soilKey

Automated soil profile classification under WRB 2022 (4th ed.), USDA Soil Taxonomy (13th ed.), and the Brazilian SiBCS (5th ed.). All three systems wired end-to-end, down to the deepest categorical level, in pure R driven from versioned YAML rules. Multimodal extraction, spatial priors, OSSL spectroscopy, and explicit per-attribute provenance — without ever delegating the taxonomic key to a language model.

✦ Status at a glance

Legend: ✅ shipped · 🟡 in queue · 🔵 idea / roadmap

✦ The headline result

A canonical Brazilian Latossolo Vermelho on tropical gneiss, classified end-to-end across the three canonical systems down to the deepest level:

library(soilKey)

pedon <- make_ferralsol_canonical()

# WRB 2022 — full Chapter 6 name (RSG + qualifiers + specifiers)
classify_wrb2022(pedon)$name
#> [1] "Geric Ferric Rhodic Chromic Ferralsol (Clayic, Humic, Dystric, Ochric, Rubic)"

# SiBCS 5 — 4th level (Subgroup) + Family (5th level)
classify_sibcs(pedon, include_familia = TRUE)$name
#> [1] "Latossolos Vermelhos Distroficos tipicos, argilosa, moderado"

# USDA Soil Taxonomy 13 — Order -> Suborder -> Great Group -> Subgroup -> Family
classify_usda(pedon, include_family = TRUE)$name
#> [1] "fine, kaolinitic, isohyperthermic Rhodic Hapludox"

• WRB delivers the complete Chapter 6 name — four principal qualifiers + five supplementary qualifiers in canonical order, with optional depth specifiers (Epi-/Endo-/Bathy-/…, via classify_wrb2022(specifiers = TRUE)).
• SiBCS descends through all four hierarchical levels (Order → Suborder → Great Group → Subgroup) plus a 5th-level Family with up to 15 orthogonal adjectival dimensions.
• USDA Soil Taxonomy walks the complete Path C (Order → Suborder → Great Group → Subgroup) per Keys to Soil Taxonomy 13th ed., plus the 5th-level family modifiers (include_family = TRUE).

All three keys are deterministic R code driven from versioned YAML rules.

✦ What’s new in v0.9.109 (2026-06-11)

• v0.9.109 — CRAN release hardening. A readiness audit found a full R CMD check was clean only because CI didn’t pass --as-cran; under --as-cran, 545 exported topics lacked a \value section (a likely CRAN rejection). The ~600 atomic taxonomic-engine predicates (qual_*, *_usda gates, carater_* / horizonte_*) are now @keywords internal — still exported and callable, but out of the public reference index, trimming the documented API from ~910 to ~195 topics; the remaining ~85 public topics gained \value. The package now passes R CMD check --as-cran with 0 errors / 0 warnings. Runnable \examples were added to the entry points, CI now runs --as-cran + pkgdown::check_pkgdown(), and the release metadata (cran-comments, CITATION.cff, lifecycle → maturing) was refreshed. No user-visible behaviour changed.

✦ What’s new in v0.9.108 (2026-06-11)

• v0.9.108 — Pro app polish. The professional Shiny app (run_classify_app(ui = "pro")) gets a thorough UX pass — the last of the three follow-up fronts (benchmarks → accuracy → app). A soil-science theme (topsoil-brown / terracotta / moss palette over flatly, plus a slim www/soilkey.css) and a navbar wordmark give it an identity; a global pedon ribbon keeps the active profile (id, horizons, coordinates, build status) visible on every tab; a “Getting started” Help modal offers a one-click Load example & classify that builds the canonical Ferralsol through the real Pedon flow and jumps straight to the results. The Spectra tab plots the attached Vis-NIR spectrum (one trace per horizon) and the Photo tab previews the uploaded image with the VLM confidence as an evidence badge. Coordinates are range-validated before a pedon is built, the USDA-family and WRB-depth-specifier toggles are surfaced in the Classify sidebar (two-way-synced with Settings), and the Report now honours both: report(pedon, include_family = TRUE, specifiers = TRUE) forwards the flags to the keys. The two new report() arguments default to FALSE, so the output stays byte-identical unless opted in. No new dependencies.

