Type:	Package
Title:	Calculate Soil Management Indicators for Agricultural Practice Assessment
Version:	1.1.0
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.
License:	MIT + file LICENSE
Encoding:	UTF-8
LazyData:	true
RoxygenNote:	7.3.1
Imports:	magrittr (≥ 2.0.3), dplyr (≥ 1.1.4), lubridate (≥ 1.9.2), ggplot2 (≥ 3.4.4), ggthemes (≥ 5.0.0), tidyr (≥ 1.3.0), tibble (≥ 3.2.1), readxl (≥ 1.4.3), Rdpack (≥ 2.6)
Depends:	R (≥ 2.10)
Suggests:	knitr, rmarkdown, testthat (≥ 3.0.0)
Config/testthat/edition:	3
VignetteBuilder:	knitr
RdMacros:	Rdpack
URL:	https://gitlab.com/SoilManageR/
BugReports:	https://gitlab.com/SoilManageR/SoilManageR/-/issues
NeedsCompilation:	no
Packaged:	2025-04-18 07:37:46 UTC; f80826871
Author:	Olivier Heller [cre, aut], Raphaël Wittwer [ctb]
Maintainer:	Olivier Heller <olivier.heller@agroscope.admin.ch>
Repository:	CRAN
Date/Publication:	2025-04-18 07:50:02 UTC

SoilManageR: 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.

Author(s)

Maintainer: Olivier Heller olivier.heller@agroscope.admin.ch (ORCID)

Other contributors:

• Raphaël Wittwer raphael.witter@agroscope.admin.ch (ORCID) [contributor]

See Also

Useful links:

• https://gitlab.com/SoilManageR/

• Report bugs at https://gitlab.com/SoilManageR/SoilManageR/-/issues

Pipe operator

Description

See magrittr::%>% for details.

Usage

lhs %>% rhs

Arguments

Value

The result of calling 'rhs(lhs)'.

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
)

Arguments

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

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.

See Also

• C_input() to calculate C inputs for a management_df

• N_input() to calculate N inputs for a management_df

• C_input_crops() to calculate C inputs by crops

• C_input_cover_crops() to calculate C input for cover crops

• CN_input_amendments_LUT() for the look-up-table for organic amendments reference values

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)

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

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

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.

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.

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)

Arguments

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.

See Also

• calculate_indicators() to calculate all management indicators for a management_df

• calculate_C_input_tibble() a helper function that calculates the C input tibble

• C_input_crops() to calculate C input for crops

• C_input_cover_crops() to calculate C input for cover crops

• CN_input_amendments() to calculate C (and N) inputs for organic amendments

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)

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 = 0,
  min_days = 0,
  max_C_abvg = 1916,
  max_days = 240,
  Cc_biomass = 450
)

Arguments

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 0 and 240 days respectively, and linearly interpolated for the period in between.

C_{Product} = \begin{cases} 0 \ kgC/ha \ , \ duration = 0 \ days \\ duration \ * \frac{1916 \ kgC/ha}{240 \ days} \ , \ 0 \ days < duration \leq 240 \ days \\ 1916 \ kgC/ha \ , \ duration > 240 \ days \end{cases}

Assumptions on the C inputs at day 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.

See Also

• C_input() to calculate C inputs for a management_df

• C_input_crops() to calculate C input for crops

• CN_input_amendments() to calculate C (and N) inputs for organic amendments

• C_input_crops_LUT() for the look-up-table for crop reference values

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)

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
)

Arguments

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.

See Also

• C_input() to calculate C inputs for a management_df

• C_input_cover_crops() to calculate C input for cover crops

• CN_input_amendments() to calculate C (and N) inputs for organic amendments

• C_input_crops_LUT() for the look-up-table for crop reference values

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) 

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

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, 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.

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.

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.

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

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]

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)

Arguments

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.

See Also

• calculate_indicators() to calculate all management indicators for a management_df

• CN_input_amendments() for the calculation of N inputs from organic amendments

• calculate_N_input_tibble() a helper function that calculates the N input tibble

• calculate_indicators() to calculate all management indicators for a management_df

• CN_input_amendments() for the calculation of N inputs from organic amendments

• calculate_N_input_tibble() a helper function that calculates the N input tibble

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) 

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
)

Arguments

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.

See Also

• tillage_intensity() to calculate STIR values for management_df

• STIR_values_LUT() for the reference data used by the STIR() function

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")

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

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

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.

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)

Arguments

Value

a rearranged management_df

Examples

#rearrange EXAMPLE data
arrange_management_df(EXAMPLE_data)

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)

Arguments

Details

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

Value

tibble with C input values

See Also

• calculate_indicators() to calculate all management indicators for a management_df

• C_input_crops() to calculate C input for crops

• C_input_cover_crops() to calculate C input for cover crops

• CN_input_amendments() to calculate C (and N) inputs for organic amendments

• C_input() a function that calculates the N input by mineral and organic fertilization and summarizes it by year

Examples

#Calculate C input tibble
calculate_C_input_tibble(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)

Arguments

Details

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

Value

tibble with N and LSU values

See Also

• calculate_indicators() to calculate all management indicators for a management_df

• CN_input_amendments() for the calculation of N inputs from organic amendments

• N_input() a function that calculates the N input by mineral and organic fertilization and summarizes it by year

Examples

#Calculate N input tibble
calculate_N_input_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)

Arguments

Details

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

Value

a tibble with all management operations and STIR values

See Also

• STIR() for the calculation of a STIR value for operations

• STIR_values_LUT() for the reference data used for tillage operations

• calculate_indicators() to calculate all management indicators for a management_df

• tillage_intensity() aggregates the STIR values

Examples

#example that returns a tibble with all operations that have a STIR value
calculate_STIR_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)

Arguments

Value

data frame with indices per year

See Also

• check_management_df()

• C_input()

• tillage_intensity()

• soil_cover()

• N_input()

• plant_diversity()

• productivity_indicator()

Examples

  #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)

Arguments

Value

a tibble with the indicator values per crop

Examples

# calculate examples

  calculate_indicators_by_crop(EXAMPLE_data)

# this would return a tibble, but it can take a while

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)

Arguments

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.

