Editorial of the Special Issue Digital Soil Mapping, Decision Support Tools and Soil Monitoring Systems in the Mediterranean

In the digital era, the role of soil surveyors has evolved significantly. With legacy soil data now being recognized as valuable assets, thanks to the increased computational capacity of geographic information systems, the potential of soil spatial assessment has been greatly enhanced [1]. International calls have led to increased collaboration between scientists, and national research projects have been instrumental in advancing innovation in the soil-mapping domain [2].

Soil mapping in the Mediterranean region involves contributions from various authors and institutions [3]. Several prominent research institutions, governmental agencies, and academic organizations are known for their contributions to soil mapping and related research in the Mediterranean region [4,5]. These includes universities with agricultural or environmental, geology and natural science departments, geological surveys, research centers specializing in soil science, and regional or international organizations focused on environmental conservation and land management.

In terms of individual authors, there are numerous experts and scholars who have made significant contributions to the field of soil mapping in the Mediterranean [6,7,8,9]. Several other scholars and researchers have authored key publications, research papers, or reports that have advanced our understanding of soils in the region [10,11,12,13,14,15].

To identify the main contributing author or institution for a specific project or study related to soil mapping in the Mediterranean, it is recommended to analyze the peer-review research literature, being as comprehensive as possible (e.g., by including SCOPUS and Web of Science databases) [16]. This can provide insights into the key contributors and institutions that have played a significant role in advancing soil mapping efforts in the Mediterranean region [17,18,19,20].

The abundance of soil information available today presents an opportunity to integrate and leverage both legacy and new soil data to gain insights into soil properties and their temporal changes, thereby enhancing our understanding of earth processes and informing better soil resource management [21]. The operational use of digital soil mapping (DSM) for precision farming has emerged as a critical activity in modern agriculture, benefiting from technological advancements such as remote sensing [22], decision support systems [23,24,25], web-based soil modeling and mapping, and cloud computing [1,26]. This aligns with the new European soil mission [27] aimed at addressing climate change [28] and environmental challenges, emphasizing the importance of soil maps in supporting sustainable development and climate mitigation efforts [29,30].

The dissemination of soil knowledge and data is crucial to meeting the needs of the broader soil user community and safeguarding soils. In line with these developments, this Special Issue (SI), “Digital Soil Mapping, Decision Support Tools and Soil Monitoring Systems in the Mediterranean”, has received seven contributions focused on various aspects of soil mapping and data management, including smart soil data management, legacy data reuse, and the extraction of spatial knowledge from soil survey data and remote sensing. The articles in this SI report on the results of field experiments and literature reviews, mostly within central European territories (Italy and Croatia), covering several assessments of and methodologies for soil properties.

The aims of the SI were to foster discussions and showcase advancements in leveraging digital technologies to enhance our understanding of soil properties spatial distribution and support sustainable soil management practices, and these objectives were partially achieved.

