Package 'SoilManageR' reference manual

Title:	Calculate Soil Management Indicators for Agricultural Practice Assessment
Description:	Calculate numerical agricultural soil management indicators from on a management timeline of an arable field. Currently, indicators for carbon (C) input into the soil system, soil tillage intensity rating (STIR), number of soil cover and living plant cover days, N fertilization and livestock intensity, and plant diversity are implemented. The functions can also be used independently of the management timeline to calculate some indicators. The package contains tables with reference information for the functions, as well as a '*.xlsx' template to collect the management data.
Authors:	Olivier Heller [cre, aut] , Raphaël Wittwer [ctb]
Maintainer:	Olivier Heller <[email protected]>
License:	MIT + file LICENSE
Version:	1.0.1.9001
Built:	2025-02-17 08:47:49 UTC
Source:	https://gitlab.com/soilmanager/soilmanager

arrange management_df by date, category

Description

the funciton arranges the management_df by date and by category of operations. The order of the operations is harvest, fertilizer_application,crop_protection, tillage, sowing, irrigation, other

Usage

arrange_management_df(var_MGMT_data, include.combination = TRUE)
arrange_management_df(var_MGMT_data, include.combination = TRUE)

Arguments

`var_MGMT_data`	management_df to be arranged
`include.combination`	logical, if the combinations should considered Default is TRUE

Value

a rearranged management_df

Examples

#rearrange EXAMPLE data
arrange_management_df(EXAMPLE_data)

#rearrange EXAMPLE data
arrange_management_df(EXAMPLE_data)

Estimate carbon input

Description

C_input() estimates the carbon (C) input into the soil system per year of a management_df.

Usage

C_input(var_MGMT_data, extended.output = FALSE)
C_input(var_MGMT_data, extended.output = FALSE)

Arguments

var_MGMT_data

a management_df that contains the management information

extended.output

an optional logical value.

If FALSE, C input values are aggregated by year.
If TRUE, a tibble with daily resolution is returned.
Default value is FALSE

Details

The function takes a management_df as input and returns a C input values per year in the management_df.

Alternatively, it can return a tibble with all managememnt operations and their respective C input values.

The functions calculates the C input with the C_input_crops(), C_input_cover_crops() and CN_input_amendments() functions.

Value

By default, a tibble with C input values (total and by category) by year is returned.
If extended.output = TRUE, a tibble with all management operations and their respecitve C inputs is returned.

Examples

#example that returns annual C input values
C_input(EXAMPLE_data)

#example that returns a tibble with all operations that have a C input
C_input(EXAMPLE_data, extended.output = TRUE)

#example that returns annual C input values
C_input(EXAMPLE_data)

#example that returns a tibble with all operations that have a C input
C_input(EXAMPLE_data, extended.output = TRUE)

Estimate C inputs by cover crops

Description

This function estimates the Carbon (C) input into the soil system by cover crops based on the duration of the cover crop stand.

Usage

C_input_cover_crops(
  abvg_biomass = NA,
  days = 180,
  min_C_abvg = 1253,
  min_days = 180,
  max_C_abvg = 1916,
  max_days = 240,
  Cc_biomass = 450
)
C_input_cover_crops(
  abvg_biomass = NA,
  days = 180,
  min_C_abvg = 1253,
  min_days = 180,
  max_C_abvg = 1916,
  max_days = 240,
  Cc_biomass = 450
)

Arguments

`abvg_biomass`	(optional): Dry weight of aboveground biomass of the cover crop (`tDM/ha`)
`days`	(optional): Number of days that the cover crop was established. If no value is provided, mind_days is assumed (`days`)
`min_C_abvg`	(optional): Minimal above ground C that the cover crop produces, given it is established for the min_days number of days. Default value is 1.253 (`kgC/ha`)
`min_days`	(optional): Number of days where the interpolation of the biomass starts. The default value is 180 (`days`)
`max_C_abvg`	(optional): Maximum biomass that the cover crop can produce. Default value is 1.916 (`kgC/ha`)
`max_days`	(optional): Number of days when the maximum biomass of the cover crop is reached. The default value is 240 (`days`)
`Cc_biomass`	(optional): Assumed C content of the cover crop biomass. Default value is 450 (`kgC/tDM`)

Details

C_input_cover_crops() internally calls C_input_crops() to calculate the different C fractions. The C in the above ground biomass ( $C_{Product}$ ) is a function of the time a cover crop is established. A minimum and a maximum cover crop biomass are assumed at 180 and 240 days respectively, and linearly interpolated for the period in between.

$C_{Product} = \begin{cases} 1253\ kgC/ha \ , \ duration < 180 \ days \\ 1253 \ kgC/ha \ + \ (duration - 180 \ days) \ * \frac{663 \ kgC/ha}{60 \ days} \ , \ 180 \ days \leq duration \leq 240 \ days \\ 1916 \ kgC/ha \ , \ duration > 240 \ days \end{cases}$

Assumptions on the C inputs at day 180 and 240 are based on values extracted from Seitz et al. (2022).

The remaining parameters to calculate the C input by cover crops are HI = 1, SER = 3.67, and REF = 0.31, all derived from Seitz et al. (2022).

Note, that with these assumptions the C input of short term cover crops (e.g. few weeks) is overestimated.

The function C_input_cover_crops() estimates the C input by applying the assumptions mentioned above. Alternatively, the user can supply an above ground biomass and a CC of the biomass, or other parameters to estimate the C input by cover crops.

Value

a tibble with the following parameters:

C_input_product: Estimated soil carbon input from product (i.e., the cover corp aboveground biomass) (kgC/ha),
C_input_straw: Estimated soil carbon input by straw or other residues (typically 0 for cover crops) (kgC/ha),
C_input_root: Estimated soil carbon input by roots (kgC/ha),
C_input_exudate: Estimated soil carbon input by roots (kgC/ha),
C_input_total: Total estimated Soil carbon input, sum of C_input_straw, C_input_root, C_input_exudate (kgC/ha),

References

Seitz D, Fischer LM, Dechow R, Wiesmeier M, Don A (2022). “The potential of cover crops to increase soil organic carbon storage in German croplands.” Plant and Soil, 488(1-2), 157–173. doi:10.1007/s11104-022-05438-w.

Examples

#example when only the duration is known
C_input_cover_crops(days = 205) 

#example if the cover crop biomass is known
C_input_cover_crops(abvg_biomass = 2.5, Cc_biomass = 450) 

#example with custom assumptions on the above ground biomass developnent over time
C_input_cover_crops(days = 60, min_C_abvg = 600 , min_days = 50, max_C_abvg = 1916, max_days = 240)

#example when only the duration is known
C_input_cover_crops(days = 205) 

#example if the cover crop biomass is known
C_input_cover_crops(abvg_biomass = 2.5, Cc_biomass = 450) 

#example with custom assumptions on the above ground biomass developnent over time
C_input_cover_crops(days = 60, min_C_abvg = 600 , min_days = 50, max_C_abvg = 1916, max_days = 240)

Estimate C inputs by crops

Description

Calculates the estimated carbon (C) input into the soil system by harvested main crops.

