Application of Self-Organizing Maps to Explore the Interactions of Microorganisms with Soil Properties in Fruit Crops Under Different Management and Pedo-Climatic Conditions

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 pe...

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Bibliographic Details
Main Authors:	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, Anne D. Jungblut, Hester J. van Schalkwyk, Corrado Costa, Eligio Malusà
Format:	Article
Language:	English
Published:	MDPI AG 2025-01-01
Series:	Soil Systems
Subjects:	apple neural networks soil microbiome diversity strawberry
Online Access:	https://www.mdpi.com/2571-8789/9/1/10
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Application of Self-Organizing Maps to Explore the Interactions of Microorganisms with Soil Properties in Fruit Crops Under Different Management and Pedo-Climatic Conditions

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