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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. [ABSTRACT FROM AUTHOR]

Aims: We aimed at assessing the influence of above- and below-ground environmental conditions over the performance of Centaurea jacea L., a drought-resistant grassland forb species. Methods: Transpiration rate, CO2 assimilation rate, leaf water potential, instantaneous and intrinsic water use efficiency, temperature, relative humidity, vapor pressure deficit and soil water content in one plant and root length density in four plants, all grown in custom-made columns, were monitored daily for 87 days in the lab. The soil water isotopic composition in eleven depths was recorded daily in a non-destructive manner. The isotopic composition of plant transpiration was inferred from gas chamber measurements. Vertical isotopic gradients in the soil column were created by adding labeled water. Daily root water uptake (RWU) profiles were computed using the multi-source mixing model Stable Isotope Analysis in R (Parnell et al. PLoS ONE 5(3):1–5, 2010). Results: RWU occurred mainly in soil layer 0–15 cm, ranging from 79 to 44%, even when water was more easily available in deeper layers. In wet soil, the transpiration rate was driven mainly by vapor pressure deficit and light intensity. Once soil water content was less than 0.12 cm3 cm− 3, the computed canopy conductance declined, which restricted leaf gas exchange. Leaf water potential dropped steeply to around − 3 MPa after soil water content was below 0.10 cm3 cm− 3. Conclusion: Our comprehensive data set contributes to a better understanding of the effects of drought on a grassland species and the limits of its acclimation in dry conditions. [ABSTRACT FROM AUTHOR]

Background and aims: Impact of drought on crop growth depends on soil and root hydraulic properties that determine the access of plant roots to soil water. Root hairs may increase the accessible water pool but their effect depends on soil hydraulic properties and adaptions of root systems to drought. These adaptions are difficult to investigate in pot experiments that focus on juvenile plants. Methods: A wild-type and its root hairless mutant maize (Zea mays) were grown in the field in loam and sand substrates during two growing seasons with a large precipitation deficit. A comprehensive dataset of soil and plant properties and monitored variables were collected and interpreted using simulations with a mechanistic root water uptake model. Results: Total crop water use was similar in both soils and for both genotypes whereas shoot biomass was larger for the wild type than for the hairless mutant and did not differ between soils. Total final root length was larger in sand than in loam but did not differ between genotypes. Simulations showed that root systems of both genotypes and in both soils extracted all plant available soil water, which was similar for sand and loam, at a potential rate. Leaf water potentials were overestimated by the model, especially for the hairless mutant in sand substrate because the water potential drop in the rhizosphere was not considered. Conclusions: A direct effect of root hairs on water uptake was not observed but root hairs might influence leaf water potential dependent growth. [ABSTRACT FROM AUTHOR]

Aims: Root hairs are important for uptake, especially for nutrients with low mobility in soils with high sorption capacity. Mutants with defective root hairs are expected to have lower nutrient uptake, unless they compensate with more root growth. Since root hairs can also contribute to the plant's water uptake their importance could change over the course of a growing season. It was our objective to investigate the role of root hairs under field conditions. Methods: The root hair mutant rth3 of Zea mays and the corresponding wild-type were grown for two years under field conditions on sand and loam. Results: Shoot growth and P and K uptake of the plants were promoted by the presence of hairs at all growth stages. Differences between genotypes were greater on loam than on sand until tassel emergence, presumably as additional exploitation by hairs is more relevant in loam. Compensation for the absence of root hairs by increased root growth was not observed in absolute terms. The root to shoot ratio was higher for rth3 than for wild-type. Root traits showed high plasticity in response to texture, the most salient being a greater mean root diameter in sand, irrespective of genotype. The mechanism causing the increase in mean root diameter is still unknown. Root length density was higher in sand, which can be explained by a greater need for exploration than exploitation in this substrate. Conclusion: The role of hairs for nutrient uptake could be confirmed under field conditions. The large impact of texture on root growth and consequences for carbon balance require further investigations. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background and aims: Characterizing root system architectures of field-grown crops is challenging as root systems are hidden in the soil. We investigate the possibility of estimating root architecture model parameters from soil core data in a Bayesian framework. Methods: In a synthetic experiment, we simulated wheat root systems in a virtual field plot with the stochastic CRootBox model. We virtually sampled soil cores from this plot to create synthetic measurement data. We used the Markov chain Monte Carlo (MCMC) DREAM(ZS) sampler to estimate the most sensitive root system architecture parameters. To deal with the CRootBox model stochasticity and limited computational resources, we essentially added a stochastic component to the likelihood function, thereby turning the MCMC sampling into a form of approximate Bayesian computation (ABC). Results: A few zero-order root parameters: maximum length, elongation rate, insertion angles, and numbers of zero-order roots, with narrow posterior distributions centered around true parameter values were identifiable from soil core data. Yet other zero-order and higher-order root parameters were not identifiable showing a sizeable posterior uncertainty. Conclusions: Bayesian inference of root architecture parameters from root density profiles is an effective method to extract information about sensitive parameters hidden in these profiles. Equally important, this method also identifies which information about root architecture is lost when root architecture is aggregated in root density profiles. [ABSTRACT FROM AUTHOR]

