Root hydraulic conductivity is a limiting factor along the water pathways between the soil and the leaf, and root radial conductivity is itself defined by cell-scale hydraulic properties and anatomical features. However, quantifying the influence of anatomical features on the radial conductivity remains challenging due to complex time-consuming experimental procedures. We present an open-source computational tool, the Generator of Root Anatomy in R (GRANAR; http://granar.github.io), that can be used to rapidly generate digital versions of contrasted monocotyledon root anatomical networks. GRANAR uses a limited set of root anatomical parameters, easily acquired with existing image analysis tools. The generated anatomical network can then be used in combination with hydraulic models to estimate the corresponding hydraulic properties. We used GRANAR to reanalyze large maize (Zea mays) anatomical datasets from the literature. Our model was successful at creating virtual anatomies for each experimental observation. We also used GRANAR to generate anatomies not observed experimentally over wider ranges of anatomical parameters. The generated anatomies were then used to estimate the corresponding radial conductivities with the hydraulic model MECHA (model of explicit cross-section hydraulic architecture). Our simulations highlight the large importance of the width of the stele and the cortex. GRANAR is a computational tool that generates root anatomical networks from experimental data. It enables the quantification of the effect of individual anatomical features on the root radial conductivity.

Functional-structural root system models combine functional and structural root traits to represent the growth and development of root systems. In general, they are characterized by a large number of growth, architectural and functional root parameters, generating contrasted root systems evolving in a highly non-linear environment (soil, atmosphere), which makes the link between local traits and functioning unclear. On the other end of the root system modelling continuum, macroscopic root system models associate to each root system a set of plant-scale, easily interpretable parameters. However, as of today, it is unclear how these macroscopic parameters relate to root-scale traits and whether the upscaling of local root traits is compatible with macroscopic parameter measurements. The aim of this study was to bridge the gap between these two modelling approaches. We describe here the MAize Root System Hydraulic Architecture soLver (MARSHAL), a new efficient and user-friendly computational tool that couples a root architecture model (CRootBox) with fast and accurate algorithms of water flow through hydraulic architectures and plant-scale parameter calculations. To illustrate the tool’s potential, we generated contrasted maize hydraulic architectures that we compared with root system architectural and hydraulic observations. Observed variability of these traits was well captured by model ensemble runs. We also analysed the multivariate sensitivity of mature root system conductance, mean depth of uptake, root system volume and convex hull to the input parameters to highlight the key model parameters to vary for virtual breeding. It is available as an R package, an RMarkdown pipeline and a web application.

Root hydraulic properties play a central role in the global water cycle, in agricultural systems productivity, and in ecosystem survival as they impact the canopy water supply. However, the existing experimental methods to quantify root hydraulic conductivities, such as the root pressure probing, are particularly challenging, and their applicability to thin roots and small root segments is limited. Therefore, there is a gap in methods enabling easy estimations of root hydraulic conductivities in diverse root types. Here, we present a new pipeline to quickly estimate root hydraulic conductivities across different root types, at high resolution along root axes. Shortly, free‐hand root cross‐sections were used to extract a selected number of key anatomical traits. We used these traits to parametrize the Generator of Root Anatomy in R (GRANAR) model to simulate root anatomical networks. Finally, we used these generated anatomical networks within the Model of Explicit Cross‐section Hydraulic Anatomy (MECHA) to compute an estimation of the root axial and radial hydraulic conductivities (k_xandk_r, respectively). Using this combination of anatomical data and computational models, we were able to create a root hydraulic conductivity atlas at the root system level, for 14‐day‐old pot‐grownZea mays(maize) plants of the var.B73. The altas highlights the significant functional variations along and between different root types. For instance, predicted variations of radial conductivity along the root axis were strongly dependent on the maturation stage of hydrophobic barriers. The same was also true for the maturation rates of the metaxylem vessels. Differences in anatomical traits along and across root types generated substantial variations in radial and axial conductivities estimated with our novel approach. Our methodological pipeline combines anatomical data and computational models to turn root cross‐section images into a detailed hydraulic atlas. It is an inexpensive, fast, and easily applicable investigation tool for root hydraulics that complements existing complex experimental methods. It opens the way to high‐throughput studies on the functional importance of root types in plant hydraulics, especially if combined with novel phenotyping techniques such as laser ablation tomography.

Mexican native maize (Zea mays ssp. mays) is adapted to a wide range of climatic and edaphic conditions. Here, we focus specifically on the potential role of root anatomical variation in this adaptation. Given the investment required to characterize root anatomy, we present a machine‐learning approach using environmental descriptors to project trait variation from a relatively small training panel onto a larger panel of genotyped and georeferenced Mexican maize accessions. The resulting models defined potential biologically relevant clines across a complex environment that we used subsequently for genotype–environment association. We found evidence of systematic variation in maize root anatomy across Mexico, notably a prevalence of trait combinations favoring a reduction in axial hydraulic conductance in varieties sourced from cooler, drier highland areas. We discuss our results in the context of previously described water‐banking strategies and present candidate genes that are associated with both root anatomical and environmental variation. Our strategy is a refinement of standard environmental genome‐wide association analysis that is applicable whenever a training set of georeferenced phenotypic data is available.

With global warming, climate zones are projected to shift poleward, and the frequency and intensity of droughts to increase, driving threats to crop production and ecosystems. Plant hydraulic traits play major roles in coping with such droughts, and process-based plant hydraulics (water flowing along decreasing pressure Ψ _p or total water potential Ψ _tot gradients ) has newly been implemented in land surface models. An enigma reported for the past 35 years is the observation of water flowing along increasing water potential gradients across roots. By combining the most advanced modelling tool from the emerging field of plant micro-hydrology with pioneering cell solute mapping data, we found that the current paradigm of water flow across roots of all vascular plants is incomplete: it lacks the impact of solute concentration (and thus negative osmotic potential Ψ _o ) gradients across living cells . This gradient acts as a water pump as it reduces water tension without loading solutes in plant vasculature (xylem). Importantly, water tension adjustments in roots may have large impacts in leaves due to the tension-cavitation feedback along stems. Here, we mathematically demonstrate the water pumping mechanism by solving water flow equations analytically on a triple-cell system. Then we show that the simplistic upscaled equations hold in 2- and 3-D maize, grapevine and Arabidopsis complex hydraulic anatomies, and that water may flow “uphill” of water potential gradients toward xylem as observed experimentally. Besides its contribution to the fundamental understanding of plant water relations, this study lays new foundations for future multidisciplinary research encompassing plant physiology and ecohydrology, and has the ambition to mathematically capture a keystone process for the accurate forecasting of plant water status in crop models and LSMs. Graphical Highlights: - We provide a scale-consistent solution of water flow equations across root tissues - Symplasmic osmotic potential gradients are missing in the current theory of root water uptake - The model solves the empirical enigma of root water uptake uphill of water potential gradients