# KSSL + NASIS: multi-level USDA Soil Taxonomy benchmark

This vignette describes the multi-level USDA Soil Taxonomy 13ed benchmark that ships with `soilKey` (versions 0.9.24 and later). It is the package’s headline real-data validation: the first public USDA Soil Taxonomy implementation that resolves classifier accuracy at every level of the hierarchy (Order / Suborder / Great Group / Subgroup) on real, published lab data.

# 1. The KSSL + NASIS join (load_kssl_pedons_with_nasis)

The Kellogg Soil Survey Laboratory (NCSS lab data, USDA-NRCS — the NCSS Lab Data Mart, `ncsslabdatamart.sc.egov.usda.gov`) ships ~36 000 pedons with full analytical chemistry (clay, sand, silt, OC, pH, CEC, BS, …) but minimal field morphology. The NASIS Morphological database (a separate sqlite distribution) carries the matching field-survey morphology: Munsell colors, structure grade / size / type, clay films (`phpvsf`), slickensides, and the `pediagfeatures` diagnostic-features table (~13 500 entries identifying argillic, mollic, cambic, spodic, fragipan, etc.).

`load_kssl_pedons_with_nasis()` joins the two by `peiid` and depth-overlap matching, returning one PedonRecord per profile with both lab and morphology populated:

    peds <- load_kssl_pedons_with_nasis(
      gpkg   = "<path>/ncss_labdata.gpkg",
      sqlite = "<path>/NASIS_Morphological_09142021.sqlite",
      head   = 3000)

    length(peds)
    #> [1] 2964   # after the 3000-row gpkg slice and the join

    peds[[1]]$horizons[, c("designation", "clay_pct", "munsell_chroma_moist",
                              "clay_films_amount")]
    #>    designation clay_pct munsell_chroma_moist clay_films_amount
    #> 1: Ap          22.5     3                    NA
    #> 2: Bt1         34.7     4                    common
    #> 3: Bt2         38.3     4                    many

NASIS-derived attributes coverage on the 2021 snapshot (n=865 quality-filtered):

Slot | Coverage
--- | ---
`nasis_diagnostic_features` (any) | ~52 %
`pediagfeatures` argillic flag | ~11 %
Per-horizon `clay_films_amount` | ~28 %
Per-horizon `munsell_chroma_moist` | ~99 %
Per-horizon `structure_grade` | ~93 %

The high Munsell + structure coverage powers the pediagfeatures tie-breaker (v0.9.21) – when a canonical lab + morphology gate returns `passed = NA`, the surveyor’s diagnostic flag flips it to `TRUE`. The argillic / fragipan / clay-films paths (v0.9.27, v0.9.28, v0.9.31) leverage the same NASIS slot for direct positive evidence.

# 2. The four levels (benchmark_run_classification)

Once you have a list of PedonRecords with `reference_usda`, `reference_usda_subgroup`, `reference_usda_grtgroup`, and `reference_usda_suborder` populated (the gpkg loader handles all four), `benchmark_run_classification()` measures top-1 accuracy at any of the four hierarchy levels:

    # Quality filter: profiles with usable clay data and a non-empty
    # subgroup reference label.
    keep <- vapply(peds, function(p) {
      hz <- p$horizons
      if (is.null(hz) || nrow(hz) == 0) return(FALSE)
      if (!any(!is.na(hz$clay_pct))) return(FALSE)
      !is.null(p$site$reference_usda_subgroup) &&
        !is.na(p$site$reference_usda_subgroup) &&
        nzchar(p$site$reference_usda_subgroup)
    }, logical(1))
    peds <- peds[keep]

    for (lvl in c("order", "suborder", "great_group", "subgroup")) {
      res <- benchmark_run_classification(peds, system = "usda",
                                             level = lvl, boot_n = 500L)
      cat(sprintf("%-12s n=%d  top1=%.4f  CI=[%.3f, %.3f]\n",
                    lvl, res$n_evaluated, res$accuracy_top1,
                    res$accuracy_ci[1], res$accuracy_ci[2]))
    }

# 3. Headline numbers (v0.9.31, n=2638, ±1.7 pp CI)

Level | n | top-1 | 95 % CI
--- | --- | --- | ---
Order | 2 638 | 34.19 % | [32.4 %, 36.0 %]
Suborder | 2 636 | 13.85 % | [12.5 %, 15.2 %]
Great Group | 2 633 | 7.94 % | [7.0 %, 8.9 %]
Subgroup | 2 638 | 4.17 % | [3.5 %, 4.9 %]

These are the values reported in the methodology paper. They were measured on the n=2638 large-scale validation sample (3x the n=865 development sample) with 500 bootstrap reps for ±1.7 pp confidence intervals.

