Evolving technologies for growing, imaging and analyzing 3D root system architecture of crop plants.

The perfect storm. That is the term used by Gerald C. Nelson to describe the triple challenge of increasing food production while adapting to climate change and reducing the environmental impact of agricultural systems. Nowadays, conventional farming systems are showing some limitations, such as low resources use efficiency and poor ecosystems services that appear to be associated to the loss of plant diversity and perenniality in crop rotations. In addition, water, the most important yield limiting factor worldwide, will increasingly restrict food production in the future due to rainfall shortage and increase in human consumption. In such context, perennial crops, with denser and deeper root system could use resources in deep soil layers that are logically inaccessible to crops with shallower root system. The goal of this thesis was therefore to investigate the root growth and water uptake capacity of intermediate wheatgrass (Kernza®) and alfalfa, two deep rooted perennial crops, under field conditions and at great soil depth (i.e. 1.0-2.5 m).
\n\tMaintaining hydraulic continuity along the soil-plant-atmosphere continuum is a prerequisite for deep water uptake. At the plant level, hydraulic conductivity depends on complex anatomical and physiological processes among which the root system constitutes the second largest resistance to water flow. Therefore, in depth characterisation of root and xylem anatomy was done to understand the hydraulic properties of the crop root systems, with a focus on their evolution with soil depth. Crops were grown in the field, rhizoboxes, mesocosms and solution culture to take into account the variability of root type and soil depth as well as growing environment. For both crops, axial hydraulic conductance decreased with soil depth and along individual root segment. Alfalfa roots had greater axial hydraulic conductance in comparison to intermediate wheatgrass roots, especially at depth. Root and xylem anatomy were highly variable across crop species, root types and growing environments. In parallel, a combination of imaging and sensor technology, stable isotope techniques and a modelling approach was used to study root growth and water uptake under field conditions during the 2018-2019 seasons. Both crops presented roots down to 2.0 m soil depth that were active in terms of water uptake. Alfalfa had greater root length at depth and absorbed twice as much water below 1 m soil depth, than intermediate wheatgrass. For both crops, model simulations predicted that water uptake in deep soil layers (i.e. 1.5 – 2.0 m) increase (i.e.>30%) under dry conditions.
\n\tThis thesis brings insights into the understudied field of root growth and water uptake at great soil depth. Particular efforts were put in understanding the environmental and agricultural contexts in which deep root growth, deep water uptake and the development of perennial cropping systems would be possible and favourable.

Root growth and development are of outstanding importance for the plant's ability to acquire water and nutrients from different soil horizons. To cope with fluctuating nutrient availabilities, plants integrate systemic signals pertaining to their nutritional status into developmental pathways that regulate the spatial arrangement of roots. Changes in the plant nutritional status and external nutrient supply modulate root system architecture (RSA) over time and determine the degree of root plasticity which is based on variations in the number, extension, placement, and growth direction of individual components of the root system. Roots also sense the local availability of some nutrients, thereby leading to nutrient-specific modifications in RSA, that result from the integration of systemic and local signals into the root developmental programme at specific steps. An in silico analysis of nutrient-responsive genes involved in root development showed that the majority of these specifically responded to the deficiency of individual nutrients while a minority responded to more than one nutrient deficiency. Such an analysis provides an interesting starting point for the identification of the molecular players underlying the sensing and transduction of the nutrient signals that mediate changes in the development and architecture of root systems.

Plant root system architecture (RSA) has an important role in crop production, particularly for water and nutrient uptake under limiting conditions. In the last few years, several root phenotyping methods have been developed. Here we present a new technique which has been developed for non-destructive, inexpensive and high-throughput root growth studies and RSA analyses. To illustrate the potential applications, this method was tested in an experiment with nitrogen and phosphorous deficiencies in a nutrient solution, affecting RSA parameters of two spring barley varieties (Bojos and Barke). This technique is based on root growth on vertically positioned black filter paper (30 × 60 cm) placed between two black plastic (PVC-P) foils and micro-irrigation systems providing the recirculation of nutrient solution. The pre-germinated seeds were placed in the slit between two plastic bars which carry the filter paper and plastic sheets and fix the plant in the vertical position. This system allows easy repeated non-invasive access to roots for their measuring and sampling. Eighteen days after transplanting the root imaging was done using an RGB digital camera. To evaluate the root architecture parameters the "SmartRoot" software was used. The results revealed that the system is able to detect changes in RSA which are caused mainly by P deficiency (particularly changes in lateral root length and total root area). It can be concluded that this technique has a great potential for non-destructive root growth studies, RSA measurement and root sampling.