✦ What’s new in v0.9.105 (2026-06-10)

• v0.9.105 — WRB depth specifiers. classify_wrb2022(pedon, specifiers = TRUE) auto-attaches the WRB 2022 Chapter 5 depth specifiers (Epi-/Endo-/Bathy-/Amphi-/Panto-/Kato-) to depth-anchored qualifiers, computed from the diagnostic feature’s actual depth — e.g. a gleyic feature confined to 50–100 cm becomes Endogleyic. The engine existed since v0.9.2.B but only fired on already-prefixed names; this wires in the automatic computation. Applied to subsurface qualifiers only (epipedons like Mollic/Umbric are excluded — their depth is definitional). Default specifiers = FALSE keeps the canonical names byte-identical (verified across every canonical fixture). Exposed on classify_all() and via a Settings toggle in the Pro app.

✦ What’s new in v0.9.104 (2026-06-10)

• v0.9.104 — USDA family (5th level). USDA Soil Taxonomy now reaches its deepest formal category: classify_usda(pedon, include_family = TRUE) prepends the family modifiers to the subgroup, e.g. “fine, kaolinitic, isohyperthermic Rhodic Hapludox”. Like the SiBCS familia, the family is computed, not keyed — six orthogonal dimensions (particle-size, mineralogy via compute_ki/compute_kr, CEC-activity, reaction, temperature regime, depth), each a FamilyAttribute carrying its evidence and missing fields. The soil temperature regime uses site$soil_temperature_regime when present, else infers it from latitude/elevation (flagged as inferred). classify_all(include_family = TRUE) and a Settings toggle in the Pro app expose it. The default (include_family = FALSE) is byte-identical to before. All three systems now classify to their deepest formal level.

✦ What’s new in v0.9.101 → v0.9.103 (2026-06-10)

The mapping roadmap, complete — the Pro Shiny app’s three cartographic surfaces.

• v0.9.101 — Interactive map tab. The Pro Shiny app gains a ninth tab, Map: an interactive leaflet surface where you click to place a point and query the SoilGrids class prior at that location via soil_classes_at_location(). The tab renders the queried buffer, the ranked class distribution (WRB 2022 / USDA ST 13 / SiBCS 5), and the canonical typical-attribute table — and it works with or without a built pedon (a map click and a built pedon’s coordinate stay in sync). This is Phase 1 of the mapping roadmap. Adds leaflet to Suggests.
• v0.9.102 — Batch soil map. The Map tab gains a Batch classify sub-tab: classify many profiles at once and map them by class. Point sources are demo fixtures spread across Brazil (zero-data demo) or an uploaded long-format CSV (one row per horizon, grouped by id into one PedonRecord each). Every profile runs through classify_all(); points are drawn on a leaflet map coloured by reference soil group / order with a legend and per-point popups, listed in a summary table, and exportable to a GeoPackage via sf. This is Phase 2 — the genuine pedon-scale soil map, each point backed by a deterministic classification.
• v0.9.103 — Gridded prediction (DSM). The Map tab gains a Grid prediction sub-tab that produces a raster class map over a bounding box, via three selectable methods: (a) SoilGrids covariates + key — sample SoilGrids covariates per cell, build a pseudo-pedon and run the deterministic key (the differentiator: the SoilGrids MostProbable layer predicts the class by ML, this applies the key); (b) interpolate points — nearest-neighbour of the Phase-2 classified points; (c) SoilGrids overlay — the MostProbable WRB raster for comparison. Each result is summarised by class and exportable as a GeoTIFF (terra). This is Phase 3 — completing the mapping roadmap. Per §14 of ARCHITECTURE.md, the covariate method is framed as complementary to, not a replacement for, pixel-scale DSM.

✦ What’s new in v0.9.97 → v0.9.100 (2026-05-19)

Four sequential roadmap releases that turn every previously pending README item into shipped functionality.