In recent years, the application of machine learning models in the field of soil science has revolutionized the way we understand and map soil properties. This editorial aims to provide an overview of seven abstracts from recent studies that highlight the innovative use of machine learning in digital soil mapping and the assessment of soil properties. Among the DSM studies, Agaba et al. (2023) [31] present a comprehensive analysis of the spatial distribution of soil organic carbon stock in an alpine valley in northern Italy using machine learning models. The authors employed different machine learning algorithms, including multivariate adaptive regression splines, random forest, support vector regression, and elastic net, to predict the soil organic carbon stock at different depths. The results demonstrated the effectiveness of the random forest model in mapping the spatial distribution of soil organic carbon stock with high accuracy. Similarly, Adeniyi et al. [32] utilized linear and nonlinear machine learning models to map soil properties in an agricultural lowland area of Lombardy, Italy. The study focused on predicting and mapping soil properties such as sand, silt, clay contents, soil organic carbon content, pH, and topsoil depth. The findings provided valuable insights for sustainable land use and management in the region. In another study, Vittori Antisari et al. [33] assessed pedodiversity and soil organic matter stock in soils developed on sandstone formations in the Northern Apennines of Italy. This research highlighted the influence of vegetation, topographic factors, and lithology on pedodiversity and soil organic matter content, emphasizing the importance of preserving soil resources in mountain regions. Conforti and Buttafuoco [34] investigated the effects of the study area size and soil sampling density on the prediction of soil organic carbon using visible and near-infrared diffuse reflectance spectroscopy in south Italian forest areas. This study emphasized the need for further research to fully realize the potential of spectroscopy in predicting soil organic carbon. Kaya et al. [35] focused on the predictive mapping of the soil Electrical Conductivity and assessments of the potential soil salinity in a Western Türkiye alluvial plain using machine learning models. This study underscored the importance of monitoring soil salinity to ensure sustainable soil management in irrigated areas. Trevisani and Bogunovic [36] conducted a diachronic mapping of soil organic matter in eastern Croatian croplands, comparing soil organic matter content from the 1970s to that from the 2010s. The study revealed a trend of soil organic matter depleting over time, highlighting the need for soil conservation and restoration actions. With the latest accepted review paper, Adeniyi et al. [37], conducted a systematic review of digital soil mapping application in lowland areas, emphasizing the growing recognition of the pivotal role of digital soil mapping in understanding soil properties in agricultural lowlands. These studies collectively demonstrate the capacity of machine learning models to advance our ability to assess spatial distribution of soil properties (e.g., electrical conductivity soil organic carbon content and stocks), thereby providing valuable insights for sustainable land management, agricultural productivity, and environmental conservation strategies. The integration of machine learning models in soil science has opened up new frontiers in digital soil mapping, enabling researchers to unravel the complex relationships between soil properties, environmental factors, and land use management. As we continue to dig deeper into soil research, these innovative approaches hold immense promise for improving soil management and fostering environmental sustainability.

Author Contributions

Conceptualization, C.S., G.L.P., M.F. and G.L.; methodology, C.S., G.L.P., M.F. and G.L.; software, C.S., G.L.P., M.F. and G.L.; validation, C.S., G.L.P., M.F. and G.L.; data curation, C.S., G.L.P., M.F. and G.L.; writing—original draft preparation, C.S., G.L.P., M.F. and G.L.; writing—review and editing, C.S., G.L.P., M.F. and G.L.; visualization, C.S., G.L.P., M.F. and G.L.; supervision, C.S., G.L.P., M.F. and G.L.; project administration, C.S., G.L.P., M.F. and G.L. All authors have read and agreed to the published version of the manuscript.

Acknowledgments

We thank all authors and the LAND managing editors who assisted us in making this Special Issue possible.

Conflicts of Interest

The authors declare no conflicts of interest.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

References

1. Baumann, P.; Merticariu, V.; Misev, D.; Pham Huu, B.; Langella, G. Datacubes as Enabler for Advanced Decision Support Systems in Land Management. Land Degrad. Dev.; 2024; [DOI: https://dx.doi.org/10.1002/ldr.5153]

2. Todd-Brown, K.E.O.; Abramoff, R.Z.; Beem-Miller, J.; Blair, H.K.; Earl, S.; Frederick, K.J.; Fuka, D.R.; Guevara Santamaria, M.; Harden, J.W.; Heckman, K. et al. Reviews and Syntheses: The Promise of Big Diverse Soil Data, Moving Current Practices towards Future Potential. Biogeosciences; 2022; 19, pp. 3505-3522. [DOI: https://dx.doi.org/10.5194/bg-19-3505-2022]

3. Yigini, Y.; Panagos, P.; Montanarella, L. Soil Resources of Mediterranean and Caucasus Countries; Office for Official Publications of the Euroean Communities: Luxembourg, 2013.

