rCTOOL is an open-source R package, that encapsulates the capabilities of C-TOOL (Petersen, Olesen, and Heidmann 2002; Taghizadeh-Toosi et al. 2014) while addressing its implementation limitations. The aim consists of providing a user-friendly interface, facilitates running multiple scenarios, and offers comprehensive documentation and licensing information. This package streamlines the use of C-TOOL, making it more accessible and effective for potential users.
You can install the development version of rCTOOL from GitHub with:
# install.packages("devtools")
devtools::install_github("francagiannini/rCTOOL")This is a simple example of the potential use of rCTOOL. The example corresponds to one of the treatments presented by (Jensen et al. 2021) and (Jensen et al. 2022) containing the C inputs for the treatment of the spring barley crop with 4 DM Mg/ha straw incorporated into the soil at a long-term experimental station Askov, Denmark.
library(rCTOOL)
library(tidyverse)
# load data ----
data('basic_example')
data('scenario_temperature') # this is equivalent to set_monthly_temperature_data(coords=c(9.114015, 55.47163), yr_start=1951, yr_end=2019)Below the basic_example and temperature data sets are exemplified.
head(basic_example, 2)
#> mon yrs id year Cin_top Cin_sub Cin_man manure_monthly_allocation
#> 1 1 1951 1 1951 3.566 0.39 0 0
#> 2 2 1951 1 1952 3.566 0.39 0 0
#> plant_monthly_allocation
#> 1 0
#> 2 0head(scenario_temperature, 2)
#> # A tibble: 2 × 6
#> # Groups: month [2]
#> month yr Tavg Tmin Tmax Range
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 1951 0.89 -0.7 2.48 3.18
#> 2 2 1951 1.17 -0.251 2.59 2.84It is mandatory to define the time period, Carbon inputs (from manure and/or plant), Management (months where the inputs are applied) and soil configurations as well as temperature.
In this basic example, the temperature was already exported using the package “easyclimate” for 1951-2019; basic_example contains the annual C inputs from manure and plants, as well as their respective monthly allocations.
# define timeperiod
period <- define_timeperiod(yr_start = 1951, yr_end = 2019)
# get annual Carbon inputs
cin <- define_Cinputs(management_filepath = basic_example)
# get management
management <- management_config(management_filepath = basic_example, f_man_humification = 0.192)
# get soil configuration
soil <- soil_config(Csoil_init = 105, # Initial C stock at 1m depth
f_hum_top = 0.533,
f_rom_top = 0.405,
f_hum_sub = 0.387,
f_rom_sub = 0.610,
Cproptop = 0.55, # landmarkensite report askov
clay_top = 0.11,
clay_sub = 0.20,
phi = 0.035,
f_co2 = 0.628,
f_romi = 0.012,
k_fom = 0.12,
k_hum = 0.0028,
k_rom = 3.85e-5,
ftr = 0.0025)We also need to initialize soil pools before the simulation starts. Initial soil pools depend on the Carbon:Nitrogen ratio, the humification and romification fractions in top- and subsoils as well as the initial C stock.
# initialize soil pools
soil_pools <- initialize_soil_pools(cn = 12, soil_config = soil)We can now start the monthly simulation. The verbose argument, currently set to FALSE, provides a check mass-balance to ensure the model is working correctly. [Note to improve the mass balance, currently wrong]
# run rCTOOL
output <- run_ctool(time_config = period,
cin_config = cin,
m_config = management,
t_config = scenario_temperature,
s_config = soil,
soil_pools = soil_pools,
verbose = F)We can plot the results.
output |>
mutate(time=make_date(year =yrs,month=mon)) |>
ggplot(aes(x=time,y=C_topsoil))+
geom_line()+
geom_smooth()+
theme_classic()
Let’s explore the output data frame.