• v0.9.97 — Professional Shiny app. run_classify_app() launches an eight-tab bslib interface: interactive pedon builder (44 canonical fixtures, editable horizon table, live plotly depth profile), tri-system classification with the full key trace, VLM photo extraction, OSSL spectral gap-fill, SoilGrids spatial prior, Monte-Carlo uncertainty, and a downloadable HTML / PDF report. The interface is bilingual (English / Portuguese) and the report follows the chosen language. The legacy single-page “classic” uploader was retired in v0.9.117 (ui = "classic" now warns and launches the Pro app).
• v0.9.98 — WRB Tier-3 strict mode. classify_wrb2022(strict = TRUE) strengthens seven Tier-2 RSG gates (Vertisols / Andosols / Gleysols / Planosols / Ferralsols / Chernozems / Kastanozems) toward the canonical WRB 2022 Chapter 4 intent — e.g. the Vertisol overlying-clay floor rises 30 → 35 %, the Chernozem base-saturation floor 50 → 80 %. strict = FALSE (the default) is byte-identical to v0.9.97; every canonical fixture classifies the same under both modes.
• v0.9.99 — Field-photo-only classification. classify_from_photos() assembles a PedonRecord entirely from VLM extraction of field photographs (Munsell colour per horizon + optional site metadata), back-fills missing attributes from a SoilGrids depth prior, and runs all three keys. Companion exports apply_soilgrids_depth_prior() (depth-resolved companion to spatial_prior_soilgrids()) and compute_per_attribute_evidence_grade() (per-cell A–E breakdown). Evidence grade E (user-assumed) was split out from D so a wholly assumed value is distinguishable from a VLM-extracted one.
• v0.9.100 — Provenance-weighted uncertainty. classify_with_uncertainty() returns a probabilistic class distribution from a Monte-Carlo perturbation of the provenance ledger — each (horizon, attribute) cell is perturbed by an amount scaled to its evidence grade (A measured ±3 %, B spectra ±7 %, C prior ±10 %, D VLM ±17 %, E assumed ±30 %). The result is a soilkey_uncertainty object: posterior P(class), Shannon entropy, and a leave-one-attribute-out sensitivity ranking that answers “what should I measure next?”. classification_robustness(provenance_aware = TRUE) exposes the same weighting on the v0.9.42 API.

✦ What’s new in v0.9.81 → v0.9.96 (2026-05-09)

The v0.9.81 → v0.9.96 release series ships 17 surgical fixes across the WRB 2022, SiBCS 5, and USDA Soil Taxonomy 13 keys, plus a CRAN-readiness polish pass. Default canonical behaviour is bit-for-bit preserved in every release; one option (soilKey.diagnostic_engine = "aqp") auto-bundles the data-quality-aware paths.

Cumulative empirical lift on five external datasets (post-v0.9.95):

Plus the v0.9.81 honest 4-level Redape benchmark: Suborder 30.9% → 39.4%, Great Group 29.1% → 35.2%, Subgroup 15.1% → 25.0%.

Highlights of the release series (full per-release diff in NEWS.md):

• v0.9.81 — benchmark_redape() now actually computes Suborder / Great Group / Subgroup accuracy.
• v0.9.82 — LUCAS Stage 3 rerun: 0% → 60% accuracy with the v0.9.66+72+77+78+79+80 stack and SoilGrids subsoil fill.
• v0.9.84 — spodic() engine-aware OC-translocation path: KSSL+NASIS Spodosols 1/14 → 5/14.
• v0.9.85 — andosol() buried-exclusion + andic OC+BD proxy thickness extension. AfSP Andosols 0/5 → 2/5.
• v0.9.86 / v0.9.89 / v0.9.90 — engine="aqp" auto-bundles the v0.9.69 ECEC fallback, the v0.9.70 texture-morphological fallback, and the v0.9.90 argic designation-inference fallback. BDsolos RJ Latossolos 14.9% → 28.1%, Order 40.3% → 46.6%.
• v0.9.91 — Strict [[reference_wrb]] access on the bundled WoSIS / KSSL / KSSL+NASIS caches (sidesteps R’s $-partial-matching footgun).
• v0.9.92 → v0.9.95 — CRAN-readiness: clean R CMD check --as-cran, lazy-fetch architecture brings the source tarball from 10 MB to 6 MB.
• v0.9.96 — README overhaul (this release): full English rewrite, expanded implementation-status table, refreshed citations.

