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The effect of root hairs on water uptake remains controversial. In particular, the key root hair and soil parameters that determine their importance have been elusive. We grew maize plants (Zea mays) in microcosms and scanned them using synchrotron-based X-ray computed microtomography. By means of image-based modelling, we investigated the parameters determining the effectiveness of root hairs in root water uptake. We explicitly accounted for rhizosphere features (e.g. root-soil contact and pore structure) and took root hair shrinkage of dehydrated root hairs into consideration. Our model suggests that > 85% of the variance in root water uptake is explained by the hair-induced increase in root-soil contact. In dry soil conditions, root hair shrinkage reduces the impact of hairs substantially. We conclude that the effectiveness of root hairs on root water uptake is determined by the hair-induced increase in root-soil contact and root hair shrinkage. Although the latter clearly reduces the effect of hairs on water uptake, our model still indicated facilitation of water uptake by root hairs at soil matric potentials from -1 to -0.1 MPa. Our findings provide new avenues towards a mechanistic understanding of the role of root hairs on water uptake., (© 2023 The Authors New Phytologist © 2023 New Phytologist Foundation.)

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., (© 2022. Springer Science+Business Media, LLC, part of Springer Nature.)

In this chapter, we discuss the issue of balance between spatial resolution and computational efficiency in the context of the R-SWMS model. Based on the equations governing the water fluxes within the model, we propose here an objective and quantitative criterion which can help fix root segment size to both minimize computational load and achieve simulation according to a given accuracy degree., (© 2022. Springer Science+Business Media, LLC, part of Springer Nature.)

For the first time, a functional-structural root-system model is validated by combining a tracer experiment monitored with magnetic resonance imaging and three-dimensional modeling of water and solute transport., (© The Author(s) 2019. Published by Oxford University Press on behalf of the Society for Experimental Biology.)

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., (© The Author(s) 2018. Published by Oxford University Press on behalf of the Society for Experimental Biology. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

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.

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. [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]

In 1978, Landsberg and Fowkes presented a solution of the water flow equation inside a root with uniform hydraulic properties. These properties are root radial conductivity and axial conductance, which control, respectively, the radial water flow between the root surface and xylem and the axial flow within the xylem. From the solution for the xylem water potential, functions that describe the radial and axial flow along the root axis were derived. These solutions can also be used to derive root macroscopic parameters that are potential input parameters of hydrological and crop models. In this paper, novel analytical solutions of the water flow equation are developed for roots whose hydraulic properties vary along their axis, which is the case for most plants. We derived solutions for single roots with linear or exponential variations of hydraulic properties with distance to root tip. These solutions were subsequently combined to construct single roots with complex hydraulic property profiles. The analytical solutions allow one to verify numerical solutions and to get a generalization of the hydric behaviour with the main influencing parameters of the solutions. The resulting flow distributions in heterogeneous roots differed from those in uniform roots and simulations led to more regular, less abrupt variations of xylem suction or radial flux along root axes. The model could successfully be applied to maize effective root conductance measurements to derive radial and axial hydraulic properties. We also show that very contrasted root water uptake patterns arise when using either uniform or heterogeneous root hydraulic properties in a soil-root model. The optimal root radius that maximizes water uptake under a carbon cost constraint was also studied. The optimal radius was shown to be highly dependent on the root hydraulic properties and close to observed properties in maize roots. We finally used the obtained functions for evaluating the impact of root maturation versus root growth on water uptake. Very diverse uptake strategies arise from the analysis. These solutions open new avenues to investigate for optimal genotype-environment-management interactions by optimization, for example, of plant-scale macroscopic hydraulic parameters used in ecohydrogolocial models. [ABSTRACT FROM AUTHOR]

This study tested a method to quantify and locate hydraulic lift (HL, defined as the passive upward water flow from wetter to dryer soil zones through the plant root system) by combining an experiment using the stable water isotope ¹H218Oas a tracer with a soil-plant water flow model. Our methodology consisted in (i) establishing the initial conditions for HL in a large rhizobox planted with Italian ryegrass (Lolium multiflorum Lam.), (ii) labeling water in the deepest soil layer with an 18O-enriched solution, (iii) monitoring the water O isotopic composition in soil layers to find out changes in the upper layers that would reflect redistribution of O-enriched water from the bottom layers by the roots, and (iv) comparing the observed soil water O isotopic composition to simulation results of a three-dimensional model of water flow and isotope transport in the soil-root system. Our main findings were that (i) the depth and strength of the observed changes in soil water O isotopic composition could be well reproduced with a modeling approach (RMSE = 0.2%o, i.e., equivalent to the precision of the isotopic measurements), (ii) the corresponding water volume involved in HL was estimated to account for 19% of the plant transpiration of the following day, i.e., 0.45 mm of water, and was in agreement with the observed soil water content changes, and (iii) the magnitude of the simulated HL was sensitive to both plant and soil hydraulic properties. [ABSTRACT FROM AUTHOR]

The coupling of soil and root water fluxes at the plant scale is a particularly challenging task. Numerical three-dimensional plant-scale models exist that consider these soil--root interactions. The influence of the hydraulic conductivity drop at the microscopic scale and especially the effect on root water uptake is not yet assessed in such models. In this study, an analytical approach describing the hydraulic conductivity drop from the bulk soil to the soil--root interface for a three-dimensional plant-scale model was derived and validated by numerical means. With these tools, quantification of the local hydraulic conductivity drop with time was possible. Furthermore, the effect of the hydraulic conductivity drop on the time occurrence of plant stress was evaluated. Root water uptake was assessed, with and without considering the hydraulic conductivity drop around single roots in a three-dimensional plant-scale model in terms of total water uptake at the root collar under different soil and root properties. It was shown that the total root water uptake was strongly affected, especially under conditions where the radial root hydraulic conductivity, which regulates root water uptake, was larger than the soil hydraulic conductivity, which regulates water flow in the soil. These findings were backed up by numerical validation of the model using mesh refinement. Incorporation of the hydraulic conductivity drop around individual roots in a three-dimensional plant-scale model can solve problems with greater accuracy for larger grid resolutions, and with smaller computational times, than not considering the hydraulic conductivity drop. [ABSTRACT FROM AUTHOR]