Aims: Demonstrating the potential of MRI as a 3D, non-invasive and continuous measurement technique to map Na+ concentration distributions in soil and around roots. Methods: Dissolved NaCl in soil and soil-plant systems was mapped by 3D 23Na-MRI. The lower limit of detectability in saturated and unsaturated porous media was evaluated, followed by evaporation experiments to test the quantification. Finally, Na+ enrichment around tomato roots, irrigated with saline solution under low/high transpiration rates (LT, HT), was imaged in parallel to the root system,. Results: A spin echo pulse sequence allowed the quantitative mapping of the volume concentration of NaCl in sandy porous medium. Evaporation experiments showed slight enrichment in the top surface layer, plus uniform temporal enrichment in the deeper layers. In the tomato experiments, enrichment was more distinct under HT than under LT. Concentration-distance correlation curves revealed thin enrichment zones ranging a few mm around the roots. Conclusions: MRI can map Na+ non-invasively in 3D at relevant concentrations for root activity. Visualizing water content, roots and Na+ on the same scale is possible, despite limitations of different scanning times and resolution. This opens a route for further quantitative investigations of salt enrichment processes in soil and soil-plant systems. [ABSTRACT FROM AUTHOR]

Aims: (1) Monitoring 'root-felt' salinity by using rhizoslides as a non-invasive method, (2) Studying how transpiration rate, salinity in irrigation water, and root water uptake affect sodium distribution around single roots, (3) Interpreting experimental results by using simulations with a 3-D root system architecture model coupled with water flow and solute transport models. Methods: Tomato plants were grown on rhizoslides under various salinity levels and two transpiration rates: high and low. Daily root images were processed with GIMP and incorporated into a 3-D numerical model. The experiments were simulated with R-SWMS, a 3-dimensional numerical model that simulates water flow and solute transport in soil, into the root and inside root systems. Results: Both experimental and simulation results displayed higher root-felt sodium concentrations compared with the bulk concentrations, and larger accumulation at higher transpiration rate. The simulations illustrated that the root-felt to bulk concentration ratio changed during the experiment depending both on the irrigation water salinity and transpiration rate. Conclusions: Changes in sodium concentrations with transpiration rates are most likely caused by root water uptake and ion exclusion. Simulation results indicate that root-scale process models are required to link root system architecture, environmental, and soil conditions with root-felt salinities. [ABSTRACT FROM AUTHOR]

Soil water evaporation plays a critical role in mass and energy exchanges across the land–atmosphere interface. Although much is known about this process, there is no agreement on the best modeling approaches to determine soil water evaporation due to the complexity of the numerical modeling scenarios and lack of experimental data available to validate such models. Existing studies show numerical and experimental discrepancies in the evaporation behavior and soil water distribution in soils at various scales, driving us to revisit the key process representation in subsurface soil. Therefore, the goal of this work is to test different mathematical formulations used to estimate evaporation from bare soils to critically evaluate the model formulations, assumptions and surface boundary conditions. This comparison required the development of three numerical models at the REV scale that vary in their complexity in characterizing water flow and evaporation, using the same modeling platform. The performance of the models was evaluated by comparing with experimental data generated from a soil tank/boundary layer wind tunnel experimental apparatus equipped with a sensor network to continuously monitor water–temperature–humidity variables. A series of experiments were performed in which the soil tank was packed with different soil types. Results demonstrate that the approaches vary in their ability to capture different stages of evaporation and no one approach can be deemed most appropriate for every scenario. When a proper top boundary condition and space discretization are defined, the Richards equation-based models (Richards model and Richards vapor model) can generally capture the evaporation behaviors across the entire range of soil saturations, comparing well with the experimental data. The simulation results of the non-equilibrium two-component two-phase model which considers vapor transport as an independent process generally agree well with the observations in terms of evaporation behavior and soil water dynamics. Certain differences in simulation results can be observed between equilibrium and non-equilibrium approaches. Comparisons of the models and the boundary layer formulations highlight the need to revisit key assumptions that influence evaporation behavior, highlighting the need to further understand water and vapor transport processes in soil to improve model accuracy. [ABSTRACT FROM AUTHOR]

