Modelling and scaling of soil properties, functions and biological processes

The importance of soil quality and its functions such as nutrient cycling, carbon sequestration, water quality and biodiversity for climate regulation and a sustainable agriculture is more and more recognized. As a limited resource, soil is permanently under pressure and also highly affected by climate change. It often remains unclear how environmental change as well as new management strategies influence the various soil functions and their interactions on the different scales.

Computational models can help to understand and predict effects of a changing environment on soil properties, functions and their relationship by describing soil processes and organism dynamics. However, combining different interrelated functions and processes of a complex system such as soil remain rather challenging especially when scale transfers are needed. Related to that, there is an ongoing debate in how far and to what level of detail biological processes and interactions need to be represented in modelling soil functions.
This is a formidable scientific challenge related to upscaling soil processes from detailed interactions at the pore scale to effective soil functions at the scale of soil profiles or even the landscape scale.

With this session, we want to address several open questions:

a. What biological processes do we need to consider for modelling the dynamics of soil functions?
What data or mechanistic knowledge is missing for modelling soil functions and biological processes?
What are the relevant metrics representing key soil functions and defining soil quality?

b. How much details are needed to adequately describe the system, while keeping models simple enough for understanding their dynamics on different scales?
How important is the incorporation of spatial heterogeneity?

c. What is the appropriate level of model complexity to advice on optimal agricultural management practices?
What can we gain from such models to optimize field experiments?

This session presents theoretical concepts, scaling approaches and mechanistic models for simulating soil properties, functions and biological processes; as well as experimental or field studies which may help to improve modelling approaches.

This session has been promoted by:
Sustainable Agroecosystems (AGRISOST,
International Soil Modeling Consortium (ISMC,

Convener: Sara König | Co-conveners: Asim Biswas, R. Murray Lark, Juan José Martin Sotoca, Holger Pagel, Thibaut Putelat, Ana Maria Tarquis, Lindsay Todman
vPICO presentations
| Tue, 27 Apr, 13:30–17:00 (CEST)

Session assets

Session materials

vPICO presentations: Tue, 27 Apr

Chairpersons: Thibaut Putelat, Ana Maria Tarquis, Sara König
1.Block: Soil functions & Scaling
Soil functions
Taro Takahashi

A holistic evaluation of agricultural systems requires mechanistic understanding of physical, chemical and biological interactions both aboveground and belowground, yet obtaining this information on commercial farms is a challenging task. In order to support practical decision making by commercial producers, it is therefore necessary to identify system-wide performance indicators that are observable presently and cost-effectively. Data acquired through commercial soil testing satisfy these conditions; however, the relationship between the density of information — thus the cost of testing — and the value of information as a guideline for on-farm managerial changes is not well-understood.

Using high-resolution soil data from the North Wyke Farm Platform in the UK as a case exemplar, this solicited talk discusses theoretical and computational frameworks to quantify the value of an information package defined by soil testing strategies. A bootstrapping experiment revealed that the information value is often a concave function of the spatial sampling frequency, indicating that “half-hearted” soil data are unlikely to be able to inform optimal farm management. On the other hand, a high degree of serial correlation as well as atemporal inter-variable correlation resulted in some measurements identified as being redundant, as the incremental value of additional information was often found to be small and occasionally negative. Given the time and budgetary constraints, therefore, it is suggested that more effort should be spent on snapshot spatial sampling of a small number of variables, rather than continuous spot sampling of a large number of variables.

How to cite: Takahashi, T.: Economic optimisation of soil testing on commercial farms: space, time and variables, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13810,, 2021.

Ulrich Weller, Sara König, Bibiana Betancur-Corredor, Birgit Lang, Mareike Ließ, Stefanie Mayer, Thomas Reitz, Bastian Stößel, Hans-Jörg Vogel, Martin Wiesmeier, and Ute Wollschläger

We developed an integrated model of soil processes – the Bodium – that enables us to predict possible changes in soil functions under varying agricultural management and climatic change.

The model combines current knowledge on soil processes by integrating state-of-the-art modules on plant growth, root development, soil carbon and matter turnover with new concepts with respect to soil hydrology and soil structure dynamics. The model domain is at profile scale, with 1D nodes of variable thickness and weight. It is tested with long-term field experiments to ensure a consistent output of the combined modules. The model is site-specific and works with different soil types and climates (weather scenarios).

The output can be interpreted towards a broad spectrum of soil functions. Plant production and nutrient balances can be determined directly. The same is possible for water dynamics, with potential surface runoff (as infiltration surplus), storage and percolation together with travel time and groundwater recharge. In addition, nitrate losses are calculated, and the travel time distribution can help with the evaluation of pesticide percolation risk. To evaluate the habitat for biological activity, the activity is calculated in terms of carbon turnover, and the state variables carbon availability, water, air and temperature for the are accessible. Also, for macrofauna the earthworm activity is included. The comparison of scenario runs can be evaluated quantitatively in terms of potential developments of soil functions.

The model is work in progress. Further modules that will be implemented are pH dynamics, more explicit microbial activity, and a more complete set of effects of agricultural management on soil structure are integrated.

How to cite: Weller, U., König, S., Betancur-Corredor, B., Lang, B., Ließ, M., Mayer, S., Reitz, T., Stößel, B., Vogel, H.-J., Wiesmeier, M., and Wollschläger, U.: Systemic soil modelling and the evaluation of functions, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11129,, 2021.

William Rickard, Marcos Paradelo Perez, Aurelie Bacq-Labreuil, Andy Neal, Xiaoxian Zhang, Sacha Mooney, Karl Ritz, Elsy Akkari, and John Crawford

Soil organic matter is associated with important biological and physical functions. There are many theories to interpret this association, as yet there is not a fully developed understanding linking soil properties to nutritional management in arable systems.

We used X-ray computed tomography to analyse soil structure at the core and aggregate scale on the Broadbalk long term experiment (Hertfordshire, England). Here we present results of the treatments that have been under continuous wheat for 175 years. Corresponding to treatments that the only difference between the treatments is the nutrient management regime, with the exception of the baseline, or ‘wilderness’ treatment in which the plot was left unmanaged and has returned to mature woodland since 1882. The other nutrient treatments correspond to inorganic fertiliser addition with and without phosphorus, farmyard manure, and no added nutrient.

At core scale (40 µm resolution) we capture macro pore structures that are responsible for convective flow, while the aggregate scale images (1.5 µm resolution) include structures responsible for retention of water by capillary forces.  Therefore, a comparison of images taken at the two resolutions 1.5 µm and 40 µm provides information on how soil partitions between drainage and storage of water, and therefore on the air water balance under different environmental contexts.

The results are presented as a state-space plot of simulated permeability vs. porosity for each treatment. We find that nutrient management resulted in two distinct states at aggregate scale corresponding to water storage potential. Inorganic nutrient management resulted in structures of lower porosity and lower simulated permeability. There was no significant difference between each treatment, or between these treatments and the treatment with no nutrient addition. By comparison, the wilderness and manure treatments had higher porosity and higher permeability, with no significant difference between them.

At core scale, the results are slightly different. Again, the inorganic nutrient management treatments had lower porosity and simulated permeability, with no significant difference between them, and between them and the treatment with no nutrient addition. However, the manure treatment had a significantly lower porosity and permeability than the wilderness treatment. We conclude that long-term cultivation with organic nutrient management results in a similar capacity for water storage and transport to roots than a wilderness control, but that long-term management using a purely inorganic nutrient regime results in a smaller capacity for water storage and a lower transport rate to roots. Organic inputs, roots and plant detritus ploughed into the soil after harvest had no significant impact. Infiltration potential is highest in the wilderness control, lower for the manure treatment, and lowest for the inorganic nutrient management treatment. Again, inputs of organic nutrients from plants had no significant impact. We interpret these findings in terms of a previously hypothesised self-organising feedback loop between microbial activity and soil structure.

