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.

Root hydraulic properties: An exploration of their variability across scales.

Root hydraulic properties are key physiological traits that determine the capacity of root systems to take up water, at a specific evaporative demand. They can strongly vary among species, cultivars or even within the same genotype, but a systematic analysis of their variation across plant functional types (PFTs) is still missing. Here, we reviewed published empirical studies on root hydraulic properties at the segment-, individual root-, or root system scale and determined its variability and the main factors contributing to it. This corresponded to a total of 241 published studies, comprising 213 species, including woody and herbaceous vegetation. We observed an extremely large range of variation (of orders of magnitude) in root hydraulic properties, but this was not caused by systematic differences among PFTs. Rather, the (combined) effect of factors such as root system age, driving force used for measurement, or stress treatments shaped the results. We found a significant decrease in root hydraulic properties under stress conditions (drought and aquaporin inhibition, p < .001) and a significant effect of the driving force used for measurement (hydrostatic or osmotic gradients, p < .001). Furthermore, whole root system conductance increased significantly with root system age across several crop species (p < .01), causing very large variation in the data (>2 orders of magnitude). Interestingly, this relationship showed an asymptotic shape, with a steep increase during the first days of growth and a flattening out at later stages of development. We confirmed this dynamic through simulations using a state-of-the-art computational model of water flow in the root system for a variety of crop species, suggesting common patterns across studies and species. These findings provide better understanding of the main causes of root hydraulic properties variations observed across empirical studies. They also open the door to better representation of hydraulic processes across multiple plant functional types and at large scales. All data collected in our analysis has been aggregated into an open access database (https://roothydraulic-properties.shinyapps.io/database/), fostering scientific exchange.

Uncovering natural variation in root system architecture and growth dynamics using a robotics-assisted phenomics platform.

The plant kingdom contains a stunning array of complex morphologies easily observed above-ground, but more challenging to visualize below-ground. Understanding the magnitude of diversity in root distribution within the soil, termed root system architecture (RSA), is fundamental in determining how this trait contributes to species adaptation in local environments. Roots are the interface between the soil environment and the shoot system and therefore play a key role in anchorage, resource uptake, and stress resilience. Previously, we presented the GLO-Roots (Growth and Luminescence Observatory for Roots) system to study the RSA of soil-grown Arabidopsis thaliana plants from germination to maturity (Rellán-Álvarez et al., 2015). In this study, we present the automation of GLO-Roots using robotics and the development of image analysis pipelines in order to examine the temporal dynamic regulation of RSA and the broader natural variation of RSA in Arabidopsis, over time. These datasets describe the developmental dynamics of two independent panels of accessions and reveal highly complex and polygenic RSA traits that show significant correlation with climate variables of the accessions' respective origins.

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.

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.

Combining cross-section images and modeling tools to create high-resolution root system hydraulic atlases in Zea mays.

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 x and k 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-grown Zea 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.

QuoVidi: An open-source web application for the organization of large-scale biological treasure hunts.

Learning biology, and in particular systematics, requires learning a substantial amount of specific vocabulary, both for botanical and zoological studies. While crucial, the precise identification of structures serving as evolutionary traits and systematic criteria is not per se a highly motivating task for students. Teaching this in a traditional teaching setting is quite challenging especially with a large crowd of students to be kept engaged. This is even more difficult if, as during the COVID-19 crisis, students are not allowed to access laboratories for hands-on observation on fresh specimens and sometimes restricted to short-range movements outside their home. Here, we present QuoVidi, a new open-source web platform for the organization of large-scale treasure hunts. The platform works as follows: students, organized in teams, receive a list of quests that contain morphologic, ecologic, or systematic terms. They have to first understand the meaning of the quests, then go and find them in the environment. Once they find the organism corresponding to a quest, they upload a geotagged picture of their finding and submit this on the platform. The correctness of each submission is evaluated by the staff. During the COVID-19 lockdown, previously validated pictures were also submitted for evaluation to students that were locked in low-biodiversity areas. From a research perspective, the system enables the creation of large image databases by the students, similar to citizen science projects. Beside the enhanced motivation of students to learn the vocabulary and perform observations on self-found specimens, this system allows instructors to remotely follow and assess the work performed by large numbers of students. The interface is freely available, open-source and customizable. Unlike existing naturalist platforms, allows the educators to fully customize the quests of interest. This enables the creation of multiple teaching scenarios, without being bound to a fixed scope. QuoVidi can be used in other disciplines with adapted quests and we expect it to be of interest in many classroom settings.