4. Fantappiè, M.; L’Abate, G.; Schillaci, C.; Costantini, E.A.C. Digital Soil Mapping of Italy to Map Derived Soil Profiles with Neural Networks. Geoderma Reg.; 2023; 32, e00619. [DOI: https://dx.doi.org/10.1016/J.GEODRS.2023.E00619]

5. Panagos, P.; Van Liedekerke, M.; Borrelli, P.; Köninger, J.; Ballabio, C.; Orgiazzi, A.; Lugato, E.; Liakos, L.; Hervas, J.; Jones, A. et al. European Soil Data Centre 2.0: Soil Data and Knowledge in Support of the EU Policies. Eur. J. Soil Sci.; 2022; 73, e13315. [DOI: https://dx.doi.org/10.1111/ejss.13315]

6. Yaalon, D.H. Soils in the Mediterranean Region: What Makes Them Different?. CATENA; 1997; 28, pp. 157-169. [DOI: https://dx.doi.org/10.1016/S0341-8162(96)00035-5]

7. Dazzi, C.; Benedetti, A.; Corti, G.; Costantini, E.A.C. Soil Science in Italy; Springer International Publishing: Cham, Switzerland, 2024; ISBN 978-3-031-52743-2

8. Montanarella, L. Soils and the European Green Deal. Ital. J. Agron.; 2020; 15, pp. 262-266. [DOI: https://dx.doi.org/10.4081/ija.2020.1761]

9. Arias-Navarro, C.; Panagos, P.; Jones, A.; Amaral, M.J.; Schneegans, A.; Van Liedekerke, M.; Wojda, P.; Montanarella, L. Forty Years of Soil Research Funded by the European Commission: Trends and Future. A Systematic Review of Research Projects. Eur. J. Soil Sci.; 2023; 74, e13423. [DOI: https://dx.doi.org/10.1111/ejss.13423]

10. Zdruli, P.; Kapur, S.; Çelik, I. Soils of the Mediterranean Region, Their Characteristics, Management and Sustainable Use. Sustainable Land Management; Springer: Berlin/Heidelberg, Germany, 2010; pp. 125-142.

11. McBratney, A.B.; Mendonça Santos, M.L.; Minasny, B. On Digital Soil Mapping. Geoderma; 2003; 117, pp. 3-52. [DOI: https://dx.doi.org/10.1016/S0016-7061(03)00223-4]

12. Daliakopoulos, I.N.; Tsanis, I.K.; Koutroulis, A.; Kourgialas, N.N.; Varouchakis, A.E.; Karatzas, G.P.; Ritsema, C.J. The Threat of Soil Salinity: A European Scale Review. Sci. Total Environ.; 2016; 573, pp. 727-739. [DOI: https://dx.doi.org/10.1016/J.SCITOTENV.2016.08.177]

13. Calvo de Anta, R.; Luís, E.; Febrero-Bande, M.; Galiñanes, J.; Macías, F.; Ortíz, R.; Casás, F. Soil Organic Carbon in Peninsular Spain: Influence of Environmental Factors and Spatial Distribution. Geoderma; 2020; 370, 114365. [DOI: https://dx.doi.org/10.1016/j.geoderma.2020.114365]

14. Minasny, B.; McBratney, A.B.B. Digital Soil Mapping: A Brief History and Some Lessons. Geoderma; 2016; 264, pp. 301-311. [DOI: https://dx.doi.org/10.1016/j.geoderma.2015.07.017]

15. Bouma, J. Soil Science Contributions towards Sustainable Development Goals and Their Implementation: Linking Soil Functions with Ecosystem Services. J. Plant Nutr. Soil Sci.; 2014; 177, pp. 111-120. [DOI: https://dx.doi.org/10.1002/jpln.201300646]

16. Tadiello, T.; Acutis, M.; Perego, A.; Schillaci, C.; Valkama, E. Soil Organic Carbon under Conservation Agriculture in Mediterranean and Humid Subtropical Climates: Global Meta-Analysis. Eur. J. Soil Sci.; 2022; 74, e13338. [DOI: https://dx.doi.org/10.1111/ejss.13338]