How to cite: Rickard, W., Paradelo Perez, M., Bacq-Labreuil, A., Neal, A., Zhang, X., Mooney, S., Ritz, K., Akkari, E., and Crawford, J.: Crop nutritional management affects soil structure and soil functions at different scales., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10043,, 2021.

Alla Yurova, Valery Kiryushin, and Anna Yudina

The key for implementation of sustainable development goals in land management is in multifunctional paradigm of landscape usage. A lot of scientific efforts were done since 1980s (e.g. Kiryushin, 2019) to develop a landscape-adaptive system which is in essence addressing

1) spatial distribution of plant varieties and farm operations adapted to topographical and lithological landscape features 2) temporal tuning of crop phenology to regional and even local weather conditions. This system proved especially useful in increasing the yield and yet reducing pollution level in experimental settings. However, there were no boost of implementation in the country of origin-Russia- due to number of reasons, social and economical included. The rapid growth of carbon tax and carbon market provides a new window of opportunity for landscape adaptive agriculture, but only in case documented benefit for carbon sequestration could be shown. Here we present theoretical proof of concept based on integrated critical zone model, 1D-ICZ (Giannakis et al, 2017), that couples computational modules for soil organic matter dynamics, soil aggregation and structure dynamics, bioturbation, plant productivity and nutrient uptake, water flow, solute speciation and transport, and mineral weathering kinetics. The model was applied to study C sequestration soil function along the regional natural soil moisture and temperature gradient. Calibration was done for three soil types (Retisols, Phaeozems, Chernozems) of increasing moisture deficits representing the well-drained landscape shoulder positions with an automorphic regime and hydromorphic footslope positions. The scenario simulation included the change in relative frequency of weather condition with low and extremely low, but also high end extremely high precipitation (from IPCC set of climate models). The model explicitly couples water infiltration storage and supply to soil structure and pedotransfer functions varying with meteorological conditions. This interaction allowed to select the current soil configuration and usage or structural and biogeochemical change in each soil and each scenario that are presumably most beneficial for C sequestration. The role of climate variables was maximum for automorphic regime and decreased with the decreasing distance to ground water. The soil textural, structural, and chemical properties on opposite played the major role on footslope positions. Accordingly, optimal land management option that promote corresponding soil structure, organic matter input and soil climate is proposed and discussed in balance with other soil functions.

How to cite: Yurova, A., Kiryushin, V., and Yudina, A.: Interactions in the soil-plant-water system as a basis for landscape-adoptive planning of carbon sequestration in agroecosystems along natural moisture gradient, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7675,, 2021.

Juan López de Herrera, Carlos Augusto Rocha de Moraes Rego, Paulo Sergio Rabello de Oliveira, Eloisa Mattei, and Antonio Saa-Requejo

Distribution of organic matter fractions of an Oxisol under different integrated agricultural production systems.

AUTHORS Juan López-Herrera, Augusto Rocha de Moraes Rego, Paulo Sergio Rabello de Oliveira, Eloisa Mattei, Antonio Saa-Requejo.



The declining organic matter content (OM) in agricultural soils is due mainly to poor agricultural management as soil fertility is closely related to OM. This work studies the variation in the different fractions of the OM in 7 plots with different agronomic management following integrated agricultural production system (IAPS) with different type of management. Six plots presented two crops per year, one of oats grown in autumn-winter and then soybeans grown in spring-summer. Seed doses per hectare and the management of livestock grazing were different among them. The seventh plot had a natural resection of rye and forage turnip during the winter, with succession of soybeans in spring-summer. Two reference plots were selected with hay and native forest production. These IAPS were compared at two areas, haymaking area and native forest, classified as Oxisols.

In each plot, random samples were analyzed at three different soil horizons, between 0.00-0.05, 0.05-0.10 and 0.10-0.20 m. Based on the soil samples the following parameters were measured: Total Organic Carbon (TOC), Particulate Organic Carbon (POC) associated with sand fraction, carbon stock (TOCst), mineral-associated organic carbon (MOC) associated with silt and clay, and humic substances (Fulvic Acids FA, Humic Acids HA and humin HUM). The relationship between these seven carbon indices and the seven IAPS were statistically analyzed using Tocher's multivariate non-hierarchical grouping methods.

The results pointed out that the different fractions of MO minus AH have a positive correlation in the three layers studied compared to the native forest. Therefore, IAPS management strategies promote beneficial modifications to soil properties and are beneficial for soil preservation. The management systems studied can serve as options for producers who wish to replace exclusive hay production with integration between crops and livestock in an Oxisol area similar to this one. Finally, the adoption of these management systems can lead to better soil preservation and increased economic benefits.


Keywords: integrated crop-livestock system, soil management, fractions of soil organic matter.



REGO, C. A. R. M.; OLIVEIRA, P. S. R.; PIANO, J. T.; ROSSET, J.S.; EGEWARTH, J. F.; MATTEI, E.; SAMPAIO, M. C.; LOPEZ-HERRERA, J.; GONÇALVES JUNIOR, A. C. (2020). Chemical properties and physical fractions of organic matter in oxisols under integrated agricultural production systems. Revista de Agricultura. Neotropical, Cassilândia-MS, v. 7, n. 3, p. 81-89, jul./set. 2020. ISSN 2358-6303.


REGO, C. A. R. M.; OLIVEIRA, P. S. R.; PIANO, J. T.; EGEWARTH, J. F.; EGEWARTH, V. A.; LOPEZ-HERRERA, J. (2020). Organic Matter Fractions and Carbon Management Index in Oxisol Under Integrated Agricultural Production Systems. Journal of Agricultural Studies, 2020, Vol. 8, No. 3 ISSN 2166-0379


How to cite: López de Herrera, J., Rocha de Moraes Rego, C. A., Rabello de Oliveira, P. S., Mattei, E., and Saa-Requejo, A.: Distribution of organic matter fractions of an Oxisol under different integrated agricultural production systems. , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9747,, 2021.

Mehmet Can Tunca, Ali Kerem Saysel, Masoud Babaei, and Günay Erpul

Soil salinity and sodicity are twin problems potentially affecting soil fertility, farmers’ livelihoods and food security. Management and control of these problems, particularly on irrigated farmlands require knowledge and expertise crafted through appropriate models and experiments. The accumulation of salts on the soil profiles may occur through natural processes (of weathering of soil minerals, saline groundwater intrusion), as well as by human actions, that are mostly related to poor agricultural and irrigation practices. While accumulation of salt in soil water impedes crop evapotranspiration, sodicity (abundance of sodium cations among others) threatens the soil structure and degrades its hydraulic qualities. These problems are more pervasive in arid and semi-arid regions, where inadequate precipitation rates compared to evapotranspiration limit leaching of salts and facilitates their accumulation in productive topsoil. Therefore, irrigation and agricultural practices are crucial in controlling these problems to avoid their undesired consequences.
We build a dynamic simulation model of salinization and sodification in soil layers so as to test the impact of alternative irrigation practices with respect to water quality, quantity and schedule, on soil fertility and farm yields. The model is developed based on the system dynamics methodology, providing a feedback rich understanding of hydraulic, solute, and crop processes. While the hydraulic flow is the driver of solute transport, salinity and sodicity influences the hydraulic flows through their impact on evapotranspiration and hydraulic conductivity. The crop growth and its demand for evapotranspiration at various stages of development is modeled, considering available moisture and the accumulation of salts in the rootzone. Moreover, the model investigates farmers’ response to salinity and sodicity through adoption of different irrigation practices and crop choices, so as to observe the long-term development of the problem under the conditions of adaptive management.
The model has a generic theoretical structure that benefits from soil physics to formulate the complex processes of hydraulic flows and solute transport. Model parameter values are selected as representative of the field conditions of Konya Plain in Turkey, which is a semi-arid region partially experiencing soil salinization problems. As a part of the research project entitled, “Soil Salinity and Sodicity Management by Sustainable Irrigation Practices in Konya Plain”, the Interdisciplinary Multi-Institutional Network, during model validation phase, we will utilize data from the soil experiments that are conducted by our research partners. These data will include, however will not be limited to the experimentally characterized porosity and hydraulic conductivity curves. Ultimately, the model will provide an experimental platform to manage and control soil salinity and sodicity under different environmental conditions and farmer responses.