✦ Why soilKey?

There is no public, maintained, end-to-end implementation of any of the three major soil classification systems. WRB acknowledges (in the 4th-edition preface) that internal classification algorithms exist within the IUSS Working Group but have not been released. The U.S. SoilTaxonomy package on CRAN provides lookup tables but not the key. There is zero public software for SiBCS in any language — until soilKey.

soilKey closes that gap with three principles:

1. The taxonomic key is never delegated to a language model. LLMs are restricted to schema-validated extraction. Every classification is a deterministic walk through versioned YAML rules with a full decision trace.
2. Every value carries a provenance tag. measured · predicted_spectra · extracted_vlm · inferred_prior · user_assumed. The result’s evidence grade (A–D) summarises that log so callers always know how robust the classification is.
3. Side modules never overrule the key. Spatial priors flag inconsistencies but cannot silently change the assigned RSG; spectral predictions fill missing attributes with explicit confidence; multimodal extraction pulls structured data without writing class names.

✦ Architecture

flowchart TB
  subgraph M2["Module 2 — Multimodal extraction"]
    A[PDF · Field report] --> V(VLM via ellmer)
    B[Profile photo]      --> V
    C[Field sheet]        --> V
    V --> J["JSON-Schema<br/>validation + retry"]
  end

  subgraph M4["Module 4 — Spectra"]
    K[Vis-NIR / SWIR / MIR] --> O("OSSL prediction<br/>MBL · PLSR-local · pretrained")
    O --> P["PI95 → confidence"]
  end

  subgraph M3["Module 3 — Spatial prior"]
    S[SoilGrids WCS]   --> R(("P(RSG)"))
    EM[National soil map]    --> R
  end

  J --> PR["PedonRecord<br/>(provenance log)"]
  P --> PR

  PR --> M1["Module 1 — Taxonomic keys"]
  M1 --> W["WRB 2022 key<br/>32 RSGs · Ch 4–6 (qualifiers + specifiers)"]
  M1 --> SC["SiBCS 5 key<br/>13 Orders · 44 Suborders · 192 GG · 938 SG · Family"]
  M1 --> U["USDA ST 13<br/>12 Orders · 68 Suborders · 339 GG · 1288 SG"]

  W --> CR["ClassificationResult<br/>name · trace · evidence grade"]
  SC --> CR
  U --> CR
  R -.consistency check.-> CR

Module 1 (the key) and the side modules (extraction / spectra / spatial) are independent. A profile with no spectra still classifies; a profile with full lab data still benefits from the spatial-prior consistency check.

✦ Coverage

soilKey faithfully reproduces three canonical books, with versioned YAML rules cross-referencing the page numbers of each diagnostic and qualifier definition.

WRB 2022 (4th edition, IUSS Working Group)

SiBCS 5th ed. (Embrapa, 2018) — all 5 levels wired

USDA Soil Taxonomy (13th edition, Soil Survey Staff 2022) — Path C complete

✦ Installation

# install.packages("remotes")
remotes::install_github("HugoMachadoRodrigues/soilKey")

# Or via devtools
# install.packages("devtools")
devtools::install_github("HugoMachadoRodrigues/soilKey")

Optional benchmark caches (4 datasets × ~1 MB each) are downloaded on demand on first call to any load_*_sample() function. To prefetch them all into the user cache:

soilKey::download_extdata_cache("all")

