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Schnepf et al., (2020) defined benchmark scenarios for root growth models, soil water flow models, root water flow models, and for water flow in the coupled soil-root system. All benchmarks and corresponding reference solutions were published in the form of Jupyter Notebooks on the GitHub repository https://github.com/RSAbenchmarks/collaborative-comparison. Several groups of functional-structural model developers have joined this benchmarking activity and provided the results of their individual implementations of the different scenarios. The focus of this contribution is on water uptake from a drying soil by a static root architecture. The numerical solutions of the different participating simulators as compared to the provided reference solution. The participating simulators are CPlantBox, DuMux, R-SWMS, OpenSimRoot and SRI. They have in common that they simulate water flow in the 3D soil domain, water flow inside the root system that is represented as a mathematical tree graph, and the coupling between the two domains in form of a volumetric sink term that describes the transfer of water between the two domains. The simulators differ in the numerical schemes used for solving the water flow equations in roots and soil domains, as well as in the way the sink term is formulated, in particular in the way the possibly increased rhizosphere resistance to water flow is accounted for. The results to the water flow in soil benchmarks show how the different simulators perform against the analytical solution to a problem of infiltration into an initially dry soil, as well as a problem of evaporation from initially moist soil. All of the simulators could accurately predict the infiltration front in different soil types as well as the actual evaporation curves. The coupled problem of root water uptake by a static root architecture from an initially already dry soil posed a bigger challenge to the different simulators and revealed some diversity between the different solutions. The Benchmark with an initially rather dry soil defined a potential transpiration that immediately induced water stress of the plant. The simulators had to simulate the consequent rhizosphere drying and associated increase in rhizosphere resistance. All of the soil simulators smoothed the gradients in the rhizosphere at the soil grid size such that root water uptake was significantly overestimated unless the rhizosphere resistance was explicitly accounted for in the root water uptake model. As a result, all simulators came close to the reference solution (that itself is a numerical solution, see Schnepf et al. 2020 for details). In this study, we showed that all simulators are generally able to solve the benchmark problems but minor differences occur amongst the simulators when simulating different soil types. Benchmarking led to model improvements and helped interpret model results in a more informed way. The availability of “reference solutions“ made modellers aware of the range of validity of their numerical solution and encouraged them to improve either their numerical solution or to introduce new processes Future efforts may aim to extend the benchmarks from water flow to further processes, such as solute transport or rhizodeposition. Schnepf et al., 2020, Front. Plant Sci.

Microorganisms possess the ability to adapt to different environmental conditions through the use of various strategies. This diversity in strategies allows us to categorize them based on their functions in the ecosystem. Copiotrophs have a fast growth rate but a low carbon use efficiency (CUE), while oligotrophs have a slow growth rate but a high CUE. In the rhizosphere, the effect of root exudation on different functional microbial groups is not well understood. Process-based modeling is a useful tool to analyze the complex feedback between roots and soil in the rhizosphere. Here, we present a rhizosphere model that explicitly considers two different microbial groups (oligotrophs and copiotrophs) classified based on their microbial traits that correlates each other due to physiological trade-offs and organic carbon accessibility (dissolved organic carbon, mucilage and sorbed carbon). The model is one-dimensional axisymmetric, simulating a soil cylinder around individual root segments. The model was conditioned using a novel constraint-based Markov chain Monte Carlo parameter sampling method. Applying this approach enabled the identification of parameter sets that led to plausible model results in agreement with experimental findings from a comprehensive literature review. The conditioned model predicts organic matter concentration curves from the root surface into the soil driven by root exudation. Our simulations show a decreasing pattern of dissolved organic carbon, which is utilized by oligotrophs and copiotrophs, away from the root surface. Furthermore, we observe a slightly higher proportion of copiotrophs than oligotrophs near the root surface and dominance of copiotrophic biomass at very high nutrient availability conditions as expected from ecological theory and experimental evidence. However, the model predictions are still highly uncertain. Thus, further experimental data and observations are required for model conditioning.

