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SQIpro

Comprehensive Soil Quality Index Computation and Visualization in R

Overview

SQIpro provides a complete, modular framework for computing the Soil Quality Index (SQI) — a single numeric score (0–1) integrating physical, chemical, and biological soil properties.

Six indexing methods

Method	Reference	Key feature
Linear Scoring	Doran & Parkin (1994)	Simple, equal-weight additive
Regression-based	Masto et al. (2008)	Weights by regression coefficients
PCA-based	Andrews et al. (2004)	Data-driven, variance-explained weights
Fuzzy Logic	Zhu et al. (2010)	Handles uncertainty; geometric mean option
Entropy Weighting	Shannon (1948)	Objective weights from information content
TOPSIS	Hwang & Yoon (1981)	Multi-criteria ideal-solution distance

Four scoring function types

"more"  — Higher is better   ▁▃▅▇█  (e.g., organic carbon)
"less"  — Lower is better    █▇▅▃▁  (e.g., bulk density)
"opt"   — Optimum range      ▁▅█▅▁  (e.g., pH 6.0–7.0)
"trap"  — Trapezoidal        ▁▅███▅▁ (explicit boundaries)

Installation

# Install from CRAN
install.packages("SQIpro")

Quick Start

library(SQIpro)

# 1. Load data
data(soil_data)

# 2. Define variable configuration
cfg <- make_config(
  variable = c("pH",  "EC",   "BD",   "OC",   "MBC",  "Clay"),
  type     = c("opt", "less", "less", "more", "more", "opt"),
  opt_low  = c(6.0,   NA,     NA,     NA,     NA,     20),
  opt_high = c(7.0,   NA,     NA,     NA,     NA,     35)
)

# 3. Score variables (0–1 transformation)
scored <- score_all(soil_data, cfg, group_cols = c("LandUse", "Depth"))

# 4. Select Minimum Data Set
mds <- select_mds(scored, group_cols = c("LandUse", "Depth"))

# 5. Compute and compare all methods
results <- sqi_compare(scored, cfg,
                        group_cols = c("LandUse", "Depth"),
                        dep_var    = "OC", mds = mds)
print(results)

Full Workflow

validate_data()  →  make_config()  →  plot_scoring_curves()
       ↓
   score_all()  →  select_mds()
       ↓
  sqi_linear()  sqi_pca()  sqi_regression()
  sqi_fuzzy()   sqi_entropy()  sqi_topsis()
       ↓
  sqi_compare()  →  sqi_anova()  →  sqi_sensitivity()
       ↓
  plot_sqi()  plot_scores()  plot_radar()  plot_sensitivity()

References

Doran, J.W., & Parkin, T.B. (1994). Defining and assessing soil quality. In: Doran, J.W., Coleman, D.C., Bezdicek, D.F., & Stewart, B.A. (Eds.), Defining Soil Quality for a Sustainable Environment (pp. 3–21). SSSA Special Publication No. 35. doi:10.2136/sssaspecpub35.c1
Andrews, S.S., Karlen, D.L., & Cambardella, C.A. (2004). The soil management assessment framework: A quantitative soil quality evaluation method. Soil Science Society of America Journal, 68(6), 1945–1962. doi:10.2136/sssaj2004.1945
Bastida, F., Zsolnay, A., Hernández, T., & García, C. (2008). Past, present and future of soil quality indices: A biological perspective. Geoderma, 147(3), 159–171. doi:10.1016/j.geoderma.2008.08.007
Masto, R.E., Chhonkar, P.K., Purakayastha, T.J., Patra, A.K., & Singh, D. (2008). Soil quality indices for evaluation of long-term land use and soil management practices in semi-arid sub-tropical India. Land Degradation & Development, 19(5), 516–529. doi:10.1002/ldr.528
Hwang, C.L., & Yoon, K. (1981). Multiple Attribute Decision Making: Methods and Applications. Springer-Verlag. doi:10.1007/978-3-642-48318-9
Shannon, C.E. (1948). A mathematical theory of communication. Bell System Technical Journal, 27(3), 379–423. doi:10.1002/j.1538-7305.1948.tb01338.x

Citation

citation("SQIpro")

License

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.