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Soil Systems
Special Issues
Use of Modern Statistical Methods in Soil Science
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Use of Modern Statistical Methods in Soil Science

A special issue of Soil Systems (ISSN 2571-8789).

Deadline for manuscript submissions: 31 October 2025 | Viewed by 1345

Special Issue Editor

Prof. Dr. Jarosław Zawadzki

E-Mail Website
Guest Editor
Hydro and Environmental Engineering, Faculty of Building Services, Warsaw University of Technology, Nowowiejska Str., 20, 00-653 Warsaw, Poland
Interests: environmental sciences; magnetism and magnetic materials; geophysics and geochemistry; soil sciences; remote sensing; geostatistics; statistics and probability
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The main objective of this Special Issue is to publish outstanding, modern papers presenting cutting-edge applications of statistical methods in soil studies. Soils contribute to the hydrological cycle and the cycles of carbon, nitrogen, sulfur, phosphorus, and other elements. They influence the climate and are vital for biological ecosystems and agriculture. However, they are also frequently contaminated by human activities. The processes within soils are dynamic and occur on various scales. Due to the exceptional variability and complexity of the soil environment, its description requires advanced statistical methods. Another reason for the continuously growing interest in using statistical methods in soil research is the rapid development of field, laboratory, and remote measurement methods of various soil parameters, ranging from microscale measurements to satellite observations. Such measurements require analysis using modern and advanced statistical methods. Simultaneously, thanks to the enormous progress in statistical software, employing statistical methods in soil research has become widespread. This allows for deepening and expanding research and for new results to be obtained. At the end of the International Decade of Soils (2015–2024), this Special Issue of Soil Systems aims to bring together current, prominent research and ideas about using modern statistical methods in soil research.

Prof. Dr. Jarosław Zawadzki
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Soil Systems is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • soils
  • statistical methods
  • soil measurements
  • soil properties
  • soil data integration
  • soil complexity
  • soil variability

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Published Papers (1 paper)

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Research

14 pages, 2833 KiB  
Article
Application of Self-Organizing Maps to Explore the Interactions of Microorganisms with Soil Properties in Fruit Crops Under Different Management and Pedo-Climatic Conditions
by Francesca Antonucci, Simona Violino, Loredana Canfora, Małgorzata Tartanus, Ewa M. Furmanczyk, Sara Turci, Maria G. Tommasini, Nika Cvelbar Weber, Jaka Razinger, Morgane Ourry, Samuel Bickel, Thomas A. J. Passey, Anne Bohr, Heinrich Maisel, Massimo Pugliese, Francesco Vitali, Stefano Mocali, Federico Pallottino, Simone Figorilli, Corrado Costa and Eligio Malusàadd Show full author list remove Hide full author list
Soil Syst. 2025, 9(1), 10; https://doi.org/10.3390/soilsystems9010010 - 26 Jan 2025
Viewed by 851
Abstract
Background: Self-organizing maps (SOMs) are a class of neural network algorithms able to visually describe a high-dimensional dataset onto a two-dimensional grid. SOMs were explored to classify soils based on an array of physical, chemical, and biological parameters. Methods: The SOM analysis was [...] Read more.
Background: Self-organizing maps (SOMs) are a class of neural network algorithms able to visually describe a high-dimensional dataset onto a two-dimensional grid. SOMs were explored to classify soils based on an array of physical, chemical, and biological parameters. Methods: The SOM analysis was performed considering soil physical, chemical, and microbial data gathered from an array of apple orchards and strawberry plantations managed by organic or conventional methods and located in different European climatic zones. Results: The SOM analysis considering the “climatic zone” categorical variables was able to discriminate the samples from the three zones for both crops. The zones were associated with different soil textures and chemical characteristics, and for both crops, the Continental zone was associated with microbial parameters—including biodiversity indices derived from the NGS data analysis. However, the SOM analysis based on the “management method” categorical variables was not able to discriminate the soils between organic and integrated management. Conclusions: This study allowed for the discrimination of soils of medium- and long-term fruit crops based on their pedo-climatic characteristics and associating these characteristics to some indicators of the soil biome, pointing to the possibility of better understanding the interactions among diverse variables, which could support unraveling the intricate web of relationships that define soil quality. Full article
(This article belongs to the Special Issue Use of Modern Statistical Methods in Soil Science)
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