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crop_model
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crop_model
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rm(list=ls())
input <- read.table(file="Weather_DesMoines.csv", header=TRUE, sep=";",dec=".")
# Average daily temperature (in °C)
# (display only first values)
head(input$T_DAILY_MEAN)
data<-input%>% # Best practice: line break after %>%,
dplyr::select( # then each new line indented by two spaces
tas = T_DAILY_MEAN,
rsds = SOLARAD_DAILY # New name = Old name
)
head(data)
data<-input%>%
dplyr::select(
tas = T_DAILY_MEAN,
rsds = SOLARAD_DAILY
)%>%
dplyr::mutate(
day_number = row_number() # Add a new column with day number
)
head(data)
day_sowing<-92 # Sowing after 1st May
day_harvest<-287 # Harvest ~ mid-october
data<-input%>%
dplyr::select(
tas = T_DAILY_MEAN,
rsds = SOLARAD_DAILY
)%>%
dplyr::mutate(
day_number = row_number()
)%>%
dplyr::filter(
day_number>=day_sowing,
day_number<=day_harvest
)
head(data)
ggplot2::ggplot(
data=data, # Name of the data frame to be used
aes(x=day_number, y=tas) # Specify x and y axis
)+
geom_point()+ # Add points to the plot
labs( # Customize labels
title = "Temperature evolution",
x = "Day number",
y = "Mean T° (°C)"
)
ggplot2::ggplot(
data=data,
aes(x=day_number, y=tas)
)+
geom_point(color="darkslategray3")+ # Change color of geom_point()
geom_smooth(color="darkslategray")+ # Add smoothing layer
labs(
title = "Temperature evolution",
x = "Day number",
y = "Mean T° (°C)"
)
T0<-6 # Set T0 for corn: 6°C
model<-data%>%
dplyr::mutate(
TT=dplyr::case_when(
tas<T0~0, # Condition 1 ~ Column value
tas>=T0~tas-T0 # Condition 2 ~ Volumn value
)
)
# Print first rows
head(model)
model<-data%>%
dplyr::mutate(
TT=dplyr::case_when(
tas<T0~0,
tas>=T0~tas-T0
)
)%>%
mutate(
GDD = cumsum(TT) # Cumulative sum of thermal time
)
# Print last rows
tail(model)
# Set parameters:
# Sum of T° for the emergence of 1 leaf
GDD_1leaf<-50
# Maximum number of leaves per plant
max_nleaf<-20
model<-data%>%
dplyr::mutate(
TT=dplyr::case_when(
tas<T0~0,
tas>=T0~tas-T0
)
)%>%
mutate(
GDD = cumsum(TT)
)%>%
# Potential number of leaves (no max values)
mutate(
pot_nleaf = GDD/GDD_1leaf
)%>%
# Estimated number of leaves (including max)
mutate(
nleaf = case_when(
pot_nleaf<=max_nleaf~round(pot_nleaf),
pot_nleaf>max_nleaf~max_nleaf
)
)
tail(model)
ggplot2::ggplot(
data=model,
aes(x=day_number, y=nleaf)
)+
geom_point(color="forestgreen")+
labs(
title = "Modelisation of the number of leaves",
x = "Day number",
y = "Number of leaves"
)
function_name <- function(arguments) {
instructions
return(results)
}
model_fun <- function(
name, # Scenario name
data, # Climatic variables to be used as inputs
GDD_1leaf # Thermal requirement for the emergence of one leaf
){
# Set parameters (without GDD_1leaf)
max_nleaf<-20
T0<-6
# Estimate nleaf
model<-data%>%
dplyr::mutate(
TT=dplyr::case_when(
tas<T0~0,
tas>=T0~tas-T0
))%>%
mutate(
GDD = cumsum(TT)
)%>%
mutate(
pot_nleaf = GDD/GDD_1leaf
)%>%
mutate(
nleaf = case_when(
pot_nleaf<=max_nleaf~round(pot_nleaf),
pot_nleaf>max_nleaf~max_nleaf
)
)%>%
add_column( # To add scenario name to data
Scenario = name # (set 'name' in argument)
)
return(model)
}
# Test the function for baseline scenario
baseline <- model_fun(name="Baseline",data=data,GDD_1leaf = 40)
tail(baseline)
baseline <- model_fun(
name="Baseline", data=data, GDD_1leaf = 50
)
breed <- model_fun(
name="Improved cultivar",data=data, GDD_1leaf = 40
)
comp<-rbind.data.frame( # Merging results
baseline, # before plotting
breed
)
ggplot(
data=comp,
aes(x=day_number,y=nleaf,color=Scenario) # Add color in aes()
)+
geom_point()+
labs(
title = "Comparison between two cultivars",
x = "Day number",
y = "Number of leaves"
)
# Load second datafile
input_sandstone <- read.table(file="Weather_Sandstone.csv", header=TRUE, sep=";",dec=".")