Background and aims: Although modelling of water and nutrient uptake by root systems has advanced considerably in recent years, steep local gradients of nutrient concentration near the root-soil interface in the rhizosphere are still a central challenge for accurate simulation of water and nutrient uptake at the root system scale. Conventionally, mesh refinement is used to resolve these gradients. However, it results in excessive computational costs. The object of the study is to present a multiscale approach which resolves the steep gradient of nutrient concentrations at rhizosphere scale and simulates nutrient and water fluxes within the entire root zone at macroscale scale in a computationally efficient way. Methods: We developed a 3D water and nutrient transport model of the root-soil system with explicit consideration of the 3D root architecture. To capture the nutrient gradients at root surfaces, 1D axisymmetric soil models at rhizosphere scale were constructed and coupled to the coarse 3D root-system-scale simulations using a mass conservative approach. The multiscale model was investigated under different scenarios for water and potassium (K+) uptake of a single root, multiple roots, and whole 3D architecture of a Zea mays L. root system in conditions of dynamic soil water and different soil buffer capacity of K+. Results: The steep gradients of K+ concentrations were efficiently resolved in the multiscale simulations thanks to the 1D model at the rhizosphere scale. In comparison with the refinement method, the multiscale model achieved a significant accuracy of K+ uptake prediction with a relative error below 5%. Meanwhile, the simulation at macroscale with coarse mesh could overestimate the K+ uptake in one order of magnitude. Moreover, the computational cost of multiscale simulations was decreased considerably by using coarse soil mesh. Conclusions: The newly developed model can describe the effect of the drying and nutrient transport in the root zone on nutrient uptake. It also allows to simulate processes in larger and complex root systems because of the considerable reduction in computational cost. [ABSTRACT FROM AUTHOR]

Aims: Traits of the plant root system architecture (RSA) play a key role in crop performance. Therefore, architectural root traits are becoming increasingly important in plant phenotyping. In this study, we use a mathematical model to investigate the sensitivity of characteristic root system measures, obtained from different classical field root sampling schemes, to RSA parameters. Methods: Root systems of wheat and maize were simulated and sampled virtually to mimic real field experiments using the root system architecture (RSA) model CRootBox. By means of a sensitivity analysis, we found RSA parameters that significantly influenced the virtual field sampling results. To identify correlations between sensitivities, we carried out a principal component analysis. Results: We found that the parameters of zero order roots are the most sensitive, and parameters of higher order roots are less sensitive. Moreover, different characteristic root system measures showed different sensitivity to RSA parameters. RSA parameters that could be derived independently from different types of field observations were identified. Conclusions: Selection of characteristic root system measures and parameters is essential to reduce the problem of parameter equifinality in inverse modeling with multi-parameter models and is an important step in the characterization of root traits from field observations. [ABSTRACT FROM AUTHOR]

Background and aims: The main difficulty in the use of 3D root architecture models is correct parameterization. We evaluated distributions of the root traits inter-branch distance, branching angle and axial root trajectories from contrasting experimental systems to improve model parameterization.Methods: We analyzed 2D root images of different wheat varieties (Triticum aestivum) from three different sources using automatic root tracking. Model input parameters and common parameter patterns were identified from extracted root system coordinates. Simulation studies were used to (1) link observed axial root trajectories with model input parameters (2) evaluate errors due to the 2D (versus 3D) nature of image sources and (3) investigate the effect of model parameter distributions on root foraging performance.Results: Distributions of inter-branch distances were approximated with lognormal functions. Branching angles showed mean values <90°. Gravitropism and tortuosity parameters were quantified in relation to downwards reorientation and segment angles of root axes. Root system projection in 2D increased the variance of branching angles. Root foraging performance was very sensitive to parameter distribution and variance.Conclusions: 2D image analysis can systematically and efficiently analyze root system architectures and parameterize 3D root architecture models. Effects of root system projection (2D from 3D) and deflection (at rhizotron face) on size and distribution of particular parameters are potentially significant. [ABSTRACT FROM AUTHOR]

