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soilquality

Overview

soilquality builds Soil Quality Indices — and, unusually, helps you find out whether the one you built is any good.

A soil quality index has no ground truth. Nothing tells you whether yours is correct. Most packages stop at producing a number; this one carries the routes the literature actually uses at every step of the pipeline, and the diagnostics that reveal when an index is not discriminating, when a model is circular, or when a comparison was never fair to begin with.

Key Features

Selecting indicators

  • PCA minimum data set selection, with the published within 10 % of the maximum loading rule available alongside the narrower default
  • Correlation-network selection by centrality rather than variance — ecological hubs instead of high-variance variables
  • Functional (EMDS) grouping: select one indicator per soil function, so a dominant function cannot crowd the others out
  • PCA adequacy testing — KMO and Bartlett’s sphericity, which most published work skips

Scoring

  • Linear (higher / lower / optimum / threshold), plus the sigmoidal curve that dominates the SQI literature
  • Reference-soil standardisation — score against an undisturbed soil instead of your own sample extremes, the documented escape from index incomparability
  • Inherent-property adjustment — stop scoring a soil down for being what its parent material made it

Weighting and aggregation

  • AHP with consistency-ratio validation, PCA loadings, or network centrality
  • Area aggregation — a weight-free route that sidesteps the pipeline’s most contested step entirely

Validation — the part most work skips

  • sqi_validate() leads with the distribution across decision categories, not a correlation
  • sqi_stability() asks whether your conclusion survives a change of recipe
  • check_circularity() refuses to let you regress an index on the indicators that built it

Also

  • Three example datasets, each for a different job
  • Interactive Shiny application for non-programmers
  • Vignettes, a full function reference, and a package website

The pipeline, and the choice at every step

An index is five decisions, and this package offers alternatives at each of them — plus the step most work skips.

select  →  group  →  score  →  weight  →  aggregate  →  VALIDATE
Step Options Default When to change it
Select pca_select_mds() (variance), na_select_mds() (network centrality), expert list PCA Network when ecological hubs matter more than variance, or when normality is doubtful
Group groups = NULL, soil_function_groups none Whenever more than one soil function matters — ungrouped selection can leave whole functions unrepresented
Score linear, sigmoid, optimum, threshold, reference-soil linear Sigmoid to reproduce most published work; reference-soil for comparability across studies
Weight AHP, PCA loading, network centrality, equal equal AHP when you have defensible expert judgement
Aggregate method = "weighted", method = "area" weighted Area to sidestep weighting entirely
Validate sqi_validate(), sqi_stability() — Always

Why validation is the point

A soil quality index has no ground truth. Nothing tells you whether yours is correct. What you can establish is whether it discriminates, and whether your conclusion survives building it a different way.

sqi_validate() leads with the distribution across decision categories, not with a correlation. Maaz et al. (2023) found two indices correlating at r = 0.96 while one placed 94 % of plots in the middle band and the other 61 %. An index that calls almost everything “medium” cannot inform a decision, however well it correlates with anything else.

sqi_stability() runs the same samples through several recipes and reports whether the ranking survives. A high rank correlation does not rescue a changed extreme, if the extreme is what you act on.

One warning worth repeating

Do not build an index from predicted soil properties. Chaudhry et al. (2024) found that computing an SQI from spectrally predicted properties gave R² = 0.23, while predicting the index directly from the same spectra gave R² = 0.90 — with individually acceptable property models. If you only have predictions, model the index itself.

See vignette("building-and-validating-an-sqi") for the pipeline end to end.

Installation

soilquality is not on CRAN. Install it from GitHub:

# Install pak if needed
if (!requireNamespace("pak", quietly = TRUE)) {
  install.packages("pak")
}

# Install soilquality
pak::pak("ccarbajal16/soilquality")

Using devtools 

# Install devtools if needed
if (!requireNamespace("devtools", quietly = TRUE)) {
  install.packages("devtools")
}

# Install soilquality
devtools::install_github("ccarbajal16/soilquality")

Optional dependency

Correlation-network selection (na_select_mds(), mds_consensus()) needs igraph, which is suggested rather than required — this package is MIT-licensed and igraph is GPL, so it is kept out of Imports deliberately. Everything else works without it.