Usage

C_input_crops(
  crop,
  crop_product = NA,
  crop_residue = NA,
  harvest_index = NA,
  variable_harvest_index = NA,
  HI_intercept = NA,
  HI_slope = NA,
  shoot_root_ratio = NA,
  root_exudation_factor = NA,
  Cc_product = 450,
  Cc_residue = 450,
  Cc_root = 450,
  straw.removal = NA,
  fixed_belowground_input = NA,
  fixed_C_input_root = NA,
  return.comment = FALSE
)
C_input_crops(
  crop,
  crop_product = NA,
  crop_residue = NA,
  harvest_index = NA,
  variable_harvest_index = NA,
  HI_intercept = NA,
  HI_slope = NA,
  shoot_root_ratio = NA,
  root_exudation_factor = NA,
  Cc_product = 450,
  Cc_residue = 450,
  Cc_root = 450,
  straw.removal = NA,
  fixed_belowground_input = NA,
  fixed_C_input_root = NA,
  return.comment = FALSE
)

Arguments

`crop`	Crop type. Must match predefined list (see `C_input_crops_LUT`)
`crop_product`	(optional) Dry weight of the exported product, i.e. yield. Default value is taken from table (tDM/ha)
`crop_residue`	(optional) Dry weight of the residues of the main crop (e.g., straw, sugar beet leaves) (tDM/ha)
`harvest_index`	(optional) Ratio of the product to the total above ground biomass
`variable_harvest_index`	(optional) Logical value that is TRUE if the variable harvest index assumptions of Fan et al. (2017) are to be applied (TRUE / FALSE)
`HI_intercept`	(optional) Intercept of the variable harvest index. Values provided by Fan et al. (2017)
`HI_slope`	(optional) Slope of the variable harvest index. Values provided by Fan et al. (2017) (ha/tDM)
`shoot_root_ratio`	(optional) Ratio of the total above ground biomass to the root biomass
`root_exudation_factor`	(optional) Ratio of the root exudated C to the C in the root biomass
`Cc_product`	(optional) C concentration in the exported product. Default value is 450 (gC/kgDM)
`Cc_residue`	(optional) C concentration in the residues of the main crop. Default value is 450 (gC/kgDM)
`Cc_root`	(optional) C concentration in the roots. Default value is 450 (gC/kgDM)
`straw.removal`	(optional) Logical value that is TRUE if straw is removed at harvest. When NA then SR value from table is take When TRUE SS is 1 When FALSE SS is 0. Default value is NA
`fixed_belowground_input`	(optional) Logical value that is TRUE if fixed below ground Carbon inputs are to be assumed (e.g. for temporary leys) (TRUE / FALSE)
`fixed_C_input_root`	(optional) amount of root C that is assumed if fixed_belowground_input is TRUE (kgC/ha)
`return.comment`	(optional) logical value if comment are returned or not. Default = FALSE (TRUE/FALSE)

Details

The annual C input by crops were estimated based on crop type and crop yield with the allometric functions of Bolinder et al. (2007):

$C_{Product} = Product * CC_{Product}$

$C_{Straw} = {Product}* \frac{1 - HI}{HI} * CC_{Straw}$

$C_{Root} = \frac{Product}{SRR*HI} * CC_{Root}$

$C_{Exudates} = C_{Root} * REF$

Where $C$ is the C per fraction (in kgC/ha) and $CC$ is the C content of given fraction (kgC/tDM). $Prodcuct$ is the dry matter yield of a crop in tDM/ha, $HI$ is the harvest index (ratio of product total of product and straw), $SRR$ is the ratio of the shoot biomass (product and straw) to the root biomass, and $REF$ is the root exudation factor (i.e., the ratio of the C exudated by the roots to the C in the root biomass). All fractions are multiplied with a crop and fraction specific $S$ -factor that determines the share of the fraction that is returned to the soil.

If not mentioned otherwise parameters were taken from the publications of Bolinder et al. (2007), Keel et al. (2017) or Wüst-Galley et al. (2020). Parameters for potatoes and sugar beets were derived from Bolinder et al. (2015) For temporary leys we assumed yield-independent annual $C_{Root}$ of 1.5 MgC/ha and a $REF$ of 0.5 (Taghizadeh-Toosi et al. 2020). Furthermore, like Wüst-Galley et al. (2020), the belowground C input ( $C_{Root} + C_{Exudates}$ ) of corn maize, silage maize and cereals were fixed to 0.46 MgC/ha, 1.1 MgC/ha and 0.6 MgC/ha respectively, based on the values from Hirte et al. (2018). Additionally, we applied the yield dependent harvest index $(HI = Intercept + Product * Slope)$ proposed by Fan et al. (2017) for cereals, faba beans, peas, corn, rapeseed, and soybeans.

Reference yields were derived from the Swiss fertilizer recommendations Sinaj et al. (2017).

All default values can be found in the look-up-table C_input_crops_LUT.

Value

a tibble with the following parameters:

C_input_product: Estimated soil carbon input from product (e.g. damaged patatos) (kgC/ha),
C_input_straw: Estimated soil carbon input by straw or other residues (kgC/ha),
C_input_root: Estimated soil carbon input by roots (kgC/ha),
C_input_exudate: Estimated soil carbon input by roots (kgC/ha),
C_input_total: Total estimated Soil carbon input, sum of C_input_straw, C_input_root, C_input_exudate (kgC/ha),
comment (optional): comment on the derived values

References

Bolinder MA, Janzen HH, Gregorich EG, Angers DA, VandenBygaart AJ (2007). “An approach for estimating net primary productivity and annual carbon inputs to soil for common agricultural crops in Canada.” Agriculture, Ecosystems & Environment, 118(1-4), 29–42. doi:10.1016/j.agee.2006.05.013.

Bolinder MA, Kätterer T, Poeplau C, Börjesson G, Parent LE (2015). “Net primary productivity and below-ground crop residue inputs for root crops: Potato (Solanum tuberosum L.) and sugar beet (Beta vulgaris L.).” Canadian Journal of Soil Science, 95(2), 87–93. doi:10.4141/cjss-2014-091.

Fan J, McConkey B, Janzen H, Townley-Smith L, Wang H (2017). “Harvest index - yield relationship for estimating crop residue in cold continental climates.” Field Crops Research, 204, 153–157. doi:10.1016/j.fcr.2017.01.014.

Hirte J, Leifeld J, Abiven S, Oberholzer H, Mayer J (2018). “Below ground carbon inputs to soil via root biomass and rhizodeposition of field-grown maize and wheat at harvest are independent of net primary productivity.” Agriculture, Ecosystems & Environment, 265, 556–566. doi:10.1016/j.agee.2018.07.010.

Keel SG, Leifeld J, Mayer J, Taghizadeh-Toosi A, Olesen JE (2017). “Large uncertainty in soil carbon modelling related to method of calculation of plant carbon input in agricultural systems.” European Journal of Soil Science, 68(6), 953–963. doi:10.1111/ejss.12454.

Sinaj S, Charles R, Baux A, Dupuis B, Hiltbrunner J, Levy Häner L, Pellet D, Blanchet G, Jeangros B (2017). “Grundlagen für die Düngung landwirtschaftlicher Kulturen in der Schweiz (GRUD): Düngung von Ackerkulturen.” Agrarforschung Schweiz, Spezialpublikation, Chapter 8(6), 1–46. https://ira.agroscope.ch/en-US/Page/Publikation/Index/36799.