Suitable training images (TIs) for multiple-point statistics (MPS) are difficult to identify in real-case three-dimensional applications, posing challenges for modelers trying to develop realistic subsurface models. This study demonstrates that two-dimensional geophysical images, when employed as training and conditioning data, can provide sufficient information for three-dimensional MPS simulations. The advantage of such a data-driven approach is that it does not rely on any external (possibly inappropriate) TI. The disadvantage is that three-dimensional MPS simulations must be carried out based on two-dimensional information. Three different approaches (two existing, one new) are tested to overcome this problem. The two existing approaches rely on three-dimensional reconstruction of incomplete datasets and on sequential two-dimensional simulations, respectively. The third approach is a newly proposed combination of the two former approaches. The three approaches are applied to model the three-dimensional facies structure of an alluvial aquifer based on high-resolution ground-penetrating radar cross-sections. The quality of each simulation outcome is evaluated based on the similarity of its multiple-point histogram (MPH) to reference MPHs derived from geophysical images. This evaluation reveals that the first approach (three-dimensional reconstruction) performs well close to conditioning data, but farther away from the data the simulation results deteriorate. Quite conversely, the second approach (sequential two-dimensional) performs well when only few conditioning data exist, but with increasing simulation sequence the quality decreases. The newly proposed third approach integrates the benefits of both approaches and is found to reproduce the reference MPHs significantly better than either of the two other approaches alone. [ABSTRACT FROM AUTHOR]

The original article can be found online at https://doi.org/10.1007/s11104-019-03993-3 B Correction to: Plant Soil (2019) 438:101-126 b https://doi.org/10.1007/s11104-019-03993-3 The authors of this erratum regret that in the article entitled "Parameter sensitivity analysis of a root system architecture model based on virtual field sampling" (Morandage et al. [1]), which was published in Plant Soil (2019) 438:101-126, corrections to the lateral roots labels are necessary. Except for the number of zero-order roots NB, each parameter is a stochastic parameter with a mean and a standard deviation (values inside the brackets indicate the standard deviations of the parameters). [Extracted from the article]

Background and aims: The use of standard dynamic root architecture models to simulate root growth in soil containing macropores failed to reproduce experimentally observed root growth patterns. We thus developed a new, more mechanistic model approach for the simulation of root growth in structured soil. Methods: In our alternative modelling approach, we distinguish between, firstly, the driving force for root growth, which is determined by the orientation of the previous root segment and the influence of gravitropism and, secondly, soil mechanical resistance to root growth. The latter is expressed by its inverse, soil mechanical conductance, and treated similarly to hydraulic conductivity in Darcy's law. At the presence of macropores, soil mechanical conductance is anisotropic, which leads to a difference between the direction of the driving force and the direction of the root tip movement. Results: The model was tested using data from the literature, at pot scale, at macropore scale, and in a series of simulations where sensitivity to gravity and macropore orientation was evaluated. Conclusions: Qualitative and quantitative comparisons between simulated and experimentally observed root systems showed good agreement, suggesting that the drawn analogy between soil water flow and root growth is a useful one. [ABSTRACT FROM AUTHOR]

Aims: Stomata can close to avoid cavitation under decreased soil water availability. This closure can be triggered by hydraulic ('H') and/or chemical signals ('C', 'H + C'). By combining plant hydraulic relations with a model for stomatal conductance, including chemical signalling, our aim was to derive direct relations that link soil water availability, expressed as fraction of roots in dry soil (f), to transpiration reduction. Methods: We used the mechanistic soil-root water flow model R-SWMS to verify this relation. Virtual split root experiments were simulated, comparing horizontal and vertical splits with varying f and different strengths of stomatal regulation by chemical and hydraulic signals. Results: Transpiration reduction predicted by the direct relations was in good agreement with numerical simulations. For small enough potential transpiration and large enough root hydraulic conductivity and stomatal sensitivity to chemical signalling isohydric plant behaviour originates from H + C control whereas anisohydric behaviour emerges from C control. For C control the relation between transpiration reduction and f becomes independent of transpiration rate whereas H + C control results in stronger reduction for higher transpiration rates. Conclusion: Direct relations that link effective soil water potential and leaf water potential can describe different stomatal control resulting in contrasting behaviour. [ABSTRACT FROM AUTHOR]