The rhizosphere, or the soil directly influenced by plant roots, is a complex and dynamic environment shaped by both plant and soil processes. Plant processes include root growth, rhizodeposition, root water and nutrient uptake or signalling; soil processes include water flow, reactive transport, organic matter decomposition or soil microbe and fauna-related processes. In this contribution, we focus on the soil-related aspects of modelling the interactions within the rhizosphere and how these interactions lead to the emergence of specific properties. Factors such as radial transport, root growth, and diurnal variation all play a role in the formation of patterns within the rhizosphere. However, modelling these processes is challenging due to their interconnected nature and the fact that they occur on multiple temporal and spatial scales. Recent research by Vetterlein et al. (2020) and Schnepf et al. (2022) have addressed these challenges and advances in our understanding of modelling the rhizosphere. For example can the effect of root elongation rate on the radial extension of the rhizosphere be quantified by means of the rhizosphere Péclet number, a dimensionless number that compares the importance of diffusive transport relative to root elongation rate. New findings of Kuppe et al. (2022), who have organized rhizosphere models within a collective framework that allows for the incorporation of microorganisms and their activity and motility, and Deckmyn et al. (2020), who combined soil carbon and food web ecosystem models, will further enhance a mechanistic description of the rhizosphere Deckmyn G, Flores O, Mayer M, Domene X, Schnepf A, Kuka K, Van Looy K, Rasse DP, Briones MJI, Barot S, Berg M, Vanguelova E, Ostonen I, Vereecken H, Suz LM, Frey B, Frossard A, Tiunov A, Frouz J, Grebenc T, Öpik M, Javaux M, Uvarov A, Vinduskova O, Henning Krogh P, Franklin O, Jiménez J, Curiel Yuste J. 2020. KEYLINK: towards a more integrative soil representation for inclusion in ecosystem scale models. I. review and model concept. PeerJ 8:e9750 DOI 10.7717/peerj.9750Kuppe CW, Schnepf A, von Lieres E, Watt M, Postma JA (2022) Rhizosphere models: their concepts and application to plant-soil ecosystems. Plant Soil 474, 17–55. doi: 10.1007/s11104-021-05201-7Schnepf A, Carminati A, Ahmed MA, Ani M, Benard P, Bentz J, Bonkowski M, Knott M, Diehl D, Duddek P, Kröner E, Javaux M, Landl M, Lehndorff E, Lippold E, Lieu A, Mueller CW, Oburger E, Otten W, Portell X, Phalempin M, Prechtel A, Schulz R, Vanderborght J, Vetterlein D (2022) Linking rhizosphere processes across scales: Opinion. Plant and Soil 478: 5-42. doi: 10.1007/s11104-022-05306-7.Vetterlein D, Carminati A, Kögel-Knabner I, Bienert GP, Smalla K, Oburger E, Schnepf A, Banitz T, Tarkka MT, Schlüter S (2020) Rhizosphere Spatiotemporal Organization–A Key to Rhizosphere Functions. Frontiers in Agronomy 2. doi: 10.3389/fagro.2020.00008.

To describe plant transpiration in drying soil, several models use ‘α-stress functions’, which represent the ratio of the maximal possible water uptake when the plant reaches the wilting point to the transpiration demand or potential transpiration, as a function of the soil water potential. Water potentials vary within the root zone, and the plant ‘senses’ with its root system an average root zone water potential and redistributes the uptake from drier to wetter zones in the root zone. This redistribution or root water uptake compensation is accounted for using an average stress index, ω, which is a weighted average of the local stress indices α at different depths in the root zone, and a critical stress index ωc (Jarvis, 2011; Simunek & Hopmans, 2009). When ω > ωc, root water uptake is equal to the potential root water uptake or the energy limited potential transpiration. The α-ω approach refers to a mechanistic description of water fluxes in the soil-root system but remains semi-empirical missing a direct link with soil and, especially, root hydraulic properties. In this contribution, we derive the α-ω approach starting from a mechanistic description of water flow in a hydraulic root architecture assuming that resistance to flow in the soil towards the soil-root interface can be neglected. In a second step, we include the non-linear soil resistance.For relatively wet soil conditions and neglecting the soil resistance, root water uptake functions can be cast in a form that is identical to the α-ω approach that was derived by Jarvis (2011), but for opposite conditions, i.e., Jarvis neglected the root resistance compared to soil resistance. Following Jarvis, the α-function should be interpreted as the ratio of the maximal possible uptake by the root system for a certain soil water potential to the maximal possible uptake by the system when the soil is fully saturated, which differs from its common interpretation. This means that the α-function is just a linear function that ranges from zero when the soil water potential is equal to the wilting point to 1 when the soil water potential is zero and that it is independent of the transpiration rate. Another outcome is that the critical stress level ωc is inverse proportional to the hydraulic conductance of the root system and is not a constant but a variable parameter that is proportional to the transpiration rate. For dry soil conditions, when soil resistance is important, we find that α and ω are non-linear functions of the soil water potential. Using α and ω functions that are derived from soil and root hydraulic properties, the uptake distributions can be calculated directly from the soil water potentials without solving a non-linear equation with iterations to derive water potentials in the plant. But, this approach is based on a simplification, which requires further testing.Jarvis, N. J. (2011). Hydrology and Earth System Sciences, 15(11), 3431-3446. doi:10.5194/hess-15-3431-2011Simunek, J., & Hopmans, J. W. (2009). Ecological Modelling, 220(4), 505-521. doi:10.1016/j.ecolmodel.2008.11.004

