Predicting Heavy Metal and Nutrient Availability in Agricultural Soils Under Climatic Variability Using Regression and Mixed-Effects Models

Vassilios Diakoloukas, Georgios Koutopoulis, Sotiria G. Papadimou, Marios-Efstathios Spiliotopoulos and Evangelia E. Golia ()
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Vassilios Diakoloukas: School of Electrical and Computer Engineering (ECE), Technical University of Crete, University Campus, Akrotiri, 731 00 Chania, Greece
Georgios Koutopoulis: Soil Science Laboratory, School of Agriculture, Faculty of Agriculture, Forestry and Natural Environment, Aristotle University of Thessaloniki, University Campus, 541 24 Thessaloniki, Greece
Sotiria G. Papadimou: Soil Science Laboratory, School of Agriculture, Faculty of Agriculture, Forestry and Natural Environment, Aristotle University of Thessaloniki, University Campus, 541 24 Thessaloniki, Greece
Marios-Efstathios Spiliotopoulos: Department of Civil Engineer, University of Thessaly, Pedion Areos, 383 34 Volos, Greece
Evangelia E. Golia: Soil Science Laboratory, School of Agriculture, Faculty of Agriculture, Forestry and Natural Environment, Aristotle University of Thessaloniki, University Campus, 541 24 Thessaloniki, Greece

Land, 2025, vol. 14, issue 9, 1-27

Abstract: It is well known that physico-chemical soil parameters can influence, or even determine, the concentrations of heavy metals in soil. Moreover, in recent decades, there has been growing concern about the role of climatic variables such as temperature fluctuations, drought, or extreme rainfall in affecting heavy metal availability. To examine the combined influence of soil properties and climatic changes on pollution levels, a 10-year study was conducted in an intensively cultivated region of central Greece. This work builds on an earlier study that established predictive relationships for Aqua Regia (Aq-Re)-extracted (pseudo)-total Fe and toxic Cd levels from a set of soil parameters, macronutrients or coexisting metals. The present investigation extends this approach by including DTPA-extracted metal concentrations and additional climatic predictors. The updated methodology applies Linear and Quadratic Regression models as well as Linear and Quadratic Mixed-Effects Models to account for the temporal variation driven by climate. The models were trained and validated on continuous, decade-long measurements. In many cases, this led to substantial revisions of the previously established correlations. Incorporating climate-related variables improved the predictive power of the models, revealing a more complex soil–metal dynamic than previously considered. The newly developed models demonstrated more accurate estimations of both total and available metal concentrations, even under the extreme weather conditions observed in autumn 2020. Given the importance of the Thessaly plain to the Greek agricultural sector, these models serve as a valuable tool for monitoring and risk assessment. Quantifying nutrient and toxic element availability under climate shifts is key to safeguarding Mediterranean soil health and addressing the broader impacts of the climate crisis in agroecosystems.

Keywords: soil pollution; heavy metals; Fe (Iron); Cd (Cadmium); machine learning; mixed-effects models; quadratic regression; climate change (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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