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Modify R requirement
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@swood-ecology
swood-ecology committed Nov 3, 2022
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2 changes: 0 additions & 2 deletions DESCRIPTION
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Expand Up @@ -23,8 +23,6 @@ Suggests:
readr,
knitr,
rmarkdown
Depends:
R (>= 4.4.0)
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.2.1
Config/testthat/edition: 3
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302 changes: 151 additions & 151 deletions R/SNAP-Graze Model.R
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@@ -1,151 +1,151 @@
library(decisionSupport)
input_table <- "Grazing_impacts_on_soil_carbon.csv"
legend_file <- "Soil_carbon_dynamics_legend.csv"
results_folder <- "Results"
figures_folder <- "Compound_figures"

# Generate a function for testing the model function 'line by line'
# by taking a single random sample of the provided estimates
make_variables<-function(est,n=1)
{ x<-random(rho=est, n=n)
for(i in colnames(x)) assign(i, as.numeric(x[1,i]),envir=.GlobalEnv)}
make_variables(estimate_read_csv(input_table))

grazing_soil_carbon_dynamics <- function(x, varnames){
#Eq.(1)
aboveground_prod_absence_grazing<-12.04-25.18/(0.0083*(mean_temp+273.15))+
0.72*log(mean_precipitation)

#Eq.(2)
biomass_absence_grazing<-aboveground_prod_absence_grazing/0.9

#Eq.(3)
biomass_onset_growing_season<-0.1*biomass_absence_grazing

#Eq.(4)
biomass_start_grazing_episode<-(biomass_absence_grazing*
biomass_onset_growing_season)/
biomass_absence_grazing*2.718^-
relative_growth_rate*
time_prior_grazing_episode+
biomass_onset_growing_season*(1-2.718^-
relative_growth_rate*
time_prior_grazing_episode)
#Eq.(5)
length_of_plant_growing_season<-22.99*mean_temp-0.94*mean_temp^2+0.073*
mean_precipitation

#Eq.(6)
days_end_grazing_to_end_growing<-length_of_plant_growing_season-
(time_prior_grazing_episode+period_of_stay)

#Eq.(7)
stocking_density<-(number_of_animals/total_area)*10^-4

#Eq.(8)
per_animal_daily_consumption<-2*(5300+770*log(mass_grazing_animals))

#Eq.(9)
relative_loss_biomass_grazing<-stocking_density*per_animal_daily_consumption*
number_of_pastures*10^-4/
biomass_start_grazing_episode
#Eq.(10)
biomass_removed_grazing<-period_of_stay*stocking_density*
per_animal_daily_consumption*number_of_pastures*10^-4

#Eq.(11)
biomass_end_grazing_episode<-(biomass_absence_grazing*
biomass_start_grazing_episode/
biomass_absence_grazing*2.718^-
(relative_growth_rate-
relative_loss_biomass_grazing)*period_of_stay+
biomass_start_grazing_episode*(1-2.718^-(
relative_growth_rate-relative_loss_biomass_grazing)*
period_of_stay))-biomass_removed_grazing
# Eq.(12)
biomass_end_growing_season<-biomass_absence_grazing*
biomass_start_grazing_episode/
biomass_absence_grazing*2.718^-relative_growth_rate*
days_end_grazing_to_end_growing+
biomass_end_grazing_episode*(1-2.718^-
relative_growth_rate*
days_end_grazing_to_end_growing)

#Eq.(13)
aboveground_prod_under_grazing<-(biomass_start_grazing_episode-
biomass_onset_growing_season)+
(biomass_end_growing_season-
biomass_start_grazing_episode)

#Eq.(14)
belowground_prod_under_grazing<-(0.602*mean_precipitation-0.00038*
mean_precipitation^2+5.88*mean_temp)*
(aboveground_prod_under_grazing/
PU)*APC*(soil_sampling_depth/40)

#Eq.(15)
biomass_removed_dormant_season<-(per_animal_daily_consumption/2)*
(365-length_of_plant_growing_season)*
stocking_density*10^-4