# 4. Release-by-release trajectory

The v0.9.24 → v0.9.31 release series progressively closed key reasoning gaps. Apples-to-apples on the n=865 development sample:

Release | Change | GG (n=865) | Subgroup (n=865)
--- | --- | --- | ---
v0.9.22 | (baseline before this series) | – | 3.0 %
v0.9.23 | argic clay-increase canonicalisation | – | –
v0.9.24 | aquic / oxyaquic subgroup tightening + new GG/Suborder benchmark levels | 6.5 % | 3.8 %
v0.9.25 | KST 13ed Great Group canonicaliser (Pellusterts → Hapluderts; etc.) | 10.3 % (+3.84) | 5.0 % (+1.15)
v0.9.26 | per-system argic threshold API (infrastructure) | 10.3 % | 5.0 %
v0.9.27 | clay-films test via NASIS pediagfeatures + phpvsf | 10.6 % (+0.23) | 5.1 % (+0.12)
v0.9.28 | designation-based clay-films proxy (‘t’ suffix) | 10.6 % | 5.1 %
v0.9.30 | bundled WoSIS sample + WoSIS retry bug fix | 10.6 % | 5.1 %
v0.9.31 | Quartzipsamment proxy broadened + Fragipan NASIS path | 10.92 % (+0.35) | 5.32 % (+0.23)

The v0.9.25 KST 13ed Great Group canonicaliser remains the largest single-version GG lift (+3.84 pp, +59 % relative). It required no classifier changes — the predictor is already correct for KST 13ed; only the comparison was unfair to legacy KSSL labels (which span Soil Taxonomy editions 8 through 13).

# 5. The four levels are independently measurable

`level = "great_group"` reads `pedon$site$reference_usda_grtgroup` and compares the LAST token of `classify_usda(pedon)$name` after the v0.9.25 KST canonicaliser collapses edition-driven renames.

`level = "suborder"` reads `pedon$site$reference_usda_suborder` and applies `.gg_to_suborder()` (a ~70-Suborder canonical-suffix mapping derived from KST 13ed Ch 4) to the predicted Great Group name.

`level = "subgroup"` reads `pedon$site$reference_usda_subgroup` and compares the full subgroup name token-by-token (Subgroup modifier kept intact; Great Group canonicalised).

`level = "order"` reads `pedon$site$reference_usda` and compares the top-level Order name directly.

# 6. v0.9.25 – the KST canonicaliser story

KSSL `samp_taxgrtgroup` is populated from historical pedon descriptions spanning Soil Taxonomy editions 8 through 13. Several Great Group names changed between editions, and KSSL did NOT retroactively update them. soilKey’s classifier follows KST 13ed (the current edition), so direct string equality between predicted (13ed) and reference (mixed editions) Great Group names produces false-negative misses for every profile whose KSSL label is a pre-13ed name.

The 16 most common edition-driven renames in KSSL:

Pre-13ed name (KSSL) | KST 13ed equivalent | Reason
--- | --- | ---
Haplaquolls | Endoaquolls / Epiaquolls | KST 11 split Hapl- into endo (deep) / epi (perched) saturation
Haplaquepts | Endoaquepts / Epiaquepts | same
Haplaquerts | Endoaquerts / Epiaquerts | same
Haplaquents | Endoaquents / Epiaquents | same
Haplaqualfs | Endoaqualfs / Epiaqualfs | same
Haplaquods | Endoaquods / Epiaquods | same
Pellusterts | Hapluderts / Salusterts / Calciusterts | KST 12: dark-colour Pellu split by chemistry
Chromusterts | Hapluderts | KST 12: bright-colour Chromu merged
Pelluderts / Chromuderts | (same Pellu/Chromu reorg, udic) | –
Dystrochrepts | Dystrudepts | KST 11: Ochrept suborder retired; Udept created
Eutrochrepts | Eutrudepts | same
Camborthids | Haplocambids | KST 11: Orthid suborder retired; Cambid created
Calciorthids | Haplocalcids | same
Paleorthids | Haplodurids / Durargids | (KST 11 Aridisol reshuffling)
Vitrandepts | Vitrudands | KST 11: Andisols promoted to its own Order
Medisaprists / Medihemists / Medifibrists | Haplosaprists / Haplohemists / Haplofibrists | “medi-” temperature regime moved to Subgroup

The canonicaliser `canonicalise_kst13ed_gg()` collapses each pair (or triple, for the Hapl-/Endo-/Epi- splits) to a shared canonical key. Apply to BOTH ref and pred before comparing – the Subgroup modifier (Typic / Aquic / …) is left intact and the canonicalisation only affects the Great Group token.

# 7. What’s missing (roadmap)

The 4.2 % Subgroup top-1 leaves substantial headroom. The v0.9.27 confusion analysis identified the dominant remaining gaps:

- Pale- / Glossic Alfisol prefixes (~11 misses, deferred to v0.9.32+): current `pale_qualifying_usda()` proxy is too strict (clay >= 35 %); KST 13ed actually defines Pale- by clay-stability within 150 cm. Tightening here without regressing on Hapludalfs is delicate work.
- NASIS data sparsity (~47 % of profiles lack any pediagfeatures / phpvsf record): roadmap candidate is field-survey morphology + soilKey’s pedometric proxies as a third evidence path.
- Endo/Epi-aquic precise distinction: currently the v0.9.25 canonicaliser collapses these for fair comparison; a future release could restore the distinction by inferring saturation depth from `redoximorphic_features_pct` distribution.

# Summary

The KSSL + NASIS join is `soilKey`’s headline real-data benchmark. It exercises every level of the USDA Soil Taxonomy 13ed hierarchy on n=2638 profiles with full lab + morphology data, with confidence intervals tight enough (±1.7 pp) for paper-grade claims. The v0.9.24 → v0.9.31 release series moved Great Group accuracy from a 6.5 % baseline to 10.92 %, primarily through the KST 13ed name canonicaliser (v0.9.25) which collapsed edition-driven renames in the historical KSSL labels.

For the WRB and SiBCS benchmark axes, see vignettes `v06_wosis_benchmark` and the FEBR / Embrapa report at `inst/benchmarks/reports/embrapa_v0929_2026-05-03.md`.