Multi-year aboveground data of minirhizotron facilities in Selhausen.

Improved understanding of crops' response to soil water stress is important to advance soil-plant system models and to support crop breeding, crop and varietal selection, and management decisions to minimize negative impacts. Studies on eco-physiological crop characteristics from leaf to canopy for different soil water conditions and crops are often carried out at controlled conditions. In-field measurements under realistic field conditions and data of plant water potential, its links with CO2 and H2O gas fluxes, and crop growth processes are rare. Here, we presented a comprehensive data set collected from leaf to canopy using sophisticated and comprehensive sensing techniques (leaf chlorophyll, stomatal conductance and photosynthesis, canopy CO2 exchange, sap flow, and canopy temperature) including detailed crop growth characteristics based on destructive methods (crop height, leaf area index, aboveground biomass, and yield). Data were acquired under field conditions with contrasting soil types, water treatments, and different cultivars of wheat and maize. The data from 2016 up to now will be made available for studying soil/water-plant relations and improving soil-plant-atmospheric continuum models.

Seedling root system adaptation to water availability during maize domestication and global expansion.

The maize root system has been reshaped by indirect selection during global adaptation to new agricultural environments. In this study, we characterized the root systems of more than 9,000 global maize accessions and its wild relatives, defining the geographical signature and genomic basis of variation in seminal root number. We demonstrate that seminal root number has increased during maize domestication followed by a decrease in response to limited water availability in locally adapted varieties. By combining environmental and phenotypic association analyses with linkage mapping, we identified genes linking environmental variation and seminal root number. Functional characterization of the transcription factor ZmHb77 and in silico root modeling provides evidence that reshaping root system architecture by reducing the number of seminal roots and promoting lateral root density is beneficial for the resilience of maize seedlings to drought.

Multi-year belowground data of minirhizotron facilities in Selhausen.

The production of crops secure the human food supply, but climate change is bringing new challenges. Dynamic plant growth and corresponding environmental data are required to uncover phenotypic crop responses to the changing environment. There are many datasets on above-ground organs of crops, but roots and the surrounding soil are rarely the subject of longer term studies. Here, we present what we believe to be the first comprehensive collection of root and soil data, obtained at two minirhizotron facilities located close together that have the same local climate but differ in soil type. Both facilities have 7m-long horizontal tubes at several depths that were used for crosshole ground-penetrating radar and minirhizotron camera systems. Soil sensors provide observations at a high temporal and spatial resolution. The ongoing measurements cover five years of maize and wheat trials, including drought stress treatments and crop mixtures. We make the processed data available for use in investigating the processes within the soil-plant continuum and the root images to develop and compare image analysis methods.

3D U-Net Segmentation Improves Root System Reconstruction from 3D MRI Images in Automated and Manual Virtual Reality Work Flows.

Magnetic resonance imaging (MRI) is used to image root systems grown in opaque soil. However, reconstruction of root system architecture (RSA) from 3-dimensional (3D) MRI images is challenging. Low resolution and poor contrast-to-noise ratios (CNRs) hinder automated reconstruction. Hence, manual reconstruction is still widely used. Here, we evaluate a novel 2-step work flow for automated RSA reconstruction. In the first step, a 3D U-Net segments MRI images into root and soil in super-resolution. In the second step, an automated tracing algorithm reconstructs the root systems from the segmented images. We evaluated the merits of both steps for an MRI dataset of 8 lupine root systems, by comparing the automated reconstructions to manual reconstructions of unaltered and segmented MRI images derived with a novel virtual reality system. We found that the U-Net segmentation offers profound benefits in manual reconstruction: reconstruction speed was doubled (+97%) for images with low CNR and increased by 27% for images with high CNR. Reconstructed root lengths were increased by 20% and 3%, respectively. Therefore, we propose to use U-Net segmentation as a principal image preprocessing step in manual work flows. The root length derived by the tracing algorithm was lower than in both manual reconstruction methods, but segmentation allowed automated processing of otherwise not readily usable MRI images. Nonetheless, model-based functional root traits revealed similar hydraulic behavior of automated and manual reconstructions. Future studies will aim to establish a hybrid work flow that utilizes automated reconstructions as scaffolds that can be manually corrected.

Development and Validation of a Deep Learning Based Automated Minirhizotron Image Analysis Pipeline.