Plant growth and development involve the integration of numerous processes, influenced by both endogenous and exogenous factors. At any given time during a plant’s life cycle, the plant architecture is a readout of this continuous integration. However, untangling the individual factors and processes involved in the plant development and quantifying their influence on the plant developmental process is experimentally challenging.Here we used a combination of computational plant models to help understand experimental findings about how local phloem anatomical features influence the root system architecture. In particular, we simulated the mutual interplay between the root system architecture development and the carbohydrate distribution to provide a plausible mechanistic explanation for several experimental results.Our in silico study highlighted the strong influence of local phloem hydraulics on the root growth rates, growth duration and final length. The model result showed that a higher phloem resistivity leads to shorter roots due to the reduced flow of carbon within the root system. This effect was due to local properties of individual roots, and not linked to any of the pleiotropic effects at the root system level.Our results open the door to a better representation of growth processes in plant computational models.

Exudation of organic carbon triggers complex spatial and temporal patterns of biophysical and biochemical processes in the root-influenced soil (rhizosphere). We use process-based modeling as a tool to gain insights into microbial interactions and carbon cycling in the rhizosphere. Here, we present a trait-based rhizosphere model that accounts for two different functional microbial groups (copiotrophs, oligotrophs) that differ according to life-history strategies, microbial physiology (e.g., dormancy) and carbon turnover (small and large polymers). The model is calibrated and validated against experimental data from the literature. We apply a parameter search algorithm that identifies plausible parameter spaces by conditioning model outputs to parameter and process constraints that reflect current ecological knowledge. We show the general concept of the model, first simulations after model conditioning, and a concept for coupling the rhizosphere model with the structural-functional plant model CPlantBox to cover the whole-plant scale.

Water and nutrient uptake are essential for plant productivity. Therefore, the development of precise functional-structural root models will enable better agricultural management in particular in resource limited environments. In such models water movement is of special importance since the rhizosphere's biochemical reactions are strongly influenced by water content and water movement. We present a general sink term for larger scale root water uptake models that includes the root-rhizosphere hydraulic architecture.We derive the new aggregated sink term from a more complex model that describes the rhizosphere around each root segment in dependence of a hydraulic root system model. We use CPlantBox (Schnepf et al. 2018) to represent root architecture, and calculate water movement within the root system using the hybrid analytical solution of Meunier et al. (2017). Around each root segment we represent water movement within the rhizosphere by a 1D axisymmetric model. Such models are flexible in the way the rhizosphere is represented (Mai et al. 2019). They are able to accurately describe water depletion and redistribution, but are computationally expensive. To simplify the model we use the analytical solution of the steady rate approximation following (Schröder et al. 2008) for water movement in the 1D axisymmetric models. The analytic solution depends on the matric potentials of the macroscopic soil (which is calculated in 1D, 2D or 3D) and the hydraulic root architecture model, root radial conductivity, and radius of the rhizosphere domain. We use fixed-point iteration to determine the matric potential at the soil root interface and store the solutions in a look up table for speedup. Moving to larger scales it is generally not useful to keep track of all root system architectures. Therefore, we aim for a coarser approximation of the root architecture by representing it as detached parallel root segments. Parallel segment conductivities are based on standard uptake fraction (SUF) and root system conductivity (Krs) of the original topology (Couvreur et al. 2012), which was shown by Vanderborght et al. (2021) to be a close approximation of the uptake by the original root topology. This approach makes the computation of the full root hydraulic architecture model superfluous, leading to a stable and performant sink term. This new sink term increases the accuracy of water uptake in a suite of larger scale models including crop modes, earth system models, and hydrological models. Using the presented approach, the sink term can be derived directly from 3D root hydraulic architecture. This avoids parameterizations based on proxy information about root system hydraulics and can acknowledge age dependent axial and radial root segment hydraulic conductances. Finally, information about rhizosphere hydraulic properties, which may differ from bulk soil hydraulic properties can be injected effectively in this sink term model. ReferencesSchnepf, A., et al. (2018) Annals of botany, 121(5) 1033-1053.Meunier, F. et al. Applied Mathematical Modelling, 52, 648-663.Mai, TH., et al. Plant and Soil, 439(1), 273-292.Schröder, et al. (2008) Vadose Zone Journal 7(3), 1089-1098.Couvreur, V., et al. (2012) Hydrology and Earth System Sciences, 16(8), 2957-2971.Vanderborght, J., et al. (2021) Hydrology and Earth System Sciences, 25(9), 4835-4860.