Taghizadeh-Toosi A, Cong W, Eriksen J, Mayer J, Olesen J, Keel SG, Glendining M, Kätterer T, Christensen BT (2020). “Visiting dark sides of model simulation of carbon stocks in European temperate agricultural soils: allometric function and model initialization.” Plant and Soil, 450(1-2), 255–272. doi:10.1007/s11104-020-04500-9.

Wüst-Galley C, Keel SG, Leifeld J (2020). “A model-based carbon inventory for Switzerland’s mineral agricultural soils using RothC.” Agroscope Science, 1–110. doi:10.34776/as105e.

Examples

#example without yield information, default yield is assumed
C_input_crops("wheat, winter")

#example with yield information and straw retention
C_input_crops("barley, spring", crop_product = 4.5, straw.removal = FALSE)

#example with more information
C_input_crops("barley, spring", crop_product = 4.5, harvest_index = 0.4,
              shoot_root_ratio = 2.4, root_exudation_factor = 0.5)

#example with variable harvest index
C_input_crops("barley, spring", crop_product = 4.5, variable_harvest_index = TRUE,
              HI_intercept = 0.35, HI_slope = 0.015, shoot_root_ratio = 2.4,
              root_exudation_factor = 0.5)

#example with fixed below ground input
C_input_crops("maize, silage", crop_product = 18.5, 
              fixed_belowground_input = TRUE, fixed_C_input_root = 1500,
              root_exudation_factor = 0.3) 
#example without yield information, default yield is assumed
C_input_crops("wheat, winter")

#example with yield information and straw retention
C_input_crops("barley, spring", crop_product = 4.5, straw.removal = FALSE)

#example with more information
C_input_crops("barley, spring", crop_product = 4.5, harvest_index = 0.4,
              shoot_root_ratio = 2.4, root_exudation_factor = 0.5)

#example with variable harvest index
C_input_crops("barley, spring", crop_product = 4.5, variable_harvest_index = TRUE,
              HI_intercept = 0.35, HI_slope = 0.015, shoot_root_ratio = 2.4,
              root_exudation_factor = 0.5)

#example with fixed below ground input
C_input_crops("maize, silage", crop_product = 18.5, 
              fixed_belowground_input = TRUE, fixed_C_input_root = 1500,
              root_exudation_factor = 0.3)

Look-up-table with default values to calculate carbon (C) inputs by crops

Description

The data set is a look-up-table that is used to calculate the C inputs by crops with the Bolinder formula, that is implemented in the function 'SoilManageR::C_input_crops()'. The data set is produced from the excel table 'C_input_crops_LUT.xlsx' file under '/inst/extdata/'.

Usage

C_input_crops_LUT
C_input_crops_LUT

Format

A tibble with 28 rows and 19 columns:

Crop: Name of the crop
RP: Ratio of the C in the product to the total carbon that is allocated by the plant (in a year)
RS: Ratio of the C in the above ground residues (e.g. straw) to the total carbon that is allocated by the plant (in a year)
RR: Ratio of the C in the plant roots to the total carbon that is allocated by the plant (in a year)
RE: Ratio of the C in the root exudates to the total carbon that is allocated by the plant (in a year)
SP: Proportion of the C in the Product that is transfered to the soil
SS: Proportion of the C in the above ground residues (e.g. straw) that is transfered to the soil
SR: Proportion of the C in the roots that is transfered to the soil
SE: Proportion of the C in root exudates that is transfered to the soil
crop_product: Reference yield, derived from the Swiss fertilizer recommendations (GRUD, 2017, Chapters 8 and 9) [tDM/ha]
harvest_index: Ratio of the product to the total of the product and the above ground residues. Calculated by RP/(RP+RS) (assuming all biomass has 45% C)
varible_harvest_index: Logical value, if the variable harvest index assumption of Fan et al. (2017) are to be aplied or not.
HI_intercept: Intercept of the variable harvest index assumption of Fan et al. (2017) are to be aplied.
HI_slope: Slope of the variable harvest index assumption of Fan et al. (2017) are to be aplied. [ha/tDM]
shoot_root_ratio: Ratio of the product and the above ground residues to the root biomass. Calculated by (RP+RS)/RR (assuming all biomass has 45% C)
root_exudation_factor: Ratio of the root exudates to the root biomass. Calculated by RE/RR (assuming all biomass has 45% C)
fixed_belowground_input: Logical value if the fixed below ground C allocation assumption of Taghizadeh-Toosi et al. (2020) is to be applied or not.
C_input_root: Fixed value of root carbon input that is to be assumed. [kgC/ha]
Source: Source where the information was derived.

References

Compilation of values from the SoilX project. Please check the 'C_input_crops_LUT.xlsx' file under '/inst/extdata/' for more information.

Bolinder MA, Kätterer T, Poeplau C, Börjesson G, Parent LE (2015). “Net primary productivity and below-ground crop residue inputs for root crops: Potato (Solanum tuberosum L.) and sugar beet (Beta vulgaris L.).” Canadian Journal of Soil Science, 95(2), 87–93. doi:10.4141/cjss-2014-091.

Fan J, McConkey B, Janzen H, Townley-Smith L, Wang H (2017). “Harvest index - yield relationship for estimating crop residue in cold continental climates.” Field Crops Research, 204, 153–157. doi:10.1016/j.fcr.2017.01.014.

Hirte J, Leifeld J, Abiven S, Oberholzer H, Mayer J (2018). “Below ground carbon inputs to soil via root biomass and rhizodeposition of field-grown maize and wheat at harvest are independent of net primary productivity.” Agriculture, Ecosystems & Environment, 265, 556–566. doi:10.1016/j.agee.2018.07.010.

Keel SG, Leifeld J, Mayer J, Taghizadeh-Toosi A, Olesen JE (2017). “Large uncertainty in soil carbon modelling related to method of calculation of plant carbon input in agricultural systems.” European Journal of Soil Science, 68(6), 953–963. doi:10.1111/ejss.12454.

Sinaj S, Charles R, Baux A, Dupuis B, Hiltbrunner J, Levy Häner L, Pellet D, Blanchet G, Jeangros B (2017). “Grundlagen für die Düngung landwirtschaftlicher Kulturen in der Schweiz (GRUD): Düngung von Ackerkulturen.” Agrarforschung Schweiz, Spezialpublikation, Chapter 8(6), 1–46. https://ira.agroscope.ch/en-US/Page/Publikation/Index/36799.

Taghizadeh-Toosi A, Cong W, Eriksen J, Mayer J, Olesen J, Keel SG, Glendining M, Kätterer T, Christensen BT (2020). “Visiting dark sides of model simulation of carbon stocks in European temperate agricultural soils: allometric function and model initialization.” Plant and Soil, 450(1-2), 255–272. doi:10.1007/s11104-020-04500-9.

Wüst-Galley C, Keel SG, Leifeld J (2020). “A model-based carbon inventory for Switzerland’s mineral agricultural soils using RothC.” Agroscope Science, 1–110. doi:10.34776/as105e.

Calculate C input tibble

Description

A function that calculates the C input by crops, cover crops and organic amendments. The output is returned as a tibble.

Usage

calculate_C_input_tibble(var_MGMT_data)
calculate_C_input_tibble(var_MGMT_data)

Arguments

var_MGMT_data

a management_df that contains the management information

Details

The function is mainly a helper function for the C_input() function.

