Computed tomography scanning can monitor the effects of soil medium on root system development: an example of salt stress in corn

Crop root system plasticity for improved yields in saline soils

Crop yields must increase to meet the demands of a growing world population. Soil salinization is increasing due to the impacts of climate change, reducing the area of arable land for crop production. Plant root systems are plastic, and their architecture can be modulated to (1) acquire nutrients and water for growth, and (2) respond to hostile soil environments. Saline soils inhibit primary root growth and alter root system architecture (RSA) of crop plants. In this review, we explore how crop root systems respond and adapt to salinity, focusing predominately on the staple cereal crops wheat, maize, rice, and barley, that all play a major role in global food security. Cereal crops are classified as glycophytes (salt-sensitive) however salt-tolerance can differ both between species and within a species. In the past, due to the inherent difficulties associated with visualising and measuring root traits, crop breeding strategies have tended to focus on optimising shoot traits. High-resolution phenotyping techniques now make it possible to visualise and measure root traits in soil systems. A steep, deep and cheap root ideotype has been proposed for water and nitrogen capture. Changes in RSA can be an adaptive strategy to avoid saline soils whilst optimising nutrient and water acquisition. In this review we propose a new model for designing crops with a salt-tolerant root ideotype. The proposed root ideotype would exhibit root plasticity to adapt to saline soils, root anatomical changes to conserve energy and restrict sodium (Na⁺) uptake, and transport mechanisms to reduce the amount of Na⁺ transported to leaves. In the future, combining high-resolution root phenotyping with advances in crop genetics will allow us to uncover root traits in complex crop species such as wheat, that can be incorporated into crop breeding programs for yield stability in saline soils.

Proximal and remote sensing in plant phenomics: 20 years of progress, challenges, and perspectives

Plant phenomics (PP) has been recognized as a bottleneck in studying the interactions of genomics and environment on plants, limiting the progress of smart breeding and precise cultivation. High-throughput plant phenotyping is challenging owing to the spatio-temporal dynamics of traits. Proximal and remote sensing (PRS) techniques are increasingly used for plant phenotyping because of their advantages in multi-dimensional data acquisition and analysis. Substantial progress of PRS applications in PP has been observed over the last two decades and is analyzed here from an interdisciplinary perspective based on 2972 publications. This progress covers most aspects of PRS application in PP, including patterns of global spatial distribution and temporal dynamics, specific PRS technologies, phenotypic research fields, working environments, species, and traits. Subsequently, we demonstrate how to link PRS to multi-omics studies, including how to achieve multi-dimensional PRS data acquisition and processing, how to systematically integrate all kinds of phenotypic information and derive phenotypic knowledge with biological significance, and how to link PP to multi-omics association analysis. Finally, we identify three future perspectives for PRS-based PP: (1) strengthening the spatial and temporal consistency of PRS data, (2) exploring novel phenotypic traits, and (3) facilitating multi-omics communication.

Learning how a tree branches out: A statistical modeling approach

The increasingly large size of the graphical and numerical data sets collected with modern technologies requires constant update and upgrade of the statistical models, methods and procedures to be used for their analysis in order to optimize learning and maximize knowledge and understanding. This is the case for plant CT scanning (CT: computed tomography), including applications aimed at studying leaf canopies and the structural complexity of the branching patterns that support them in trees. Therefore, we first show after a brief review, how the CT scanning data can be leveraged by constructing an analytical representation of a tree branching structure where each branch is represented by a line segment in 3D and classified in a level of a hierarchy, starting with the trunk (level 1). Each segment, or branch, is characterized by four variables: (i) the position on its parent, (ii) its orientation, a unit vector in 3D, (iii) its length, and (iv) the number of offspring that it bears. The branching structure of a tree can then be investigated by calculating descriptive statistics on these four variables. A deeper analysis, based on statistical models aiming to explain how the characteristics of a branch are associated with those of its parents, is also presented. The branching patterns of three miniature trees that were CT scanned are used to showcase the statistical modeling framework, and the differences in their structural complexity are reflected in the results. Overall, the most important determinant of a tree structure appears to be the length of the branches attached to the trunk. This variable impacts the characteristics of all the other branches of the tree.

Hydrogel-based transparent soils for root phenotyping in vivo

Root phenotypes are increasingly explored as predictors of crop performance but are still challenging to characterize. Media that mimic field conditions (e.g., soil, sand) are opaque to most forms of radiation, while transparent media do not provide field-relevant growing conditions and phenotypes. We describe here a "transparent soil" formed by the spherification of hydrogels of biopolymers. It is specifically designed to support root growth in the presence of air, water, and nutrients, and allows the time-resolved phenotyping of roots in vivo by both photography and microscopy. The roots developed by soybean plants in this medium are significantly more similar to those developed in real soil than those developed in hydroponic conditions and do not show signs of hypoxia. Lastly, we show that the granular nature and tunable properties of these hydrogel beads can be leveraged to investigate the response of roots to gradients in water availability and soil stiffness.