Root systems of crops play a significant role in agroecosystems. The root system is essential for water and nutrient uptake, plant stability, symbiosis with microbes, and a good soil structure. Minirhizotrons have shown to be effective to noninvasively investigate the root system. Root traits, like root length, can therefore be obtained throughout the crop growing season. Analyzing datasets from minirhizotrons using common manual annotation methods, with conventional software tools, is time-consuming and labor-intensive. Therefore, an objective method for high-throughput image analysis that provides data for field root phenotyping is necessary. In this study, we developed a pipeline combining state-of-the-art software tools, using deep neural networks and automated feature extraction. This pipeline consists of two major components and was applied to large root image datasets from minirhizotrons. First, a segmentation by a neural network model, trained with a small image sample, is performed. Training and segmentation are done using "RootPainter." Then, an automated feature extraction from the segments is carried out by "RhizoVision Explorer." To validate the results of our automated analysis pipeline, a comparison of root length between manually annotated and automatically processed data was realized with more than 36,500 images. Mainly the results show a high correlation (r = 0.9) between manually and automatically determined root lengths. With respect to the processing time, our new pipeline outperforms manual annotation by 98.1-99.6%. Our pipeline, combining state-of-the-art software tools, significantly reduces the processing time for minirhizotron images. Thus, image analysis is no longer the bottle-neck in high-throughput phenotyping approaches.

Simulating Root Growth as a Function of Soil Strength and Yield With a Field-Scale Crop Model Coupled With a 3D Architectural Root Model.

Accurate prediction of root growth and related resource uptake is crucial to accurately simulate crop growth especially under unfavorable environmental conditions. We coupled a 1D field-scale crop-soil model running in the SIMPLACE modeling framework with the 3D architectural root model CRootbox on a daily time step and implemented a stress function to simulate root elongation as a function of soil bulk density and matric potential. The model was tested with field data collected during two growing seasons of spring barley and winter wheat on Haplic Luvisol. In that experiment, mechanical strip-wise subsoil loosening (30-60 cm) (DL treatment) was tested, and effects on root and shoot growth at the melioration strip as well as in a control treatment were evaluated. At most soil depths, strip-wise deep loosening significantly enhanced observed root length densities (RLDs) of both crops as compared to the control. However, the enhanced root growth had a beneficial effect on crop productivity only in the very dry season in 2018 for spring barley where the observed grain yield at the strip was 18% higher as compared to the control. To understand the underlying processes that led to these yield effects, we simulated spring barley and winter wheat root and shoot growth using the described field data and the model. For comparison, we simulated the scenarios with the simpler 1D conceptual root model. The coupled model showed the ability to simulate the main effects of strip-wise subsoil loosening on root and shoot growth. It was able to simulate the adaptive plasticity of roots to local soil conditions (more and thinner roots in case of dry and loose soil). Additional scenario runs with varying weather conditions were simulated to evaluate the impact of deep loosening on yield under different conditions. The scenarios revealed that higher spring barley yields in DL than in the control occurred in about 50% of the growing seasons. This effect was more pronounced for spring barley than for winter wheat. Different virtual root phenotypes were tested to assess the potential of the coupled model to simulate the effect of varying root traits under different conditions.

Root System Scale Models Significantly Overestimate Root Water Uptake at Drying Soil Conditions.

Soil hydraulic conductivity (k soil ) drops significantly in dry soils, resulting in steep soil water potential gradients (ψ s ) near plant roots during water uptake. Coarse soil grid resolutions in root system scale (RSS) models of root water uptake (RWU) generally do not spatially resolve this gradient in drying soils which can lead to a large overestimation of RWU. To quantify this, we consider a benchmark scenario of RWU from drying soil for which a numerical reference solution is available. We analyze this problem using a finite volume scheme and investigate the impact of grid size on the RSS model results. At dry conditions, the cumulative RWU was overestimated by up to 300% for the coarsest soil grid of 4.0 cm and by 30% for the finest soil grid of 0.2 cm, while the computational demand increased from 19 s to 21 h. As an accurate and computationally efficient alternative to the RSS model, we implemented a continuum multi-scale model where we keep a coarse grid resolution for the bulk soil, but in addition, we solve a 1-dimensional radially symmetric soil model at rhizosphere scale around individual root segments. The models at the two scales are coupled in a mass-conservative way. The multi-scale model compares best to the reference solution (-20%) at much lower computational costs of 4 min. Our results demonstrate the need to shift to improved RWU models when simulating dry soil conditions and highlight that results for dry conditions obtained with RSS models of RWU should be interpreted with caution.

Investigating Soil-Root Interactions with the Numerical Model R-SWMS.

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.

Nutrient deficiency effects on root architecture and root-to-shoot ratio in arable crops.

