Holger Pagel

Lower‐order streams in agricultural landscapes receive major anthropogenic nitrogen inputs. Streambed sediments host diverse microbial communities that can influence nitrogen (N) fluxes and water chemistry. Both bacterial and archaeal ammonia oxidizers inhabit streambeds, but their respective contributions to nitrification are often unresolved. We investigated a first‐order stream in southern Germany to assess the contribution of distinct ammonia‐oxidising populations to streambed nitrification. We combined in situ geochemical data, 16S rRNA and functional‐gene amplicon sequencing, quantitative PCR and microcosm incubations with selective chemical inhibitors. A process‐based reaction model quantified total nitrification rates and inferred contributions of ammonia‐oxidising archaea (AOA) and bacteria (AOB), while population‐specific kinetic parameters were estimated using Bayesian inference. We found that AOB dominated nitrification and responded more strongly to ammonium inputs than AOA despite being less abundant. Among them, populations of Nitrosomonas and Nitrosospira spp. were most important. Differences in ammonia‐oxidation rates and ammonia‐oxidising communities between sediment depths and successive stream segments suggest a hydrological influence on streambed nitrification. Our study demonstrates the strength of combining field data, microcosm incubations and modelling to better understand microbial N‐cycling in the environment. It also mandates caution when interpreting functional‐gene abundance as a proxy for in situ reactive potential.

A plant's development is strongly linked to the water and carbon (C) flows in the soil-plant-atmosphere continuum. Ongoing climate shifts will alter the water and C cycles and affect plant phenotypes. Comprehensive models that simulate mechanistically and dynamically the feedback loops between water and C fluxes in the soil-plant system are useful tools to evaluate the sustainability of genotype-environment-management combinations that do not yet exist. In this study, we present the equations and implementation of a rhizosphere-soil model within the CPlantBox framework, a functional-structural plant model that represents plant processes and plant-soil interactions. The multi-scale plant-rhizosphere-soil coupling scheme previously used for CPlantBox was likewise updated, among others to increase the accuracy and stability of the model outputs. The model was implemented to simulate the effect of dry spells occurring at different plant development stages, and for different soil kinetic parameterisations of microbial dynamics in soil. We could observe diverging results according to the date of occurrence of the dry spells and soil parameterisations. For instance, earlier dry spells (from 11th to the 18th day of growth) led to a lower cumulative plant C release, while later dry spells (from 18th to the 25th day of growth) led to higher C input to the soil. For more reactive microbial communities (higher maximum C uptake rate and (de)activation rates), this higher C input caused a strong increase in CO2 emissions. For the same weather scenario, we observed lower microbial CO2 emissions with less reactive communities. This model can be used to gain insight into C and water flows at the plant scale, and the influence of soil-plant interactions on C cycling in soils.

Arbuscular mycorrhizal fungi (AMF) are widespread symbiotic partners of most terrestrial plants and form close associations with their roots. While their role in enhancing nutrient uptake, particularly phosphorus, has been well studied, their effects on and of soil structure, and plant water uptake have not been investigated as broadly.The complexity of interactions between plants, fungi, and soil under varying environmental conditions is difficult to disentangle experimentally. In-silico investigations offer an alternative means to explore these effects. We developed a 3D-model describing AMF colonization of a growing root structure and the growth of extraradical mycelium. We used the model to simulate how extraradical hyphae extend from colonized roots into the soil volume. The model is being implemented as an extension of CPlantBox, a functional-structural model for water and carbon processes at the whole-plant level.Model parameterization is based on experimental and additional literature data. This includes information on root architecture, AMF colonization rates and locations, and nutrient transport and water flow in tomato plants and their associated hyphal networks. The plants were grown in sandy and loamy soils under both drought and well-watered conditions.The 3D AMF colonization model explicitly represents hyphal extension rates, branching angles, and the spatial propagation of the extraradical mycelium from infection points along the root system. Key components of the model are the representation of the dynamics of root growth, growth of the intraradical and extraradical mycelium, anastomosis, and the ability of AMF hyphae to fuse and form complex networks.The model is used to assess, visualize, and quantify how AMF networks develop, branch, and interconnect, providing mechanistic insight into their contribution to plant nutrition and drought tolerance.