Investigating soil, roots and their interaction is important to optimize agricultural practices like irrigation and fertilization and therefore increase the sustainability and productivity of crop production. In this study, we are combining two methods to examine non-invasively, characterize and monitor the soil-root zone throughout crop growing seasons: crosshole ground penetrating radar (GPR) and root-images within horizontal mini-rhizotrons. Over three maize crop growing seasons, we acquired in-situ time-lapse crosshole ground penetrating radar data and time-lapse root images, at two mini-rhizotron facilities in Selhausen, Germany. These facilities allow to horizontally measure data at six different depths, ranging between 0.1 m - 1.2 m and below three different plots with varying agricultural treatments, such as irrigation, sowing density, sowing date and cultivars. The GPR measurements result in the dielectric permittivity slices by applying standard ray-based analysis to zero-offset measurements along a pair of rhizotubes. Such horizontal permittivity slices can be linked to soil water content using petro‑physical relationships. Additionally, the root images provide a root fraction per image, which is derived by using a workflow combining state-of-the-art software tools, deep neural networks and automated feature extraction. The dielectric permittivity slices suggest a permittivity variation along the horizontal and vertical axes, depending on atmospheric conditions, soil properties, and root architecture. To quantify the influence of the roots on the spatial and temporal distribution of dielectric permittivity, we used statistical methods to reduce the impacting factors like soil heterogeneity, tube deviations and changing atmospheric conditions, which results in the spatial and temporal variability. For verification these permittivity variabilities are compared to the root fraction values. In general, using the spatial and temporal permittivity variations, we can detect the presence of roots and additionally recognize a varying influence of the roots over the duration of the crop growing season. Using these first results, we demonstrate that GPR can be applied to improve the characterization of the root-soil system related to maize plants. This could be the first step towards developing proxies e.g. for irrigation and fertilization applications using this non-invasive method.

