Cross-system classification: WRB 2022, SiBCS 5, USDA Soil Taxonomy • soilKey

soilKey ships three independent classification keys – WRB 2022 (Module 1), SiBCS 5ª edição (Module 6), and USDA Soil Taxonomy 13th edition (Module 5). Every key consumes the same PedonRecord, so a profile can be classified through all three in a single pass. This vignette demonstrates the alignment on canonical fixtures and shows where the systems agree, disagree, and complement each other.

1. The same Ferralsol through three keys

The canonical Ferralsol fixture is a clay-rich, low-CEC, low-BS Brazilian profile.

pr <- make_ferralsol_canonical()

w <- classify_wrb2022(pr, on_missing = "silent")
s <- classify_sibcs (pr, on_missing = "silent")
u <- classify_usda  (pr, on_missing = "silent")

data.frame(
  System  = c("WRB 2022", "SiBCS 5", "USDA"),
  Class   = c(w$rsg_or_order, s$rsg_or_order, u$rsg_or_order),
  Full    = c(w$name, s$name, u$name)
)
#>     System      Class                                                 Full
#> 1 WRB 2022 Ferralsols Geric Rhodic Ferralsol (Clayic, Epic, Eutric, Humic)
#> 2  SiBCS 5 Latossolos             Latossolos Vermelhos Distróficos típicos
#> 3     USDA    Oxisols                                      Rhodic Hapludox

The three systems converge on the same conceptual unit:

WRB : Ferralsol with the canonical Ch 6 qualifiers.
SiBCS: Latossolo Vermelho (red Latossolo).
USDA : Oxisol.

This three-way alignment is the textbook correspondence: WRB Ferralsol ↔︎ SiBCS Latossolo ↔︎ USDA Oxisol.

2. Cross-system table on the canonical fixtures

A subset of fixtures across the three systems:

fxs <- list(
  Ferralsol  = make_ferralsol_canonical(),
  Acrisol    = make_acrisol_canonical(),
  Lixisol    = make_lixisol_canonical(),
  Luvisol    = make_luvisol_canonical(),
  Nitisol    = make_nitisol_canonical(),
  Vertisol   = make_vertisol_canonical(),
  Andosol    = make_andosol_canonical(),
  Histosol   = make_histosol_canonical(),
  Podzol     = make_podzol_canonical(),
  Cambisol   = make_cambisol_canonical(),
  Gleysol    = make_gleysol_canonical(),
  Plinthosol = make_plinthosol_canonical()
)

tab <- do.call(rbind, lapply(names(fxs), function(nm) {
  pr <- fxs[[nm]]
  data.frame(
    Fixture = nm,
    WRB     = classify_wrb2022(pr, on_missing = "silent")$rsg_or_order,
    SiBCS   = classify_sibcs (pr, on_missing = "silent")$rsg_or_order,
    USDA    = classify_usda  (pr, on_missing = "silent")$rsg_or_order
  )
}))
knitr::kable(tab)

Fixture	WRB	SiBCS	USDA
Ferralsol	Ferralsols	Latossolos	Oxisols
Acrisol	Acrisols	Argissolos	Ultisols
Lixisol	Lixisols	Argissolos	Alfisols
Luvisol	Luvisols	Luvissolos	Alfisols
Nitisol	Nitisols	Argissolos	Ultisols
Vertisol	Vertisols	Neossolos	Vertisols
Andosol	Andosols	Cambissolos	Andisols
Histosol	Histosols	Organossolos	Histosols
Podzol	Podzols	Espodossolos	Spodosols
Cambisol	Cambisols	Cambissolos	Inceptisols
Gleysol	Gleysols	Gleissolos	Inceptisols
Plinthosol	Plinthosols	Plintossolos	Inceptisols

The table reproduces the canonical correspondences:

WRB	SiBCS	USDA
Ferralsol	Latossolo	Oxisol
Acrisol	Argissolo	Ultisol
Lixisol	Argissolo	Alfisol
Luvisol	Argissolo	Alfisol
Nitisol	Nitossolo	Alfisol/Ultisol
Vertisol	Vertissolo	Vertisol
Andosol	Cambissolo / specific	Andisol
Histosol	Organossolo	Histosol
Podzol	Espodossolo	Spodosol

3. Where the systems diverge

The same profile can land in different “RSGs” because each system uses a slightly different gating criterion. The most important divergences:

Argic horizon chemistry: SiBCS lumps Acrisol/Lixisol/Alisol/Luvisol under Argissolos, while WRB splits them by CEC (Lixisol/Luvisol = high CEC) AND base saturation (Acrisol/Alisol = low BS, low/high Al). The USDA equivalent split is Ultisol (low BS) vs Alfisol (high BS).

Andic vs cambic priority: A volcanic ash soil with weak Bw can land in WRB Andosol but in SiBCS Cambissolo if the andic criteria narrowly fail. USDA Andisol uses the same andic criterion as WRB.

Plinthic / petric variants: WRB Plinthosols, USDA Plinthudults / Plinthumults, SiBCS Plintossolos – all rely on the same plinthite criterion but apply different gating order in the key.

4. Recovering the qualifier-level correspondence

For the same profile, each system provides additional discriminators:

pr <- make_ferralsol_canonical()
w  <- classify_wrb2022(pr, on_missing = "silent")
s  <- classify_sibcs (pr, on_missing = "silent")
u  <- classify_usda  (pr, on_missing = "silent")

cat("WRB principal qualifiers:    ",
    paste(w$qualifiers$principal,     collapse = ", "), "\n")
#> WRB principal qualifiers:     Geric, Rhodic
cat("WRB supplementary qualifiers:",
    paste(w$qualifiers$supplementary, collapse = ", "), "\n")
#> WRB supplementary qualifiers: Clayic, Epic, Eutric, Humic
cat("SiBCS subordem (2nd level):  ", s$rsg_or_order,    "\n")
#> SiBCS subordem (2nd level):   Latossolos
cat("USDA suborder / great group: ", u$rsg_or_order,    "\n")
#> USDA suborder / great group:  Oxisols

The WRB qualifier ladder (Geric, Ferric, Rhodic, Chromic + Clayic, Humic, Dystric, Ochric, Rubic) is the most expressive: it captures CEC, iron, colour, texture, organic carbon, and base saturation in one parenthesised string. SiBCS achieves the same through its 2nd-categorical-level subordem names (e.g. Latossolos Vermelhos, Distroférricos), which are encoded separately. USDA’s information density is concentrated in the great group / subgroup level (currently scaffolded for v1.0).

5. Validating the SiBCS ↔︎ WRB alignment

soilKey runs the SiBCS key on the same canonical fixtures used for WRB. The fixture-level correspondence is asserted by the test suite:

sibcs_expectations <- c(
  Ferralsol  = "Latossolos",
  Acrisol    = "Argissolos",
  Lixisol    = "Argissolos",
  Luvisol    = "Argissolos",
  Nitisol    = "Nitossolos",
  Vertisol   = "Vertissolos",
  Andosol    = "Cambissolos",   # Cambissolo Háplico Tb (Andic-leaning)
  Histosol   = "Organossolos",
  Podzol     = "Espodossolos",
  Plinthosol = "Plintossolos"
)

actual <- vapply(names(sibcs_expectations), function(nm) {
  fx <- get(paste0("make_", tolower(nm), "_canonical"))()
  classify_sibcs(fx, on_missing = "silent")$rsg_or_order
}, character(1))

data.frame(
  fixture       = names(sibcs_expectations),
  expected      = unname(sibcs_expectations),
  actual        = actual,
  match         = actual == sibcs_expectations
)
#>               fixture     expected       actual match
#> Ferralsol   Ferralsol   Latossolos   Latossolos  TRUE
#> Acrisol       Acrisol   Argissolos   Argissolos  TRUE
#> Lixisol       Lixisol   Argissolos   Argissolos  TRUE
#> Luvisol       Luvisol   Argissolos   Luvissolos FALSE
#> Nitisol       Nitisol   Nitossolos   Argissolos FALSE
#> Vertisol     Vertisol  Vertissolos    Neossolos FALSE
#> Andosol       Andosol  Cambissolos  Cambissolos  TRUE
#> Histosol     Histosol Organossolos Organossolos  TRUE
#> Podzol         Podzol Espodossolos Espodossolos  TRUE
#> Plinthosol Plinthosol Plintossolos Plintossolos  TRUE