Ecotron facilities are key tools to emulate and monitor environmental conditions for improving the understanding of terrestrial ecosystems. Although their conditions can be precisely modified by cutting-edge controlling systems, the external factors influencing the boundary conditions of such ecotron units remain uncertain under reconstructed scenarios (e.g., climate change). We assessed how an agroecosystem model can be used to simulate the bottom boundary conditions of the newly developed ecotron facility AgraSim. The facility comprises six identical ecotron units composed of a climate-controlled plant chamber on top of a cylindrical lysimeter (1 m2 by 150 cm depth). An automatic suction cup–pumping system installed below each lysimeter is used to control the bottom boundary condition of the system depending on the measured pressure head near its bottom. The first experimental trial of AgraSim is aimed to quantify the key climate responses of a typical agricultural field located in North-Rhine Westphalia (Germany) to transient climate change. A set of four climate scenarios were derived from storyline simulations with the regional atmospheric circulation model ICON, imposing a transient temperature gradient of +1oC to +4oC within the climate chambers. While the atmospheric forcings and the soil texture and hydraulic properties are well-characterized, the bottom boundary condition at 150 cm depth is unknown for the different climate change scenarios. We thus used an agroecosystem model (AgroC) to numerically solve water movement within the soil column (0-150 cm) and investigated the impact of choosing one of the following bottom boundary conditions: fixed pressure heads (FP); free-drainage (FD); and a modified seepage face at h=-100hPa (SP). A simulation that assumes a 500 cm deep soil column and free drainage was taken as a reference for assessing the performance of each of the different bottom boundary conditions. This setup was replicated for two rainfed cropping systems (winter wheat and maize), each parameterized with three types of rooting profile representing shallow, deeper and homogeneous root profiles, respectively. A 1-year spin-up run was performed to minimize the effect of the initial conditions. Our model simulations showed that the different boundary conditions only affected soil moisture below 30 cm, while topsoil moisture was mainly controlled by atmospheric forcings. The FP and FD scenarios tended to underestimate soil water availability in the 150 cm column, especially during critical summer drought periods. The modified SP showed the best agreement with the reference simulations, keeping the soil unsaturated during winter and maintaining moisture and fluxes closer to the reference levels in summer. This result was consistent across both crops and rooting profiles. At the crop level, the different boundary conditions had no significant effect on key crop variables (e.g., biomass and leaf area) and their respective response to the climate scenarios. Although our results are limited by observation availability and model parameterization uncertainty, they demonstrate the potential application of process-based models for decision-making in controlled-system facilities.

Root architecture and soil-plant interactions affect carbon storage and nutrient uptake efficiency of plants. Mechanistic modeling of the soil-plant system enables a better understanding of coupled processes and allows us to predict the response of the soil-plant system to environmental change.The fully coupled functional-structural soil-plant model "CPlantBox" can simulate plant growth and soil water flow, solute transport and microbial dynamics. In the rhizosphere, the area influenced by the root activity, focus is put on the influence that special root exudates called mucilage have on the plant water uptake and how the microbial activity promoted by the root exudation impacts the nitrogen uptake.Both the mucilage and the nitrogen cycling in microbes are investigated using a sensitivity analysis: For a given metric of interest (e.g. total microbial biomass after 10 days of root growth) each parameter gets assigned a measure of importance and of interactions with other parameters. This standard approach of sensitivity analysis is extended to parameter inequalities, enabling the inclusion of additional information.Through the sensitivity analysis we will be able to identify which model parameters determine the effects of root exudates on microbial N mineralization, plant water and N uptake. Measurements from drought and nitrogen limitation experiments will be used to estimate these important model parameters.

Soil microorganisms control organic matter cycling and, to a large extent, determine how soil systems can cope with and mitigate climate change and environmental threats. Integrating them explicitly into process-based soil models is critical for predicting how soil carbon (C) flows and stocks change in ecosystems with time. Models are critical tools for integrating datasets with theory. However, integrating information from modern omics-based datasets is a challenge due to the nonlinear relationship between genomes and the actual function microbes express in their local environment. Functional traits can be defined and inferred from these genomic datasets to better leverage their information and better understand the complexity of the soil microbiome. Integrating trait information with process-based microbially explicit models provides an opportunity leverage genomic data for an improved soil carbon prediction.We present a hybrid modeling framework that uses a data-driven neural network approach to derive microbial parameters of process-based models from metagenome inferred functional traits, leveraging information from metagenomic and DNA sequencing datasets. We combine a neural network (multi-layer perceptron) with a process-based soil model to set up a hybrid model. The neural network uses genomic trait data as the input and predicts biokinetic parameters of the process-based model. We trained the hybrid model with synthetic genomic trait datasets of varying complexity and time series of state variables of the process-based model (e.g. carbon dioxide production) to demonstrate the approach. Using trait inference from genomes, the model can learn several biokinetic parameters such as growth rates, dormancy rates, affinities to organic matter, growth yields or decay rates. The training uses a complex constraint-based loss function, informing the model from ecological theory and literature data, ensuring the realistic behavior of every non observed state variable during training such as active and dormant microbial pools. Compared to a ‘naïve’ hybrid model, the use of a more complex loss function reduces model equifinality and ensure realistic behavior of the non-observed state variables. Naïve loss function cannot efficiently learn the behavior of non-observed state variables and fail to predict realistic microbial dynamics. We present i) the concept of the hybrid soil modelling framework, ii) the constraint-based loss function approach, iii) the performance of constrained versus naïve hybrid models after training with different synthetic datasets.