Keywords: Soil Salinity, Soil Sodicity, System Dynamics, Irrigation, Agriculture

Acknowledgement: This work was supported by the Scientific and Technological Research Council of Turkey [Project Number: TUBITAK-118Y343]

How to cite: Tunca, M. C., Saysel, A. K., Babaei, M., and Erpul, G.: A dynamic simulation approach to soil salinity and sodicity control, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15984,, 2021.

Scale effects characterisation
Deise Cristina Santos Nogueira, Antonio Paz-González, Eva Vidal-Vázquez, Mário Luiz Teixeira de Moraes, José Marques Júnior, Rafael Montanari, Debora Marcondes Bastos Pereira, Newton La Scala Júnior, and Alan Rodrigo Panosso

Soil is a major source and also a sink of CO2. Agricultural management practices influence soil  carbon sequestration. Identification of CO2 emission hotspots may be instrumental in implemented strategies for managing carbon cycling in agricultural soils. We used multifractal analysis to assess the spatial variability of both, soil CO2 emissions and associated soil physico-chemical attributes. The objectives of this study were: i) to characterize patterns of spatial variability of CO2 emissions and related soil properties using single multifractal spectra, and ii) to compare the scale‐dependent relationship between soil CO2 emissions and selected soil attributes by joint multifractal analysis. The study site was an experimental field managed as a sylvopastoral system, located in Selviria, South Mato Grosso state, Brazil. The soil was an Oxisol developed over basalt. Soil CO2 emission, soil water content and soil temperature were measured at 128 points every meter. In addition, soil was sampled at the marked points to analyze clay content, macro and microporosity, air free porosity, magnetic susceptibility, bulk density, and humification index of soil organic matter in absolute values and relative to organic carbon content. The generalized dimension, Dq versus q, and singularity spectra, f(α) versus α, of the spatial distributions of the 11 variables studied showed various degrees of multifractality. In general, the amplitude of the generalized dimension and singularity spectra was much higher for negative than for positive q order statistical moments. Joint multifractal spectra show a positive relationship between the scaling indices of the spatial distributions of CO2 and all of the other soil variables studied. However, contour plots were diagonally oriented for higher values of scaling indices and showed no distinct trend for the lower ones. Joint multifractal analysis corroborates different degrees of association between the scaling indices of CO2 and all of the remaining variables studied. It also showed that CO2 was stronger correlated at multiple scales than at the observation scale. Therefore, single scale analysis may not be sufficient to fully describe relationships between soil testing methods.Our study suggests that soil factors and processes driven the spatial variability of CO2 and the associated variables studied may be not very different.


How to cite: Nogueira, D. C. S., Paz-González, A., Vidal-Vázquez, E., de Moraes, M. L. T., Júnior, J. M., Montanari, R., Pereira, D. M. B., Júnior, N. L. S., and Panosso, A. R.: Multifractal and joint multifractal analysis of the spatial variability of CO2 emission and other soil properties, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16174,, 2021.

Estimation of Synthetic Aperture Radar (SAR) soil moisture with the use of fractal roughness
Ju Hyoung Lee, Notarnicola Claudia, and Jeff Walker
Hanna Zeitfogel, Moritz Feigl, and Karsten Schulz

To assess future groundwater recharge rates in Austria under climate change conditions, detailed spatial soil information is required.  Different data sources such as global soil maps (SoilGrids), regional soil maps of arable land (eBOD) and local soil profiles are available. However, they differ in scale and degree of data aggregation, as well as in spatial coverage.

Soil properties are characterized by a high spatial variability at all scales and it is well known that averaging will cause biases in the statistical relationships between different soil data sets, and between soil and landscape physio-geographical properties. Aiming for a best quality Austrian-wide soil map synthesizing all information, scale dependent multi-level relations between soil data bases are examined following two approaches:

Firstly, a linear relationship of soil variables at different scales is assumed. The statistical and geostatistical characteristics are analyzed at different aggregation levels to explore the scale-related behavior of our data. Secondly, machine learning algorithms (random forests and boosting methods) are applied to predict soil characteristics of existing regional soil maps by using all other available data sources as input features. Additional locally available variables such as elevation, slope, aspect, vegetation and climate data are evaluated for significance when predicting the missing soil information.  

In summary, this study analyzes the statistical behavior and patterns of variability of soil properties at different scales and derives a modelling approach that can be used to predict regional soil properties from data sources spanning a range of different scales.

How to cite: Zeitfogel, H., Feigl, M., and Schulz, K.: Variability across scales - exploring methods for predicting soil properties from multiple sources, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4142,, 2021.

Juan José Martin Sotoca, Antonio Saa Requejo, Sergio Zubelzu, and Ana M. Tarquis

The characterization of the spatial distribution of soil pore structures is essential to obtain different parameters that will be useful in developing predictive models for a range of physical, chemical, and biological processes in soils. Over the last decade, major technological advances in X-ray computed tomography (CT) have allowed for the investigation and reconstruction of natural porous soils at very fine scales. Delimiting the pore structure (pore space) from the CT soil images applying image segmentation methods is crucial when attempting to extract complex pore space geometry information.

Different segmentation methods can result in different spatial distributions of pores influencing the parameters used in the models [1]. A new combined global & local segmentation (2D) method called “Combining Singularity-CA method” was successfully applied [2]. This method combines a local scaling method (Singularity-CA method) with a global one (Maximum Entropy method). The Singularity-CA method, based on fractal concepts, creates singularity maps, and the CA (Concentration Area) method is used to define local thresholds that can be applied to binarize CT images [3]. Comparing Singularity-CA method with classical methods, such as Otsu and Maximum Entropy, we observed that more pores can be detected mainly due to its ability to amplify anomalous concentrations. However, some small pores were detected incorrectly. Combining Singularity-CA (2D) method gives better pore detection performance than the Singularity-CA and the Maximum Entropy method applied individually to the images.

The Combining Singularity-CV (3D) method is presented in this work. It combines the Singularity – CV (Concentration Volume) method [4] and a global one to improve 3D pore space detection.



[1] Zhang, Y.J. (2001). A review of recent evaluation methods for image segmentation: International symposium on signal processing and its applications. Kuala Lumpur, Malaysia, 13–16, pp. 148–151.

[2] Martín-Sotoca, J.J., Saa-Requejo, A., Grau, J.B., Paz-González, A., and Tarquis, A.M. (2018). Combining global and local scaling methods to detect soil pore space. J. of Geo. Exploration, vol. 189, June 2018, pp 72-84.

[3] Martín-Sotoca, J.J., Saa-Requejo, A., Grau, J.B. and Tarquis, A.M. (2017). New segmentation method based on fractal properties using singularity maps. Geoderma, vol. 287, February 2017, pp 40-53.