#Eq.(16)
dung_derived_carbon_input<-lignin_and_cellulose_content*0.45*0.3*
(period_of_stay*per_animal_daily_consumption*
number_of_pastures*stocking_density+
biomass_removed_dormant_season)

#SOC calculations
baseline_carbon_stocks<-vv(sampling_depth*soil_organic_cab_conc*
bulk_density*(1-sand_percentage/100),
general_CV,n=n_years)
#Eq.(17)
daily_microbial_respiration<--0.579+0.00044*exp(10.18)*baseline_carbon_stocks^
1.298

#Eq.(18)
wetdays<-(0.00044*mean_precipitation-0.025)*length_of_plant_growing_season

#Eq.(19)
microbial_respiration_rate<-wetdays*(0.7+0.3*sand_percentage/100)*
daily_microbial_respiration

#Eq.(20)
plant_derived_soil_carbon<-0.45*(lignin_and_cellulose_content*
(biomass_end_growing_season-
biomass_removed_dormant_season/2)*
(1-fire_frequency)+
(lignin_and_cellulose_content+0.05)*
belowground_production)*
belowground_prod_under_grazing

#Eq.(21)
change_soil_carbon_density<-plant_derived_soil_carbon+
dung_derived_carbon_input-
microbial_respiration_rate
carbon_revenues<-change_soil_carbon_density*price_ton_carbon-
interventions_cost

carbon_stocks_NPV<-discount(carbon_revenues,carbon_discount_rate,
calculate_NPV=TRUE)

return(list(change_soil_carbon_density=change_soil_carbon_density,
cashflow_carbon_stocks_NPV=carbon_revenues,
carbon_project_NPV=carbon_stocks_NPV))
}

# Run the Monte Carlo using the decisionSupport function ####
decisionSupport(inputFilePath = input_table, #input file with estimates
outputPath = results_folder, #output folder
welfareFunction = grazing_soil_carbon_dynamics, #the function created above
numberOfModelRuns = 10000,
functionSyntax = "plainNames",
write_table = TRUE,)







