allocate: Allocate soil properties within various classification systems.

Description

Generic function to allocate soil properties to different classification schemes.

Usage

allocate(
  ...,
  to = c("FAO Salt Severity", "FAO Black Soil", "ST Diagnostic Features"),
  droplevels = TRUE
)

Arguments

...

arguments to specific allocation functions, see details and examples

character specifying the classification scheme: FAO Salt Severity, FAO Black Soil (see details for the required ...)

droplevels

logical indicating whether to drop unused levels in factors. This is useful when the results have a large number of unused classes, which can waste space in tables and figures.

Value

A vector or data.frame object.

Details

This function is intended to allocate a set of soil properties to an established soil classification scheme, such as Salt Severity or Black Soil. Allocation is semantically different from classification. While classification is the 'act' of developing a grouping scheme, allocation is the assignment or identification of measurements to a established class (Powell, 2008).

Usage Details

Each classification scheme (to argument) uses a different set of arguments.

FAO Salt Severity
- EC: electrical conductivity column name, dS/m
- pH: pH column name, saturated paste extract
- ESP: exchangeable sodium percentage column name, percent
FAO Black Soils
- object: a data.frame or SoilProfileCollection
- pedonid: pedon ID column name, required when object is a data.frame
- hztop: horizon top depth column name, required when object is a data.frame
- hzbot: horizon bottom depth column name, required when object is a data.frame
- OC: organic carbon column name, percent
- m_chroma: moist Munsell chroma column name
- m_value: moist Munsell value column name
- d_value: dry Munsell value column name
- CEC: cation exchange capacity column name (NH4OAc at pH 7), units of cmol(+)/kg soil
- BS: base saturation column name (NH4OAc at pH 7), percent
- tropical: logical, data are associated with "tropical soils"
ST Diagnostic Features
- object: a data.frame or SoilProfileCollection
- pedonid: pedon ID column name, required when object is a data.frame
- hzname: horizon name column, required when object is a data.frame
- hztop: horizon top depth column name, required when object is a data.frame
- hzbot: horizon bottom depth column name, required when object is a data.frame
- texture: soil texture class (USDA) column name
- rupresblkcem: rupture resistance column name
- m_value: moist Munsell value column name
- m_chroma: moist Munsell chroma column name
- d_value: dry Munsell value column name
- BS: base saturation column name (method ??), percent
- OC: organic carbon column name, percent
- n_value: ??
- featkind: ??

References

Abrol, I., Yadav, J. & Massoud, F. 1988. Salt-affected soils and their management. No. Bulletin 39. Rome, FAO Soils.

FAO. 2006. Guidelines for soil description. Rome, Food and Agriculture Organization of the United Nations.

FAO. 2020. DEFINITION | What is a black soil? (online). (Cited 28 December 2020). http://www.fao.org/global-soil-partnership/intergovernmental-technical-panel-soils/gsoc17-implementation/internationalnetworkblacksoils/more-on-black-soils/definition-what-is-a-black-soil/es/

Powell, B., 2008. Classifying soil and land, in: McKenzie, N.J., Grundy, M.J., Webster, R., Ringrose-Voase, A.J. (Eds.), Guidelines for Survey Soil and Land Resources, Australian Soil and Land Survey Handbook Series. CSIRO, Melbourne, p. 572.

Richards, L.A. 1954. Diagnosis and Improvement of Saline and Alkali Soils. U. S. Government Printing Office. 166 pp.

Soil Survey Staff, 2014. Keys to Soil Taxonomy, 12th ed. USDA-Natural Resources Conservation Service, Washington, D.C.

Examples

Run this code

# NOT RUN {
# Salt Severity
test <- expand.grid(
  EC  = sort(sapply(c(0, 0.75, 2, 4, 8, 15, 30), function(x) x + c(0, -0.05, 0.05))),
  pH  = c(8.1, 8.2, 8.3, 8.4, 8.5, 8.6),
  ESP = sort(sapply(c(0, 15, 30, 50, 70, 100), function(x) x + c(0, 0.1, -0.1)))
)
test$ss      <- with(test, allocate(EC = EC, pH = pH, ESP = ESP, to = "FAO Salt Severity"))
table(test$ss)

# Black Soil Category 1 (BS1)
test <- expand.grid(
  dept = seq(0, 50, 10),
  OC   = sort(sapply(c(0, 0.6, 1.2, 20, 40), function(x) x + c(0, -0.05, 0.05))),
  chroma_moist  = 2:4,
  value_moist   = 2:4,
  value_dry     = 4:6,
  thickness     = 24:26,
  CEC           = 24:26,
  BS            = 49:51,
  tropical      = c(TRUE, FALSE)
)
test$pedon_id <- rep(1:21870, each = 6)
test$depb     <- test$dept + 10

bs1 <- allocate(test, pedonid = "pedon_id", hztop = "dept", hzbot = "depb", 
                OC = "OC", m_chroma = "chroma_moist", m_value = "value_moist", 
                d_value = "value_dry", CEC = "CEC", BS = "BS", 
                to = "FAO Black Soil"
)

table(BS1 = bs1$BS1, BS2 = bs1$BS2)


# SoilProfileCollection interface

data(sp3)
depths(sp3) <- id ~ top + bottom
hzdesgnname(sp3) <- 'name'

# fake base saturation
horizons(sp3)$bs <- 75

plotSPC(sp3)

allocate(
  sp3, 
  to = 'FAO Black Soil', 
  OC = 'tc', 
  m_chroma = 'chroma', 
  m_value = 'value', 
  d_value = 'value',
  CEC = 'cec',
  BS = 'bs'
)

# make a copy and edit horizon values
x <- sp3
x$value <- 2
x$chroma <- 2
x$cec <- 26
x$tc <- 2

x$soil_color <- munsell2rgb(x$hue, x$value, x$chroma)

plotSPC(x)

allocate(
  x, 
  to = 'FAO Black Soil', 
  OC = 'tc', 
  m_chroma = 'chroma', 
  m_value = 'value', 
  d_value = 'value',
  CEC = 'cec',
  BS = 'bs'
)


# Soil Taxonomy Diagnostic Features
data(sp1)
df <- allocate(object = sp1, pedonid = "id", hzname = "name", 
               hzdept = "top", hzdepb = "bot", texture = "texture", 
               to = "ST Diagnostic Features"
)
aggregate(featdept ~ id, data = df, summary)

# }

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