Value

tibble with C input values

Examples

#Calculate C input tibble
calculate_C_input_tibble(EXAMPLE_data)
#Calculate C input tibble
calculate_C_input_tibble(EXAMPLE_data)

Calculate all soil management indicators

Description

Checks the management_df for consitency with the check_management_df() function. Then it calculates the C_input(), tillage_intensity(), soil_cover(),plant_diversity() N_input() and productivity_indicator().

Usage

calculate_indicators(var_MGMT_data)
calculate_indicators(var_MGMT_data)

Arguments

var_MGMT_data

a management_df with a management history

Value

data frame with indices per year

Examples



  #example input
  calculate_indicators(EXAMPLE_data)


#example input
  calculate_indicators(EXAMPLE_data)

Calculate soil management indicators by crop

Description

Alternative to 'calculate_indicators()' that C inputs, N inputs, STIR and soil cover for each crop separately. The function identifies the last harvest of each crop per year and aggregates the management intensities for the periods in between. Harvests of temporary leys that are sown after a main crop and harvest in the same year are not considered.

Usage

calculate_indicators_by_crop(var_management_df)
calculate_indicators_by_crop(var_management_df)

Arguments

var_management_df

a 'management_df()'

Value

a tibble with the indicator values per crop

Examples

# calculate examples
calculate_indicators_by_crop(EXAMPLE_data)

# calculate examples
calculate_indicators_by_crop(EXAMPLE_data)

Calculate N input tibble

Description

A function that calculates the N input by mineral and organic fertilization as well as the livestock intensity of the animal derived N inputs.

Usage

calculate_N_input_tibble(var_MGMT_data)
calculate_N_input_tibble(var_MGMT_data)

Arguments

var_MGMT_data

a management_df that contains the management information

Details

The function is mainly a helper function for the N_input() function.

Value

tibble with N and LSU values

Examples

#Calculate N input tibble
calculate_N_input_tibble(EXAMPLE_data)
#Calculate N input tibble
calculate_N_input_tibble(EXAMPLE_data)

Caculate soil cover tibble

Description

The function takes a management_df as input and returns a soil_cover_tibble with daily resolution of the estimated soil cover.

Usage

calculate_soil_cover_tibble(var_MGMT_data)
calculate_soil_cover_tibble(var_MGMT_data)

Arguments

var_MGMT_data

a management_df that contains the management information

Details

The function is mainly a helper function for the soil_cover() function.

Value

object of the class soil_cover_tibble with daily resolution is returned.

Examples


  #example that returns a soil_cover_tibble
  calculate_soil_cover_tibble(EXAMPLE_data)


#example that returns a soil_cover_tibble
  calculate_soil_cover_tibble(EXAMPLE_data)

Calculate STIR tibble

Description

A function that calculates the STIR value of all operations in a management_df. The output is returned as a tibble.

Usage

calculate_STIR_tibble(var_MGMT_data)
calculate_STIR_tibble(var_MGMT_data)

Arguments

var_MGMT_data

a management_df that contains the management information

Details

The function is mainly a helper function for the tillage_intensity() function.

Value

a tibble with all management operations and STIR values

Examples


#example that returns a tibble with all operations that have a STIR value
calculate_STIR_tibble(EXAMPLE_data)

#example that returns a tibble with all operations that have a STIR value
calculate_STIR_tibble(EXAMPLE_data)

Check management_df for consitency

Description

The function checkes objects of the class management_df() for internal consistency. It formally checks the class and the column names. Additionally, the function checks if dates are consistently increasing and if all organic amendments, tillage and sowing devices and crops are in the relevant look-up-tables. Furthermore, the amount of organic amendments (<100t/ha) and N fertilizer (<100kgN/ha) application rates per event are checked. The depth of tillage operations are compared with the min and max depth from the STIR_value_LUT. Finally, the order of tillage, sowing and harvest operations are checked for plausibility (see details for more information).

Usage

check_management_df(var_MGMT_data)
check_management_df(var_MGMT_data)

Arguments

var_MGMT_data

an object of the management_df() class

Details

The order of tillage, sowing and harvest operations are checked with the following assumptions:

after "stubble_cultivation" the following operations are allowed: "stubble_cultivation", "primary_tillage", "seedbed_preparation", "sowing_main_crop", "sowing_cover_crop"
after "primary_tillage" the following operations are allowed: "primary_tillage", "seedbed_preparation", "sowing_main_crop", "sowing_cover_crop"
after "seedbed_preparation" the following operations are allowed: "stubble_cultivation", "seedbed_preparation", "sowing_main_crop", "sowing_cover_crop"
after "sowing_main_crop" the following operations are allowed: "sowing_main_crop", "harvest_main_crop", "sowing_cover_crop"
after "sowing_cover_crop" the following operations are allowed: "stubble_cultivation", "primary_tillage", "seedbed_preparation", "sowing_main_crop", "sowing_cover_crop", "harvest_main_crop"
after "harvest_main_crop" the following operations are allowed: "harvest_main_crop", "straw_removal", "hay_removal", "stubble_cultivation", "primary_tillage", "seedbed_preparation", "sowing_main_crop", "sowing_cover_crop"
after "straw_removal" the following operations are allowed: "harvest_main_crop", "straw_removal", "stubble_cultivation", "primary_tillage", "seedbed_preparation", "sowing_main_crop", "sowing_cover_crop"
after "hay_removal" the following operations are allowed: "harvest_main_crop", "hay_removal", "stubble_cultivation", "primary_tillage", "seedbed_preparation", "sowing_main_crop", "sowing_cover_crop"

Additionally, there are exceptions for potato crops: "bedder" can be used after a "potato_planter" and "mulching" can be applied before a "potato_harvester"

Value

a test_report list (only returned if some tests failed)

Examples

#example input
check_management_df(EXAMPLE_data)
#example input
check_management_df(EXAMPLE_data)

Estimate C and N inputs of organic amendments

Description

Estimates the carbon (C) and total nitrogen (N) input into the soil system by organic amendments.

Usage

CN_input_amendments(
  amount,
  amd_type = NA,
  DMC = NA,
  C_content = NA,
  N_content = NA,
  return.comment = FALSE,
  concentration_liquids = 0.5
)
CN_input_amendments(
  amount,
  amd_type = NA,
  DMC = NA,
  C_content = NA,
  N_content = NA,
  return.comment = FALSE,
  concentration_liquids = 0.5
)

Arguments

`amount`	Amount of organic amendment in fresh weight (t/ha)
`amd_type`	Type of organic manure, there are default values available for "Slurry_dairy_cow", "Manure_dairy_cow", "Slurry_cattle", "Manure_cattle", "Slurry_pig", "Manure_pig", "Compost", "Biorga_Quick_12N" and others (see `CN_input_amendments_LUT`)
`DMC`	dry matter content of the organic amendment (gDM/kgFM)
`C_content`	C content of the dry matter (gC/kgDM)
`N_content`	N content of the dry matter (gN/kgDM)
`return.comment`	(optional): logical value if comments are returned or not. Default = FALSE
`concentration_liquids`	concentration factor for liquid amendments. Default value is .5.

Details

The C and N inputs by organic amendments is calculated based on the dry matter content (DMC) and the C and N content of the dry matter of the amendment. If the contents are not specified, default values from the Swiss fertilizer recommendations (Sinaj et al. 2017) are assumed. The default values are available the look-up-table CN_input_amendments_LUT For all slurries and liquid amendments (DMC < 150 g/kg), a dilution of 50% is assumed if default DMC values from the CN_input_amendments_LUT are used.