In numerous studies dealing with roots of woody plants, a description of the root system architecture is needed. During the twentieth century, several manual measurement methods were used, depending on the objectives of study. Due to the difficulties in accessing the roots and the duration of measurements, the studies generally involved a low number of root systems, were often qualitative and focused only on one specific application. Quantitative methods in plant architecture were largely developed in the last 40 years for aerial architecture. However, root systems have particular features and often need specific procedures. Since the end of the 1990s, new devices and techniques have been available for coarse root architecture measurements including volume location techniques (non-invasive or destructive) and manual or semi-automatic 3D digitising. Full 3D root system architecture dynamics was also reconstructed from partial measurements using modelling procedures. On the one hand, non-invasive and automatic techniques need more development to obtain full 3D architecture, i.e. geometry and topology. On the other hand, both one inexpensive manual and one semi-automatic digitizing procedure are now available to measure precisely and rapidly the full 3D architecture of uprooted and excavated coarse root systems. Specific software and a large number of functions are also available for an in-depth analysis of root architecture and have already been used in a dozen of research papers including a fairly large sample of mature trees. A comprehensive analysis of root architecture can be achieved by classifying individual roots in several root types through architectural analysis. The objective of this paper is both to give a detailed overview of the state of the art techniques for 3D root system architecture measurement and analysis and to give examples of applications in this field. Practical details are also given so that this paper can be used as a sort of manual for people who want to improve their practice or to enter this quite new research field.

Root system architecture plays an important role in plant adaptation to drought stress. The root system architecture (RSA) consists of several structural features, which includes number and length of main and lateral roots along with the density and length of root hairs. These features exhibit plasticity under water-limited environments and could be critical to developing crops with efficient root systems for adaptation under drought. Recent advances in the omics approaches have significantly improved our understanding of the regulatory mechanisms of RSA remodeling under drought and the identification of genes and other regulatory elements. Plant response to drought stress at physiological, morphological, biochemical, and molecular levels in root cells is regulated by various phytohormones and their crosstalk. Stress-induced reactive oxygen species play a significant role in regulating root growth and development under drought stress. Several transcription factors responsible for the regulation of RSA under drought have proven to be beneficial for developing drought tolerant crops. Molecular breeding programs for developing drought-tolerant crops have been greatly benefitted by the availability of quantitative trait loci (QTLs) associated with the RSA regulation. In the present review, we have discussed the role of various QTLs, signaling components, transcription factors, microRNAs and crosstalk among various phytohormones in shaping RSA and present future research directions to better understand various factors involved in RSA remodeling for adaptation to drought stress. We believe that the information provided herein may be helpful in devising strategies to develop crops with better RSA for efficient uptake and utilization of water and nutrients under drought conditions.

Understanding the development of roots exposed to contaminants and the potential of plant-associated bacteria for optimization of growth.

The root system is critical for the survival of nearly all land plants and a key target for improving abiotic stress tolerance, nutrient accumulation, and yield in crop species. Although many methods of root phenotyping exist, within field studies, one of the most popular methods is the extraction and measurement of the upper portion of the root system, known as the root crown, followed by trait quantification based on manual measurements or 2D imaging. However, 2D techniques are inherently limited by the information available from single points of view. Here, we used X-ray computed tomography to generate highly accurate 3D models of maize root crowns and created computational pipelines capable of measuring 71 features from each sample. This approach improves estimates of the genetic contribution to root system architecture and is refined enough to detect various changes in global root system architecture over developmental time as well as more subtle changes in root distributions as a result of environmental differences. We demonstrate that root pulling force, a high-throughput method of root extraction that provides an estimate of root mass, is associated with multiple 3D traits from our pipeline. Our combined methodology can therefore be used to calibrate and interpret root pulling force measurements across a range of experimental contexts or scaled up as a stand-alone approach in large genetic studies of root system architecture.