Quick Start

Basic Workflow 

library(soilquality)

# Load example data
data(soil_data)

# Compute SQI with automatic property detection
result <- compute_sqi_properties(
  data = soil_data,
  properties = c("Sand", "Silt", "Clay", "pH", "OM", "P", "K")
)

# View results summary
print(result)

# Visualize SQI distribution
plot(result, type = "distribution")

# Create comprehensive report
plot_sqi_report(result)

Using Pre-defined Property Sets 

# Use standard property set
result <- compute_sqi_properties(
  data = soil_data,
  properties = soil_property_sets$standard
)

Custom Scoring Rules 

# Define custom scoring for specific properties
custom_rules <- list(
  pH = optimum_range(optimal = 6.5, tolerance = 1),
  OM = higher_better(),
  BD = lower_better(),
  P = threshold_scoring(
    thresholds = c(0, 10, 20, 50),
    scores = c(0, 0.5, 0.8, 1.0)
  )
)

result <- compute_sqi_properties(
  data = soil_data,
  properties = c("pH", "OM", "BD", "P"),
  scoring_rules = custom_rules
)

Interactive AHP Matrix Creation 

# Create AHP matrix interactively
indicators <- c("pH", "OM", "P", "K")
ahp_matrix <- create_ahp_matrix(indicators, mode = "interactive")

# Use computed AHP weights in SQI calculation
result <- compute_sqi_properties(
  data = soil_data,
  properties = ahp_matrix$indicators,
  pairwise_matrix = ahp_matrix$weights
)

Validating What You Built

Producing an index is the easy half. This is the other one.

props <- c("pH", "OM", "N", "P", "K", "CEC", "BD")
index <- compute_sqi_properties(soil_data, properties = props,
                                id_column = "SampleID")

sqi_validate(index)

On this dataset that call raises a warning, and it is not a bug — it is the diagnostic working. The default recipe puts 100 % of samples in the middle bands, with “very low” and “very high” both empty. An index that declines to call any sample clearly good or clearly bad cannot inform a decision.

Then ask whether the conclusion depends on how you built it:

sigmoid <- compute_sqi_properties(
  soil_data, properties = props, id_column = "SampleID",
  scoring_rules = standard_scoring_rules(props, scoring = "sigmoid")
)

sqi_stability(linear = index, sigmoid = sigmoid)

Here the two recipes agree at ρ = 0.80 and keep the same best field — but the worst one changes. If the field you were about to intervene on depends on which scoring function you happened to pick, you do not have a result; you have an artefact of the recipe. A high rank correlation does not rescue that.

And before modelling what drives the index, check that the question is not circular:

check_circularity(index, ~ Sand + Clay)          # outside the index: fine
try(check_circularity(index, ~ OM))              # a component: refused

Launch Interactive Application 

# Start Shiny app for GUI-based analysis
run_sqi_app()

Main Functions

SQI Computation

Data Handling

Selecting a Minimum Data Set

  • pca_select_mds() - Selection by variance. within gives the published relative-loading rule, groups selects within soil functions, selector = "norm" uses Yuan’s norm value
  • na_select_mds() - Selection by centrality in a Spearman correlation network (needs the suggested igraph)
  • mds_consensus() - The intersection of both routes, as a robustness check
  • pca_adequacy() - KMO and Bartlett’s test of sphericity

Functional Grouping

  • soil_function_groups - The five soil functions, for grouped selection
  • assign_function_groups() - Map property names onto those functions

Scoring

Scoring Constructors

Weighting and Aggregation

Validation

  • sqi_validate() - Distribution across decision categories, sensitivity, fidelity to a total-data-set index, and an optional external criterion
  • sqi_stability() - Does the ranking survive a change of recipe?
  • check_circularity() - Refuses a model that regresses an index on its own components

Correcting for What Cannot Be Managed

Visualization

Interactive Tools

Note: the Shiny app currently exposes the 1.0.0 workflow. The routes added since — sigmoid and reference scoring, area aggregation, network and grouped selection, and the whole validation layer — are available from the console only.

Example Data

Three datasets, because they do three different jobs:

Dataset Rows For
soil_data 50 Scoring, weighting, aggregation. Properties drawn independently, so it carries almost no covariance
soil_structured 120 Anything reading the structure between indicators. Texture sums to 100, SOC = OM/1.724, CEC generated by clay and organic colloids
soil_inherent 180 Inherent-property adjustment and nested designs. Carries soil_type, land_use_history, management and PlotID, five samples per plot

All three are simulated, and each generating script in data-raw/ states every relationship it used.

Documentation

Comprehensive documentation is available through:

Citation

If you use this package in your research, please cite it as:

citation("soilquality")

Or use:

Carbajal, Carlos (2026). soilquality: Soil Quality Index Calculation with PCA and AHP. R package version 2.3.0. https://github.com/ccarbajal16/soilquality

citation("soilquality") reads the installed DESCRIPTION, so it is the authoritative version; the block above will drift as releases go by.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request or open an Issue for:

  • Bug reports
  • Feature requests
  • Documentation improvements
  • Code contributions

Issues and Support

Please report issues at: https://github.com/ccarbajal16/soilquality/issues

For questions and discussions, use the GitHub Discussions page.

License

This package is licensed under the MIT License. See the LICENSE file for details.

Acknowledgments

This package implements methodologies from soil quality assessment literature, including PCA-based MDS selection and AHP weighting approaches widely used in soil science research.