Genetic Variability of Arabidopsis thaliana Mature Root System Architecture and Genome-Wide Association Study.

Root system architecture (RSA) has a direct influence on the efficiency of nutrient uptake and plant growth, but the genetics of RSA are often studied only at the seedling stage. To get an insight into the genetic blueprint of a more mature RSA, we exploited natural variation and performed a detailed in vitro study of 241 Arabidopsis thaliana accessions using large petri dishes. A comprehensive analysis of 17 RSA traits showed high variability among the different accessions, unveiling correlations between traits and conditions of the natural habitat of the plants. A sub-selection of these accessions was grown in water-limiting conditions in a rhizotron set-up, which revealed that especially the spatial distribution showed a high consistency between in vitro and ex vitro conditions, while in particular, a large root area in the lower zone favored drought tolerance. The collected RSA phenotype data were used to perform genome-wide association studies (GWAS), which stands out from the previous studies by its exhaustive measurements of RSA traits on more mature Arabidopsis accessions used for GWAS. As a result, we found not only several genes involved in the lateral root (LR) development or auxin signaling pathways to be associated with RSA traits but also new candidate genes that are potentially involved in the adaptation to the natural habitats.

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.

GRANAR, a Computational Tool to Better Understand the Functional Importance of Monocotyledon Root Anatomy.

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.

Lateral Roots: Random Diversity in Adversity.

Lateral roots are essential for soil foraging and uptake of minerals and water. They feature a large morphological diversity that results from divergent primordia or root growth and development patterns. Besides a structured diversity, resulting from the hierarchical and developmental organization of root systems, there exists a random diversity, occurring between roots of similar age, of the same hierarchical order, and exposed to uniform conditions. The physiological bases and functional consequences of this random diversity are largely ignored. Here we review the evidence for such random diversity throughout the plant kingdom, present innovative approaches based on statistical modeling to account for such diversity, and set the list of its potential benefits in front of a variable and unpredictable soil environment.

Demystifying roots: A need for clarification and extended concepts in root phenotyping.

Plant roots have major roles in plant anchorage, resource acquisition and offer environmental benefits including carbon sequestration and soil erosion mitigation. As such, the study of root system architecture, anatomy and functional properties is of crucial interest to plant breeding, with the aim of sustainable yield production and environmental stewardship. Due to the importance of the root system studies, there is a need for clarification of terms and concepts in the root phenotyping community. In particular in this contribution, we advocate for the use of a reference naming system (ontologies) for roots and root phenes. Such uniformity would not only allow better understanding of research results, but would also enable a better sharing of data. In addition, we highlight the need to incorporate the concept of plasticity in breeding programs, as it is an essential component of root system development in heterogeneous environments.

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.

Going with the Flow: Multiscale Insights into the Composite Nature of Water Transport in Roots.

As water often limits crop production, a more complete understanding of plant water capture and transport is necessary. Here, we developed MECHA, a mathematical model that computes the flow of water across the root at the scale of walls, membranes, and plasmodesmata of individual cells, and used it to test hypotheses related to root water transport in maize (Zea mays). The model uses detailed root anatomical descriptions and a minimal set of experimental cell properties, including the conductivity of plasma membranes, cell walls, and plasmodesmata, which yield quantitative and scale-consistent estimations of water pathways and root radial hydraulic conductivity (k r). MECHA revealed that the mainstream hydraulic theories derived independently at the cell and root segment scales are compatible only if osmotic potentials within the apoplastic domains are uniform. The results suggested that the convection-diffusion of apoplastic solutes explained most of the offset between estimated k r in pressure clamp and osmotic experiments, while the contribution of water-filled intercellular spaces was limited. Furthermore, sensitivity analyses quantified the relative impact of cortex and endodermis cell conductivity of plasma membranes on root k r and suggested that only the latter contributed substantially to k r due to the composite nature of water flow across roots. The explicit root hydraulic anatomy framework brings insights into contradictory interpretations of experiments from the literature and suggests experiments to efficiently address questions pertaining to root water relations. Its scale consistency opens avenues for cross-scale communication in the world of root hydraulics.

EZ-Root-VIS: A Software Pipeline for the Rapid Analysis and Visual Reconstruction of Root System Architecture.