✦ Quick start

1. Build a PedonRecord from horizon data

library(soilKey)

hz <- data.table::data.table(
  top_cm    = c(0,    20,   55,   115),
  bottom_cm = c(20,   55,   115,  200),
  designation = c("Ap", "AB", "Bw1", "Bw2"),
  munsell_hue_moist    = c("10YR","7.5YR","2.5YR","2.5YR"),
  munsell_value_moist  = c(4, 4, 3, 3),
  munsell_chroma_moist = c(3, 5, 6, 6),
  clay_pct = c(35, 45, 65, 65),
  sand_pct = c(25, 20, 15, 15),
  silt_pct = c(40, 35, 20, 20),
  cec_cmolc_kg = c(8, 6, 5, 4),
  bs_pct  = c(35, 30, 25, 20),
  oc_pct  = c(2.0, 1.0, 0.5, 0.3),
  ph_h2o  = c(5.0, 5.2, 5.3, 5.4),
  bulk_density_g_cm3 = c(1.0, 1.1, 1.2, 1.2)
)
hz <- ensure_horizon_schema(hz)

pedon <- PedonRecord$new(
  site = list(id = "demo-001", lat = -22.4, lon = -43.7, country = "BR"),
  horizons = hz
)

2. Classify across three systems in one pass

# WRB 2022 — full Chapter 6 name
classify_wrb2022(pedon)$name

# SiBCS 5 — 4th level (Subgroup) + 5th level (Family)
classify_sibcs(pedon, include_familia = TRUE)$name

# USDA Soil Taxonomy 13 — Subgroup
classify_usda(pedon)$name

3. Inspect the trace and evidence grade

res <- classify_wrb2022(pedon)
res$evidence_grade   # one of "A", "B", "C", "D"
res$trace            # full decision walk: which RSGs were tested, why each failed/passed
res$missing_data     # attributes the key wanted but couldn't find
res$ambiguities      # alternative classifications still viable on the data

4. Gap-fill missing attributes from spectra

# Vis-NIR spectrum per horizon, OSSL backbone:
pr <- predict_horizon_attributes(
  pedon,
  spectra      = list(Ap = vnir_ap, Bw1 = vnir_bw1, Bw2 = vnir_bw2),
  models       = c("clay_pct", "oc_pct", "cec_cmolc_kg"),
  ossl_engine  = "PLSR-local"
)
# Each filled attribute carries provenance = "predicted_spectra" + PI95 confidence.
# Now classify_wrb2022(pr)$evidence_grade may be "B" (predicted_spectra)
# instead of "A" (measured) — provenance survives.

5. Cross-check against a spatial prior

# SoilGrids 250 m WCS at the site coordinates:
prior <- spatial_prior(pedon, source = "soilgrids")
res   <- classify_wrb2022(pedon, prior = prior)
res$prior_check
# If the assigned RSG is inconsistent with the SoilGrids posterior,
# `res$warnings` flags it. The prior never overrides the key.

6. Render a self-contained report (HTML or PDF)

# All three results in a single one-pager (HTML, no external deps):
classify_all_to_html(pedon, output_file = "demo-001.html")

# Or pass an explicit list of results:
classify_all_to_html(
  list(
    wrb   = classify_wrb2022(pedon),
    sibcs = classify_sibcs(pedon),
    usda  = classify_usda(pedon)
  ),
  output_file = "demo-001.html"
)

# PDF (requires rmarkdown + LaTeX):
classify_all_to_pdf(pedon, output_file = "demo-001.pdf")

✦ Empirical validation

soilKey ships eleven benchmark drivers under inst/benchmarks/. The post-v0.9.95 cumulative sweep on five external datasets (reproduced from a clean session by inst/benchmarks/run_v0987_post_086_sweep.R in ~30 seconds, plus the LUCAS Stage 3 SoilGrids fill at ~60 minutes from the v0.9.82 RDS):

1. Canonical-fixture run (release-time CI)

26 hand-built canonical fixtures (one per WRB Reference Soil Group, sourced from the WRB 2022 didactic exemplars + ISRIC ISMC monoliths + the Soil Atlas of Europe) achieve WRB 26 / 26, SiBCS 20 / 20, USDA 26 / 26 at every release. Runs offline in <2 s; gated on every PR.

2. KSSL + NASIS multi-level (USDA Soil Taxonomy 13)

NCSS Lab Data Mart joined with the companion NASIS Morphological sqlite. n = 99 profiles; full four-level USDA hierarchy (Order → Suborder → Great Group → Subgroup) measured. WRB 2022 cross-walk via IUSS WRB 2022 Annex 6 yields 24.2% Order accuracy with engine = "aqp" (vs 21.2% canonical). v0.9.84 spodic OC-translocation lifts spodic-test recall on KSSL+NASIS Podzols from 1/14 to 5/14.