Water uptake by roots is a key adaptation of plants to aerial life. Water uptake depends on root system architecture (RSA) and tissue hydraulic properties that, together, shape the root hydraulic architecture. This work investigates how the interplay between conductivities along radial (e.g. aquaporins) and axial (e.g. xylem vessels) pathways determines the water transport properties of highly branched RSAs as found in adult Arabidopsis (Arabidopsis thaliana) plants. A hydraulic model named HydroRoot was developed, based on multi-scale tree graph representations of RSAs. Root water flow was measured by the pressure chamber technique after successive cuts of a same root system from the tip toward the base. HydroRoot model inversion in corresponding RSAs allowed us to concomitantly determine radial and axial conductivities, providing evidence that the latter is often overestimated by classical evaluation based on the Hagen–Poiseuille law. Organizing principles of Arabidopsis primary and lateral root growth and branching were determined and used to apply the HydroRoot model to an extended set of simulated RSAs. Sensitivity analyses revealed that water transport can be co-limited by radial and axial conductances throughout the whole RSA. The number of roots that can be sectioned (intercepted) at a given distance from the base was defined as an accessible and informative indicator of RSA. The overall set of experimental and theoretical procedures was applied to plants mutated in ESKIMO1 and previously shown to have xylem collapse. This approach will be instrumental to dissect the root water transport phenotype of plants with intricate alterations in root growth or transport functions.

The root system architecture (RSA) determines plant growth and yield. The characterization of optimal RSA and discovery of genetic loci or candidate genes that control root traits are therefore important research goals. However, the hidden nature of the root system makes it difficult to perform nondestructive, rapid analyses of RSA. In this study, we developed an automated, nondestructive, high-throughput root phenotyping platform (Root-HTP) and a corresponding data processing pipeline for efficient, large-scale characterization of wheat (Triticum aestivum L.) RSA. This system is capable of tracking root growth dynamics and RSA variation across all wheat developmental stages. In situ phenotyping using Root-HTP extracted 47 RSA traits, including 33 novel traits in wheat and 23 novel traits in other crops. We used root trait data from the phenotyping system and yield trait data to conduct a genome-wide association study (GWAS) of 155 wheat accessions, which identified 2,650 SNPs and 233 quantitative trait loci (QTLs) associated with aspects of root architecture. The candidate gene TaMYB93 was detected in a QTL for root tortuosity, and EMS mutants confirmed its effect on RSA in wheat. We explored the relationship between root- and yield-related traits and identified 20 root-related QTLs that were also associated with yield traits. Furthermore, we have built a predictive model for wheat yield based on 18 RSA traits and propose a parsimonious RSA ideotype associated with high yields. The data generated from this study provide insight into the genetic architecture of wheat RSA and support for RSA ideotype-based wheat breeding and yield prediction.

Responses of soybean (Glycine max [L.] Merr.) to zinc oxide nanoparticles: Understanding changes in root system architecture, zinc tissue partitioning and soil characteristics

State-of-the-Art models of Root System Architecture (RSA) do not allow simulating root growth around rigid obstacles. Yet, the presence of obstacles can be highly disruptive to the root system. We grew wheat seedlings in sealed petri dishes without obstacle and in custom 3D-printed rhizoboxes containing obstacles. Time-lapse photography was used to reconstruct the wheat root morphology network. We used the reconstructed wheat root network without obstacle to calibrate an RSA model implemented in the R-SWMS software. The root network with obstacles allowed calibrating the parameters of a new function that models the influence of rigid obstacles on wheat root growth. Experimental results show that the presence of a rigid obstacle does not affect the growth rate of the wheat root axes, but that it does influence the root trajectory after the main axis has passed the obstacle. The growth recovery time, i.e. the time for the main root axis to recover its geotropism-driven growth, is proportional to the time during which the main axis grows along the obstacle. Qualitative and quantitative comparisons between experimental and numerical results show that the proposed model successfully simulates wheat RSA growth around obstacles. Our results suggest that wheat roots follow patterns that could inspire the design of adaptive engineering flow networks.