Value

a tibble with the following parameters:

C_input_org: C input by organic ammendment (kgC/ha)
N_input_org: N input by organic ammendment (kgN/ha)
comment (optional): Source of information on properties of organic amendment

References

Examples

#example where amount, dry matter content, C and N content are known.
CN_input_amendments(40, DMC = 300, C_content = 300, N_content = 30)

#example where only amount and type of amendment are known 
CN_input_amendments(20, "Manure_pig")

#example of a diluted slurry
CN_input_amendments(20, amd_type = "Slurry_dairy_cow", DMC = 50)


#example where amount, dry matter content, C and N content are known.
CN_input_amendments(40, DMC = 300, C_content = 300, N_content = 30)

#example where only amount and type of amendment are known 
CN_input_amendments(20, "Manure_pig")

#example of a diluted slurry
CN_input_amendments(20, amd_type = "Slurry_dairy_cow", DMC = 50)

Look-up-table with default values to calculate C and N inputs by organic amendments

Description

The dataset is a look-up-table that is used to calculate the carbon (C) and nitrogen (N) inputs by organic amendments with the function 'SoilManageR::CN_input_amendments()'. The data set is produced from the excel table 'CN_input_amendments_LUT.xlsx' file under '/inst/extdata/'.

Usage

CN_input_amendments_LUT
CN_input_amendments_LUT

Format

A tibble with 27 rows and 7 columns:

Amendment: Name of the amendment
DMC: Dry matter content of the amendment [gDM/kgFM]
OM: Organic matter content of the amendment, relative to its fresh weight [gDM/kgFM]
C_content: Carbon content of the amendment, relative to its dry matter [gC/kgDM]
N_tot: Total N conent of the amendment, relative to it's dry fresh weight [gN/kgFM]
N_content: Nitrogen content of the amendment, relative to its dry matter [gN/kgDM]
Comment: Comment, e.g. source of the information. These lines are shown as part of the function output

References

Compilation of Values from the SoilX project. Please check the 'CN_input_amendments_LUT.xlsx' file under '/inst/extdata/' for more information

Richner W, Flisch R, Mayer J, Schlegel P, Zähner M, Menzi H (2017). “Grundlagen für die Düngung landwirtschaftlicher Kulturen in der Schweiz (GRUD): Eigenschaften und Anwendung von Düngern.” Agrarforschung Schweiz, Spezialpublikation, Chapter 4(6), 1–24. https://ira.agroscope.ch/en-US/publication/41476.

Example of a management_df

Description

The dataset is derived from a Swiss long term agricultural field experiment. It is intended for demonstration purposes only

Usage

EXAMPLE_data
EXAMPLE_data

Format

A tibble with 130 rows and 13 columns:

crop: Name of the main crop. Cover crop related operations are linked to the next main crop in the rotation [String from list]
date: Date of the management operation [Date]
year: Year of the management operation [Integer]
category: Categorization of the managment operation [1 level] [String from list]
operation: Categorization of the managment operation [2 level] [String from list]
device: Categorization of the managment operation [3 level] [String from list]
value: Numerical value linked to managment operation (e.g., depth of tillage operation, mass of organic amendment) [Integer]
unit: Unit of the numerical value (e.g. cm, t/ha) [String from list]
machine: Further information on the machine used (e.g., type, manufacturer, tool) [String [UTF-8]]
product: Further information on the applied product (e.g., name, manufacturer, C content) [String [UTF-8]]
combination: Indicate if a operation was done in combination with others. Use consequtive integer numbers if combinded operations occur. Leave empty if not combined
comments: Comments related to the management operation [String [UTF-8]]
DMC: Dry matter content of organic amendments [gDM/kgFM]
C_content: Carbon content of the amendments, relative to its dry matter [gC/kgDM]
N_content: Nitrogen content of organic amendments, relative to its dry matter [gN/kgDM]
crop_product: Crop product yield [tDM/ha]
crop_residue: Crop residue mass [tDM/ha]
Cc_product: Carbon content of the crop product [gC/kgDM$]
Cc_residue: Carbon content of the crop residue [gC/kgDM]

Filter management_df for pattern in comments

Description

Excludes operations (lines) from management_df based on a character pattern in the comments column.

Usage

filter_management_df(var_MGMT_data, filter_pattern)
filter_management_df(var_MGMT_data, filter_pattern)

Arguments

`var_MGMT_data`	management_df
`filter_pattern`	string based on which the lines should be excluded

Value

a filtered management_df

Examples

# filter EXAMPLE_data and exclude all lines that contain "UFA 330" in comments
filter_management_df(EXAMPLE_data,"UFA 330")

# filter EXAMPLE_data and exclude all lines that contain "UFA 330" in comments
filter_management_df(EXAMPLE_data,"UFA 330")

Constructor for management_df

Description

This function is a constructor for empty objects of the management_df() class, the core of the SoilManageR package.

Usage

management_df(
  crop = NA,
  year = NA,
  date = NA,
  category = NA,
  operation = NA,
  device = NA,
  value = NA,
  unit = NA,
  machine = NA,
  product = NA,
  combination = NA,
  comments = NA,
  DMC = NA,
  C_content = NA,
  N_content = NA,
  crop_product = NA,
  crop_residue = NA,
  Cc_product = NA,
  Cc_residue = NA
)
management_df(
  crop = NA,
  year = NA,
  date = NA,
  category = NA,
  operation = NA,
  device = NA,
  value = NA,
  unit = NA,
  machine = NA,
  product = NA,
  combination = NA,
  comments = NA,
  DMC = NA,
  C_content = NA,
  N_content = NA,
  crop_product = NA,
  crop_residue = NA,
  Cc_product = NA,
  Cc_residue = NA
)

Arguments

`crop`	Name of the main crop. Must match a pre-existing list
`year`	Year of the management operation ("YYYY")
`date`	Date of the management operation ("YYYY-MM-DD")
`category`	Categorization of the managment operation (1 level). Must match a pre-existing list.
`operation`	Categorization of the managment operation (2 level). Must match a pre-existing list.
`device`	Categorization of the managment operation (3 level). Must match a pre-existing list.
`value`	Numerical value linked to managment operation (e.g., depth of tillage operation, mass of organic amendment)
`unit`	Unit of the numerical value (e.g. cm, t/ha)
`machine`	Further information on the machine used (e.g., type, manufacturer, tool)
`product`	Further information on the applied product (e.g., name, manufacturer). Must match pre-existing list for organic amendments.
`combination`	Indicate if a operation was done in combination with others. Use consequtive integer numbers if combinded operations occur. Leave empty if not combined.
`comments`	Comments related to the management operation
`DMC`	Dry matter content of organic amendments (gDM/kgFM)
`C_content`	Carbon content of the amendments, relative to its dry matter (gC/kgDM)
`N_content`	Nitrogen content of organic amendments, relative to its dry matter (gN/kgDM)
`crop_product`	Crop product yield (tDM/ha)
`crop_residue`	Crop residue mass (tDM/ha)
`Cc_product`	Carbon content of the crop product (gC/kgDM)
`Cc_residue`	Carbon content of the crop residue (gC/kgDM)

Value

a management_df()

Examples

#creation of an empty management_df
management_df()
#creation of an empty management_df
management_df()

Import management_df from excel file

Description

This function imports management data from an excel template and transforms it into a management_df. Additionaly, it checks if all columns that are expected for a managment_df are available. The excel template can be found in the SoilManageR Package under inst/extdata/SoilManageR_mgmt_data_template. Optionaly, the parameter year can be overwritten by the year extracted from date.