3. Embrapa Redape (curated SiBCS gold standard, Vaz et al. 2023)

The 96-profile curated GeoTab dataset published by Vaz, Silva Jr & Silva Neto (2023) at the Embrapa Redape repository (DOI 10.48432/PYKKA7). Pedologists hand-reviewed every profile, making it the gold-standard benchmark for SiBCS classification. v0.9.81 wires honest 4-level accuracy:

4. WoSIS GraphQL stratified (paper-grade WRB baseline)

ISRIC WoSIS bundled cache; n = 130 profiles balanced across 26 WRB Reference Soil Groups (5 per RSG). v0.9.88 fixed the loader’s reference-field aliasing; v0.9.91 hardened it against R’s $-partial-matching footgun. Default canonical 17.7%, engine = "aqp" 18.5%.

5. AfSP — ISRIC Africa Soil Profiles Database v1.2

n = 120 African profiles. Default 21.7% Order accuracy; with engine = "aqp" + andic_oc_bd_proxy + extension: 30.8% (+9.1pp). v0.9.85 lifts AfSP Andosols 0/5 → 2/5 by relaxing the buried-diagnostic exclusion (per WRB 2022 Ch 4 p 104).

6. LUCAS 2018 — EU topsoil + SoilGrids subsoil fill

n = 30 (FR / PL / IT, seed 20260508). Stage 3 (engine = "aqp" + full opt-in stack + SoilGrids 30–60 cm subsoil fill) reaches 60.0% accuracy, with 100% recall on Cambisols (18 / 18). Stage 1 / 2 (no fill) sit at 0% — the LUCAS topsoil-only horizons cannot satisfy cambic / argic / spodic depth requirements without a synthesised subsoil.

✦ Two user-facing helpers that guide classification

soil_classes_at_location(lat, lon) — spatial classification aid

soil_classes_at_location(lat = -22.4, lon = -43.7)
#> $wrb     [1] "Ferralsols"   $confidence 0.71
#> $sibcs   [1] "Latossolos"   $confidence 0.66  (SoilGrids does not split SiBCS Suborder)
#> $usda    [1] "Oxisols"      $confidence 0.71

Convenience wrapper around the SoilGrids 250 m WCS + the IUSS WRB 2022 Annex 6 cross-walk. Returns a probabilistic prior at the site coordinates; does not classify, only suggests.

classify_by_spectral_neighbours(spectrum, ossl_library) — spectral analogy

Given a Vis-NIR / MIR spectrum, retrieves the k spectrally most similar profiles in the OSSL library, looks up their canonical classifications, and returns the modal label. Useful for sanity-checking a classification that came out unexpected.

✦ Multimodal extraction (VLM / Gemma 4 / one-liner pipeline)

# One-liner. Local-first; no API key needed; data never leaves your machine.
pedon <- extract_pedon_from_pdf(
  "field_survey_2024.pdf",
  vlm_engine = ellmer::chat_ollama("gemma3:4b")
)

classify_wrb2022(pedon)$name
#> [1] "Geric Ferric Rhodic Chromic Ferralsol (Clayic, Humic, Dystric, Ochric, Rubic)"

The VLM extracts a JSON-Schema-validated PedonRecord from a field-report PDF (or photo); the deterministic key takes it from there. The schema rejects any LLM hallucination of class names — extraction is restricted to per-attribute observations.

✦ Documentation

• Vignettes: 10+ vignettes under vignettes/ covering getting-started, end-to-end classification, cross-system correlation, VLM extraction, spatial + spectra pipeline, the WoSIS benchmark, KSSL+NASIS multi-level, and a fully-worked Embrapa profile.
• pkgdown reference site: hugomachadorodrigues.github.io/soilKey — every exported function with full API docs and runnable examples.
• Architecture document: ARCHITECTURE.md — full design rationale, module separation, and v1.0 roadmap.
• Per-release diff: NEWS.md — every fix, every benchmark uplift, every test added.