Time period variables:
mon: month of the year (1-12)yrs: year of the simulation
C fluxes variables by depth (Mg/ha):
C_topsoil: Carbon stock in the topsoil (0-25 cm)C_subsoil: Carbon stock in the subsoil (26-100 cm)SOC_stock: Total Soil Organic Carbon stock in the soil (0-100 cm)C_transport: Carbon transported to the subsoilem_CO2_top: Total CO2 emissions from the topsoilem_CO2_sub: Total CO2 emissions from the subsoilem_CO2_total: Total CO2 emissions from the soil
C pools variables (Mg/ha):
Fresh Organic Matter (FOM) pool:
FOM_top: FOM in the topsoilFOM_top_decomposition: Monthly decomposition of FOM in the topsoilsubstrate_FOM_decomp_top: Substrate for FOM decomposition in the topsoilFOM_humified_top: FOM that has been “humified” in the topsoil (becomes part of topsoil HUM)em_CO2_FOM_top: CO2 emissions from the decomposition of FOM in the topsoilFOM_tr: FOM transported from the topsoil to the subsoilFOM_sub: FOM in the subsoilFOM_sub_decomposition: Decomposition of FOM in the subsoilsubstrate_FOM_decomp_sub: Substrate for FOM decomposition in the subsoilFOM_humified_sub: FOM that has been “humified” in the subsoil (becomes part of subsoil HUM)em_CO2_FOM_sub: CO2 emissions from the decomposition of FOM in the subsoil
Humified Organic Matter (HUM) pool:
HUM_top: HUM in the topsoilHUM_top_decomposition: Decomposition of HUM in the topsoilsubstrate_HUM_decomp_top: Substrate for HUM decomposition in the topsoilHUM_romified_top: HUM that has been “romified” in the topsoil (becomes part of topsoil ROM)em_CO2_HUM_top: CO2 emissions from the decomposition of HUM in the topsoilHUM_tr: HUM transported from the topsoil to the subsoilHUM_sub: HUM in the subsoilHUM_sub_decomposition: Decomposition of HUM in the subsoilsubstrate_HUM_decomp_sub: Substrate for HUM decomposition in the subsoilHUM_romified_sub: HUM that has been “romified” in the subsoil (becomes part of subsoil ROM)em_CO2_HUM_sub: CO2 emissions from the decomposition of HUM in the subsoil
Resistant Organic Matter (ROM) pool:
ROM_top: ROM in the topsoilROM_top_decomposition: Decomposition of ROM in the topsoilsubstrate_ROM_decomp_top: Substrate for ROM decomposition in the topsoilem_CO2_ROM_top: CO2 emissions from the decomposition of ROM in the topsoilROM_tr: ROM transported from the topsoil to the subsoilROM_sub: ROM in the subsoilROM_sub_decomposition: Decomposition of ROM in the subsoilsubstrate_ROM_decomp_sub: Substrate for ROM decomposition in the subsoilem_CO2_ROM_sub: CO2 emissions from the decomposition of ROM in the subsoil
Aiming to show how to run and compare multiple scenarios, we have set ourselves the following challenge:
We have inherited an old field where cereals have been grown for many many years and we want to increase C in Soil. We need to choose between…
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Creating a football pitch for students at AU Viborg recreation.
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Starting an organic dairy farm.
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Establishing a municipal (kommunal) pet cemetery…
Loading scenario management data for the there options.
# load data ----
data('scenario')
data('scenario_temperature') # this is equivalent to set_monthly_temperature_data(coords=c(9.114015, 55.47163), yr_start=1951, yr_end=2019)The scenario data set contains three different C input estimation for the different scenarios:
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For the football court scenario we assume a well-maintained stomped ryegrass cover,
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for the organic dairy farming we assume a crop rotation with grass, maize and cereals for happy milking cows,
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and finally for the pet cemetery we assume a less healthily reygrass and a certain number of beloved dogs and cats from Viborg municipality burred in the subsoil.
Now we will play with rCTOOL to explore the implications in terms of soil C dynamics.