Usage

management_df_from_excel(
  path_to_xlsx,
  var_sheet = "Management_template",
  overwrite_year = TRUE
)
management_df_from_excel(
  path_to_xlsx,
  var_sheet = "Management_template",
  overwrite_year = TRUE
)

Arguments

`path_to_xlsx`	path to the excel file with the management data
`var_sheet`	name of the sheet with the management data template in the excel sheet, default is "Management_template"
`overwrite_year`	logical: if TRUE (default), the year will be set to the year extracted from the date

Value

a management_df

Examples

#create path
path_to_xlsx_template <- system.file(
"/extdata/SoilManageR_mgmt_data_template_V2.6.xlsx", package = "SoilManageR")

#load management_df
management_df_from_excel(path_to_xlsx_template)
#create path
path_to_xlsx_template <- system.file(
"/extdata/SoilManageR_mgmt_data_template_V2.6.xlsx", package = "SoilManageR")

#load management_df
management_df_from_excel(path_to_xlsx_template)

Estimate nitrogen input

Description

This function estimates the nitrogen (N) input by mineral and organic fertilization into the soil system per year.

Usage

N_input(var_MGMT_data, extended.output = FALSE)
N_input(var_MGMT_data, extended.output = FALSE)

Arguments

var_MGMT_data

a management_df that contains the management information

extended.output

an optional logical value:

If FALSE, N input values are aggregated by year
If TRUE, a tibble with all management operations is returned
Default value is FALSE

Details

The function takes a management_df as input and returns N input values per year in the management_df.

Alternatively, it can return a extensive tibble with all management operations and their N input values.

The functions calculates the N input by organic fertilization with the calculate_N_input_tibble() and the CN_input_amendments() function. Furthermore, it calculates the livestock intensity (LSU/ha) by deviding the animal derived N by ⁠105kgN/LSU⁠.

Be aware that the function currently neglects N that is fixated by plants (e.g. legumes)

Value

by default, a tibble with N input values (organic N, mineral N and total N) by year is returned. If extended.output = TRUE, a tibble with daily resolution is returned.

Examples

#example that returns annual N input values
N_input(EXAMPLE_data)

#example that returns a tibble with all management operations and their N input
N_input(EXAMPLE_data, extended.output = TRUE) 
#example that returns annual N input values
N_input(EXAMPLE_data)

#example that returns a tibble with all management operations and their N input
N_input(EXAMPLE_data, extended.output = TRUE)

Estimate soil cover percentage by plants

Description

This function estimates the percentage of soil cover based on the number of days since sowing. The parameters used are derived from Mosimann and Rüttimann (2006).

Usage

plant_cover(varCrop, varDays = 0)
plant_cover(varCrop, varDays = 0)

Arguments

`varCrop`	Crop type, must match with crop name in `plant_cover_LUT`
`varDays`	Number of days since sowing of the crop

Details

The function assumes that plant cover unfolds in four phases with different soil cover rates:

0 to 10 % of soil cover
10 to 50 % of soil cover
50 to 75 % of soil cover
75 to 100 % of soil cover

Value

percentage of soil cover by plants, value of 0 to 100 %.

References

Mosimann T, Rüttimann M (2006). “Berechnungsgrundlagen zum Fruchtfolgefaktor zentrales Mittelland 2005 im Modell Erosion CH (V2.02).” Terragon, Bubendorf. https://uwe.lu.ch/-/media/UWE/Dokumente/Themen/Bodenschutz/Bodenschutz_Landwirtschaft/dokumentationbodenerosionsschluessel_terragon2006.pdf.

Examples

plant_cover("wheat, winter", 140)


plant_cover("wheat, winter", 140)

Look-up-table with default values to estimate soil cover by plants

Description

The dataset is a look-up-table that is used to estimate the soil cover percentage by plant with the function 'SoilManageR::plant_cover()'. The data set is produced from the excel table 'plant_cover_LUT.xlsx' file under '/inst/extdata/'.

Usage

plant_cover_LUT
plant_cover_LUT

Format

A tibble with 28 rows and 7 columns:

Crop: Name of the crop
Slope_0_10: Increase of crop cover per day between 0 and 10% soil cover [%/day]
Slope_10_50: Increase of crop cover per day between 10 and 50% soil cover [%/day]
Slope_50_75: Increase of crop cover per day between 50 and 75% soil cover [%/day]
Slope_75_100: Increase of crop cover per day between 75 and 100% soil cover [%/day]
days_30: Number of days it takes to reach 30% soil cover [day]
Comments: Source where the information was derived

References

Calculate plant diversity indicators

Description

Derives three indicators for plant diversity of a crop rotation based on management information (mainly sowing events).

Usage

plant_diversity(var_MGMT_data, start_year, end_year)
plant_diversity(var_MGMT_data, start_year, end_year)

Arguments

`var_MGMT_data`	a 'management_df' with a management history
`start_year`	start year of the cropping sequence of interest
`end_year`	end year of the cropping sequence of interest

Details

For the function to work properly the species (or variety) must be mentioned in the "product" column, and all sown species of mixtures must be represented in a single row of the management_df each.

The function calculates the plant diversity index ( $PDI$ ), the total number of different species, and the Shannon index for all sown species.

The $PDI$ is calculated in the following way inspired by Tiemann et al. (2015):

$PDI = \overline{S_{year}} * S_{rotation}$

where $\overline{S_{year}}$ is the average number of sown species per year and $S_{rotation}$ is the total number of different species sown in the full crop rotation or cropping sequence.

The Shannon Index is calculated with the shannon_index() function.

Value

a tibble with three indicators for plant diversity

References

Tiemann LK, Grandy AS, Atkinson EE, Marin-Spiotta E, McDaniel MD (2015). “Crop rotational diversity enhances belowground communities and functions in an agroecosystem.” Ecology Letters, 18(8), 761-771. doi:10.1111/ele.12453.

Examples

plant_diversity(EXAMPLE_data,2013,2020)

plant_diversity(EXAMPLE_data,2013,2020)

Plot a management dataframe

Description

Visual representation of a management_df.

Usage

plot_management_df(management_df, title = "Management Timeline")
plot_management_df(management_df, title = "Management Timeline")

Arguments

`management_df`	a management data frame
`title`	the title of the plot

Details

The colors in the background represent the soil cover by plants (green), and crop residues (brown). "T" indicates tillage event with a STIR value >= 0. "P" indicates a crop protection event. Dotted vertical lines represent sowing events, the sown crop is noted in the lowest quarter at the right side of the line. The values in the middle indicate C inputs into the soil system im MgC/ha, either by crops (C), residue removal (R), cover crops (CC) or organic amendments (OA). Negative values are withdrawls by residue removal. Note that large management_df can be heavy to compute and the plots may be messy.