[4] Martín-Sotoca, J.J., Saa-Requejo, A., Grau, J.B. and Tarquis, A.M. (2018). Local 3D segmentation of soil pore space based on fractal properties using singularity maps. Geoderma, vol. 311, February 2018, pp 175-188.



The authors acknowledge support from Project No. PGC2018-093854-B-I00 of the Spanish Ministerio de Ciencia Innovación y Universidades of Spain and the funding from the Comunidad de Madrid (Spain), Structural Funds 2014-2020 512 (ERDF and ESF), through project AGRISOST-CM S2018/BAA-4330.

How to cite: Martin Sotoca, J. J., Saa Requejo, A., Zubelzu, S., and Tarquis, A. M.: Combining global and local scaling (3D) methods to detect pore spaces in CT soil images, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13209,, 2021.

Ivana Pavlů, Renáta Talská, Daniel Šimíček, Karel Hron, and Ondřej Bábek

To describe the relationship between the distribution of particle sizes in soil (particle size distribution, PSD) and the geochemical composition of sediment samples, specific attributes of the variables need to be considered.  In this case, the explanatory variable can be described in form of the probability density function while the response is a real variable represented by log-ratios of the original chemical concentrations. Due to the relative character of density functions, an adequate methodology must be used to satisfy their specific properties. Here, the Bayes space methodology was employed, specifically the centred logratio (clr) transformation played the role to represent the PSDs (densities) in the standard $L^2$ space which is suitable for multivariate statistical methods, including regression. The idea of smoothing splines was used to represent the discretized input densities while fulfilling the zero-integral constraint imposed by the clr transformation. The resulting regression parameters (densities) can be interpreted in both the original and clr space, however, in the latter the interpretation is more straightforward. The newly developed regression model, called compositional scalar-on-function regression was then used for real-world geological data consisting of samples from four loess-paleosol sequences (LPS) in the Czech Republic (Brodek u Přerova, Dobšice, Ivaň, Rozvadovice). The regression modeling allows to distinguish local effects on the PSD and elemental composition of loess, which were not apparent by the standard approach where the PSD and compositions are usually plotted separately. The high mixing capacity of the aeolian transport caused a similar mineral and chemical composition, despite the different source areas of the studied LPSs. Local variability in the PSDs and distribution of selected elements in different grain fractions reflect some microclimatic features, especially the annual precipitation totals, which affected the particle size distribution of dust material blown by wind as well as the intensity of subsequent post-deposition and pedogenic processes.

How to cite: Pavlů, I., Talská, R., Šimíček, D., Hron, K., and Bábek, O.: Compositional scalar-on-function regression between geochemical composition and particle size distribution, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7047,, 2021.

Hugo Fagundes, Fernando Fan, Rodrigo Paiva, Vinicius Siqueira, Diogo Buarque, Luisa Kornowski, Leonardo Laipelt, and Walter Collischonn

Suspended sediments (SS) have an important role in the maintenance of several ecosystems by supplying them with nutrients. On the other hand, erosion and sediment transport can carry pollutants and pesticides, contributing to the negative impacts on the aquatic biota. Besides that, sediment supply for the rivers is often a driver to geomorphologic changes occurring in the rivers. Erosion and sediment rates in South America are considerably high in comparison to northern continents in the world. In this study we modeled the natural (non affected by reservoirs) spatio-temporal dynamic of suspended sediments in South America, including deposition rates in floodplain areas, using the sediment continental model MGB-SED SA. The model performance was evaluated aga inst 595 in-situ stations; 80 sites using results from regional studies; and 51 sites using results from a global sediment model. For most places, model performance analysis shows a better agreement between simulated and observed (in-situ) data than when results were compared to regional studies and a global model data. A better representation of sediment flow in rivers and floodplains was possible due to the use of hydrodynamic river routing. Based on MGB-SED SA estimates, South America delivers to the oceans 1.00×109 t/year of SS. The bigger suppliers are the Amazon (4.36×108 t/year), Orinoco (1.37×108 t/year), La Plata (1.11×108 t/year), and Magdalena (3.26×107) rivers. Around 12% (2.40×108 t/year) of SS loads reaching the rivers are stored in the floodplains, showing the importance of these regions.  

How to cite: Fagundes, H., Fan, F., Paiva, R., Siqueira, V., Buarque, D., Kornowski, L., Laipelt, L., and Collischonn, W.: Simulated suspended sediment flows in South America using hydrological-hydrodynamic modeling, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8490,, 2021.

Ahsan Raza, Thomas Gaiser, Muhammad Habib-Ur-Rahman, and Hella Ahrends

Information on field scale soil erosion and related sedimentation process is very important for natural resource management and sustainable farming. Plenty of models are available for study of these processes but only a few are suitable for dynamic small scale soil erosion assessments. The available models vary greatly in terms of their input requirements, analysis capabilities, process [t1] complexities, spatial and temporal scale of their intended use, practicality, the manner they represent the processes, and the type of output information they provide. The study aims in examining, theoretically, 51 models classified as physical, conceptual, and empirical based on their representation of the processes of soil erosion. The literature review shows that there is no specific model available for soil erosion prediction under agroforestry systems.   It is further suggested that models like EPIC, PERFECT, GUEST, EPM, TCRP, SLEMSA, APSIM, RillGrow, and CREAMS can be potentially used for soil erosion assessment at plot/field scale at daily time steps. Most of these models are capable to simulate the soil erosion process at small scale; further model development is needed regarding their limitations with respect to components interaction i.e., rainfall intensity, overland flow, crop cover, and their difficulties in upscaling. The research suggested that SIMPLACE network can provide modules with LintulBiomass, HillFlow, Runoff to develop new dynamic components to simulate overland flow and soil erosion incorporating improved upscaling capabilities

How to cite: Raza, A., Gaiser, T., Habib-Ur-Rahman, M., and Ahrends, H.: Erosion and sediment transport models with reference to the needs of small scale, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5513,, 2021.

Roland Szatmári and Ferenc Kun

Layers of dense pastes, colloids attached to a substrate often undergo sequential cracking due to shrinkage stresses caused by desiccation. From the spectacular crack patterns of dried out lake beds through the polygonal ground patterns of permafrost regions to the formation of columnar joints in cooling volcanic lava, shrinkage induced cracking is responsible for a large variety of complex crack structures in nature. Under laboratory conditions this phenomenon is usually investigated by desiccating thin layers of dense colloidal suspensions in a container, which typically leads to polygonal crack patterns with a high degree of isotropy.

It is of great interest how to control the structure of shrinkage induced two-dimensional crack patterns also due to its high importance for technological applications. Recently, it has been demonstrated experimentally for dense calcium carbonate and magnesium carbonate hydroxide pastes that applying mechanical excitation by means of vibration or flow of the paste the emerging desiccation crack pattern remembers the direction of excitation, i.e. main cracks get aligned and their orientation can be tuned by the direction of mechanical excitation.

In order to understand the mechanism of this memory effect, we investigate a fragmentation process of a brittle, cylindrical sample, where the driving force of the cracking coming from a continous shrinkage, which sooner or later destroys the cohesive forces between the structure’s building blocks. Our study is based on a two dimensional discrete element model, where the material is discretised via a special form of the Voronoi-tesselation, with the so-called randomised vector lattice which allows to fine-tune the initial disorder of the system. We assume that the initial mechanical vibration imprints plastic deformation into the paste, which is captured in the model by assuming that the local cohesive strength of the layer has a directional dependence: the layer is stronger along the direction of vibration. We demonstrate that - based on this simple assumption - the model well reproduces the qualitative features of the anisotropic crack patterns observed in experiments. Gradually increasing the degree of anisotropy the system exhibits a crossover from an isotropic cellular structure to an anisotropic one.