# library(decisionSupport)
# input_table <- "Grazing_impacts_on_soil_carbon.csv"
# legend_file <- "Soil_carbon_dynamics_legend.csv"
# results_folder <- "Results"
# figures_folder <- "Compound_figures"
#
# # Generate a function for testing the model function 'line by line'
# # by taking a single random sample of the provided estimates
# make_variables<-function(est,n=1)
# { x<-random(rho=est, n=n)
# for(i in colnames(x)) assign(i, as.numeric(x[1,i]),envir=.GlobalEnv)}
# make_variables(estimate_read_csv(input_table))
#
# grazing_soil_carbon_dynamics <- function(x, varnames){
#
# #Eq.(1)
# aboveground_prod_absence_grazing<-12.04-25.18/(0.0083*(mean_temp+273.15))+
# 0.72*log(mean_precipitation)
#
# #Eq.(2)
# biomass_absence_grazing<-aboveground_prod_absence_grazing/0.9
#
# #Eq.(3)
# biomass_onset_growing_season<-0.1*biomass_absence_grazing
#
# #Eq.(4)
# biomass_start_grazing_episode<-(biomass_absence_grazing*
# biomass_onset_growing_season)/
# biomass_absence_grazing*2.718^-
# relative_growth_rate*
# time_prior_grazing_episode+
# biomass_onset_growing_season*(1-2.718^-
# relative_growth_rate*
# time_prior_grazing_episode)
# #Eq.(5)
# length_of_plant_growing_season<-22.99*mean_temp-0.94*mean_temp^2+0.073*
# mean_precipitation
#
# #Eq.(6)
# days_end_grazing_to_end_growing<-length_of_plant_growing_season-
# (time_prior_grazing_episode+period_of_stay)
#
# #Eq.(7)
# stocking_density<-(number_of_animals/total_area)*10^-4
#
# #Eq.(8)
# per_animal_daily_consumption<-2*(5300+770*log(mass_grazing_animals))
#
# #Eq.(9)
# relative_loss_biomass_grazing<-stocking_density*per_animal_daily_consumption*
# number_of_pastures*10^-4/
# biomass_start_grazing_episode
# #Eq.(10)
# biomass_removed_grazing<-period_of_stay*stocking_density*
# per_animal_daily_consumption*number_of_pastures*10^-4
#
# #Eq.(11)
# biomass_end_grazing_episode<-(biomass_absence_grazing*
# biomass_start_grazing_episode/
# biomass_absence_grazing*2.718^-
# (relative_growth_rate-
# relative_loss_biomass_grazing)*period_of_stay+
# biomass_start_grazing_episode*(1-2.718^-(
# relative_growth_rate-relative_loss_biomass_grazing)*
# period_of_stay))-biomass_removed_grazing
# # Eq.(12)
# biomass_end_growing_season<-biomass_absence_grazing*
# biomass_start_grazing_episode/
# biomass_absence_grazing*2.718^-relative_growth_rate*
# days_end_grazing_to_end_growing+
# biomass_end_grazing_episode*(1-2.718^-
# relative_growth_rate*
# days_end_grazing_to_end_growing)
#
# #Eq.(13)
# aboveground_prod_under_grazing<-(biomass_start_grazing_episode-
# biomass_onset_growing_season)+
# (biomass_end_growing_season-
# biomass_start_grazing_episode)
#
# #Eq.(14)
# belowground_prod_under_grazing<-(0.602*mean_precipitation-0.00038*
# mean_precipitation^2+5.88*mean_temp)*
# (aboveground_prod_under_grazing/
# PU)*APC*(soil_sampling_depth/40)
#
# #Eq.(15)
# biomass_removed_dormant_season<-(per_animal_daily_consumption/2)*
# (365-length_of_plant_growing_season)*
# stocking_density*10^-4
#
# #Eq.(16)
# dung_derived_carbon_input<-lignin_and_cellulose_content*0.45*0.3*
# (period_of_stay*per_animal_daily_consumption*
# number_of_pastures*stocking_density+
# biomass_removed_dormant_season)
#
# #SOC calculations
# baseline_carbon_stocks<-vv(sampling_depth*soil_organic_cab_conc*
# bulk_density*(1-sand_percentage/100),
# general_CV,n=n_years)
# #Eq.(17)
# daily_microbial_respiration<--0.579+0.00044*exp(10.18)*baseline_carbon_stocks^
# 1.298
#
# #Eq.(18)
# wetdays<-(0.00044*mean_precipitation-0.025)*length_of_plant_growing_season
#
# #Eq.(19)
# microbial_respiration_rate<-wetdays*(0.7+0.3*sand_percentage/100)*
# daily_microbial_respiration
#
# #Eq.(20)
# plant_derived_soil_carbon<-0.45*(lignin_and_cellulose_content*
# (biomass_end_growing_season-
# biomass_removed_dormant_season/2)*
# (1-fire_frequency)+
# (lignin_and_cellulose_content+0.05)*
# belowground_production)*
# belowground_prod_under_grazing
#
# #Eq.(21)
# change_soil_carbon_density<-plant_derived_soil_carbon+
# dung_derived_carbon_input-
# microbial_respiration_rate
#
# carbon_revenues<-change_soil_carbon_density*price_ton_carbon-
# interventions_cost
#
# carbon_stocks_NPV<-discount(carbon_revenues,carbon_discount_rate,
# calculate_NPV=TRUE)
#
# return(list(change_soil_carbon_density=change_soil_carbon_density,
# cashflow_carbon_stocks_NPV=carbon_revenues,
# carbon_project_NPV=carbon_stocks_NPV))
# }
#
# # Run the Monte Carlo using the decisionSupport function ####
# decisionSupport(inputFilePath = input_table, #input file with estimates
# outputPath = results_folder, #output folder
# welfareFunction = grazing_soil_carbon_dynamics, #the function created above
# numberOfModelRuns = 10000,
# functionSyntax = "plainNames",
# write_table = TRUE,)
#
#
#
#
#
#
#

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