2025-04-08, 16:30–16:45, HugoTECH
Soils are increasingly rediscovered as a vital resource that underpins many natural and societal services. Over more than half a century, agricultural mechanization and a singular focus on plant production, supported by chemical fertilizers, have led to widespread soil degradation. This reductionistic perspective has relied on soil observations focused on physico-chemical properties; properties that can be boosted by chemical additions but ignore the biological and ecological status and functions of the soil. Recognizing the importance of natural soil processes, which have evolved and been fine-tuned over billions of years, a new set of indicators for describing soil health beyond the physico-chemical properties is required. These indicators should preferably be observable and analyzable by farmers, advisors, extension workers and other citizen scientists. Methods that directly or indirectly capture the biological and ecological functions include, for instance i) environmental DNA (eDNA) metabarcoding to characterize the diversity and composition of soil microbial communities, ii) activity rates of key enzymes involved in the main biogeochemical cycles, iii) the ratio of soil fungi to bacteria, an indicator of the extent of disturbance in soil ecosystems, iv) aggregate stability, which is important for soil erosion resistance, and water and nutrient holding capacity, and v) water infiltration capacity as a key measure of the soil water absorption, holding and release potentials. While eDNA requires specialist laboratories and databases, the other methods are currently available for “Do-It-Yourself” (DIY) testing. In this study, as part of the EU-funded project AI4SoilHealth (https://ai4soilhealth.eu) we sampled soils in Greece, Sweden, Finland, Croatia and Denmark. We applied the outlined methods alongside traditional wet chemistry analysis of properties such as carbon, pH and electrical conductivity, and the particle size distribution. These properties were also estimated by leveraging their correlations with diffuse reflectance Near InfraRed (NIR) spectra and applying machine learning models. We are testing both the robustness of the novel methods and their interdependence with more traditional physico-chemical properties and soil spectroscopy. We hypothesize that there is a significant positive correlation between novel indicators (e.g. eDNA richness is correlated to enzymatic activity, which is correlated to aggregate stability, which in turn is correlated to infiltration capacity) and that high scores of the biological and ecological properties are correlated with, for instance, soil carbon content. This study explores the potential of these novel methods for more holistic understanding of soil health.
Dr. Lur Epelde (https://orcid.org/0000-0002-4624-4946) is a researcher at NEIKER-Basque Institute of Agricultural Research & Development and the coordinator of its Soil Microbial Ecology Group (www.soilmicrobialecology.com). During her PhD (University of the Basque Country, 2009), she gained extensive experience in using microbial indicators of soil health to assess the efficiency of phytoremediation processes. Currently, she continues to study soil microbial properties, including high-throughput sequencing technologies, to monitor the impact of various environmental stressors (e.g., pollution, agricultural practices, and climate change). She is also interested in the spread of antibiotic resistance in agricultural soils fertilized with organic amendments of animal or human origin. Finally, she takes part in outreach activities promoting soil health using Soil Health Cards as a tool (www.lurzain.eus). Throughout her career, she has completed research stays at the Netherlands Institute of Ecology, the Institute for Environmental Genomics at the University of Oklahoma, the Genetics in Ecology department at the University of Vienna, and the Lawrence Berkeley National Laboratory.
Sonia is the Chief Technology Officer at Digit Soil, where she leads the development of affordable, advanced soil sensors to promote sustainable soil management on a global scale. With a PhD from ETH Zürich’s Plant Nutrition Group and a Master’s in Biotechnology from the Jagiellonian University in Krakow, Sonia combines a deep understanding of natural systems with cutting-edge engineering. Her work bridges soil science with practical technology, helping land managers access real-time data for smarter, more sustainable decision-making in soil health.
I am Fatemeh Hateffard, a postdoctoral researcher at Stockholm University. My background is in soil science, with interests in soil mapping, remote sensing, and machine learning. Currently, I am engaged in the AI4SoilHealth project (WP4), focusing on soil spectroscopy and evaluating and testing new soil health indicators across different pilot sites.
I am a Senior Scientist at ETH Zurich (Environmental Systems Science). I study soil physical processes and their controlling structures at various scales ranging from pores to global mapping. As a lecturer, I teach soil physics and experimental methods to quantify processes in the vadose zone. I'm part of the AI4SoilHealth Project and manage the Swiss pilot site.
I am an environmental and soil scientist, having a broad education from Geoecology (Bachelor at the KIT, Karlsruhe), over Environmental Sciences (Master at the ETH Zürich), to Soil Science (PhD at the ETH Zürich the Federal Research Institute WSL, Birmensdorf). Now, I am working with our Startup Digit Soil on a portable device that can measure soil extracellular enzymatic activity. Within the AI4SoilHealth project we contribute to the section novel sensors and soil health parameters.
Robert holds PhD in Physical Geography and has a background in Geoinformatics. He has experience with digital soil mapping and predictive modeling of forest disturbance using machine learning. At OpenGeoHub is a postdoctoral researcher. At OpenGeoHub, Robert supports current and future high profile European Commission-funded and other international projects where there is a need to develop new solutions for geocomputing, optimizing modeling frameworks and publishing the scientific outputs.