How to cite: Szatmári, R. and Kun, F.: Anisotropic fragmentation of shrinking thin layers on a substrate, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12679,, 2021.

Chairpersons: Holger Pagel, Lindsay Todman, Sara König
2. Block: Biological processes and soil data analysis
Biological processes
Katharina Meurer, Thomas Keller, and Nicholas Jarvis

The pore structure of soil is known to be dynamic at time scales ranging from seconds (e.g. compaction) to seasons (e.g. root growth, macro-faunal activity) and even decades to centuries (e.g. changes in organic matter content). Nevertheless, soil physical and hydraulic functions are generally treated as static properties in most soil-crop models. Some models account for seasonal variations in soil properties (e.g. bulk density) due to tillage loosening and post-tillage consolidation or soil sealing. However, no model can account for longer-term changes in soil structure due to biological agents and processes. The development of such a model remains a challenge due to the enormous complexity of the interactions in the soil-plant system. Here, we present a new concept for modelling soil structure evolution impacted by biological processes such as root growth and earthworm activity. In this preliminary test of the model, we compare simulations against field observations made at the Soil Structure Observatory (SSO) in Zürich, Switzerland, that was designed to provide information on soil structure recovery following a severe compaction event. In this simple application, we modelled changes in the pore size distribution in a bare soil treatment resulting from soil ingestion and egestion by earthworms and the loosening of compacted soil by casting at the soil surface. Following calibration, the model was able to reproduce the observed temporal development of total porosity, soil bulk density and pore size distribution during a four-year period following severe traffic compaction. The modelling approach presented here appears promising and could help support the development of cost-efficient strategies for sustainable soil management and the restoration of degraded soils.

How to cite: Meurer, K., Keller, T., and Jarvis, N.: An approach to modelling soil structure dynamics and soil structure recovery due to earthworm bioturbation and root growth, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1172,, 2021.

Bibiana Betancur Corredor, Birgit Lang, and David Russell

The impact of agricultural activities on soil fauna can be highly variable, depending on the management options adopted. High-input agricultural practices can promote a reduction in diversity of soil microarthropod communities but, at the same time can also favor bacterial-feeding fauna through the increase of bacterial food web pathways. In contrast, low-input practices can increase the dominance of fungal-feeding fauna through the promotion of fungal pathways. Responses also vary with time after fertilizer application and are strongly dependent on crop species or shifts in plant species composition due to fertilization. The type of fertilizer, organic or inorganic, can also have diverse effects on soil organisms. Organic fertilizers can increase the population of soil decomposers serving as nutrient sources for other soil organisms. Nitrogen fertilization may disturb soil organisms in a manner that affects ecosystem functioning, but the links are not yet well quantified. Therefore, a systematic compilation of available experimental data on the effects of nitrogen fertilization on taxonomic and functional groups of soil fauna is needed to clarify the patterns and mechanisms of responses. 

Paired observations for meta-analysis were collected from 198 studies published in the last 30 years across 37 countries. First results show that nitrogen fertilization increased the biomass of earthworms (mean increase of 19.7%), the abundance of nematodes (mean increase of 36.6%), springtails (mean increase of 29.7%), and mites (mean increase of 35.2%), and reduced the abundance of earthworms (mean reduction of 9.2%) compared to when no fertilizer was applied. The population responses of all organisms were larger when organic fertilizers were applied. The meta-analyses for different earthworm ecological groups showed that the biomass of epigeic and endogeic earthworms were most sensitive to organic fertilization, and this effect was magnified when higher rates of nitrogen are applied. The meta-analyses for different nematode feeding groups, life-form groups of springtails and mite suborders showed that each group is affected differently by organic and inorganic fertilization. Additional meta-analysis also showed that the responses of the soil organisms to nitrogen fertilization can also be modulated by physicochemical properties of the soil as well as climatic conditions. 

How to cite: Betancur Corredor, B., Lang, B., and Russell, D.: Effects of nitrogen fertilization on soil fauna – A global meta-analysis, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16437,, 2021.

Sara König, Ulrich Weller, Thomas Reitz, Bibiana Betancur-Corredor, Birgit Lang, Hans-Jörg Vogel, and Ute Wollschläger

Mechanistic simulation models are an essential tool for predicting soil functions such as nutrient cycling, water filtering and storage, productivity and carbon storage as well as the complex interactions between these functions. Most soil functions are driven or affected by soil organisms. Yet, biological processes are often neglected in soil function models or implicitly described by rate parameters. This can be explained by the high complexity of the soil ecosystem with its dynamic and heterogeneous environment, and by the range of temporal and spatial scales these processes are taking place at. On the other hand, the technical capabilities to explore microbial activity and communities in soil has greatly improved, resulting in new possibilities to understand soil microbial processes on various scales.

However, to integrate such biological processes in soil modelling, we need to find the right level of detail. Here, we present a systemic soil model approach to simulate the impact of different management options and changing climate on soil functions integrating biological activity on the profile scale. We use stoichiometric considerations to simulate microbial processes involved in different soil functions without explicitly describing community dynamics or functional groups. With this approach we are able to mechanistically describe microbial activity and its impact on the turnover of organic matter and nutrient cycling as driven by agricultural soil management.

Further, we discuss general challenges and ongoing developments to additionally consider, e.g., microbe-fauna-interactions or microbial feedback with soil structure dynamics.

How to cite: König, S., Weller, U., Reitz, T., Betancur-Corredor, B., Lang, B., Vogel, H.-J., and Wollschläger, U.: Biological processes in modelling soil functions – a balancing act between too complex and too simple, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7458,, 2021.

Alice Lieu, Simon Zech, Alexander Prechtel, Nadja Ray, and Raphael Schulz

We assess the complex coupling of biological, chemical and physical processes with the help of a mechanistic modeling approach extending [Rupp 2019] The aim is to study the interplay of relevant mechanisms in silico and consequently gain a model-based understanding of dynamics in soils.
The hybrid discrete-continuum model used explicitly represents the pore structure and allows for dynamic structural organization of the medium at the pore scale. The movement of interacting entities - nutrients, bacteria and possibly charged chemicals - in the fluid is described by means of the diffusion and Nernst-Planck equations with Henry's law at the liquid/gas interfaces. Homogeneous chemical reactions are considered using for instance the mass action law whereas heterogeneous reactions on the solid surface are incorporated via a kinetic Langmuir isotherm. A biomass phase can develop from agglomerations of bacteria and stabilising sticky agents may grow or decay at the solid surfaces. Root cells and an explicit phase of exudate as well as attachment properties of root hairs can be included. In addition to solving the continuous partial differential equations, a discrete cellular automaton method [Ray et al. 2017, Rupp et al 2018, Tang and Valocchi 2013] is used, enabling structural changes in the solid and biomass/mucilage phases at each time step. The partial differential equations are discretised with a local discontinuous Galerkin method which is able to handle discontinuities induced by the evolving geometry. Upscaling techniques enable the incorporation of information from the pore scale into the macroscale.
In this study, we illustrate the ability of the approach to advance the understanding of specific process mechanisms. Microaggregates are the fundamental building blocks of soils and are thus important for soil structure, properties, and functions. Although there has been much research investigating the dynamics, stability, and structure of microaggregates, there is still a substantial lack in quantifying the relationships between the major driving forces (soil fauna, microorganisms, roots, organic and inorganic matter, and physical processes). As an example, we study structure formation of microaggregates as a function of the size and shape of the solid building units taking into account the effect of attraction and repulsion by charges. Biomass development and root exudate can significantly alter the macroscopic soil hydraulic properties. Using the model in hand, this effect can be quantified for different amount and spatial distribution of root exudate with geometries from CT-scans. In the natural environment, microbial communities are highly diverse. We employ the model to investigate the way spatial distribution of organic matter can influence bacterial dynamics.