17. Ferreira, C.S.S.; Seifollahi-Aghmiuni, S.; Destouni, G.; Ghajarnia, N.; Kalantari, Z. Soil Degradation in the European Mediterranean Region: Processes, Status and Consequences. Sci. Total Environ.; 2022; 805, 150106. [DOI: https://dx.doi.org/10.1016/J.SCITOTENV.2021.150106] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/34537691]

18. Bogunovic, I.; Pereira, P.; Galic, M.; Bilandzija, D.; Kisic, I. Tillage System and Farmyard Manure Impact on Soil Physical Properties, CO2 Emissions, and Crop Yield in an Organic Farm Located in a Mediterranean Environment (Croatia). Environ. Earth Sci.; 2020; 79, 70. [DOI: https://dx.doi.org/10.1007/s12665-020-8813-z]

19. Egli, M.; Plötze, M.; Tikhomirov, D.; Kraut, T.; Wiesenberg, G.L.B.; Lauria, G.; Raimondi, S. Soil Development on Sediments and Evaporites of the Messinian Crisis. Catena; 2020; 187, 104368. [DOI: https://dx.doi.org/10.1016/j.catena.2019.104368]

20. Lagacherie, P.; Álvaro-Fuentes, J.; Annabi, M.; Bernoux, M.; Bouarfa, S.; Douaoui, A.; Grünberger, O.; Hammani, A.; Montanarella, L.; Mrabet, R. et al. Managing Mediterranean Soil Resources under Global Change: Expected Trends and Mitigation Strategies. Reg. Environ. Change; 2018; 18, pp. 663-675. [DOI: https://dx.doi.org/10.1007/s10113-017-1239-9]

21. Erdogan, H.E.; Havlicek, E.; Dazzi, C.; Montanarella, L.; Van Liedekerke, M.; Vrščaj, B.; Krasilnikov, P.; Khasankhanova, G.; Vargas, R. Soil Conservation and Sustainable Development Goals(SDGs) Achievement in Europe and Central Asia: Which Role for the European Soil Partnership?. Int. Soil Water Conserv. Res.; 2021; 9, pp. 360-369. [DOI: https://dx.doi.org/10.1016/j.iswcr.2021.02.003]

22. Wang, J.; Zhen, J.; Hu, W.; Chen, S.; Lizaga, I.; Zeraatpisheh, M.; Yang, X. Remote Sensing of Soil Degradation: Progress and Perspective. Int. Soil Water Conserv. Res.; 2023; 11, pp. 429-454. [DOI: https://dx.doi.org/10.1016/j.iswcr.2023.03.002]

23. McBratney, A.B.; Minasny, B.; Cattle, S.R.; Vervoort, R.W. From Pedotransfer Functions to Soil Inference Systems. Geoderma; 2002; 109, pp. 41-73. [DOI: https://dx.doi.org/10.1016/S0016-7061(02)00139-8]

24. Karavitis, C.A.; Tsesmelis, D.E.; Oikonomou, P.D.; Kairis, O.; Kosmas, C.; Fassouli, V.; Ritsema, C.; Hessel, R.; Jetten, V.; Moustakas, N. et al. A Desertification Risk Assessment Decision Support Tool (DRAST). Catena; 2020; 187, 104413. [DOI: https://dx.doi.org/10.1016/j.catena.2019.104413]

25. Zwetsloot, M.J.; van Leeuwen, J.; Hemerik, L.; Martens, H.; Simó Josa, I.; Van de Broek, M.; Debeljak, M.; Rutgers, M.; Sandén, T.; Wall, D.P. et al. Soil Multifunctionality: Synergies and Trade-Offs across European Climatic Zones and Land Uses. Eur. J. Soil Sci.; 2020; 72, pp. 1640-1654. [DOI: https://dx.doi.org/10.1111/ejss.13051]