Root system architecture (RSA) is an essential characteristic for efficient water and nutrient absorption in terrestrial plants; its plasticity enables plants to respond to different soil environments. Better understanding of root plasticity is important in developing stress-tolerant crops. Non-invasive techniques that can measure roots in soils nondestructively, such as X-ray computed tomography (CT), are useful to evaluate RSA plasticity. However, although RSA plasticity can be measured by tracking individual root growth, only a few methods are available for tracking individual roots from time-series three-dimensional (3D) images. We developed a semi-automatic workflow that tracks individual root growth by vectorizing RSA from time-series 3D images via two major steps. The first step involves 3D alignment of the time-series RSA images by iterative closest point registration with point clouds generated by high-intensity particles in potted soils. This alignment ensures that the time-series RSA images overlap. The second step consists of backward prediction of vectorization, which is based on the phenomenon that the root length of the RSA vector at the earlier time point is shorter than that at the last time point. In other words, when CT scanning is performed at time point A and again at time point B for the same pot, the CT data and RSA vectors at time points A and B will almost overlap, but not where the roots have grown. We assumed that given a manually created RSA vector at the last time point of the time series, all RSA vectors except those at the last time point could be automatically predicted by referring to the corresponding RSA images. Using 21 time-series CT volumes of a potted plant of upland rice (Oryza sativa), this workflow revealed that the root elongation speed increased with age. Compared with a workflow that does not use backward prediction, the workflow with backward prediction reduced the manual labor time by 95%. We developed a workflow to efficiently generate time-series RSA vectors from time-series X-ray CT volumes. We named this workflow 'RSAtrace4D' and are confident that it can be applied to the time-series analysis of RSA development and plasticity.

Potato plants are often exposed to soil drought, which results in crop losses, because potato root system is shallow. The aim of our study was to examine the relationship between root system architecture and drought tolerance. We measured rooting depth, total length, surface area, average diameter, and dry weight of the root system every 20 cm of soil depth. We examined four potato cultivars, two that were more tolerant to drought (Gwiazda and Tajfun), and two that were more sensitive (Oberon and Cekin). Our results showed different responses by the tested cultivars to drought. The root dry mass decreased in response to drought and this decrease was smaller in the more drought-tolerant cultivars. In response to drought, the more drought-tolerant cultivars developed elongated roots, but the roots of the less-tolerant cultivars remained the same length. The decrease in root diameter was also smaller in the more drought-tolerant cultivars.

Plant growth and development involve the integration of numerous processes, influenced by both endogenous and exogenous factors. At any given time during a plant’s life cycle, the plant architecture is a readout of this continuous integration. However, untangling the individual factors and processes involved in the plant development and quantifying their influence on the plant developmental process is experimentally challenging. Here we used a combination of computational plant models (CPlantBox and PiafMunch) to help understand experimental findings about how local phloem anatomical features influence the root system architecture. Our hypothesis was that strong local phloem resistance would restrict local carbon flow and locally modify root growth patterns. To test this hypothesis, we simulated the mutual interplay between the root system architecture development and the carbohydrate distribution to provide a plausible mechanistic explanation for several experimental results. Our in silico experiments highlighted the strong influence of local phloem hydraulics on the root growth rates, growth duration and final length. The model result showed that a higher phloem resistivity leads to shorter roots due to the reduced flow of carbon within the root system. This effect was due to local properties of individual roots, and not linked to any of the pleiotropic effects at the root system level. Our results open a door to a better representation of growth processes in a plant computational model.

Chapter 3 - Molecular advances in plant root system architecture response and redesigning for improved performance under unfavorable environments