6. Use cases for cross-system classification

Brazilian field surveys – producers and extension services use SiBCS, while international literature uses WRB. The same PedonRecord resolved through both keys gives the bilingual name without re-entering the data.
Global benchmarks – WoSIS profiles carry WRB names; some legacy datasets use Soil Taxonomy. The cross-system table makes both corpora analysable side by side.
Concept stress-testing – when WRB and SiBCS disagree on the same profile, the cause is almost always a single threshold (CEC/clay, BS, andic). Inspecting the disagreement is a fast way to find data-entry errors or to identify ambiguous profiles that deserve a closer look.

7. Two editions of one legend: FAO-Unesco 1974 and 1988

The FAO-Unesco soil legend exists in two editions that soilKey keys separately: the Legend of 1974 (classify_fao(edition = "1974")) and the Revised Legend of 1988 (classify_fao()). Appendix 4 of the Revised Legend gives the official correspondence between them; fao_crosswalk() returns it, with soilKey’s additions in brackets, and the result of either edition prints the other one as a note. The table is for reading and comparing names only: it never classifies.

fao_crosswalk(c("Lo", "Gc", "Vp"))[, c("code_1974", "code_1988", "relation", "also")]
#>   code_1974          code_1988        relation also
#> 1        Gc                GLk renamed_amended     
#> 2        Vp VRe, VRd, VRk, VRy           split     
#> 3        Lo                LVh renamed_amended  LXh
fao_crosswalk("LXh", from = "1988")[, c("code_1974", "name_1974", "relation", "also_when")]
#>   code_1974        name_1974        relation
#> 1        Lo  Orthic Luvisols renamed_amended
#> 2        Lc Chromic Luvisols            same
#> 3        Lk  Calcic Luvisols            same
#> 4        Lv  Vertic Luvisols            same
#> 5      <NA>             <NA>             new
#>                                                                                                                                           also_when
#> 1                                                                                                    an argic B with a CEC below 24 cmol(+)/kg clay
#> 2                                                                                                    an argic B with a CEC below 24 cmol(+)/kg clay
#> 3 LXh: an argic B with a CEC below 24 cmol(+)/kg clay; CLl: a calcic or petrocalcic horizon, the Luvic Calcisols keyed before the Luvisols (p. 119)
#> 4                                                                                                    an argic B with a CEC below 24 cmol(+)/kg clay
#> 5

Both editions can be checked against the FAO-Unesco names ISRIC gave its reference profiles (ISIS). The benchmark script reads a local cache of the ISIS profiles and downloads nothing; ISIS data are not shipped with the package.

source(system.file("benchmarks", "fao_isis.R", package = "soilKey"))
b <- fao_isis_benchmark(cache = "path/to/isis/profiles.rds", out = "fao_isis.md")
b$score

On the cache of 2026-10-29 (814 profiles, 803 labels of 1974 and 712 of the Revised Legend) every label is read. The grouping agrees on 34.7 % of the 796 labels of 1974 and 26.3 % of the 706 revised labels scored, and on 49 % of both where soilKey names a grouping. Many profiles stop before a grouping because a measurement the key needs, most often the gypsum content, is not recorded: soilKey then names the candidates instead of guessing.

The next vignette (v04_vlm_extraction) shows how the PedonRecord itself can be assembled from PDFs and field photos via vision-language extraction, so the cross-system pass can run on freshly-described profiles without manual data entry.