How to cite: Lieu, A., Zech, S., Prechtel, A., Ray, N., and Schulz, R.: Soils in silico - solutes, biofilms and structure formation, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14591,, 2021.

Xavier Raynaud, Hannes Schmidt, and Naoise Nunan

Heterogeneity is a fundamental property of soil that is often overlooked in microbial ecology. Although it is generally accepted that the heterogeneity of soil underpins the emergence and maintenance of microbial diversity, the profound and far-reaching consequences that heterogeneity can have on many aspects of microbial ecology and activity have yet to be fully apprehended and have not been fully integrated into our understanding of microbial functioning.

Heterogeneity in soils has multiple facets, from the molecular heterogeneity of the diversity of substrate available, the activity heterogeneity due to the activity of microbial species and the spatial heterogeneity of the soil structure and the distribution of organisms.

In this contribution we present a simple, spatially explicit model that can be used to understand how the interactions between the heterogeneity of decomposers (in terms of species and spatial distribution) and environmental heterogeneity (in terms of the diversity of substrates and their spatial distribution) affect the bacterial decomposition of organic matter. We found that environmental heterogeneity is a key element in determining the variability of the decomposition process.

How to cite: Raynaud, X., Schmidt, H., and Nunan, N.: The ecology of heterogeneity: soil bacterial communities and C dynamics, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13399,, 2021.

Erik Schwarz, Swamini Khurana, Luciana Chavez Rodriguez, Johannes Wirsching, Christian Poll, Ellen Kandeler, Thilo Streck, Martin Thullner, and Holger Pagel

Despite all legislative efforts, pesticides persist in soils at low concentrations and are leached to groundwater. This environmental issue has previously been associated with control factors relevant in natural soils but elusive in lab experiments and standard modeling approaches. One such factor is the small-scale spatial distribution of pesticide-degrading microorganisms in soil. Microbes are distributed heterogeneously in natural soils. They are aggregated in biogeochemical “hotspots” at the centimeter scale. The aim of our study is to investigate the relevance of such aggregation for pesticide degradation. For this, we upscaled the effect of the heterogeneity-induced accessibility limitations to degradation to the soil-column scale and analyzed kinetic constraints and amplifying factors under contrasting unsaturated flow regimes.

We performed a 2D spatially explicit, site-specific model-based scenario analysis for bioreactive transport of the model pesticide 4-chloro-2-methylphenoxyacetic acid (MCPA) in an arable soil (Luvisol). Stochastic centimeter-scale spatial distributions of microbial degraders were simulated with a spatial statistical model (log Gaussian Cox process), parametrized to meet experimentally observed spatial distribution metrics. Three heterogeneity levels were considered, representing homogenized soil conditions, and the lower and upper limit of expected microbial spatial aggregation in natural soils. Additionally, two contrasting precipitation scenarios (continuous light rain vs. heavy rain events directly following MCPA application) were assessed. A reactive transport model was set up to simulate a 0.3 m x 0.9 m soil column based on hydraulic and bioreactive measurements from a soil monitoring station (Germany, SM#3/ DFG CRC 1253 CAMPOS).

Our simulations revealed that heavy precipitation events were the main driver of pesticide leaching. Leached amounts from the topsoil increased by two to five orders of magnitude compared to the light rain scenario and at max. ca. 20 ng was leached from 90 cm after one year. With the increasing spatial aggregation of microbial degraders, upscaled pesticide degradation rates decreased, and considerable differences emerged between homogeneous and highly aggregated scenarios. In the latter, leaching from the plow layer into the subsoil was more pronounced and MCPA was detectable (LOD = 4 µg/kg) 5-6 times longer. In heterogeneous scenarios, degradation in microbial hotspots was mainly diffusion-limited during “hot moments” (times of high substrate availability), with a fraction of MCPA simultaneously “locked in” in coldspots with low microbial abundance. During intense precipitation events MCPA was remobilised from these coldspots by advective-dispersive transport, thereby increasing pesticide accessibility.

Our results indicate that predicted environmental concentrations and detectability of pesticides might be underestimated if spatial heterogeneity of microbial degraders is neglected, and they highlight the importance of heavy rain events as drivers of leaching and substrate accessibility.

How to cite: Schwarz, E., Khurana, S., Chavez Rodriguez, L., Wirsching, J., Poll, C., Kandeler, E., Streck, T., Thullner, M., and Pagel, H.: Spatial controls of microbial pesticide degradation in soils – A model-based scenario analysis, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15697,, 2021.

Albert C. Brangarí, Stefano Manzoni, and Johannes Rousk

Soil moisture is one of the most important variables controlling the activity and diversity of resident microorganisms and play a mediating role in biogeochemical cycling and soil functioning. Yet, natural ecosystems are not exposed to constant moisture conditions but to successive drying and rewetting (D/RW) cycles where periods of drought are interspersed with sudden rainfall events. Soil scientists have known for more than 60 years about the existence of the Birch effect, that is, that the rewetting of a dry soil causes a profound remobilization of resources and a large emission of CO2 to the atmosphere. However, recent empirical evidence at high temporal resolution has demonstrated that respiration and microbial growth follow strongly disconnected patterns. Moreover, these microbial patterns can be categorized into two general responses: the microbial community starts synthesizing new biomass immediately after rewetting (“type-1”), or after a lag period of several hours (“type-2”). Despite the enormous implications of these short-term dynamics for the stabilization of C in soils and the C budget, they have been surprisingly ignored in biogeochemical models at all scales.

To address this critical issue, we developed a new process-based model (EcoSMMARTS) that incorporated a long list of soil and microbial mechanisms thought to affect the responses to D/RW, based on previous literature. The model was proven useful to reproduce the disconnected behaviour between microbial growth and respiration, and captured the patterns characterizing both types of response. We identified the physiological and structural characteristics of the community at the moment of rewetting as the main factor controlling the patterns of the response. And these characteristics were, in turn, determined by the history of climate, which defined the stress-level of cells and selected for microbial groups with the most suitable survival strategies. The communities better adapted to dry environments could start growing immediately after rewetting and depicted a resilient or “type-1” response, where the elimination of osmolytes to adapt the internal osmotic pressure of cells played a major role. In contrast, those communities from continuously moist environments could not withstand the harshness of the D/RW event and depicted a sensitive response or “type-2”. The small population surviving (and still active) after the drying phase caused a delay in the synthesis of biomass, while cell residues from dead organisms contributed largely to respiration. The C fuelling the emissions was sourced from the accumulation of dead microbial biomass during droughts, and from multiple sources after rewetting, including microbial foraging, the disruption of soil aggregates, and the reuse of osmolytes. The good qualitative agreement between the model results and empirical observations represents a critical step towards unravelling the drivers and key mechanisms that govern the functioning of soils and their feedbacks on climate.

How to cite: Brangarí, A. C., Manzoni, S., and Rousk, J.: Unravelling the processes that govern the emission/sequestration of carbon in soils subjected to moisture changes, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5546,, 2021.