26. Terribile, F.; Acutis, M.; Agrillo, A.; Anzalone, E.; Azam-Ali, S.; Bancheri, M.; Baumann, P.; Birli, B.; Bonfante, A.; Botta, M. et al. The LANDSUPPORT Geospatial Decision Support System (S-DSS) Vision: Operational Tools to Implement Sustainability Policies in Land Planning and Management. L. Degrad. Dev.; 2023; 35, pp. 813-834. [DOI: https://dx.doi.org/10.1002/ldr.4954]

27. Veerman, C.P. Activity Update of the Mission Board of European Union on Soil Health and Food. Soil Environ. Health; 2023; 1, 100018. [DOI: https://dx.doi.org/10.1016/j.seh.2023.100018]

28. Cramer, W.; Guiot, J.; Fader, M.; Garrabou, J.; Gattuso, J.-P.; Iglesias, A.; Lange, M.A.; Lionello, P.; Llasat, M.C.; Paz, S. et al. Climate Change and Interconnected Risks to Sustainable Development in the Mediterranean. Nat. Clim. Chang.; 2018; 8, pp. 972-980. [DOI: https://dx.doi.org/10.1038/s41558-018-0299-2]

29. Panagos, P.; Borrelli, P.; Jones, A.; Robinson, D.A. A 1 Billion Euro Mission: A Soil Deal for Europe. Eur. J. Soil Sci.; 2024; 75, e13466. [DOI: https://dx.doi.org/10.1111/ejss.13466]

30. Cammarano, D. Climate Variability and Change Impacts on Crop Productivity. Ital. J. Agron.; 2022; 17, [DOI: https://dx.doi.org/10.4081/ija.2022.2177]

31. Agaba, S.; Ferré, C.; Musetti, M.; Comolli, R. Mapping Soil Organic Carbon Stock and Uncertainties in an Alpine Valley (Northern Italy) Using Machine Learning Models. Land; 2024; 13, 78. [DOI: https://dx.doi.org/10.3390/land13010078]

32. Adeniyi, O.D.; Brenning, A.; Bernini, A.; Brenna, S.; Maerker, M. Digital Mapping of Soil Properties Using Ensemble Machine Learning Approaches in an Agricultural Lowland Area of Lombardy, Italy. Land; 2023; 12, 494. [DOI: https://dx.doi.org/10.3390/land12020494]

33. Vittori Antisari, L.; Trenti, W.; Buscaroli, A.; Falsone, G.; Vianello, G.; De Feudis, M. Pedodiversity and Organic Matter Stock of Soils Developed on Sandstone Formations in the Northern Apennines (Italy). Land; 2022; 12, 79. [DOI: https://dx.doi.org/10.3390/land12010079]

34. Conforti, M.; Buttafuoco, G. Insights into the Effects of Study Area Size and Soil Sampling Density in the Prediction of Soil Organic Carbon by Vis-NIR Diffuse Reflectance Spectroscopy in Two Forest Areas. Land; 2022; 12, 44. [DOI: https://dx.doi.org/10.3390/land12010044]

35. Kaya, F.; Schillaci, C.; Keshavarzi, A.; Başayiğit, L. Predictive Mapping of Electrical Conductivity and Assessment of Soil Salinity in a Western Türkiye Alluvial Plain. Land; 2022; 11, 2148. [DOI: https://dx.doi.org/10.3390/land11122148]

36. Trevisani, S.; Bogunovic, I. Diachronic Mapping of Soil Organic Matter in Eastern Croatia Croplands. Land; 2022; 11, 861. [DOI: https://dx.doi.org/10.3390/land11060861]

37. Adeniyi, O.D.; Bature, H.; Mearker, M. A Systematic Review on Digital Soil Mapping Approaches in Lowland Areas. Land; 2024; 13, 379. [DOI: https://dx.doi.org/10.3390/land13030379]