3D models of root growth, architecture and function are becoming important tools to aid the design of agricultural management schemes and the selection of beneficial root traits. While benchmarking is common for water and solute transport models in soil, 3D root-soil interaction models have not yet been systematically analysed. Several interacting processes might induce disagreement between models: root growth, sink term definitions of root water and solute uptake and representation of the rhizosphere. Schnepf et al. (2020) proposed a framework for quantitatively comparing such models. It builds upon benchmark scenarios that test individual components, followed by benchmark scenarios for the coupled root-rhizosphere-soil system.Here we present the results of benchmarking different well-known models (“simulators”) with respect to water flow in soil, water flow in roots, and water flow and root water uptake in a coupled soil-root system for the case of a given prescribed root architecture as observed from an MRI experiment. The participating simulators areCPlantBox and DuMux (Koch et al. 2021; Mai et al. 2019), R-SWMS (Javaux et al. 2008), OpenSimRoot (Postma et al. 2017) and ArchiSimple, RootTyp and SRI (Beudez et al. 2013; Pagès et al. 2014; Pagès et al. 2004).In the benchmark scenarios that represent individual modules, the different simulators solved the same mathematical model but with different numerical approaches; all perform well with respect to the given analytical reference solution. For the coupled problem of root water uptake from a drying soil, the different simulators make different choices for the coupling of the different sub-problems. Thus, the results of the different simulators show a larger heterogeneity amongst each other.We expect that this benchmarking will result in improved models, with which we can simulate various scenarios with greater confidence, avoiding that future work is based on accidental results caused by bugs, numerical errors or conceptual misunderstandings and will set a standard for model development.Beudez N, Doussan C, Lefeuve-Mesgouez G, Mesgouez A (2013) Procedia Environmental Sciences 19: 37-46. doi:Javaux M, Schröder T, Vanderborght J, Vereecken H (2008) Vadose Zone Journal 7: 1079-1088.Koch T, Wu H, Schneider M (2021) Journal of Computational Physics: 110823.Mai TH, Schnepf A, Vereecken H, Vanderborght J (2019) Plant and Soil 439: 273-292. doi: 10.1007/s11104-018-3890-4.Pagès L, Bécel C, Boukcim H, Moreau D, Nguyen C, Voisin A-S (2014) Ecological Modelling 290: 76-84.Pagès L, Vercambre G, Drouet J-L, Lecompte F, Collet C, Le Bot J (2004) Plant and Soil 258: 103-119.Postma JA, Kuppe C, Owen MR, Mellor N, Griffiths M, Bennett MJ, Lynch JP, Watt M (2017) New Phytologist 215: 1274-1286.Schnepf A, Black CK, Couvreur V, et al. (2020) Frontiers in Plant Science 11.

Home-field advantage (HFA) encompasses all the processes leading to faster litter decomposition in the 'home' environment as compared to that of ‘away’ environments. To determine the occurrence of HFA in a forest and adjacent clear-cut, we set up a reciprocal litter decomposition experiment within the forest and clear-cut for two soil types (Cambisols and Gleysols) in temperate Germany. The forest was dominated by Norway spruce (Picea abies), whereas forest regeneration of European Beech (Fagus sylvatica) after clearcutting was encouraged. Our observation that Norway spruce decomposed faster than European beech in 70-yr-old spruce forest, was therefore most likely related to specialized litter-microbial interaction under existing spruce, and leading to a HFA. The ‘spruce’ microbial biome and soil conditions of original forest were then rapidly changed even after a short-term regeneration following clearcutting, resulting in faster beech decomposition also masking any HFA for spruce, particularly in moisture- and nutrient-deficient Cambisols. Divergence between forest and clear-cut in the Cambisol of their litter δ15N values beyond 9 months, implied litter N decomposition was only initially independent of soil and residual C status. We conclude that clearcutting modifies HFA and helps promote the establishment or regeneration of European beech in this and similar forest mountain upland areas.

Soil water status, which refers to the wetness or dryness of soils, is crucial for the productivity of agroecosystems, as it determines nutrient cycling and uptake physically via transport, biologically via the moisture-dependent activity of soil flora, fauna, and plants, and chemically via specific hydrolyses and redox reactions. Here, we focus on the dynamics of nitrogen (N), phosphorus (P), and sulfur (S) and review how soil water is coupled to the cycling of these elements and related stoichiometric controls across different scales within agroecosystems. These scales span processes at the molecular level, where nutrients and water are consumed, to processes in the soil pore system, within a soil profile and across the landscape. We highlight that with increasing mobility of the nutrients in water, water-based nutrient flux may alleviate or even exacerbate imbalances in nutrient supply within soils, for example, by transport of mobile nutrients towards previously depleted microsites (alleviating imbalances), or by selective loss of mobile nutrients from microsites (increasing imbalances). These imbalances can be modulated by biological activity, especially by fungal hyphae and roots, which contribute to nutrient redistribution within soils, and which are themselves dependent on specific, optimal water availability. At larger scales, such small-scale effects converge with nutrient inputs from atmospheric (wet deposition) or nonlocal sources and with nutrient losses from the soil system towards aquifers. Hence, water acts as a major control in nutrient cycling across scales in agroecosystems and may either exacerbate or remove spatial disparities in the availability of the individual nutrients (N, P, S) required for biological activity., Journal of Plant Nutrition and Soil Science, 185 (6), ISSN:1436-8730, ISSN:0044-3263, ISSN:1522-2624