Holger Pagel, Marie Uksa, Christian Poll, Ellen Kandeler, and Thilo Streck

Soil microbial functional traits control carbon (C) decomposition and stabilization in soil. Integrating metabolic trade-offs and life-history strategies of microbial communities into models enhances the representation of feedbacks between microbial diversity and soil biogeochemical functions. This has great potential to improve our understanding of microbial C allocation and how microbial processes affect C storage and use efficiency in soil. The current challenge is, however, to quantify and identify ecologically meaningful microbial traits. This study utilizes data from a 13C pulse-labelling litter decomposition experiment to inform a new soil C turnover model that captures microbial life-history traits and dormancy in combination with soil organic matter accessibility. Quantitative data from 13C DNA stable isotope probing and high-throughput sequencing is used to parameterize the C utilization of copiotrophic and oligotrophic microorganisms. The new model is then applied to quantify C utilization of functional microbial groups and C turnover in soil.  In scenario analyses we investigate the sensitivity of functional microbial groups and its feedback on C cycling to C input.

How to cite: Pagel, H., Uksa, M., Poll, C., Kandeler, E., and Streck, T.: Data-informed trait-based modeling of microbial carbon cycling in soil, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11255,, 2021.

Ahmet Sircan, Mona Giraud, Guillaume Lobet, Andrea Schnepf, Thilo Streck, and Holger Pagel

The rhizosphere shows complex spatial and temporal patterns of biophysical and biochemical processes. Process-based modeling that accounts for functional microbial traits provides a tool to gain a better understanding of microbial interactions involved in carbon cycling in the rhizosphere. Here, we present a trait-based rhizosphere model that accounts for microbial life-history strategies (copiotrophs, oligotrophs), microbial physiology (e.g., dormancy), and organic carbon bioaccessibility (small and large polymers). The model reflects the mm-scale microbial and carbon dynamics around a cylindrical root segment and will be linked with a structural-functional soil-plant model (CPlantBox), which enables to connect water, carbon and nitrogen dynamics in the rhizosphere to plant and bulk soil dynamics. We show the concept of trait-based rhizosphere modeling, first simulations, and our model coupling approach to CPlantBox.


How to cite: Sircan, A., Giraud, M., Lobet, G., Schnepf, A., Streck, T., and Pagel, H.: Trait-Based Modeling of Microbial Interactions and Carbon Cycling in the Rhizosphere, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15191,, 2021.

Arjun Chakrawal, Anke M. Herrmann, and Stefano Manzoni

Knowledge of functional traits such as the maximum substrate uptake rate and growth efficiency of microorganisms is crucial in understanding the turnover and storage of soil organic carbon. In addition to CO2 measurements, heat dissipation from organic matter decomposition is also a well-recognized proxy for microbial activity in soils. However, only a few attempts have been made to utilize heat signals for quantifying microbial traits.

To leverage high-resolution heat dissipation data, a coupled mass-energy balance model is proposed and used to estimate microbial traits encoded in model parameters. Our underlying question was whether heat dissipation data alone would be sufficient to quantify key microbial traits, or whether respiration rates were also necessary to constrain the model. To this aim, we parametrized four variants of the model using heat dissipation and respiration rate data at different time scales: during the initial lag-phase (5 hours) and throughout the growth-phase until substrate depletion (48 hours) in an isothermal calorimeter combined with a gas analyzer. The four different variants of the model were: (i) a complex physiological model (including active and inactive biomass), (ii) a simplified physiological model (only active microbial biomass), (iii) a model describing only the lag-phase (no growth, only maintenance), and (iv) a model describing only the growth phase (growth under substrate-abundant conditions). Microbial traits were determined using three combinations of data: A) only the heat dissipation rate, B) only the respiration rate, and C) both heat dissipation and respiration rates. We assumed that the ‘best’ parameter estimates would be obtained when using all the available data (i.e., option C).

Our results show that all model variants were able to fit the observed heat dissipation and respiration rates at the respective time scales. Parameters shared among different model variants were generally comparable, indicating that our model simplifications led to structurally sound models. The parameters estimated using only heat dissipation data were similar to the ‘best’ estimates compared to using only respiration rate data, suggesting that the observed heat dissipation rate can be used to constrain microbial models and estimate microbial traits.

How to cite: Chakrawal, A., Herrmann, A. M., and Manzoni, S.: Can we use heat flows to quantify microbial traits in soils?, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4191,, 2021.

Lindsay Todman and Andrew Neal

Soil microbial communities (microbiomes) are dynamic, responding continually to their surrounding environment.  These dynamics may relate to changes in the taxonomic/phylogenetic community structure as well as the functional capacity of the entire microbiome. This dynamism makes it challenging to define resilience for such ecosystems. Here, resilient communities are those able to adjust their taxonomic composition under environmental pulse or press stresses to maintain or restore a particular function.  Trait-based models typically assume trade-offs between life cycle strategies because of the resources required to enable different behaviours. An individual trait may be advantageous depending on the environmental conditions at a particular time and location. However, recent experiments addressing resilience in which soils were repeatedly exposed to stress cycles show soils developed the ability to maintain function despite a repeatedly imposed pulse stress. This suggests that a stress tolerance strategy operates in conjunction with other life cycle strategies. Here, we consider conceptual approaches to reconcile these findings – such as the inclusion of additional life strategies to represent further dimensions of soil community function and a community level trait-based approach that represents the dynamics of functional change in trait space. We also consider the challenge, pertinent to resilience modelling, of distinguishing between stress tolerance and the exposure to stress in heterogeneous soil; both aspects will affect the soil microbial community response, yet the latter could erroneously affect stress tolerance parameters in a trait-based model.

How to cite: Todman, L. and Neal, A.: Can trait-based approaches model the resilience of soil microbial communities?, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12297,, 2021.

Soil data analysis
Luciana Chavez Rodriguez, Ana González-Nicolás, Brian Ingalls, Wolfgang Nowak, Thilo Streck, Sinan Xiao, and Holger Pagel

The natural degradation pathways of the herbicide atrazine (AT) are highly complex. These pathways involve the metabolic activity of several bacterial guilds (that use AT as a source of carbon, nitrogen or both) and abiotic degradation mechanisms. The co-occurrence of multiple degradation pathways, combined with challenges in quantifying bacterial guilds and relevant intermediate metabolites, has led to the development of competing model formulations, which all represent valid descriptions of the fate of AT. A proper understanding of the fate of this complex compound is needed to develop effective management and mitigation strategies.

Here, we propose a model discrimination process in combination with prospective optimal design of experiments. We performed Monte-Carlo simulations using a first-order model that reflects a simple reaction chain of complete AT degradation and a set of Monod-based model variants that consider different bacterial consortia and degradation pathways. We used a Bayesian statistical analysis of these simulation ensembles to simulate virtual degradation experiments and chemical analysis strategies, thus obtaining predictions on the utility of experiments to deliver conclusive data for model discrimination. To do so, we defined different experimental protocols including a combination of: i) the metabolites to measure (AT, metabolites and CO2), ii) sampling frequency (sampling every day, every two days and every four days), iii) features difficult to quantify (specific bacterial guilds). As a statistical metric to measure the conclusiveness of these virtual experiments, we used the so-called energy distance.

Our results show that simulated AT degradation pathways following first-order reaction chains can be clearly distinguished from simulations using Monod-based models. Within the Monod-based models, we detected three clusters of models that differ in the number of bacterial guilds involved in AT degradation. Experimental designs considering main AT metabolites and sampling frequencies of once every two or four days at durations of 50 or 100 days provided the most informative data to discriminate models. Including measurements of bacterial guilds only slightly improved model discrimination. Our study highlights that environmental fate studies should prioritize measuring metabolites to elucidate active AT degradation pathways in soil and identify robust model formulations supporting risk assessment and mitigation strategies. 