Plant root traits play a crucial role in resource acquisition and crop performance when soil nutrient availability is low. However, the respective trait responses are complex, particularly at the field scale, and poorly understood due to difficulties in root phenotyping monitoring, inaccurate sampling, and environmental conditions. Here, we conducted a systematic review and meta-analysis of 50 field studies to identify the effects of nitrogen (N), phosphorous (P), or potassium (K) deficiencies on the root systems of common crops. Root length and biomass were generally reduced, while root length per shoot biomass was enhanced under N and P deficiency. Root length decreased by 9% under N deficiency and by 14% under P deficiency, while root biomass was reduced by 7% in N-deficient and by 25% in P-deficient soils. Root length per shoot biomass increased by 33% in N deficient and 51% in P deficient soils. The root-to-shoot ratio was often enhanced (44%) under N-poor conditions, but no consistent response of the root-to-shoot ratio to P-deficiency was found. Only a few K-deficiency studies suited our approach and, in those cases, no differences in morphological traits were reported. We encountered the following drawbacks when performing this analysis: limited number of root traits investigated at field scale, differences in the timing and severity of nutrient deficiencies, missing data (e.g., soil nutrient status and time of stress), and the impact of other conditions in the field. Nevertheless, our analysis indicates that, in general, nutrient deficiencies increased the root-length-to-shoot-biomass ratios of crops, with impacts decreasing in the order deficient P > deficient N > deficient K. Our review resolved inconsistencies that were often found in the individual field experiments, and led to a better understanding of the physiological mechanisms underlying root plasticity in fields with low nutrient availability.

Mechanistic modeling of pesticide uptake with a 3D plant architecture model.

Meaningful assessment of pesticide fate in soils and plants is based on fate models that represent all relevant processes. With mechanistic models, these processes can be simulated based on soil, substance, and plant properties. We present a mechanistic model that simulates pesticide uptake from soil and investigate how it is influenced, depending on the governing uptake process, by root and substance properties and by distributions of the substance and water in the soil profile. A new root solute uptake model based on a lumped version of the Trapp model (Trapp, 2000) was implemented in a coupled version of R-SWMS-ParTrace models for 3-D water flow and solute transport in soil and root systems. Solute uptake was modeled as two individual processes: advection with the transpiration stream and diffusion through the root membrane. We set up the model for a FOCUS scenario used in the European Union (EU) for pesticide registration. Considering a single vertical root and advective uptake only, the root hydraulic properties could be defined so that water and substance uptake and substance fate in soil showed a good agreement with the results of the 1D PEARL model, one of the reference models used in the EU for pesticide registration. Simulations with a complex root system and using root hydraulic parameters reported in the literature predicted larger water uptake from the upper root zone, leading to larger pesticide uptake when pesticides are concentrated in the upper root zone. Dilution of root water concentrations at the top root zone with water with low pesticide concentration taken up from the bottom of the root zone leads to larger uptake of solute when uptake was simulated as a diffusive process. This illustrates the importance of modeling uptake mechanistically and considering root and solute physical and chemical properties, especially when root-zone pesticide concentrations are non-uniform.

Tracing uptake and translocation of phosphorus in wheat using oxygen isotopes and mathematical modelling.

Understanding P uptake in soil-plant systems requires suitable P tracers. The stable oxygen isotope ratio in phosphate (expressed as δ18 OP ) is an alternative to radioactive labelling, but the degree to which plants preserve the δ18 OP value of the P source is unclear. We hypothesised that the source signal will be preserved in roots rather than shoots. In soil and hydroponic experiments with spring wheat (Triticum aestivum), we replaced irrigation water by 18 O-labelled water for up to 10 d. We extracted plant inorganic phosphates with trichloroacetic acid (TCA), assessed temporal dynamics of δ18 OTCA-P values after changing to 18 O-labelled water and combined the results with a mathematical model. Within 1 wk, full equilibration of δ18 OTCA-P values with the isotope value of the water in the growth medium occurred in shoots but not in roots. Model results further indicated that root δ18 OTCA-P values were affected by back transport of phosphate from shoots to roots, with a greater contribution of source P at higher temperatures when back transport was reduced. Root δ18 OTCA-P partially preserved the source signal, providing an indicator of P uptake sources. This now needs to be tested extensively for different species, soil and climate conditions to enable application in future ecosystem studies.

KEYLINK: towards a more integrative soil representation for inclusion in ecosystem scale models-II: model description, implementation and testing.

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.

Bioavailability and -accessibility of subsoil allocated 33P-labelled hydroxyapatite to wheat under different moisture supply.