✦ Provenance & evidence grade

Every attribute on a PedonRecord carries a provenance tag:

The ClassificationResult$evidence_grade (A–D) summarises the trace:

• A — every attribute the key consulted was measured.
• B — every attribute was measured or predicted_spectra with PI95 ≤ threshold.
• C — at least one attribute was extracted_vlm with VLM-confidence ≤ 0.85.
• D — at least one attribute was inferred_prior or user_assumed.

✦ Citing

If soilKey contributes to your work, please cite the package via the Zenodo concept-DOI 10.5281/zenodo.19930112 (always resolves to the latest version):

Rodrigues, H. (2026). soilKey: Automated soil profile classification per WRB 2022, SiBCS 5, and USDA Soil Taxonomy 13. R package. https://github.com/HugoMachadoRodrigues/soilKey. https://doi.org/10.5281/zenodo.19930112.

Run citation("soilKey") to get the canonical BibTeX block plus the four upstream-data citations the package carries (see below).

Cite these too — depending on what you used

When you use classify_via_smartsolos_api() to cross-validate against Embrapa’s SmartSolos Expert REST API:

Vaz, G. J., Silva Neto, L. de F. da, & Barbedo, J. G. A. (2025). SmartSolos Expert: an expert system for Brazilian soil classification. Smart Agricultural Technology, 10, 100735. https://doi.org/10.1016/j.atech.2024.100735.

Vaz, G. J., Silva Neto, L. de F. da, Lima, R. N., & Oliveira, S. R. de M. (2019). Uma API para a classificação de solos do Brasil. In: 12. Congresso Brasileiro de Agroinformática, Indaiatuba. Anais, p. 63–72. SBIAGRO, Ponta Grossa.

The API is publicly available at https://www.agroapi.cnptia.embrapa.br/store/apis/info?name=SmartSolosExpert&version=v1&provider=agroapi.

When you use benchmark_redape() or load_redape_pedons():

Vaz, G. J., Silva Jr, A. F., & Silva Neto, L. de F. da (2023). Brazilian soil data for taxonomic classification. Redape (Embrapa Research Data Repository), V1. https://doi.org/10.48432/PYKKA7.

✦ References (canonical books + datasets)

• WRB 2022 — IUSS Working Group WRB (2022). World Reference Base for Soil Resources, 4th edition. International Union of Soil Sciences, Vienna, Austria. FAO OpenKnowledge PDF (https://openknowledge.fao.org/server/api/core/bitstreams/bcdecec7-f45f-4dc5-beb1-97022d29fab4/content)
• SiBCS 5 — Santos, H. G. et al. (2018). Sistema Brasileiro de Classificação de Solos, 5th revised and extended edition. Embrapa, Brasília.
• USDA Soil Taxonomy 13 — Soil Survey Staff (2022). Keys to Soil Taxonomy, 13th edition. USDA-NRCS, Washington, DC.
• OSSL — Sanderman, J., Savage, K., & Dangal, S. R. S. (2020). Mid-infrared spectroscopy for prediction of soil health indicators in the United States. Soil Science Society of America Journal, 84(1), 251–261.
• WoSIS — Batjes, N. H., Ribeiro, E., & van Oostrum, A. (2020). Standardised soil profile data to support global mapping and modelling (WoSIS snapshot 2019). Earth System Science Data, 12, 299–320. https://doi.org/10.5194/essd-12-299-2020
• AfSP — Leenaars, J. G. B., van Oostrum, A. J. M., & Ruiperez Gonzalez, M. (2014). Africa Soil Profiles Database, Version 1.2. ISRIC Report 2014/01. ISRIC — World Soil Information, Wageningen. Project page (https://isric.org/projects/africa-soil-profiles-database-afsp). The bundled afsp_sample.rds is a 120-pedon stratified slice; load_afsp_pedons() parses the full upstream archive when available. (Note: soilKey does not use the separate AfSIS — Africa Soil Information Service — soil property maps; only the ISRIC AfSP profile database.)
• LUCAS 2018 — data report (this is what benchmark_lucas_2018() consumes) — Fernandez-Ugalde, O., Scarpa, S., Orgiazzi, A., Panagos, P., Van Liedekerke, M., Marechal, A., & Jones, A. (2022). LUCAS 2018 SOIL Component: sampling intensity, harmonisation and procedures for the collection of soil samples. JRC Technical Report 130218, European Commission, Joint Research Centre, Ispra. https://doi.org/10.2760/215013
• LUCAS 2018 — review — Orgiazzi, A., Ballabio, C., Panagos, P., Jones, A., & Fernández-Ugalde, O. (2018). LUCAS Soil, the largest expandable soil dataset for Europe: a review. European Journal of Soil Science, 69(1), 140–153. https://doi.org/10.1111/ejss.12499
• SmartSolos Expert — Vaz, G. J., Silva Neto, L. de F. da, & Barbedo, J. G. A. (2025). SmartSolos Expert: an expert system for Brazilian soil classification. Smart Agricultural Technology, 10, 100735.
• SmartSolos REST API announcement — Vaz, G. J., Silva Neto, L. de F. da, Lima, R. N., & Oliveira, S. R. de M. (2019). Uma API para a classificação de solos do Brasil. 12 SBIAGRO, Indaiatuba.
• Redape curated SiBCS dataset — Vaz, G. J., Silva Jr, A. F., & Silva Neto, L. de F. da (2023). Brazilian soil data for taxonomic classification. Redape, V1. https://doi.org/10.48432/PYKKA7.
• NCSS-tech ecosystem (aqp) — Beaudette, D., Skovlin, J., Roecker, S., & Brown, A. (2024). aqp: Algorithms for Quantitative Pedology. R package. https://github.com/ncss-tech/aqp