Value

a plot

Examples



  # creates a visual representation of the EXAMPLE data
  plot_management_df(EXAMPLE_data)
 
# creates a visual representation of the EXAMPLE data
  plot_management_df(EXAMPLE_data)

Plotting soil_cover_tibbles

Description

This function plots objects of the class soil_cover_tibble in a custom format

Usage

## S3 method for class 'soil_cover_tibble'
plot(x, ...)
## S3 method for class 'soil_cover_tibble'
plot(x, ...)

Arguments

`x`	an soil_cover_tibble object
`...`	arguments to be passed to methods

Value

none, it plots the soil_cover_tibble

Examples


  data <- soil_cover(EXAMPLE_data, extended.output = TRUE)
  plot(data)



data <- soil_cover(EXAMPLE_data, extended.output = TRUE)
  plot(data)

Calculate average productivity

Description

Estimates estimates the relative yield of a cropping sequence per year. The function takes a management_df as input and returns a relative_yield value per year in the management_df. Alternatively, it can return a tibble with additional information on each crop. The productivity_indicator() calculates the relative yields with the relative_yield() function.

Usage

productivity_indicator(var_MGMT_data, extended.output = FALSE)
productivity_indicator(var_MGMT_data, extended.output = FALSE)

Arguments

var_MGMT_data

a management_df that contains the management information

extended.output

an optional logical value.

If FALSE, relative yields are aggregated by year.
If TRUE, a tibble with relative yield of each crop is returned.
Default value is FALSE

Value

By default, a tibble with relative yields by year is returned.
If extended.output = TRUE, a tibble with additional information is returned.

Examples

#example that returns annual relative yield values
productivity_indicator(EXAMPLE_data)

#example that returns a tibble with additional information
productivity_indicator(EXAMPLE_data, extended.output = TRUE)

#example that returns annual relative yield values
productivity_indicator(EXAMPLE_data)

#example that returns a tibble with additional information
productivity_indicator(EXAMPLE_data, extended.output = TRUE)

Calculate relative yield

Description

This function calculates the relative yield of an observed dry matter yield compared to the reference dry matter yield in the Swiss fertilizing guidelines (Sinaj et al. 2017).

Usage

relative_yield(var_crop, var_yield)
relative_yield(var_crop, var_yield)

Arguments

`var_crop`	string with name of crop type, must match with the Crops in the C_input_crops_LUT
`var_yield`	observed dry matter yield (tDM/ha)

Value

the numeric value for the relative yield

References

Examples

relative_yield("wheat, winter", 4.8)
relative_yield("wheat, winter", 4.8)

Calculate Shannon Index for diversity

Description

This function calculates the Index of Shannon (1948) for a tibble with different species.

The formula that is used is

$SI = -\sum_{S=1}^i (p_i * ln(p_i))$

where $p_i$ is the relative abundance of each species ( $S$ ).

Usage

shannon_index(var_tibble)
shannon_index(var_tibble)

Arguments

var_tibble

a tibble with two columns (name of species, count per species), the second column must be called "count"

Value

double of the Shannon Index

References

Shannon CE (1948). “A Mathematical Theory of Communication.” Bell System Technical Journal, 27(3), 379-423.

Spellerberg IF, Fedor PJ (2003). “A tribute to Claude Shannon (1916–2001) and a plea for more rigorous use of species richness, species diversity and the ‘Shannon–Wiener’Index.” Global ecology and biogeography, 12(3), 177–179. doi:10.1046/j.1466-822X.2003.00015.x.

Examples

#create tibble
tibble_example <- tibble::tibble(Plant = c("A","B","C","D","E"), count = c(10,5,8,20,10))

#calculate Shannon Index
shannon_index(tibble_example)  # = 1.505...

#create tibble
tibble_example <- tibble::tibble(Plant = c("A","B","C","D","E"), count = c(10,5,8,20,10))

#calculate Shannon Index
shannon_index(tibble_example)  # = 1.505...

Estimate soil cover by plants and residues

Description

Derives the days where soil cover by living plants or resiudes is ⁠>=30%⁠.

Usage

soil_cover(var_MGMT_data, extended.output = FALSE)
soil_cover(var_MGMT_data, extended.output = FALSE)

Arguments

var_MGMT_data

a management_df that contains the management information

extended.output

an optional logical value.

If FALSE, soil cover days are aggregated by year.
If TRUE, a soil_cover_tibble with daily resolution is returned.
Default value is FALSE

Details

The function takes a management_df as input and returns soil cover days per year in the management_df. Alternatively, it can return a soil_cover_tibble with daily resolution of the estimated soil cover.

The function calculates plant soil cover with the plant_cover() function and the soil coverage by residues.

The residue mass is dependent on the residue supply by crops, its decay and its incorporation by tillage operations (Büchi et al. 2016).

Residue supply is estimated with the yield dependent residue C C_input_straw provided by the function C_input_crops() and a C content of $450 \ [mgC/gDM]$ . If residues are removed, the removed residue mass is subtracted.

Residue decay is calculated with the formula of Steiner et al. (1999):

$M_t = M_{t-1} * (1 - k_{decay})$

Where $M_{t-1}$ is the residue mass of the prior day $[g/m^2]$ and $k_{decay}$ is the daily decay rate that was assumed to be 0.028 $g/g$ , the average decomposition rate of winter wheat straw (Steiner et al. 1999).

Residue incorporation by tillage was estimated with the operation-specific burial coefficient extracted from the RUSLE2 database (USDA-NRCS 2023) that are provided in the look-up-table STIR_values_LUT.

Residue mass is translated into percentage of soil cover by the formula of Steiner et al. (2000):

$cover_{residues} = (1-e^{-k(M)})* 100 \%$

Where $M$ is the residue mass $[g/m^2]$ and $k$ is a cover coefficient $[m^2/g]$ . $k$ was assumed to be 0.0175 (Steiner et al. 2000).

Value

By default, a tibble with soil cover days by year is returned.
If extended.output = TRUE, an object of the class soil_cover_tibble with daily resolution is returned.

References

Büchi L, Valsangiacomo A, Burel E, Charles R (2016). “Integrating simulation data from a crop model in the development of an agri-environmental indicator for soil cover in Switzerland.” European Journal of Agronomy, 76, 149–159. doi:10.1016/j.eja.2015.11.004.

Steiner JL, Schomberg HH, Unger PW, Cresap J (1999). “Crop residue decomposition in no-tillage small-grain fields.” Soil Science Society of America Journal, 63(6), 1817–1824. doi:10.2136/sssaj1999.6361817x.

Steiner JL, Schomberg HH, Unger PW, Cresap J (2000). “Biomass and residue cover relationships of fresh and decomposing small grain residue.” Soil Science Society of America Journal, 64(6), 2109–2114. doi:10.2136/sssaj2000.6462109x.

USDA-NRCS (2023). “Revised Universal Soil Loss Equation, Version 2 (RUSLE2), Official NRCS RUSLE2 Program and Database (V 2023-02-24).” USDA-NCRS. https://fargo.nserl.purdue.edu/rusle2_dataweb/RUSLE2_Index.htm.

