Allocate soil properties within various classification systems.

Source: R/allocate.R

Generic function to allocate soil properties to different classification schemes.

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

Arguments

...

arguments to specific allocation functions, see details and examples

to

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

    • texcl: 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: ??

Note

The results returned by allocate(to = "ST Diagnostic Features") currently return a limited set of diagnostic features that are easily defined. Also, the logic implemented for some features does not include all the criteria defined in the Keys to Soil Taxonomy.

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


# 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)
#> 
#>     extremely saline very strongly saline      strongly saline 
#>                  150                   90                   90 
#>    moderately saline      slightly saline                 none 
#>                   90                   90                  120 
#>       slightly sodic     moderately sodic       strongly sodic 
#>                  132                  198                  198 
#>  very strongly sodic         saline-sodic 
#>                  330                  780 

# 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)
#>        BS2
#> BS1     FALSE  TRUE
#>   FALSE 20142   960
#>   TRUE      0   768


# 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'
)
#>    peiid   BS1   BS2
#> 1      1 FALSE FALSE
#> 2     10 FALSE FALSE
#> 3      2 FALSE FALSE
#> 4      3 FALSE FALSE
#> 5      4 FALSE FALSE
#> 6      5 FALSE FALSE
#> 7      6 FALSE FALSE
#> 8      7 FALSE FALSE
#> 9      8 FALSE FALSE
#> 10     9 FALSE FALSE

# 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'
)
#>    peiid  BS1  BS2
#> 1      1 TRUE TRUE
#> 2     10 TRUE TRUE
#> 3      2 TRUE TRUE
#> 4      3 TRUE TRUE
#> 5      4 TRUE TRUE
#> 6      5 TRUE TRUE
#> 7      6 TRUE TRUE
#> 8      7 TRUE TRUE
#> 9      8 TRUE TRUE
#> 10     9 TRUE TRUE


# Soil Taxonomy Diagnostic Features
data(sp1)
sp1$texcl = gsub("gr|grv|cbv", "", sp1$texture)
df <- allocate(object = sp1, pedonid = "id", hzname = "name", 
               hzdept = "top", hzdepb = "bottom", texcl = "texcl", 
               to = "ST Diagnostic Features"
)
#> Warning: the minimum dataset includes: pedonid, hzdept, hzdepb, and hzname; if texcl or rupreblkcem are missing the resulting diagnostic features are inferred from the available information
#> guessing lithic contact
#> Warning: the minimum dataset includes: pedonid, hzdept, hzdepb, and hzname; if texcl or rupreblkcem are missing the resulting diagnostic features are inferred from the available information
#> guessing paralithic contact
#> Warning: the minimum dataset includes: pedonid, hzdept, hzdepb, and hzname; if texcl or rupreblkcem are missing the resulting diagnostic features are inferred from the available information
#> guessing densic contact
#> Warning: the minimum dataset includes: pedonid, hzdept, hzdepb, and hzname; if texcl or rupreblkcem are missing the resulting diagnostic features are inferred from the available information
#> guessing petrocalcic horizon
#> Warning: the minimum dataset includes: pedonid, hzdept, hzdepb, and hzname; if texcl or rupreblkcem are missing the resulting diagnostic features are inferred from the available information
#> guessing calcic horizon
#> Warning: the minimum dataset includes: pedonid, hzdept, hzdepb, and hzname; if texcl or rupreblkcem are missing the resulting diagnostic features are inferred from the available information
#> guessing secondary carbonates
#> Warning: the minimum dataset includes: pedonid, hzdept, hzdepb, and hzname; if texcl or rupreblkcem are missing the resulting diagnostic features are inferred from the available information
#> guessing mollic epipedon
aggregate(featdept ~ id, data = df, summary)
#>     id featdept.Min. featdept.1st Qu. featdept.Median featdept.Mean
#> 1 P001            89               89              89            89
#>   featdept.3rd Qu. featdept.Max.
#> 1               89            89