Geospatial Analysis of Agricultural Soil Suitability in Mainland Portugal

This study examines the spatial distribution of key soil health indicators—pH (of CaCl2 and H2O), electrical conductivity (EC), and organic carbon (OC) in Portugal. Spatial interpolation methods, namely Inverse Distance Weighting (IDW) and Empirical Bayesian Kriging (EBK), were utilized to analyse soil properties. EBK outperformed IDW in accuracy and spatial coherence, making it more reflective of gradual changes in soil properties. A weighted map of soil health indicators identified the central and southern regions of Portugal as the most agriculturally suitable. This study highlights spatial statistics as a valuable tool for targeted soil management and demonstrates a scalable and reproducible framework using globally recognized soil health metrics.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-05-04
DOI
https://doi.org/10.5281/zenodo.15335907
Primary Topic
Morphological variations and asymmetry
Type
article
Field-Weighted Citation Impact
0.00
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article

Geospatial Analysis of Agricultural Soil Suitability in Mainland Portugal

Zenodo (CERN European Organization for Nuclear Research)
Morphological variations and asymmetry
article

Geospatial Analysis of Agricultural Soil Suitability in Mainland Portugal

article en

Abstract

This study examines the spatial distribution of key soil health indicators—pH (of CaCl2 and H2O), electrical conductivity (EC), and organic carbon (OC) in Portugal. Spatial interpolation methods, namely Inverse Distance Weighting (IDW) and Empirical Bayesian Kriging (EBK), were utilized to analyse soil properties. EBK outperformed IDW in accuracy and spatial coherence, making it more reflective of gradual changes in soil properties. A weighted map of soil health indicators identified the central and southern regions of Portugal as the most agriculturally suitable. This study highlights spatial statistics as a valuable tool for targeted soil management and demonstrates a scalable and reproducible framework using globally recognized soil health metrics.

Zenodo (CERN European Organization for Nuclear Research)
Zero hunger
Openalex Percentile: Top 49%
Morphological variations and asymmetry
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