Chemotropic vs Hydrotropic Stimuli for Root Growth Orientation in Microgravity

Understanding how plants respond to spaceflight and extraterrestrial environments is crucial to develop life-support systems intended for long-term human explorations. Gravity is a main factor influencing root development and orientation, typically masking other tropisms. Considering that reduced levels of gravity affect many plant responses in space, the interaction of other tropic stimuli in microgravity represents the frontier to be investigated aiming at life-support systems optimization. In this paper we report on MULTITROP (Multiple-Tropism: interaction of gravity, nutrient and water stimuli for root orientation in microgravity), an experiment performed on the International Space Station during the Expedition 52/53. Scientific aim of the experiment was to disentangle hydrotropism from chemotropism for root orientation in absence of the gravity stimulus. Among several species relevant to space farming, Daucus carota was selected for the experiment because of its suitability with the experimental hardware and setup. At launch site, carrot seeds were placed between two disks of inert substrate (one imbibed with water and the other with a disodium phosphate solution) and integrated into a hardware developed, refurbished and flight-certificated by Kayser Italia. Post-flight, a Ground Reference Experiment was performed. Root development and orientation of seedlings grown in microgravity and at 1g condition were measured through 3D-image analysis procedures after imaging with X-ray microtomography. Radicle protruded preferentially from the ventral side of the seed due to the asymmetric position of the embryo. Such a phenomenon did not prevent the achievement of MULTITROP scientific goal but should be considered for further experiments on radicle growth orientation in microgravity. The experiment conducted in space verified that the primary root of carrot shows a positive chemotropism towards disodium phosphate solution in the absence of the gravity stimulus. On Earth, the positive chemotropism was masked by the dominant effect of gravity and roots developed downward regardless of the presence/absence of nutrients in the substrate. Taking advantage of altered gravity conditions and using other chemical compounds, further studies should be performed to deepen our understanding of root chemotropic response and its interaction with other tropisms.

Fractal based complexity analysis of wheat root system under different heavy metals

In this study, fractal geometry was applied to characterize the complexity of the root system morphology of wheat plants under the exposure of heavy metals, namely cadmium (Cd), copper (Cu) and zinc (Zn). We proposed a measure called, relative complexity index (RCI), a ratio based on fractal dimension (FD) before and after exposure to heavy metals. FDs were calculated by box-counting method with digitized and skeletonized images of roots of wheat plants cultivated in hydroculture system. RCI, and relative weight were mesuared under different concentrations of Cd (0.001, 0.01 and 0.05 mM), Cu (0.016, 0.4 and 1.2 mM) and Zn (0.3 and 0.75 mM). Results showed significant reduction of RCI for Cd stress with 0.01 and 0.05, all Cu concentrations and promotion at all zinc concentrations. In comparison, no statistically significant changes were found in conventional relative weight measurement at low concentrations of Cu, Cd and Zn. RCI were more sensitive and were reliable in reflecting the influence of heavy metals than the conventional measure. These results imply that RCI can be an effective measure of the negative and positive effects of heavy metals on the development of complexity of root system under heavy metal exposures.

Box-Counting Dimension Revisited: Presenting an Efficient Method of Minimizing Quantization Error and an Assessment of the Self-Similarity of Structural Root Systems

Fractal dimension (FD), estimated by box-counting, is a metric used to characterize plant anatomical complexity or space-filling characteristic for a variety of purposes. The vast majority of published studies fail to evaluate the assumption of statistical self-similarity, which underpins the validity of the procedure. The box-counting procedure is also subject to error arising from arbitrary grid placement, known as quantization error (QE), which is strictly positive and varies as a function of scale, making it problematic for the procedure's slope estimation step. Previous studies either ignore QE or employ inefficient brute-force grid translations to reduce it. The goals of this study were to characterize the effect of QE due to translation and rotation on FD estimates, to provide an efficient method of reducing QE, and to evaluate the assumption of statistical self-similarity of coarse root datasets typical of those used in recent trait studies. Coarse root systems of 36 shrubs were digitized in 3D and subjected to box-counts. A pattern search algorithm was used to minimize QE by optimizing grid placement and its efficiency was compared to the brute force method. The degree of statistical self-similarity was evaluated using linear regression residuals and local slope estimates. QE, due to both grid position and orientation, was a significant source of error in FD estimates, but pattern search provided an efficient means of minimizing it. Pattern search had higher initial computational cost but converged on lower error values more efficiently than the commonly employed brute force method. Our representations of coarse root system digitizations did not exhibit details over a sufficient range of scales to be considered statistically self-similar and informatively approximated as fractals, suggesting a lack of sufficient ramification of the coarse root systems for reiteration to be thought of as a dominant force in their development. FD estimates did not characterize the scaling of our digitizations well: the scaling exponent was a function of scale. Our findings serve as a caution against applying FD under the assumption of statistical self-similarity without rigorously evaluating it first.

Concepts and Analyses in the CT Scanning of Root Systems and Leaf Canopies: A Timely Summary

Non-medical applications of computed tomography (CT) scanning have flourished in recent years, including in Plant Science. This Perspective article on CT scanning of root systems and leaf canopies is intended to be of interest to three categories of readers: those who have not yet tried plant CT scanning, and should find inspiration for new research objectives; readers who are on the learning curve with applications-here is helpful advice for them; and researchers with greater experience-the field is evolving quickly and it is easy to miss aspects. Our conclusion is that CT scanning of roots and canopies is highly demanding in terms of technology, multidisciplinarity and big-data analysis, to name a few areas of expertise, but eventually, the reward for researchers is directly proportional!