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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2025-01-01
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| author | 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 |
| author_facet | 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 |
| author_sort | Francesca Antonucci |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-c65cecdea73348a59cc8e7ccebdc48c3 |
| institution | Kabale University |
| issn | 2571-8789 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Soil Systems |
| spelling | doaj-art-c65cecdea73348a59cc8e7ccebdc48c32025-08-20T04:02:28ZengMDPI AGSoil Systems2571-87892025-01-01911010.3390/soilsystems9010010Application of Self-Organizing Maps to Explore the Interactions of Microorganisms with Soil Properties in Fruit Crops Under Different Management and Pedo-Climatic ConditionsFrancesca Antonucci0Simona Violino1Loredana Canfora2Małgorzata Tartanus3Ewa M. Furmanczyk4Sara Turci5Maria G. Tommasini6Nika Cvelbar Weber7Jaka Razinger8Morgane Ourry9Samuel Bickel10Thomas A. J. Passey11Anne Bohr12Heinrich Maisel13Massimo Pugliese14Francesco Vitali15Stefano Mocali16Federico Pallottino17Simone Figorilli18Anne D. Jungblut19Hester J. van Schalkwyk20Corrado Costa21Eligio Malusà22Centro di Ricerca Ingegneria e Trasformazioni Agroalimentari, Consiglio per la Ricerca in Agricoltura e l’Analisi dell’Economia Agraria (CREA), Via Della Pascolare 16, 00015 Monterotondo, ItalyCentro di Ricerca Ingegneria e Trasformazioni Agroalimentari, Consiglio per la Ricerca in Agricoltura e l’Analisi dell’Economia Agraria (CREA), Via Della Pascolare 16, 00015 Monterotondo, ItalyCentro di Ricerca Agricoltura e Ambiente, Consiglio per la Ricerca in Agricoltura e l’Analisi dell’Economia Agraria (CREA), Via della Navicella 4, 00184 Rome, ItalyThe National Institute of Horticultural Research, ul. Konstytucji 3 Maja 1/3, 96-100 Skierniewice, PolandThe National Institute of Horticultural Research, ul. Konstytucji 3 Maja 1/3, 96-100 Skierniewice, PolandRI.NOVA Research and Innovation Soc. Coop., Via Dell’Arrigoni 120, 47522 Cesena, ItalyRI.NOVA Research and Innovation Soc. Coop., Via Dell’Arrigoni 120, 47522 Cesena, ItalyAgricultural Institute of Slovenia, Hacquetova ulica 17, SI-1000 Ljubljana, SloveniaAgricultural Institute of Slovenia, Hacquetova ulica 17, SI-1000 Ljubljana, SloveniaDepartment of Plant and Environmental Sciences, University of Copenhagen, Thorvaldsensvej 40, 1871 Frederiksberg C, DenmarkInstitute of Environmental Biotechnology, Technische Universitaet Graz, Rechbauerstrasse 12, 8010 Graz, AustriaNational Institute of Agricultural Botany (NIAB), New Road, East Malling, Kent ME19 6BJ, UKCompetence Center for Fruit Crops at the Lake of Constance, Schuhmacherhof 6, 88213 Ravensburg, GermanyFordergemeinschaft Okologischer Obstbau EV, Traubenplatz 5, 74189 Weinsberg, GermanyAgroinnova, Università degli Studi di Torino, Largo Paolo Braccini 2, 10095 Grugliasco, ItalyCentro di Ricerca Agricoltura e Ambiente, Consiglio per la Ricerca in Agricoltura e l’Analisi dell’Economia Agraria (CREA), Via di Lanciola 12/A, 50125 Firenze, ItalyCentro di Ricerca Agricoltura e Ambiente, Consiglio per la Ricerca in Agricoltura e l’Analisi dell’Economia Agraria (CREA), Via di Lanciola 12/A, 50125 Firenze, ItalyCentro di Ricerca Ingegneria e Trasformazioni Agroalimentari, Consiglio per la Ricerca in Agricoltura e l’Analisi dell’Economia Agraria (CREA), Via Della Pascolare 16, 00015 Monterotondo, ItalyCentro di Ricerca Ingegneria e Trasformazioni Agroalimentari, Consiglio per la Ricerca in Agricoltura e l’Analisi dell’Economia Agraria (CREA), Via Della Pascolare 16, 00015 Monterotondo, ItalyNatural History Museum, Department of Sciences, Cromwell Road, London SW7 5BD, UKNatural History Museum, Department of Sciences, Cromwell Road, London SW7 5BD, UKCentro di Ricerca Ingegneria e Trasformazioni Agroalimentari, Consiglio per la Ricerca in Agricoltura e l’Analisi dell’Economia Agraria (CREA), Via Della Pascolare 16, 00015 Monterotondo, ItalyThe National Institute of Horticultural Research, ul. Konstytucji 3 Maja 1/3, 96-100 Skierniewice, PolandBackground: 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.https://www.mdpi.com/2571-8789/9/1/10appleneural networkssoil microbiome diversitystrawberry |
| spellingShingle | 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 Application of Self-Organizing Maps to Explore the Interactions of Microorganisms with Soil Properties in Fruit Crops Under Different Management and Pedo-Climatic Conditions Soil Systems apple neural networks soil microbiome diversity strawberry |
| title | Application of Self-Organizing Maps to Explore the Interactions of Microorganisms with Soil Properties in Fruit Crops Under Different Management and Pedo-Climatic Conditions |
| title_full | Application of Self-Organizing Maps to Explore the Interactions of Microorganisms with Soil Properties in Fruit Crops Under Different Management and Pedo-Climatic Conditions |
| title_fullStr | Application of Self-Organizing Maps to Explore the Interactions of Microorganisms with Soil Properties in Fruit Crops Under Different Management and Pedo-Climatic Conditions |
| title_full_unstemmed | Application of Self-Organizing Maps to Explore the Interactions of Microorganisms with Soil Properties in Fruit Crops Under Different Management and Pedo-Climatic Conditions |
| title_short | Application of Self-Organizing Maps to Explore the Interactions of Microorganisms with Soil Properties in Fruit Crops Under Different Management and Pedo-Climatic Conditions |
| title_sort | application of self organizing maps to explore the interactions of microorganisms with soil properties in fruit crops under different management and pedo climatic conditions |
| topic | apple neural networks soil microbiome diversity strawberry |
| url | https://www.mdpi.com/2571-8789/9/1/10 |
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