If we want to understand how the environment has shaped the appearance and behavior of living creatures, we need to compare groups of individuals that differ in genetic makeup and environment experience. For complex phenotypic features, such as body posture or facial expression in humans, comparison is not straightforward because some of the contributing factors cannot easily be quantified or averaged across individuals. Therefore, computational methods are used to reconstruct representative prototypes using a range of algorithms for filling in missing information and calculating means. The same problem applies to the root system architecture (RSA) of plants. Several computer programs are available for extracting numerical data from root images, but they usually do not offer customized data analysis or visual reconstruction of RSA. We developed Root-VIS, a free software tool that facilitates the determination of means and variance of many different RSA features across user-selected sets of root images. Furthermore, Root-VIS offers several options to generate visual reconstructions of root systems from the averaged data to enable screening and modeling. We confirmed the suitability of Root-VIS, combined with a new version of EZ-Rhizo, for the rapid characterization of genotype-environment interactions and gene discovery through genome-wide association studies in Arabidopsis (Arabidopsis thaliana).

Impact of crop residue management on crop production and soil chemistry after seven years of crop rotation in temperate climate, loamy soils.

Society is increasingly demanding a more sustainable management of agro-ecosystems in a context of climate change and an ever growing global population. The fate of crop residues is one of the important management aspects under debate, since it represents an unneglectable quantity of organic matter which can be kept in or removed from the agro-ecosystem. The topic of residue management is not new, but the need for global conclusion on the impact of crop residue management on the agro-ecosystem linked to local pedo-climatic conditions has become apparent with an increasing amount of studies showing a diversity of conclusions. This study specifically focusses on temperate climate and loamy soil using a seven-year data set. Between 2008 and 2016, we compared four contrasting residue management strategies differing in the amount of crop residues returned to the soil (incorporation vs. exportation of residues) and in the type of tillage (reduced tillage (10 cm depth) vs. conventional tillage (ploughing at 25 cm depth)) in a field experiment. We assessed the impact of the crop residue management on crop production (three crops-winter wheat, faba bean and maize-cultivated over six cropping seasons), soil organic carbon content, nitrate ([Formula: see text]), phosphorus (P) and potassium (K) soil content and uptake by the crops. The main differences came primarily from the tillage practice and less from the restitution or removal of residues. All years and crops combined, conventional tillage resulted in a yield advantage of 3.4% as compared to reduced tillage, which can be partly explained by a lower germination rate observed under reduced tillage, especially during drier years. On average, only small differences were observed for total organic carbon (TOC) content of the soil, but reduced tillage resulted in a very clear stratification of TOC and also of P and K content as compared to conventional tillage. We observed no effect of residue management on the [Formula: see text] content, since the effect of fertilization dominated the effect of residue management. To confirm the results and enhance early tendencies, we believe that the experiment should be followed up in the future to observe whether more consistent changes in the whole agro-ecosystem functioning are present on the long term when managing residues with contrasted strategies.

A New Phenotyping Pipeline Reveals Three Types of Lateral Roots and a Random Branching Pattern in Two Cereals.

Recent progress in root phenotyping has focused mainly on increasing throughput for genetic studies, while identifying root developmental patterns has been comparatively underexplored. We introduce a new phenotyping pipeline for producing high-quality spatiotemporal root system development data and identifying developmental patterns within these data. The SmartRoot image-analysis system and temporal and spatial statistical models were applied to two cereals, pearl millet (Pennisetum glaucum) and maize (Zea mays). Semi-Markov switching linear models were used to cluster lateral roots based on their growth rate profiles. These models revealed three types of lateral roots with similar characteristics in both species. The first type corresponds to fast and accelerating roots, the second to rapidly arrested roots, and the third to an intermediate type where roots cease elongation after a few days. These types of lateral roots were retrieved in different proportions in a maize mutant affected in auxin signaling, while the first most vigorous type was absent in maize plants exposed to severe shading. Moreover, the classification of growth rate profiles was mirrored by a ranking of anatomical traits in pearl millet. Potential dependencies in the succession of lateral root types along the primary root were then analyzed using variable-order Markov chains. The lateral root type was not influenced by the shootward neighbor root type or by the distance from this root. This random branching pattern of primary roots was remarkably conserved, despite the high variability of root systems in both species. Our phenotyping pipeline opens the door to exploring the genetic variability of lateral root developmental patterns.

archiDART v3.0: A new data analysis pipeline allowing the topological analysis of plant root systems.

Quantifying plant morphology is a very challenging task that requires methods able to capture the geometry and topology of plant organs at various spatial scales. Recently, the use of persistent homology as a mathematical framework to quantify plant morphology has been successfully demonstrated for leaves, shoots, and root systems. In this paper, we present a new data analysis pipeline implemented in the R package archiDART to analyse root system architectures using persistent homology. In addition, we also show that both geometric and topological descriptors are necessary to accurately compare root systems and assess their natural complexity.

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.