SSS10.2

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, https://doi.org/10.5194/egusphere-egu21-10043, 2021.

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, https://doi.org/10.5194/egusphere-egu21-7675, 2021.

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.

E-mail: juan.lz.herrera@upm.es, cassielcarlos@hotmail.com, rabelo.oliveira@hotmail.com, eloisa-mattei@hotmail.com, antonio.saa@upm.es

Abstract

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.

Reference

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 https://doi.org/10.5296/jas.v8i3.16325

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, https://doi.org/10.5194/egusphere-egu21-9747, 2021.

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, https://doi.org/10.5194/egusphere-egu21-15984, 2021.

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, https://doi.org/10.5194/egusphere-egu21-4142, 2021.

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.

References:

[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. http://dx.doi.org/10.1016/j.geoderma.2016.09.005.

[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. http://dx.doi.org/10.1016/j.geoderma.2016.11.029.

Acknowledgements:

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, https://doi.org/10.5194/egusphere-egu21-13209, 2021.

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, https://doi.org/10.5194/egusphere-egu21-7047, 2021.

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, https://doi.org/10.5194/egusphere-egu21-8490, 2021.

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, https://doi.org/10.5194/egusphere-egu21-5513, 2021.

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, https://doi.org/10.5194/egusphere-egu21-15697, 2021.

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, https://doi.org/10.5194/egusphere-egu21-11255, 2021.

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, https://doi.org/10.5194/egusphere-egu21-15191, 2021.

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, https://doi.org/10.5194/egusphere-egu21-4191, 2021.

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, https://doi.org/10.5194/egusphere-egu21-12297, 2021.

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, https://doi.org/10.5194/egusphere-egu21-7035, 2021.

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, https://doi.org/10.5194/egusphere-egu21-1320, 2021.