Information on the bioavailability and -accessibility of subsoil phosphorus (P) and how soil moisture affects its utilization by plants is scarce. The current study examined whether and to which degree wheat acquires P from subsoil allocated hydroxyapatite and how this could be affected by soil moisture. We investigated the 33P uptake by growing wheat in two rhizotron trials (soil and sand) with integrated 33P-labelled hydroxyapatite hotspots over a period of 44 days using digital autoradiography imaging and liquid scintillation counting. We applied two irrigation scenarios, mimicking either rainfall via topsoil watering or subsoil water storage. The plants showed similar biomass development when grown in soil, but a reduced growth in sand rhizotrons. Total plant P(tot) stocks were significantly larger in plants grown under improved subsoil moisture supply, further evidenced by enhanced P stocks in the ears of wheat in the sand treatment due to an earlier grain filling. This P uptake is accompanied by larger 33P signals, indicating that the plants accessed the hydroxyapatite because subsoil irrigation also promoted root proliferation within and around the hotspots. We conclude that even within a single season plants access subsoil mineral P sources, and this process is influenced by water management.

KEYLINK: towards a more integrative soil representation for inclusion in ecosystem scale models. I. review and model concept.

The relatively poor simulation of the below-ground processes is a severe drawback for many ecosystem models, especially when predicting responses to climate change and management. For a meaningful estimation of ecosystem production and the cycling of water, energy, nutrients and carbon, the integration of soil processes and the exchanges at the surface is crucial. It is increasingly recognized that soil biota play an important role in soil organic carbon and nutrient cycling, shaping soil structure and hydrological properties through their activity, and in water and nutrient uptake by plants through mycorrhizal processes. In this article, we review the main soil biological actors (microbiota, fauna and roots) and their effects on soil functioning. We review to what extent they have been included in soil models and propose which of them could be included in ecosystem models. We show that the model representation of the soil food web, the impact of soil ecosystem engineers on soil structure and the related effects on hydrology and soil organic matter (SOM) stabilization are key issues in improving ecosystem-scale soil representation in models. Finally, we describe a new core model concept (KEYLINK) that integrates insights from SOM models, structural models and food web models to simulate the living soil at an ecosystem scale.

A functional-structural model of upland rice root systems reveals the importance of laterals and growing root tips for phosphate uptake from wet and dry soils.

BACKGROUND AND AIMS: Upland rice is often grown where water and phosphorus (P) are limited. To better understand the interaction between water and P availability, functional-structural models that mechanistically represent small-scale nutrient gradients and water dynamics in the rhizosphere are needed. METHODS: Rice was grown in large columns using a P-deficient soil at three P supplies in the topsoil (deficient, sub-optimal and non-limiting) in combination with two water regimes (field capacity vs. drying periods). Root system characteristics, such as nodal root number, lateral types, interbranch distance, root diameters and the distribution of biomass with depth, as well as water and P uptake, were measured. Based on the observed root data, 3-D root systems were reconstructed by calibrating the structural architecure model CRootBox for each scenario. Water flow and P transport in the soil to each of the individual root segments of the generated 3-D root architectures were simulated using a multiscale flow and transport model. Total water and P uptake were then computed by adding up the uptake by all the root segments. KEY RESULTS: Measurements showed that root architecture was significantly affected by the treatments. The moist, high P scenario had 2.8 times the root mass, double the number of nodal roots and more S-type laterals than the dry, low P scenario. Likewise, measured plant P uptake increased >3-fold by increasing P and water supply. However, drying periods reduced P uptake at high but not at low P supply. Simulation results adequately predicted P uptake in all scenarios when the Michaelis-Menten constant (Km) was corrected for diffusion limitation. They showed that the key drivers for P uptake are the different types of laterals (i.e. S- and L-type) and growing root tips. The L-type laterals become more important for overall water and P uptake than the S-type laterals in the dry scenarios. This is true across all the P treatments, but the effect is more pronounced as the P availability decreases. CONCLUSIONS: This functional-structural model can predict the function of specific rice roots in terms of P and water uptake under different P and water supplies, when the structure of the root system is known. A future challenge is to predict how the structure root systems responds to nutrient and water availability.

Call for Participation: Collaborative Benchmarking of Functional-Structural Root Architecture Models. The Case of Root Water Uptake.