In this chapter, we present the Root and Soil Water Movement and Solute transport model R-SWMS, which can be used to simulate flow and transport in the soil-plant system. The equations describing water flow in soil-root systems are presented and numerical solutions are provided. An application of R-SWMS is then briefly discussed, in which we combine in vivo and in silico experiments in order to decrypt water flow in the soil-root domain. More precisely, light transmission imaging experiments were conducted to generate data that can serve as input for the R-SWMS model. These data include the root system architecture, the soil hydraulic properties and the environmental conditions (initial soil water content and boundary conditions, BC). Root hydraulic properties were not acquired experimentally, but set to theoretical values found in the literature. In order to validate the results obtained by the model, the simulated and experimental water content distributions were compared. The model was then used to estimate variables that were not experimentally accessible, such as the actual root water uptake distribution and xylem water potential.

In this chapter, we present the Root and Soil Water Movement and Solute transport model R-SWMS, which can be used to simulate flow and transport in the soil-plant system. The equations describing water flow in soil-root systems are presented and numerical solutions are provided. An application of R-SWMS is then briefly discussed, in which we combine in vivo and in silico experiments in order to decrypt water flow in the soil-root domain. More precisely, light transmission imaging experiments were conducted to generate data that can serve as input for the R-SWMS model. These data include the root system architecture, the soil hydraulic properties and the environmental conditions (initial soil water content and boundary conditions, BC). Root hydraulic properties were not acquired experimentally, but set to theoretical values found in the literature. In order to validate the results obtained by the model, the simulated and experimental water content distributions were compared. The model was then used to estimate variables that were not experimentally accessible, such as the actual root water uptake distribution and xylem water potential.

Purpose Simultaneously interacting rhizosphere processes determine emergent plant behaviour, including growth, transpiration, nutrient uptake, soil carbon storage and transformation by microorganisms. However, these processes occur on multiple scales, challenging modelling of rhizosphere and plant behaviour. Current advances in modelling and experimental methods open the path to unravel the importance and interconnectedness of those processes across scales. Methods We present a series of case studies of state-of-the art simulations addressing this multi-scale, multi-process problem from a modelling point of view, as well as from the point of view of integrating newly available rhizosphere data and images. Results Each case study includes a model that links scales and experimental data to explain and predict spatial and temporal distribution of rhizosphere components. We exemplify the state-of-the-art modelling tools in this field: image-based modelling, pore-scale modelling, continuum scale modelling, and functional-structural plant modelling. We show how to link the pore scale to the continuum scale by homogenisation or by deriving effective physical parameters like viscosity from nano-scale chemical properties. Furthermore, we demonstrate ways of modelling the links between rhizodeposition and plant nutrient uptake or soil microbial activity. Conclusion Modelling allows to integrate new experimental data across different rhizosphere processes and scales and to explore more variables than is possible with experiments. Described models are tools to test hypotheses and consequently improve our mechanistic understanding of how rhizosphere processes impact plant-scale behaviour. Linking multiple scales and processes including the dynamics of root growth is the logical next step for future research., Plant and Soil, 478, ISSN:0032-079X, ISSN:1573-5036

We developed root scale models that simulate water and nutrient uptake by crops. Water flow in the soil and root systems was linked in order to describe root water uptake as a function of root properties and distributions, soil and leaf water potentials. One of the underlying motivations is to predict the crop water stress level and its impact on transpiration and growth. The mechanistic description of water fluxes resulted in models that were sensitive to hydraulic properties of the root system, including root density, and root distribution with depth. These sensitivities improved predictions of crop water uptake and water stress in different soils and for different water treatments. Crucial was the correct representation of the root system and its response to different ‘treatments’. Thus, in order to predict the impact of water stress on growth, the growth response to the water stress must be predicted. So far, these response functions and especially the distribution of carbon within the plant to the different plant organs are empirical functions. A coupled carbon and water flow model within the plant is a way forward to more mechanistic descriptions of these responses. A similar storyline can be developed for nutrient uptake. Mechanistic nutrient uptake models do not consider nutrient transport within the root system but focus on transport towards the root surface. Multi-scale flow and transport simulations demonstrated that small scale transport towards growing root tips and root system scale water and nutrient distributions controlled nutrient uptake. These simulations predicted the observed interaction between water and phosphate uptake of an upland rice crop. However, here again, simulated uptake depended on the root development in response to nutrient and water stress. Mechanistic descriptions of root growth response to nutrients require a further understanding of plant physiological processes that cause these responses.