How to cite: Chavez Rodriguez, L., González-Nicolás, A., Ingalls, B., Nowak, W., Streck, T., Xiao, S., and Pagel, H.: Optimal design of experiments for effective modeling of atrazine degradation in soils, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13340,, 2021.

Sen Lu

Knowledge on the components of apparent soil thermal conductivity (λ) across various water contents (θ) and temperatures is important to accurately understand soil heat transfer mechanisms. In this study, soil thermal conductivity was measured for sandy loam and silty clay soils at various temperatures and air pressures using a transient method. Four components of λ, namely, heat conduction, latent heat transfer by water vapor diffusion, sensible heat transfer by liquid water, and sensible heat transfer by water vapor diffusion were quantified. Results showed that in uniform soils, the magnitudes of sensible heat transfers by liquid water and water vapor were negligible during these transient measurements. The contribution of latent heat transfer through vapor diffusion to total heat transfer increased as temperature increased, and the peak value occurred at an intermediate water content. The water content at which the maximum vapor diffusion occurred varied with soil texture. In addition to the four calculated components, a significant residual contribution to λ caused by an unidentified vapor transfer mechanism was observed between 3.5°C and 81°C. For example, calculations indicated that approximately 66% of the sandy loam λ at θ=0.11 m3 m−3 was caused by an unidentified vapor transfer mechanism at 81°C. This extra contribution by vapor transfer could be explained either as enhanced vapor diffusion or by an advection mechanism. Further investigation is needed to clarify whether enhanced diffusion or advection is occurring in unsaturated soils. 

How to cite: Lu, S.: Components of apparent soil thermal conductivity measured by the heat pulse method, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-25,, 2021.

Karel Hron, Alessandra Menafoglio, Javier Palarea-Albaladejo, Peter Filzmoser, and Juan Jose Egozcue

For varied reasons, in practical analysis of geochemical (compositional) data we are often interested in adjusting the role or the influence of variables on the final results. For instance, a measuring device used to analyse the chemical mixture of soil samples might not necessarily have the same accuracy for all components, particularly for those with low concentrations. This can have a severe impact on results and interpretation of popular methods like principal component analysis, regression analysis or clustering, but also on the quality of imputation of values below detection limit of a measurement device. In all these cases, a sensible weighting scheme for the variables would generally lead to a statistical analysis better reflecting the underlying phenomenon of interest and less influenced by some limitations or issues with the data collection process. In addition, the relative nature of geochemical data (i.e., those in units like mg/kg, proportions or percentages), where the relevant information is contained in ratios between components, needs to be taken into account for a reliable statistical processing. In this contribution we propose a sensible way of weighting of geochemical components using a generalisation of the logratio methodology for compositional data analysis, namely, the Bayes space approach. We provide practical examples of such weighting and also highlight that the Bayes space approach enables one to develop a methodological framework where it is possibly to apply any weighting strategy in a controlled way.

How to cite: Hron, K., Menafoglio, A., Palarea-Albaladejo, J., Filzmoser, P., and Egozcue, J. J.: Purposeful weighting of components in geochemical (compositional) data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7035,, 2021.

Yili Lu, Tusheng Ren, Sen Lu, and Robert Horton

Soil thermal conductivity (λ) is affected by the energy status of water and is closely related to soil matric potential (h). In this study, a soil water retention curve and a soil thermal conductivity curve were linked via the critical point that separated the adsorption water and capillary water regimes. Based on existing water retention curve and a thermal conductivity curve models, we derived a new implicit mathematical formulation of the λ-h relationship. The λ-h relationship was valid for the entire water content range at room temperature. The new model parameter values for adsorption, capillarity and soil thermal conduction were optimized, and a linear relationship between critical water content and maximum adsorption capacity was established by fitting the SWRC and STCC models to measurements from eight soils. Laboratory evaluations using λ and h measurements on a loam soil and a clay loam soil showed that the new model well described observed values with coefficients of determination greater than 0.97. The implicit model can quantify λ-h behaviors for various soil textures over the entire water content range.

How to cite: Lu, Y., Ren, T., Lu, S., and Horton, R.: An implicit model for soil thermal conductivity and matric potential, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1320,, 2021.

Gabriela Naibo, Rafael Ramon, Gustavo Pesini, Jean Michel Moura-Bueno, Claudia Alessandra Peixoto Barros, Laurent Caner, Jean Paolo Gomes Minella, Danilo Santos Rheinheimer, and Tales Tiecher

The intense soil use with inadequate management can result in the constant transport of sediments with chemical elements absorbed to aquatic systems. The diffuse reflectance spectroscopy in the near infrared (NIR) and medium (MIR) spectral bands associated with chemometry and machine learning, is an analytical technique that has the potential to quantify the concentration of chemical elements in the environment. However, there is no consensus on the best combination of calibration methods, spectral pre-processing and spectral ranges. Thus, the objective of this study was to evaluate the use of this technique, with the combination of different spectral bands, pre-processing techniques and machine learning to estimate the concentration of chemical elements on soil and sediment samples. In this study we used a soil and sediment database from samples collected in the Guaporé River catchment, in southern Brazil. A total of 316 soil samples and 196 sediment samples were dried, disaggregated and sieved at 63 μm. Organic carbon (CO) was quantified by wet oxidation and the total concentration of 21 elements (Al, Ba, Be, Ca, Co, Cr, Cu, Fe, K, La, Li, Mg, Mn, Na, Ni, P, Pb, Sr, Ti V and Zn) were quantified by ICP-OES after microwave assisted digestion for 9,5 min at 182ºC with HCl and HNO3 concentrated in the proportion of 3:1. The NIR (1000-2500 nm) and MIR (2500-25000 nm) spectra were obtained in all soil and sediment samples. Two machine-learning methods were tested: Partial Least Squares Regression (PLSR) and Support Vector Machine (SVM), associated with three different spectrum pre-processing methods: Detrend (DET), Savitzky-Golay Derivative (SGD) and Standard Normal Variate (SNV), compared to raw data (RAW). Performance was assessed by the coefficient of determination (R²) and the relationship between performance and interquartile distance (RPIQ). The SVM model resulted in better predictions compared to the PLSR in all evaluated cases, as indicated by the average adjustment values of the model (R²=0.87 for SVM and 0.62 for PLSR), and by the RPIQ values (7.14 for SVM and 2.22 for PLSR). The pre-processing method increased the accuracy of the estimates in the following order: RAW<SNV< DET<SGD. The best performance in relation to the spectral range was observed for the MIR region, being significantly superior to the NIR and NIR+MIR combination. The adjustment of the models calibrated with soil (R²=0.91) and sediment (R²=0.90) data was higher compared to the calibrated with the combination soil + sediment (R²=0.78). For RPIQ, the calibration model with soil data showed the highest RPIQ value (9.29), being higher and differing significantly from the others. In general, the results show that the combination of different calibration methods, spectral pre-processing and spectral ranges has an effect on the accuracy of the estimates. The studied elements can be estimated by means of diffuse reflectance spectroscopy, however it should be noted that this technique has an associated error in the estimates due to the heterogeneity of the chemical structure of the elements in the soil and sediment matrix and the reference samples obtained by chemical methods.

How to cite: Naibo, G., Ramon, R., Pesini, G., Moura-Bueno, J. M., Peixoto Barros, C. A., Caner, L., Gomes Minella, J. P., Santos Rheinheimer, D., and Tiecher, T.: Diffuse reflectance spectroscopy to estimate the concentration of chemical elements in soil and sediment combining pre-processing methods with machine learning , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12979,, 2021.