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Comprehensive Soil Quality Index Computation and Visualization in R
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.
| 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 |
"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)# Install from CRAN
install.packages("SQIpro")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)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()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("SQIpro")GPL-3 © Sadikul Islam
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.
Health stats visible at Monitor.