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.

Root water uptake is an important process in the terrestrial water cycle. How this process depends on soil water content, root distributions, and root properties is a soil–root hydraulic problem. We compare different approaches to implement root hydraulics in macroscopic soil water flow and land surface models. By upscaling a three-dimensional hydraulic root architecture model, we derived an exact macroscopic root hydraulic model. The macroscopic model uses the following three characteristics: the root system conductance, Krs, the standard uptake fraction, SUF, which represents the uptake from a soil profile with a uniform hydraulic head, and a compensatory matrix that describes the redistribution of water uptake in a non-uniform hydraulic head profile. The two characteristics, Krs and SUF, are sufficient to describe the total uptake as a function of the collar and soil water potential, and water uptake redistribution does not depend on the total uptake or collar water potential. We compared the exact model with two hydraulic root models that make a priori simplifications of the hydraulic root architecture, i.e., the parallel and big root model. The parallel root model uses only two characteristics, Krs and SUF, which can be calculated directly following a bottom-up approach from the 3D hydraulic root architecture. The big root model uses more parameters than the parallel root model, but these parameters cannot be obtained straightforwardly with a bottom-up approach. The big root model was parameterized using a top-down approach, i.e., directly from root segment hydraulic properties, assuming a priori a single big root architecture. This simplification of the hydraulic root architecture led to less accurate descriptions of root water uptake than by the parallel root model. To compute root water uptake in macroscopic soil water flow and land surface models, we recommend the use of the parallel root model with Krs and SUF computed in a bottom-up approach from a known 3D root hydraulic architecture.

New knowledge on soil structure highlights its importance for hydrology and soil organic matter (SOM) stabilization, which however remains neglected in many wide used models. We present here a new model, KEYLINK, in which soil structure is integrated with the existing concepts on SOM pools, and elements from food web models, that is, those from direct trophic interactions among soil organisms. KEYLINK is, therefore, an attempt to integrate soil functional diversity and food webs in predictions of soil carbon (C) and soil water balances. We present a selection of equations that can be used for most models as well as basic parameter intervals, for example, key pools, functional groups’ biomasses and growth rates. Parameter distributions can be determined with Bayesian calibration, and here an example is presented for food web growth rate parameters for a pine forest in Belgium. We show how these added equations can improve the functioning of the model in describing known phenomena. For this, five test cases are given as simulation examples: changing the input litter quality (recalcitrance and carbon to nitrogen ratio), excluding predators, increasing pH and changing initial soil porosity. These results overall show how KEYLINK is able to simulate the known effects of these parameters and can simulate the linked effects of biopore formation, hydrology and aggregation on soil functioning. Furthermore, the results show an important trophic cascade effect of predation on the complete C cycle with repercussions on the soil structure as ecosystem engineers are predated, and on SOM turnover when predation on fungivore and bacterivore populations are reduced. In summary, KEYLINK shows how soil functional diversity and trophic organization and their role in C and water cycling in soils should be considered in order to improve our predictions on C sequestration and C emissions from soils., This article is based upon work from COST Actions FP1305 (BioLink) and ES1406 (KEYSOM), supported by COST (European Cooperation in Science and Technology), and their Short Term Scientific Mission (STSM) programs. Omar Flores’ work was funded by FPU PhD grant program of the Spanish Ministry of Science, Innovation and Universities. Jorge Curiel Yuste received funding from the Spanish Ministry of Economy and Competitiveness (MINECO) under projects IBERYCA (CGL2017-84723-P) and the BC3 María de Maeztu excellence accreditation (MDM-2017-0714). Jorge Curiel Yuste also received funding from the Basque Government through the BERC 2018-2021 program., We also acknowledge institutional support from the Unit of Information Resources for Research at the Unit of Information Resources for Research at the "Consejo Superior de Investigaciones Científicas" (CSIC) for the article-processing charges contribution.