# Cleaning data
data_sandstone<-input_sandstone%>%
dplyr::select(
tas = T_DAILY_MEAN,
rsds = SOLARAD_DAILY
)%>%
dplyr::mutate(
day_number = row_number()
)%>%
dplyr::filter(
day_number>=day_sowing,
day_number<=day_harvest
)
# Apply function for both datasets
baseline <- model_fun(
name="DesMoines", data=data, GDD_1leaf = 50
)
sandstone <- model_fun(
name="Sandstone",data=data_sandstone, GDD_1leaf = 50
)
# Merging results before plotting
comp<-rbind.data.frame(
baseline,
sandstone
)
# Plotting
ggplot(
data=comp,
aes(x=day_number,y=nleaf,color=Scenario)
)+
geom_point()+
labs(
title = "Comparison between two cities",
x = "Day number",
y = "Number of leaves"
)
# Outside the function:
# Required parameters to compute C
# Light extinction coefficient
K <- 0.56
# Individual leaf area (m-2)
S <- 0.05
# Plant density (m-2)
d <- 90000/10000
# Model function
model_fun <- function(
name, # Scenario name
data, # Climatic variables to be used as inputs
GDD_1leaf, # Thermal requirement for the emergence of one leaf
C, # C=f(K,S,d)
RUE, # Radiation use efficiency (gDM.MJ-1)
nthresh # Number of leaves before grain filling
){
# Set parameters (without GDD_1leaf)
max_nleaf<-20
T0<-6
f<-0.5 # Active fraction of incoming radiation
frac<-0.7 # Fraction of Net Primary Productivity dedicated to grain
# Estimate yield
model<-data%>%
dplyr::mutate(
TT=dplyr::case_when(
tas<T0~0,
tas>=T0~tas-T0
))%>%
mutate(
GDD = cumsum(TT)
)%>%
mutate(
pot_nleaf = GDD/GDD_1leaf
)%>%
mutate(
nleaf = case_when(
pot_nleaf<=max_nleaf~round(pot_nleaf),
pot_nleaf>max_nleaf~max_nleaf
)
)%>%
# Incoming photosynthetic active radiation (MJ.m-2.day-1)
mutate(
PAR_inc = f*rsds
)%>%
# Absorbed PAR by the canopy (MJ.m-2.day-1)
mutate(
APAR = PAR_inc*(1-exp(-C*nleaf))
)%>%
# Net primary productivity dedicated to the aboveground biomass
mutate(
NPP = RUE*APAR
)%>%
# Sum of aboveground biomass
mutate(
biom = cumsum(NPP)
)%>%
# Net primary productivity dedicated to the variable grain
mutate(
NPPgrain = case_when(
nleaf<nthresh ~ 0,
nleaf>=nthresh ~ frac*NPP
)
)%>%
# Total grain production (g.m-2)
mutate(
grain = cumsum(NPPgrain)
)%>%
# Total grain production (t.ha-1)
mutate(
grain_t = grain/100
)%>%
add_column( # To add scenario name to data
Scenario = name # (set 'name' in argument)
)
return(model)
}
# Apply function for both datasets
baseline <- model_fun(
name="DesMoines",
data=data,
GDD_1leaf = 50,
C=K*S*d,
RUE=2,
nthresh = 16
)
sandstone <- model_fun(
name="Sandstone",
data=data_sandstone,
GDD_1leaf = 50,
C=K*S*d,
RUE=2,
nthresh = 16
)
# Merging results before plotting
comp<-rbind.data.frame(
baseline,
sandstone
)
# Plotting
ggplot(
data=comp,
aes(x=day_number,y=grain_t,color=Scenario)
)+
geom_point()+
labs(
title = "Comparison between two cities",
x = "Day number",
y = "Potential max yield (t.ha-1)"
)