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Recent advances in multivariate analysis coupled with chemical analysis for soil surveys: a review

  • Soils, Sec 1 • Soil Organic Matter Dynamics and Nutrient Cycling • Review Article
  • Published:
Journal of Soils and Sediments Aims and scope Submit manuscript

Abstract

Purpose

Soil is a complex open system covering various physical and chemical attributes. In soil testing, multivariate analysis (MVA) has an important application because it allows the interpretation of a large amount of data for the design of relevant environmental scenarios. The purpose of this research is to summarize recent applications of MVA for identifying soil types or characteristics and for predicting soil attributes with a critical evaluation.

Methods

Based on a comprehensive search of the available database, in this review, we have provided updated information on the most representative classification and regression MVA applied in the past decade in soil surveys. Regression MVA were compared in terms of applicability, efficiency, and predictive power of different soil attributes.

Results

Principal component analysis (PCA) allows the grouping of soils into independent clusters according to their differences in texture or physicochemical composition, which may mirror local or regional environmental signatures. PCA is also used to reduce the dimensionality of spectral data before their application in regression MVA. Partial least square regression (PLSR) is the most commonly applied regression MVA for predicting soil attributes after the correlation of spectra (e.g., Vis–NIR) vs. conventional analysis results. The resulting PLSR models, evaluated by correlation coefficient (R2) and root mean squared error (RMSE), can be valid for the estimation of several soil attributes (e.g., organic carbon, clay).

Conclusions

Application of regression MVA may have limitations in predicting some soil attributes. Objective interpretation of the dynamic nature of soils requires the selection of representative samples as well as appropriate MVA, which can have significant potential in an effective soil survey.

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Funding

This research was supported by the project “SHS—Soil health surrounding former mining areas: characterization, risk analysis, and intervention,” which is financed by national funds from the Norte Portugal Regional Coordination and Development Commission (CCDR-N), Grant Number: NORTE-01–0145-FEDER-000056.

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  1. CIQ-UP, Institute of Molecular Sciences (IMS), Chemistry and Biochemistry Department, Faculty of Sciences, University of Porto, Rua do Campo Alegre s/n, 4169-007, Porto, Portugal

    Jelena Milinovic & Manuel Azenha

  2. CIIMAR, Interdisciplinary Centre of Marine and Environmental Research, Terminal de Cruzeiros Do Porto, University of Porto, Av. General Norton de Matos S/N, 4450-208, Matosinhos, Portugal

    Carlos Vale

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  1. Jelena Milinovic
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  2. Carlos Vale
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Correspondence to Jelena Milinovic.

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Milinovic, J., Vale, C. & Azenha, M. Recent advances in multivariate analysis coupled with chemical analysis for soil surveys: a review. J Soils Sediments 23, 1085–1098 (2023). https://doi.org/10.1007/s11368-022-03377-8

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