Peter Lehmann
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.
Sessions
Compaction reduces the infiltration capacity of the soil surface and may result in water ponding during heavy rainfall events. The same ponding effect is expected for soils with low organic content, limiting the formation of structural pores with high drainage capacity. The resulting presence of ponding water generates run-off and anaerobic conditions in the topsoil, increasing erosion rate and reducing the biological activity and productivity. The longer the duration of a rainfall event, the lower the infiltration capacity of the soil and the higher the risk that the infiltration capacity becomes limiting, defining the ‘ponding time’ as the time of onset of water accumulation at the surface. The ponding time is a comprehensive soil physical property, integrating aspects of soil water retention, hydraulic conductivity, and initial water content. The lower the ponding time for a certain precipitation rate, the more frequent is the expected occurrence of water ponding at the surface. In this study, we apply an analytical expression for ponding time to compute it at the Pan-European scale. Using maps of basic soil information (soil texture, organic carbon, and bulk density), we can estimate values of ponding time and thus the frequency of ponding (and run-off) by infiltration excess. This map provides a reference for the quantification of soil health by reducing ponding time by compaction and loss of organic carbon (‘unhealthy’) and increasing ponding time by structure formation (‘healthy’). Applying pedotransfer functions linking soil hydraulic properties with basic soil information and land use, we provide a sensitivity map to estimate the change in ponding time statistics by modifying land management. The generated maps have a spatial resolution of 1 km and allow a comparison with national statistics of hydromorphic constraints, revealing the relationship between infiltration excess and saturation excess. To apply ponding maps in soil health assessment and land use management, they must be developed at higher resolution. We will show the potential of providing ponding maps at higher resolution (30 m, Soil Health Data Cube for Europe) and linking them to remote sensing observations of ponding using satellite data.
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.