First lets take a look on the C inputs distribution:
Then we provide a simulation parameters to the different modules time period, management, soil parameters and pools initial distribution.
period <- define_timeperiod(yr_start = 1951, yr_end = 2019)
management <- management_config(
manure_monthly_allocation = c(0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0),
plant_monthly_allocation = c(0, 0, 0, 8, 12, 16, 64, 0, 0, 0, 0, 0) / 100
) # set to default
soil <- soil_config(Csoil_init = 100,
f_hum_top = 0.4803,
f_rom_top = 0.4881,
f_hum_sub = 0.3123,
f_rom_sub = 0.6847,
Cproptop = 0.47,
clay_top = 0.1,
clay_sub = 0.15,
phi = 0.035,
f_co2 = 0.628,
f_romi = 0.012,
k_fom = 0.12,
k_hum = 0.0028,
k_rom = 3.85e-5,
ftr = 0.003)
soil_pools <- initialize_soil_pools(cn = 10, soil_config = soil)Then we provide configuration for running each of the three treatment/scenarios.
treatment <- unique(scenario$treatment)
cin_treatment <- lapply(treatment, function(x) { define_Cinputs(management_filepath = subset(scenario, treatment==x)) })
names(cin_treatment) <- treatmentFinally we run the simulation for each treatment/scenario.
output_treatment <- lapply(treatment, function(x) {
output <- run_ctool(time_config = period,
cin_config = cin_treatment[[x]],
m_config = management,
t_config = scenario_temperature,
s_config = soil,
soil_pools = soil_pools)
output$treatment = x
return(output)
})
output_treatment <- data.table::rbindlist(output_treatment)And the winning one is…
Lets explore:
plot_df <- output_treatment[, c('mon','yrs','C_topsoil','C_subsoil','em_CO2_total', 'treatment')]
plot_df <- reshape2::melt(plot_df, c('mon','yrs','treatment'))
labels <- c(
C_topsoil = 'SOC topsoil',
C_subsoil = 'SOC subsoil',
em_CO2_total = 'CO2 emissions'
)
ggplot(plot_df, aes(x=yrs, y=value, colour=treatment)) +
geom_point(size=0.02, alpha=0.2) +
geom_smooth() +
facet_wrap(variable~., scales = 'free_y', ncol = 1,
labeller = as_labeller(labels)) +
labs(x='Years', y='Output (Mg/ha)', colour='Treatment') +
scale_x_continuous(breaks=c(1960, 2010))+
theme_classic() 
Jensen, Johannes L., Jørgen Eriksen, Ingrid K. Thomsen, Lars J. Munkholm, and Bent T. Christensen. 2022. “Cereal Straw Incorporation and Ryegrass Cover Crops: The Path to Equilibrium in Soil Carbon Storage Is Short.” Journal Article. European Journal of Soil Science 73 (1). https://doi.org/10.1111/ejss.13173.
Jensen, Johannes L., Ingrid K. Thomsen, Jørgen Eriksen, and Bent T. Christensen. 2021. “Spring Barley Grown for Decades with Straw Incorporation and Cover Crops: Effects on Crop Yields and n Uptake.” Journal Article. Field Crops Research 270. https://doi.org/10.1016/j.fcr.2021.108228.
Petersen, Bjørn M., Jørgen E. Olesen, and Tove Heidmann. 2002. “A Flexible Tool for Simulation of Soil Carbon Turnover.” Journal Article. www.elsevier.com/locate/ecolmodel.
Taghizadeh-Toosi, Arezoo, Bent T. Christensen, Nicholas J. Hutchings, Jonas Vejlin, Thomas Kätterer, Margaret Glendining, and Jørgen E. Olesen. 2014. “C-TOOL: A Simple Model for Simulating Whole-Profile Carbon Storage in Temperate Agricultural Soils.” Journal Article. Ecological Modelling 292: 11–25. https://doi.org/https://doi.org/10.1016/j.ecolmodel.2014.08.016.