✦ Acknowledgements

soilKey was developed at the Universidade Federal Rural do Rio de Janeiro (UFRRJ), Departamento de Solos. The benchmark datasets were generously made public by ISRIC (AfSP, WoSIS), USDA-NRCS (KSSL Lab Data Mart, NASIS Morphological), the European Soil Data Centre (LUCAS), Embrapa (BDsolos, Redape, SmartSolos Expert API), and the FEBR consortium (UFSM). The deterministic-key separation is inspired by the IUSS Working Group WRB’s stated commitment to open taxonomic logic.

Special thanks to Glauber José Vaz and colleagues at Embrapa for opening up the SmartSolos Expert REST API and curating the Redape gold-standard SiBCS dataset — both directly enable the soilKey cross-validation and benchmark axes for the Brazilian system.

✦ License

MIT © 2026 Hugo Rodrigues. CRAN-style template at LICENSE; full text at LICENSE.md.

The package source is MIT. The bundled benchmark caches retain their respective upstream licenses (ISRIC AfSP / WoSIS public-domain; NCSS Lab Data Mart public-domain US Federal data). The Redape SiBCS dataset is published by Vaz et al. (2023) under their original repository terms — see the DOI for details.

_{Status (v0.9.96, 2026-05-09): CRAN-submit-ready. R CMD check --as-cran returns 0 errors / 0 warnings / 2 trivial NOTEs. All seven CI matrix runs (macOS, Ubuntu × 3 R versions, Windows, pkgdown, test-coverage) green on every PR merged to main since v0.9.65. All three classification systems wired end-to-end down to the deepest categorical level. WRB 2022 (32 RSGs + 229 of 234 canonical qualifiers deliverable, 5 honest gaps), SiBCS 5 (Order → Suborder → Great Group → Subgroup → Family; 13 / 44 / 192 / 938 registered classes), USDA Soil Taxonomy 13 (Order → Suborder → Great Group → Subgroup, 339/339 great groups + 2 003 of 2 715 subgroups = 73.8%; run coverage_report() for the live by-name diff at every level). DOI: https://doi.org/10.5281/zenodo.19930112 (resolves to the latest version on Zenodo). Per-release changes in NEWS.md; roadmap in ARCHITECTURE.md; CRAN submission instructions in inst/cran-submission/HOW_TO_SUBMIT.md.}