Three-dimensional models of root growth, architecture and function are becoming important tools that aid the design of agricultural management schemes and the selection of beneficial root traits. However, while benchmarking is common in many disciplines that use numerical models, such as natural and engineering sciences, functional-structural root architecture models have never been systematically compared. The following reasons might induce disagreement between the simulation results of different models: different representation of root growth, sink term of root water and solute uptake and representation of the rhizosphere. Presently, the extent of discrepancies is unknown, and a framework for quantitatively comparing functional-structural root architecture models is required. We propose, in a first step, to define benchmarking scenarios that test individual components of complex models: root architecture, water flow in soil and water flow in roots. While the latter two will focus mainly on comparing numerical aspects, the root architectural models have to be compared at a conceptual level as they generally differ in process representation. Therefore, defining common inputs that allow recreating reference root systems in all models will be a key challenge. In a second step, benchmarking scenarios for the coupled problems are defined. We expect that the results of step 1 will enable us to better interpret differences found in step 2. This benchmarking will result in a better understanding of the different models and contribute toward improving them. Improved models will allow us to simulate various scenarios with greater confidence and avoid bugs, numerical errors or conceptual misunderstandings. This work will set a standard for future model development.

Mechanical and Hydric Stress Effects on Maize Root System Development at Different Soil Compaction Levels.

Soil mechanical resistance, aeration, and water availability directly affect plant root growth. The objective of this work was to identify the contribution of mechanical and hydric stresses on maize root elongation, by modeling root growth while taking the dynamics of these stresses in an Oxisol into consideration. The maize crop was cultivated under four compaction levels (soil chiseling, no-tillage system, areas trafficked by a tractor, and trafficked by a harvester), and we present a new model, which allows to distinguish between mechanical and hydric stresses. Root length density profiles, soil bulk density, and soil water retention curves were determined for four compaction levels up to 50 cm in depth. Furthermore, grain yield and shoot biomass of maize were quantified. The new model described the mechanical and hydric stresses during maize growth with field data for the first time in maize crop. Simulations of root length density in 1D and 2D showed adequate agreement with the values measured under field conditions. Simulation makes it possible to identify the interaction between the soil physical conditions and maize root growth. Compared to the no-tillage system, grain yield was reduced due to compaction caused by harvester traffic and by soil chiseling. The root growth was reduced by the occurrence of mechanical and hydric stresses during the crop cycle, the principal stresses were mechanical in origin for areas with agricultural traffic, and water based in areas with soil chiseling. Including mechanical and hydric stresses in root growth models can help to predict future scenarios, and coupling soil biophysical models with weather, soil, and crop responses will help to improve agricultural management.

Connecting the dots between computational tools to analyse soil-root water relations.

In recent years, many computational tools, such as image analysis, data management, process-based simulation, and upscaling tools, have been developed to help quantify and understand water flow in the soil-root system, at multiple scales (tissue, organ, plant, and population). Several of these tools work together or at least are compatible. However, for the uninformed researcher, they might seem disconnected, forming an unclear and disorganized succession of tools. In this article, we show how different studies can be further developed by connecting them to analyse soil-root water relations in a comprehensive and structured network. This 'explicit network of soil-root computational tools' informs readers about existing tools and helps them understand how their data (past and future) might fit within the network. We also demonstrate the novel possibilities of scale-consistent parameterizations made possible by the network with a set of case studies from the literature. Finally, we discuss existing gaps in the network and how we can move forward to fill them.

CRootBox: a structural-functional modelling framework for root systems.

Background and Aims: Root architecture development determines the sites in soil where roots provide input of carbon and take up water and solutes. However, root architecture is difficult to determine experimentally when grown in opaque soil. Thus, root architecture models have been widely used and been further developed into functional-structural models that simulate the fate of water and solutes in the soil-root system. The root architecture model CRootBox presented here is a flexible framework to model root architecture and its interactions with static and dynamic soil environments. Methods: CRootBox is a C++-based root architecture model with Python binding, so that CRootBox can be included via a shared library into any Python code. Output formats include VTP, DGF, RSML and a plain text file containing coordinates of root nodes. Furthermore, a database of published root architecture parameters was created. The capabilities of CRootBox for the unconfined growth of single root systems, as well as the different parameter sets, are highlighted in a freely available web application. Key results: The capabilities of CRootBox are demonstrated through five different cases: (1) free growth of individual root systems; (2) growth of root systems in containers as a way to mimic experimental setups; (3) field-scale simulation; (4) root growth as affected by heterogeneous, static soil conditions; and (5) coupling CRootBox with code from the book Soil physics with Python to dynamically compute water flow in soil, root water uptake and water flow inside roots. Conclusions: CRootBox is a fast and flexible functional-structural root model that is based on state-of-the-art computational science methods. Its aim is to facilitate modelling of root responses to environmental conditions as well as the impact of roots on soil. In the future, this approach will be extended to the above-ground part of the plant.