A plant's development is strongly linked to the water and carbon (C) flows in the soil-plant-atmosphere continuum. Ongoing climate shifts will alter the water and C cycles and affect plant phenotypes. Comprehensive models that simulate mechanistically and dynamically the feedback loops between water and C fluxes in the soil-plant system are useful tools to evaluate the sustainability of genotype-environment-management combinations that do not yet exist. In this study, we present the equations and implementation of a rhizosphere-soil model within the CPlantBox framework, a functional-structural plant model that represents plant processes and plant-soil interactions. The multi-scale plant-rhizosphere-soil coupling scheme previously used for CPlantBox was likewise updated, among others to include an implicit time-stepping. The model was implemented to simulate the effect of dry spells occurring at different plant development stages, and for different soil biokinetic parametrisations of microbial dynamics in soil. We could observe diverging results according to the date of occurrence of the dry spells and soil parametrisations. For instance, an earlier dry spell led to a lower cumulative plant C release, while later dry spells led to higher C input to the soil. For more reactive microbial communities, this higher C input caused a strong increase in CO₂ emissions, while, for the same weather scenario, we observed a lasting stabilisation of soil C with less reactive communities. This model can be used to gain insight into C and water flows at the plant scale, and the influence of soil-plant interactions on C cycling in soil.

A plant's development is strongly linked to the water and carbon (C) flows in the soil-plant-atmosphere continuum. Ongoing climate shifts will alter the water and C cycles and affect plant phenotypes. Comprehensive models that simulate mechanistically and dynamically the feedback loops between water and C fluxes in the soil-plant system are useful tools to evaluate the sustainability of genotype-environment-management combinations that do not yet exist. In this study, we present the equations and implementation of a rhizosphere-soil model within the CPlantBox framework, a functional-structural plant model that represents plant processes and plant-soil interactions. The multi-scale plant-rhizosphere-soil coupling scheme previously used for CPlantBox was likewise updated, among others to include an implicit time-stepping. The model was implemented to simulate the effect of dry spells occurring at different plant development stages, and for different soil biokinetic parametrisations of microbial dynamics in soil. We could observe diverging results according to the date of occurrence of the dry spells and soil parametrisations. For instance, an earlier dry spell led to a lower cumulative plant C release, while later dry spells led to higher C input to the soil. For more reactive microbial communities, this higher C input caused a strong increase in CO₂ emissions, while, for the same weather scenario, we observed a lasting stabilisation of soil C with less reactive communities. This model can be used to gain insight into C and water flows at the plant scale, and the influence of soil-plant interactions on C cycling in soil.

A plant's development is strongly linked to the water and carbon (C) flows in the soil-plant-atmosphere continuum. Ongoing climate shifts will alter the water and C cycles and affect plant phenotypes. Comprehensive models that simulate mechanistically and dynamically the feedback loops between water and C fluxes in the soil-plant system are useful tools to evaluate the sustainability of genotype-environment-management combinations that do not yet exist. In this study, we present the equations and implementation of a rhizosphere-soil model within the CPlantBox framework, a functional-structural plant model that represents plant processes and plant-soil interactions. The multi-scale plant-rhizosphere-soil coupling scheme previously used for CPlantBox was likewise updated, among others to include an implicit time-stepping. The model was implemented to simulate the effect of dry spells occurring at different plant development stages, and for different soil biokinetic parametrisations of microbial dynamics in soil. We could observe diverging results according to the date of occurrence of the dry spells and soil parametrisations. For instance, an earlier dry spell led to a lower cumulative plant C release, while later dry spells led to higher C input to the soil. For more reactive microbial communities, this higher C input caused a strong increase in CO₂ emissions, while, for the same weather scenario, we observed a lasting stabilisation of soil C with less reactive communities. This model can be used to gain insight into C and water flows at the plant scale, and the influence of soil-plant interactions on C cycling in soil.