20.500.12556/DiRROS-21443 Application of self-organizing maps to explore the interactions of microorganisms with soil properties in fruit crops under different management and pedo-climatic conditions 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 Soil Syst. 2025, 9, 10 https://doi.org/10.3390/soilsystems9010010 Soil Syst. 2025, 9, 10 2 of 14 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. apple neural networks soil microbiome diversity strawberry true false true Angleški jezik Ni določen Neznano 2025-02-03 14:35:49 2025-02-03 14:35:50 2025-06-28 03:38:45 0000-00-00 00:00:00 2025 0 0 Nasl. z nasl. zaslona; Opis vira z dne 3. 2. 2025; 14 str. iss. 1, art. 10 Vol. 9 2025 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 2025-01-26 634.1/.7 2571-8789 10.3390/soilsystems9010010 224921091 https://www.mdpi.com/2571-8789/9/1/10 0 ca210d23-e233-11ef-ba3f-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=30570 RAZ_Antonucci_Francesca_2025.pdf RAZ_Antonucci_Francesca_2025.pdf 1 A62A0BF46B48F75DE247B6A2A715E83F 613ac86bd537461cb892c66e3adda7599bcdd9c900b54c1c3dc9cfd08694ab49 fb5d5065-5333-11f0-8080-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=33135 Kmetijski inštitut Slovenije 0 0 0