Background and aims: Hedgerow cropping decreases erosion in hillside agriculture but also competes for water and nutrients with crops. This study combined two methods for an improved understanding of water and nutrient competition at the crop-soil-hedge interface. Methods: δC isotopic discrimination in plants and soil electrical resistivity tomography (ERT) imaging were used in a field trial with maize monocropping (MM) vs. leucaena hedgerow intercropping with and without fertilizer (MHF and MHF) in Thailand. Results: Hedges significantly reduced maize grain yield and aboveground biomass in rows close to hedgerows. ERT revealed water depletion was stronger in MM than in MHF and MHF confirming time domain reflectometry and leaf area data. In MHF, water depletion was higher in maize rows close to the hedge compared to rows distant to hedges and maize grain δC was significantly less negative in rows close to hedges (-10.33‰) compared to distant ones (-10.64‰). Lack of N increased grain δC in MHF (-9.32‰, p ≤ 0.001). Both methods were correlated with each other (r = 0.66, p ≤ 0.001). Combining ERT with grain δC and %N allowed identifying that maize growth close to hedges was limited by N and not by water supply. Conclusion: Combining ERT imaging and C isotopic discrimination approaches improved the understanding of spatial-temporal patterns of competition at the hedge-soil-crop interface and allowed distinguishing between water and N competition in maize based hedgerow systems. [ABSTRACT FROM AUTHOR]

Aims: A simulation model to demonstrate that soil water potential can regulate transpiration, by influencing leaf water potential and/or inducing root production of chemical signals that are transported to the leaves. Methods: Signalling impacts on the relationship between soil water potential and transpiration were simulated by coupling a 3D model for water flow in soil, into and through roots (Javaux et al. ) with a model for xylem transport of chemicals (produced as a function of local root water potential). Stomatal conductance was regulated by simulated leaf water potential (H) and/or foliar chemical signal concentrations (C; H + C). Split-root experiments were simulated by varying transpiration demands and irrigation placement. Results: While regulation of stomatal conductance by chemical transport was unstable and oscillatory, simulated transpiration over time and root water uptake from the two soil compartments were similar for both H and H + C regulation. Increased stomatal sensitivity more strongly decreased transpiration, and decreased threshold root water potential (below which a chemical signal is produced) delayed transpiration reduction. Conclusions: Although simulations with H + C regulation qualitatively reproduced transpiration of plants exposed to partial rootzone drying (PRD), long-term effects seemed negligible. Moreover, most transpiration responses to PRD could be explained by hydraulic signalling alone. [ABSTRACT FROM AUTHOR]

Aims: Soil salinity can cause salt plant stress by reducing plant transpiration and yield due to very low osmotic potentials in the soil. For predicting this reduction, we present a simulation study to (i) identify a suitable functional form of the transpiration reduction function and (ii) to explain the different shapes of empirically observed reduction functions. Methods: We used high resolution simulations with a model that couples 3D water flow and salt transport in the soil towards individual roots with flow in the root system. Results: The simulations demonstrated that the local total water potential at the soil-root interface, i.e. the sum of the matric and osmotic potentials, is for a given root system, uniquely and piecewise linearly related to the transpiration rate. Using bulk total water potentials, i.e. spatially and temporally averaged potentials in the soil around roots, sigmoid relations were obtained. Unlike for the local potentials, the sigmoid relations were non-unique functions of the total bulk potential but depended on the contribution of the bulk osmotic potential. Conclusions: To a large extent, Transpiration reduction is controlled by water potentials at the soil-root interface. Since spatial gradients in water potentials around roots are different for osmotic and matric potentials, depending on the root density and on soil hydraulic properties, transpiration reduction functions in terms of bulk water potentials cannot be transferred to other conditions, i.e. soil type, salt content, root density, beyond the conditions for which they were derived. Such a transfer could be achieved by downscaling to the soil-root interface using simulations with a high resolution process model. [ABSTRACT FROM AUTHOR]