Examples


  #example that returns annual soil cover days by plants and residues
  soil_cover(EXAMPLE_data)

  #example that returns a soil_cover_tibble
  soil_cover(EXAMPLE_data, extended.output = TRUE)


#example that returns annual soil cover days by plants and residues
  soil_cover(EXAMPLE_data)

  #example that returns a soil_cover_tibble
  soil_cover(EXAMPLE_data, extended.output = TRUE)

Calculate STIR value

Description

The function calculates the soil tillage intensity rating (STIR) value of tillage and sowing operations. By default, the STIR() function of SoilManageR operates on SI units (cm and km/h) and not in imperial units (inch, mph) as the original STIR equation (USDA-NRCS 2023). However, the user can specifiy that the input is in imperial units. The function can process custom input, if no such input is provided it assumes default values from the STIR_value_LUT.

Usage

STIR(
  device = NA,
  speed = NA,
  speed_unit = "km/h",
  type_modifier = NA,
  depth = NA,
  depth_unit = "cm",
  area_disturbed = NA,
  original.STIR.value = FALSE
)
STIR(
  device = NA,
  speed = NA,
  speed_unit = "km/h",
  type_modifier = NA,
  depth = NA,
  depth_unit = "cm",
  area_disturbed = NA,
  original.STIR.value = FALSE
)

Arguments

`device`	tillage or sowing implement or operation, must match predefined list, or all necessary inputs (speed, type modidifier, depth, area_disturbed) must be provided
`speed`	speed of the operation (km/h or mph)
`speed_unit`	unit of the speed input, must be either NA, "km/h" or "mph". Default value is "km/h"
`type_modifier`	tillage operation type modifier, must be a value between 0 and 1
`depth`	depth of the soil that is affected by the operation (cm or inch)
`depth_unit`	unit of the depth input, must be either NA, "cm" or "inch". Default value is "cm"
`area_disturbed`	share of the surfave that is disturbed by the operation (0 to 1)
`original.STIR.value`	logical value, if TRUE, the original STIR value of the operation in the `STIR_value_LUT` (instead of the calculated STIR value) is returned. Default value is FALSE

Details

The concept of the STIR value was developed within the RUSLE2 framework by the USDA-NRCS (2023). The STIR equation is defined as

$STIR = (0.5 * speed) * (3.25 * type_{modifier}) * depth * area_{disturbed}$

where $speed$ and $depth$ are provided in mph and inches(!). For the purpose of this function we assume that ⁠1 inch = 2.54 cm⁠ and ⁠1 mph = 1.609 km/h⁠. The tillage $type_{modifier}$ is defined to be:

1.00 for inversion operation
0.80 for mixing and some inversion operations
0.70 for mixing operations
0.40 for lifting and fracturing operations
0.15 for compression operations

In the STIR_value_LUT there are more than 400 operations, incl. the original operations from the RUSLE2 software (as of 2023-02-24) and operations defined by the SoilX project.

For further details on the STIR please consider the RUSLE2 website (https://fargo.nserl.purdue.edu/rusle2_dataweb/RUSLE2_Index.htm) or the description of the SoilManageR package

Value

STIR value of the operation

References

USDA-NRCS (2023). “Revised Universal Soil Loss Equation, Version 2 (RUSLE2), Official NRCS RUSLE2 Program and Database (V 2023-02-24).” USDA-NCRS. https://fargo.nserl.purdue.edu/rusle2_dataweb/RUSLE2_Index.htm.

Examples

#example without additional information
STIR("plough")

#example with additional information
STIR("rotary_harrow", depth = 15)

#custom example
STIR(speed = 10, type_modifier = 0.8, depth = 15, area_disturbed = 0.45)

#example that returns orginial STIR value
STIR("plough", original.STIR.value = TRUE)

#example that uses imperial units
STIR("plough", depth = 5, depth_unit = "inch", speed = 8, speed_unit = "mph")
#example without additional information
STIR("plough")

#example with additional information
STIR("rotary_harrow", depth = 15)

#custom example
STIR(speed = 10, type_modifier = 0.8, depth = 15, area_disturbed = 0.45)

#example that returns orginial STIR value
STIR("plough", original.STIR.value = TRUE)

#example that uses imperial units
STIR("plough", depth = 5, depth_unit = "inch", speed = 8, speed_unit = "mph")

Look-up-table with default values for tillage operations

Description

The dataset is a look-up-table that is used to derive STIR values with the function 'SoilManageR::STIR()' and residue incorperation by tillage operations with the function 'SoilManageR::soil_cover()'. The data set is produced from the excel table 'STIR_value_LUT.xlsx' file under '/inst/extdata/' and was mostly derived from the official RUSLE2 database (USDA-NRCS 2023).

Usage

STIR_values_LUT
STIR_values_LUT

Format

A tibble with 50 rows and 15 columns:

Operation: Name of the operation
Speed: Average speed of the operation [km/h]
Speed_MIN: Min speed of the operation [km/h]
Speed_Max: Max speed of the operation [km/h]
Surf_Disturbance: Share of the disturbed soil surface [%]
Depth: Average depth of the operation [cm]
Depth_MIN: Min depth of the operation [cm]
Depth_MAX: Max depth of the operation [cm]
TILLAGE_TYPE: Type of tillage operation
TILLAGE_TYPE_Modifier: Numerical value of the tillage type modifier [0-1]
STIR: Soil tillage intensity rating value, based on default values
Diesel_use: iesel use of the operation per area [l/ha]
Burial_Coefficient: Burial of plant residues on the soil surface [0-1]
Source: Source of the values in the table
Description: Description of the operation

References

Estimate tillage intensity

Description

Calculates the soil tillage intensity per year. The function takes a management_df as input and returns a STIR value per year in the management_df. Alternatively, it can return a extensive tibble with each operation and their respective STIR values.

Usage

tillage_intensity(var_MGMT_data, extended.output = FALSE)
tillage_intensity(var_MGMT_data, extended.output = FALSE)

Arguments

var_MGMT_data

a management_df that contains the management information

extended.output

an optional logical value. Default value is FALSE

If FALSE, STIR values are aggregated by year.
If TRUE, a tibble with all management operations is returned.

Value

by default, a tibble with STIR values by year is returned. If extended.output = TRUE, a tibble with daily resolution is returned.

Examples

#example that returns annual STIR values
tillage_intensity(EXAMPLE_data)

#example that returns a tibble with all operations that have a STIR value
tillage_intensity(EXAMPLE_data, extended.output = TRUE)

#example that returns annual STIR values
tillage_intensity(EXAMPLE_data)

#example that returns a tibble with all operations that have a STIR value
tillage_intensity(EXAMPLE_data, extended.output = TRUE)

Package 'SoilManageR'

Help Index

arrange management_df by date, category

Description

Usage

Arguments

Value

Examples

Estimate carbon input

Description

Usage

Arguments

Details

Value

See Also

Examples

Estimate C inputs by cover crops

Description

Usage

Arguments

Details

Value

References

See Also

Examples

Estimate C inputs by crops

Description

Usage

Arguments

Details

Value

References

See Also

Examples

Look-up-table with default values to calculate carbon (C) inputs by crops

Description

Usage

Format

References

Calculate C input tibble

Description

Usage

Arguments

Details

Value

See Also

Examples

Calculate all soil management indicators

Description

Usage

Arguments

Value

See Also

Examples

Calculate soil management indicators by crop

Description

Usage

Arguments

Value

Examples

Calculate N input tibble

Description

Usage

Arguments

Details

Value

See Also

Examples

Caculate soil cover tibble

Description

Usage

Arguments

Details

Value

See Also

Examples

Calculate STIR tibble

Description

Usage

Arguments