Lur Epelde
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.
Sessions
Policy demands for robust soil health monitoring are steadily growing. Given that soil biota are critical to the ecosystem services soils provide, biological properties are well-suited as relevant indicators, complementing physicochemical characteristics. However, biological properties are highly dynamic across spatial and temporal scales, which presents a challenge when using them for monitoring purposes.
As part of AI4SoilHealth project, we conducted a comprehensive soil health assessment in the experimental grasslands of NEIKER, where a rotational grazing system has been in place since 2013, compared to a free grazing system. Our objectives were to test innovative methods for measuring soil health and to analyze the temporal dynamics of soil biological properties in relation to climatic and pasture conditions.
Plant and soil samples were collected every three weeks from April to November 2024 at two depths (0-20 cm and 20-50 cm). A broad array of descriptors related to pasture quality, production, and soil physicochemical and biological properties were assessed. Novel methods tested and compared to conventional approaches included: (i) DigitSoil – a tool that measures enzymatic activity, providing real-time data on organic matter decomposition and other key biological processes; (ii) microBIOMETER – a portable kit measuring microbial biomass and the fungi-to-bacteria ratio; (iii) Slakes – assessing aggregate stability through a mobile app; (iv) eDNA and eRNA metabarcoding of 16S rRNA and ITS, to differentiate total and active prokaryotic and fungal communities; (v) remote sensing from Planet to provide data on vegetation growth and greenness.
The novel diagnostic tools provided cost-effective and high quality soil health assessments. Nevertheless, preliminary results suggest that differences in soil biological properties were more pronounced across soil depths and over time than between grazing types. Therefore, their spatial and temporal variability must be considered when designing a soil health monitoring program.
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.