x=w_s, y=w_y), color='red', size=4) + ggtitle("Broccoli") +
xlab("Level of shade (%)") + ylab("Relative Yield (%)") + theme(plot.title = element_text(hjust = 0.5)) +
theme_bw(base_size = 16) +
geom_text(data = data.frame(w_s = 30, w_y = predict(lm(w_y ~ w_y, dat1), newdata = data.frame(w_s = 30))), aes(x = w_s, y = w_y, label = paste(round(w_y, 2), "%")), color = "red", size = 6)
pw
#Cauliflower
w_s <- c(0, 38, 50, 60, 0, 38, 50, 60, 100)
w_y <- c(100 ,97.64705882, 95.52941176, 90.11764706, 100, 94.91525424, 91.52542373, 88.13559322, 0)
df_w <- data.frame(w_s, w_y)
highlight_df_w <- df_w %>%
filter(w_y > 100)
pw <- ggplot(df_w, aes(x = w_s, y = w_y)) + geom_point(color="cornflowerblue", size=4) + geom_smooth(method = "lm" ,linetype = "dotted" , se= F) + ylim(0, 120) +
geom_point(data=highlight_df_w, aes(x=w_s, y=w_y), color='red', size=4) + ggtitle("Cauliflower") +
xlab("Level of shade (%)") + ylab("Relative Yield (%)") + theme(plot.title = element_text(hjust = 0.5)) +
theme_bw(base_size = 16) +
geom_text(data = data.frame(w_s = 30, w_y = predict(lm(w_y ~ w_y, dat1), newdata = data.frame(w_s = 30))), aes(x = w_s, y = w_y, label = paste(round(w_y, 2), "%")), color = "red", size = 6)
pw
#Celeriac
w_s <- c(0, 30, 0, 30, 100)
w_y <- c(100 , 81.1836, 100, 111.1416, 0)
df_w <- data.frame(w_s, w_y)
highlight_df_w <- df_w %>%
filter(w_y > 100)
pw <- ggplot(df_w, aes(x = w_s, y = w_y)) + geom_point(color="cornflowerblue", size=4) + geom_smooth(method = "lm" ,linetype = "dotted" , se= F) + ylim(0, 120) +
geom_point(data=highlight_df_w, aes(x=w_s, y=w_y), color='red', size=4) + ggtitle("Celereiac") +
xlab("Level of shade (%)") + ylab("Relative Yield (%)") + theme(plot.title = element_text(hjust = 0.5)) +
theme_bw(base_size = 16) +
geom_text(data = data.frame(w_s = 30, w_y = predict(lm(w_y ~ w_y, dat1), newdata = data.frame(w_s = 30))), aes(x = w_s, y = w_y, label = paste(round(w_y, 2), "%")), color = "red", size = 6)
pw
#Cabbage
w_s <- c(0, 45, 0, 45, 100)
w_y <- c(100 , 90.3, 100, 98.8, 0)
df_w <- data.frame(w_s, w_y)
highlight_df_w <- df_w %>%
filter(w_y > 100)
pw <- ggplot(df_w, aes(x = w_s, y = w_y)) + geom_point(color="cornflowerblue", size=4) + geom_smooth(method = "lm" ,linetype = "dotted" , se= F) + ylim(0, 120) +
geom_point(data=highlight_df_w, aes(x=w_s, y=w_y), color='red', size=4) + ggtitle("Cabbage") +
xlab("Level of shade (%)") + ylab("Relative Yield (%)") + theme(plot.title = element_text(hjust = 0.5)) +
theme_bw(base_size = 16) +
geom_text(data = data.frame(w_s = 30, w_y = predict(lm(w_y ~ w_y, dat1), newdata = data.frame(w_s = 30))), aes(x = w_s, y = w_y, label = paste(round(w_y, 2), "%")), color = "red", size = 6)
pw
#Apples
w_s <- c(0, 50, 0, 50, 0, 40, 100)
w_y <- c(100 , 68, 100, 73, 100, 190, 0)
df_w <- data.frame(w_s, w_y)
highlight_df_w <- df_w %>%
filter(w_y > 100)
pw <- ggplot(df_w, aes(x = w_s, y = w_y)) + geom_point(color="cornflowerblue", size=4) + geom_smooth(method = "lm" ,linetype = "dotted" , se= F) + ylim(0, 200) +
geom_point(data=highlight_df_w, aes(x=w_s, y=w_y), color='red', size=4) + ggtitle("Apples") +
xlab("Level of shade (%)") + ylab("Relative Yield (%)") + theme(plot.title = element_text(hjust = 0.5)) +
theme_bw(base_size = 16) +
geom_text(data = data.frame(w_s = 30, w_y = predict(lm(w_y ~ w_y, dat1), newdata = data.frame(w_s = 30))), aes(x = w_s, y = w_y, label = paste(round(w_y, 2), "%")), color = "red", size = 6)
pw