See Also

• calculate_indicators() to calculate all management indicators for a management_df

• soil_cover() aggregates the soil cover information

• plant_cover() for more detail on the plant cover function

• plot.soil_cover_tibble() for plotting the soil_cover_tibble

• STIR_values_LUT for tillage operation specific burial coefficients

Examples

  #example that returns a soil_cover_tibble
  calculate_soil_cover_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. It is checked if the are operations where the device is not mentioned. 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)

Arguments

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)

See Also

• management_df() for creating an management_df

• management_df_from_excel() for importing a management_df from an excel template

Examples

#example input
check_management_df(EXAMPLE_data)

Count number of pesticide applications

Description

Counts the number of pesticide applications in the management dataframe. Besides, seperate counts of herbicide, fungicide and insecticide applications are provided.

Usage

count_pesticide_applications(var_management_df)

Arguments

Value

list with two tibbles: yearly and cropwise counts of pesticide applications

Examples

# count pesticide applications in the EXAMPLE_data
count_pesticide_applications(EXAMPLE_data)

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)

Arguments

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")

Identify main crops

Description

Identifies all main crops that are harvested in the management dataframe.

Usage

identify_main_crops(var_management_df)

Arguments

Value

a management dataframe with a column "crop_no" that identifies the main crops

Examples

# identify main crop in the EXAMPLE data
identify_main_crops(EXAMPLE_data)

Extract indicators by a specific date

Description

Calculate for a specific date (e.g. day of sampling) and discount the indicator values of events in the past. The default half life time of all discounts is set to 1 year (365 days).

Usage

indicators_by_date(
  var_management_df,
  var_date,
  half_life_time_STIR = 365,
  half_life_time_C_input = 365,
  half_life_time_N_input = 365,
  half_life_time_soil_cover = 365
)

Arguments

Value

tibble with the discounted indicator values per date

Examples

# calculate indicators by a specific date

  indicators_by_date(EXAMPLE_data, "2019-10-03")
 
# this would return a tibble, but it may take a while

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
)

Arguments

Value

a management_df()

See Also

• management_df_from_excel() for importing a management_df from an excel template

• check_management_df() to check the integrity of a management_df

• EXAMPLE_data() for an example of a management_df

Examples

#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
)

Arguments

Value

a management_df

See Also

• management_df() for creating an management_df

• check_management_df() to check the integrity of 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)

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)

Arguments

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.

See Also

• soil_cover() to calculate soil coverage by plants and residue for a management_df

• plant_cover_LUT() for the data used by the plant_cover() function

Examples

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

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

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.

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)

Arguments

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 crop diversity index (CDI), the total number of different species, and the Shannon index for all sown species.

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

CDI = \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.

See Also

• calculate_indicators() to calculate all management indicators for a management_df

• shannon_index() for more detail on the Shannon index

Examples

plant_diversity(EXAMPLE_data,2013,2020)

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, ...)

Arguments

Value

none, it plots the soil_cover_tibble

Examples

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

Plot a management dataframe

Description

Visual representation of a management_df.

Usage

plot_management_df(management_df, title = "Management Timeline")

Arguments

Details

The colors in the background represent the soil cover by main crops (dark green), cover crops (light green), and crop residues (brown). "T" indicates tillage event with a STIR value >= 0. "P" indicates a crop protection event. 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. Dotted vertical lines represent sowing events, the sown crop or cover crop is noted in the lowest quarter at the right side of the line. 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)
 

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)

Arguments

Value

• By default, a tibble with relative yields by year is returned.

• If extended.output = TRUE, a tibble with additional information is returned.

See Also

• relative_yield() for caluclating relative yields

• calculate_indicators() for calculating all soil management indicators

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)

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)

Arguments

Value

the numeric value for the relative yield

References

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.

See Also

• productivity_indicator() to calculate relative yields for a management_df

• C_input_crops_LUT() for reference yield used in the relative_yield() function

Examples

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_{i=1}^S (p_i * ln(p_i))

where p_i is the relative abundance of each species (i) of the total number of species (S).

Usage

shannon_index(var_tibble)

Arguments

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.

See Also

plant_diversity() to calculate the shannon_index() (and other diversity indices) for a management_df

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...

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)

Arguments

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.

See Also

• calculate_indicators() to calculate all management indicators for a management_df

• calculate_soil_cover_tibble() a helper function that calculates the soil cover tibble

• plant_cover() for more detail on the plant cover function

• plot.soil_cover_tibble() for plotting the soil_cover_tibble

• STIR_values_LUT for tillage operation specific burial coefficients

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)

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)

Arguments

Value

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

See Also

• STIR() for the calculation of a STIR value for operations

• STIR_values_LUT() for the reference data used for tillage operations

• calculate_indicators() to calculate all management indicators for a management_df

• calculate_STIR_tibble() a helper function that calculates the STIR input tibble

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)